Add files using upload-large-folder tool

.gitattributes

@@ -1951,3 +1951,5 @@ parrot/lib/python3.10/site-packages/matplotlib/_ttconv.cpython-310-x86_64-linux-
 parrot/lib/python3.10/site-packages/matplotlib/axes/__pycache__/_base.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 parrot/lib/python3.10/site-packages/altair/vegalite/v5/schema/__pycache__/core.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 parrot/lib/python3.10/site-packages/matplotlib/_path.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+videollama2/lib/python3.10/site-packages/sklearn/preprocessing/_csr_polynomial_expansion.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+llava_next/lib/python3.10/site-packages/scipy.libs/libscipy_openblas-c128ec02.so filter=lfs diff=lfs merge=lfs -text

llava_next/lib/python3.10/site-packages/scipy.libs/libscipy_openblas-c128ec02.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95121ba2173f1838ca08d988af0cc03a4959d50d9f8adea42d10d8fe8f3be61d
+size 36060905

videollama2/lib/python3.10/site-packages/cmake_modules/FindAzure.cmake

@@ -0,0 +1,45 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+if(Azure_FOUND)
+  return()
+endif()
+
+set(find_package_args)
+list(APPEND find_package_args CONFIG)
+if(Azure_FIND_QUIETLY)
+  list(APPEND find_package_args QUIET)
+endif()
+
+if(Azure_FIND_REQUIRED)
+  list(APPEND find_package_args REQUIRED)
+endif()
+
+find_package(azure-core-cpp ${find_package_args})
+find_package(azure-identity-cpp ${find_package_args})
+find_package(azure-storage-blobs-cpp ${find_package_args})
+find_package(azure-storage-common-cpp ${find_package_args})
+find_package(azure-storage-files-datalake-cpp ${find_package_args})
+
+find_package_handle_standard_args(
+  Azure
+  REQUIRED_VARS azure-core-cpp_FOUND
+                azure-identity-cpp_FOUND
+                azure-storage-blobs-cpp_FOUND
+                azure-storage-common-cpp_FOUND
+                azure-storage-files-datalake-cpp_FOUND
+  VERSION_VAR azure-core-cpp_VERSION)

videollama2/lib/python3.10/site-packages/cmake_modules/UseCython.cmake

@@ -0,0 +1,187 @@
+# Define a function to create Cython modules.
+#
+# For more information on the Cython project, see http://cython.org/.
+# "Cython is a language that makes writing C extensions for the Python language
+# as easy as Python itself."
+#
+# This file defines a CMake function to build a Cython Python module.
+# To use it, first include this file.
+#
+#   include( UseCython )
+#
+# Then call cython_add_module to create a module.
+#
+#   cython_add_module( <target_name> <pyx_target_name> <output_files> <src1> <src2> ... <srcN> )
+#
+# Where <module_name> is the desired name of the target for the resulting Python module,
+# <pyx_target_name> is the desired name of the target that runs the Cython compiler
+# to generate the needed C or C++ files, <output_files> is a variable to hold the
+# files generated by Cython, and <src1> <src2> ... are source files
+# to be compiled into the module, e.g. *.pyx, *.c, *.cxx, etc.
+# only one .pyx file may be present for each target
+# (this is an inherent limitation of Cython).
+#
+# The sample paths set with the CMake include_directories() command will be used
+# for include directories to search for *.pxd when running the Cython compiler.
+#
+# Cache variables that effect the behavior include:
+#
+#  CYTHON_ANNOTATE
+#  CYTHON_NO_DOCSTRINGS
+#  CYTHON_FLAGS
+#
+# Source file properties that effect the build process are
+#
+#  CYTHON_IS_CXX
+#  CYTHON_IS_PUBLIC
+#  CYTHON_IS_API
+#
+# If this is set of a *.pyx file with CMake set_source_files_properties()
+# command, the file will be compiled as a C++ file.
+#
+# See also FindCython.cmake
+
+#=============================================================================
+# Copyright 2011 Kitware, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#=============================================================================
+
+# Configuration options.
+set(CYTHON_ANNOTATE OFF CACHE BOOL "Create an annotated .html file when compiling *.pyx.")
+set(CYTHON_NO_DOCSTRINGS OFF CACHE BOOL "Strip docstrings from the compiled module.")
+set(CYTHON_FLAGS "" CACHE STRING "Extra flags to the cython compiler.")
+mark_as_advanced(CYTHON_ANNOTATE CYTHON_NO_DOCSTRINGS CYTHON_FLAGS)
+
+find_package(Python3Alt REQUIRED)
+
+# (using another C++ extension breaks coverage)
+set(CYTHON_CXX_EXTENSION "cpp")
+set(CYTHON_C_EXTENSION "c")
+
+# Create a *.c or *.cpp file from a *.pyx file.
+# Input the generated file basename.  The generate files will put into the variable
+# placed in the "generated_files" argument. Finally all the *.py and *.pyx files.
+function(compile_pyx
+         _name
+         pyx_target_name
+         generated_files
+         pyx_file)
+  # Default to assuming all files are C.
+  set(cxx_arg "")
+  set(extension ${CYTHON_C_EXTENSION})
+  set(pyx_lang "C")
+  set(comment "Compiling Cython C source for ${_name}...")
+
+  get_filename_component(pyx_file_basename "${pyx_file}" NAME_WE)
+
+  # Determine if it is a C or C++ file.
+  get_source_file_property(property_is_cxx ${pyx_file} CYTHON_IS_CXX)
+  if(${property_is_cxx})
+    set(cxx_arg "--cplus")
+    set(extension ${CYTHON_CXX_EXTENSION})
+    set(pyx_lang "CXX")
+    set(comment "Compiling Cython CXX source for ${_name}...")
+  endif()
+  get_source_file_property(pyx_location ${pyx_file} LOCATION)
+
+  set(output_file "${_name}.${extension}")
+
+  # Set additional flags.
+  if(CYTHON_ANNOTATE)
+    set(annotate_arg "--annotate")
+  endif()
+
+  if(CYTHON_NO_DOCSTRINGS)
+    set(no_docstrings_arg "--no-docstrings")
+  endif()
+
+  if(NOT WIN32)
+    string( TOLOWER "${CMAKE_BUILD_TYPE}" build_type )
+    if("${build_type}" STREQUAL "debug"
+       OR "${build_type}" STREQUAL "relwithdebinfo")
+      set(cython_debug_arg "--gdb")
+    endif()
+  endif()
+
+  # Determining generated file names.
+  get_source_file_property(property_is_public ${pyx_file} CYTHON_PUBLIC)
+  get_source_file_property(property_is_api ${pyx_file} CYTHON_API)
+  if(${property_is_api})
+    set(_generated_files "${output_file}" "${_name}.h" "${_name}_api.h")
+  elseif(${property_is_public})
+    set(_generated_files "${output_file}" "${_name}.h")
+  else()
+    set(_generated_files "${output_file}")
+  endif()
+  set_source_files_properties(${_generated_files} PROPERTIES GENERATED TRUE)
+
+  if(NOT WIN32)
+    # Cython creates a lot of compiler warning detritus on clang
+    set_source_files_properties(${_generated_files} PROPERTIES COMPILE_FLAGS
+                                -Wno-unused-function)
+  endif()
+
+  set(${generated_files} ${_generated_files} PARENT_SCOPE)
+
+  # Add the command to run the compiler.
+  add_custom_target(
+    ${pyx_target_name}
+    COMMAND ${PYTHON_EXECUTABLE}
+            -m
+            cython
+            ${cxx_arg}
+            ${annotate_arg}
+            ${no_docstrings_arg}
+            ${cython_debug_arg}
+            ${CYTHON_FLAGS}
+            # Necessary for autodoc of function arguments
+            --directive embedsignature=True
+            # Necessary for Cython code coverage
+            --working
+            ${CMAKE_CURRENT_SOURCE_DIR}
+            --output-file
+            "${CMAKE_CURRENT_BINARY_DIR}/${output_file}"
+            "${CMAKE_CURRENT_SOURCE_DIR}/${pyx_file}"
+    DEPENDS ${pyx_location}
+            # Do not specify byproducts for now since they don't work with the older
+            # version of cmake available in the apt repositories.
+            #BYPRODUCTS ${_generated_files}
+    COMMENT ${comment})
+
+  # Remove their visibility to the user.
+  set(corresponding_pxd_file "" CACHE INTERNAL "")
+  set(header_location "" CACHE INTERNAL "")
+  set(pxd_location "" CACHE INTERNAL "")
+endfunction()
+
+# cython_add_module( <name> src1 src2 ... srcN )
+# Build the Cython Python module.
+function(cython_add_module _name pyx_target_name generated_files)
+  set(pyx_module_source "")
+  set(other_module_sources "")
+  foreach(_file ${ARGN})
+    if(${_file} MATCHES ".*\\.py[x]?$")
+      list(APPEND pyx_module_source ${_file})
+    else()
+      list(APPEND other_module_sources ${_file})
+    endif()
+  endforeach()
+  compile_pyx(${_name} ${pyx_target_name} _generated_files ${pyx_module_source})
+  set(${generated_files} ${_generated_files} PARENT_SCOPE)
+  include_directories(${PYTHON_INCLUDE_DIRS})
+  python_add_module(${_name} ${_generated_files} ${other_module_sources})
+  add_dependencies(${_name} ${pyx_target_name})
+endfunction()
+
+include(CMakeParseArguments)

videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2017 Just van Rossum
+
+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.

videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/RECORD

@@ -0,0 +1,643 @@
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+../../../bin/ttx,sha256=QCaFJ4V_iauAfNWpUTV7NFAgeZ_YS3L160YQbS9PDEM,229
+../../../share/man/man1/ttx.1,sha256=cLbm_pOOj1C76T2QXvDxzwDj9gk-GTd5RztvTMsouFw,5377
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+Generator: setuptools (70.2.0)
+Root-Is-Purelib: false
+Tag: cp310-cp310-manylinux_2_17_x86_64
+Tag: cp310-cp310-manylinux2014_x86_64
+

videollama2/lib/python3.10/site-packages/fonttools-4.53.1.dist-info/entry_points.txt

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+[console_scripts]
+fonttools = fontTools.__main__:main
+pyftmerge = fontTools.merge:main
+pyftsubset = fontTools.subset:main
+ttx = fontTools.ttx:main

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videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/INSTALLER

