diff --git a/env-llmeval/lib/python3.10/site-packages/certifi/__init__.py b/env-llmeval/lib/python3.10/site-packages/certifi/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1c91f3ec932d465dece996df658b92880772a077 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/certifi/__init__.py @@ -0,0 +1,4 @@ +from .core import contents, where + +__all__ = ["contents", "where"] +__version__ = "2024.02.02" diff --git a/env-llmeval/lib/python3.10/site-packages/certifi/__main__.py b/env-llmeval/lib/python3.10/site-packages/certifi/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..8945b5da857f4a7dec2b84f1225f012f6098418c --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/certifi/__main__.py @@ -0,0 +1,12 @@ +import argparse + +from certifi import contents, where + +parser = argparse.ArgumentParser() +parser.add_argument("-c", "--contents", action="store_true") +args = parser.parse_args() + +if args.contents: + print(contents()) +else: + print(where()) diff --git a/env-llmeval/lib/python3.10/site-packages/certifi/__pycache__/__init__.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/certifi/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ca76f741e6fa705bc53b51360fba65de53b9d415 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/certifi/__pycache__/__init__.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/certifi/__pycache__/__main__.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/certifi/__pycache__/__main__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e85c26b8f6462f309f670a9853223121d2a3dc17 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/certifi/__pycache__/__main__.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/certifi/__pycache__/core.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/certifi/__pycache__/core.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..779c8f9f139711ada282c4e56dfd71ca0c6d7ac3 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/certifi/__pycache__/core.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/INSTALLER b/env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/LICENSE b/env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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python_version < "3.8" +Provides-Extra: dev +Requires-Dist: absl-py ; extra == 'dev' +Requires-Dist: charcut >=1.1.1 ; extra == 'dev' +Requires-Dist: cer >=1.2.0 ; extra == 'dev' +Requires-Dist: nltk ; extra == 'dev' +Requires-Dist: pytest ; extra == 'dev' +Requires-Dist: pytest-datadir ; extra == 'dev' +Requires-Dist: pytest-xdist ; extra == 'dev' +Requires-Dist: tensorflow !=2.6.0,!=2.6.1,<=2.10,>=2.3 ; extra == 'dev' +Requires-Dist: torch ; extra == 'dev' +Requires-Dist: accelerate ; extra == 'dev' +Requires-Dist: bert-score >=0.3.6 ; extra == 'dev' +Requires-Dist: rouge-score >=0.1.2 ; extra == 'dev' +Requires-Dist: sacrebleu ; extra == 'dev' +Requires-Dist: sacremoses ; extra == 'dev' +Requires-Dist: scipy ; extra == 'dev' +Requires-Dist: seqeval ; extra == 'dev' +Requires-Dist: scikit-learn ; extra == 'dev' +Requires-Dist: jiwer ; extra == 'dev' +Requires-Dist: sentencepiece ; extra == 'dev' +Requires-Dist: transformers ; extra == 'dev' +Requires-Dist: mauve-text ; extra == 'dev' +Requires-Dist: trectools ; extra == 'dev' +Requires-Dist: toml >=0.10.1 ; extra == 'dev' +Requires-Dist: requests-file >=1.5.1 ; extra == 'dev' +Requires-Dist: tldextract >=3.1.0 ; extra == 'dev' +Requires-Dist: texttable >=1.6.3 ; extra == 'dev' +Requires-Dist: unidecode >=1.3.4 ; extra == 'dev' +Requires-Dist: Werkzeug >=1.0.1 ; extra == 'dev' +Requires-Dist: six ~=1.15.0 ; extra == 'dev' +Requires-Dist: black ~=22.0 ; extra == 'dev' +Requires-Dist: flake8 >=3.8.3 ; extra == 'dev' +Requires-Dist: isort >=5.0.0 ; extra == 'dev' +Requires-Dist: pyyaml >=5.3.1 ; extra == 'dev' +Provides-Extra: docs +Requires-Dist: s3fs ; extra == 'docs' +Provides-Extra: evaluator +Requires-Dist: transformers ; extra == 'evaluator' +Requires-Dist: scipy >=1.7.1 ; extra == 'evaluator' +Provides-Extra: quality +Requires-Dist: black ~=22.0 ; extra == 'quality' +Requires-Dist: flake8 >=3.8.3 ; extra == 'quality' +Requires-Dist: isort >=5.0.0 ; extra == 'quality' +Requires-Dist: pyyaml >=5.3.1 ; extra == 'quality' +Provides-Extra: template +Requires-Dist: cookiecutter ; extra == 'template' +Requires-Dist: gradio >=3.0.0 ; extra == 'template' +Provides-Extra: tensorflow +Requires-Dist: tensorflow !=2.6.0,!=2.6.1,>=2.2.0 ; extra == 'tensorflow' +Provides-Extra: tensorflow_gpu +Requires-Dist: tensorflow-gpu !=2.6.0,!=2.6.1,>=2.2.0 ; extra == 'tensorflow_gpu' +Provides-Extra: tests +Requires-Dist: absl-py ; extra == 'tests' +Requires-Dist: charcut >=1.1.1 ; extra == 'tests' +Requires-Dist: cer >=1.2.0 ; extra == 'tests' +Requires-Dist: nltk ; extra == 'tests' +Requires-Dist: pytest ; extra == 'tests' +Requires-Dist: pytest-datadir ; extra == 'tests' +Requires-Dist: pytest-xdist ; extra == 'tests' +Requires-Dist: tensorflow !=2.6.0,!=2.6.1,<=2.10,>=2.3 ; extra == 'tests' +Requires-Dist: torch ; extra == 'tests' +Requires-Dist: accelerate ; extra == 'tests' +Requires-Dist: bert-score >=0.3.6 ; extra == 'tests' +Requires-Dist: rouge-score >=0.1.2 ; extra == 'tests' +Requires-Dist: sacrebleu ; extra == 'tests' +Requires-Dist: sacremoses ; extra == 'tests' +Requires-Dist: scipy ; extra == 'tests' +Requires-Dist: seqeval ; extra == 'tests' +Requires-Dist: scikit-learn ; extra == 'tests' +Requires-Dist: jiwer ; extra == 'tests' +Requires-Dist: sentencepiece ; extra == 'tests' +Requires-Dist: transformers ; extra == 'tests' +Requires-Dist: mauve-text ; extra == 'tests' +Requires-Dist: trectools ; extra == 'tests' +Requires-Dist: toml >=0.10.1 ; extra == 'tests' +Requires-Dist: requests-file >=1.5.1 ; extra == 'tests' +Requires-Dist: tldextract >=3.1.0 ; extra == 'tests' +Requires-Dist: texttable >=1.6.3 ; extra == 'tests' +Requires-Dist: unidecode >=1.3.4 ; extra == 'tests' +Requires-Dist: Werkzeug >=1.0.1 ; extra == 'tests' +Requires-Dist: six ~=1.15.0 ; extra == 'tests' +Provides-Extra: torch +Requires-Dist: torch ; extra == 'torch' + +

+
+ +
+

+ +

+ + Build + + + GitHub + + + Documentation + + + GitHub release + + + Contributor Covenant + +

+ +🤗 Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized. + +It currently contains: + +- **implementations of dozens of popular metrics**: the existing metrics cover a variety of tasks spanning from NLP to Computer Vision, and include dataset-specific metrics for datasets. With a simple command like `accuracy = load("accuracy")`, get any of these metrics ready to use for evaluating a ML model in any framework (Numpy/Pandas/PyTorch/TensorFlow/JAX). +- **comparisons and measurements**: comparisons are used to measure the difference between models and measurements are tools to evaluate datasets. +- **an easy way of adding new evaluation modules to the 🤗 Hub**: you can create new evaluation modules and push them to a dedicated Space in the 🤗 Hub with `evaluate-cli create [metric name]`, which allows you to see easily compare different metrics and their outputs for the same sets of references and predictions. + +[🎓 **Documentation**](https://huggingface.co/docs/evaluate/) + +🔎 **Find a [metric](https://huggingface.co/evaluate-metric), [comparison](https://huggingface.co/evaluate-comparison), [measurement](https://huggingface.co/evaluate-measurement) on the Hub** + +[🌟 **Add a new evaluation module**](https://huggingface.co/docs/evaluate/) + +🤗 Evaluate also has lots of useful features like: + +- **Type checking**: the input types are checked to make sure that you are using the right input formats for each metric +- **Metric cards**: each metrics comes with a card that describes the values, limitations and their ranges, as well as providing examples of their usage and usefulness. +- **Community metrics:** Metrics live on the Hugging Face Hub and you can easily add your own metrics for your project or to collaborate with others. + + +# Installation + +## With pip + +🤗 Evaluate can be installed from PyPi and has to be installed in a virtual environment (venv or conda for instance) + +```bash +pip install evaluate +``` + +# Usage + +🤗 Evaluate's main methods are: + +- `evaluate.list_evaluation_modules()` to list the available metrics, comparisons and measurements +- `evaluate.load(module_name, **kwargs)` to instantiate an evaluation module +- `results = module.compute(*kwargs)` to compute the result of an evaluation module + +# Adding a new evaluation module + +First install the necessary dependencies to create a new metric with the following command: +```bash +pip install evaluate[template] +``` +Then you can get started with the following command which will create a new folder for your metric and display the necessary steps: +```bash +evaluate-cli create "Awesome Metric" +``` +See this [step-by-step guide](https://huggingface.co/docs/evaluate/creating_and_sharing) in the documentation for detailed instructions. + +## Credits + +Thanks to [@marella](https://github.com/marella) for letting us use the `evaluate` namespace on PyPi previously used by his [library](https://github.com/marella/evaluate). + + diff --git a/env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/entry_points.txt b/env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/entry_points.txt new file mode 100644 index 0000000000000000000000000000000000000000..cbb3e69c055695cd6dccee54dec460f319c3fd25 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/entry_points.txt @@ -0,0 +1,3 @@ +[console_scripts] +evaluate-cli = evaluate.commands.evaluate_cli:main + diff --git a/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/INSTALLER b/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/LICENSE.txt b/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..a76d0ed5ad8501d70722dd7e819d528e1190fb41 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/LICENSE.txt @@ -0,0 +1,29 @@ +Copyright (c) 2004 Infrae. