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llmeval-env/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/INSTALLER

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+pip

llmeval-env/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/LICENSE

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+The MIT License (MIT)
+
+Copyright (c) 2016 Tsuyoshi Hombashi
+
+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.

llmeval-env/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: DataProperty
+Version: 1.0.1
+Summary: Python library for extract property from data.
+Home-page: https://github.com/thombashi/DataProperty
+Author: Tsuyoshi Hombashi
+Author-email: [email protected]
+Maintainer: Tsuyoshi Hombashi
+Maintainer-email: [email protected]
+License: MIT License
+Project-URL: Source, https://github.com/thombashi/DataProperty
+Project-URL: Tracker, https://github.com/thombashi/DataProperty/issues
+Keywords: data,library,property
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Information Technology
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: mbstrdecoder (<2,>=1.0.0)
+Requires-Dist: typepy[datetime] (<2,>=1.2.0)
+Provides-Extra: logging
+Requires-Dist: loguru (<1,>=0.4.1) ; extra == 'logging'
+Provides-Extra: test
+Requires-Dist: pytest (>=6.0.1) ; extra == 'test'
+Requires-Dist: pytest-md-report (>=0.3) ; extra == 'test'
+Requires-Dist: tcolorpy (>=0.1.2) ; extra == 'test'
+
+.. contents:: **DataProperty**
+   :backlinks: top
+   :local:
+
+
+Summary
+=======
+A Python library for extract property from data.
+
+
+.. image:: https://badge.fury.io/py/DataProperty.svg
+    :target: https://badge.fury.io/py/DataProperty
+    :alt: PyPI package version
+
+.. image:: https://anaconda.org/conda-forge/DataProperty/badges/version.svg
+    :target: https://anaconda.org/conda-forge/DataProperty
+    :alt: conda-forge package version
+
+.. image:: https://img.shields.io/pypi/pyversions/DataProperty.svg
+   :target: https://pypi.org/project/DataProperty
+    :alt: Supported Python versions
+
+.. image:: https://img.shields.io/pypi/implementation/DataProperty.svg
+    :target: https://pypi.org/project/DataProperty
+    :alt: Supported Python implementations
+
+.. image:: https://github.com/thombashi/DataProperty/actions/workflows/ci.yml/badge.svg
+    :target: https://github.com/thombashi/DataProperty/actions/workflows/ci.yml
+    :alt: CI status of Linux/macOS/Windows
+
+.. image:: https://coveralls.io/repos/github/thombashi/DataProperty/badge.svg?branch=master
+    :target: https://coveralls.io/github/thombashi/DataProperty?branch=master
+    :alt: Test coverage
+
+.. image:: https://github.com/thombashi/DataProperty/actions/workflows/github-code-scanning/codeql/badge.svg
+    :target: https://github.com/thombashi/DataProperty/actions/workflows/github-code-scanning/codeql
+    :alt: CodeQL
+
+
+Installation
+============
+
+Installation: pip
+------------------------------
+::
+
+    pip install DataProperty
+
+Installation: conda
+------------------------------
+::
+
+    conda install -c conda-forge dataproperty
+
+Installation: apt
+------------------------------
+::
+
+    sudo add-apt-repository ppa:thombashi/ppa
+    sudo apt update
+    sudo apt install python3-dataproperty
+
+
+Usage
+=====
+
+Extract property of data
+------------------------
+
+e.g. Extract a ``float`` value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> from dataproperty import DataProperty
+    >>> DataProperty(-1.1)
+    data=-1.1, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=1, extra_len=1
+
+e.g. Extract a ``int`` value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> from dataproperty import DataProperty
+    >>> DataProperty(123456789)
+    data=123456789, type=INTEGER, align=right, ascii_width=9, int_digits=9, decimal_places=0, extra_len=0
+
+e.g. Extract a ``str`` (ascii) value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> from dataproperty import DataProperty
+    >>> DataProperty("sample string")
+    data=sample string, type=STRING, align=left, length=13, ascii_width=13, extra_len=0
+
+e.g. Extract a ``str`` (multi-byte) value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> from dataproperty import DataProperty
+    >>> str(DataProperty("吾輩は猫である"))
+    data=吾輩は猫である, type=STRING, align=left, length=7, ascii_width=14, extra_len=0
+
+e.g. Extract a time (``datetime``) value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> import datetime
+    >>> from dataproperty import DataProperty
+    >>> DataProperty(datetime.datetime(2017, 1, 1, 0, 0, 0))
+    data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0
+
+e.g. Extract a ``bool`` value property
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. code:: python
+
+    >>> from dataproperty import DataProperty
+    >>> DataProperty(True)
+    data=True, type=BOOL, align=left, ascii_width=4, extra_len=0
+
+
+Extract data property for each element from a matrix
+----------------------------------------------------
+``DataPropertyExtractor.to_dp_matrix`` method returns a matrix of ``DataProperty`` instances from a data matrix.
+An example data set and the result are as follows:
+
+:Sample Code:
+    .. code:: python
+
+        import datetime
+        from dataproperty import DataPropertyExtractor
+
+        dp_extractor = DataPropertyExtractor()
+        dt = datetime.datetime(2017, 1, 1, 0, 0, 0)
+        inf = float("inf")
+        nan = float("nan")
+
+        dp_matrix = dp_extractor.to_dp_matrix([
+            [1, 1.1, "aa", 1, 1, True, inf, nan, dt],
+            [2, 2.2, "bbb", 2.2, 2.2, False, "inf", "nan", dt],
+            [3, 3.33, "cccc", -3, "ccc", "true", inf, "NAN", "2017-01-01T01:23:45+0900"],
+        ])
+
+        for row, dp_list in enumerate(dp_matrix):
+            for col, dp in enumerate(dp_list):
+                print("row={:d}, col={:d}, {}".format(row, col, str(dp)))
+
+:Output:
+    ::
+
+        row=0, col=0, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
+        row=0, col=1, data=1.1, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
+        row=0, col=2, data=aa, type=STRING, align=left, ascii_width=2, length=2, extra_len=0
+        row=0, col=3, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
+        row=0, col=4, data=1, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
+        row=0, col=5, data=True, type=BOOL, align=left, ascii_width=4, extra_len=0
+        row=0, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0
+        row=0, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0
+        row=0, col=8, data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0
+        row=1, col=0, data=2, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
+        row=1, col=1, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
+        row=1, col=2, data=bbb, type=STRING, align=left, ascii_width=3, length=3, extra_len=0
+        row=1, col=3, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
+        row=1, col=4, data=2.2, type=REAL_NUMBER, align=right, ascii_width=3, int_digits=1, decimal_places=1, extra_len=0
+        row=1, col=5, data=False, type=BOOL, align=left, ascii_width=5, extra_len=0
+        row=1, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0
+        row=1, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0
+        row=1, col=8, data=2017-01-01 00:00:00, type=DATETIME, align=left, ascii_width=19, extra_len=0
+        row=2, col=0, data=3, type=INTEGER, align=right, ascii_width=1, int_digits=1, decimal_places=0, extra_len=0
+        row=2, col=1, data=3.33, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=2, extra_len=0
+        row=2, col=2, data=cccc, type=STRING, align=left, ascii_width=4, length=4, extra_len=0
+        row=2, col=3, data=-3, type=INTEGER, align=right, ascii_width=2, int_digits=1, decimal_places=0, extra_len=1
+        row=2, col=4, data=ccc, type=STRING, align=left, ascii_width=3, length=3, extra_len=0
+        row=2, col=5, data=True, type=BOOL, align=left, ascii_width=4, extra_len=0
+        row=2, col=6, data=Infinity, type=INFINITY, align=left, ascii_width=8, extra_len=0
+        row=2, col=7, data=NaN, type=NAN, align=left, ascii_width=3, extra_len=0
+        row=2, col=8, data=2017-01-01T01:23:45+0900, type=STRING, align=left, ascii_width=24, length=24, extra_len=0
+
+
+Full example source code can be found at *examples/py/to_dp_matrix.py*
+
+
+Extract properties for each column from a matrix
+------------------------------------------------------
+``DataPropertyExtractor.to_column_dp_list`` method returns a list of ``DataProperty`` instances from a data matrix. The list represents the properties for each column.
+An example data set and the result are as follows:
+
+Example data set and result are as follows:
+
+:Sample Code:
+    .. code:: python
+
+        import datetime
+        from dataproperty import DataPropertyExtractor
+
+        dp_extractor = DataPropertyExtractor()
+        dt = datetime.datetime(2017, 1, 1, 0, 0, 0)
+        inf = float("inf")
+        nan = float("nan")
+
+        data_matrix = [
+            [1, 1.1,  "aa",   1,   1,     True,   inf,   nan,   dt],
+            [2, 2.2,  "bbb",  2.2, 2.2,   False,  "inf", "nan", dt],
+            [3, 3.33, "cccc", -3,  "ccc", "true", inf,   "NAN", "2017-01-01T01:23:45+0900"],
+        ]
+
+        dp_extractor.headers = ["int", "float", "str", "num", "mix", "bool", "inf", "nan", "time"]
+        col_dp_list = dp_extractor.to_column_dp_list(dp_extractor.to_dp_matrix(dp_matrix))
+
+        for col_idx, col_dp in enumerate(col_dp_list):
+            print(str(col_dp))
+
+:Output:
+    ::
+
+        column=0, type=INTEGER, align=right, ascii_width=3, bit_len=2, int_digits=1, decimal_places=0
+        column=1, type=REAL_NUMBER, align=right, ascii_width=5, int_digits=1, decimal_places=(min=1, max=2)
+        column=2, type=STRING, align=left, ascii_width=4
+        column=3, type=REAL_NUMBER, align=right, ascii_width=4, int_digits=1, decimal_places=(min=0, max=1), extra_len=(min=0, max=1)
+        column=4, type=STRING, align=left, ascii_width=3, int_digits=1, decimal_places=(min=0, max=1)
+        column=5, type=BOOL, align=left, ascii_width=5
+        column=6, type=INFINITY, align=left, ascii_width=8
+        column=7, type=NAN, align=left, ascii_width=3
+        column=8, type=STRING, align=left, ascii_width=24
+
+
+Full example source code can be found at *examples/py/to_column_dp_list.py*
+
+
+Dependencies
+============
+- Python 3.7+
+- `Python package dependencies (automatically installed) <https://github.com/thombashi/DataProperty/network/dependencies>`__
+
+Optional dependencies
+---------------------
+- `loguru <https://github.com/Delgan/loguru>`__
+    - Used for logging if the package installed

llmeval-env/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/RECORD

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llmeval-env/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/WHEEL

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+Wheel-Version: 1.0
+Generator: bdist_wheel (0.40.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

llmeval-env/lib/python3.10/site-packages/DataProperty-1.0.1.dist-info/top_level.txt

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+dataproperty

llmeval-env/lib/python3.10/site-packages/PyYAML-6.0.1.dist-info/INSTALLER

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

llmeval-env/lib/python3.10/site-packages/PyYAML-6.0.1.dist-info/LICENSE

@@ -0,0 +1,20 @@
+Copyright (c) 2017-2021 Ingy döt Net
+Copyright (c) 2006-2016 Kirill Simonov
+
+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.

llmeval-env/lib/python3.10/site-packages/PyYAML-6.0.1.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: PyYAML
+Version: 6.0.1
+Summary: YAML parser and emitter for Python
+Home-page: https://pyyaml.org/
+Download-URL: https://pypi.org/project/PyYAML/
+Author: Kirill Simonov
+Author-email: [email protected]
+License: MIT
+Project-URL: Bug Tracker, https://github.com/yaml/pyyaml/issues
+Project-URL: CI, https://github.com/yaml/pyyaml/actions
+Project-URL: Documentation, https://pyyaml.org/wiki/PyYAMLDocumentation
+Project-URL: Mailing lists, http://lists.sourceforge.net/lists/listinfo/yaml-core
+Project-URL: Source Code, https://github.com/yaml/pyyaml
+Platform: Any
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Cython
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: Text Processing :: Markup
+Requires-Python: >=3.6
+License-File: LICENSE
+
+YAML is a data serialization format designed for human readability
+and interaction with scripting languages.  PyYAML is a YAML parser
+and emitter for Python.
+
+PyYAML features a complete YAML 1.1 parser, Unicode support, pickle
+support, capable extension API, and sensible error messages.  PyYAML
+supports standard YAML tags and provides Python-specific tags that
+allow to represent an arbitrary Python object.
+
+PyYAML is applicable for a broad range of tasks from complex
+configuration files to object serialization and persistence.

llmeval-env/lib/python3.10/site-packages/PyYAML-6.0.1.dist-info/RECORD

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llmeval-env/lib/python3.10/site-packages/PyYAML-6.0.1.dist-info/top_level.txt

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+yaml

llmeval-env/lib/python3.10/site-packages/aiosignal-1.3.1.dist-info/INSTALLER

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+pip

llmeval-env/lib/python3.10/site-packages/aiosignal-1.3.1.dist-info/LICENSE

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+Apache License
+                           Version 2.0, January 2004
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llmeval-env/lib/python3.10/site-packages/aiosignal-1.3.1.dist-info/METADATA

@@ -0,0 +1,128 @@
+Metadata-Version: 2.1
+Name: aiosignal
+Version: 1.3.1
+Summary: aiosignal: a list of registered asynchronous callbacks
+Home-page: https://github.com/aio-libs/aiosignal
+Maintainer: aiohttp team <[email protected]>
+Maintainer-email: [email protected]
+License: Apache 2.0
+Project-URL: Chat: Gitter, https://gitter.im/aio-libs/Lobby
+Project-URL: CI: GitHub Actions, https://github.com/aio-libs/aiosignal/actions
+Project-URL: Coverage: codecov, https://codecov.io/github/aio-libs/aiosignal
+Project-URL: Docs: RTD, https://docs.aiosignal.org
+Project-URL: GitHub: issues, https://github.com/aio-libs/aiosignal/issues
+Project-URL: GitHub: repo, https://github.com/aio-libs/aiosignal
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Intended Audience :: Developers
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Framework :: AsyncIO
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: frozenlist (>=1.1.0)
+
+=========
+aiosignal
+=========
+
+.. image:: https://github.com/aio-libs/aiosignal/workflows/CI/badge.svg
+   :target: https://github.com/aio-libs/aiosignal/actions?query=workflow%3ACI
+   :alt: GitHub status for master branch
+
+.. image:: https://codecov.io/gh/aio-libs/aiosignal/branch/master/graph/badge.svg
+   :target: https://codecov.io/gh/aio-libs/aiosignal
+   :alt: codecov.io status for master branch
+
+.. image:: https://badge.fury.io/py/aiosignal.svg
+   :target: https://pypi.org/project/aiosignal
+   :alt: Latest PyPI package version
+
+.. image:: https://readthedocs.org/projects/aiosignal/badge/?version=latest
+   :target: https://aiosignal.readthedocs.io/
+   :alt: Latest Read The Docs
+
+.. image:: https://img.shields.io/discourse/topics?server=https%3A%2F%2Faio-libs.discourse.group%2F
+   :target: https://aio-libs.discourse.group/
+   :alt: Discourse group for io-libs
+
+.. image:: https://badges.gitter.im/Join%20Chat.svg
+   :target: https://gitter.im/aio-libs/Lobby
+   :alt: Chat on Gitter
+
+Introduction
+============
+
+A project to manage callbacks in `asyncio` projects.
+
+``Signal`` is a list of registered asynchronous callbacks.
+
+The signal's life-cycle has two stages: after creation its content
+could be filled by using standard list operations: ``sig.append()``
+etc.
+
+After you call ``sig.freeze()`` the signal is *frozen*: adding, removing
+and dropping callbacks is forbidden.
+
+The only available operation is calling the previously registered
+callbacks by using ``await sig.send(data)``.
+
+For concrete usage examples see the `Signals
+<https://docs.aiohttp.org/en/stable/web_advanced.html#aiohttp-web-signals>
+section of the `Web Server Advanced
+<https://docs.aiohttp.org/en/stable/web_advanced.html>` chapter of the `aiohttp
+documentation`_.
+
+
+Installation
+------------
+
+::
+
+   $ pip install aiosignal
+
+The library requires Python 3.6 or newer.
+
+
+Documentation
+=============
+
+https://aiosignal.readthedocs.io/
+
+Communication channels
+======================
+
+*gitter chat* https://gitter.im/aio-libs/Lobby
+
+Requirements
+============
+
+- Python >= 3.6
+- frozenlist >= 1.0.0
+
+License
+=======
+
+``aiosignal`` is offered under the Apache 2 license.
+
+Source code
+===========
+
+The project is hosted on GitHub_
+
+Please file an issue in the `bug tracker
+<https://github.com/aio-libs/aiosignal/issues>`_ if you have found a bug
+or have some suggestions to improve the library.
+
+.. _GitHub: https://github.com/aio-libs/aiosignal
+.. _aiohttp documentation: https://docs.aiohttp.org/

