diff --git a/.gitattributes b/.gitattributes index d2f70b70f0cf941091472bf0642c4645496a3a2d..96f856b33d357296b9b35602119fe442fdfe0f31 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1013,3 +1013,4 @@ omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMon omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf filter=lfs diff=lfs merge=lfs -text omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf filter=lfs diff=lfs merge=lfs -text omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf filter=lfs diff=lfs merge=lfs -text +omnilmm/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf filter=lfs diff=lfs merge=lfs -text diff --git a/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/INSTALLER b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/LICENSE.md b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..19b6b45242c16a1025465309eec2ca5009319de3 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/LICENSE.md @@ -0,0 +1,31 @@ +BSD 3-Clause License + +Copyright (c) 2013-2024, Kim Davies and contributors. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED +TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/METADATA b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..c42623e9423c23b555d9d352bc5dab518ede02c2 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/METADATA @@ -0,0 +1,250 @@ +Metadata-Version: 2.1 +Name: idna +Version: 3.10 +Summary: Internationalized Domain Names in Applications (IDNA) +Author-email: Kim Davies +Requires-Python: >=3.6 +Description-Content-Type: text/x-rst +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: System Administrators +Classifier: License :: OSI Approved :: BSD License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Topic :: Internet :: Name Service (DNS) +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Classifier: Topic :: Utilities +Requires-Dist: ruff >= 0.6.2 ; extra == "all" +Requires-Dist: mypy >= 1.11.2 ; extra == "all" +Requires-Dist: pytest >= 8.3.2 ; extra == "all" +Requires-Dist: flake8 >= 7.1.1 ; extra == "all" +Project-URL: Changelog, https://github.com/kjd/idna/blob/master/HISTORY.rst +Project-URL: Issue tracker, https://github.com/kjd/idna/issues +Project-URL: Source, https://github.com/kjd/idna +Provides-Extra: all + +Internationalized Domain Names in Applications (IDNA) +===================================================== + +Support for the Internationalized Domain Names in +Applications (IDNA) protocol as specified in `RFC 5891 +`_. This is the latest version of +the protocol and is sometimes referred to as “IDNA 2008”. + +This library also provides support for Unicode Technical +Standard 46, `Unicode IDNA Compatibility Processing +`_. + +This acts as a suitable replacement for the “encodings.idna” +module that comes with the Python standard library, but which +only supports the older superseded IDNA specification (`RFC 3490 +`_). + +Basic functions are simply executed: + +.. code-block:: pycon + + >>> import idna + >>> idna.encode('ドメイン.テスト') + b'xn--eckwd4c7c.xn--zckzah' + >>> print(idna.decode('xn--eckwd4c7c.xn--zckzah')) + ドメイン.テスト + + +Installation +------------ + +This package is available for installation from PyPI: + +.. code-block:: bash + + $ python3 -m pip install idna + + +Usage +----- + +For typical usage, the ``encode`` and ``decode`` functions will take a +domain name argument and perform a conversion to A-labels or U-labels +respectively. + +.. code-block:: pycon + + >>> import idna + >>> idna.encode('ドメイン.テスト') + b'xn--eckwd4c7c.xn--zckzah' + >>> print(idna.decode('xn--eckwd4c7c.xn--zckzah')) + ドメイン.テスト + +You may use the codec encoding and decoding methods using the +``idna.codec`` module: + +.. code-block:: pycon + + >>> import idna.codec + >>> print('домен.испытание'.encode('idna2008')) + b'xn--d1acufc.xn--80akhbyknj4f' + >>> print(b'xn--d1acufc.xn--80akhbyknj4f'.decode('idna2008')) + домен.испытание + +Conversions can be applied at a per-label basis using the ``ulabel`` or +``alabel`` functions if necessary: + +.. code-block:: pycon + + >>> idna.alabel('测试') + b'xn--0zwm56d' + +Compatibility Mapping (UTS #46) ++++++++++++++++++++++++++++++++ + +As described in `RFC 5895 `_, the +IDNA specification does not normalize input from different potential +ways a user may input a domain name. This functionality, known as +a “mapping”, is considered by the specification to be a local +user-interface issue distinct from IDNA conversion functionality. + +This library provides one such mapping that was developed by the +Unicode Consortium. Known as `Unicode IDNA Compatibility Processing +`_, it provides for both a regular +mapping for typical applications, as well as a transitional mapping to +help migrate from older IDNA 2003 applications. Strings are +preprocessed according to Section 4.4 “Preprocessing for IDNA2008” +prior to the IDNA operations. + +For example, “Königsgäßchen” is not a permissible label as *LATIN +CAPITAL LETTER K* is not allowed (nor are capital letters in general). +UTS 46 will convert this into lower case prior to applying the IDNA +conversion. + +.. code-block:: pycon + + >>> import idna + >>> idna.encode('Königsgäßchen') + ... + idna.core.InvalidCodepoint: Codepoint U+004B at position 1 of 'Königsgäßchen' not allowed + >>> idna.encode('Königsgäßchen', uts46=True) + b'xn--knigsgchen-b4a3dun' + >>> print(idna.decode('xn--knigsgchen-b4a3dun')) + königsgäßchen + +Transitional processing provides conversions to help transition from +the older 2003 standard to the current standard. For example, in the +original IDNA specification, the *LATIN SMALL LETTER SHARP S* (ß) was +converted into two *LATIN SMALL LETTER S* (ss), whereas in the current +IDNA specification this conversion is not performed. + +.. code-block:: pycon + + >>> idna.encode('Königsgäßchen', uts46=True, transitional=True) + 'xn--knigsgsschen-lcb0w' + +Implementers should use transitional processing with caution, only in +rare cases where conversion from legacy labels to current labels must be +performed (i.e. IDNA implementations that pre-date 2008). For typical +applications that just need to convert labels, transitional processing +is unlikely to be beneficial and could produce unexpected