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+pip

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+Metadata-Version: 2.3
+Name: orjson
+Version: 3.10.7
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: MacOS
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+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: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Rust
+Classifier: Typing :: Typed
+License-File: LICENSE-APACHE
+License-File: LICENSE-MIT
+Summary: Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
+Keywords: fast,json,dataclass,dataclasses,datetime,rfc,8259,3339
+Home-Page: https://github.com/ijl/orjson
+Author: ijl <ijl@mailbox.org>
+Author-email: ijl <ijl@mailbox.org>
+License: Apache-2.0 OR MIT
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
+Project-URL: Changelog, https://github.com/ijl/orjson/blob/master/CHANGELOG.md
+
+# orjson
+
+orjson is a fast, correct JSON library for Python. It
+[benchmarks](https://github.com/ijl/orjson?tab=readme-ov-file#performance) as the fastest Python
+library for JSON and is more correct than the standard json library or other
+third-party libraries. It serializes
+[dataclass](https://github.com/ijl/orjson?tab=readme-ov-file#dataclass),
+[datetime](https://github.com/ijl/orjson?tab=readme-ov-file#datetime),
+[numpy](https://github.com/ijl/orjson?tab=readme-ov-file#numpy), and
+[UUID](https://github.com/ijl/orjson?tab=readme-ov-file#uuid) instances natively.
+
+Its features and drawbacks compared to other Python JSON libraries:
+
+* serializes `dataclass` instances 40-50x as fast as other libraries
+* serializes `datetime`, `date`, and `time` instances to RFC 3339 format,
+e.g., "1970-01-01T00:00:00+00:00"
+* serializes `numpy.ndarray` instances 4-12x as fast with 0.3x the memory
+usage of other libraries
+* pretty prints 10x to 20x as fast as the standard library
+* serializes to `bytes` rather than `str`, i.e., is not a drop-in replacement
+* serializes `str` without escaping unicode to ASCII, e.g., "好" rather than
+"\\\u597d"
+* serializes `float` 10x as fast and deserializes twice as fast as other
+libraries
+* serializes subclasses of `str`, `int`, `list`, and `dict` natively,
+requiring `default` to specify how to serialize others
+* serializes arbitrary types using a `default` hook
+* has strict UTF-8 conformance, more correct than the standard library
+* has strict JSON conformance in not supporting Nan/Infinity/-Infinity
+* has an option for strict JSON conformance on 53-bit integers with default
+support for 64-bit
+* does not provide `load()` or `dump()` functions for reading from/writing to
+file-like objects
+
+orjson supports CPython 3.8, 3.9, 3.10, 3.11, 3.12, and 3.13. It distributes
+amd64/x86_64, aarch64/armv8, arm7, POWER/ppc64le, and s390x wheels for Linux,
+amd64 and aarch64 wheels for macOS, and amd64 and i686/x86 wheels for Windows.
+orjson does not and will not support PyPy. orjson does not and will not
+support PEP 554 subinterpreters. Releases follow semantic versioning and
+serializing a new object type without an opt-in flag is considered a
+breaking change.
+
+orjson is licensed under both the Apache 2.0 and MIT licenses. The
+repository and issue tracker is
+[github.com/ijl/orjson](https://github.com/ijl/orjson), and patches may be
+submitted there. There is a
+[CHANGELOG](https://github.com/ijl/orjson/blob/master/CHANGELOG.md)
+available in the repository.
+
+1. [Usage](https://github.com/ijl/orjson?tab=readme-ov-file#usage)
+    1. [Install](https://github.com/ijl/orjson?tab=readme-ov-file#install)
+    2. [Quickstart](https://github.com/ijl/orjson?tab=readme-ov-file#quickstart)
+    3. [Migrating](https://github.com/ijl/orjson?tab=readme-ov-file#migrating)
+    4. [Serialize](https://github.com/ijl/orjson?tab=readme-ov-file#serialize)
+        1. [default](https://github.com/ijl/orjson?tab=readme-ov-file#default)
+        2. [option](https://github.com/ijl/orjson?tab=readme-ov-file#option)
+        3. [Fragment](https://github.com/ijl/orjson?tab=readme-ov-file#fragment)
+    5. [Deserialize](https://github.com/ijl/orjson?tab=readme-ov-file#deserialize)
+2. [Types](https://github.com/ijl/orjson?tab=readme-ov-file#types)
+    1. [dataclass](https://github.com/ijl/orjson?tab=readme-ov-file#dataclass)
+    2. [datetime](https://github.com/ijl/orjson?tab=readme-ov-file#datetime)
+    3. [enum](https://github.com/ijl/orjson?tab=readme-ov-file#enum)
+    4. [float](https://github.com/ijl/orjson?tab=readme-ov-file#float)
+    5. [int](https://github.com/ijl/orjson?tab=readme-ov-file#int)
+    6. [numpy](https://github.com/ijl/orjson?tab=readme-ov-file#numpy)
+    7. [str](https://github.com/ijl/orjson?tab=readme-ov-file#str)
+    8. [uuid](https://github.com/ijl/orjson?tab=readme-ov-file#uuid)
+3. [Testing](https://github.com/ijl/orjson?tab=readme-ov-file#testing)
+4. [Performance](https://github.com/ijl/orjson?tab=readme-ov-file#performance)
+    1. [Latency](https://github.com/ijl/orjson?tab=readme-ov-file#latency)
+    2. [Memory](https://github.com/ijl/orjson?tab=readme-ov-file#memory)
+    3. [Reproducing](https://github.com/ijl/orjson?tab=readme-ov-file#reproducing)
+5. [Questions](https://github.com/ijl/orjson?tab=readme-ov-file#questions)
+6. [Packaging](https://github.com/ijl/orjson?tab=readme-ov-file#packaging)
+7. [License](https://github.com/ijl/orjson?tab=readme-ov-file#license)
+
+## Usage
+
+### Install
+
+To install a wheel from PyPI:
+
+```sh
+pip install --upgrade "pip>=20.3" # manylinux_x_y, universal2 wheel support
+pip install --upgrade orjson
+```
+
+To build a wheel, see [packaging](https://github.com/ijl/orjson?tab=readme-ov-file#packaging).
+
+### Quickstart
+
+This is an example of serializing, with options specified, and deserializing:
+
+```python
+>>> import orjson, datetime, numpy
+>>> data = {
+    "type": "job",
+    "created_at": datetime.datetime(1970, 1, 1),
+    "status": "🆗",
+    "payload": numpy.array([[1, 2], [3, 4]]),
+}
+>>> orjson.dumps(data, option=orjson.OPT_NAIVE_UTC | orjson.OPT_SERIALIZE_NUMPY)
+b'{"type":"job","created_at":"1970-01-01T00:00:00+00:00","status":"\xf0\x9f\x86\x97","payload":[[1,2],[3,4]]}'
+>>> orjson.loads(_)
+{'type': 'job', 'created_at': '1970-01-01T00:00:00+00:00', 'status': '🆗', 'payload': [[1, 2], [3, 4]]}
+```
+
+### Migrating
+
+orjson version 3 serializes more types than version 2. Subclasses of `str`,
+`int`, `dict`, and `list` are now serialized. This is faster and more similar
+to the standard library. It can be disabled with
+`orjson.OPT_PASSTHROUGH_SUBCLASS`.`dataclasses.dataclass` instances
+are now serialized by default and cannot be customized in a
+`default` function unless `option=orjson.OPT_PASSTHROUGH_DATACLASS` is
+specified. `uuid.UUID` instances are serialized by default.
+For any type that is now serialized,
+implementations in a `default` function and options enabling them can be
+removed but do not need to be. There was no change in deserialization.
+
+To migrate from the standard library, the largest difference is that
+`orjson.dumps` returns `bytes` and `json.dumps` returns a `str`. Users with
+`dict` objects using non-`str` keys should specify
+`option=orjson.OPT_NON_STR_KEYS`. `sort_keys` is replaced by
+`option=orjson.OPT_SORT_KEYS`. `indent` is replaced by
+`option=orjson.OPT_INDENT_2` and other levels of indentation are not
+supported.
+
+### Serialize
+
+```python
+def dumps(
+    __obj: Any,
+    default: Optional[Callable[[Any], Any]] = ...,
+    option: Optional[int] = ...,
+) -> bytes: ...
+```
+
+`dumps()` serializes Python objects to JSON.
+
+It natively serializes
+`str`, `dict`, `list`, `tuple`, `int`, `float`, `bool`, `None`,
+`dataclasses.dataclass`, `typing.TypedDict`, `datetime.datetime`,
+`datetime.date`, `datetime.time`, `uuid.UUID`, `numpy.ndarray`, and
+`orjson.Fragment` instances. It supports arbitrary types through `default`. It
+serializes subclasses of `str`, `int`, `dict`, `list`,
+`dataclasses.dataclass`, and `enum.Enum`. It does not serialize subclasses
+of `tuple` to avoid serializing `namedtuple` objects as arrays. To avoid
+serializing subclasses, specify the option `orjson.OPT_PASSTHROUGH_SUBCLASS`.
+
+The output is a `bytes` object containing UTF-8.
+
+The global interpreter lock (GIL) is held for the duration of the call.
+
+It raises `JSONEncodeError` on an unsupported type. This exception message
+describes the invalid object with the error message
+`Type is not JSON serializable: ...`. To fix this, specify
+[default](https://github.com/ijl/orjson?tab=readme-ov-file#default).
+
+It raises `JSONEncodeError` on a `str` that contains invalid UTF-8.
+
+It raises `JSONEncodeError` on an integer that exceeds 64 bits by default or,
+with `OPT_STRICT_INTEGER`, 53 bits.
+
+It raises `JSONEncodeError` if a `dict` has a key of a type other than `str`,
+unless `OPT_NON_STR_KEYS` is specified.
+
+It raises `JSONEncodeError` if the output of `default` recurses to handling by
+`default` more than 254 levels deep.
+
+It raises `JSONEncodeError` on circular references.
+
+It raises `JSONEncodeError`  if a `tzinfo` on a datetime object is
+unsupported.
+
+`JSONEncodeError` is a subclass of `TypeError`. This is for compatibility
+with the standard library.
+
+If the failure was caused by an exception in `default` then
+`JSONEncodeError` chains the original exception as `__cause__`.
+
+#### default
+
+To serialize a subclass or arbitrary types, specify `default` as a
+callable that returns a supported type. `default` may be a function,
+lambda, or callable class instance. To specify that a type was not
+handled by `default`, raise an exception such as `TypeError`.
+
+```python
+>>> import orjson, decimal
+>>>
+def default(obj):
+    if isinstance(obj, decimal.Decimal):
+        return str(obj)
+    raise TypeError
+
+>>> orjson.dumps(decimal.Decimal("0.0842389659712649442845"))
+JSONEncodeError: Type is not JSON serializable: decimal.Decimal
+>>> orjson.dumps(decimal.Decimal("0.0842389659712649442845"), default=default)
+b'"0.0842389659712649442845"'
+>>> orjson.dumps({1, 2}, default=default)
+orjson.JSONEncodeError: Type is not JSON serializable: set
+```
+
+The `default` callable may return an object that itself
+must be handled by `default` up to 254 times before an exception
+is raised.
+
+It is important that `default` raise an exception if a type cannot be handled.
+Python otherwise implicitly returns `None`, which appears to the caller
+like a legitimate value and is serialized:
+
+```python
+>>> import orjson, json, rapidjson
+>>>
+def default(obj):
+    if isinstance(obj, decimal.Decimal):
+        return str(obj)
+
+>>> orjson.dumps({"set":{1, 2}}, default=default)
+b'{"set":null}'
+>>> json.dumps({"set":{1, 2}}, default=default)
+'{"set":null}'
+>>> rapidjson.dumps({"set":{1, 2}}, default=default)
+'{"set":null}'
+```
+
+#### option
+
+To modify how data is serialized, specify `option`. Each `option` is an integer
+constant in `orjson`. To specify multiple options, mask them together, e.g.,
+`option=orjson.OPT_STRICT_INTEGER | orjson.OPT_NAIVE_UTC`.
+
+##### OPT_APPEND_NEWLINE
+
+Append `\n` to the output. This is a convenience and optimization for the
+pattern of `dumps(...) + "\n"`. `bytes` objects are immutable and this
+pattern copies the original contents.
+
+```python
+>>> import orjson
+>>> orjson.dumps([])
+b"[]"
+>>> orjson.dumps([], option=orjson.OPT_APPEND_NEWLINE)
+b"[]\n"
+```
+
+##### OPT_INDENT_2
+
+Pretty-print output with an indent of two spaces. This is equivalent to
+`indent=2` in the standard library. Pretty printing is slower and the output
+larger. orjson is the fastest compared library at pretty printing and has
+much less of a slowdown to pretty print than the standard library does. This
+option is compatible with all other options.
+
+```python
+>>> import orjson
+>>> orjson.dumps({"a": "b", "c": {"d": True}, "e": [1, 2]})
+b'{"a":"b","c":{"d":true},"e":[1,2]}'
+>>> orjson.dumps(
+    {"a": "b", "c": {"d": True}, "e": [1, 2]},
+    option=orjson.OPT_INDENT_2
+)
+b'{\n  "a": "b",\n  "c": {\n    "d": true\n  },\n  "e": [\n    1,\n    2\n  ]\n}'
+```
+
+If displayed, the indentation and linebreaks appear like this:
+
+```json
+{
+  "a": "b",
+  "c": {
+    "d": true
+  },
+  "e": [
+    1,
+    2
+  ]
+}
+```
+
+This measures serializing the github.json fixture as compact (52KiB) or
+pretty (64KiB):
+
+| Library    |   compact (ms) |   pretty (ms) |   vs. orjson |
+|------------|----------------|---------------|--------------|
+| orjson     |           0.03 |          0.04 |          1   |
+| ujson      |           0.18 |          0.19 |          4.6 |
+| rapidjson  |           0.1  |          0.12 |          2.9 |
+| simplejson |           0.25 |          0.89 |         21.4 |
+| json       |           0.18 |          0.71 |         17   |
+
+This measures serializing the citm_catalog.json fixture, more of a worst
+case due to the amount of nesting and newlines, as compact (489KiB) or
+pretty (1.1MiB):
+
+| Library    |   compact (ms) |   pretty (ms) |   vs. orjson |
+|------------|----------------|---------------|--------------|
+| orjson     |           0.59 |          0.71 |          1   |
+| ujson      |           2.9  |          3.59 |          5   |
+| rapidjson  |           1.81 |          2.8  |          3.9 |
+| simplejson |          10.43 |         42.13 |         59.1 |
+| json       |           4.16 |         33.42 |         46.9 |
+
+This can be reproduced using the `pyindent` script.
+
+##### OPT_NAIVE_UTC
+
+Serialize `datetime.datetime` objects without a `tzinfo` as UTC. This
+has no effect on `datetime.datetime` objects that have `tzinfo` set.
+
+```python
+>>> import orjson, datetime
+>>> orjson.dumps(
+        datetime.datetime(1970, 1, 1, 0, 0, 0),
+    )
+b'"1970-01-01T00:00:00"'
+>>> orjson.dumps(
+        datetime.datetime(1970, 1, 1, 0, 0, 0),
+        option=orjson.OPT_NAIVE_UTC,
+    )
+b'"1970-01-01T00:00:00+00:00"'
+```
+
+##### OPT_NON_STR_KEYS
+
+Serialize `dict` keys of type other than `str`. This allows `dict` keys
+to be one of `str`, `int`, `float`, `bool`, `None`, `datetime.datetime`,
+`datetime.date`, `datetime.time`, `enum.Enum`, and `uuid.UUID`. For comparison,
+the standard library serializes `str`, `int`, `float`, `bool` or `None` by
+default. orjson benchmarks as being faster at serializing non-`str` keys
+than other libraries. This option is slower for `str` keys than the default.
+
+```python
+>>> import orjson, datetime, uuid
+>>> orjson.dumps(
+        {uuid.UUID("7202d115-7ff3-4c81-a7c1-2a1f067b1ece"): [1, 2, 3]},
+        option=orjson.OPT_NON_STR_KEYS,
+    )
+b'{"7202d115-7ff3-4c81-a7c1-2a1f067b1ece":[1,2,3]}'
+>>> orjson.dumps(
+        {datetime.datetime(1970, 1, 1, 0, 0, 0): [1, 2, 3]},
+        option=orjson.OPT_NON_STR_KEYS | orjson.OPT_NAIVE_UTC,
+    )
+b'{"1970-01-01T00:00:00+00:00":[1,2,3]}'
+```
+
+These types are generally serialized how they would be as
+values, e.g., `datetime.datetime` is still an RFC 3339 string and respects
+options affecting it. The exception is that `int` serialization does not
+respect `OPT_STRICT_INTEGER`.
+
+This option has the risk of creating duplicate keys. This is because non-`str`
+objects may serialize to the same `str` as an existing key, e.g.,
+`{"1": true, 1: false}`. The last key to be inserted to the `dict` will be
+serialized last and a JSON deserializer will presumably take the last
+occurrence of a key (in the above, `false`). The first value will be lost.
+
+This option is compatible with `orjson.OPT_SORT_KEYS`. If sorting is used,
+note the sort is unstable and will be unpredictable for duplicate keys.
+
+```python
+>>> import orjson, datetime
+>>> orjson.dumps(
+    {"other": 1, datetime.date(1970, 1, 5): 2, datetime.date(1970, 1, 3): 3},
+    option=orjson.OPT_NON_STR_KEYS | orjson.OPT_SORT_KEYS
+)
+b'{"1970-01-03":3,"1970-01-05":2,"other":1}'
+```
+
+This measures serializing 589KiB of JSON comprising a `list` of 100 `dict`
+in which each `dict` has both 365 randomly-sorted `int` keys representing epoch
+timestamps as well as one `str` key and the value for each key is a
+single integer. In "str keys", the keys were converted to `str` before
+serialization, and orjson still specifes `option=orjson.OPT_NON_STR_KEYS`
+(which is always somewhat slower).
+
+| Library    |   str keys (ms) | int keys (ms)   | int keys sorted (ms)   |
+|------------|-----------------|-----------------|------------------------|
+| orjson     |            1.53 | 2.16            | 4.29                   |
+| ujson      |            3.07 | 5.65            |                        |
+| rapidjson  |            4.29 |                 |                        |
+| simplejson |           11.24 | 14.50           | 21.86                  |
+| json       |            7.17 | 8.49            |                        |
+
+ujson is blank for sorting because it segfaults. json is blank because it