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+Tag: py3-none-any
+

llmeval-env/lib/python3.10/site-packages/aiosignal-1.3.1.dist-info/top_level.txt

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+aiosignal

llmeval-env/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/INSTALLER

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

llmeval-env/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/LICENSE

@@ -0,0 +1,27 @@
+Copyright (c) 2005-2021 Fredrik Johansson and mpmath contributors
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+  a. Redistributions of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+  b. Redistributions in binary form must reproduce the above copyright
+     notice, this list of conditions and the following disclaimer in the
+     documentation and/or other materials provided with the distribution.
+  c. Neither the name of the copyright holder nor the names of its
+     contributors may be used to endorse or promote products derived
+     from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
+DAMAGE.

llmeval-env/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/METADATA

@@ -0,0 +1,233 @@
+Metadata-Version: 2.1
+Name: mpmath
+Version: 1.3.0
+Summary: Python library for arbitrary-precision floating-point arithmetic
+Home-page: http://mpmath.org/
+Author: Fredrik Johansson
+Author-email: [email protected]
+License: BSD
+Project-URL: Source, https://github.com/fredrik-johansson/mpmath
+Project-URL: Tracker, https://github.com/fredrik-johansson/mpmath/issues
+Project-URL: Documentation, http://mpmath.org/doc/current/
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 2
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+License-File: LICENSE
+Provides-Extra: develop
+Requires-Dist: pytest (>=4.6) ; extra == 'develop'
+Requires-Dist: pycodestyle ; extra == 'develop'
+Requires-Dist: pytest-cov ; extra == 'develop'
+Requires-Dist: codecov ; extra == 'develop'
+Requires-Dist: wheel ; extra == 'develop'
+Provides-Extra: docs
+Requires-Dist: sphinx ; extra == 'docs'
+Provides-Extra: gmpy
+Requires-Dist: gmpy2 (>=2.1.0a4) ; (platform_python_implementation != "PyPy") and extra == 'gmpy'
+Provides-Extra: tests
+Requires-Dist: pytest (>=4.6) ; extra == 'tests'
+
+mpmath
+======
+
+|pypi version| |Build status| |Code coverage status| |Zenodo Badge|
+
+.. |pypi version| image:: https://img.shields.io/pypi/v/mpmath.svg
+   :target: https://pypi.python.org/pypi/mpmath
+.. |Build status| image:: https://github.com/fredrik-johansson/mpmath/workflows/test/badge.svg
+   :target: https://github.com/fredrik-johansson/mpmath/actions?workflow=test
+.. |Code coverage status| image:: https://codecov.io/gh/fredrik-johansson/mpmath/branch/master/graph/badge.svg
+   :target: https://codecov.io/gh/fredrik-johansson/mpmath
+.. |Zenodo Badge| image:: https://zenodo.org/badge/2934512.svg
+   :target: https://zenodo.org/badge/latestdoi/2934512
+
+A Python library for arbitrary-precision floating-point arithmetic.
+
+Website: http://mpmath.org/
+Main author: Fredrik Johansson <[email protected]>
+
+Mpmath is free software released under the New BSD License (see the
+LICENSE file for details)
+
+0. History and credits
+----------------------
+
+The following people (among others) have contributed major patches
+or new features to mpmath:
+
+* Pearu Peterson <[email protected]>
+* Mario Pernici <[email protected]>
+* Ondrej Certik <[email protected]>
+* Vinzent Steinberg <[email protected]>
+* Nimish Telang <[email protected]>
+* Mike Taschuk <[email protected]>
+* Case Van Horsen <[email protected]>
+* Jorn Baayen <[email protected]>
+* Chris Smith <[email protected]>
+* Juan Arias de Reyna <[email protected]>
+* Ioannis Tziakos <[email protected]>
+* Aaron Meurer <[email protected]>
+* Stefan Krastanov <[email protected]>
+* Ken Allen <[email protected]>
+* Timo Hartmann <[email protected]>
+* Sergey B Kirpichev <[email protected]>
+* Kris Kuhlman <[email protected]>
+* Paul Masson <[email protected]>
+* Michael Kagalenko <[email protected]>
+* Jonathan Warner <[email protected]>
+* Max Gaukler <[email protected]>
+* Guillermo Navas-Palencia <[email protected]>
+* Nike Dattani <[email protected]>
+
+Numerous other people have contributed by reporting bugs,
+requesting new features, or suggesting improvements to the
+documentation.
+
+For a detailed changelog, including individual contributions,
+see the CHANGES file.
+
+Fredrik's work on mpmath during summer 2008 was sponsored by Google
+as part of the Google Summer of Code program.
+
+Fredrik's work on mpmath during summer 2009 was sponsored by the
+American Institute of Mathematics under the support of the National Science
+Foundation Grant No. 0757627 (FRG: L-functions and Modular Forms).
+
+Any opinions, findings, and conclusions or recommendations expressed in this
+material are those of the author(s) and do not necessarily reflect the
+views of the sponsors.
+
+Credit also goes to:
+
+* The authors of the GMP library and the Python wrapper
+  gmpy, enabling mpmath to become much faster at
+  high precision
+* The authors of MPFR, pari/gp, MPFUN, and other arbitrary-
+  precision libraries, whose documentation has been helpful
+  for implementing many of the algorithms in mpmath
+* Wikipedia contributors; Abramowitz & Stegun; Gradshteyn & Ryzhik;
+  Wolfram Research for MathWorld and the Wolfram Functions site.
+  These are the main references used for special functions
+  implementations.
+* George Brandl for developing the Sphinx documentation tool
+  used to build mpmath's documentation
+
+Release history:
+
+* Version 1.3.0 released on March 7, 2023
+* Version 1.2.0 released on February 1, 2021
+* Version 1.1.0 released on December 11, 2018
+* Version 1.0.0 released on September 27, 2017
+* Version 0.19 released on June 10, 2014
+* Version 0.18 released on December 31, 2013
+* Version 0.17 released on February 1, 2011
+* Version 0.16 released on September 24, 2010
+* Version 0.15 released on June 6, 2010
+* Version 0.14 released on February 5, 2010
+* Version 0.13 released on August 13, 2009
+* Version 0.12 released on June 9, 2009
+* Version 0.11 released on January 26, 2009
+* Version 0.10 released on October 15, 2008
+* Version 0.9 released on August 23, 2008
+* Version 0.8 released on April 20, 2008
+* Version 0.7 released on March 12, 2008
+* Version 0.6 released on January 13, 2008
+* Version 0.5 released on November 24, 2007
+* Version 0.4 released on November 3, 2007
+* Version 0.3 released on October 5, 2007
+* Version 0.2 released on October 2, 2007
+* Version 0.1 released on September 27, 2007
+
+1. Download & installation
+--------------------------
+
+Mpmath requires Python 2.7 or 3.5 (or later versions). It has been tested
+with CPython 2.7, 3.5 through 3.7 and for PyPy.
+
+The latest release of mpmath can be downloaded from the mpmath
+website and from https://github.com/fredrik-johansson/mpmath/releases
+
+It should also be available in the Python Package Index at
+https://pypi.python.org/pypi/mpmath
+
+To install latest release of Mpmath with pip, simply run
+
+``pip install mpmath``
+
+Or unpack the mpmath archive and run
+
+``python setup.py install``
+
+Mpmath can also be installed using
+
+``python -m easy_install mpmath``
+
+The latest development code is available from
+https://github.com/fredrik-johansson/mpmath
+
+See the main documentation for more detailed instructions.
+
+2. Running tests
+----------------
+
+The unit tests in mpmath/tests/ can be run via the script
+runtests.py, but it is recommended to run them with py.test
+(https://pytest.org/), especially
+to generate more useful reports in case there are failures.
+
+You may also want to check out the demo scripts in the demo
+directory.
+
+The master branch is automatically tested by Travis CI.
+
+3. Documentation
+----------------
+
+Documentation in reStructuredText format is available in the
+doc directory included with the source package. These files
+are human-readable, but can be compiled to prettier HTML using
+the build.py script (requires Sphinx, http://sphinx.pocoo.org/).
+
+See setup.txt in the documentation for more information.
+
+The most recent documentation is also available in HTML format:
+
+http://mpmath.org/doc/current/
+
+4. Known problems
+-----------------
+
+Mpmath is a work in progress. Major issues include:
+
+* Some functions may return incorrect values when given extremely
+  large arguments or arguments very close to singularities.
+
+* Directed rounding works for arithmetic operations. It is implemented
+  heuristically for other operations, and their results may be off by one
+  or two units in the last place (even if otherwise accurate).
+
+* Some IEEE 754 features are not available. Inifinities and NaN are
+  partially supported; denormal rounding is currently not available
+  at all.
+
+* The interface for switching precision and rounding is not finalized.
+  The current method is not threadsafe.
+
+5. Help and bug reports
+-----------------------
+
+General questions and comments can be sent to the mpmath mailinglist,
+[email protected]
+
+You can also report bugs and send patches to the mpmath issue tracker,
+https://github.com/fredrik-johansson/mpmath/issues

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llmeval-env/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.38.4)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

llmeval-env/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+mpmath

llmeval-env/lib/python3.10/site-packages/networkx-3.3.dist-info/INSTALLER

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

llmeval-env/lib/python3.10/site-packages/networkx-3.3.dist-info/LICENSE.txt

@@ -0,0 +1,37 @@
+NetworkX is distributed with the 3-clause BSD license.
+
+::
+
+   Copyright (C) 2004-2024, NetworkX Developers
+   Aric Hagberg <[email protected]>
+   Dan Schult <[email protected]>
+   Pieter Swart <[email protected]>
+   All rights reserved.
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions are
+   met:
+
+     * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+     * Redistributions in binary form must reproduce the above
+       copyright notice, this list of conditions and the following
+       disclaimer in the documentation and/or other materials provided
+       with the distribution.
+
+     * Neither the name of the NetworkX Developers nor the names of its
+       contributors may be used to endorse or promote products derived
+       from this software without specific prior written permission.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+   "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+   A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+   OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+   SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+   LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+   DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+   THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+   (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+   OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

llmeval-env/lib/python3.10/site-packages/networkx-3.3.dist-info/METADATA

@@ -0,0 +1,133 @@
+Metadata-Version: 2.1
+Name: networkx
+Version: 3.3
+Summary: Python package for creating and manipulating graphs and networks
+Author-email: Aric Hagberg <[email protected]>
+Maintainer-email: NetworkX Developers <[email protected]>
+Project-URL: Homepage, https://networkx.org/
+Project-URL: Bug Tracker, https://github.com/networkx/networkx/issues
+Project-URL: Documentation, https://networkx.org/documentation/stable/
+Project-URL: Source Code, https://github.com/networkx/networkx
+Keywords: Networks,Graph Theory,Mathematics,network,graph,discrete mathematics,math
+Platform: Linux
+Platform: Mac OSX
+Platform: Windows
+Platform: Unix
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
+Classifier: Topic :: Scientific/Engineering :: Information Analysis
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Physics
+Requires-Python: >=3.10
+Description-Content-Type: text/x-rst
+License-File: LICENSE.txt
+Provides-Extra: default
+Requires-Dist: numpy >=1.23 ; extra == 'default'
+Requires-Dist: scipy !=1.11.0,!=1.11.1,>=1.9 ; extra == 'default'
+Requires-Dist: matplotlib >=3.6 ; extra == 'default'
+Requires-Dist: pandas >=1.4 ; extra == 'default'
+Provides-Extra: developer
+Requires-Dist: changelist ==0.5 ; extra == 'developer'
+Requires-Dist: pre-commit >=3.2 ; extra == 'developer'
+Requires-Dist: mypy >=1.1 ; extra == 'developer'
+Requires-Dist: rtoml ; extra == 'developer'
+Provides-Extra: doc
+Requires-Dist: sphinx >=7 ; extra == 'doc'
+Requires-Dist: pydata-sphinx-theme >=0.14 ; extra == 'doc'
+Requires-Dist: sphinx-gallery >=0.14 ; extra == 'doc'
+Requires-Dist: numpydoc >=1.7 ; extra == 'doc'
+Requires-Dist: pillow >=9.4 ; extra == 'doc'
+Requires-Dist: texext >=0.6.7 ; extra == 'doc'
+Requires-Dist: myst-nb >=1.0 ; extra == 'doc'
+Provides-Extra: extra
+Requires-Dist: lxml >=4.6 ; extra == 'extra'
+Requires-Dist: pygraphviz >=1.12 ; extra == 'extra'
+Requires-Dist: pydot >=2.0 ; extra == 'extra'
+Requires-Dist: sympy >=1.10 ; extra == 'extra'
+Provides-Extra: test
+Requires-Dist: pytest >=7.2 ; extra == 'test'
+Requires-Dist: pytest-cov >=4.0 ; extra == 'test'
+
+NetworkX
+========
+
+
+.. image:: https://github.com/networkx/networkx/workflows/test/badge.svg?branch=main
+  :target: https://github.com/networkx/networkx/actions?query=workflow%3A%22test%22
+
+.. image:: https://codecov.io/gh/networkx/networkx/branch/main/graph/badge.svg
+   :target: https://app.codecov.io/gh/networkx/networkx/branch/main
+   
+.. image:: https://img.shields.io/github/labels/networkx/networkx/Good%20First%20Issue?color=green&label=Contribute%20&style=flat-square
+   :target: https://github.com/networkx/networkx/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+First+Issue%22
+   
+
+NetworkX is a Python package for the creation, manipulation,
+and study of the structure, dynamics, and functions
+of complex networks.
+
+- **Website (including documentation):** https://networkx.org
+- **Mailing list:** https://groups.google.com/forum/#!forum/networkx-discuss
+- **Source:** https://github.com/networkx/networkx
+- **Bug reports:** https://github.com/networkx/networkx/issues
+- **Report a security vulnerability:** https://tidelift.com/security
+- **Tutorial:** https://networkx.org/documentation/latest/tutorial.html
+- **GitHub Discussions:** https://github.com/networkx/networkx/discussions
+
+Simple example
+--------------
+
+Find the shortest path between two nodes in an undirected graph:
+
+.. code:: pycon
+
+    >>> import networkx as nx
+    >>> G = nx.Graph()
+    >>> G.add_edge("A", "B", weight=4)
+    >>> G.add_edge("B", "D", weight=2)
+    >>> G.add_edge("A", "C", weight=3)
+    >>> G.add_edge("C", "D", weight=4)
+    >>> nx.shortest_path(G, "A", "D", weight="weight")
+    ['A', 'B', 'D']
+
+Install
+-------
+
+Install the latest version of NetworkX::
+
+    $ pip install networkx
+
+Install with all optional dependencies::
+
+    $ pip install networkx[all]
+
+For additional details, please see `INSTALL.rst`.
+
+Bugs
+----
+
+Please report any bugs that you find `here <https://github.com/networkx/networkx/issues>`_.
+Or, even better, fork the repository on `GitHub <https://github.com/networkx/networkx>`_
+and create a pull request (PR). We welcome all changes, big or small, and we
+will help you make the PR if you are new to `git` (just ask on the issue and/or
+see `CONTRIBUTING.rst`).
+
+License
+-------
+
+Released under the 3-Clause BSD license (see `LICENSE.txt`)::
+
+   Copyright (C) 2004-2024 NetworkX Developers
+   Aric Hagberg <[email protected]>
+   Dan Schult <[email protected]>
+   Pieter Swart <[email protected]>