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + + 3. Neither the name of Infrae nor the names of its contributors may + be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL INFRAE OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/LICENSES.txt b/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/LICENSES.txt new file mode 100644 index 0000000000000000000000000000000000000000..9f97c18aa0b162ffcc3e745d80efb9def1c82515 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/LICENSES.txt @@ -0,0 +1,29 @@ +lxml is copyright Infrae and distributed under the BSD license (see +doc/licenses/BSD.txt), with the following exceptions: + +Some code, such a selftest.py, selftest2.py and +src/lxml/_elementpath.py are derived from ElementTree and +cElementTree. See doc/licenses/elementtree.txt for the license text. + +lxml.cssselect and lxml.html are copyright Ian Bicking and distributed +under the BSD license (see doc/licenses/BSD.txt). + +test.py, the test-runner script, is GPL and copyright Shuttleworth +Foundation. See doc/licenses/GPL.txt. It is believed the unchanged +inclusion of test.py to run the unit test suite falls under the +"aggregation" clause of the GPL and thus does not affect the license +of the rest of the package. + +The isoschematron implementation uses several XSL and RelaxNG resources: + * The (XML syntax) RelaxNG schema for schematron, copyright International + Organization for Standardization (see + src/lxml/isoschematron/resources/rng/iso-schematron.rng for the license + text) + * The skeleton iso-schematron-xlt1 pure-xslt schematron implementation + xsl stylesheets, copyright Rick Jelliffe and Academia Sinica Computing + Center, Taiwan (see the xsl files here for the license text: + src/lxml/isoschematron/resources/xsl/iso-schematron-xslt1/) + * The xsd/rng schema schematron extraction xsl transformations are unlicensed + and copyright the respective authors as noted (see + src/lxml/isoschematron/resources/xsl/RNG2Schtrn.xsl and + src/lxml/isoschematron/resources/xsl/XSD2Schtrn.xsl) diff --git a/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..14af69834c6b3cac3671862238ba671a3ce2ed48 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/METADATA @@ -0,0 +1,89 @@ +Metadata-Version: 2.1 +Name: lxml +Version: 5.2.1 +Summary: Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API. +Home-page: https://lxml.de/ +Author: lxml dev team +Author-email: lxml-dev@lxml.de +Maintainer: lxml dev team +Maintainer-email: lxml-dev@lxml.de +License: BSD-3-Clause +Project-URL: Source, https://github.com/lxml/lxml +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Information Technology +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: Cython +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: C +Classifier: Operating System :: OS Independent +Classifier: Topic :: Text Processing :: Markup :: HTML +Classifier: Topic :: Text Processing :: Markup :: XML +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Requires-Python: >=3.6 +License-File: LICENSE.txt +License-File: LICENSES.txt +Provides-Extra: cssselect +Requires-Dist: cssselect >=0.7 ; extra == 'cssselect' +Provides-Extra: html5 +Requires-Dist: html5lib ; extra == 'html5' +Provides-Extra: html_clean +Requires-Dist: lxml-html-clean ; extra == 'html_clean' +Provides-Extra: htmlsoup +Requires-Dist: BeautifulSoup4 ; extra == 'htmlsoup' +Provides-Extra: source +Requires-Dist: Cython >=3.0.10 ; extra == 'source' + +lxml is a Pythonic, mature binding for the libxml2 and libxslt libraries. It +provides safe and convenient access to these libraries using the ElementTree +API. + +It extends the ElementTree API significantly to offer support for XPath, +RelaxNG, XML Schema, XSLT, C14N and much more. + +To contact the project, go to the `project home page +`_ or see our bug tracker at +https://launchpad.net/lxml + +In case you want to use the current in-development version of lxml, +you can get it from the github repository at +https://github.com/lxml/lxml . Note that this requires Cython to +build the sources, see the build instructions on the project home +page. To the same end, running ``easy_install lxml==dev`` will +install lxml from +https://github.com/lxml/lxml/tarball/master#egg=lxml-dev if you have +an appropriate version of Cython installed. + + +After an official release of a new stable series, bug fixes may become +available at +https://github.com/lxml/lxml/tree/lxml-5.2 . +Running ``easy_install lxml==5.2bugfix`` will install +the unreleased branch state from +https://github.com/lxml/lxml/tarball/lxml-5.2#egg=lxml-5.2bugfix +as soon as a maintenance branch has been established. Note that this +requires Cython to be installed at an appropriate version for the build. + +5.2.1 (2024-04-02) +================== + +Bugs fixed +---------- + +* LP#2059910: The minimum CPU architecture for the Linux x86 binary wheels was set back to + "core2", but with SSE 4.2 enabled. + +* LP#2059977: ``Element.iterfind("//absolute_path")`` failed with a ``SyntaxError`` + where it should have issued a warning. + +* GH#416: The documentation build was using the non-standard ``which`` command. + Patch by Michał Górny. + + diff --git a/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/RECORD b/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..b63ecf22c7de485df50f23eb3a93412eca2ebea4 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/lxml-5.2.1.dist-info/RECORD @@ -0,0 +1,201 @@ +lxml-5.2.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 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b/env-llmeval/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.2.106.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.2.106.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.2.106.dist-info/License.txt b/env-llmeval/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.2.106.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..b491c70e0aef319022ded661e111ddbd45b8a17f --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.2.106.dist-info/License.txt @@ -0,0 +1,1568 @@ +End User License Agreement +-------------------------- + + +Preface +------- + +The Software License Agreement in Chapter 1 and the Supplement +in Chapter 2 contain license terms and conditions that govern +the use of 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This SDK may include software and materials from + NVIDIA’s licensors, and these licensors are intended + third party beneficiaries that may enforce this Agreement + with respect to their intellectual property rights. + + 2. You hold all rights, title and interest in and to your + applications and your derivative works of the sample + source code delivered in the SDK, including their + respective intellectual property rights, subject to + NVIDIA’s rights described in this section. + + 3. You may, but don’t have to, provide to NVIDIA + suggestions, feature requests or other feedback regarding + the SDK, including possible enhancements or modifications + to the SDK. 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No Warranties + +THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL +FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND +ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND +OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING, +BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE +ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO +WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF +DEALING OR COURSE OF TRADE. + + +1.5. 