+raises `TypeError` on attempting to sort before converting all keys to `str`.
+rapidjson is blank because it does not support non-`str` keys. This can
+be reproduced using the `pynonstr` script.
+
+##### OPT_OMIT_MICROSECONDS
+
+Do not serialize the `microsecond` field on `datetime.datetime` and
+`datetime.time` instances.
+
+```python
+>>> import orjson, datetime
+>>> orjson.dumps(
+        datetime.datetime(1970, 1, 1, 0, 0, 0, 1),
+    )
+b'"1970-01-01T00:00:00.000001"'
+>>> orjson.dumps(
+        datetime.datetime(1970, 1, 1, 0, 0, 0, 1),
+        option=orjson.OPT_OMIT_MICROSECONDS,
+    )
+b'"1970-01-01T00:00:00"'
+```
+
+##### OPT_PASSTHROUGH_DATACLASS
+
+Passthrough `dataclasses.dataclass` instances to `default`. This allows
+customizing their output but is much slower.
+
+
+```python
+>>> import orjson, dataclasses
+>>>
+@dataclasses.dataclass
+class User:
+    id: str
+    name: str
+    password: str
+
+def default(obj):
+    if isinstance(obj, User):
+        return {"id": obj.id, "name": obj.name}
+    raise TypeError
+
+>>> orjson.dumps(User("3b1", "asd", "zxc"))
+b'{"id":"3b1","name":"asd","password":"zxc"}'
+>>> orjson.dumps(User("3b1", "asd", "zxc"), option=orjson.OPT_PASSTHROUGH_DATACLASS)
+TypeError: Type is not JSON serializable: User
+>>> orjson.dumps(
+        User("3b1", "asd", "zxc"),
+        option=orjson.OPT_PASSTHROUGH_DATACLASS,
+        default=default,
+    )
+b'{"id":"3b1","name":"asd"}'
+```
+
+##### OPT_PASSTHROUGH_DATETIME
+
+Passthrough `datetime.datetime`, `datetime.date`, and `datetime.time` instances
+to `default`. This allows serializing datetimes to a custom format, e.g.,
+HTTP dates:
+
+```python
+>>> import orjson, datetime
+>>>
+def default(obj):
+    if isinstance(obj, datetime.datetime):
+        return obj.strftime("%a, %d %b %Y %H:%M:%S GMT")
+    raise TypeError
+
+>>> orjson.dumps({"created_at": datetime.datetime(1970, 1, 1)})
+b'{"created_at":"1970-01-01T00:00:00"}'
+>>> orjson.dumps({"created_at": datetime.datetime(1970, 1, 1)}, option=orjson.OPT_PASSTHROUGH_DATETIME)
+TypeError: Type is not JSON serializable: datetime.datetime
+>>> orjson.dumps(
+        {"created_at": datetime.datetime(1970, 1, 1)},
+        option=orjson.OPT_PASSTHROUGH_DATETIME,
+        default=default,
+    )
+b'{"created_at":"Thu, 01 Jan 1970 00:00:00 GMT"}'
+```
+
+This does not affect datetimes in `dict` keys if using OPT_NON_STR_KEYS.
+
+##### OPT_PASSTHROUGH_SUBCLASS
+
+Passthrough subclasses of builtin types to `default`.
+
+```python
+>>> import orjson
+>>>
+class Secret(str):
+    pass
+
+def default(obj):
+    if isinstance(obj, Secret):
+        return "******"
+    raise TypeError
+
+>>> orjson.dumps(Secret("zxc"))
+b'"zxc"'
+>>> orjson.dumps(Secret("zxc"), option=orjson.OPT_PASSTHROUGH_SUBCLASS)
+TypeError: Type is not JSON serializable: Secret
+>>> orjson.dumps(Secret("zxc"), option=orjson.OPT_PASSTHROUGH_SUBCLASS, default=default)
+b'"******"'
+```
+
+This does not affect serializing subclasses as `dict` keys if using
+OPT_NON_STR_KEYS.
+
+##### OPT_SERIALIZE_DATACLASS
+
+This is deprecated and has no effect in version 3. In version 2 this was
+required to serialize  `dataclasses.dataclass` instances. For more, see
+[dataclass](https://github.com/ijl/orjson?tab=readme-ov-file#dataclass).
+
+##### OPT_SERIALIZE_NUMPY
+
+Serialize `numpy.ndarray` instances. For more, see
+[numpy](https://github.com/ijl/orjson?tab=readme-ov-file#numpy).
+
+##### OPT_SERIALIZE_UUID
+
+This is deprecated and has no effect in version 3. In version 2 this was
+required to serialize `uuid.UUID` instances. For more, see
+[UUID](https://github.com/ijl/orjson?tab=readme-ov-file#UUID).
+
+##### OPT_SORT_KEYS
+
+Serialize `dict` keys in sorted order. The default is to serialize in an
+unspecified order. This is equivalent to `sort_keys=True` in the standard
+library.
+
+This can be used to ensure the order is deterministic for hashing or tests.
+It has a substantial performance penalty and is not recommended in general.
+
+```python
+>>> import orjson
+>>> orjson.dumps({"b": 1, "c": 2, "a": 3})
+b'{"b":1,"c":2,"a":3}'
+>>> orjson.dumps({"b": 1, "c": 2, "a": 3}, option=orjson.OPT_SORT_KEYS)
+b'{"a":3,"b":1,"c":2}'
+```
+
+This measures serializing the twitter.json fixture unsorted and sorted:
+
+| Library    |   unsorted (ms) |   sorted (ms) |   vs. orjson |
+|------------|-----------------|---------------|--------------|
+| orjson     |            0.32 |          0.54 |          1   |
+| ujson      |            1.6  |          2.07 |          3.8 |
+| rapidjson  |            1.12 |          1.65 |          3.1 |
+| simplejson |            2.25 |          3.13 |          5.8 |
+| json       |            1.78 |          2.32 |          4.3 |
+
+The benchmark can be reproduced using the `pysort` script.
+
+The sorting is not collation/locale-aware:
+
+```python
+>>> import orjson
+>>> orjson.dumps({"a": 1, "ä": 2, "A": 3}, option=orjson.OPT_SORT_KEYS)
+b'{"A":3,"a":1,"\xc3\xa4":2}'
+```
+
+This is the same sorting behavior as the standard library, rapidjson,
+simplejson, and ujson.
+
+`dataclass` also serialize as maps but this has no effect on them.
+
+##### OPT_STRICT_INTEGER
+
+Enforce 53-bit limit on integers. The limit is otherwise 64 bits, the same as
+the Python standard library. For more, see [int](https://github.com/ijl/orjson?tab=readme-ov-file#int).
+
+##### OPT_UTC_Z
+
+Serialize a UTC timezone on `datetime.datetime` instances as `Z` instead
+of `+00:00`.
+
+```python
+>>> import orjson, datetime, zoneinfo
+>>> orjson.dumps(
+        datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=zoneinfo.ZoneInfo("UTC")),
+    )
+b'"1970-01-01T00:00:00+00:00"'
+>>> orjson.dumps(
+        datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=zoneinfo.ZoneInfo("UTC")),
+        option=orjson.OPT_UTC_Z
+    )
+b'"1970-01-01T00:00:00Z"'
+```
+
+#### Fragment
+
+`orjson.Fragment` includes already-serialized JSON in a document. This is an
+efficient way to include JSON blobs from a cache, JSONB field, or separately
+serialized object without first deserializing to Python objects via `loads()`.
+
+```python
+>>> import orjson
+>>> orjson.dumps({"key": "zxc", "data": orjson.Fragment(b'{"a": "b", "c": 1}')})
+b'{"key":"zxc","data":{"a": "b", "c": 1}}'
+```
+
+It does no reformatting: `orjson.OPT_INDENT_2` will not affect a
+compact blob nor will a pretty-printed JSON blob be rewritten as compact.
+
+The input must be `bytes` or `str` and given as a positional argument.
+
+This raises `orjson.JSONEncodeError` if a `str` is given and the input is
+not valid UTF-8. It otherwise does no validation and it is possible to
+write invalid JSON. This does not escape characters. The implementation is
+tested to not crash if given invalid strings or invalid JSON.
+
+This is similar to `RawJSON` in rapidjson.
+
+### Deserialize
+
+```python
+def loads(__obj: Union[bytes, bytearray, memoryview, str]) -> Any: ...
+```
+
+`loads()` deserializes JSON to Python objects. It deserializes to `dict`,
+`list`, `int`, `float`, `str`, `bool`, and `None` objects.
+
+`bytes`, `bytearray`, `memoryview`, and `str` input are accepted. If the input
+exists as a `memoryview`, `bytearray`, or `bytes` object, it is recommended to
+pass these directly rather than creating an unnecessary `str` object. That is,
+`orjson.loads(b"{}")` instead of `orjson.loads(b"{}".decode("utf-8"))`. This
+has lower memory usage and lower latency.
+
+The input must be valid UTF-8.
+
+orjson maintains a cache of map keys for the duration of the process. This
+causes a net reduction in memory usage by avoiding duplicate strings. The
+keys must be at most 64 bytes to be cached and 2048 entries are stored.
+
+The global interpreter lock (GIL) is held for the duration of the call.
+
+It raises `JSONDecodeError` if given an invalid type or invalid
+JSON. This includes if the input contains `NaN`, `Infinity`, or `-Infinity`,
+which the standard library allows, but is not valid JSON.
+
+It raises `JSONDecodeError` if a combination of array or object recurses
+1024 levels deep.
+
+`JSONDecodeError` is a subclass of `json.JSONDecodeError` and `ValueError`.
+This is for compatibility with the standard library.
+
+## Types
+
+### dataclass
+
+orjson serializes instances of `dataclasses.dataclass` natively. It serializes
+instances 40-50x as fast as other libraries and avoids a severe slowdown seen
+in other libraries compared to serializing `dict`.
+
+It is supported to pass all variants of dataclasses, including dataclasses
+using `__slots__`, frozen dataclasses, those with optional or default
+attributes, and subclasses. There is a performance benefit to not
+using `__slots__`.
+
+| Library    | dict (ms)   | dataclass (ms)   | vs. orjson   |
+|------------|-------------|------------------|--------------|
+| orjson     | 1.40        | 1.60             | 1            |
+| ujson      |             |                  |              |
+| rapidjson  | 3.64        | 68.48            | 42           |
+| simplejson | 14.21       | 92.18            | 57           |
+| json       | 13.28       | 94.90            | 59           |
+
+This measures serializing 555KiB of JSON, orjson natively and other libraries
+using `default` to serialize the output of `dataclasses.asdict()`. This can be
+reproduced using the `pydataclass` script.
+
+Dataclasses are serialized as maps, with every attribute serialized and in
+the order given on class definition:
+
+```python
+>>> import dataclasses, orjson, typing
+
+@dataclasses.dataclass
+class Member:
+    id: int
+    active: bool = dataclasses.field(default=False)
+
+@dataclasses.dataclass
+class Object:
+    id: int
+    name: str
+    members: typing.List[Member]
+
+>>> orjson.dumps(Object(1, "a", [Member(1, True), Member(2)]))
+b'{"id":1,"name":"a","members":[{"id":1,"active":true},{"id":2,"active":false}]}'
+```
+
+### datetime
+
+orjson serializes `datetime.datetime` objects to
+[RFC 3339](https://tools.ietf.org/html/rfc3339) format,
+e.g., "1970-01-01T00:00:00+00:00". This is a subset of ISO 8601 and is
+compatible with `isoformat()` in the standard library.
+
+```python
+>>> import orjson, datetime, zoneinfo
+>>> orjson.dumps(
+    datetime.datetime(2018, 12, 1, 2, 3, 4, 9, tzinfo=zoneinfo.ZoneInfo("Australia/Adelaide"))
+)
+b'"2018-12-01T02:03:04.000009+10:30"'
+>>> orjson.dumps(
+    datetime.datetime(2100, 9, 1, 21, 55, 2).replace(tzinfo=zoneinfo.ZoneInfo("UTC"))
+)
+b'"2100-09-01T21:55:02+00:00"'
+>>> orjson.dumps(
+    datetime.datetime(2100, 9, 1, 21, 55, 2)
+)
+b'"2100-09-01T21:55:02"'
+```
+
+`datetime.datetime` supports instances with a `tzinfo` that is `None`,
+`datetime.timezone.utc`, a timezone instance from the python3.9+ `zoneinfo`
+module, or a timezone instance from the third-party `pendulum`, `pytz`, or
+`dateutil`/`arrow` libraries.
+
+It is fastest to use the standard library's `zoneinfo.ZoneInfo` for timezones.
+
+`datetime.time` objects must not have a `tzinfo`.
+
+```python
+>>> import orjson, datetime
+>>> orjson.dumps(datetime.time(12, 0, 15, 290))
+b'"12:00:15.000290"'
+```
+
+`datetime.date` objects will always serialize.
+
+```python
+>>> import orjson, datetime
+>>> orjson.dumps(datetime.date(1900, 1, 2))
+b'"1900-01-02"'
+```
+
+Errors with `tzinfo` result in `JSONEncodeError` being raised.
+
+To disable serialization of `datetime` objects specify the option
+`orjson.OPT_PASSTHROUGH_DATETIME`.
+
+To use "Z" suffix instead of "+00:00" to indicate UTC ("Zulu") time, use the option
+`orjson.OPT_UTC_Z`.
+
+To assume datetimes without timezone are UTC, use the option `orjson.OPT_NAIVE_UTC`.
+
+### enum
+
+orjson serializes enums natively. Options apply to their values.
+
+```python
+>>> import enum, datetime, orjson
+>>>
+class DatetimeEnum(enum.Enum):
+    EPOCH = datetime.datetime(1970, 1, 1, 0, 0, 0)
+>>> orjson.dumps(DatetimeEnum.EPOCH)
+b'"1970-01-01T00:00:00"'
+>>> orjson.dumps(DatetimeEnum.EPOCH, option=orjson.OPT_NAIVE_UTC)
+b'"1970-01-01T00:00:00+00:00"'
+```
+
+Enums with members that are not supported types can be serialized using
+`default`:
+
+```python
+>>> import enum, orjson
+>>>
+class Custom:
+    def __init__(self, val):
+        self.val = val
+
+def default(obj):
+    if isinstance(obj, Custom):
+        return obj.val
+    raise TypeError
+
+class CustomEnum(enum.Enum):
+    ONE = Custom(1)
+
+>>> orjson.dumps(CustomEnum.ONE, default=default)
+b'1'
+```
+
+### float
+
+orjson serializes and deserializes double precision floats with no loss of
+precision and consistent rounding.
+
+`orjson.dumps()` serializes Nan, Infinity, and -Infinity, which are not
+compliant JSON, as `null`:
+
+```python
+>>> import orjson, ujson, rapidjson, json
+>>> orjson.dumps([float("NaN"), float("Infinity"), float("-Infinity")])
+b'[null,null,null]'
+>>> ujson.dumps([float("NaN"), float("Infinity"), float("-Infinity")])
+OverflowError: Invalid Inf value when encoding double
+>>> rapidjson.dumps([float("NaN"), float("Infinity"), float("-Infinity")])
+'[NaN,Infinity,-Infinity]'
+>>> json.dumps([float("NaN"), float("Infinity"), float("-Infinity")])
+'[NaN, Infinity, -Infinity]'
+```
+
+### int
+
+orjson serializes and deserializes 64-bit integers by default. The range
+supported is a signed 64-bit integer's minimum (-9223372036854775807) to
+an unsigned 64-bit integer's maximum (18446744073709551615). This
+is widely compatible, but there are implementations
+that only support 53-bits for integers, e.g.,
+web browsers. For those implementations, `dumps()` can be configured to
+raise a `JSONEncodeError` on values exceeding the 53-bit range.
+
+```python
+>>> import orjson
+>>> orjson.dumps(9007199254740992)
+b'9007199254740992'
+>>> orjson.dumps(9007199254740992, option=orjson.OPT_STRICT_INTEGER)
+JSONEncodeError: Integer exceeds 53-bit range
+>>> orjson.dumps(-9007199254740992, option=orjson.OPT_STRICT_INTEGER)
+JSONEncodeError: Integer exceeds 53-bit range
+```
+
+### numpy
+
+orjson natively serializes `numpy.ndarray` and individual
+`numpy.float64`, `numpy.float32`, `numpy.float16` (`numpy.half`),
+`numpy.int64`, `numpy.int32`, `numpy.int16`, `numpy.int8`,
+`numpy.uint64`, `numpy.uint32`, `numpy.uint16`, `numpy.uint8`,
+`numpy.uintp`, `numpy.intp`, `numpy.datetime64`, and `numpy.bool`
+instances.
+
+orjson is compatible with both numpy v1 and v2.
+
+orjson is faster than all compared libraries at serializing
+numpy instances. Serializing numpy data requires specifying
+`option=orjson.OPT_SERIALIZE_NUMPY`.
+
+```python
+>>> import orjson, numpy
+>>> orjson.dumps(
+        numpy.array([[1, 2, 3], [4, 5, 6]]),
+        option=orjson.OPT_SERIALIZE_NUMPY,
+)
+b'[[1,2,3],[4,5,6]]'
+```
+
+The array must be a contiguous C array (`C_CONTIGUOUS`) and one of the
+supported datatypes.
+
+Note a difference between serializing `numpy.float32` using `ndarray.tolist()`
+or `orjson.dumps(..., option=orjson.OPT_SERIALIZE_NUMPY)`: `tolist()` converts
+to a `double` before serializing and orjson's native path does not. This
+can result in different rounding.
+
+`numpy.datetime64` instances are serialized as RFC 3339 strings and
+datetime options affect them.
+
+```python
+>>> import orjson, numpy
+>>> orjson.dumps(
+        numpy.datetime64("2021-01-01T00:00:00.172"),
+        option=orjson.OPT_SERIALIZE_NUMPY,
+)
+b'"2021-01-01T00:00:00.172000"'
+>>> orjson.dumps(
+        numpy.datetime64("2021-01-01T00:00:00.172"),
+        option=(
+            orjson.OPT_SERIALIZE_NUMPY |
+            orjson.OPT_NAIVE_UTC |
+            orjson.OPT_OMIT_MICROSECONDS
+        ),
+)
+b'"2021-01-01T00:00:00+00:00"'
+```
+
+If an array is not a contiguous C array, contains an unsupported datatype,
+or contains a `numpy.datetime64` using an unsupported representation
+(e.g., picoseconds), orjson falls through to `default`. In `default`,
+`obj.tolist()` can be specified.
+
+If an array is not in the native endianness, e.g., an array of big-endian values
+on a little-endian system, `orjson.JSONEncodeError`  is raised.
+
+If an array is malformed, `orjson.JSONEncodeError` is raised.
+
+This measures serializing 92MiB of JSON from an `numpy.ndarray` with
+dimensions of `(50000, 100)` and `numpy.float64` values:
+
+| Library    | Latency (ms)   | RSS diff (MiB)   | vs. orjson   |
+|------------|----------------|------------------|--------------|
+| orjson     | 194            | 99               | 1.0          |
+| ujson      |                |                  |              |
+| rapidjson  | 3,048          | 309              | 15.7         |
+| simplejson | 3,023          | 297              | 15.6         |
+| json       | 3,133          | 297              | 16.1         |
+
+This measures serializing 100MiB of JSON from an `numpy.ndarray` with
+dimensions of `(100000, 100)` and `numpy.int32` values:
+
+| Library    | Latency (ms)   | RSS diff (MiB)   | vs. orjson   |
+|------------|----------------|------------------|--------------|
+| orjson     | 178            | 115              | 1.0          |