llmeval-env/lib/python3.10/site-packages/networkx-3.3.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.43.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

llmeval-env/lib/python3.10/site-packages/networkx-3.3.dist-info/entry_points.txt

@@ -0,0 +1,2 @@
+[networkx.backends]
+nx-loopback = networkx.classes.tests.dispatch_interface:dispatcher

llmeval-env/lib/python3.10/site-packages/networkx-3.3.dist-info/top_level.txt

@@ -0,0 +1 @@
+networkx

llmeval-env/lib/python3.10/site-packages/pandas/arrays/__init__.py

@@ -0,0 +1,53 @@
+"""
+All of pandas' ExtensionArrays.
+
+See :ref:`extending.extension-types` for more.
+"""
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    ArrowStringArray,
+    BooleanArray,
+    Categorical,
+    DatetimeArray,
+    FloatingArray,
+    IntegerArray,
+    IntervalArray,
+    NumpyExtensionArray,
+    PeriodArray,
+    SparseArray,
+    StringArray,
+    TimedeltaArray,
+)
+
+__all__ = [
+    "ArrowExtensionArray",
+    "ArrowStringArray",
+    "BooleanArray",
+    "Categorical",
+    "DatetimeArray",
+    "FloatingArray",
+    "IntegerArray",
+    "IntervalArray",
+    "NumpyExtensionArray",
+    "PeriodArray",
+    "SparseArray",
+    "StringArray",
+    "TimedeltaArray",
+]
+
+
+def __getattr__(name: str) -> type[NumpyExtensionArray]:
+    if name == "PandasArray":
+        # GH#53694
+        import warnings
+
+        from pandas.util._exceptions import find_stack_level
+
+        warnings.warn(
+            "PandasArray has been renamed NumpyExtensionArray. Use that "
+            "instead. This alias will be removed in a future version.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return NumpyExtensionArray
+    raise AttributeError(f"module 'pandas.arrays' has no attribute '{name}'")

llmeval-env/lib/python3.10/site-packages/safetensors/mlx.py

@@ -0,0 +1,138 @@
+import os
+from typing import Dict, Optional, Union
+
+import numpy as np
+
+import mlx.core as mx
+from safetensors import numpy, safe_open
+
+
+def save(tensors: Dict[str, mx.array], metadata: Optional[Dict[str, str]] = None) -> bytes:
+    """
+    Saves a dictionary of tensors into raw bytes in safetensors format.
+
+    Args:
+        tensors (`Dict[str, mx.array]`):
+            The incoming tensors. Tensors need to be contiguous and dense.
+        metadata (`Dict[str, str]`, *optional*, defaults to `None`):
+            Optional text only metadata you might want to save in your header.
+            For instance it can be useful to specify more about the underlying
+            tensors. This is purely informative and does not affect tensor loading.
+
+    Returns:
+        `bytes`: The raw bytes representing the format
+
+    Example:
+
+    ```python
+    from safetensors.mlx import save
+    import mlx.core as mx
+
+    tensors = {"embedding": mx.zeros((512, 1024)), "attention": mx.zeros((256, 256))}
+    byte_data = save(tensors)
+    ```
+    """
+    np_tensors = _mx2np(tensors)
+    return numpy.save(np_tensors, metadata=metadata)
+
+
+def save_file(
+    tensors: Dict[str, mx.array],
+    filename: Union[str, os.PathLike],
+    metadata: Optional[Dict[str, str]] = None,
+) -> None:
+    """
+    Saves a dictionary of tensors into raw bytes in safetensors format.
+
+    Args:
+        tensors (`Dict[str, mx.array]`):
+            The incoming tensors. Tensors need to be contiguous and dense.
+        filename (`str`, or `os.PathLike`)):
+            The filename we're saving into.
+        metadata (`Dict[str, str]`, *optional*, defaults to `None`):
+            Optional text only metadata you might want to save in your header.
+            For instance it can be useful to specify more about the underlying
+            tensors. This is purely informative and does not affect tensor loading.
+
+    Returns:
+        `None`
+
+    Example:
+
+    ```python
+    from safetensors.mlx import save_file
+    import mlx.core as mx
+
+    tensors = {"embedding": mx.zeros((512, 1024)), "attention": mx.zeros((256, 256))}
+    save_file(tensors, "model.safetensors")
+    ```
+    """
+    np_tensors = _mx2np(tensors)
+    return numpy.save_file(np_tensors, filename, metadata=metadata)
+
+
+def load(data: bytes) -> Dict[str, mx.array]:
+    """
+    Loads a safetensors file into MLX format from pure bytes.
+
+    Args:
+        data (`bytes`):
+            The content of a safetensors file
+
+    Returns:
+        `Dict[str, mx.array]`: dictionary that contains name as key, value as `mx.array`
+
+    Example:
+
+    ```python
+    from safetensors.mlx import load
+
+    file_path = "./my_folder/bert.safetensors"
+    with open(file_path, "rb") as f:
+        data = f.read()
+
+    loaded = load(data)
+    ```
+    """
+    flat = numpy.load(data)
+    return _np2mx(flat)
+
+
+def load_file(filename: Union[str, os.PathLike]) -> Dict[str, mx.array]:
+    """
+    Loads a safetensors file into MLX format.
+
+    Args:
+        filename (`str`, or `os.PathLike`)):
+            The name of the file which contains the tensors
+
+    Returns:
+        `Dict[str, mx.array]`: dictionary that contains name as key, value as `mx.array`
+
+    Example:
+
+    ```python
+    from safetensors.flax import load_file
+
+    file_path = "./my_folder/bert.safetensors"
+    loaded = load_file(file_path)
+    ```
+    """
+    result = {}
+    with safe_open(filename, framework="mlx") as f:
+        for k in f.keys():
+            result[k] = f.get_tensor(k)
+    return result
+
+
+def _np2mx(numpy_dict: Dict[str, np.ndarray]) -> Dict[str, mx.array]:
+    for k, v in numpy_dict.items():
+        numpy_dict[k] = mx.array(v)
+    return numpy_dict
+
+
+def _mx2np(mx_dict: Dict[str, mx.array]) -> Dict[str, np.array]:
+    new_dict = {}
+    for k, v in mx_dict.items():
+        new_dict[k] = np.asarray(v)
+    return new_dict

llmeval-env/lib/python3.10/site-packages/tcolorpy-0.1.6.dist-info/INSTALLER

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

llmeval-env/lib/python3.10/site-packages/tcolorpy-0.1.6.dist-info/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Tsuyoshi Hombashi
+
+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.

llmeval-env/lib/python3.10/site-packages/tcolorpy-0.1.6.dist-info/METADATA

@@ -0,0 +1,171 @@
+Metadata-Version: 2.1
+Name: tcolorpy
+Version: 0.1.6
+Summary: tcolopy is a Python library to apply true color for terminal text.
+Home-page: https://github.com/thombashi/tcolorpy
+Author: Tsuyoshi Hombashi
+Author-email: [email protected]
+License: MIT License
+Project-URL: Changlog, https://github.com/thombashi/tcolorpy/blob/master/CHANGELOG.md
+Project-URL: Source, https://github.com/thombashi/tcolorpy
+Project-URL: Tracker, https://github.com/thombashi/tcolorpy/issues
+Keywords: ANSI escape,terminal color,truecolor
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Information Technology
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: Terminals
+Classifier: Topic :: Text Processing
+Classifier: Typing :: Typed
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Provides-Extra: test
+Requires-Dist: pytest >=6.0.1 ; extra == 'test'
+Requires-Dist: pytest-md-report >=0.5 ; extra == 'test'
+
+.. contents:: **tcolorpy**
+   :backlinks: top
+   :depth: 2
+
+
+Summary
+============================================
+tcolopy is a Python library to apply true color for terminal text.
+
+|PyPI pkg ver| |conda pkg ver| |Supported Python implementations| |Supported Python versions| |CI status| |CodeQL| |coverage|
+
+.. |PyPI pkg ver| image:: https://badge.fury.io/py/tcolorpy.svg
+    :target: https://badge.fury.io/py/tcolorpy
+    :alt: PyPI package version
+
+.. |conda pkg ver| image:: https://anaconda.org/conda-forge/tcolorpy/badges/version.svg
+    :target: https://anaconda.org/conda-forge/tcolorpy
+    :alt: conda-forge package version
+
+.. |Supported Python implementations| image:: https://img.shields.io/pypi/implementation/tcolorpy.svg
+    :target: https://pypi.org/project/tcolorpy
+    :alt: Supported Python implementations
+
+.. |Supported Python versions| image:: https://img.shields.io/pypi/pyversions/tcolorpy.svg
+    :target: https://pypi.org/project/tcolorpy
+    :alt: Supported Python versions
+
+.. |CI status| image:: https://github.com/thombashi/tcolorpy/actions/workflows/ci.yml/badge.svg
+    :target: https://github.com/thombashi/tcolorpy/actions/workflows/ci.yml
+    :alt: CI status of Linux/macOS/Windows
+
+.. |CodeQL| image:: https://github.com/thombashi/tcolorpy/actions/workflows/github-code-scanning/codeql/badge.svg
+    :target: https://github.com/thombashi/tcolorpy/actions/workflows/github-code-scanning/codeql
+    :alt: CodeQL
+
+.. |coverage| image:: https://coveralls.io/repos/github/thombashi/tcolorpy/badge.svg?branch=master
+    :target: https://coveralls.io/github/thombashi/tcolorpy?branch=master
+    :alt: Test coverage: coveralls
+
+
+Installation
+============================================
+
+Installation: pip
+------------------------------
+::
+
+    pip install tcolorpy
+
+Installation: conda
+------------------------------
+::
+
+    conda install -c conda-forge tcolorpy
+
+
+Usage
+============================================
+
+Library usage
+--------------------------------------------
+
+:Sample Code:
+    .. code-block:: python
+
+        from tcolorpy import tcolor
+
+        print(tcolor("tcolopy example", color="#ee1177", styles=["bold", "italic", "underline"]))
+
+:Output:
+    .. figure:: https://cdn.jsdelivr.net/gh/thombashi/tcolorpy@master/ss/oneline.png
+        :scale: 60%
+        :alt: https://github.com/thombashi/tcolorpy/blob/master/ss/oneline.png
+
+You can set the following ``tcolor`` arguments:
+
+- ``color``/``bg_color``
+    - color names (``"red"``, ``"green"``, etc.) or color code (``"#RRGGBB"``)
+- ``styles``
+    - ``"bold"``, ``"italic"``, etc.
+
+
+Other examples
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Apply true color and styles to text:
+
+.. figure:: https://cdn.jsdelivr.net/gh/thombashi/tcolorpy@master/ss/styles.png
+    :scale: 60%
+    :alt: https://github.com/thombashi/tcolorpy/blob/master/ss/styles.png
+
+    `example source code <https://github.com/thombashi/tcolorpy/blob/master/examples/ansi_styles.py>`__
+
+You can also specify colors by name:
+
+.. figure:: https://cdn.jsdelivr.net/gh/thombashi/tcolorpy@master/ss/ansi_colors.png
+    :scale: 60%
+    :alt: https://github.com/thombashi/tcolorpy/blob/master/ss/ansi_colors.png
+
+    `example source code <https://github.com/thombashi/tcolorpy/blob/master/examples/ansi_colors.py>`__
+
+
+CLI usage
+--------------------------------------------
+``tcolorpy`` can be used via CLI:
+
+::
+
+    $ python3 -m tcolorpy "tcolopy example" -c "#ee1177" -s bold,italic,underline
+
+Command help
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+::
+
+    usage: __main__.py [-h] [-c COLOR] [-b BG_COLOR] [-s STYLES] [--encode ENCODE] string
+
+    positional arguments:
+      string                string to apply styles.
+
+    options:
+      -h, --help            show this help message and exit
+      -c COLOR, --color COLOR
+                            specify a color code (#XXXXXX) or a name. valid names are: black, red, green, yellow, blue, magenta, cyan, white, lightblack, lightred, lightgreen, lightyellow, lightblue, lightmagenta, lightcyan, lightwhite
+      -b BG_COLOR, --bg-color BG_COLOR
+                            specify a background color code (#XXXXXX) or a name. valid names are: black, red, green, yellow, blue, magenta, cyan, white, lightblack, lightred, lightgreen, lightyellow, lightblue, lightmagenta, lightcyan, lightwhite
+      -s STYLES, --styles STYLES
+                            specify a comma-separated style. valid values are: bold, dim, italic, underline, blink, invert, strike
+      --encode ENCODE       output a text encoded with the specified encoding
+
+
+Dependencies
+============================================
+Python 3.7+
+no external dependencies.