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THE NATURE OF THE LIABILITY OR THE +NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS +LIMIT. + +These exclusions and limitations of liability shall apply +regardless if NVIDIA or its affiliates have been advised of +the possibility of such damages, and regardless of whether a +remedy fails its essential purpose. These exclusions and +limitations of liability form an essential basis of the +bargain between the parties, and, absent any of these +exclusions or limitations of liability, the provisions of this +Agreement, including, without limitation, the economic terms, +would be substantially different. + + +1.6. Termination + + 1. This Agreement will continue to apply until terminated by + either you or NVIDIA as described below. + + 2. If you want to terminate this Agreement, you may do so by + stopping to use the SDK. + + 3. NVIDIA may, at any time, terminate this Agreement if: + + a. (i) you fail to comply with any term of this + Agreement and the non-compliance is not fixed within + thirty (30) days following notice from NVIDIA (or + immediately if you violate NVIDIA’s intellectual + property rights); + + b. (ii) you commence or participate in any legal + proceeding against NVIDIA with respect to the SDK; or + + c. (iii) NVIDIA decides to no longer provide the SDK in + a country or, in NVIDIA’s sole discretion, the + continued use of it is no longer commercially viable. + + 4. Upon any termination of this Agreement, you agree to + promptly discontinue use of the SDK and destroy all copies + in your possession or control. Your prior distributions in + accordance with this Agreement are not affected by the + termination of this Agreement. Upon written request, you + will certify in writing that you have complied with your + commitments under this section. Upon any termination of + this Agreement all provisions survive except for the + license grant provisions. + + +1.7. General + +If you wish to assign this Agreement or your rights and +obligations, including by merger, consolidation, dissolution +or operation of law, contact NVIDIA to ask for permission. Any +attempted assignment not approved by NVIDIA in writing shall +be void and of no effect. NVIDIA may assign, delegate or +transfer this Agreement and its rights and obligations, and if +to a non-affiliate you will be notified. + +You agree to cooperate with NVIDIA and provide reasonably +requested information to verify your compliance with this +Agreement. + +This Agreement will be governed in all respects by the laws of +the United States and of the State of Delaware as those laws +are applied to contracts entered into and performed entirely +within Delaware by Delaware residents, without regard to the +conflicts of laws principles. 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Unless otherwise specified, +remedies are cumulative. + +Each party acknowledges and agrees that the other is an +independent contractor in the performance of this Agreement. + +The SDK has been developed entirely at private expense and is +“commercial items” consisting of “commercial computer +software” and “commercial computer software +documentation” provided with RESTRICTED RIGHTS. Use, +duplication or disclosure by the U.S. Government or a U.S. +Government subcontractor is subject to the restrictions in +this Agreement pursuant to DFARS 227.7202-3(a) or as set forth +in subparagraphs (c)(1) and (2) of the Commercial Computer +Software - Restricted Rights clause at FAR 52.227-19, as +applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas +Expressway, Santa Clara, CA 95051. + +The SDK is subject to United States export laws and +regulations. You agree that you will not ship, transfer or +export the SDK into any country, or use the SDK in any manner, +prohibited by the United States Bureau of Industry and +Security or economic sanctions regulations administered by the +U.S. Department of Treasury’s Office of Foreign Assets +Control (OFAC), or any applicable export laws, restrictions or +regulations. These laws include restrictions on destinations, +end users and end use. By accepting this Agreement, you +confirm that you are not a resident or citizen of any country +currently embargoed by the U.S. and that you are not otherwise +prohibited from receiving the SDK. + +Any notice delivered by NVIDIA to you under this Agreement +will be delivered via mail, email or fax. You agree that any +notices that NVIDIA sends you electronically will satisfy any +legal communication requirements. Please direct your legal +notices or other correspondence to NVIDIA Corporation, 2788 +San Tomas Expressway, Santa Clara, California 95051, United +States of America, Attention: Legal Department. + +This Agreement and any exhibits incorporated into this +Agreement constitute the entire agreement of the parties with +respect to the subject matter of this Agreement and supersede +all prior negotiations or documentation exchanged between the +parties relating to this SDK license. Any additional and/or +conflicting terms on documents issued by you are null, void, +and invalid. Any amendment or waiver under this Agreement +shall be in writing and signed by representatives of both +parties. + + +2. CUDA Toolkit Supplement to Software License Agreement for +NVIDIA Software Development Kits +------------------------------------------------------------ + + +Release date: August 16, 2018 +----------------------------- + +The terms in this supplement govern your use of the NVIDIA +CUDA Toolkit SDK under the terms of your license agreement +(“Agreement”) as modified by this supplement. Capitalized +terms used but not defined below have the meaning assigned to +them in the Agreement. + +This supplement is an exhibit to the Agreement and is +incorporated as an integral part of the Agreement. In the +event of conflict between the terms in this supplement and the +terms in the Agreement, the terms in this supplement govern. + + +2.1. License Scope + +The SDK is licensed for you to develop applications only for +use in systems with NVIDIA GPUs. + + +2.2. Distribution + +The portions of the SDK that are distributable under the +Agreement are listed in Attachment A. + + +2.3. Operating Systems + +Those portions of the SDK designed exclusively for use on the +Linux or FreeBSD operating systems, or other operating systems +derived from the source code to these operating systems, may +be copied and redistributed for use in accordance with this +Agreement, provided that the object code files are not +modified in any way (except for unzipping of compressed +files). + + +2.4. Audio and Video Encoders and Decoders + +You acknowledge and agree that it is your sole responsibility +to obtain any additional third-party licenses required to +make, have made, use, have used, sell, import, and offer for +sale your products or services that include or incorporate any +third-party software and content relating to audio and/or +video encoders and decoders from, including but not limited +to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A., +MPEG-LA, and Coding Technologies. NVIDIA does not grant to you +under this Agreement any necessary patent or other rights with +respect to any audio and/or video encoders and decoders. + + +2.5. Licensing + +If the distribution terms in this Agreement are not suitable +for your organization, or for any questions regarding this +Agreement, please contact NVIDIA at +nvidia-compute-license-questions@nvidia.com. + + +2.6. Attachment A + +The following portions of the SDK are distributable under the +Agreement: + +Component + +CUDA Runtime + +Windows + +cudart.dll, cudart_static.lib, cudadevrt.lib + +Mac OSX + +libcudart.dylib, libcudart_static.a, libcudadevrt.a + +Linux + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Android + +libcudart.so, libcudart_static.a, libcudadevrt.a + +Component + +CUDA FFT Library + +Windows + +cufft.dll, cufftw.dll, cufft.lib, cufftw.lib + +Mac OSX + +libcufft.dylib, libcufft_static.a, libcufftw.dylib, +libcufftw_static.a + +Linux + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Android + +libcufft.so, libcufft_static.a, libcufftw.so, +libcufftw_static.a + +Component + +CUDA BLAS Library + +Windows + +cublas.dll, cublasLt.dll + +Mac OSX + +libcublas.dylib, libcublasLt.dylib, libcublas_static.a, +libcublasLt_static.a + +Linux + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Android + +libcublas.so, libcublasLt.so, libcublas_static.a, +libcublasLt_static.a + +Component + +NVIDIA "Drop-in" BLAS Library + +Windows + +nvblas.dll + +Mac OSX + +libnvblas.dylib + +Linux + +libnvblas.so + +Component + +CUDA Sparse Matrix Library + +Windows + +cusparse.dll, cusparse.lib + +Mac OSX + +libcusparse.dylib, libcusparse_static.a + +Linux + +libcusparse.so, libcusparse_static.a + +Android + +libcusparse.so, libcusparse_static.a + +Component + +CUDA Linear Solver Library + +Windows + +cusolver.dll, cusolver.lib + +Mac OSX + +libcusolver.dylib, libcusolver_static.a + +Linux + +libcusolver.so, libcusolver_static.a + +Android + +libcusolver.so, libcusolver_static.a + +Component + +CUDA Random Number Generation Library + +Windows + +curand.dll, curand.lib + +Mac OSX + +libcurand.dylib, libcurand_static.a + +Linux + +libcurand.so, libcurand_static.a + +Android + +libcurand.so, libcurand_static.a + +Component + +CUDA Accelerated Graph Library + +Component + +NVIDIA Performance Primitives Library + +Windows + +nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll, +nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll, +nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib, +nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll, +nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib + +Mac OSX + +libnppc.dylib, libnppc_static.a, libnppial.dylib, +libnppial_static.a, libnppicc.dylib, libnppicc_static.a, +libnppicom.dylib, libnppicom_static.a, libnppidei.dylib, +libnppidei_static.a, libnppif.dylib, libnppif_static.a, +libnppig.dylib, libnppig_static.a, libnppim.dylib, +libnppisu_static.a, libnppitc.dylib, libnppitc_static.a, +libnpps.dylib, libnpps_static.a + +Linux + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Android + +libnppc.so, libnppc_static.a, libnppial.so, +libnppial_static.a, libnppicc.so, libnppicc_static.a, +libnppicom.so, libnppicom_static.a, libnppidei.so, +libnppidei_static.a, libnppif.so, libnppif_static.a +libnppig.so, libnppig_static.a, libnppim.so, +libnppim_static.a, libnppist.so, libnppist_static.a, +libnppisu.so, libnppisu_static.a, libnppitc.so +libnppitc_static.a, libnpps.so, libnpps_static.a + +Component + +NVIDIA JPEG Library + +Linux + +libnvjpeg.so, libnvjpeg_static.a + +Component + +Internal common library required for statically linking to +cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP + +Mac OSX + +libculibos.a + +Linux + +libculibos.a + +Component + +NVIDIA Runtime Compilation Library and Header + +All + +nvrtc.h + +Windows + +nvrtc.dll, nvrtc-builtins.dll + +Mac OSX + +libnvrtc.dylib, libnvrtc-builtins.dylib + +Linux + +libnvrtc.so, libnvrtc-builtins.so + +Component + +NVIDIA Optimizing Compiler Library + +Windows + +nvvm.dll + +Mac OSX + +libnvvm.dylib + +Linux + +libnvvm.so + +Component + +NVIDIA Common Device Math Functions Library + +Windows + +libdevice.10.bc + +Mac OSX + +libdevice.10.bc + +Linux + +libdevice.10.bc + +Component + +CUDA Occupancy Calculation Header Library + +All + +cuda_occupancy.h + +Component + +CUDA Half Precision Headers + +All + +cuda_fp16.h, cuda_fp16.hpp + +Component + +CUDA Profiling Tools Interface (CUPTI) Library + +Windows + +cupti.dll + +Mac OSX + +libcupti.dylib + +Linux + +libcupti.so + +Component + +NVIDIA Tools Extension Library + +Windows + +nvToolsExt.dll, nvToolsExt.lib + +Mac OSX + +libnvToolsExt.dylib + +Linux + +libnvToolsExt.so + +Component + +NVIDIA CUDA Driver Libraries + +Linux + +libcuda.so, libnvidia-fatbinaryloader.so, +libnvidia-ptxjitcompiler.so + +The NVIDIA CUDA Driver Libraries are only distributable in +applications that meet this criteria: + + 1. The application was developed starting from a NVIDIA CUDA + container obtained from Docker Hub or the NVIDIA GPU + Cloud, and + + 2. The resulting application is packaged as a Docker + container and distributed to users on Docker Hub or the + NVIDIA GPU Cloud only. + + +2.7. Attachment B + + +Additional Licensing Obligations + +The following third party components included in the SOFTWARE +are licensed to Licensee pursuant to the following terms and +conditions: + + 1. Licensee's use of the GDB third party component is + subject to the terms and conditions of GNU GPL v3: + + This product includes copyrighted third-party software licensed + under the terms of the GNU General Public License v3 ("GPL v3"). + All third-party software packages are copyright by their respective + authors. GPL v3 terms and conditions are hereby incorporated into + the Agreement by this reference: http://www.gnu.org/licenses/gpl.txt + + Consistent with these licensing requirements, the software + listed below is provided under the terms of the specified + open source software licenses. To obtain source code for + software provided under licenses that require + redistribution of source code, including the GNU General + Public License (GPL) and GNU Lesser General Public License + (LGPL), contact oss-requests@nvidia.com. This offer is + valid for a period of three (3) years from the date of the + distribution of this product by NVIDIA CORPORATION. + + Component License + CUDA-GDB GPL v3 + + 2. Licensee represents and warrants that any and all third + party licensing and/or royalty payment obligations in + connection with Licensee's use of the H.264 video codecs + are solely the responsibility of Licensee. + + 3. Licensee's use of the Thrust library is subject to the + terms and conditions of the Apache License Version 2.0. + All third-party software packages are copyright by their + respective authors. Apache License Version 2.0 terms and + conditions are hereby incorporated into the Agreement by + this reference. + http://www.apache.org/licenses/LICENSE-2.0.html + + In addition, Licensee acknowledges the following notice: + Thrust includes source code from the Boost Iterator, + Tuple, System, and Random Number libraries. + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + 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, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 4. Licensee's use of the LLVM third party component is + subject to the following terms and conditions: + + ====================================================== + LLVM Release License + ====================================================== + University of Illinois/NCSA + Open Source License + + Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign. + All rights reserved. + + Developed by: + + LLVM Team + + University of Illinois at Urbana-Champaign + + http://llvm.org + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to + deal with 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: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimers. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimers in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the LLVM Team, University of Illinois at Urbana- + Champaign, nor the names of its contributors may be used to endorse or + promote products derived from this Software without specific prior + written permission. + + 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 CONTRIBUTORS 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 WITH THE SOFTWARE. + + 5. Licensee's use (e.g. nvprof) of the PCRE third party + component is subject to the following terms and + conditions: + + ------------ + PCRE LICENCE + ------------ + PCRE is a library of functions to support regular expressions whose syntax + and semantics are as close as possible to those of the Perl 5 language. + Release 8 of PCRE is distributed under the terms of the "BSD" licence, as + specified below. The documentation for PCRE, supplied in the "doc" + directory, is distributed under the same terms as the software itself. The + basic library functions are written in C and are freestanding. Also + included in the distribution is a set of C++ wrapper functions, and a just- + in-time compiler that can be used to optimize pattern matching. These are + both optional features that can be omitted when the library is built. + + THE BASIC LIBRARY FUNCTIONS + --------------------------- + Written by: Philip Hazel + Email local part: ph10 + Email domain: cam.ac.uk + University of Cambridge Computing Service, + Cambridge, England. + Copyright (c) 1997-2012 University of Cambridge + All rights reserved. + + PCRE JUST-IN-TIME COMPILATION SUPPORT + ------------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2010-2012 Zoltan Herczeg + All rights reserved. + + STACK-LESS JUST-IN-TIME COMPILER + -------------------------------- + Written by: Zoltan Herczeg + Email local part: hzmester + Emain domain: freemail.hu + Copyright(c) 2009-2012 Zoltan Herczeg + All rights reserved. + + THE C++ WRAPPER FUNCTIONS + ------------------------- + Contributed by: Google Inc. + Copyright (c) 2007-2012, Google Inc. + All rights reserved. + + THE "BSD" LICENCE + ----------------- + 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 University of Cambridge nor the name of Google + Inc. nor the names of their 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. + + 6. Some of the cuBLAS library routines were written by or + derived from code written by Vasily Volkov and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2007-2009, Regents of the University of California + + 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 University of California, Berkeley 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 AUTHOR "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 AUTHOR 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. + + 7. Some of the cuBLAS library routines were written by or + derived from code