+| ujson      |                |                  |              |
+| rapidjson  | 1,512          | 551              | 8.5          |
+| simplejson | 1,606          | 504              | 9.0          |
+| json       | 1,506          | 503              | 8.4          |
+
+This measures serializing 105MiB of JSON from an `numpy.ndarray` with
+dimensions of `(100000, 200)` and `numpy.bool` values:
+
+| Library    | Latency (ms)   | RSS diff (MiB)   | vs. orjson   |
+|------------|----------------|------------------|--------------|
+| orjson     | 157            | 120              | 1.0          |
+| ujson      |                |                  |              |
+| rapidjson  | 710            | 327              | 4.5          |
+| simplejson | 931            | 398              | 5.9          |
+| json       | 996            | 400              | 6.3          |
+
+In these benchmarks, orjson serializes natively, ujson is blank because it
+does not support a `default` parameter, and the other libraries serialize
+`ndarray.tolist()` via `default`. The RSS column measures peak memory
+usage during serialization. This can be reproduced using the `pynumpy` script.
+
+orjson does not have an installation or compilation dependency on numpy. The
+implementation is independent, reading `numpy.ndarray` using
+`PyArrayInterface`.
+
+### str
+
+orjson is strict about UTF-8 conformance. This is stricter than the standard
+library's json module, which will serialize and deserialize UTF-16 surrogates,
+e.g., "\ud800", that are invalid UTF-8.
+
+If `orjson.dumps()` is given a `str` that does not contain valid UTF-8,
+`orjson.JSONEncodeError` is raised. If `loads()` receives invalid UTF-8,
+`orjson.JSONDecodeError` is raised.
+
+orjson and rapidjson are the only compared JSON libraries to consistently
+error on bad input.
+
+```python
+>>> import orjson, ujson, rapidjson, json
+>>> orjson.dumps('\ud800')
+JSONEncodeError: str is not valid UTF-8: surrogates not allowed
+>>> ujson.dumps('\ud800')
+UnicodeEncodeError: 'utf-8' codec ...
+>>> rapidjson.dumps('\ud800')
+UnicodeEncodeError: 'utf-8' codec ...
+>>> json.dumps('\ud800')
+'"\\ud800"'
+>>> orjson.loads('"\\ud800"')
+JSONDecodeError: unexpected end of hex escape at line 1 column 8: line 1 column 1 (char 0)
+>>> ujson.loads('"\\ud800"')
+''
+>>> rapidjson.loads('"\\ud800"')
+ValueError: Parse error at offset 1: The surrogate pair in string is invalid.
+>>> json.loads('"\\ud800"')
+'\ud800'
+```
+
+To make a best effort at deserializing bad input, first decode `bytes` using
+the `replace` or `lossy` argument for `errors`:
+
+```python
+>>> import orjson
+>>> orjson.loads(b'"\xed\xa0\x80"')
+JSONDecodeError: str is not valid UTF-8: surrogates not allowed
+>>> orjson.loads(b'"\xed\xa0\x80"'.decode("utf-8", "replace"))
+'���'
+```
+
+### uuid
+
+orjson serializes `uuid.UUID` instances to
+[RFC 4122](https://tools.ietf.org/html/rfc4122) format, e.g.,
+"f81d4fae-7dec-11d0-a765-00a0c91e6bf6".
+
+``` python
+>>> import orjson, uuid
+>>> orjson.dumps(uuid.UUID('f81d4fae-7dec-11d0-a765-00a0c91e6bf6'))
+b'"f81d4fae-7dec-11d0-a765-00a0c91e6bf6"'
+>>> orjson.dumps(uuid.uuid5(uuid.NAMESPACE_DNS, "python.org"))
+b'"886313e1-3b8a-5372-9b90-0c9aee199e5d"'
+```
+
+## Testing
+
+The library has comprehensive tests. There are tests against fixtures in the
+[JSONTestSuite](https://github.com/nst/JSONTestSuite) and
+[nativejson-benchmark](https://github.com/miloyip/nativejson-benchmark)
+repositories. It is tested to not crash against the
+[Big List of Naughty Strings](https://github.com/minimaxir/big-list-of-naughty-strings).
+It is tested to not leak memory. It is tested to not crash
+against and not accept invalid UTF-8. There are integration tests
+exercising the library's use in web servers (gunicorn using multiprocess/forked
+workers) and when
+multithreaded. It also uses some tests from the ultrajson library.
+
+orjson is the most correct of the compared libraries. This graph shows how each
+library handles a combined 342 JSON fixtures from the
+[JSONTestSuite](https://github.com/nst/JSONTestSuite) and
+[nativejson-benchmark](https://github.com/miloyip/nativejson-benchmark) tests:
+
+| Library    |   Invalid JSON documents not rejected |   Valid JSON documents not deserialized |
+|------------|---------------------------------------|-----------------------------------------|
+| orjson     |                                     0 |                                       0 |
+| ujson      |                                    31 |                                       0 |
+| rapidjson  |                                     6 |                                       0 |
+| simplejson |                                    10 |                                       0 |
+| json       |                                    17 |                                       0 |
+
+This shows that all libraries deserialize valid JSON but only orjson
+correctly rejects the given invalid JSON fixtures. Errors are largely due to
+accepting invalid strings and numbers.
+
+The graph above can be reproduced using the `pycorrectness` script.
+
+## Performance
+
+Serialization and deserialization performance of orjson is better than
+ultrajson, rapidjson, simplejson, or json. The benchmarks are done on
+fixtures of real data:
+
+* twitter.json, 631.5KiB, results of a search on Twitter for "一", containing
+CJK strings, dictionaries of strings and arrays of dictionaries, indented.
+
+* github.json, 55.8KiB, a GitHub activity feed, containing dictionaries of
+strings and arrays of dictionaries, not indented.
+
+* citm_catalog.json, 1.7MiB, concert data, containing nested dictionaries of
+strings and arrays of integers, indented.
+
+* canada.json, 2.2MiB, coordinates of the Canadian border in GeoJSON
+format, containing floats and arrays, indented.
+
+### Latency
+
+![Serialization](doc/serialization.png)
+
+![Deserialization](doc/deserialization.png)
+
+#### twitter.json serialization
+
+| Library    |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
+|------------|---------------------------------|-------------------------|----------------------|
+| orjson     |                             0.1 |                    8377 |                  1   |
+| ujson      |                             0.9 |                    1088 |                  7.3 |
+| rapidjson  |                             0.8 |                    1228 |                  6.8 |
+| simplejson |                             1.9 |                     531 |                 15.6 |
+| json       |                             1.4 |                     744 |                 11.3 |
+
+#### twitter.json deserialization
+
+| Library    |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
+|------------|---------------------------------|-------------------------|----------------------|
+| orjson     |                             0.6 |                    1811 |                  1   |
+| ujson      |                             1.2 |                     814 |                  2.1 |
+| rapidjson  |                             2.1 |                     476 |                  3.8 |
+| simplejson |                             1.6 |                     626 |                  3   |
+| json       |                             1.8 |                     557 |                  3.3 |
+
+#### github.json serialization
+
+| Library    |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
+|------------|---------------------------------|-------------------------|----------------------|
+| orjson     |                            0.01 |                  104424 |                  1   |
+| ujson      |                            0.09 |                   10594 |                  9.8 |
+| rapidjson  |                            0.07 |                   13667 |                  7.6 |
+| simplejson |                            0.2  |                    5051 |                 20.6 |
+| json       |                            0.14 |                    7133 |                 14.6 |
+
+#### github.json deserialization
+
+| Library    |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
+|------------|---------------------------------|-------------------------|----------------------|
+| orjson     |                            0.05 |                   20069 |                  1   |
+| ujson      |                            0.11 |                    8913 |                  2.3 |
+| rapidjson  |                            0.13 |                    8077 |                  2.6 |
+| simplejson |                            0.11 |                    9342 |                  2.1 |
+| json       |                            0.11 |                    9291 |                  2.2 |
+
+#### citm_catalog.json serialization
+
+| Library    |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
+|------------|---------------------------------|-------------------------|----------------------|
+| orjson     |                             0.3 |                    3757 |                  1   |
+| ujson      |                             1.7 |                     598 |                  6.3 |
+| rapidjson  |                             1.3 |                     768 |                  4.9 |
+| simplejson |                             8.3 |                     120 |                 31.1 |
+| json       |                             3   |                     331 |                 11.3 |
+
+#### citm_catalog.json deserialization
+
+| Library    |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
+|------------|---------------------------------|-------------------------|----------------------|
+| orjson     |                             1.4 |                     730 |                  1   |
+| ujson      |                             2.6 |                     384 |                  1.9 |
+| rapidjson  |                             4   |                     246 |                  3   |
+| simplejson |                             3.7 |                     271 |                  2.7 |
+| json       |                             3.7 |                     267 |                  2.7 |
+
+#### canada.json serialization
+
+| Library    |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
+|------------|---------------------------------|-------------------------|----------------------|
+| orjson     |                             2.4 |                     410 |                  1   |
+| ujson      |                             9.6 |                     104 |                  3.9 |
+| rapidjson  |                            28.7 |                      34 |                 11.8 |
+| simplejson |                            49.3 |                      20 |                 20.3 |
+| json       |                            30.6 |                      32 |                 12.6 |
+
+#### canada.json deserialization
+
+| Library    |   Median latency (milliseconds) |   Operations per second |   Relative (latency) |
+|------------|---------------------------------|-------------------------|----------------------|
+| orjson     |                             3   |                     336 |                  1   |
+| ujson      |                             7.1 |                     141 |                  2.4 |
+| rapidjson  |                            20.1 |                      49 |                  6.7 |
+| simplejson |                            16.8 |                      59 |                  5.6 |
+| json       |                            18.2 |                      55 |                  6.1 |
+
+### Memory
+
+orjson as of 3.7.0 has higher baseline memory usage than other libraries
+due to a persistent buffer used for parsing. Incremental memory usage when
+deserializing is similar to the standard library and other third-party
+libraries.
+
+This measures, in the first column, RSS after importing a library and reading
+the fixture, and in the second column, increases in RSS after repeatedly
+calling `loads()` on the fixture.
+
+#### twitter.json
+
+| Library    |   import, read() RSS (MiB) |   loads() increase in RSS (MiB) |
+|------------|----------------------------|---------------------------------|
+| orjson     |                       15.7 |                             3.4 |
+| ujson      |                       16.4 |                             3.4 |
+| rapidjson  |                       16.6 |                             4.4 |
+| simplejson |                       14.5 |                             1.8 |
+| json       |                       13.9 |                             1.8 |
+
+#### github.json
+
+| Library    |   import, read() RSS (MiB) |   loads() increase in RSS (MiB) |
+|------------|----------------------------|---------------------------------|
+| orjson     |                       15.2 |                             0.4 |
+| ujson      |                       15.4 |                             0.4 |
+| rapidjson  |                       15.7 |                             0.5 |
+| simplejson |                       13.7 |                             0.2 |
+| json       |                       13.3 |                             0.1 |
+
+#### citm_catalog.json
+
+| Library    |   import, read() RSS (MiB) |   loads() increase in RSS (MiB) |
+|------------|----------------------------|---------------------------------|
+| orjson     |                       16.8 |                            10.1 |
+| ujson      |                       17.3 |                            10.2 |
+| rapidjson  |                       17.6 |                            28.7 |
+| simplejson |                       15.8 |                            30.1 |
+| json       |                       14.8 |                            20.5 |
+
+#### canada.json
+
+| Library    |   import, read() RSS (MiB) |   loads() increase in RSS (MiB) |
+|------------|----------------------------|---------------------------------|
+| orjson     |                       17.2 |                            22.1 |
+| ujson      |                       17.4 |                            18.3 |
+| rapidjson  |                       18   |                            23.5 |
+| simplejson |                       15.7 |                            21.4 |
+| json       |                       15.4 |                            20.4 |
+
+### Reproducing
+
+The above was measured using Python 3.11.9 on Linux (amd64) with
+orjson 3.10.6, ujson 5.10.0, python-rapidson 1.18, and simplejson 3.19.2.
+
+The latency results can be reproduced using the `pybench` and `graph`
+scripts. The memory results can be reproduced using the `pymem` script.
+
+## Questions
+
+### Why can't I install it from PyPI?
+
+Probably `pip` needs to be upgraded to version 20.3 or later to support
+the latest manylinux_x_y or universal2 wheel formats.
+
+### "Cargo, the Rust package manager, is not installed or is not on PATH."
+
+This happens when there are no binary wheels (like manylinux) for your
+platform on PyPI. You can install [Rust](https://www.rust-lang.org/) through
+`rustup` or a package manager and then it will compile.
+
+### Will it deserialize to dataclasses, UUIDs, decimals, etc or support object_hook?
+
+No. This requires a schema specifying what types are expected and how to
+handle errors etc. This is addressed by data validation libraries a
+level above this.
+
+### Will it serialize to `str`?
+
+No. `bytes` is the correct type for a serialized blob.
+
+## Packaging
+
+To package orjson requires at least [Rust](https://www.rust-lang.org/) 1.72
+and the [maturin](https://github.com/PyO3/maturin) build tool. The recommended
+build command is:
+
+```sh
+maturin build --release --strip
+```
+
+It benefits from also having a C build environment to compile a faster
+deserialization backend. See this project's `manylinux_2_28` builds for an
+example using clang and LTO.
+
+The project's own CI tests against `nightly-2024-08-05` and stable 1.72. It
+is prudent to pin the nightly version because that channel can introduce
+breaking changes.
+
+orjson is tested for amd64, aarch64, arm7, ppc64le, and s390x on Linux. It
+is tested for either aarch64 or amd64 on macOS and cross-compiles for the other,
+depending on version. For Windows it is tested on amd64 and i686.
+
+There are no runtime dependencies other than libc.
+
+The source distribution on PyPI contains all dependencies' source and can be
+built without network access. The file can be downloaded from
+`https://files.pythonhosted.org/packages/source/o/orjson/orjson-${version}.tar.gz`.
+
+orjson's tests are included in the source distribution on PyPI. The
+requirements to run the tests are specified in `test/requirements.txt`. The
+tests should be run as part of the build. It can be run with
+`pytest -q test`.
+
+## License
+
+orjson was written by ijl <<ijl@mailbox.org>>, copyright 2018 - 2024, available
+to you under either the Apache 2 license or MIT license at your choice.
+