llmeval-env/lib/python3.10/site-packages/tcolorpy-0.1.6.dist-info/RECORD

@@ -0,0 +1,17 @@
+tcolorpy-0.1.6.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+tcolorpy-0.1.6.dist-info/LICENSE,sha256=9BoEVtXyu6Jf1NflC1GpXeMEdw_x21p5UV0DOXqRTY0,1074
+tcolorpy-0.1.6.dist-info/METADATA,sha256=IDGYAt_oFtLBO4jHLKx8SETH0FP33K-RaszTkTLhMes,6358
+tcolorpy-0.1.6.dist-info/RECORD,,
+tcolorpy-0.1.6.dist-info/WHEEL,sha256=GJ7t_kWBFywbagK5eo9IoUwLW6oyOeTKmQ-9iHFVNxQ,92
+tcolorpy-0.1.6.dist-info/top_level.txt,sha256=g8LDaQz0FVP61jibPz7OTwQqiseVV9pxUYDeGp2lFAI,9
+tcolorpy/__init__.py,sha256=6fI5Y7N04ZgSFfienFNtd7hjJtAmBO8j4zxcDpk4OYk,913
+tcolorpy/__main__.py,sha256=gjNpi78hE-X6CpY20ZLMmQ_yaWYIh_eOu2XrLnoGkBE,1701
+tcolorpy/__pycache__/__init__.cpython-310.pyc,,
+tcolorpy/__pycache__/__main__.cpython-310.pyc,,
+tcolorpy/__pycache__/__version__.cpython-310.pyc,,
+tcolorpy/__pycache__/_const.cpython-310.pyc,,
+tcolorpy/__pycache__/_truecolor.cpython-310.pyc,,
+tcolorpy/__version__.py,sha256=FfUl1ix-FI5DHv8TmnpAYpPWggJASYcLGQ0s-sVO6Ko,201
+tcolorpy/_const.py,sha256=XS2rzsxY7SKxg0HreYTR_kEGeSi_59gOrrntI2_kG1o,1080
+tcolorpy/_truecolor.py,sha256=nzu2GCc6Tu_4no5_Qcksm88-Vm75sCdeOMDQHG_2DhM,7495
+tcolorpy/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0

llmeval-env/lib/python3.10/site-packages/tcolorpy-0.1.6.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.43.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

llmeval-env/lib/python3.10/site-packages/tcolorpy-0.1.6.dist-info/top_level.txt

@@ -0,0 +1 @@
+tcolorpy

llmeval-env/lib/python3.10/site-packages/torch/__config__.py

@@ -0,0 +1,22 @@
+import torch
+
+
+def show():
+    """
+    Return a human-readable string with descriptions of the
+    configuration of PyTorch.
+    """
+    return torch._C._show_config()
+
+
+# TODO: In principle, we could provide more structured version/config
+# information here. For now only CXX_FLAGS is exposed, as Timer
+# uses them.
+def _cxx_flags():
+    """Returns the CXX_FLAGS used when building PyTorch."""
+    return torch._C._cxx_flags()
+
+
+def parallel_info():
+    r"""Returns detailed string with parallelization settings"""
+    return torch._C._parallel_info()

llmeval-env/lib/python3.10/site-packages/torch/__future__.py

@@ -0,0 +1,75 @@
+_overwrite_module_params_on_conversion: bool = False
+_swap_module_params_on_conversion: bool = False
+
+
+def set_overwrite_module_params_on_conversion(value: bool) -> None:
+    """
+    Sets whether to assign new tensors to the parameters instead of changing the
+    existing parameters in-place when converting an ``nn.Module``.
+
+    When enabled, the following methods will assign new parameters to the module:
+
+    #. ``module.{device}()`` (e.g. :meth:`nn.Module.cuda()`) for moving a module between devices
+    #. ``module.{dtype}()`` (e.g. :meth:`nn.Module.float()`) for converting a module to a different dtype
+    #. :meth:`nn.Module.to`
+    #. :meth:`nn.Module.to_empty`
+
+    Args:
+        value (bool): Whether to assign new tensors or not.
+
+    """
+    global _overwrite_module_params_on_conversion
+    _overwrite_module_params_on_conversion = value
+
+
+def get_overwrite_module_params_on_conversion() -> bool:
+    """
+    Returns whether to assign new tensors to the parameters instead of changing the
+    existing parameters in-place when converting an :class:`torch.nn.Module`. Defaults to ``False``.
+
+    See :func:`~torch.__future__.set_overwrite_module_params_on_conversion` for more information.
+    """
+    return _overwrite_module_params_on_conversion
+
+
+def set_swap_module_params_on_conversion(value: bool) -> None:
+    """
+    Sets whether to use :func:`~torch.utils.swap_tensors` instead of setting ``.data`` to
+    change the existing parameters in-place when converting an ``nn.Module`` and instead
+    of ``param.copy_(state_dict[key])`` when loading a state dict into an ``nn.Module``.
+
+    .. note::
+        This function takes precedence over :func:`~torch.__future__.get_overwrite_module_params_on_conversion`
+
+    When enabled, the following methods will swap the existing parameters in-place:
+
+    #. ``module.{device}()`` (e.g. :meth:`nn.Module.cuda()`) for moving a module between devices
+    #. ``module.{dtype}()`` (e.g. :meth:`nn.Module.float()`) for converting a module to a different dtype
+    #. :meth:`nn.Module.to`
+    #. :meth:`nn.Module.to_empty`
+    #. :meth:`nn.Module.load_state_dict`
+
+    The semantics for :meth:`~nn.Module.load_state_dict` when this is set are as follows:
+
+    #. For each parameter/buffer, its corresponding ``state_dict['key']`` is transformed via
+       :meth:`~torch.Tensor.module_load` (i.e. ``res = param.module_load(state_dict['key'])``)
+    #. If necessary, ``res`` will be wrapped in an :class:`~nn.Parameter`
+    #. The parameter/buffer in the module will be swapped via :func:`~torch.utils.swap_tensors`
+       with ``res``
+
+    Args:
+        value (bool): Whether to use :func:`~torch.utils.swap_tensors` or not.
+
+    """
+    global _swap_module_params_on_conversion
+    _swap_module_params_on_conversion = value
+
+
+def get_swap_module_params_on_conversion() -> bool:
+    """
+    Returns whether to use :func:`~torch.utils.swap_tensors` instead of setting .data to
+    change the existing parameters in-place when converting an ``nn.Module``. Defaults to ``False``.
+
+    See :func:`~torch.__future__.set_swap_module_params_on_conversion` for more information.
+    """
+    return _swap_module_params_on_conversion