written by Davide Barbieri and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata. + + 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. + * The name of the author may not be used to endorse or promote + products derived from this software without specific prior + written permission. + + THIS SOFTWARE IS PROVIDED BY THE AUTHOR "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 AUTHOR 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. + + 8. Some of the cuBLAS library routines were derived from + code developed by the University of Tennessee and are + subject to the Modified Berkeley Software Distribution + License as follows: + + Copyright (c) 2010 The University of Tennessee. + + 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 listed in this license in the documentation and/or + other materials provided with the distribution. + * Neither the name of the copyright holders 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. + + 9. Some of the cuBLAS library routines were written by or + derived from code written by Jonathan Hogg and are subject + to the Modified Berkeley Software Distribution License as + follows: + + Copyright (c) 2012, The Science and Technology Facilities Council (STFC). + + 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 STFC 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 STFC 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. + + 10. Some of the cuBLAS library routines were written by or + derived from code written by Ahmad M. Abdelfattah, David + Keyes, and Hatem Ltaief, and are subject to the Apache + License, Version 2.0, as follows: + + -- (C) Copyright 2013 King Abdullah University of Science and Technology + Authors: + Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa) + David Keyes (david.keyes@kaust.edu.sa) + Hatem Ltaief (hatem.ltaief@kaust.edu.sa) + + 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 King Abdullah University of Science and + Technology 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 + HOLDERS 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 + + 11. Some of the cuSPARSE library routines were written by or + derived from code written by Li-Wen Chang and are subject + to the NCSA Open Source License as follows: + + Copyright (c) 2012, University of Illinois. + + All rights reserved. + + Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal with 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: + * 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 + disclaimers in the documentation and/or other materials provided + with the distribution. + * Neither the names of IMPACT Group, University of Illinois, nor + the names of its contributors may be used to endorse or promote + products derived from this Software without specific prior + written permission. + + 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 CONTRIBUTORS 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 WITH THE + SOFTWARE. + + 12. Some of the cuRAND library routines were written by or + derived from code written by Mutsuo Saito and Makoto + Matsumoto and are subject to the following license: + + Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima + University. All rights reserved. + + Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima + University and University of Tokyo. 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 Hiroshima University 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. + + 13. Some of the cuRAND library routines were derived from + code developed by D. E. Shaw Research and are subject to + the following license: + + Copyright 2010-2011, D. E. Shaw Research. + + 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 D. E. Shaw Research 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. + + 14. Some of the Math library routines were written by or + derived from code developed by Norbert Juffa and are + subject to the following license: + + Copyright (c) 2015-2017, Norbert Juffa + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + 15. Licensee's use of the lz4 third party component is + subject to the following terms and conditions: + + Copyright (C) 2011-2013, Yann Collet. + BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php) + + 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. + + 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. + + 16. The NPP library uses code from the Boost Math Toolkit, + and is subject to the following license: + + Boost Software License - Version 1.0 - August 17th, 2003 + . . . . + + Permission is hereby granted, free of charge, to any person or + organization obtaining a copy of the software and accompanying + documentation covered by this license (the "Software") to use, + reproduce, display, distribute, execute, and transmit the Software, + and to prepare derivative works of the Software, and to permit + third-parties to whom the Software is furnished to do so, all + subject to the following: + + The copyright notices in the Software and this entire statement, + including the above license grant, this restriction and the following + disclaimer, must be included in all copies of the Software, in whole + or in part, and all derivative works of the Software, unless such + copies or derivative works are solely in the form of machine-executable + object code generated by a source language processor. + + 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, TITLE AND + NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR + ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR + OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING + FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR + OTHER DEALINGS IN THE SOFTWARE. + + 17. Portions of the Nsight Eclipse Edition is subject to the + following license: + + The Eclipse Foundation makes available all content in this plug-in + ("Content"). Unless otherwise indicated below, the Content is provided + to you under the terms and conditions of the Eclipse Public License + Version 1.0 ("EPL"). A copy of the EPL is available at http:// + www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program" + will mean the Content. + + If you did not receive this Content directly from the Eclipse + Foundation, the Content is being redistributed by another party + ("Redistributor") and different terms and conditions may apply to your + use of any object code in the Content. Check the Redistributor's + license that was provided with the Content. If no such license exists, + contact the Redistributor. Unless otherwise indicated below, the terms + and conditions of the EPL still apply to any source code in the + Content and such source code may be obtained at http://www.eclipse.org. + + 18. Some of the cuBLAS library routines uses code from + OpenAI, which is subject to the following license: + + License URL + https://github.com/openai/openai-gemm/blob/master/LICENSE + + License Text + The MIT License + + Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc. + + 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. + + 19. Licensee's use of the Visual Studio Setup Configuration + Samples is subject to the following license: + + The MIT License (MIT) + Copyright (C) Microsoft Corporation. All rights reserved. + + 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. + + 20. Licensee's use of linmath.h header for CPU functions for + GL vector/matrix operations from lunarG is subject to the + Apache License Version 2.0. + + 21. The DX12-CUDA sample uses the d3dx12.h header, which is + subject to the MIT license . + +----------------- diff --git a/env-llmeval/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.2.106.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.2.106.