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videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/WHEEL

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videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/license_files/LICENSE-APACHE

@@ -0,0 +1,201 @@
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videollama2/lib/python3.10/site-packages/orjson-3.10.7.dist-info/license_files/LICENSE-MIT

@@ -0,0 +1,23 @@
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videollama2/lib/python3.10/site-packages/scipy-1.14.1.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

videollama2/lib/python3.10/site-packages/scipy-1.14.1.dist-info/LICENSE.txt

@@ -0,0 +1,933 @@
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+
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+  17. Interpretation of Sections 15 and 16.
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+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
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+
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+Also add information on how to contact you by electronic and paper mail.
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+The hypothetical commands `show w' and `show c' should show the appropriate
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+
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+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
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+may consider it more useful to permit linking proprietary applications with
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+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
+
+
+Name: libquadmath
+Files: scipy.libs/libquadmath*.so
+Description: dynamically linked to files compiled with gcc
+Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
+License: LGPL-2.1-or-later
+
+    GCC Quad-Precision Math Library
+    Copyright (C) 2010-2019 Free Software Foundation, Inc.
+    Written by Francois-Xavier Coudert  <fxcoudert@gcc.gnu.org>
+
+    This file is part of the libquadmath library.
+    Libquadmath is free software; you can redistribute it and/or
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+    https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html

videollama2/lib/python3.10/site-packages/scipy-1.14.1.dist-info/METADATA

@@ -0,0 +1,1077 @@
+Metadata-Version: 2.1
+Name: scipy
+Version: 1.14.1
+Summary: Fundamental algorithms for scientific computing in Python
+Home-page: https://scipy.org/
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+        <http://www.gnu.org/licenses/>.
+        
+          The GNU General Public License does not permit incorporating your program
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+        may consider it more useful to permit linking proprietary applications with
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+        Public License instead of this License.  But first, please read
+        <http://www.gnu.org/philosophy/why-not-lgpl.html>.
+        
+        
+        Name: libquadmath
+        Files: scipy.libs/libquadmath*.so
+        Description: dynamically linked to files compiled with gcc
+        Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
+        License: LGPL-2.1-or-later
+        
+            GCC Quad-Precision Math Library
+            Copyright (C) 2010-2019 Free Software Foundation, Inc.
+            Written by Francois-Xavier Coudert  <fxcoudert@gcc.gnu.org>
+        
+            This file is part of the libquadmath library.
+            Libquadmath is free software; you can redistribute it and/or
+            modify it under the terms of the GNU Library General Public
+            License as published by the Free Software Foundation; either
+            version 2.1 of the License, or (at your option) any later version.
+        
+            Libquadmath is distributed in the hope that it will be useful,
+            but WITHOUT ANY WARRANTY; without even the implied warranty of
+            MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+            Lesser General Public License for more details.
+            https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: C
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Scientific/Engineering
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Unix
+Classifier: Operating System :: MacOS
+Project-URL: Homepage, https://scipy.org/
+Project-URL: Documentation, https://docs.scipy.org/doc/scipy/
+Project-URL: Source, https://github.com/scipy/scipy
+Project-URL: Download, https://github.com/scipy/scipy/releases
+Project-URL: Tracker, https://github.com/scipy/scipy/issues
+Requires-Python: >=3.10
+Requires-Dist: numpy<2.3,>=1.23.5
+Requires-Dist: pytest; extra == "test"
+Requires-Dist: pytest-cov; extra == "test"
+Requires-Dist: pytest-timeout; extra == "test"
+Requires-Dist: pytest-xdist; extra == "test"
+Requires-Dist: asv; extra == "test"
+Requires-Dist: mpmath; extra == "test"
+Requires-Dist: gmpy2; extra == "test"
+Requires-Dist: threadpoolctl; extra == "test"
+Requires-Dist: scikit-umfpack; extra == "test"
+Requires-Dist: pooch; extra == "test"
+Requires-Dist: hypothesis>=6.30; extra == "test"
+Requires-Dist: array-api-strict>=2.0; extra == "test"
+Requires-Dist: Cython; extra == "test"
+Requires-Dist: meson; extra == "test"
+Requires-Dist: ninja; sys_platform != "emscripten" and extra == "test"
+Requires-Dist: sphinx<=7.3.7,>=5.0.0; extra == "doc"
+Requires-Dist: pydata-sphinx-theme>=0.15.2; extra == "doc"
+Requires-Dist: sphinx-design>=0.4.0; extra == "doc"
+Requires-Dist: matplotlib>=3.5; extra == "doc"
+Requires-Dist: numpydoc; extra == "doc"
+Requires-Dist: jupytext; extra == "doc"
+Requires-Dist: myst-nb; extra == "doc"
+Requires-Dist: pooch; extra == "doc"
+Requires-Dist: jupyterlite-sphinx>=0.13.1; extra == "doc"
+Requires-Dist: jupyterlite-pyodide-kernel; extra == "doc"
+Requires-Dist: mypy==1.10.0; extra == "dev"
+Requires-Dist: typing_extensions; extra == "dev"
+Requires-Dist: types-psutil; extra == "dev"
+Requires-Dist: pycodestyle; extra == "dev"
+Requires-Dist: ruff>=0.0.292; extra == "dev"
+Requires-Dist: cython-lint>=0.12.2; extra == "dev"
+Requires-Dist: rich-click; extra == "dev"
+Requires-Dist: doit>=0.36.0; extra == "dev"
+Requires-Dist: pydevtool; extra == "dev"
+Provides-Extra: test
+Provides-Extra: doc
+Provides-Extra: dev
+Description-Content-Type: text/x-rst
+
+.. image:: https://raw.githubusercontent.com/scipy/scipy/main/doc/source/_static/logo.svg
+  :target: https://scipy.org
+  :width: 110
+  :height: 110
+  :align: left 
+
+.. image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A
+  :target: https://numfocus.org
+
+.. image:: https://img.shields.io/pypi/dm/scipy.svg?label=Pypi%20downloads
+  :target: https://pypi.org/project/scipy/
+
+.. image:: https://img.shields.io/conda/dn/conda-forge/scipy.svg?label=Conda%20downloads
+  :target: https://anaconda.org/conda-forge/scipy
+
+.. image:: https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg
+  :target: https://stackoverflow.com/questions/tagged/scipy
+
+.. image:: https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue.svg
+  :target: https://www.nature.com/articles/s41592-019-0686-2
+
+SciPy (pronounced "Sigh Pie") is an open-source software for mathematics,
+science, and engineering. It includes modules for statistics, optimization,
+integration, linear algebra, Fourier transforms, signal and image processing,
+ODE solvers, and more.
+
+- **Website:** https://scipy.org
+- **Documentation:** https://docs.scipy.org/doc/scipy/
+- **Development version of the documentation:** https://scipy.github.io/devdocs
+- **SciPy development forum:** https://discuss.scientific-python.org/c/contributor/scipy 
+- **Source code:** https://github.com/scipy/scipy
+- **Contributing:** https://scipy.github.io/devdocs/dev/index.html
+- **Bug reports:** https://github.com/scipy/scipy/issues
+- **Code of Conduct:** https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html
+- **Report a security vulnerability:** https://tidelift.com/docs/security
+- **Citing in your work:** https://www.scipy.org/citing-scipy/
+
+SciPy is built to work with
+NumPy arrays, and provides many user-friendly and efficient numerical routines,
+such as routines for numerical integration and optimization. Together, they
+run on all popular operating systems, are quick to install, and are free of
+charge. NumPy and SciPy are easy to use, but powerful enough to be depended
+upon by some of the world's leading scientists and engineers. If you need to
+manipulate numbers on a computer and display or publish the results, give
+SciPy a try!
+
+For the installation instructions, see `our install
+guide <https://scipy.org/install/>`__.
+
+
+Call for Contributions
+----------------------
+
+We appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as "good
+first issue" may be a good starting point. Have a look at `our contributing
+guide <https://scipy.github.io/devdocs/dev/index.html>`__.
+
+Writing code isn’t the only way to contribute to SciPy. You can also:
+
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+- develop graphic design for our brand assets and promotional materials
+- help with outreach and onboard new contributors
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+
+If you’re unsure where to start or how your skills fit in, reach out! You can
+ask on the `forum <https://discuss.scientific-python.org/c/contributor/scipy>`__
+or here, on GitHub, by leaving a comment on a relevant issue that is already
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+
+If you are new to contributing to open source, `this
+guide <https://opensource.guide/how-to-contribute/>`__ helps explain why, what,
+and how to get involved.