incompatible +results. + +``encodings.idna`` Compatibility +++++++++++++++++++++++++++++++++ + +Function calls from the Python built-in ``encodings.idna`` module are +mapped to their IDNA 2008 equivalents using the ``idna.compat`` module. +Simply substitute the ``import`` clause in your code to refer to the new +module name. + +Exceptions +---------- + +All errors raised during the conversion following the specification +should raise an exception derived from the ``idna.IDNAError`` base +class. + +More specific exceptions that may be generated as ``idna.IDNABidiError`` +when the error reflects an illegal combination of left-to-right and +right-to-left characters in a label; ``idna.InvalidCodepoint`` when +a specific codepoint is an illegal character in an IDN label (i.e. +INVALID); and ``idna.InvalidCodepointContext`` when the codepoint is +illegal based on its positional context (i.e. it is CONTEXTO or CONTEXTJ +but the contextual requirements are not satisfied.) + +Building and Diagnostics +------------------------ + +The IDNA and UTS 46 functionality relies upon pre-calculated lookup +tables for performance. These tables are derived from computing against +eligibility criteria in the respective standards. These tables are +computed using the command-line script ``tools/idna-data``. + +This tool will fetch relevant codepoint data from the Unicode repository +and perform the required calculations to identify eligibility. There are +three main modes: + +* ``idna-data make-libdata``. Generates ``idnadata.py`` and + ``uts46data.py``, the pre-calculated lookup tables used for IDNA and + UTS 46 conversions. Implementers who wish to track this library against + a different Unicode version may use this tool to manually generate a + different version of the ``idnadata.py`` and ``uts46data.py`` files. + +* ``idna-data make-table``. Generate a table of the IDNA disposition + (e.g. PVALID, CONTEXTJ, CONTEXTO) in the format found in Appendix + B.1 of RFC 5892 and the pre-computed tables published by `IANA + `_. + +* ``idna-data U+0061``. Prints debugging output on the various + properties associated with an individual Unicode codepoint (in this + case, U+0061), that are used to assess the IDNA and UTS 46 status of a + codepoint. This is helpful in debugging or analysis. + +The tool accepts a number of arguments, described using ``idna-data +-h``. Most notably, the ``--version`` argument allows the specification +of the version of Unicode to be used in computing the table data. For +example, ``idna-data --version 9.0.0 make-libdata`` will generate +library data against Unicode 9.0.0. + + +Additional Notes +---------------- + +* **Packages**. The latest tagged release version is published in the + `Python Package Index `_. + +* **Version support**. This library supports Python 3.6 and higher. + As this library serves as a low-level toolkit for a variety of + applications, many of which strive for broad compatibility with older + Python versions, there is no rush to remove older interpreter support. + Removing support for older versions should be well justified in that the + maintenance burden has become too high. + +* **Python 2**. Python 2 is supported by version 2.x of this library. + Use "idna<3" in your requirements file if you need this library for + a Python 2 application. Be advised that these versions are no longer + actively developed. + +* **Testing**. The library has a test suite based on each rule of the + IDNA specification, as well as tests that are provided as part of the + Unicode Technical Standard 46, `Unicode IDNA Compatibility Processing + `_. + +* **Emoji**. It is an occasional request to support emoji domains in + this library. Encoding of symbols like emoji is expressly prohibited by + the technical standard IDNA 2008 and emoji domains are broadly phased + out across the domain industry due to associated security risks. For + now, applications that need to support these non-compliant labels + may wish to consider trying the encode/decode operation in this library + first, and then falling back to using `encodings.idna`. See `the Github + project `_ for more discussion. + diff --git a/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/RECORD b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..508951d11ccce467fbd5e025b20d8429776db32a --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/RECORD @@ -0,0 +1,23 @@ +idna-3.10.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +idna-3.10.dist-info/LICENSE.md,sha256=pZ8LDvNjWHQQmkRhykT_enDVBpboFHZ7-vch1Mmw2w8,1541 +idna-3.10.dist-info/METADATA,sha256=URR5ZyDfQ1PCEGhkYoojqfi2Ra0tau2--lhwG4XSfjI,10158 +idna-3.10.dist-info/RECORD,, +idna-3.10.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +idna-3.10.dist-info/WHEEL,sha256=EZbGkh7Ie4PoZfRQ8I0ZuP9VklN_TvcZ6DSE5Uar4z4,81 +idna/__init__.py,sha256=MPqNDLZbXqGaNdXxAFhiqFPKEQXju2jNQhCey6-5eJM,868 +idna/__pycache__/__init__.cpython-310.pyc,, +idna/__pycache__/codec.cpython-310.pyc,, +idna/__pycache__/compat.cpython-310.pyc,, +idna/__pycache__/core.cpython-310.pyc,, +idna/__pycache__/idnadata.cpython-310.pyc,, +idna/__pycache__/intranges.cpython-310.pyc,, +idna/__pycache__/package_data.cpython-310.pyc,, +idna/__pycache__/uts46data.cpython-310.pyc,, +idna/codec.py,sha256=PEew3ItwzjW4hymbasnty2N2OXvNcgHB-JjrBuxHPYY,3422 +idna/compat.py,sha256=RzLy6QQCdl9784aFhb2EX9EKGCJjg0P3PilGdeXXcx8,316 +idna/core.py,sha256=YJYyAMnwiQEPjVC4-Fqu_p4CJ6yKKuDGmppBNQNQpFs,13239 +idna/idnadata.py,sha256=W30GcIGvtOWYwAjZj4ZjuouUutC6ffgNuyjJy7fZ-lo,78306 +idna/intranges.py,sha256=amUtkdhYcQG8Zr-CoMM_kVRacxkivC1WgxN1b63KKdU,1898 +idna/package_data.py,sha256=q59S3OXsc5VI8j6vSD0sGBMyk6zZ4vWFREE88yCJYKs,21 +idna/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +idna/uts46data.py,sha256=rt90K9J40gUSwppDPCrhjgi5AA6pWM65dEGRSf6rIhM,239289 diff --git a/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/REQUESTED b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/WHEEL b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..3b5e64b5e6c4a210201d1676a891fd57b15cda99 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/idna-3.10.dist-info/WHEEL @@ -0,0 +1,4 @@ +Wheel-Version: 1.0 +Generator: flit 3.9.0 +Root-Is-Purelib: true +Tag: py3-none-any diff --git a/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/INSTALLER b/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/LICENSE.txt b/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..910537bd33412dd9b70c4d07cedd41b519be7fb5 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/LICENSE.txt @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2008-2021, The joblib developers. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/METADATA b/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..92e3724687ad41e98a70d993e79513e36ff67bc0 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/METADATA @@ -0,0 +1,165 @@ +Metadata-Version: 2.1 +Name: joblib +Version: 1.4.2 +Summary: Lightweight pipelining with Python functions +Author-email: Gael Varoquaux +License: BSD 3-Clause +Project-URL: Homepage, https://joblib.readthedocs.io +Project-URL: Source, https://github.com/joblib/joblib +Platform: any +Classifier: Development Status :: 5 - Production/Stable +Classifier: Environment :: Console +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Science/Research +Classifier: Intended Audience :: Education +Classifier: License :: OSI Approved :: BSD License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Utilities +Classifier: Topic :: Software Development :: Libraries +Requires-Python: >=3.8 +Description-Content-Type: text/x-rst +License-File: LICENSE.txt + +|PyPi| |Azure| |ReadTheDocs| |Codecov| + +.. |PyPi| image:: https://badge.fury.io/py/joblib.svg + :target: https://badge.fury.io/py/joblib + :alt: Joblib version + +.. |Azure| image:: https://dev.azure.com/joblib/joblib/_apis/build/status/joblib.joblib?branchName=main + :target: https://dev.azure.com/joblib/joblib/_build?definitionId=3&_a=summary&branchFilter=40 + :alt: Azure CI status + +.. |ReadTheDocs| image:: https://readthedocs.org/projects/joblib/badge/?version=latest + :target: https://joblib.readthedocs.io/en/latest/?badge=latest + :alt: Documentation Status + +.. |Codecov| image:: https://codecov.io/gh/joblib/joblib/branch/main/graph/badge.svg + :target: https://codecov.io/gh/joblib/joblib + :alt: Codecov coverage + + +The homepage of joblib with user documentation is located on: + +https://joblib.readthedocs.io + +Getting the latest code +======================= + +To get the latest code using git, simply type:: + + git clone https://github.com/joblib/joblib.git + +If you don't have git installed, you can download a zip +of the latest code: https://github.com/joblib/joblib/archive/refs/heads/main.zip + +Installing +========== + +You can use `pip` to install joblib:: + + pip install joblib + +from any directory or:: + + python setup.py install + +from the source directory. + +Dependencies +============ + +- Joblib has no mandatory dependencies besides Python (supported versions are + 3.8+). +- Joblib has an optional dependency on Numpy (at least version 1.6.1) for array + manipulation. +- Joblib includes its own vendored copy of + `loky `_ for process management. +- Joblib can efficiently dump and load numpy arrays but does not require numpy + to be installed. +- Joblib has an optional dependency on + `python-lz4 `_ as a faster alternative to + zlib and gzip for compressed serialization. +- Joblib has an optional dependency on psutil to mitigate memory leaks in + parallel worker processes. +- Some examples require external dependencies such as pandas. See the + instructions in the `Building the docs`_ section for details. + +Workflow to contribute +====================== + +To contribute to joblib, first create an account on `github +`_. Once this is done, fork the `joblib repository +`_ to have your own repository, +clone it using 'git clone' on the computers where you want to work. Make +your changes in your clone, push them to your github account, test them +on several computers, and when you are happy with them, send a pull +request to the main repository. + +Running the test suite +====================== + +To run the test suite, you need the pytest (version >= 3) and coverage modules. +Run the test suite using:: + + pytest joblib + +from the root of the project. + +Building the docs +================= + +To build the docs you need to have sphinx (>=1.4) and some dependencies +installed:: + + pip install -U -r .readthedocs-requirements.txt + +The docs can then be built with the following command:: + + make doc + +The html docs are located in the ``doc/_build/html`` directory. + + +Making a source tarball +======================= + +To create a source tarball, eg for packaging or distributing, run the +following command:: + + python setup.py sdist + +The tarball will be created in the `dist` directory. This command will +compile the docs, and the resulting tarball can be installed with +no extra dependencies than the Python standard library. You will need +setuptool and sphinx. + +Making a release and uploading it to PyPI +========================================= + +This command is only run by project manager, to make a release, and +upload in to PyPI:: + + python setup.py sdist bdist_wheel + twine upload dist/* + + +Note that the documentation should automatically get updated at each git +push. If that is not the case, try building th doc locally and resolve +any doc build error (in particular when running the examples). + +Updating the changelog +====================== + +Changes are listed in the CHANGES.rst file. They must be manually updated +but, the following git command may be used to generate the lines:: + + git log --abbrev-commit --date=short --no-merges --sparse + diff --git a/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/RECORD b/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..2702c454bcf415675be5caa29a59dc496ff1ef55 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/joblib-1.4.2.dist-info/RECORD @@ -0,0 +1,218 @@ +joblib-1.4.2.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +joblib-1.4.2.dist-info/LICENSE.txt,sha256=QmEpEcGHLF5LQ_auDo7llGfNNQMyJBz3LOkGQCZPrmo,1527 +joblib-1.4.2.dist-info/METADATA,sha256=-PL7OT7ROjEST8XDvFsNetiT1C39xP0HjiPNfR51N78,5364 +joblib-1.4.2.dist-info/RECORD,, +joblib-1.4.2.