llmeval-env/lib/python3.10/site-packages/torch/__init__.py

@@ -0,0 +1,2038 @@
+
+r"""
+The torch package contains data structures for multi-dimensional
+tensors and defines mathematical operations over these tensors.
+Additionally, it provides many utilities for efficient serialization of
+Tensors and arbitrary types, and other useful utilities.
+
+It has a CUDA counterpart, that enables you to run your tensor computations
+on an NVIDIA GPU with compute capability >= 3.0.
+"""
+
+import math
+import os
+import sys
+import platform
+import textwrap
+import ctypes
+import inspect
+import threading
+
+# multipy/deploy is setting this import before importing torch, this is the most
+# reliable way we have to detect if we're running within deploy.
+# https://github.com/pytorch/multipy/blob/d60f34ad38c371e441fe7ffdb77a3c3dda5a5d19/multipy/runtime/interpreter/interpreter_impl.cpp#L134-L137
+def _running_with_deploy():
+    return sys.modules.get("torch._meta_registrations", None) is object
+
+from ._utils import _import_dotted_name, classproperty
+from ._utils import _functionalize_sync as _sync
+from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
+    USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS
+
+# TODO(torch_deploy) figure out how to freeze version.py in fbcode build
+if _running_with_deploy():
+    __version__ = "torch-deploy-1.8"
+else:
+    from .torch_version import __version__ as __version__
+
+from typing import Any, Callable, Dict, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, List
+import builtins
+
+__all__ = [
+    'typename', 'is_tensor', 'is_storage',
+    'set_default_tensor_type', 'set_default_device', 'get_default_device',
+    'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
+    'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
+    'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode',
+    'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
+    'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
+    'TypedStorage', 'UntypedStorage',
+    'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
+    'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
+    'lobpcg', 'use_deterministic_algorithms',
+    'are_deterministic_algorithms_enabled',
+    'is_deterministic_algorithms_warn_only_enabled',
+    'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
+    'set_float32_matmul_precision', 'get_float32_matmul_precision',
+    'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
+    'SymBool', 'sym_not', 'unravel_index',
+    'sym_int', 'sym_float', 'sym_max', 'sym_min', 'sym_ite', 'compile', 'vmap',
+    'export', 'autocast', 'cond', 'GradScaler',
+]
+
+################################################################################
+# Load the extension module
+################################################################################
+
+if sys.platform == 'win32':
+    pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files')
+    py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
+    th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
+
+    # When users create a virtualenv that inherits the base environment,
+    # we will need to add the corresponding library directory into
+    # DLL search directories. Otherwise, it will rely on `PATH` which
+    # is dependent on user settings.
+    if sys.exec_prefix != sys.base_exec_prefix:
+        base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin')
+    else:
+        base_py_dll_path = ''
+
+    dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
+
+    if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
+        nvtoolsext_dll_path = os.path.join(
+            os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
+    else:
+        nvtoolsext_dll_path = ''
+
+    from .version import cuda as cuda_version
+    import glob
+    if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
+        cuda_version_1 = cuda_version.replace('.', '_')
+        cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
+        default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)
+        cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
+    else:
+        cuda_path = ''
+
+    dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path]))
+
+    kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True)
+    with_load_library_flags = hasattr(kernel32, 'AddDllDirectory')
+    prev_error_mode = kernel32.SetErrorMode(0x0001)
+
+    kernel32.LoadLibraryW.restype = ctypes.c_void_p
+    if with_load_library_flags:
+        kernel32.LoadLibraryExW.restype = ctypes.c_void_p
+
+    for dll_path in dll_paths:
+        os.add_dll_directory(dll_path)
+
+    try:
+        ctypes.CDLL('vcruntime140.dll')
+        ctypes.CDLL('msvcp140.dll')
+        ctypes.CDLL('vcruntime140_1.dll')
+    except OSError:
+        print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.
+                 It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''')
+
+    dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
+    path_patched = False
+    for dll in dlls:
+        is_loaded = False
+        if with_load_library_flags:
+            res = kernel32.LoadLibraryExW(dll, None, 0x00001100)
+            last_error = ctypes.get_last_error()
+            if res is None and last_error != 126:
+                err = ctypes.WinError(last_error)
+                err.strerror += f' Error loading "{dll}" or one of its dependencies.'
+                raise err
+            elif res is not None:
+                is_loaded = True
+        if not is_loaded:
+            if not path_patched:
+                os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']])
+                path_patched = True
+            res = kernel32.LoadLibraryW(dll)
+            if res is None:
+                err = ctypes.WinError(ctypes.get_last_error())
+                err.strerror += f' Error loading "{dll}" or one of its dependencies.'
+                raise err
+
+    kernel32.SetErrorMode(prev_error_mode)
+
+
+def _preload_cuda_deps(lib_folder, lib_name):
+    """Preloads cuda deps if they could not be found otherwise."""
+    # Should only be called on Linux if default path resolution have failed
+    assert platform.system() == 'Linux', 'Should only be called on Linux'
+    import glob
+    lib_path = None
+    for path in sys.path:
+        nvidia_path = os.path.join(path, 'nvidia')
+        if not os.path.exists(nvidia_path):
+            continue
+        candidate_lib_paths = glob.glob(os.path.join(nvidia_path, lib_folder, 'lib', lib_name))
+        if candidate_lib_paths and not lib_path:
+            lib_path = candidate_lib_paths[0]
+        if lib_path:
+            break
+    if not lib_path:
+        raise ValueError(f"{lib_name} not found in the system path {sys.path}")
+    ctypes.CDLL(lib_path)
+
+
+# See Note [Global dependencies]
+def _load_global_deps() -> None:
+    if _running_with_deploy() or platform.system() == 'Windows':
+        return
+
+    lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
+    here = os.path.abspath(__file__)
+    lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
+
+    try:
+        ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
+    except OSError as err:
+        # Can only happen for wheel with cuda libs as PYPI deps
+        # As PyTorch is not purelib, but nvidia-*-cu12 is
+        cuda_libs: Dict[str, str] = {
+            'cublas': 'libcublas.so.*[0-9]',
+            'cudnn': 'libcudnn.so.*[0-9]',
+            'cuda_nvrtc': 'libnvrtc.so.*[0-9]',
+            'cuda_runtime': 'libcudart.so.*[0-9]',
+            'cuda_cupti': 'libcupti.so.*[0-9]',
+            'cufft': 'libcufft.so.*[0-9]',
+            'curand': 'libcurand.so.*[0-9]',
+            'cusolver': 'libcusolver.so.*[0-9]',
+            'cusparse': 'libcusparse.so.*[0-9]',
+            'nccl': 'libnccl.so.*[0-9]',
+            'nvtx': 'libnvToolsExt.so.*[0-9]',
+        }
+        is_cuda_lib_err = [lib for lib in cuda_libs.values() if lib.split('.')[0] in err.args[0]]
+        if not is_cuda_lib_err:
+            raise err
+        for lib_folder, lib_name in cuda_libs.items():
+            _preload_cuda_deps(lib_folder, lib_name)
+        ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
+
+
+if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
+        (_running_with_deploy() or platform.system() != 'Windows'):
+    # Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a
+    # few circumstances:
+    #
+    #   1. You're in a build environment (e.g., fbcode) where
+    #      libtorch_global_deps is not available, but you still need
+    #      to get mkl to link in with RTLD_GLOBAL or it will just
+    #      not work.
+    #
+    #   2. You're trying to run PyTorch under UBSAN and you need
+    #      to ensure that only one copy of libtorch is loaded, so
+    #      vptr checks work properly
+    #
+    # If you're using this setting, you must verify that all the libraries
+    # you load consistently use the same libstdc++, or you may have
+    # mysterious segfaults.
+    #
+    old_flags = sys.getdlopenflags()
+    sys.setdlopenflags(os.RTLD_GLOBAL | os.RTLD_LAZY)
+    from torch._C import *  # noqa: F403
+    sys.setdlopenflags(old_flags)
+    del old_flags
+
+else:
+    # Easy way.  You want this most of the time, because it will prevent
+    # C++ symbols from libtorch clobbering C++ symbols from other
+    # libraries, leading to mysterious segfaults.
+    #
+    # If building in an environment where libtorch_global_deps isn't available
+    # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will
+    # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False
+    #
+    # See Note [Global dependencies]
+    if USE_GLOBAL_DEPS:
+        _load_global_deps()
+    from torch._C import *  # noqa: F403
+
+# Appease the type checker; ordinarily this binding is inserted by the
+# torch._C module initialization code in C
+if TYPE_CHECKING:
+    from . import _C as _C
+
+class SymInt:
+    """
+    Like an int (including magic methods), but redirects all operations on the
+    wrapped node. This is used in particular to symbolically record operations
+    in the symbolic shape workflow.
+    """
+
+    def __init__(self, node):
+        # This field MUST be named node; C++ binding code assumes that this
+        # class has a field named node that stores SymNode
+        self.node = node
+
+    def __bool__(self):
+        return builtins.bool(self != 0)
+
+    def __int__(self):
+        return self.node.int_()
+
+    def __index__(self):
+        return self.node.int_()
+
+    # Magic methods installed by torch.fx.experimental.sym_node
+
+    def __eq__(self, other: object) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __lt__(self, other) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __gt__(self, other) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __le__(self, other) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __ge__(self, other) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __add__(self, other) -> "SymInt":
+        raise AssertionError("type stub not overridden")
+
+    def __mul__(self, other) -> "SymInt":
+        raise AssertionError("type stub not overridden")
+
+    def __sym_max__(self, other):
+        raise AssertionError("type stub not overridden")
+
+    def __sym_min__(self, other):
+        raise AssertionError("type stub not overridden")
+
+    def __sym_float__(self):
+        raise AssertionError("type stub not overridden")
+
+    def __neg__(self):
+        raise AssertionError("type stub not overridden")
+
+    def __repr__(self):
+        return str(self.node)
+
+    def __hash__(self) -> builtins.int:
+        if self.node.is_nested_int():
+            return hash(self.node.nested_int())
+        else:
+            # We could support constant SymInts as well, but not doing it for now
+            raise TypeError("unhashable type: non-nested SymInt")
+
+class SymFloat:
+    """
+    Like an float (including magic methods), but redirects all operations on the
+    wrapped node. This is used in particular to symbolically record operations
+    in the symbolic shape workflow.
+    """
+
+    def __init__(self, node):
+        # This field MUST be named node; C++ binding code assumes that this
+        # class has a field named node that stores SymNode
+        self.node = node
+
+    def __bool__(self):
+        return self.node.bool_()
+
+    # Magic methods installed by torch.fx.experimental.sym_node
+
+    def __eq__(self, other: object) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __lt__(self, other) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __gt__(self, other) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __le__(self, other) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __ge__(self, other) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __sym_max__(self, other):
+        raise AssertionError("type stub not overridden")
+
+    def __sym_min__(self, other):
+        raise AssertionError("type stub not overridden")
+
+    def __sym_int__(self):
+        raise AssertionError("type stub not overridden")
+
+    def is_integer(self):
+        """Return True if the float is an integer."""
+        raise AssertionError("type stub not overridden")
+
+    def __repr__(self):
+        return self.node.str()
+
+class SymBool:
+    """
+    Like an bool (including magic methods), but redirects all operations on the
+    wrapped node. This is used in particular to symbolically record operations
+    in the symbolic shape workflow.
+
+    Unlike regular bools, regular boolean operators will force extra guards instead
+    of symbolically evaluate.  Use the bitwise operators instead to handle this.
+    """
+
+    def __init__(self, node):
+        # This field MUST be named node; C++ binding code assumes that this
+        # class has a field named node that stores SymNode
+        self.node = node
+
+    def __bool__(self):
+        return self.node.bool_()
+
+    def __int__(self):
+        return builtins.int(self.node.bool_())
+
+    # Magic methods installed by torch.fx.experimental.sym_node
+    def __and__(self, other) -> "SymBool":
+        raise AssertionError("type stub not overridden")
+
+    def __or__(self, other) -> "SymBool":
+        raise AssertionError("type stub not overridden")
+
+    # We very carefully define __sym_not__, and not a number of other
+    # plausible alternatives:
+    #
+    #   - We do not override __not__ because this is not a real magic
+    #     method; you cannot override the meaning of the not builtin in
+    #     Python.  We use the name 'sym_not' to clarify that in user code you
+    #     cannot use the builtin not or operator.not_ or operator.__not__ and
+    #     hit this magic method; you must use our custom sym_not operator.
+    #
+    #   - We do not override the __invert__ method because SymBool is
+    #     meant to be usable in situations where bool is expected.  However,
+    #     bitwise negation ~a does the wrong thing with booleans (because
+    #     bool is a subclass of int, so ~1 = -2 which is not falseish.)
+    #     This would be a giant footgun, so we get around it by defining
+    #     our own operator.  Note that bitwise and/or do the right thing,
+    #     so we reuse the conventional operators there for readability.
+    #
+    def __sym_not__(self) -> "SymBool":
+        raise AssertionError("type stub not overridden")
+
+    def __sym_ite__(self, then_val, else_val):
+        raise AssertionError("type stub not overridden")
+
+    def __eq__(self, other) -> builtins.bool:
+        raise AssertionError("type stub not overridden")
+
+    def __repr__(self):
+        return str(self.node)
+
+    def __hash__(self):
+        if self.node.is_constant():
+            return hash(self.node.bool_())
+        else:
+            raise TypeError("unhashable type: SymBool")
+
+def sym_not(a):
+    r""" SymInt-aware utility for logical negation.
+
+    Args:
+        a (SymBool or bool): Object to negate
+    """
+    import sympy
+    from .overrides import has_torch_function_unary, handle_torch_function
+
+    if has_torch_function_unary(a):
+        return handle_torch_function(sym_not, (a,), a)
+    if hasattr(a, '__sym_not__'):
+        return a.__sym_not__()
+    if isinstance(a, sympy.Basic):
+        return ~a  # type: ignore[operator]
+    return not a
+
+def sym_float(a):
+    r""" SymInt-aware utility for float casting.
+
+    Args:
+        a (SymInt, SymFloat, or object): Object to cast
+    """
+    from .overrides import has_torch_function_unary, handle_torch_function
+
+    if has_torch_function_unary(a):
+        return handle_torch_function(sym_float, (a,), a)
+    if isinstance(a, SymFloat):
+        return a
+    elif hasattr(a, '__sym_float__'):
+        return a.__sym_float__()
+    return py_float(a)  # type: ignore[operator]
+
+
+def sym_int(a):
+    r""" SymInt-aware utility for int casting.
+
+    Args:
+        a (SymInt, SymFloat, or object): Object to cast
+    """
+    from .overrides import has_torch_function_unary, handle_torch_function
+
+    if has_torch_function_unary(a):
+        return handle_torch_function(sym_int, (a,), a)
+    if isinstance(a, SymInt):
+        return a
+    elif isinstance(a, SymFloat):
+        return math.floor(a) if a >= 0 else math.ceil(a)  # type: ignore[arg-type, call-overload]
+    return py_int(a)  # type: ignore[operator]
+
+def sym_max(a, b):
+    """ SymInt-aware utility for max()."""
+    from .overrides import has_torch_function, handle_torch_function
+
+    if has_torch_function((a, b)):
+        return handle_torch_function(sym_max, (a, b), a, b)
+    if isinstance(a, (SymInt, SymFloat)):
+        return a.__sym_max__(b)
+    elif isinstance(b, (SymInt, SymFloat)):
+        # NB: If you actually care about preserving output type exactly
+        # if you do something like max(0, 0.0), it is NOT sound to treat
+        # min/max as commutative
+        return b.__sym_max__(a)
+    return builtins.max(a, b)  # type: ignore[operator]
+
+def sym_min(a, b):
+    """ SymInt-aware utility for max()."""
+    from .overrides import has_torch_function, handle_torch_function
+
+    if has_torch_function((a, b)):
+        return handle_torch_function(sym_min, (a, b), a, b)
+    if isinstance(a, (SymInt, SymFloat)):
+        return a.__sym_min__(b)
+    elif isinstance(b, (SymInt, SymFloat)):
+        return b.__sym_min__(a)
+    return builtins.min(a, b)  # type: ignore[operator]
+
+# Drop in replacement for math.sqrt, math.sin, math.cos etc
+current_module = sys.modules[__name__]
+
+def _get_sym_math_fn(name):
+    def fn(a):
+        from .overrides import has_torch_function_unary, handle_torch_function
+
+        if has_torch_function_unary(a):
+            return handle_torch_function(fn, (a,), a)
+        if hasattr(a, f"__sym_{name}__"):
+            return getattr(a, f"__sym_{name}__")()
+        return getattr(math, name)(a)
+
+    return fn
+
+for name in ("sqrt", "cos", "cosh", "sin", "sinh", "tan", "tanh", "asin", "acos", "atan"):
+    sym_name = f"_sym_{name}"
+    fn = _get_sym_math_fn(name)
+    fn.__qualname__ = fn.__name__ = sym_name
+    setattr(current_module, sym_name, fn)
+
+# Adding temporary shortcut
+sym_sqrt = current_module._sym_sqrt
+__all__.append("sym_sqrt")
+
+del fn, name, sym_name, current_module  # type: ignore[possibly-undefined]
+
+
+def sym_ite(b, t, f):
+    from .overrides import has_torch_function, handle_torch_function
+