videollama2/lib/python3.10/site-packages/scipy-1.14.1.dist-info/WHEEL

@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: meson
+Root-Is-Purelib: false
+Tag: cp310-cp310-manylinux_2_17_x86_64
+Tag: cp310-cp310-manylinux2014_x86_64
+

videollama2/lib/python3.10/site-packages/sklearn/compose/__init__.py

@@ -0,0 +1,21 @@
+"""Meta-estimators for building composite models with transformers
+
+In addition to its current contents, this module will eventually be home to
+refurbished versions of Pipeline and FeatureUnion.
+
+"""
+
+from ._column_transformer import (
+    ColumnTransformer,
+    make_column_transformer,
+    make_column_selector,
+)
+from ._target import TransformedTargetRegressor
+
+
+__all__ = [
+    "ColumnTransformer",
+    "make_column_transformer",
+    "TransformedTargetRegressor",
+    "make_column_selector",
+]

videollama2/lib/python3.10/site-packages/sklearn/compose/_column_transformer.py

@@ -0,0 +1,1124 @@
+"""
+The :mod:`sklearn.compose._column_transformer` module implements utilities
+to work with heterogeneous data and to apply different transformers to
+different columns.
+"""
+# Author: Andreas Mueller
+#         Joris Van den Bossche
+# License: BSD
+from itertools import chain
+from collections import Counter
+
+import numpy as np
+from scipy import sparse
+
+from ..base import clone, TransformerMixin
+from ..utils._estimator_html_repr import _VisualBlock
+from ..pipeline import _fit_transform_one, _transform_one, _name_estimators
+from ..preprocessing import FunctionTransformer
+from ..utils import Bunch
+from ..utils import _safe_indexing
+from ..utils import _get_column_indices
+from ..utils._set_output import _get_output_config, _safe_set_output
+from ..utils import check_pandas_support
+from ..utils.metaestimators import _BaseComposition
+from ..utils.validation import check_array, check_is_fitted, _check_feature_names_in
+from ..utils.parallel import delayed, Parallel
+
+
+__all__ = ["ColumnTransformer", "make_column_transformer", "make_column_selector"]
+
+
+_ERR_MSG_1DCOLUMN = (
+    "1D data passed to a transformer that expects 2D data. "
+    "Try to specify the column selection as a list of one "
+    "item instead of a scalar."
+)
+
+
+class ColumnTransformer(TransformerMixin, _BaseComposition):
+    """Applies transformers to columns of an array or pandas DataFrame.
+
+    This estimator allows different columns or column subsets of the input
+    to be transformed separately and the features generated by each transformer
+    will be concatenated to form a single feature space.
+    This is useful for heterogeneous or columnar data, to combine several
+    feature extraction mechanisms or transformations into a single transformer.
+
+    Read more in the :ref:`User Guide <column_transformer>`.
+
+    .. versionadded:: 0.20
+
+    Parameters
+    ----------
+    transformers : list of tuples
+        List of (name, transformer, columns) tuples specifying the
+        transformer objects to be applied to subsets of the data.
+
+        name : str
+            Like in Pipeline and FeatureUnion, this allows the transformer and
+            its parameters to be set using ``set_params`` and searched in grid
+            search.
+        transformer : {'drop', 'passthrough'} or estimator
+            Estimator must support :term:`fit` and :term:`transform`.
+            Special-cased strings 'drop' and 'passthrough' are accepted as
+            well, to indicate to drop the columns or to pass them through
+            untransformed, respectively.
+        columns :  str, array-like of str, int, array-like of int, \
+                array-like of bool, slice or callable
+            Indexes the data on its second axis. Integers are interpreted as
+            positional columns, while strings can reference DataFrame columns
+            by name.  A scalar string or int should be used where
+            ``transformer`` expects X to be a 1d array-like (vector),
+            otherwise a 2d array will be passed to the transformer.
+            A callable is passed the input data `X` and can return any of the
+            above. To select multiple columns by name or dtype, you can use
+            :obj:`make_column_selector`.
+
+    remainder : {'drop', 'passthrough'} or estimator, default='drop'
+        By default, only the specified columns in `transformers` are
+        transformed and combined in the output, and the non-specified
+        columns are dropped. (default of ``'drop'``).
+        By specifying ``remainder='passthrough'``, all remaining columns that
+        were not specified in `transformers`, but present in the data passed
+        to `fit` will be automatically passed through. This subset of columns
+        is concatenated with the output of the transformers. For dataframes,
+        extra columns not seen during `fit` will be excluded from the output
+        of `transform`.
+        By setting ``remainder`` to be an estimator, the remaining
+        non-specified columns will use the ``remainder`` estimator. The
+        estimator must support :term:`fit` and :term:`transform`.
+        Note that using this feature requires that the DataFrame columns
+        input at :term:`fit` and :term:`transform` have identical order.
+
+    sparse_threshold : float, default=0.3
+        If the output of the different transformers contains sparse matrices,
+        these will be stacked as a sparse matrix if the overall density is
+        lower than this value. Use ``sparse_threshold=0`` to always return
+        dense.  When the transformed output consists of all dense data, the
+        stacked result will be dense, and this keyword will be ignored.
+
+    n_jobs : int, default=None
+        Number of jobs to run in parallel.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    transformer_weights : dict, default=None
+        Multiplicative weights for features per transformer. The output of the
+        transformer is multiplied by these weights. Keys are transformer names,
+        values the weights.
+
+    verbose : bool, default=False
+        If True, the time elapsed while fitting each transformer will be
+        printed as it is completed.
+
+    verbose_feature_names_out : bool, default=True
+        If True, :meth:`get_feature_names_out` will prefix all feature names
+        with the name of the transformer that generated that feature.
+        If False, :meth:`get_feature_names_out` will not prefix any feature
+        names and will error if feature names are not unique.
+
+        .. versionadded:: 1.0
+
+    Attributes
+    ----------
+    transformers_ : list
+        The collection of fitted transformers as tuples of
+        (name, fitted_transformer, column). `fitted_transformer` can be an
+        estimator, 'drop', or 'passthrough'. In case there were no columns
+        selected, this will be the unfitted transformer.
+        If there are remaining columns, the final element is a tuple of the
+        form:
+        ('remainder', transformer, remaining_columns) corresponding to the
+        ``remainder`` parameter. If there are remaining columns, then
+        ``len(transformers_)==len(transformers)+1``, otherwise
+        ``len(transformers_)==len(transformers)``.
+
+    named_transformers_ : :class:`~sklearn.utils.Bunch`
+        Read-only attribute to access any transformer by given name.
+        Keys are transformer names and values are the fitted transformer
+        objects.
+
+    sparse_output_ : bool
+        Boolean flag indicating whether the output of ``transform`` is a
+        sparse matrix or a dense numpy array, which depends on the output
+        of the individual transformers and the `sparse_threshold` keyword.
+
+    output_indices_ : dict
+        A dictionary from each transformer name to a slice, where the slice
+        corresponds to indices in the transformed output. This is useful to
+        inspect which transformer is responsible for which transformed
+        feature(s).
+
+        .. versionadded:: 1.0
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying transformers expose such an attribute when fit.
+
+        .. versionadded:: 0.24
+
+    See Also
+    --------
+    make_column_transformer : Convenience function for
+        combining the outputs of multiple transformer objects applied to
+        column subsets of the original feature space.
+    make_column_selector : Convenience function for selecting
+        columns based on datatype or the columns name with a regex pattern.
+
+    Notes
+    -----
+    The order of the columns in the transformed feature matrix follows the
+    order of how the columns are specified in the `transformers` list.
+    Columns of the original feature matrix that are not specified are
+    dropped from the resulting transformed feature matrix, unless specified
+    in the `passthrough` keyword. Those columns specified with `passthrough`
+    are added at the right to the output of the transformers.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.compose import ColumnTransformer
+    >>> from sklearn.preprocessing import Normalizer
+    >>> ct = ColumnTransformer(
+    ...     [("norm1", Normalizer(norm='l1'), [0, 1]),
+    ...      ("norm2", Normalizer(norm='l1'), slice(2, 4))])
+    >>> X = np.array([[0., 1., 2., 2.],
+    ...               [1., 1., 0., 1.]])
+    >>> # Normalizer scales each row of X to unit norm. A separate scaling
+    >>> # is applied for the two first and two last elements of each
+    >>> # row independently.
+    >>> ct.fit_transform(X)
+    array([[0. , 1. , 0.5, 0.5],
+           [0.5, 0.5, 0. , 1. ]])
+
+    :class:`ColumnTransformer` can be configured with a transformer that requires
+    a 1d array by setting the column to a string:
+
+    >>> from sklearn.feature_extraction import FeatureHasher
+    >>> from sklearn.preprocessing import MinMaxScaler
+    >>> import pandas as pd   # doctest: +SKIP
+    >>> X = pd.DataFrame({
+    ...     "documents": ["First item", "second one here", "Is this the last?"],
+    ...     "width": [3, 4, 5],
+    ... })  # doctest: +SKIP
+    >>> # "documents" is a string which configures ColumnTransformer to
+    >>> # pass the documents column as a 1d array to the FeatureHasher
+    >>> ct = ColumnTransformer(
+    ...     [("text_preprocess", FeatureHasher(input_type="string"), "documents"),
+    ...      ("num_preprocess", MinMaxScaler(), ["width"])])
+    >>> X_trans = ct.fit_transform(X)  # doctest: +SKIP
+    """
+
+    _required_parameters = ["transformers"]
+
+    def __init__(
+        self,
+        transformers,
+        *,
+        remainder="drop",
+        sparse_threshold=0.3,
+        n_jobs=None,
+        transformer_weights=None,
+        verbose=False,
+        verbose_feature_names_out=True,
+    ):
+        self.transformers = transformers
+        self.remainder = remainder
+        self.sparse_threshold = sparse_threshold
+        self.n_jobs = n_jobs
+        self.transformer_weights = transformer_weights
+        self.verbose = verbose
+        self.verbose_feature_names_out = verbose_feature_names_out
+
+    @property
+    def _transformers(self):
+        """
+        Internal list of transformer only containing the name and
+        transformers, dropping the columns. This is for the implementation
+        of get_params via BaseComposition._get_params which expects lists
+        of tuples of len 2.
+        """
+        try:
+            return [(name, trans) for name, trans, _ in self.transformers]
+        except (TypeError, ValueError):
+            return self.transformers
+
+    @_transformers.setter
+    def _transformers(self, value):
+        try:
+            self.transformers = [
+                (name, trans, col)
+                for ((name, trans), (_, _, col)) in zip(value, self.transformers)
+            ]
+        except (TypeError, ValueError):
+            self.transformers = value
+
+    def set_output(self, *, transform=None):
+        """Set the output container when `"transform"` and `"fit_transform"` are called.
+
+        Calling `set_output` will set the output of all estimators in `transformers`
+        and `transformers_`.
+
+        Parameters
+        ----------
+        transform : {"default", "pandas"}, default=None
+            Configure output of `transform` and `fit_transform`.
+
+            - `"default"`: Default output format of a transformer
+            - `"pandas"`: DataFrame output
+            - `None`: Transform configuration is unchanged
+
+        Returns
+        -------
+        self : estimator instance
+            Estimator instance.
+        """
+        super().set_output(transform=transform)
+        transformers = (
+            trans
+            for _, trans, _ in chain(
+                self.transformers, getattr(self, "transformers_", [])
+            )
+            if trans not in {"passthrough", "drop"}
+        )
+        for trans in transformers:
+            _safe_set_output(trans, transform=transform)
+
+        return self
+
+    def get_params(self, deep=True):
+        """Get parameters for this estimator.
+
+        Returns the parameters given in the constructor as well as the
+        estimators contained within the `transformers` of the
+        `ColumnTransformer`.
+
+        Parameters
+        ----------
+        deep : bool, default=True
+            If True, will return the parameters for this estimator and
+            contained subobjects that are estimators.
+
+        Returns
+        -------
+        params : dict
+            Parameter names mapped to their values.
+        """
+        return self._get_params("_transformers", deep=deep)
+
+    def set_params(self, **kwargs):
+        """Set the parameters of this estimator.
+
+        Valid parameter keys can be listed with ``get_params()``. Note that you
+        can directly set the parameters of the estimators contained in
+        `transformers` of `ColumnTransformer`.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Estimator parameters.
+
+        Returns
+        -------
+        self : ColumnTransformer
+            This estimator.
+        """
+        self._set_params("_transformers", **kwargs)
+        return self
+
+    def _iter(self, fitted=False, replace_strings=False, column_as_strings=False):
+        """
+        Generate (name, trans, column, weight) tuples.
+
+        If fitted=True, use the fitted transformers, else use the
+        user specified transformers updated with converted column names
+        and potentially appended with transformer for remainder.
+
+        """
+        if fitted:
+            if replace_strings:
+                # Replace "passthrough" with the fitted version in
+                # _name_to_fitted_passthrough
+                def replace_passthrough(name, trans, columns):
+                    if name not in self._name_to_fitted_passthrough:
+                        return name, trans, columns
+                    return name, self._name_to_fitted_passthrough[name], columns
+
+                transformers = [
+                    replace_passthrough(*trans) for trans in self.transformers_
+                ]
+            else:
+                transformers = self.transformers_
+        else:
+            # interleave the validated column specifiers
+            transformers = [
+                (name, trans, column)
+                for (name, trans, _), column in zip(self.transformers, self._columns)
+            ]
+            # add transformer tuple for remainder
+            if self._remainder[2]:
+                transformers = chain(transformers, [self._remainder])
+        get_weight = (self.transformer_weights or {}).get
+
+        output_config = _get_output_config("transform", self)
+        for name, trans, columns in transformers:
+            if replace_strings:
+                # replace 'passthrough' with identity transformer and
+                # skip in case of 'drop'
+                if trans == "passthrough":
+                    trans = FunctionTransformer(
+                        accept_sparse=True,
+                        check_inverse=False,
+                        feature_names_out="one-to-one",
+                    ).set_output(transform=output_config["dense"])
+                elif trans == "drop":
+                    continue
+                elif _is_empty_column_selection(columns):
+                    continue
+
+            if column_as_strings:
+                # Convert all columns to using their string labels
+                columns_is_scalar = np.isscalar(columns)
+
+                indices = self._transformer_to_input_indices[name]
+                columns = self.feature_names_in_[indices]
+
+                if columns_is_scalar:
+                    # selection is done with one dimension
+                    columns = columns[0]
+
+            yield (name, trans, columns, get_weight(name))
+
+    def _validate_transformers(self):
+        if not self.transformers:
+            return
+
+        names, transformers, _ = zip(*self.transformers)
+
+        # validate names
+        self._validate_names(names)
+
+        # validate estimators
+        for t in transformers:
+            if t in ("drop", "passthrough"):
+                continue
+            if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr(
+                t, "transform"
+            ):
+                raise TypeError(
+                    "All estimators should implement fit and "
+                    "transform, or can be 'drop' or 'passthrough' "
+                    "specifiers. '%s' (type %s) doesn't." % (t, type(t))
+                )
+
+    def _validate_column_callables(self, X):
+        """
+        Converts callable column specifications.
+        """
+        all_columns = []
+        transformer_to_input_indices = {}
+        for name, _, columns in self.transformers:
+            if callable(columns):
+                columns = columns(X)
+            all_columns.append(columns)
+            transformer_to_input_indices[name] = _get_column_indices(X, columns)
+
+        self._columns = all_columns
+        self._transformer_to_input_indices = transformer_to_input_indices
+
+    def _validate_remainder(self, X):
+        """
+        Validates ``remainder`` and defines ``_remainder`` targeting
+        the remaining columns.
+        """
+        is_transformer = (
+            hasattr(self.remainder, "fit") or hasattr(self.remainder, "fit_transform")
+        ) and hasattr(self.remainder, "transform")
+        if self.remainder not in ("drop", "passthrough") and not is_transformer:
+            raise ValueError(
+                "The remainder keyword needs to be one of 'drop', "
+                "'passthrough', or estimator. '%s' was passed instead"
+                % self.remainder
+            )
+
+        self._n_features = X.shape[1]
+        cols = set(chain(*self._transformer_to_input_indices.values()))
+        remaining = sorted(set(range(self._n_features)) - cols)
+        self._remainder = ("remainder", self.remainder, remaining)
+        self._transformer_to_input_indices["remainder"] = remaining
+
+    @property
+    def named_transformers_(self):
+        """Access the fitted transformer by name.
+
+        Read-only attribute to access any transformer by given name.
+        Keys are transformer names and values are the fitted transformer
+        objects.
+        """
+        # Use Bunch object to improve autocomplete
+        return Bunch(**{name: trans for name, trans, _ in self.transformers_})
+
+    def _get_feature_name_out_for_transformer(
+        self, name, trans, column, feature_names_in
+    ):
+        """Gets feature names of transformer.
+
+        Used in conjunction with self._iter(fitted=True) in get_feature_names_out.
+        """
+        column_indices = self._transformer_to_input_indices[name]
+        names = feature_names_in[column_indices]
+        if trans == "drop" or _is_empty_column_selection(column):
+            return
+        elif trans == "passthrough":
+            return names
+
+        # An actual transformer
+        if not hasattr(trans, "get_feature_names_out"):
+            raise AttributeError(
+                f"Transformer {name} (type {type(trans).__name__}) does "
+                "not provide get_feature_names_out."
+            )
+        return trans.get_feature_names_out(names)
+
+    def get_feature_names_out(self, input_features=None):
+        """Get output feature names for transformation.
+
+        Parameters
+        ----------
+        input_features : array-like of str or None, default=None
+            Input features.
+
+            - If `input_features` is `None`, then `feature_names_in_` is
+              used as feature names in. If `feature_names_in_` is not defined,
+              then the following input feature names are generated:
+              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
+            - If `input_features` is an array-like, then `input_features` must
+              match `feature_names_in_` if `feature_names_in_` is defined.
+
+        Returns
+        -------
+        feature_names_out : ndarray of str objects
+            Transformed feature names.
+        """
+        check_is_fitted(self)
+        input_features = _check_feature_names_in(self, input_features)
+
+        # List of tuples (name, feature_names_out)
+        transformer_with_feature_names_out = []
+        for name, trans, column, _ in self._iter(fitted=True):
+            feature_names_out = self._get_feature_name_out_for_transformer(
+                name, trans, column, input_features
+            )
+            if feature_names_out is None:
+                continue
+            transformer_with_feature_names_out.append((name, feature_names_out))
+
+        if not transformer_with_feature_names_out:
+            # No feature names
+            return np.array([], dtype=object)
+
+        return self._add_prefix_for_feature_names_out(
+            transformer_with_feature_names_out
+        )
+
+    def _add_prefix_for_feature_names_out(self, transformer_with_feature_names_out):
+        """Add prefix for feature names out that includes the transformer names.
+
+        Parameters
+        ----------
+        transformer_with_feature_names_out : list of tuples of (str, array-like of str)
+            The tuple consistent of the transformer's name and its feature names out.
+
+        Returns
+        -------
+        feature_names_out : ndarray of shape (n_features,), dtype=str
+            Transformed feature names.
+        """
+        if self.verbose_feature_names_out:
+            # Prefix the feature names out with the transformers name
+            names = list(
+                chain.from_iterable(
+                    (f"{name}__{i}" for i in feature_names_out)
+                    for name, feature_names_out in transformer_with_feature_names_out
+                )
+            )