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +joblib-1.4.2.dist-info/WHEEL,sha256=2wepM1nk4DS4eFpYrW1TTqPcoGNfHhhO_i5m4cOimbo,92 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0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/LICENSE.txt b/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..a76d0ed5ad8501d70722dd7e819d528e1190fb41 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/LICENSE.txt @@ -0,0 +1,29 @@ +Copyright (c) 2004 Infrae. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + + 3. Neither the name of Infrae nor the names of its contributors may + be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL INFRAE OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/LICENSES.txt b/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/LICENSES.txt new file mode 100644 index 0000000000000000000000000000000000000000..9f97c18aa0b162ffcc3e745d80efb9def1c82515 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/LICENSES.txt @@ -0,0 +1,29 @@ +lxml is copyright Infrae and distributed under the BSD license (see +doc/licenses/BSD.txt), with the following exceptions: + +Some code, such a selftest.py, selftest2.py and +src/lxml/_elementpath.py are derived from ElementTree and +cElementTree. See doc/licenses/elementtree.txt for the license text. + +lxml.cssselect and lxml.html are copyright Ian Bicking and distributed +under the BSD license (see doc/licenses/BSD.txt). + +test.py, the test-runner script, is GPL and copyright Shuttleworth +Foundation. See doc/licenses/GPL.txt. It is believed the unchanged +inclusion of test.py to run the unit test suite falls under the +"aggregation" clause of the GPL and thus does not affect the license +of the rest of the package. + +The isoschematron implementation uses several XSL and RelaxNG resources: + * The (XML syntax) RelaxNG schema for schematron, copyright International + Organization for Standardization (see + src/lxml/isoschematron/resources/rng/iso-schematron.rng for the license + text) + * The skeleton iso-schematron-xlt1 pure-xslt schematron implementation + xsl stylesheets, copyright Rick Jelliffe and Academia Sinica Computing + Center, Taiwan (see the xsl files here for the license text: + src/lxml/isoschematron/resources/xsl/iso-schematron-xslt1/) + * The xsd/rng schema schematron extraction xsl transformations are unlicensed + and copyright the respective authors as noted (see + src/lxml/isoschematron/resources/xsl/RNG2Schtrn.xsl and + src/lxml/isoschematron/resources/xsl/XSD2Schtrn.xsl) diff --git a/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/METADATA b/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..33c83682e1672c71d37ea7c687c60f038bfea92a --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/METADATA @@ -0,0 +1,103 @@ +Metadata-Version: 2.1 +Name: lxml +Version: 5.3.0 +Summary: Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API. +Home-page: https://lxml.de/ +Author: lxml dev team +Author-email: lxml-dev@lxml.de +Maintainer: lxml dev team +Maintainer-email: lxml-dev@lxml.de +License: BSD-3-Clause +Project-URL: Source, https://github.com/lxml/lxml +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Information Technology +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: Cython +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: C +Classifier: Operating System :: OS Independent +Classifier: Topic :: Text Processing :: Markup :: HTML +Classifier: Topic :: Text Processing :: Markup :: XML +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Requires-Python: >=3.6 +License-File: LICENSE.txt +License-File: LICENSES.txt +Provides-Extra: cssselect +Requires-Dist: cssselect>=0.7; extra == "cssselect" +Provides-Extra: html5 +Requires-Dist: html5lib; extra == "html5" +Provides-Extra: html_clean +Requires-Dist: lxml-html-clean; extra == "html-clean" +Provides-Extra: htmlsoup +Requires-Dist: BeautifulSoup4; extra == "htmlsoup" +Provides-Extra: source +Requires-Dist: Cython>=3.0.11; extra == "source" + +lxml is a Pythonic, mature binding for the libxml2 and libxslt libraries. It +provides safe and convenient access to these libraries using the ElementTree +API. + +It extends the ElementTree API significantly to offer support for XPath, +RelaxNG, XML Schema, XSLT, C14N and much more. + +To contact the project, go to the `project home page +`_ or see our bug tracker at +https://launchpad.net/lxml + +In case you want to use the current in-development version of lxml, +you can get it from the github repository at +https://github.com/lxml/lxml . Note that this requires Cython to +build the sources, see the build instructions on the project home +page. To the same end, running ``easy_install lxml==dev`` will +install lxml from +https://github.com/lxml/lxml/tarball/master#egg=lxml-dev if you have +an appropriate version of Cython installed. + + +After an official release of a new stable series, bug fixes may become +available at +https://github.com/lxml/lxml/tree/lxml-5.3 . +Running ``easy_install lxml==5.3bugfix`` will install +the unreleased branch state from +https://github.com/lxml/lxml/tarball/lxml-5.3#egg=lxml-5.3bugfix +as soon as a maintenance branch has been established. Note that this +requires Cython to be installed at an appropriate version for the build. + +5.3.0 (2024-08-10) +================== + +Features added +-------------- + +* GH#421: Nested ``CDATA`` sections are no longer rejected but split on output + to represent ``]]>`` correctly. + Patch by Gertjan Klein. + +Bugs fixed +---------- + +* LP#2060160: Attribute values serialised differently in ``xmlfile.element()`` and ``xmlfile.write()``. + +* LP#2058177: The ISO-Schematron implementation could fail on unknown prefixes. + Patch by David Lakin. + +Other changes +------------- + +* LP#2067707: The ``strip_cdata`` option in ``HTMLParser()`` turned out to be useless and is now deprecated. + +* Binary wheels use the library versions libxml2 2.12.9 and libxslt 1.1.42. + +* Windows binary wheels use the library versions libxml2 2.11.8 and libxslt 1.1.39. + +* Built with Cython 3.0.11. + + diff --git a/omnilmm/lib/python3.10/site-packages/lxml-5.3.0.dist-info/RECORD 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Preiss +Emile Fourcini +Alberto Jiménez Ruiz +João