+    if has_torch_function((b, t, f)):
+        return handle_torch_function(sym_ite, (b, t, f), b, t, f)
+    assert isinstance(b, (SymBool, builtins.bool)) and type(t) == type(f)
+    if isinstance(b, SymBool):
+        return b.__sym_ite__(t, f)
+    return t if b else f
+
+# Check to see if we can load C extensions, and if not provide some guidance
+# on what the problem might be.
+try:
+    # _initExtension is chosen (arbitrarily) as a sentinel.
+    from torch._C import _initExtension
+except ImportError:
+    import torch._C as _C_for_compiled_check
+
+    # The __file__ check only works for Python 3.7 and above.
+    if _C_for_compiled_check.__file__ is None:
+        raise ImportError(textwrap.dedent('''
+            Failed to load PyTorch C extensions:
+                It appears that PyTorch has loaded the `torch/_C` folder
+                of the PyTorch repository rather than the C extensions which
+                are expected in the `torch._C` namespace. This can occur when
+                using the `install` workflow. e.g.
+                    $ python setup.py install && python -c "import torch"
+
+                This error can generally be solved using the `develop` workflow
+                    $ python setup.py develop && python -c "import torch"  # This should succeed
+                or by running Python from a different directory.
+            ''').strip()) from None
+    raise  # If __file__ is not None the cause is unknown, so just re-raise.
+
+for name in dir(_C):
+    if name[0] != '_' and not name.endswith('Base'):
+        __all__.append(name)
+        obj = getattr(_C, name)
+        if (isinstance(obj, Callable) or inspect.isclass(obj)):  # type: ignore[arg-type]
+            if (obj.__module__ != 'torch'):
+                # TODO: fix their module from C++ side
+                if name not in ['DisableTorchFunctionSubclass', 'DisableTorchFunction', 'Generator']:
+                    obj.__module__ = 'torch'
+    elif name == 'TensorBase':
+        # issue 109438 / pr 109940. Prevent TensorBase from being copied into torch.
+        delattr(sys.modules[__name__], name)
+
+if not TYPE_CHECKING:
+    # issue 38137 and python issue 43367. Submodules of a C extension are
+    # non-standard, and attributes of those submodules cannot be pickled since
+    # pickle expect to be able to import them as "from _C.sub import attr"
+    # which fails with "_C is not a package
+    for attr in dir(_C):
+        candidate = getattr(_C, attr)
+        if type(candidate) is type(_C):
+            # submodule
+            if f'torch._C.{attr}' not in sys.modules:
+                sys.modules[f'torch._C.{attr}'] = candidate
+
+
+################################################################################
+# Define basic utilities
+################################################################################
+
+
+def typename(o):
+    if isinstance(o, torch.Tensor):
+        return o.type()
+
+    module = ''
+    class_name = ''
+    if hasattr(o, '__module__') and o.__module__ != 'builtins' \
+            and o.__module__ != '__builtin__' and o.__module__ is not None:
+        module = o.__module__ + '.'
+
+    if hasattr(o, '__qualname__'):
+        class_name = o.__qualname__
+    elif hasattr(o, '__name__'):
+        class_name = o.__name__
+    else:
+        class_name = o.__class__.__name__
+
+    return module + class_name
+
+
+def is_tensor(obj):
+    r"""Returns True if `obj` is a PyTorch tensor.
+
+    Note that this function is simply doing ``isinstance(obj, Tensor)``.
+    Using that ``isinstance`` check is better for typechecking with mypy,
+    and more explicit - so it's recommended to use that instead of
+    ``is_tensor``.
+
+    Args:
+        obj (Object): Object to test
+    Example::
+
+        >>> x = torch.tensor([1, 2, 3])
+        >>> torch.is_tensor(x)
+        True
+
+    """
+    return isinstance(obj, torch.Tensor)
+
+
+def is_storage(obj):
+    r"""Returns True if `obj` is a PyTorch storage object.
+
+    Args:
+        obj (Object): Object to test
+    """
+    return type(obj) in _storage_classes
+
+
+_GLOBAL_DEVICE_CONTEXT = threading.local()
+
+
+def get_default_device() -> "torch.device":
+    r"""Gets the default ``torch.Tensor`` to be allocated on ``device``"""
+    global _GLOBAL_DEVICE_CONTEXT
+    if hasattr(_GLOBAL_DEVICE_CONTEXT, "device_context"):
+        device = _GLOBAL_DEVICE_CONTEXT.device_context.device
+        if device.index is not None:
+            return device
+        else:
+            # TODO: Call like get_device_index() method corresponding to
+            # each device type
+            return torch.tensor([]).device
+    else:
+        return torch.device("cpu")
+
+
+def set_default_device(device):
+    """Sets the default ``torch.Tensor`` to be allocated on ``device``.  This
+    does not affect factory function calls which are called with an explicit
+    ``device`` argument.  Factory calls will be performed as if they
+    were passed ``device`` as an argument.
+
+    To only temporarily change the default device instead of setting it
+    globally, use ``with torch.device(device):`` instead.
+
+    The default device is initially ``cpu``.  If you set the default tensor
+    device to another device (e.g., ``cuda``) without a device index, tensors
+    will be allocated on whatever the current device for the device type,
+    even after :func:`torch.cuda.set_device` is called.
+
+    .. warning::
+
+        This function imposes a slight performance cost on every Python
+        call to the torch API (not just factory functions).  If this
+        is causing problems for you, please comment on
+        https://github.com/pytorch/pytorch/issues/92701
+
+    .. note::
+
+        This doesn't affect functions that create tensors that share the same memory as the input, like:
+        :func:`torch.from_numpy` and :func:`torch.frombuffer`
+
+    Args:
+        device (device or string): the device to set as default
+
+    Example::
+
+        >>> # xdoctest: +SKIP("requires cuda, changes global state")
+        >>> torch.get_default_device()
+        device(type='cpu')
+        >>> torch.set_default_device('cuda')  # current device is 0
+        >>> torch.get_default_device()
+        device(type='cuda', index=0)
+        >>> torch.set_default_device('cuda')
+        >>> torch.cuda.set_device('cuda:1')  # current device is 1
+        >>> torch.get_default_device()
+        device(type='cuda', index=1)
+        >>> torch.set_default_device('cuda:1')
+        >>> torch.get_default_device()
+        device(type='cuda', index=1)
+
+    """
+    global _GLOBAL_DEVICE_CONTEXT
+    if hasattr(_GLOBAL_DEVICE_CONTEXT, "device_context"):
+        device_context = _GLOBAL_DEVICE_CONTEXT.device_context
+        if device_context is not None:
+            device_context.__exit__(None, None, None)
+
+    if device is None:
+        device_context = None
+    else:
+        from torch.utils._device import DeviceContext
+        device_context = DeviceContext(device)
+        device_context.__enter__()
+    _GLOBAL_DEVICE_CONTEXT.device_context = device_context
+
+
+def set_default_tensor_type(t):
+    r"""
+    .. warning::
+
+        This function is deprecated as of PyTorch 2.1, please use :func:`torch.set_default_dtype()` and
+        :func:`torch.set_default_device()` as alternatives.
+
+    Sets the default ``torch.Tensor`` type to floating point tensor type
+    ``t``. This type will also be used as default floating point type for
+    type inference in :func:`torch.tensor`.
+
+    The default floating point tensor type is initially ``torch.FloatTensor``.
+
+    Args:
+        t (type or string): the floating point tensor type or its name
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Other tests may have changed the default type. Can we reset it?")
+        >>> torch.tensor([1.2, 3]).dtype    # initial default for floating point is torch.float32
+        torch.float32
+        >>> torch.set_default_tensor_type(torch.DoubleTensor)
+        >>> torch.tensor([1.2, 3]).dtype    # a new floating point tensor
+        torch.float64
+
+    """
+    if isinstance(t, str):
+        t = _import_dotted_name(t)
+    _C._set_default_tensor_type(t)
+
+
+def set_default_dtype(d):
+    r"""
+
+    Sets the default floating point dtype to :attr:`d`. Supports torch.float32
+    and torch.float64 as inputs. Other dtypes may be accepted without complaint
+    but are not supported and are unlikely to work as expected.
+
+    When PyTorch is initialized its default floating point dtype is torch.float32,
+    and the intent of set_default_dtype(torch.float64) is to facilitate NumPy-like
+    type inference. The default floating point dtype is used to:
+
+    1. Implicitly determine the default complex dtype. When the default floating point
+       type is float32 the default complex dtype is complex64, and when the default
+       floating point type is float64 the default complex type is complex128.
+    2. Infer the dtype for tensors constructed using Python floats or complex Python
+       numbers. See examples below.
+    3. Determine the result of type promotion between bool and integer tensors and
+       Python floats and complex Python numbers.
+
+    Args:
+        d (:class:`torch.dtype`): the floating point dtype to make the default.
+                                  Either torch.float32 or torch.float64.
+
+    Example:
+        >>> # xdoctest: +SKIP("Other tests may have changed the default type. Can we reset it?")
+        >>> # initial default for floating point is torch.float32
+        >>> # Python floats are interpreted as float32
+        >>> torch.tensor([1.2, 3]).dtype
+        torch.float32
+        >>> # initial default for floating point is torch.complex64
+        >>> # Complex Python numbers are interpreted as complex64
+        >>> torch.tensor([1.2, 3j]).dtype
+        torch.complex64
+
+        >>> torch.set_default_dtype(torch.float64)
+
+        >>> # Python floats are now interpreted as float64
+        >>> torch.tensor([1.2, 3]).dtype    # a new floating point tensor
+        torch.float64
+        >>> # Complex Python numbers are now interpreted as complex128
+        >>> torch.tensor([1.2, 3j]).dtype   # a new complex tensor
+        torch.complex128
+
+    """
+    _C._set_default_dtype(d)
+
+def use_deterministic_algorithms(mode: builtins.bool, *, warn_only: builtins.bool = False) -> None:
+    r""" Sets whether PyTorch operations must use "deterministic"
+    algorithms. That is, algorithms which, given the same input, and when
+    run on the same software and hardware, always produce the same output.
+    When enabled, operations will use deterministic algorithms when available,
+    and if only nondeterministic algorithms are available they will throw a
+    :class:`RuntimeError` when called.
+
+    .. note:: This setting alone is not always enough to make an application
+        reproducible. Refer to :ref:`reproducibility` for more information.
+
+    .. note:: :func:`torch.set_deterministic_debug_mode` offers an alternative
+        interface for this feature.
+
+    The following normally-nondeterministic operations will act
+    deterministically when ``mode=True``:
+
+        * :class:`torch.nn.Conv1d` when called on CUDA tensor
+        * :class:`torch.nn.Conv2d` when called on CUDA tensor
+        * :class:`torch.nn.Conv3d` when called on CUDA tensor
+        * :class:`torch.nn.ConvTranspose1d` when called on CUDA tensor
+        * :class:`torch.nn.ConvTranspose2d` when called on CUDA tensor
+        * :class:`torch.nn.ConvTranspose3d` when called on CUDA tensor
+        * :class:`torch.nn.ReplicationPad2d` when attempting to differentiate a CUDA tensor
+        * :func:`torch.bmm` when called on sparse-dense CUDA tensors
+        * :func:`torch.Tensor.__getitem__` when attempting to differentiate a CPU tensor
+          and the index is a list of tensors
+        * :func:`torch.Tensor.index_put` with ``accumulate=False``
+        * :func:`torch.Tensor.index_put` with ``accumulate=True`` when called on a CPU
+          tensor
+        * :func:`torch.Tensor.put_` with ``accumulate=True`` when called on a CPU
+          tensor
+        * :func:`torch.Tensor.scatter_add_` when called on a CUDA tensor
+        * :func:`torch.gather` when called on a CUDA tensor that requires grad
+        * :func:`torch.index_add` when called on CUDA tensor
+        * :func:`torch.index_select` when attempting to differentiate a CUDA tensor
+        * :func:`torch.repeat_interleave` when attempting to differentiate a CUDA tensor
+        * :func:`torch.Tensor.index_copy` when called on a CPU or CUDA tensor
+        * :func:`torch.Tensor.scatter` when `src` type is Tensor and called on CUDA tensor
+        * :func:`torch.Tensor.scatter_reduce` when ``reduce='sum'`` or ``reduce='mean'`` and called on CUDA tensor
+
+    The following normally-nondeterministic operations will throw a
+    :class:`RuntimeError` when ``mode=True``:
+
+        * :class:`torch.nn.AvgPool3d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.AdaptiveAvgPool2d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.AdaptiveAvgPool3d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.MaxPool3d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.AdaptiveMaxPool2d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.FractionalMaxPool2d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.FractionalMaxPool3d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.MaxUnpool1d`
+        * :class:`torch.nn.MaxUnpool2d`
+        * :class:`torch.nn.MaxUnpool3d`
+        * :func:`torch.nn.functional.interpolate` when attempting to differentiate a CUDA tensor
+          and one of the following modes is used:
+
+          - ``linear``
+          - ``bilinear``
+          - ``bicubic``
+          - ``trilinear``
+
+        * :class:`torch.nn.ReflectionPad1d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.ReflectionPad2d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.ReflectionPad3d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.ReplicationPad1d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.ReplicationPad3d` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.NLLLoss` when called on a CUDA tensor
+        * :class:`torch.nn.CTCLoss` when attempting to differentiate a CUDA tensor
+        * :class:`torch.nn.EmbeddingBag` when attempting to differentiate a CUDA tensor when
+          ``mode='max'``
+        * :func:`torch.Tensor.put_` when ``accumulate=False``
+        * :func:`torch.Tensor.put_` when ``accumulate=True`` and called on a CUDA tensor
+        * :func:`torch.histc` when called on a CUDA tensor
+        * :func:`torch.bincount` when called on a CUDA tensor and ``weights``
+          tensor is given
+        * :func:`torch.kthvalue` with called on a CUDA tensor
+        * :func:`torch.median` with indices output when called on a CUDA tensor
+        * :func:`torch.nn.functional.grid_sample` when attempting to differentiate a CUDA tensor
+        * :func:`torch.cumsum` when called on a CUDA tensor when dtype is floating point or complex
+        * :func:`torch.Tensor.scatter_reduce` when ``reduce='prod'`` and called on CUDA tensor
+        * :func:`torch.Tensor.resize_` when called with a quantized tensor
+
+    In addition, several operations fill uninitialized memory when this setting
+    is turned on and when
+    :attr:`torch.utils.deterministic.fill_uninitialized_memory` is turned on.
+    See the documentation for that attribute for more information.
+
+    A handful of CUDA operations are nondeterministic if the CUDA version is
+    10.2 or greater, unless the environment variable ``CUBLAS_WORKSPACE_CONFIG=:4096:8``
+    or ``CUBLAS_WORKSPACE_CONFIG=:16:8`` is set. See the CUDA documentation for more
+    details: `<https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility>`_
+    If one of these environment variable configurations is not set, a :class:`RuntimeError`
+    will be raised from these operations when called with CUDA tensors:
+
+        * :func:`torch.mm`
+        * :func:`torch.mv`
+        * :func:`torch.bmm`
+
+    Note that deterministic operations tend to have worse performance than
+    nondeterministic operations.
+
+    .. note::
+
+        This flag does not detect or prevent nondeterministic behavior caused
+        by calling an inplace operation on a tensor with an internal memory
+        overlap or by giving such a tensor as the :attr:`out` argument for an
+        operation. In these cases, multiple writes of different data may target
+        a single memory location, and the order of writes is not guaranteed.
+
+    Args:
+        mode (:class:`bool`): If True, makes potentially nondeterministic
+            operations switch to a deterministic algorithm or throw a runtime
+            error. If False, allows nondeterministic operations.
+
+    Keyword args:
+        warn_only (:class:`bool`, optional): If True, operations that do not
+            have a deterministic implementation will throw a warning instead of
+            an error. Default: ``False``
+
+    Example::
+
+        >>> # xdoctest: +SKIP
+        >>> torch.use_deterministic_algorithms(True)
+
+        # Forward mode nondeterministic error
+        >>> torch.randn(10, device='cuda').kthvalue(1)
+        ...
+        RuntimeError: kthvalue CUDA does not have a deterministic implementation...
+
+        # Backward mode nondeterministic error
+        >>> torch.nn.AvgPool3d(1)(torch.randn(3, 4, 5, 6, requires_grad=True).cuda()).sum().backward()
+        ...
+        RuntimeError: avg_pool3d_backward_cuda does not have a deterministic implementation...
+    """
+    _C._set_deterministic_algorithms(mode, warn_only=warn_only)
+
+def are_deterministic_algorithms_enabled() -> builtins.bool:
+    r"""Returns True if the global deterministic flag is turned on. Refer to
+    :func:`torch.use_deterministic_algorithms` documentation for more details.
+    """
+    return _C._get_deterministic_algorithms()
+
+def is_deterministic_algorithms_warn_only_enabled() -> builtins.bool:
+    r"""Returns True if the global deterministic flag is set to warn only.
+    Refer to :func:`torch.use_deterministic_algorithms` documentation for more
+    details.
+    """
+    return _C._get_deterministic_algorithms_warn_only()
+
+def set_deterministic_debug_mode(debug_mode: Union[builtins.int, str]) -> None:
+    r"""Sets the debug mode for deterministic operations.
+
+    .. note:: This is an alternative interface for
+        :func:`torch.use_deterministic_algorithms`. Refer to that function's
+        documentation for details about affected operations.
+
+    Args:
+        debug_mode(str or int): If "default" or 0, don't error or warn on
+            nondeterministic operations. If "warn" or 1, warn on
+            nondeterministic operations. If "error" or 2, error on
+            nondeterministic operations.
+    """
+
+    # NOTE: builtins.int is used here because int in this scope resolves
+    # to torch.int
+    if not isinstance(debug_mode, (builtins.int, str)):
+        raise TypeError(f'debug_mode must be str or int, but got {type(debug_mode)}')
+
+    if isinstance(debug_mode, str):
+        if debug_mode == 'default':
+            debug_mode = 0
+        elif debug_mode == 'warn':
+            debug_mode = 1
+        elif debug_mode == 'error':
+            debug_mode = 2
+        else:
+            raise RuntimeError(
+                'invalid value of debug_mode, expected one of `default`, '
+                f'`warn`, `error`, but got {debug_mode}')
+
+    if debug_mode == 0:
+        _C._set_deterministic_algorithms(False)
+    elif debug_mode == 1:
+        _C._set_deterministic_algorithms(True, warn_only=True)
+    elif debug_mode == 2:
+        _C._set_deterministic_algorithms(True)
+    else:
+        raise RuntimeError(
+            'invalid value of debug_mode, expected 0, 1, or 2, '
+            f'but got {debug_mode}')
+
+def get_deterministic_debug_mode() -> builtins.int:
+    r"""Returns the current value of the debug mode for deterministic
+    operations. Refer to :func:`torch.set_deterministic_debug_mode`
+    documentation for more details.
+    """
+
+    if _C._get_deterministic_algorithms():
+        if _C._get_deterministic_algorithms_warn_only():
+            return 1
+        else:
+            return 2
+    else:
+        return 0
+
+def get_float32_matmul_precision() -> builtins.str:
+    r"""Returns the current value of float32 matrix multiplication precision. Refer to
+    :func:`torch.set_float32_matmul_precision` documentation for more details.
+    """
+    return _C._get_float32_matmul_precision()
+
+def set_float32_matmul_precision(precision: str) -> None:
+    r"""Sets the internal precision of float32 matrix multiplications.
+
+    Running float32 matrix multiplications in lower precision may significantly increase
+    performance, and in some programs the loss of precision has a negligible impact.
+
+    Supports three settings:
+
+        * "highest", float32 matrix multiplications use the float32 datatype (24 mantissa
+          bits with 23 bits explicitly stored) for internal computations.
+        * "high", float32 matrix multiplications either use the TensorFloat32 datatype (10
+          mantissa bits explicitly stored) or treat each float32 number as the sum of two bfloat16 numbers
+          (approximately 16 mantissa bits with 14 bits explicitly stored), if the appropriate fast matrix multiplication
+          algorithms are available.  Otherwise float32 matrix multiplications are computed
+          as if the precision is "highest".  See below for more information on the bfloat16
+          approach.
+        * "medium", float32 matrix multiplications use the bfloat16 datatype (8 mantissa
+          bits with 7 bits explicitly stored) for internal computations, if a fast matrix multiplication algorithm
+          using that datatype internally is available. Otherwise float32
+          matrix multiplications are computed as if the precision is "high".
+
+    When using "high" precision, float32 multiplications may use a bfloat16-based algorithm
+    that is more complicated than simply truncating to some smaller number mantissa bits
+    (e.g. 10 for TensorFloat32, 7 for bfloat16 explicitly stored).  Refer to [Henry2019]_ for a complete
+    description of this algorithm.  To briefly explain here, the first step is to realize
+    that we can perfectly encode a single float32 number as the sum of three bfloat16
+    numbers (because float32 has 23 mantissa bits while bfloat16 has 7 explicitly stored, and both have the
+    same number of exponent bits).  This means that the product of two float32 numbers can
+    be exactly given by the sum of nine products of bfloat16 numbers.  We can then trade
+    accuracy for speed by dropping some of these products.  The "high" precision algorithm
+    specifically keeps only the three most significant products, which conveniently excludes
+    all of the products involving the last 8 mantissa bits of either input.  This means that
+    we can represent our inputs as the sum of two bfloat16 numbers rather than three.
+    Because bfloat16 fused-multiply-add (FMA) instructions are typically >10x faster than
+    float32 ones, it's faster to do three multiplications and 2 additions with bfloat16
+    precision than it is to do a single multiplication with float32 precision.
+
+    .. [Henry2019] http://arxiv.org/abs/1904.06376
+
+    .. note::
+
+        This does not change the output dtype of float32 matrix multiplications,
+        it controls how the internal computation of the matrix multiplication is performed.
+
+    .. note::
+
+        This does not change the precision of convolution operations. Other flags,
+        like `torch.backends.cudnn.allow_tf32`, may control the precision of convolution
+        operations.
+
+    .. note::
+
+        This flag currently only affects one native device type: CUDA.
+        If "high" or "medium" are set then the TensorFloat32 datatype will be used
+        when computing float32 matrix multiplications, equivalent to setting
+        `torch.backends.cuda.matmul.allow_tf32 = True`. When "highest" (the default)
+        is set then the float32 datatype is used for internal computations, equivalent
+        to setting `torch.backends.cuda.matmul.allow_tf32 = False`.
+
+    Args:
+        precision(str): can be set to "highest" (default), "high", or "medium" (see above).
+
+    """
+    _C._set_float32_matmul_precision(precision)
+
+def set_warn_always(b: builtins.bool) -> None:
+    r"""When this flag is False (default) then some PyTorch warnings may only
+    appear once per process. This helps avoid excessive warning information.
+    Setting it to True causes these warnings to always appear, which may be
+    helpful when debugging.
+
+    Args:
+        b (:class:`bool`): If True, force warnings to always be emitted
+                           If False, set to the default behaviour
+    """
+    _C._set_warnAlways(b)
+
+def is_warn_always_enabled() -> builtins.bool:
+    r"""Returns True if the global warn_always flag is turned on. Refer to
+    :func:`torch.set_warn_always` documentation for more details.
+    """
+    return _C._get_warnAlways()
+
+################################################################################
+# Define error checking functions
+################################################################################
+
+# These error checking functions must be kept consistent with their C++
+# equivalents. Their C++ equivalents are mentioned where applicable.
+
+def _check_with(error_type, cond: Union[builtins.bool, SymBool], message: Callable[[], str]):  # noqa: F811
+    if not isinstance(cond, (builtins.bool, torch.SymBool)):
+        raise TypeError(f'cond must be a bool, but got {type(cond)}')
+
+    from torch.fx.experimental.symbolic_shapes import expect_true
+    if expect_true(cond):
+        return
+
+    # error_type must be a subclass of Exception and not subclass of Warning
+    assert issubclass(error_type, Exception) and not issubclass(error_type, Warning)
+
+    if message is None:
+        message_evaluated = (
+            'Expected cond to be True, but got False. (Could this error '
+            'message be improved? If so, please report an enhancement request '
+            'to PyTorch.)')
+
+    else:
+        if not callable(message):
+            raise TypeError('message must be a callable')
+
+        message_evaluated = str(message())
+
+    raise error_type(message_evaluated)
+
+def _check(cond, message=None):  # noqa: F811
+    r"""Throws error containing an optional message if the specified condition
+    is False.
+
+    Error type: ``RuntimeError``
+
+    C++ equivalent: ``TORCH_CHECK``
+
+    Args:
+        cond (:class:`bool`): If False, throw error
+
+        message (Callable, optional): Callable that returns either a string or
+            an object that has a ``__str__()`` method to be used as the error
+            message. Default: ``None``
+    """
+    _check_with(RuntimeError, cond, message)
+
+def _check_is_size(i, message=None):
+    """Checks that a given integer is a valid size (i.e., is non-negative).
+    You should use this over _check(i >= 0) because we can use the semantic
+    information (that i is a size) to make some further inferences in case
+    i is an unbacked SymInt.
+
+    NB: Do NOT use this in contexts where a -1 size would be valid (indicating
+    to infer the size from context, or if you should wrap-around or truncate).
+    Only use this if the only valid value is an honest to goodness size.
+    """
+    # This is responsible for the expect_true
+    _check(i >= 0, message)
+    from torch.fx.experimental.symbolic_shapes import _advise_is_size
+    _advise_is_size(i)
+
+def _check_index(cond, message=None):  # noqa: F811
+    r"""Throws error containing an optional message if the specified condition
+    is False.
+
+    Error type: ``IndexError``
+
+    C++ equivalent: ``TORCH_CHECK_INDEX``
+
+    Args:
+        cond (:class:`bool`): If False, throw error
+
+        message (Callable, optional): Callable that returns either a string or
+            an object that has a ``__str__()`` method to be used as the error
+            message. Default: ``None``
+    """
+    _check_with(IndexError, cond, message)
+
+def _check_value(cond, message=None):  # noqa: F811
+    r"""Throws error containing an optional message if the specified condition
+    is False.
+
+    Error type: ``ValueError``
+
+    C++ equivalent: ``TORCH_CHECK_VALUE``
+
+    Args:
+        cond (:class:`bool`): If False, throw error
+
+        message (Callable, optional): Callable that returns either a string or
+            an object that has a ``__str__()`` method to be used as the error
+            message. Default: ``None``
+    """
+    _check_with(ValueError, cond, message)
+
+def _check_type(cond, message=None):  # noqa: F811
+    r"""Throws error containing an optional message if the specified condition
+    is False.
+
+    Error type: ``TypeError``
+
+    C++ equivalent: ``TORCH_CHECK_TYPE``
+
+    Args:
+        cond (:class:`bool`): If False, throw error
+
+        message (Callable, optional): Callable that returns either a string or
+            an object that has a ``__str__()`` method to be used as the error
+            message. Default: ``None``
+    """
+    _check_with(TypeError, cond, message)
+
+def _check_not_implemented(cond, message=None):  # noqa: F811
+    r"""Throws error containing an optional message if the specified condition
+    is False.
+
+    Error type: ``NotImplementedError``
+
+    C++ equivalent: ``TORCH_CHECK_NOT_IMPLEMENTED``
+
+    Args:
+        cond (:class:`bool`): If False, throw error
+
+        message (Callable, optional): Callable that returns either a string or
+            an object that has a ``__str__()`` method to be used as the error
+            message. Default: ``None``
+    """
+    _check_with(NotImplementedError, cond, message)
+
+def _check_tensor_all_with(error_type, cond, message=None):  # noqa: F811
+    if not torch.is_tensor(cond):
+        raise TypeError(f'cond must be a tensor, but got {type(cond)}')
+
+    if not cond.dtype == torch.bool:
+        raise TypeError(
+            f'cond tensor must have dtype torch.bool, but got {cond.dtype}')
+
+    _check_with(error_type, cond._is_all_true().item(), message)
+
+# C++ equivalent: `TORCH_CHECK_TENSOR_ALL`
+def _check_tensor_all(cond, message=None):  # noqa: F811
+    r"""Throws error containing an optional message if the specified condition
+    is False.
+
+    Error type: ``RuntimeError``
+
+    C++ equivalent: ``TORCH_CHECK_TENSOR_ALL``
+
+    Args:
+        cond (:class:`torch.Tensor`): Tensor of dtype ``torch.bool``. If any
+            element is ``False``, throw error
+
+        message (Callable, optional): Callable that returns either a string or
+            an object that has a ``__str__()`` method to be used as the error
+            message. Default: ``None``
+    """
+    _check_tensor_all_with(RuntimeError, cond, message)
+
+################################################################################
+# Define numeric constants
+################################################################################
+
+# For Python Array API (https://data-apis.org/array-api/latest/API_specification/constants.html) and
+# NumPy consistency (https://numpy.org/devdocs/reference/constants.html)
+from math import e , nan , inf , pi
+__all__.extend(['e', 'pi', 'nan', 'inf'])
+
+################################################################################
+# Define Storage and Tensor classes
+################################################################################
+
+from ._tensor import Tensor
+from .storage import _StorageBase, TypedStorage, _LegacyStorage, UntypedStorage, _warn_typed_storage_removal
+
+# NOTE: New <type>Storage classes should never be added. When adding a new
+# dtype, use torch.storage.TypedStorage directly.
+
+class ByteStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.uint8
+
+class DoubleStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.double
+
+class FloatStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.float
+
+class HalfStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.half
+
+class LongStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.long
+
+class IntStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.int
+
+class ShortStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.short
+
+class CharStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.int8
+
+class BoolStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.bool
+
+class BFloat16Storage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.bfloat16
+
+class ComplexDoubleStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.cdouble
+
+class ComplexFloatStorage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.cfloat
+
+class QUInt8Storage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.quint8
+
+class QInt8Storage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.qint8
+
+class QInt32Storage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.qint32
+
+class QUInt4x2Storage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.quint4x2
+
+class QUInt2x4Storage(_LegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal(stacklevel=3)
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.quint2x4
+
+_storage_classes = {
+    UntypedStorage, DoubleStorage, FloatStorage, LongStorage, IntStorage,
+    ShortStorage, CharStorage, ByteStorage, HalfStorage, BoolStorage,
+    QUInt8Storage, QInt8Storage, QInt32Storage, BFloat16Storage,
+    ComplexFloatStorage, ComplexDoubleStorage, QUInt4x2Storage, QUInt2x4Storage,
+    TypedStorage
+}
+
+# The _tensor_classes set is initialized by the call to initialize_python_bindings.
+_tensor_classes: Set[Type] = set()
+
+# If you edit these imports, please update torch/__init__.py.in as well
+from .random import set_rng_state, get_rng_state, manual_seed, initial_seed, seed
+from .serialization import save, load
+from ._tensor_str import set_printoptions
+
+################################################################################
+# Initialize extension
+################################################################################
+
+def manager_path():
+    if _running_with_deploy() or platform.system() == 'Windows':
+        return b""
+    path = get_file_path('torch', 'bin', 'torch_shm_manager')
+    prepare_multiprocessing_environment(get_file_path('torch'))
+    if not os.path.exists(path):
+        raise RuntimeError("Unable to find torch_shm_manager at " + path)
+    return path.encode('utf-8')
+
+from torch.amp import autocast, GradScaler
+
+# Initializing the extension shadows the built-in python float / int classes;
+# store them for later use by SymInt / SymFloat.
+py_float = float
+py_int = int
+
+# Shared memory manager needs to know the exact location of manager executable
+_C._initExtension(manager_path())
+del manager_path
+
+# Appease the type checker: it can't deal with direct setting of globals().
+# Note that we will see "too many" functions when reexporting this way; there
+# is not a good way to fix this problem.  Perhaps, try to redesign VariableFunctions
+# so that this import is good enough
+if TYPE_CHECKING:
+    # Some type signatures pulled in from _VariableFunctions here clash with
+    # signatures already imported. For now these clashes are ignored; see
+    # PR #43339 for details.
+    from torch._C._VariableFunctions import *  # type: ignore[assignment, misc] # noqa: F403
+    # Fixup segment_reduce visibility
+    _segment_reduce = segment_reduce
+    del segment_reduce  # noqa: F821
+
+# Ops not to be exposed in `torch` namespace,
+# mostly helper ops.
+PRIVATE_OPS = (
+    'unique_dim',
+)
+
+for name in dir(_C._VariableFunctions):
+    if name.startswith('__') or name in PRIVATE_OPS:
+        continue
+    obj = getattr(_C._VariableFunctions, name)
+    obj.__module__ = 'torch'
+    # Hide some APIs that should not be public
+    if name == "segment_reduce":
+        # TODO: Once the undocumented FC window is passed, remove the line bellow
+        globals()[name] = obj
+        name = "_" + name
+    globals()[name] = obj
+    if not name.startswith("_"):
+        __all__.append(name)
+
+
+################################################################################
+# Add torch.dtype instances to the public API
+################################################################################
+
+import torch
+
+for attribute in dir(torch):
+    if isinstance(getattr(torch, attribute), torch.dtype):
+        __all__.append(attribute)
+
+################################################################################
+# Import TorchDynamo's lazy APIs to avoid circular dependenices
+################################################################################
+
+# needs to be before from .functional import * to avoid circular dependencies
+from ._compile import _disable_dynamo
+
+################################################################################
+# Import interface functions defined in Python
+################################################################################
+
+# needs to be after the above ATen bindings so we can overwrite from Python side
+from .functional import *  # noqa: F403
+
+
+################################################################################
+# Remove unnecessary members
+################################################################################
+
+del _StorageBase
+del _LegacyStorage
+
+################################################################################
+# Define _assert
+################################################################################
+
+# needs to be before the submodule imports to avoid circular dependencies
+def _assert(condition, message):
+    r"""A wrapper around Python's assert which is symbolically traceable.
+    """
+    from .overrides import has_torch_function, handle_torch_function
+
+    if type(condition) is not torch.Tensor and has_torch_function((condition,)):
+        return handle_torch_function(_assert, (condition,), condition, message)
+    assert condition, message
+
+################################################################################
+# Import most common subpackages
+################################################################################
+
+# Use the redundant form so that type checkers know that these are a part of
+# the public API. The "regular" import lines are there solely for the runtime
+# side effect of adding to the imported module's members for other users.
+from torch import cuda as cuda
+from torch import cpu as cpu
+from torch import mps as mps
+from torch import xpu as xpu
+from torch import autograd as autograd
+from torch.autograd import (
+    no_grad as no_grad,