+            return np.asarray(names, dtype=object)
+
+        # verbose_feature_names_out is False
+        # Check that names are all unique without a prefix
+        feature_names_count = Counter(
+            chain.from_iterable(s for _, s in transformer_with_feature_names_out)
+        )
+        top_6_overlap = [
+            name for name, count in feature_names_count.most_common(6) if count > 1
+        ]
+        top_6_overlap.sort()
+        if top_6_overlap:
+            if len(top_6_overlap) == 6:
+                # There are more than 5 overlapping names, we only show the 5
+                # of the feature names
+                names_repr = str(top_6_overlap[:5])[:-1] + ", ...]"
+            else:
+                names_repr = str(top_6_overlap)
+            raise ValueError(
+                f"Output feature names: {names_repr} are not unique. Please set "
+                "verbose_feature_names_out=True to add prefixes to feature names"
+            )
+
+        return np.concatenate(
+            [name for _, name in transformer_with_feature_names_out],
+        )
+
+    def _update_fitted_transformers(self, transformers):
+        # transformers are fitted; excludes 'drop' cases
+        fitted_transformers = iter(transformers)
+        transformers_ = []
+        self._name_to_fitted_passthrough = {}
+
+        for name, old, column, _ in self._iter():
+            if old == "drop":
+                trans = "drop"
+            elif old == "passthrough":
+                # FunctionTransformer is present in list of transformers,
+                # so get next transformer, but save original string
+                func_transformer = next(fitted_transformers)
+                trans = "passthrough"
+
+                # The fitted FunctionTransformer is saved in another attribute,
+                # so it can be used during transform for set_output.
+                self._name_to_fitted_passthrough[name] = func_transformer
+            elif _is_empty_column_selection(column):
+                trans = old
+            else:
+                trans = next(fitted_transformers)
+            transformers_.append((name, trans, column))
+
+        # sanity check that transformers is exhausted
+        assert not list(fitted_transformers)
+        self.transformers_ = transformers_
+
+    def _validate_output(self, result):
+        """
+        Ensure that the output of each transformer is 2D. Otherwise
+        hstack can raise an error or produce incorrect results.
+        """
+        names = [
+            name for name, _, _, _ in self._iter(fitted=True, replace_strings=True)
+        ]
+        for Xs, name in zip(result, names):
+            if not getattr(Xs, "ndim", 0) == 2:
+                raise ValueError(
+                    "The output of the '{0}' transformer should be 2D (scipy "
+                    "matrix, array, or pandas DataFrame).".format(name)
+                )
+
+    def _record_output_indices(self, Xs):
+        """
+        Record which transformer produced which column.
+        """
+        idx = 0
+        self.output_indices_ = {}
+
+        for transformer_idx, (name, _, _, _) in enumerate(
+            self._iter(fitted=True, replace_strings=True)
+        ):
+            n_columns = Xs[transformer_idx].shape[1]
+            self.output_indices_[name] = slice(idx, idx + n_columns)
+            idx += n_columns
+
+        # `_iter` only generates transformers that have a non empty
+        # selection. Here we set empty slices for transformers that
+        # generate no output, which are safe for indexing
+        all_names = [t[0] for t in self.transformers] + ["remainder"]
+        for name in all_names:
+            if name not in self.output_indices_:
+                self.output_indices_[name] = slice(0, 0)
+
+    def _log_message(self, name, idx, total):
+        if not self.verbose:
+            return None
+        return "(%d of %d) Processing %s" % (idx, total, name)
+
+    def _fit_transform(self, X, y, func, fitted=False, column_as_strings=False):
+        """
+        Private function to fit and/or transform on demand.
+
+        Return value (transformers and/or transformed X data) depends
+        on the passed function.
+        ``fitted=True`` ensures the fitted transformers are used.
+        """
+        transformers = list(
+            self._iter(
+                fitted=fitted, replace_strings=True, column_as_strings=column_as_strings
+            )
+        )
+        try:
+            return Parallel(n_jobs=self.n_jobs)(
+                delayed(func)(
+                    transformer=clone(trans) if not fitted else trans,
+                    X=_safe_indexing(X, column, axis=1),
+                    y=y,
+                    weight=weight,
+                    message_clsname="ColumnTransformer",
+                    message=self._log_message(name, idx, len(transformers)),
+                )
+                for idx, (name, trans, column, weight) in enumerate(transformers, 1)
+            )
+        except ValueError as e:
+            if "Expected 2D array, got 1D array instead" in str(e):
+                raise ValueError(_ERR_MSG_1DCOLUMN) from e
+            else:
+                raise
+
+    def fit(self, X, y=None):
+        """Fit all transformers using X.
+
+        Parameters
+        ----------
+        X : {array-like, dataframe} of shape (n_samples, n_features)
+            Input data, of which specified subsets are used to fit the
+            transformers.
+
+        y : array-like of shape (n_samples,...), default=None
+            Targets for supervised learning.
+
+        Returns
+        -------
+        self : ColumnTransformer
+            This estimator.
+        """
+        # we use fit_transform to make sure to set sparse_output_ (for which we
+        # need the transformed data) to have consistent output type in predict
+        self.fit_transform(X, y=y)
+        return self
+
+    def fit_transform(self, X, y=None):
+        """Fit all transformers, transform the data and concatenate results.
+
+        Parameters
+        ----------
+        X : {array-like, dataframe} of shape (n_samples, n_features)
+            Input data, of which specified subsets are used to fit the
+            transformers.
+
+        y : array-like of shape (n_samples,), default=None
+            Targets for supervised learning.
+
+        Returns
+        -------
+        X_t : {array-like, sparse matrix} of \
+                shape (n_samples, sum_n_components)
+            Horizontally stacked results of transformers. sum_n_components is the
+            sum of n_components (output dimension) over transformers. If
+            any result is a sparse matrix, everything will be converted to
+            sparse matrices.
+        """
+        self._check_feature_names(X, reset=True)
+
+        X = _check_X(X)
+        # set n_features_in_ attribute
+        self._check_n_features(X, reset=True)
+        self._validate_transformers()
+        self._validate_column_callables(X)
+        self._validate_remainder(X)
+
+        result = self._fit_transform(X, y, _fit_transform_one)
+
+        if not result:
+            self._update_fitted_transformers([])
+            # All transformers are None
+            return np.zeros((X.shape[0], 0))
+
+        Xs, transformers = zip(*result)
+
+        # determine if concatenated output will be sparse or not
+        if any(sparse.issparse(X) for X in Xs):
+            nnz = sum(X.nnz if sparse.issparse(X) else X.size for X in Xs)
+            total = sum(
+                X.shape[0] * X.shape[1] if sparse.issparse(X) else X.size for X in Xs
+            )
+            density = nnz / total
+            self.sparse_output_ = density < self.sparse_threshold
+        else:
+            self.sparse_output_ = False
+
+        self._update_fitted_transformers(transformers)
+        self._validate_output(Xs)
+        self._record_output_indices(Xs)
+
+        return self._hstack(list(Xs))
+
+    def transform(self, X):
+        """Transform X separately by each transformer, concatenate results.
+
+        Parameters
+        ----------
+        X : {array-like, dataframe} of shape (n_samples, n_features)
+            The data to be transformed by subset.
+
+        Returns
+        -------
+        X_t : {array-like, sparse matrix} of \
+                shape (n_samples, sum_n_components)
+            Horizontally stacked results of transformers. sum_n_components is the
+            sum of n_components (output dimension) over transformers. If
+            any result is a sparse matrix, everything will be converted to
+            sparse matrices.
+        """
+        check_is_fitted(self)
+        X = _check_X(X)
+
+        fit_dataframe_and_transform_dataframe = hasattr(
+            self, "feature_names_in_"
+        ) and hasattr(X, "columns")
+
+        if fit_dataframe_and_transform_dataframe:
+            named_transformers = self.named_transformers_
+            # check that all names seen in fit are in transform, unless
+            # they were dropped
+            non_dropped_indices = [
+                ind
+                for name, ind in self._transformer_to_input_indices.items()
+                if name in named_transformers
+                and isinstance(named_transformers[name], str)
+                and named_transformers[name] != "drop"
+            ]
+
+            all_indices = set(chain(*non_dropped_indices))
+            all_names = set(self.feature_names_in_[ind] for ind in all_indices)
+
+            diff = all_names - set(X.columns)
+            if diff:
+                raise ValueError(f"columns are missing: {diff}")
+        else:
+            # ndarray was used for fitting or transforming, thus we only
+            # check that n_features_in_ is consistent
+            self._check_n_features(X, reset=False)
+
+        Xs = self._fit_transform(
+            X,
+            None,
+            _transform_one,
+            fitted=True,
+            column_as_strings=fit_dataframe_and_transform_dataframe,
+        )
+        self._validate_output(Xs)
+
+        if not Xs:
+            # All transformers are None
+            return np.zeros((X.shape[0], 0))
+
+        return self._hstack(list(Xs))
+
+    def _hstack(self, Xs):
+        """Stacks Xs horizontally.
+
+        This allows subclasses to control the stacking behavior, while reusing
+        everything else from ColumnTransformer.
+
+        Parameters
+        ----------
+        Xs : list of {array-like, sparse matrix, dataframe}
+        """
+        if self.sparse_output_:
+            try:
+                # since all columns should be numeric before stacking them
+                # in a sparse matrix, `check_array` is used for the
+                # dtype conversion if necessary.
+                converted_Xs = [
+                    check_array(X, accept_sparse=True, force_all_finite=False)
+                    for X in Xs
+                ]
+            except ValueError as e:
+                raise ValueError(
+                    "For a sparse output, all columns should "
+                    "be a numeric or convertible to a numeric."
+                ) from e
+
+            return sparse.hstack(converted_Xs).tocsr()
+        else:
+            Xs = [f.toarray() if sparse.issparse(f) else f for f in Xs]
+            config = _get_output_config("transform", self)
+            if config["dense"] == "pandas" and all(hasattr(X, "iloc") for X in Xs):
+                pd = check_pandas_support("transform")
+                output = pd.concat(Xs, axis=1)
+
+                # If all transformers define `get_feature_names_out`, then transform
+                # will adjust the column names to be consistent with
+                # verbose_feature_names_out. Here we prefix the feature names if
+                # verbose_feature_names_out=True.
+
+                if not self.verbose_feature_names_out:
+                    return output
+
+                transformer_names = [
+                    t[0] for t in self._iter(fitted=True, replace_strings=True)
+                ]
+                # Selection of columns might be empty.
+                # Hence feature names are filtered for non-emptiness.
+                feature_names_outs = [X.columns for X in Xs if X.shape[1] != 0]
+                names_out = self._add_prefix_for_feature_names_out(
+                    list(zip(transformer_names, feature_names_outs))
+                )
+                output.columns = names_out
+                return output
+
+            return np.hstack(Xs)
+
+    def _sk_visual_block_(self):
+        if isinstance(self.remainder, str) and self.remainder == "drop":
+            transformers = self.transformers
+        elif hasattr(self, "_remainder"):
+            remainder_columns = self._remainder[2]
+            if (
+                hasattr(self, "feature_names_in_")
+                and remainder_columns
+                and not all(isinstance(col, str) for col in remainder_columns)
+            ):
+                remainder_columns = self.feature_names_in_[remainder_columns].tolist()
+            transformers = chain(
+                self.transformers, [("remainder", self.remainder, remainder_columns)]
+            )
+        else:
+            transformers = chain(self.transformers, [("remainder", self.remainder, "")])
+
+        names, transformers, name_details = zip(*transformers)
+        return _VisualBlock(
+            "parallel", transformers, names=names, name_details=name_details
+        )
+
+
+def _check_X(X):
+    """Use check_array only on lists and other non-array-likes / sparse"""
+    if hasattr(X, "__array__") or sparse.issparse(X):
+        return X
+    return check_array(X, force_all_finite="allow-nan", dtype=object)
+
+
+def _is_empty_column_selection(column):
+    """
+    Return True if the column selection is empty (empty list or all-False
+    boolean array).
+
+    """
+    if hasattr(column, "dtype") and np.issubdtype(column.dtype, np.bool_):
+        return not column.any()
+    elif hasattr(column, "__len__"):
+        return (
+            len(column) == 0
+            or all(isinstance(col, bool) for col in column)
+            and not any(column)
+        )
+    else:
+        return False
+
+
+def _get_transformer_list(estimators):
+    """
+    Construct (name, trans, column) tuples from list
+
+    """
+    transformers, columns = zip(*estimators)
+    names, _ = zip(*_name_estimators(transformers))
+
+    transformer_list = list(zip(names, transformers, columns))
+    return transformer_list
+
+
+def make_column_transformer(
+    *transformers,
+    remainder="drop",
+    sparse_threshold=0.3,
+    n_jobs=None,
+    verbose=False,
+    verbose_feature_names_out=True,
+):
+    """Construct a ColumnTransformer from the given transformers.
+
+    This is a shorthand for the ColumnTransformer constructor; it does not
+    require, and does not permit, naming the transformers. Instead, they will
+    be given names automatically based on their types. It also does not allow
+    weighting with ``transformer_weights``.
+
+    Read more in the :ref:`User Guide <make_column_transformer>`.
+
+    Parameters
+    ----------
+    *transformers : tuples
+        Tuples of the form (transformer, columns) specifying the
+        transformer objects to be applied to subsets of the data.
+
+        transformer : {'drop', 'passthrough'} or estimator
+            Estimator must support :term:`fit` and :term:`transform`.
+            Special-cased strings 'drop' and 'passthrough' are accepted as
+            well, to indicate to drop the columns or to pass them through
+            untransformed, respectively.
+        columns : str,  array-like of str, int, array-like of int, slice, \
+                array-like of bool or callable
+            Indexes the data on its second axis. Integers are interpreted as
+            positional columns, while strings can reference DataFrame columns
+            by name. A scalar string or int should be used where
+            ``transformer`` expects X to be a 1d array-like (vector),
+            otherwise a 2d array will be passed to the transformer.
+            A callable is passed the input data `X` and can return any of the
+            above. To select multiple columns by name or dtype, you can use
+            :obj:`make_column_selector`.
+
+    remainder : {'drop', 'passthrough'} or estimator, default='drop'
+        By default, only the specified columns in `transformers` are
+        transformed and combined in the output, and the non-specified
+        columns are dropped. (default of ``'drop'``).
+        By specifying ``remainder='passthrough'``, all remaining columns that
+        were not specified in `transformers` will be automatically passed
+        through. This subset of columns is concatenated with the output of
+        the transformers.
+        By setting ``remainder`` to be an estimator, the remaining
+        non-specified columns will use the ``remainder`` estimator. The
+        estimator must support :term:`fit` and :term:`transform`.
+
+    sparse_threshold : float, default=0.3
+        If the transformed output consists of a mix of sparse and dense data,
+        it will be stacked as a sparse matrix if the density is lower than this
+        value. Use ``sparse_threshold=0`` to always return dense.
+        When the transformed output consists of all sparse or all dense data,
+        the stacked result will be sparse or dense, respectively, and this
+        keyword will be ignored.
+
+    n_jobs : int, default=None
+        Number of jobs to run in parallel.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+
+    verbose : bool, default=False
+        If True, the time elapsed while fitting each transformer will be
+        printed as it is completed.
+
+    verbose_feature_names_out : bool, default=True
+        If True, :meth:`get_feature_names_out` will prefix all feature names
+        with the name of the transformer that generated that feature.
+        If False, :meth:`get_feature_names_out` will not prefix any feature
+        names and will error if feature names are not unique.
+
+        .. versionadded:: 1.0
+
+    Returns
+    -------
+    ct : ColumnTransformer
+        Returns a :class:`ColumnTransformer` object.
+
+    See Also
+    --------
+    ColumnTransformer : Class that allows combining the
+        outputs of multiple transformer objects used on column subsets
+        of the data into a single feature space.
+
+    Examples
+    --------
+    >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
+    >>> from sklearn.compose import make_column_transformer
+    >>> make_column_transformer(
+    ...     (StandardScaler(), ['numerical_column']),
+    ...     (OneHotEncoder(), ['categorical_column']))
+    ColumnTransformer(transformers=[('standardscaler', StandardScaler(...),
+                                     ['numerical_column']),
+                                    ('onehotencoder', OneHotEncoder(...),
+                                     ['categorical_column'])])
+    """
+    # transformer_weights keyword is not passed through because the user
+    # would need to know the automatically generated names of the transformers
+    transformer_list = _get_transformer_list(transformers)
+    return ColumnTransformer(
+        transformer_list,
+        n_jobs=n_jobs,
+        remainder=remainder,
+        sparse_threshold=sparse_threshold,
+        verbose=verbose,
+        verbose_feature_names_out=verbose_feature_names_out,
+    )
+
+
+class make_column_selector:
+    """Create a callable to select columns to be used with
+    :class:`ColumnTransformer`.
+
+    :func:`make_column_selector` can select columns based on datatype or the
+    columns name with a regex. When using multiple selection criteria, **all**
+    criteria must match for a column to be selected.
+
+    Parameters
+    ----------
+    pattern : str, default=None
+        Name of columns containing this regex pattern will be included. If
+        None, column selection will not be selected based on pattern.
+
+    dtype_include : column dtype or list of column dtypes, default=None
+        A selection of dtypes to include. For more details, see
+        :meth:`pandas.DataFrame.select_dtypes`.
+
+    dtype_exclude : column dtype or list of column dtypes, default=None
+        A selection of dtypes to exclude. For more details, see
+        :meth:`pandas.DataFrame.select_dtypes`.
+
+    Returns
+    -------
+    selector : callable
+        Callable for column selection to be used by a
+        :class:`ColumnTransformer`.
+
+    See Also
+    --------
+    ColumnTransformer : Class that allows combining the
+        outputs of multiple transformer objects used on column subsets
+        of the data into a single feature space.
+
+    Examples
+    --------
+    >>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
+    >>> from sklearn.compose import make_column_transformer
+    >>> from sklearn.compose import make_column_selector
+    >>> import numpy as np
+    >>> import pandas as pd  # doctest: +SKIP
+    >>> X = pd.DataFrame({'city': ['London', 'London', 'Paris', 'Sallisaw'],
+    ...                   'rating': [5, 3, 4, 5]})  # doctest: +SKIP
+    >>> ct = make_column_transformer(
+    ...       (StandardScaler(),
+    ...        make_column_selector(dtype_include=np.number)),  # rating
+    ...       (OneHotEncoder(),
+    ...        make_column_selector(dtype_include=object)))  # city
+    >>> ct.fit_transform(X)  # doctest: +SKIP
+    array([[ 0.90453403,  1.        ,  0.        ,  0.        ],
+           [-1.50755672,  1.        ,  0.        ,  0.        ],
+           [-0.30151134,  0.        ,  1.        ,  0.        ],
+           [ 0.90453403,  0.        ,  0.        ,  1.        ]])
+    """
+
+    def __init__(self, pattern=None, *, dtype_include=None, dtype_exclude=None):
+        self.pattern = pattern
+        self.dtype_include = dtype_include
+        self.dtype_exclude = dtype_exclude
+
+    def __call__(self, df):
+        """Callable for column selection to be used by a
+        :class:`ColumnTransformer`.
+
+        Parameters
+        ----------
+        df : dataframe of shape (n_features, n_samples)
+            DataFrame to select columns from.
+        """
+        if not hasattr(df, "iloc"):
+            raise ValueError(
+                "make_column_selector can only be applied to pandas dataframes"
+            )
+        df_row = df.iloc[:1]
+        if self.dtype_include is not None or self.dtype_exclude is not None:
+            df_row = df_row.select_dtypes(
+                include=self.dtype_include, exclude=self.dtype_exclude
+            )
+        cols = df_row.columns
+        if self.pattern is not None:
+            cols = cols[cols.str.contains(self.pattern, regex=True)]
+        return cols.tolist()