Bravo +Dean Price +Hugo Kerstens +Jan Jancar +Matt Wang diff --git a/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/INSTALLER b/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/LICENSE b/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..0744f229d697ca3ed1b1b257bfdb70e3eecf0b9e --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/LICENSE @@ -0,0 +1,153 @@ +Copyright (c) 2006-2023 SymPy Development Team + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of SymPy nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY +OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH +DAMAGE. + +-------------------------------------------------------------------------------- + +Patches that were taken from the Diofant project (https://github.com/diofant/diofant) +are licensed as: + +Copyright (c) 2006-2018 SymPy Development Team, + 2013-2023 Sergey B Kirpichev + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of Diofant or SymPy nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. 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IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY +OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH +DAMAGE. + +-------------------------------------------------------------------------------- + +The files under the directory sympy/parsing/autolev/tests/pydy-example-repo +are directly copied from PyDy project and are licensed as: + +Copyright (c) 2009-2023, PyDy Authors +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. +* Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. +* Neither the name of this project nor the names of its contributors may be + used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL PYDY AUTHORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF +ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +-------------------------------------------------------------------------------- + +The files under the directory sympy/parsing/latex +are directly copied from latex2sympy project and are licensed as: + +Copyright 2016, latex2sympy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/METADATA b/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..2ee82bf5e1020fe9b877981367a0d7d2ac6dd562 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/METADATA @@ -0,0 +1,304 @@ +Metadata-Version: 2.1 +Name: sympy +Version: 1.13.3 +Summary: Computer algebra system (CAS) in Python +Home-page: https://sympy.org +Author: SymPy development team +Author-email: sympy@googlegroups.com +License: BSD +Project-URL: Source, https://github.com/sympy/sympy +Keywords: Math CAS +Classifier: License :: OSI Approved :: BSD License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Physics +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Requires-Python: >=3.8 +Description-Content-Type: text/markdown +License-File: LICENSE +License-File: AUTHORS +Requires-Dist: mpmath <1.4,>=1.1.0 +Provides-Extra: dev +Requires-Dist: pytest >=7.1.0 ; extra == 'dev' +Requires-Dist: hypothesis >=6.70.0 ; extra == 'dev' + +# SymPy + +[![pypi version](https://img.shields.io/pypi/v/sympy.svg)](https://pypi.python.org/pypi/sympy) +[![Join the chat at https://gitter.im/sympy/sympy](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/sympy/sympy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) +[![Zenodo Badge](https://zenodo.org/badge/18918/sympy/sympy.svg)](https://zenodo.org/badge/latestdoi/18918/sympy/sympy) +[![Downloads](https://pepy.tech/badge/sympy/month)](https://pepy.tech/project/sympy) +[![GitHub Issues](https://img.shields.io/badge/issue_tracking-github-blue.svg)](https://github.com/sympy/sympy/issues) +[![Git Tutorial](https://img.shields.io/badge/PR-Welcome-%23FF8300.svg?)](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project) +[![Powered by NumFocus](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) +[![Commits since last release](https://img.shields.io/github/commits-since/sympy/sympy/latest.svg?longCache=true&style=flat-square&logo=git&logoColor=fff)](https://github.com/sympy/sympy/releases) + +[![SymPy Banner](https://github.com/sympy/sympy/raw/master/banner.svg)](https://sympy.org/) + + +See the [AUTHORS](AUTHORS) file for the list of authors. + +And many more people helped on the SymPy mailing list, reported bugs, +helped organize SymPy's participation in the Google Summer of Code, the +Google Highly Open Participation Contest, Google Code-In, wrote and +blogged about SymPy... + +License: New BSD License (see the [LICENSE](LICENSE) file for details) covers all +files in the sympy repository unless stated otherwise. + +Our mailing list is at +. + +We have a community chat at [Gitter](https://gitter.im/sympy/sympy). Feel +free to ask us anything there. We have a very welcoming and helpful +community. + +## Download + +The recommended installation method is through Anaconda, + + +You can also get the latest version of SymPy from + + +To get the git version do + + $ git clone https://github.com/sympy/sympy.git + +For other options (tarballs, debs, etc.), see +. + +## Documentation and Usage + +For in-depth instructions on installation and building the +documentation, see the [SymPy Documentation Style Guide](https://docs.sympy.org/dev/documentation-style-guide.html). + +Everything is at: + + + +You can generate everything at the above site in your local copy of +SymPy by: + + $ cd doc + $ make html + +Then the docs will be in \_build/html. If +you don't want to read that, here is a short usage: + +From this directory, start Python and: + +``` python +>>> from sympy import Symbol, cos +>>> x = Symbol('x') +>>> e = 1/cos(x) +>>> print(e.series(x, 0, 10)) +1 + x**2/2 + 5*x**4/24 + 61*x**6/720 + 277*x**8/8064 + O(x**10) +``` + +SymPy also comes with a console that is a simple wrapper around the +classic python console (or IPython when available) that loads the SymPy +namespace and executes some common commands for you. + +To start it, issue: + + $ bin/isympy + +from this directory, if SymPy is not installed or simply: + + $ isympy + +if SymPy is installed. + +## Installation + +To install SymPy using PyPI, run the following command: + + $ pip install sympy + +To install SymPy using Anaconda, run the following command: + + $ conda install -c anaconda sympy + +To install SymPy from GitHub source, first clone SymPy using `git`: + + $ git clone https://github.com/sympy/sympy.git + +Then, in the `sympy` repository that you cloned, simply run: + + $ pip install . + +See for more information. + +## Contributing + +We welcome contributions from anyone, even if you are new to open +source. Please read our [Introduction to Contributing](https://docs.sympy.org/dev/contributing/introduction-to-contributing.html) +page and the [SymPy Documentation Style Guide](https://docs.sympy.org/dev/documentation-style-guide.html). If you +are new and looking for some way to contribute, a good place to start is +to look at the issues tagged [Easy to Fix](https://github.com/sympy/sympy/issues?q=is%3Aopen+is%3Aissue+label%3A%22Easy+to+Fix%22). + +Please note that all participants in this project are expected to follow +our Code of Conduct. By participating in this project you agree to abide +by its terms. See [CODE\_OF\_CONDUCT.md](CODE_OF_CONDUCT.md). + +## Tests + +To execute all tests, run: + + $./setup.py test + +in the current directory. + +For the more fine-grained running of tests or doctests, use `bin/test` +or respectively `bin/doctest`. The master branch is automatically tested +by GitHub Actions. + +To test pull requests, use +[sympy-bot](https://github.com/sympy/sympy-bot). + +## Regenerate Experimental LaTeX Parser/Lexer + +The parser and lexer were generated with the [ANTLR4](http://antlr4.org) +toolchain in `sympy/parsing/latex/_antlr` and checked into the repo. +Presently, most users should not need to regenerate these files, but +if you plan to work on this feature, you will need the `antlr4` +command-line tool (and you must ensure that it is in your `PATH`). +One way to get it is: + + $ conda install -c conda-forge antlr=4.11.1 + +Alternatively, follow the instructions on the ANTLR website and download +the `antlr-4.11.1-complete.jar`. Then export the `CLASSPATH` as instructed +and instead of creating `antlr4` as an alias, make it an executable file +with the following contents: +``` bash +#!/bin/bash +java -jar /usr/local/lib/antlr-4.11.1-complete.jar "$@" +``` + +After making changes to `sympy/parsing/latex/LaTeX.g4`, run: + + $ ./setup.py antlr + +## Clean + +To clean everything (thus getting the same tree as in the repository): + + $ git clean -Xdf + +which will clear everything ignored by `.gitignore`, and: + + $ git clean -df + +to clear all untracked files. You can revert the most recent changes in +git with: + + $ git reset --hard + +WARNING: The above commands will all clear changes you may have made, +and you will lose them forever. Be sure to check things with `git +status`, `git diff`, `git clean -Xn`, and `git clean -n` before doing any +of those. + +## Bugs + +Our issue tracker is at . Please +report any bugs that you find. Or, even better, fork the repository on +GitHub and create a pull request. We welcome all changes, big or small, +and we will help you make the pull request if you are new to git (just +ask on our mailing list or Gitter Channel). If you further have any queries, you can find answers +on Stack Overflow using the [sympy](https://stackoverflow.com/questions/tagged/sympy) tag. + +## Brief History + +SymPy was started by Ondřej Čertík in 2005, he wrote some code during +the summer, then he wrote some more code during summer 2006. In February +2007, Fabian Pedregosa joined the project and helped fix many things, +contributed documentation, and made it alive again. 5 students (Mateusz +Paprocki, Brian Jorgensen, Jason Gedge, Robert Schwarz, and Chris Wu) +improved SymPy incredibly during summer 2007 as part of the Google +Summer of Code. Pearu Peterson joined the development during the summer +2007 and he has made SymPy much more competitive by rewriting the core +from scratch, which has made it from 10x to 100x faster. Jurjen N.E. Bos +has contributed pretty-printing and other patches. Fredrik Johansson has +written mpmath and contributed a lot of patches. + +SymPy has participated in every Google Summer of Code since 2007. You +can see for +full details. Each year has improved SymPy by bounds. Most of SymPy's +development has come from Google Summer of Code students. + +In 2011, Ondřej Čertík stepped down as lead developer, with Aaron +Meurer, who also started as a Google Summer of Code student, taking his +place. Ondřej Čertík is still active in the community but is too busy +with work and family to play a lead development role. + +Since then, a lot more people have joined the development and some +people have also left. You can see the full list in doc/src/aboutus.rst, +or online at: + + + +The git history goes back to 2007 when development moved from svn to hg. +To see the history before that point, look at +. + +You can use git to see the biggest developers. The command: + + $ git shortlog -ns + +will show each developer, sorted by commits to the project. The command: + + $ git shortlog -ns --since="1 year" + +will show the top developers from the last year. + +## Citation + +To cite SymPy in publications use + +> Meurer A, Smith CP, Paprocki M, Čertík O, Kirpichev SB, Rocklin M, +> Kumar A, Ivanov S, Moore JK, Singh S, Rathnayake T, Vig S, Granger BE, +> Muller RP, Bonazzi F, Gupta H, Vats S, Johansson F, Pedregosa F, Curry +> MJ, Terrel AR, Roučka Š, Saboo A, Fernando I, Kulal S, Cimrman R, +> Scopatz A. (2017) SymPy: symbolic computing in Python. *PeerJ Computer +> Science* 3:e103 + +A BibTeX entry for LaTeX users is + +``` bibtex +@article{10.7717/peerj-cs.103, + title = {SymPy: symbolic computing in Python}, + author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, Amit and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony}, + year = 2017, + month = Jan, + keywords = {Python, Computer algebra system, Symbolics}, + abstract = { + SymPy is an open-source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provides additional examples and further outlines details of the architecture and features of SymPy. + }, + volume = 3, + pages = {e103}, + journal = {PeerJ Computer Science}, + issn = {2376-5992}, + url = {https://doi.org/10.7717/peerj-cs.103}, + doi = {10.7717/peerj-cs.103} +} +``` + +SymPy is BSD licensed, so you are free to use it whatever you like, be +it academic, commercial, creating forks or derivatives, as long as you +copy the BSD statement if you redistribute it (see the LICENSE file for +details). That said, although not required by the SymPy license, if it +is convenient for you, please cite SymPy when using it in your work and +also consider contributing all your changes back, so that we can +incorporate it and all of us will benefit in the end. diff --git a/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/RECORD b/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..660be5c9f3dd72bcaa7495067909a8bcb11c89f1 --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/sympy-1.13.3.dist-info/RECORD @@ -0,0 +1,3076 @@ +../../../bin/isympy,sha256=65QSYJa_4Rr8r_V2CqulrbYnNg0m9KUucQ0r75mcF7w,218 +../../../share/man/man1/isympy.1,sha256=9DZdSOIQLikrATHlbkdDZ04LBQigZDUE0_oCXBDvdBs,6659 +__pycache__/isympy.cpython-310.pyc,, +isympy.py,sha256=gAoHa7OM0y9G5IBO7wO-uTpD-CPnd6sbmjJ_GGB0yzg,11207 +sympy-1.13.3.dist-info/AUTHORS,sha256=LU7_WXv-LYwodkUQMT_Xc7n9BVkcoDbRfeoXvn3HrvQ,53081 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+ + + + Hugging Face Transformers Library + +
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State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow

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+ +🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. + +These models can be applied on: + +* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages. +* 🖼️ Images, for tasks like image classification, object detection, and segmentation. +* 🗣️ Audio, for tasks like speech recognition and audio classification. + +Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering. + +🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments. + +🤗 Transformers is backed by the three most popular deep learning libraries — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other. + +## Online demos + +You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) for public and private models. + +Here are a few examples: + +In Natural Language Processing: +- [Masked word completion with BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) +- [Named Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) +- [Text generation with Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) +- [Natural Language Inference with RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) +- [Summarization with BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) +- [Question answering with DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) +- [Translation with T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) + +In Computer Vision: +- [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224) +- [Object Detection with DETR](https://huggingface.co/facebook/detr-resnet-50) +- [Semantic Segmentation with SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) +- [Panoptic Segmentation with Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic) +- [Depth Estimation with Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything) +- [Video Classification with VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae) +- [Universal Segmentation with OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) + +In Audio: +- [Automatic Speech Recognition with Whisper](https://huggingface.co/openai/whisper-large-v3) +- [Keyword Spotting with Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) +- [Audio Classification with Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) + +In Multimodal tasks: +- [Table Question Answering with TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq) +- [Visual Question Answering with ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) +- [Image captioning with LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf) +- [Zero-shot Image Classification with SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) +- [Document Question Answering with LayoutLM](https://huggingface.co/impira/layoutlm-document-qa) +- [Zero-shot Video Classification with X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip) +- [Zero-shot Object Detection with OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2) +- [Zero-shot Image Segmentation with CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg) +- [Automatic Mask Generation with SAM](https://huggingface.co/docs/transformers/model_doc/sam) + + +## 100 projects using Transformers + +Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the +Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone +else to build their dream projects. + +In order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on the +community, and we have created the [awesome-transformers](./awesome-transformers.md) page which lists 100 +incredible projects built in the vicinity of transformers. + +If you own or use a project that you believe should be part of the list, please open a PR to add it! + +## Serious about AI in your organisation? Build faster with the Hugging Face Enterprise Hub. + + + Hugging Face Enterprise Hub +
+ +## Quick tour + +To immediately use a model on a given input (text, image, audio, ...), we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts: + +```python +>>> from transformers import pipeline + +# Allocate a pipeline for sentiment-analysis +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +The second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here, the answer is "positive" with a confidence of 99.97%. + +Many tasks have a pre-trained `pipeline` ready to go, in NLP but also in computer vision and speech. For example, we can easily extract detected objects in an image: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Download an image with cute cats +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Allocate a pipeline for object detection +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +Here, we get a list of objects detected in the image, with a box surrounding the object and a confidence score. Here is the original image on the left, with the predictions displayed on the right: + +

+ + +