+    enable_grad as enable_grad,
+    set_grad_enabled as set_grad_enabled,
+    inference_mode as inference_mode,
+)
+from torch import fft as fft
+from torch import futures as futures
+from torch import _awaits as _awaits
+from torch import nested as nested
+from torch import nn as nn
+from torch.signal import windows as windows
+from torch import optim as optim
+import torch.optim._multi_tensor
+from torch import multiprocessing as multiprocessing
+from torch import sparse as sparse
+from torch import special as special
+import torch.utils.backcompat
+from torch import jit as jit
+from torch import linalg as linalg
+from torch import hub as hub
+from torch import random as random
+from torch import distributions as distributions
+from torch import testing as testing
+from torch import backends as backends
+import torch.utils.data
+from torch import __config__ as __config__
+from torch import __future__ as __future__
+from torch import profiler as profiler
+
+# Quantized, sparse, AO, etc. should be last to get imported, as nothing
+# is expected to depend on them.
+from torch import ao as ao
+# nn.quant* depends on ao -- so should be after those.
+import torch.nn.quantizable
+import torch.nn.quantized
+import torch.nn.qat
+import torch.nn.intrinsic
+
+_C._init_names(list(torch._storage_classes))
+
+# attach docstrings to torch and tensor functions
+from . import _torch_docs, _tensor_docs, _storage_docs
+del _torch_docs, _tensor_docs, _storage_docs
+
+
+def compiled_with_cxx11_abi() -> builtins.bool:
+    r"""Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1"""
+    return _C._GLIBCXX_USE_CXX11_ABI
+
+
+# Import the ops "namespace"
+from torch._ops import ops
+from torch._classes import classes
+import torch._library
+
+# quantization depends on torch.fx
+# Import quantization
+from torch import quantization as quantization
+
+# Import the quasi random sampler
+from torch import quasirandom as quasirandom
+
+# If you are seeing this, it means that this call site was not checked if
+# the memory format could be preserved, and it was switched to old default
+# behaviour of contiguous
+legacy_contiguous_format = contiguous_format
+
+# Register fork handler to initialize OpenMP in child processes (see gh-28389)
+from torch.multiprocessing._atfork import register_after_fork
+register_after_fork(torch.get_num_threads)
+del register_after_fork
+
+# Import tools that require fully imported torch (for applying
+# torch.jit.script as a decorator, for instance):
+from ._lobpcg import lobpcg as lobpcg
+
+# These were previously defined in native_functions.yaml and appeared on the
+# `torch` namespace, but we moved them to c10 dispatch to facilitate custom
+# class usage. We add these lines here to preserve backward compatibility.
+quantized_lstm = torch.ops.aten.quantized_lstm
+quantized_gru = torch.ops.aten.quantized_gru
+
+from torch.utils.dlpack import from_dlpack, to_dlpack
+
+# Import experimental masked operations support. See
+# [RFC-0016](https://github.com/pytorch/rfcs/pull/27) for more
+# information.
+from . import masked
+
+# Import removed ops with error message about removal
+from ._linalg_utils import (  # type: ignore[misc]
+    matrix_rank,
+    eig,
+    solve,
+    lstsq,
+)
+from ._linalg_utils import _symeig as symeig  # type: ignore[misc]
+
+class _TorchCompileInductorWrapper:
+    compiler_name = "inductor"
+
+    def __init__(self, mode, options, dynamic):
+        self.config: Dict[str, Any] = dict()
+        self.dynamic = dynamic
+        self.apply_mode(mode)
+        self.apply_options(options)
+
+        if self.config.get("triton.cudagraphs", False):
+            os.environ["DISABLE_CUPTI_LAZY_REINIT"] = "1"
+            # FIXME: CUDA Graph does not work well with CUPTI teardown.
+            #   1) crashes on 1st lazy CUPTI re-init after teardown (CUDA 11)
+            #   2) crashes on 2nd non-lazy CUPTI re-init after teardown (CUDA 12)
+            # Workaround: turn off CUPTI teardown when using CUDA Graphs.
+            os.environ["TEARDOWN_CUPTI"] = "0"
+
+    def __eq__(self, other):
+        return (isinstance(other, _TorchCompileInductorWrapper) and
+                self.config == other.config and
+                self.dynamic == other.dynamic)
+
+    def apply_mode(self, mode: Optional[str]):
+        if mode is None or mode == "default":
+            pass
+        elif mode in ("reduce-overhead", "max-autotune", "max-autotune-no-cudagraphs"):
+            from torch._inductor import list_mode_options
+            self.apply_options(list_mode_options(mode, self.dynamic))
+        else:
+            raise RuntimeError(
+                f"Unrecognized mode={mode}, should be one of: default, reduce-overhead, max-autotune, max-autotune-no-cudagraphs"
+            )
+
+    def apply_options(self, options: Optional[Dict[str, Any]]):
+        if not options:
+            return
+
+        from torch._inductor import config
+        current_config: Dict[str, Any] = config.shallow_copy_dict()
+
+        for key, val in options.items():
+            attr_name = key.replace("-", "_")
+            if attr_name not in current_config:
+                raise RuntimeError(
+                    f"Unexpected optimization option {key}, known options are {list(current_config.keys())}"
+                )
+            if type(val) is not type(current_config[attr_name]):
+                val_type_str = type(val).__name__
+                expected_type_str = type(current_config[attr_name]).__name__
+                raise RuntimeError(
+                    f"Unexpected type of attr {key}, got {val_type_str} should be {expected_type_str}"
+                )
+            self.config[attr_name] = val
+
+    def __call__(self, model_, inputs_):
+        from torch._inductor.compile_fx import compile_fx
+
+        return compile_fx(model_, inputs_, config_patches=self.config)
+
+    def get_compiler_config(self):
+        from torch._inductor.compile_fx import get_patched_config_dict
+        return get_patched_config_dict(config_patches=self.config)
+
+    def reset(self):
+        from torch._inductor import config
+        if "triton.cudagraphs" in self.config or config.triton.cudagraphs:
+            if self.config.get("triton.cudagraphs", True):
+                from torch._inductor.cudagraph_trees import reset_cudagraph_trees
+                reset_cudagraph_trees()
+
+class _TorchCompileWrapper:
+    def __init__(self, backend, mode, options, dynamic):
+        from torch._dynamo.backends.registry import lookup_backend
+
+        if isinstance(backend, str):
+            self.compiler_name = backend
+        elif hasattr(backend, "__name__"):
+            self.compiler_name = backend.__name__
+        else:
+            self.compiler_name = str(backend)
+        self.dynamic = dynamic
+        self.compiler_fn = lookup_backend(backend)
+        self.kwargs = {}
+        # only pass the args if they non-empty
+        if mode and mode != "default":
+            self.kwargs["mode"] = mode
+        if options:
+            self.kwargs["options"] = options
+
+    def __eq__(self, other):
+        return (isinstance(other, _TorchCompileWrapper) and
+                self.compiler_fn == other.compiler_fn and
+                self.kwargs == other.kwargs and
+                self.dynamic == other.dynamic)
+
+    def __call__(self, model_, inputs_):
+        return self.compiler_fn(model_, inputs_, **self.kwargs)
+
+    def reset(self):
+        if hasattr(self.compiler_fn, "reset"):
+            self.compiler_fn.reset()
+
+
+def compile(model: Optional[Callable] = None, *,
+            fullgraph: builtins.bool = False,
+            dynamic: Optional[builtins.bool] = None,
+            backend: Union[str, Callable] = "inductor",
+            mode: Union[str, None] = None,
+            options: Optional[Dict[str, Union[str, builtins.int, builtins.bool]]] = None,
+            disable: builtins.bool = False) -> Callable:
+    """
+    Optimizes given model/function using TorchDynamo and specified backend.
+
+    Concretely, for every frame executed within the compiled region, we will attempt
+    to compile it and cache the compiled result on the code object for future
+    use.  A single frame may be compiled multiple times if previous compiled
+    results are not applicable for subsequent calls (this is called a "guard
+    failure), you can use TORCH_LOGS=guards to debug these situations.
+    Multiple compiled results can be associated with a frame up to
+    ``torch._dynamo.config.cache_size_limit``, which defaults to 64; at which
+    point we will fall back to eager.  Note that compile caches are per
+    *code object*, not frame; if you dynamically create multiple copies of a
+    function, they will all share the same code cache.
+
+    Args:
+       model (Callable): Module/function to optimize
+       fullgraph (bool): If False (default), torch.compile attempts to discover compileable regions
+        in the function that it will optimize. If True, then we require that the entire function be
+        capturable into a single graph. If this is not possible (that is, if there are graph breaks),
+        then this will raise an error.
+       dynamic (bool or None): Use dynamic shape tracing.  When this is True, we will up-front attempt
+        to generate a kernel that is as dynamic as possible to avoid recompilations when
+        sizes change.  This may not always work as some operations/optimizations will
+        force specialization; use TORCH_LOGS=dynamic to debug overspecialization.
+        When this is False, we will NEVER generate dynamic kernels, we will always specialize.
+        By default (None), we automatically detect if dynamism has occurred and compile a more
+        dynamic kernel upon recompile.
+       backend (str or Callable): backend to be used
+
+        - "inductor" is the default backend, which is a good balance between performance and overhead
+
+        - Non experimental in-tree backends can be seen with `torch._dynamo.list_backends()`
+
+        - Experimental or debug in-tree backends can be seen with `torch._dynamo.list_backends(None)`
+
+        - To register an out-of-tree custom backend: https://pytorch.org/docs/main/compile/custom-backends.html
+       mode (str): Can be either "default", "reduce-overhead", "max-autotune" or "max-autotune-no-cudagraphs"
+
+        - "default" is the default mode, which is a good balance between performance and overhead
+
+        - "reduce-overhead" is a mode that reduces the overhead of python with CUDA graphs,
+          useful for small batches.  Reduction of overhead can come at the cost of more memory
+          usage, as we will cache the workspace memory required for the invocation so that we
+          do not have to reallocate it on subsequent runs.  Reduction of overhead is not guaranteed
+          to work; today, we only reduce overhead for CUDA only graphs which do not mutate inputs.
+          There are other circumstances where CUDA graphs are not applicable; use TORCH_LOG=perf_hints
+          to debug.
+
+        - "max-autotune" is a mode that leverages Triton based matrix multiplications and convolutions
+          It enables CUDA graphs by default.
+
+        - "max-autotune-no-cudagraphs" is a mode similar to "max-autotune" but without CUDA graphs
+
+        - To see the exact configs that each mode sets you can call `torch._inductor.list_mode_options()`
+
+       options (dict): A dictionary of options to pass to the backend. Some notable ones to try out are
+
+        - `epilogue_fusion` which fuses pointwise ops into templates. Requires `max_autotune` to also be set
+
+        - `max_autotune` which will profile to pick the best matmul configuration
+
+        - `fallback_random` which is useful when debugging accuracy issues
+
+        - `shape_padding` which pads matrix shapes to better align loads on GPUs especially for tensor cores
+
+        - `triton.cudagraphs` which will reduce the overhead of python with CUDA graphs
+
+        - `trace.enabled` which is the most useful debugging flag to turn on
+
+        - `trace.graph_diagram` which will show you a picture of your graph after fusion
+
+        - For inductor you can see the full list of configs that it supports by calling `torch._inductor.list_options()`
+       disable (bool): Turn torch.compile() into a no-op for testing
+
+    Example::
+
+        @torch.compile(options={"triton.cudagraphs": True}, fullgraph=True)
+        def foo(x):
+            return torch.sin(x) + torch.cos(x)
+
+    """
+    _C._log_api_usage_once("torch.compile")
+    # Temporary until we get proper support for python 3.12
+    if sys.version_info >= (3, 12):
+        raise RuntimeError("Dynamo is not supported on Python 3.12+")
+
+    # Decorator mode
+    if model is None:
+        def fn(model: Callable):
+            if model is None:
+                raise RuntimeError("Model can't be None")
+            return compile(model,
+                           fullgraph=fullgraph,
+                           dynamic=dynamic,
+                           backend=backend,
+                           mode=mode,
+                           options=options,
+                           disable=disable)
+        return fn
+
+    if mode is not None and options is not None:
+        raise RuntimeError("Either mode or options can be specified, but both can't be specified at the same time.")
+    if mode is None and options is None:
+        mode = "default"
+    if backend == "inductor":
+        backend = _TorchCompileInductorWrapper(mode, options, dynamic)
+    else:
+        backend = _TorchCompileWrapper(backend, mode, options, dynamic)
+
+    return torch._dynamo.optimize(backend=backend, nopython=fullgraph, dynamic=dynamic, disable=disable)(model)
+
+
+from torch import export as export
+
+from torch._higher_order_ops import cond
+
+def _register_device_module(device_type, module):
+    r"""Register an external runtime module of the specific :attr:`device_type`
+    supported by torch.
+
+    After the :attr:`module` is registered correctly, the user can refer
+    the external runtime module as part of torch with attribute torch.xxx.
+    """
+    # Make sure the device_type represent a supported device type for torch.
+    device_type = torch.device(device_type).type
+    m = sys.modules[__name__]
+    if hasattr(m, device_type):
+        raise RuntimeError(f"The runtime module of '{device_type}' has already "
+                           f"been registered with '{getattr(m, device_type)}'")
+    setattr(m, device_type, module)
+    torch_module_name = '.'.join([__name__, device_type])
+    sys.modules[torch_module_name] = module
+
+# expose return_types
+from . import return_types
+from . import library
+if not TYPE_CHECKING:
+    from . import _meta_registrations
+
+# Enable CUDA Sanitizer
+if 'TORCH_CUDA_SANITIZER' in os.environ:
+    import torch.cuda._sanitizer as csan
+
+    csan.enable_cuda_sanitizer()
+
+# Populate magic methods on SymInt and SymFloat
+import torch.fx.experimental.sym_node
+
+from torch import func as func
+from torch.func import vmap
+
+
+# The function _sparse_coo_tensor_unsafe is removed from PyTorch
+# Python API (v. 1.13), here we temporarily provide its replacement
+# with a deprecation warning.
+# TODO: remove the function for PyTorch v 1.15.
+def _sparse_coo_tensor_unsafe(*args, **kwargs):
+    import warnings
+    warnings.warn('torch._sparse_coo_tensor_unsafe is deprecated, '
+                  'use torch.sparse_coo_tensor(..., check_invariants=False) instead.')
+    kwargs['check_invariants'] = False
+    return torch.sparse_coo_tensor(*args, **kwargs)
+
+# Register MPS specific decomps
+torch.backends.mps._init()
+
+if not _running_with_deploy():
+    from torch import compiler as compiler
+
+    class _TritonLibrary:
+        lib = torch.library.Library("triton", "DEF")
+        ops_table: Dict[Tuple[str, str], Callable] = {}
+
+        @classmethod
+        def registerOp(cls, op_key, full_schema, op_impl, dispatch_key):
+            if (op_key, dispatch_key) not in cls.ops_table:
+                cls.lib.define(full_schema)
+                cls.lib.impl("triton::" + op_key, op_impl, dispatch_key)
+                cls.ops_table[(op_key, dispatch_key)] = op_impl
+
+            return cls.ops_table[(op_key, dispatch_key)]
+
+
+# Deprecated attributes
+_deprecated_attrs = {
+    "has_mps": torch.backends.mps.is_built,
+    "has_cuda": torch.backends.cuda.is_built,
+    "has_cudnn": torch.backends.cudnn.is_available,
+    "has_mkldnn": torch.backends.mkldnn.is_available,
+}
+
+if TYPE_CHECKING:
+    # Import the following modules during type checking to enable code intelligence features,
+    # such as auto-completion in tools like pylance, even when these modules are not explicitly
+    # imported in user code.
+    from torch import _dynamo as _dynamo
+    from torch import _inductor as _inductor
+    from torch import onnx as onnx
+
+else:
+    _lazy_modules = {
+        "_dynamo",
+        "_inductor",
+        "_export",
+        # ONNX must be imported after _dynamo, _ops, _subclasses, fx, func and jit
+        "onnx",
+    }
+
+    def __getattr__(name):
+        # Deprecated attrs
+        replacement = _deprecated_attrs.get(name)
+        if replacement is not None:
+            import warnings
+            warnings.warn(f"'{name}' is deprecated, please use '{replacement.__module__}.{replacement.__name__}()'", stacklevel=2)
+            return replacement()
+
+        # Lazy modules
+        if name in _lazy_modules:
+            import importlib
+            return importlib.import_module(f".{name}", __name__)
+
+        raise AttributeError(f"module '{__name__}' has no attribute '{name}'")
+
+
+def _constrain_as_value(symbol, min: Optional[builtins.int] = None, max: Optional[builtins.int] = None):
+    """
+    Add min/max constraint on the intermediate symbol at tracing time. If called in eager mode,
+    it will still check if the input value is within the specified range.
+    """
+    torch.sym_constrain_range(symbol, min=min, max=max)
+
+
+def _constrain_as_size(symbol, min: Optional[builtins.int] = None, max: Optional[builtins.int] = None):
+    """
+    This indicates that a given int is size-like, and can be used in any context where a size is expected.
+    You will typically use this when reading out integers from Tensors, e.g., max.item() or lengths.tolist()
+    which then need to be used as tensor constructors. Providing these assertions to PyTorch can help resolve
+      GuardOnDataDependentSymNode errors upon export, since we cannot guard on unbacked SymInts.
+
+    This function has unusual semantics which distinguish it from
+    constrain_as_value.  Specifically, in some circumstances in framework
+    code, we will treat this int as >= 2 (when we do a size-oblivious guard).
+    This makes it easier to This makes it easier to use the unbacked int in
+    size contexts, as we will often attempt to guard on a size being zero/one
+    (e.g., when computing the contiguity of a tensor, or testing if
+    broadcasting can occur), which will not work on unbacked SymInts.
+    However, if we conservatively assume that the size is not zero/one, we will
+    end up with a graph that will still work even if the size is zero/one.
+
+    For more details, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit
+    ```
+    """
+    torch.sym_constrain_range_for_size(symbol, min=min, max=max)
+
+
+from . import _logging
+_logging._init_logs()