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videollama2/lib/python3.10/site-packages/sklearn/preprocessing/__init__.py

@@ -0,0 +1,70 @@
+"""
+The :mod:`sklearn.preprocessing` module includes scaling, centering,
+normalization, binarization methods.
+"""
+
+from ._function_transformer import FunctionTransformer
+
+from ._data import Binarizer
+from ._data import KernelCenterer
+from ._data import MinMaxScaler
+from ._data import MaxAbsScaler
+from ._data import Normalizer
+from ._data import RobustScaler
+from ._data import StandardScaler
+from ._data import QuantileTransformer
+from ._data import add_dummy_feature
+from ._data import binarize
+from ._data import normalize
+from ._data import scale
+from ._data import robust_scale
+from ._data import maxabs_scale
+from ._data import minmax_scale
+from ._data import quantile_transform
+from ._data import power_transform
+from ._data import PowerTransformer
+
+from ._encoders import OneHotEncoder
+from ._encoders import OrdinalEncoder
+
+from ._label import label_binarize
+from ._label import LabelBinarizer
+from ._label import LabelEncoder
+from ._label import MultiLabelBinarizer
+
+from ._discretization import KBinsDiscretizer
+
+from ._polynomial import PolynomialFeatures
+from ._polynomial import SplineTransformer
+
+
+__all__ = [
+    "Binarizer",
+    "FunctionTransformer",
+    "KBinsDiscretizer",
+    "KernelCenterer",
+    "LabelBinarizer",
+    "LabelEncoder",
+    "MultiLabelBinarizer",
+    "MinMaxScaler",
+    "MaxAbsScaler",
+    "QuantileTransformer",
+    "Normalizer",
+    "OneHotEncoder",
+    "OrdinalEncoder",
+    "PowerTransformer",
+    "RobustScaler",
+    "SplineTransformer",
+    "StandardScaler",
+    "add_dummy_feature",
+    "PolynomialFeatures",
+    "binarize",
+    "normalize",
+    "scale",
+    "robust_scale",
+    "maxabs_scale",
+    "minmax_scale",
+    "label_binarize",
+    "quantile_transform",
+    "power_transform",
+]