+ +You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/docs/transformers/task_summary). + +In addition to `pipeline`, to download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version: +```python +>>> from transformers import AutoTokenizer, AutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="pt") +>>> outputs = model(**inputs) +``` + +And here is the equivalent code for TensorFlow: +```python +>>> from transformers import AutoTokenizer, TFAutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="tf") +>>> outputs = model(**inputs) +``` + +The tokenizer is responsible for all the preprocessing the pretrained model expects and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator. + +The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use as usual. [This tutorial](https://huggingface.co/docs/transformers/training) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset. + +## Why should I use transformers? + +1. Easy-to-use state-of-the-art models: + - High performance on natural language understanding & generation, computer vision, and audio tasks. + - Low barrier to entry for educators and practitioners. + - Few user-facing abstractions with just three classes to learn. + - A unified API for using all our pretrained models. + +1. Lower compute costs, smaller carbon footprint: + - Researchers can share trained models instead of always retraining. + - Practitioners can reduce compute time and production costs. + - Dozens of architectures with over 400,000 pretrained models across all modalities. + +1. Choose the right framework for every part of a model's lifetime: + - Train state-of-the-art models in 3 lines of code. + - Move a single model between TF2.0/PyTorch/JAX frameworks at will. + - Seamlessly pick the right framework for training, evaluation, and production. + +1. Easily customize a model or an example to your needs: + - We provide examples for each architecture to reproduce the results published by its original authors. + - Model internals are exposed as consistently as possible. + - Model files can be used independently of the library for quick experiments. + +## Why shouldn't I use transformers? + +- This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files. +- The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library (possibly, [Accelerate](https://huggingface.co/docs/accelerate)). +- While we strive to present as many use cases as possible, the scripts in our [examples folder](https://github.com/huggingface/transformers/tree/main/examples) are just that: examples. It is expected that they won't work out-of-the-box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. + +## Installation + +### With pip + +This repository is tested on Python 3.9+, Flax 0.4.1+, PyTorch 2.0+, and TensorFlow 2.6+. + +You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). + +First, create a virtual environment with the version of Python you're going to use and activate it. + +Then, you will need to install at least one of Flax, PyTorch, or TensorFlow. +Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) installation pages regarding the specific installation command for your platform. + +When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows: + +```bash +pip install transformers +``` + +If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source). + +### With conda + +🤗 Transformers can be installed using conda as follows: + +```shell script +conda install conda-forge::transformers +``` + +> **_NOTE:_** Installing `transformers` from the `huggingface` channel is deprecated. + +Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda. + +> **_NOTE:_** On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062). + +## Model architectures + +**[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co/models), where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations). + +Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen) + +🤗 Transformers currently provides the following architectures: see [here](https://huggingface.co/docs/transformers/model_summary) for a high-level summary of each them. + +To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/docs/transformers/index#supported-frameworks). + +These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://github.com/huggingface/transformers/tree/main/examples). + + +## Learn more + +| Section | Description | +|-|-| +| [Documentation](https://huggingface.co/docs/transformers/) | Full API documentation and tutorials | +| [Task summary](https://huggingface.co/docs/transformers/task_summary) | Tasks supported by 🤗 Transformers | +| [Preprocessing tutorial](https://huggingface.co/docs/transformers/preprocessing) | Using the `Tokenizer` class to prepare data for the models | +| [Training and fine-tuning](https://huggingface.co/docs/transformers/training) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API | +| [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/main/examples) | Example scripts for fine-tuning models on a wide range of tasks | +| [Model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing) | Upload and share your fine-tuned models with the community | + +## Citation + +We now have a [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) you can cite for the 🤗 Transformers library: +```bibtex +@inproceedings{wolf-etal-2020-transformers, + title = "Transformers: State-of-the-Art Natural Language Processing", + author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = oct, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", + pages = "38--45" +} +``` diff --git a/omnilmm/lib/python3.10/site-packages/transformers-4.48.0.dist-info/RECORD b/omnilmm/lib/python3.10/site-packages/transformers-4.48.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..89d6d1f4a33706c52ea9318fb58d44d0f7e5f8ea --- /dev/null +++ b/omnilmm/lib/python3.10/site-packages/transformers-4.48.0.dist-info/RECORD @@ -0,0 +1,3382 @@ +../../../bin/transformers-cli,sha256=sSIVBEgseSJz1T8oq-Ku8RHjiU58fLbMI8XN03abLng,250 +transformers-4.48.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +transformers-4.48.0.dist-info/LICENSE,sha256=d_1HEN757DwPYiWADgI18VpCWr1KiwNVkSf814JhIEk,11418 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