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..301ffe540f0242521617353143a2ee3ad715a44e --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/nvidia_curand_cu12-10.3.2.106.dist-info/METADATA @@ -0,0 +1,35 @@ +Metadata-Version: 2.1 +Name: nvidia-curand-cu12 +Version: 10.3.2.106 +Summary: CURAND native runtime libraries +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: cuda_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience 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+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 :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Requires-Python: >=3.7 +License-File: LICENSE.txt + +pip - The Python Package Installer +================================== + +.. image:: https://img.shields.io/pypi/v/pip.svg + :target: https://pypi.org/project/pip/ + +.. image:: https://readthedocs.org/projects/pip/badge/?version=latest + :target: https://pip.pypa.io/en/latest + +pip is the `package installer`_ for Python. You can use pip to install packages from the `Python Package Index`_ and other indexes. + +Please take a look at our documentation for how to install and use pip: + +* `Installation`_ +* `Usage`_ + +We release updates regularly, with a new version every 3 months. Find more details in our documentation: + +* `Release notes`_ +* `Release process`_ + +In pip 20.3, we've `made a big improvement to the heart of pip`_; `learn more`_. We want your input, so `sign up for our user experience research studies`_ to help us do it right. + +**Note**: pip 21.0, in January 2021, removed Python 2 support, per pip's `Python 2 support policy`_. Please migrate to Python 3. + +If you find bugs, need help, or want to talk to the developers, please use our mailing lists or chat rooms: + +* `Issue tracking`_ +* `Discourse channel`_ +* `User IRC`_ + +If you want to get involved head over to GitHub to get the source code, look at our development documentation and feel free to jump on the developer mailing lists and chat rooms: + +* `GitHub page`_ +* `Development documentation`_ +* `Development mailing list`_ +* `Development IRC`_ + +Code of Conduct +--------------- + +Everyone interacting in the pip project's codebases, issue trackers, chat +rooms, and mailing lists is expected to follow the `PSF Code of Conduct`_. + +.. _package installer: https://packaging.python.org/guides/tool-recommendations/ +.. _Python Package Index: https://pypi.org +.. _Installation: https://pip.pypa.io/en/stable/installation/ +.. _Usage: https://pip.pypa.io/en/stable/ +.. _Release notes: https://pip.pypa.io/en/stable/news.html +.. _Release process: https://pip.pypa.io/en/latest/development/release-process/ +.. _GitHub page: https://github.com/pypa/pip +.. _Development documentation: https://pip.pypa.io/en/latest/development +.. _made a big improvement to the heart of pip: https://pyfound.blogspot.com/2020/11/pip-20-3-new-resolver.html +.. _learn more: https://pip.pypa.io/en/latest/user_guide/#changes-to-the-pip-dependency-resolver-in-20-3-2020 +.. _sign up for our user experience research studies: https://pyfound.blogspot.com/2020/03/new-pip-resolver-to-roll-out-this-year.html +.. _Python 2 support policy: https://pip.pypa.io/en/latest/development/release-process/#python-2-support +.. _Issue tracking: https://github.com/pypa/pip/issues +.. _Discourse channel: https://discuss.python.org/c/packaging +.. _Development mailing list: https://mail.python.org/mailman3/lists/distutils-sig.python.org/ +.. _User IRC: https://kiwiirc.com/nextclient/#ircs://irc.libera.chat:+6697/pypa +.. _Development IRC: 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--- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip-22.0.2.dist-info/entry_points.txt @@ -0,0 +1,5 @@ +[console_scripts] +pip = pip._internal.cli.main:main +pip3 = pip._internal.cli.main:main +pip3.10 = pip._internal.cli.main:main + diff --git a/env-llmeval/lib/python3.10/site-packages/pip-22.0.2.dist-info/top_level.txt b/env-llmeval/lib/python3.10/site-packages/pip-22.0.2.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/pip-22.0.2.dist-info/top_level.txt @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/python_dateutil-2.9.0.post0.dist-info/LICENSE b/env-llmeval/lib/python3.10/site-packages/python_dateutil-2.9.0.post0.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..1e65815cf0b3132689485874a93034ede7206bf4 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/python_dateutil-2.9.0.post0.dist-info/LICENSE @@ -0,0 +1,54 @@ +Copyright 2017- Paul Ganssle +Copyright 2017- dateutil contributors (see AUTHORS file) + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + +The above license applies to all contributions after 2017-12-01, as well as +all contributions that have been re-licensed (see AUTHORS file for the list of +contributors who have re-licensed their code). +-------------------------------------------------------------------------------- +dateutil - Extensions to the standard Python datetime module. + +Copyright (c) 2003-2011 - Gustavo Niemeyer +Copyright (c) 2012-2014 - Tomi Pieviläinen +Copyright (c) 2014-2016 - Yaron de Leeuw +Copyright (c) 2015- - Paul Ganssle +Copyright (c) 2015- - dateutil contributors (see AUTHORS file) + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + * Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 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. + +The above BSD License Applies to all code, even that also covered by Apache 2.0. \ No newline at end of file diff --git a/env-llmeval/lib/python3.10/site-packages/python_dateutil-2.9.0.post0.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/python_dateutil-2.9.0.post0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..577f2bf2b7749e1b123b8225d610b1b257e430cc --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/python_dateutil-2.9.0.post0.dist-info/METADATA @@ -0,0 +1,204 @@ +Metadata-Version: 2.1 +Name: python-dateutil +Version: 2.9.0.post0 +Summary: Extensions to the standard Python datetime module +Home-page: https://github.com/dateutil/dateutil +Author: Gustavo Niemeyer +Author-email: gustavo@niemeyer.net +Maintainer: Paul Ganssle +Maintainer-email: dateutil@python.org +License: Dual License +Project-URL: Documentation, https://dateutil.readthedocs.io/en/stable/ +Project-URL: Source, https://github.com/dateutil/dateutil +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: BSD License +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 2 +Classifier: Programming Language :: Python :: 2.7 +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.3 +Classifier: Programming Language :: Python :: 3.4 +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 :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Topic :: Software Development :: Libraries +Requires-Python: !=3.0.*,!=3.1.*,!=3.2.*,>=2.7 +Description-Content-Type: text/x-rst +License-File: LICENSE +Requires-Dist: six >=1.5 + +dateutil - powerful extensions to datetime +========================================== + +|pypi| |support| |licence| + +|gitter| |readthedocs| + +|travis| |appveyor| |pipelines| |coverage| + +.. |pypi| image:: https://img.shields.io/pypi/v/python-dateutil.svg?style=flat-square + :target: https://pypi.org/project/python-dateutil/ + :alt: pypi version + +.. |support| image:: https://img.shields.io/pypi/pyversions/python-dateutil.svg?style=flat-square + :target: https://pypi.org/project/python-dateutil/ + :alt: supported Python version + +.. |travis| image:: https://img.shields.io/travis/dateutil/dateutil/master.svg?style=flat-square&label=Travis%20Build + :target: https://travis-ci.org/dateutil/dateutil + :alt: travis build status + +.. |appveyor| image:: https://img.shields.io/appveyor/ci/dateutil/dateutil/master.svg?style=flat-square&logo=appveyor + :target: https://ci.appveyor.com/project/dateutil/dateutil + :alt: appveyor build status + +.. |pipelines| image:: https://dev.azure.com/pythondateutilazure/dateutil/_apis/build/status/dateutil.dateutil?branchName=master + :target: https://dev.azure.com/pythondateutilazure/dateutil/_build/latest?definitionId=1&branchName=master + :alt: azure pipelines build status + +.. |coverage| image:: https://codecov.io/gh/dateutil/dateutil/branch/master/graphs/badge.svg?branch=master + :target: https://codecov.io/gh/dateutil/dateutil?branch=master + :alt: Code coverage + +.. |gitter| image:: https://badges.gitter.im/dateutil/dateutil.svg + :alt: Join the chat at https://gitter.im/dateutil/dateutil + :target: https://gitter.im/dateutil/dateutil + +.. |licence| image:: https://img.shields.io/pypi/l/python-dateutil.svg?style=flat-square + :target: https://pypi.org/project/python-dateutil/ + :alt: licence + +.. |readthedocs| image:: https://img.shields.io/readthedocs/dateutil/latest.svg?style=flat-square&label=Read%20the%20Docs + :alt: Read the documentation at https://dateutil.readthedocs.io/en/latest/ + :target: https://dateutil.readthedocs.io/en/latest/ + +The `dateutil` module provides powerful extensions to +the standard `datetime` module, available in Python. + +Installation +============ +`dateutil` can be installed from PyPI using `pip` (note that the package name is +different from the importable name):: + + pip install python-dateutil + +Download +======== +dateutil is available on PyPI +https://pypi.org/project/python-dateutil/ + +The documentation is hosted at: +https://dateutil.readthedocs.io/en/stable/ + +Code +==== +The code and issue tracker are hosted on GitHub: +https://github.com/dateutil/dateutil/ + +Features +======== + +* Computing of relative deltas (next month, next year, + next Monday, last week of month, etc); +* Computing of relative deltas between two given + date and/or datetime objects; +* Computing of dates based on very flexible recurrence rules, + using a superset of the `iCalendar `_ + specification. Parsing of RFC strings is supported as well. +* Generic parsing of dates in almost any string format; +* Timezone (tzinfo) implementations for tzfile(5) format + files (/etc/localtime, /usr/share/zoneinfo, etc), TZ + environment string (in all known formats), iCalendar + format files, given ranges (with help from relative deltas), + local machine timezone, fixed offset timezone, UTC timezone, + and Windows registry-based time zones. +* Internal up-to-date world timezone information based on + Olson's database. +* Computing of Easter Sunday dates for any given year, + using Western, Orthodox or Julian algorithms; +* A comprehensive test suite. + +Quick example +============= +Here's a snapshot, just to give an idea about the power of the +package. For more examples, look at the documentation. + +Suppose you want to know how much time is left, in +years/months/days/etc, before the next easter happening on a +year with a Friday 13th in August, and you want to get today's +date out of the "date" unix system command. Here is the code: + +.. code-block:: python3 + + >>> from dateutil.relativedelta import * + >>> from dateutil.easter import * + >>> from dateutil.rrule import * + >>> from dateutil.parser import * + >>> from datetime import * + >>> now = parse("Sat Oct 11 17:13:46 UTC 2003") + >>> today = now.date() + >>> year = rrule(YEARLY,dtstart=now,bymonth=8,bymonthday=13,byweekday=FR)[0].year + >>> rdelta = relativedelta(easter(year), today) + >>> print("Today is: %s" % today) + Today is: 2003-10-11 + >>> print("Year with next Aug 13th on a Friday is: %s" % year) + Year with next Aug 13th on a Friday is: 2004 + >>> print("How far is the Easter of that year: %s" % rdelta) + How far is the Easter of that year: relativedelta(months=+6) + >>> print("And the Easter of that year is: %s" % (today+rdelta)) + And the Easter of that year is: 2004-04-11 + +Being exactly 6 months ahead was **really** a coincidence :) + +Contributing +============ + +We welcome many types of contributions - bug reports, pull requests (code, infrastructure or documentation fixes). For more information about how to contribute to the project, see the ``CONTRIBUTING.md`` file in the repository. + + +Author +====== +The dateutil module was written by Gustavo Niemeyer +in 2003. + +It is maintained by: + +* Gustavo Niemeyer 2003-2011 +* Tomi Pieviläinen 2012-2014 +* Yaron de Leeuw 2014-2016 +* Paul Ganssle 2015- + +Starting with version 2.4.1 and running until 2.8.2, all source and binary +distributions will be signed by a PGP key that has, at the very least, been +signed by the key which made the previous release. A table of release signing +keys can be found below: + +=========== ============================ +Releases Signing key fingerprint +=========== ============================ +2.4.1-2.8.2 `6B49 ACBA DCF6 BD1C A206 67AB CD54 FCE3 D964 BEFB`_ +=========== ============================ + +New releases *may* have signed tags, but binary and source distributions +uploaded to PyPI will no longer have GPG signatures attached. + +Contact +======= +Our mailing list is available at `dateutil@python.org `_. As it is hosted by the PSF, it is subject to the `PSF code of +conduct `_. + +License +======= + +All contributions after December 1, 2017 released under dual license - either `Apache 2.0 License `_ or the `BSD 3-Clause License `_. Contributions before December 1, 2017 - except those those explicitly relicensed - are released only under the BSD 3-Clause License. + + +.. _6B49 ACBA DCF6 BD1C A206 67AB CD54 FCE3 D964 BEFB: + https://pgp.mit.edu/pks/lookup?op=vindex&search=0xCD54FCE3D964BEFB diff --git a/env-llmeval/lib/python3.10/site-packages/python_dateutil-2.9.0.post0.dist-info/RECORD b/env-llmeval/lib/python3.10/site-packages/python_dateutil-2.9.0.post0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..176502e4e04b23de8580d433f4ee9101ac917a93 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/python_dateutil-2.9.0.post0.dist-info/RECORD @@ -0,0 +1,44 @@ +dateutil/__init__.py,sha256=Mqam67WO9IkTmUFyI66vS6IoSXTp9G388DadH2LCMLY,620 +dateutil/__pycache__/__init__.cpython-310.pyc,, +dateutil/__pycache__/_common.cpython-310.pyc,, +dateutil/__pycache__/_version.cpython-310.pyc,, +dateutil/__pycache__/easter.cpython-310.pyc,, 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0000000000000000000000000000000000000000..de6633112c1f9951fd688e1fb43457a1ec11d6d8 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/LICENSE @@ -0,0 +1,18 @@ +Copyright (c) 2010-2020 Benjamin Peterson + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..6d7525c2ebcfe25cb6787579bf5324da1fd6f28a --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/METADATA @@ -0,0 +1,49 @@ +Metadata-Version: 2.1 +Name: six +Version: 1.16.0 +Summary: Python 2 and 3 compatibility utilities +Home-page: https://github.com/benjaminp/six +Author: Benjamin Peterson +Author-email: benjamin@python.org +License: MIT +Platform: UNKNOWN +Classifier: Development Status :: 5 - Production/Stable +Classifier: Programming Language :: Python :: 2 +Classifier: Programming Language :: Python :: 3 +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: MIT License +Classifier: Topic :: Software Development :: Libraries +Classifier: Topic :: Utilities +Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.* + +.. image:: https://img.shields.io/pypi/v/six.svg + :target: https://pypi.org/project/six/ + :alt: six on PyPI + +.. image:: https://travis-ci.org/benjaminp/six.svg?branch=master + :target: https://travis-ci.org/benjaminp/six + :alt: six on TravisCI + +.. image:: https://readthedocs.org/projects/six/badge/?version=latest + :target: https://six.readthedocs.io/ + :alt: six's documentation on Read the Docs + +.. image:: https://img.shields.io/badge/license-MIT-green.svg + :target: https://github.com/benjaminp/six/blob/master/LICENSE + :alt: MIT License badge + +Six is a Python 2 and 3 compatibility library. It provides utility functions +for smoothing over the differences between the Python versions with the goal of +writing Python code that is compatible on both Python versions. See the +documentation for more information on what is provided. + +Six supports Python 2.7 and 3.3+. It is contained in only one Python +file, so it can be easily copied into your project. (The copyright and license +notice must be retained.) + +Online documentation is at https://six.readthedocs.io/. + +Bugs can be reported to https://github.com/benjaminp/six. The code can also +be found there. + + diff --git a/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/RECORD b/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..ed0b59873cf4b0e6dcb600c4ebf5db608b49b01f --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/RECORD @@ -0,0 +1,8 @@ +__pycache__/six.cpython-310.pyc,, +six-1.16.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +six-1.16.0.dist-info/LICENSE,sha256=i7hQxWWqOJ_cFvOkaWWtI9gq3_YPI5P8J2K2MYXo5sk,1066 +six-1.16.0.dist-info/METADATA,sha256=VQcGIFCAEmfZcl77E5riPCN4v2TIsc_qtacnjxKHJoI,1795 +six-1.16.0.dist-info/RECORD,, +six-1.16.0.dist-info/WHEEL,sha256=Z-nyYpwrcSqxfdux5Mbn_DQ525iP7J2DG3JgGvOYyTQ,110 +six-1.16.0.dist-info/top_level.txt,sha256=_iVH_iYEtEXnD8nYGQYpYFUvkUW9sEO1GYbkeKSAais,4 +six.py,sha256=TOOfQi7nFGfMrIvtdr6wX4wyHH8M7aknmuLfo2cBBrM,34549 diff --git a/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/WHEEL b/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..01b8fc7d4a10cb8b4f1d21f11d3398d07d6b3478 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/WHEEL @@ -0,0 +1,6 @@ +Wheel-Version: 1.0 +Generator: bdist_wheel (0.36.2) +Root-Is-Purelib: true +Tag: py2-none-any +Tag: py3-none-any + diff --git a/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/top_level.txt b/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..ffe2fce498955b628014618b28c6bcf152466a4a --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/six-1.16.0.dist-info/top_level.txt @@ -0,0 +1 @@ +six diff --git a/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/PKG-INFO b/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/PKG-INFO new file mode 100644 index 0000000000000000000000000000000000000000..5a5226e0a866ab60221b295653899239b8bb8092 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/PKG-INFO @@ -0,0 +1,298 @@ +Metadata-Version: 2.1 +Name: sqlitedict +Version: 2.1.0 +Summary: Persistent dict in Python, backed up by sqlite3 and pickle, multithread-safe. +Home-page: https://github.com/piskvorky/sqlitedict +Author: Radim Rehurek, Victor R. Escobar, Andrey Usov, Prasanna Swaminathan, Jeff Quast +Author-email: me@radimrehurek.com +Maintainer: Radim Rehurek +Maintainer-email: me@radimrehurek.com +License: Apache 2.0 +Download-URL: http://pypi.python.org/pypi/sqlitedict +Keywords: sqlite,persistent dict,multithreaded +Platform: any +Classifier: Development Status :: 5 - Production/Stable +Classifier: Environment :: Console +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Operating System :: OS Independent +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: Topic :: Database :: Front-Ends +License-File: LICENSE.md + +=================================================== +sqlitedict -- persistent ``dict``, backed by SQLite +=================================================== + +|GithubActions|_ +|License|_ + +.. |GithubActions| image:: https://github.com/RaRe-Technologies/sqlitedict/actions/workflows/python-package.yml/badge.svg +.. |Downloads| image:: https://img.shields.io/pypi/dm/sqlitedict.svg +.. |License| image:: https://img.shields.io/pypi/l/sqlitedict.svg +.. _GithubActions: https://github.com/RaRe-Technologies/sqlitedict/actions/workflows/python-package.yml +.. _Downloads: https://pypi.python.org/pypi/sqlitedict +.. _License: https://pypi.python.org/pypi/sqlitedict + +A lightweight wrapper around Python's sqlite3 database with a simple, Pythonic +dict-like interface and support for multi-thread access: + +Usage +===== + +Write +----- + +.. code-block:: python + + >>> from sqlitedict import SqliteDict + >>> db = SqliteDict("example.sqlite") + >>> + >>> db["1"] = {"name": "first item"} + >>> db["2"] = {"name": "second item"} + >>> db["3"] = {"name": "yet another item"} + >>> + >>> # Commit to save the objects. + >>> db.commit() + >>> + >>> db["4"] = {"name": "yet another item"} + >>> # Oops, forgot to commit here, that object will never be saved. + >>> # Always remember to commit, or enable autocommit with SqliteDict("example.sqlite", autocommit=True) + >>> # Autocommit is off by default for performance. + >>> + >>> db.close() + +Read +---- + +.. code-block:: python + + >>> from sqlitedict import SqliteDict + >>> db = SqliteDict("example.sqlite") + >>> + >>> print("There are %d items in the database" % len(db)) + There are 3 items in the database + >>> + >>> # Standard dict interface. items() values() keys() etc... + >>> for key, item in db.items(): + ... print("%s=%s" % (key, item)) + 1={'name': 'first item'} + 2={'name': 'second item'} + 3={'name': 'yet another item'} + >>> + >>> db.close() + +Efficiency +---------- + +By default, sqlitedict's exception handling favors verbosity over efficiency. +It extracts and outputs the outer exception stack to the error logs. +If you favor efficiency, then initialize the DB with outer_stack=False. + +.. code-block:: python + + >>> from sqlitedict import SqliteDict + >>> db = SqliteDict("example.sqlite", outer_stack=False) # True is the default + >>> db[1] + {'name': 'first item'} + +Context Manager +--------------- + +.. code-block:: python + + >>> from sqlitedict import SqliteDict + >>> + >>> # The database is automatically closed when leaving the with section. + >>> # Uncommitted objects are not saved on close. REMEMBER TO COMMIT! + >>> + >>> with SqliteDict("example.sqlite") as db: + ... print("There are %d items in the database" % len(db)) + There are 3 items in the database + +Tables +------ + +A database file can store multiple tables. +A default table is used when no table name is specified. + +Note: Writes are serialized, having multiple tables does not improve performance. + +.. code-block:: python + + >>> from sqlitedict import SqliteDict + >>> + >>> products = SqliteDict("example.sqlite", tablename="product", autocommit=True) + >>> manufacturers = SqliteDict("example.sqlite", tablename="manufacturer", autocommit=True) + >>> + >>> products["1"] = {"name": "first item", "manufacturer_id": "1"} + >>> products["2"] = {"name": "second item", "manufacturer_id": "1"} + >>> + >>> manufacturers["1"] = {"manufacturer_name": "afactory", "location": "US"} + >>> manufacturers["2"] = {"manufacturer_name": "anotherfactory", "location": "UK"} + >>> + >>> tables = products.get_tablenames('example.sqlite') + >>> print(tables) + ['unnamed', 'product', 'manufacturer'] + >>> + >>> products.close() + >>> manufacturers.close() + +In case you're wondering, the unnamed table comes from the previous examples, +where we did not specify a table name. + +Serialization +------------- + +Keys are strings. Values are any serializeable object. + +By default Pickle is used internally to (de)serialize the values. + +It's possible to use a custom (de)serializer, notably for JSON and for compression. + +.. code-block:: python + + >>> # Use JSON instead of pickle + >>> import json + >>> with SqliteDict("example.sqlite", encode=json.dumps, decode=json.loads) as mydict: + ... pass + >>> + >>> # Apply zlib compression after pickling + >>> import zlib, pickle, sqlite3 + >>> + >>> def my_encode(obj): + ... return sqlite3.Binary(zlib.compress(pickle.dumps(obj, pickle.HIGHEST_PROTOCOL))) + >>> + >>> def my_decode(obj): + ... return pickle.loads(zlib.decompress(bytes(obj))) + >>> + >>> with SqliteDict("example.sqlite", encode=my_encode, decode=my_decode) as mydict: + ... pass + +It's also possible to use a custom (de)serializer for keys to allow non-string keys. + +.. code-block:: python + + >>> # Use key encoding instead of default string keys only + >>> from sqlitedict import encode_key, decode_key + >>> with SqliteDict("example.sqlite", encode_key=encode_key, decode_key=decode_key) as mydict: + ... pass + +More +---- + +Functions are well documented, see docstrings directly in ``sqlitedict.py`` or call ``help(sqlitedict)``. + +**Beware**: because of Python semantics, ``sqlitedict`` cannot know when a mutable +SqliteDict-backed entry was modified in RAM. You'll need to +explicitly assign the mutated object back to SqliteDict: + +.. code-block:: python + + >>> from sqlitedict import SqliteDict + >>> db = SqliteDict("example.sqlite") + >>> db["colors"] = {"red": (255, 0, 0)} + >>> db.commit() + >>> + >>> colors = db["colors"] + >>> colors["blue"] = (0, 0, 255) # sqlite DB not updated here! + >>> db["colors"] = colors # now updated + >>> + >>> db.commit() # remember to commit (or set autocommit) + >>> db.close() + +Features +======== + +* Values can be **any picklable objects** (uses ``pickle`` with the highest protocol). +* Support for **multiple tables** (=dicts) living in the same database file. +* Support for **access from multiple threads** to the same connection (needed by e.g. Pyro). + Vanilla sqlite3 gives you ``ProgrammingError: SQLite objects created in a thread can + only be used in that same thread.`` + + Concurrent requests are still serialized internally, so this "multithreaded support" + **doesn't** give you any performance benefits. It is a work-around for sqlite limitations in Python. + +* Support for **custom serialization or compression**: + +.. code-block:: python + + # use JSON instead of pickle + >>> import json + >>> mydict = SqliteDict('./my_db.sqlite', encode=json.dumps, decode=json.loads) + + # apply zlib compression after pickling + >>> import zlib, pickle, sqlite3 + >>> def my_encode(obj): + ... return sqlite3.Binary(zlib.compress(pickle.dumps(obj, pickle.HIGHEST_PROTOCOL))) + >>> def my_decode(obj): + ... return pickle.loads(zlib.decompress(bytes(obj))) + >>> mydict = SqliteDict('./my_db.sqlite', encode=my_encode, decode=my_decode) + +* sqlite is efficient and can work effectively with large databases (multi gigabytes), not limited by memory. +* sqlitedict is mostly a thin wrapper around sqlite. +* ``items()`` ``keys()`` ``values()`` are iterating one by one, the rows are loaded in a worker thread and queued in memory. +* ``len()`` is calling sqlite to count rows, that is scanning the whole table. +* For better performance, write objects in batch and ``commit()`` once. + +Installation +============ + +The module has no dependencies beyond Python itself. +The minimum supported Python version is 3.7, continuously tested on Python 3.7, 3.8, 3.9, and 3.10 `on Travis `_. + +Install or upgrade with:: + + pip install -U sqlitedict + +or from the `source tar.gz `_:: + + python setup.py install + +Contributions +============= + +Testing +------- + +Install:: + + $ pip install pytest coverage pytest-coverage + +To perform all tests:: + + $ mkdir -p tests/db + $ pytest tests + $ python -m doctest README.rst + +To perform all tests with coverage:: + + $ pytest tests --cov=sqlitedict + +Comments, bug reports +--------------------- + +``sqlitedict`` resides on `github `_. You can file +issues or pull requests there. + +License +======= + +``sqlitedict`` is open source software released under the `Apache 2.0 license `_. +Copyright (c) 2011-now `Radim Řehůřek `_ and contributors. + +Housekeeping +============ + +Clean up the test database to keep each doctest run idempotent: + +.. code-block:: python + + >>> import os + >>> if __name__ == '__main__': + ... os.unlink('example.sqlite') + + diff --git a/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/SOURCES.txt b/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/SOURCES.txt new file mode 100644 index 0000000000000000000000000000000000000000..5031dd45e8b86e4833887cd9608413603e86906b --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/SOURCES.txt @@ -0,0 +1,18 @@ +LICENSE.md +MANIFEST.in +Makefile +README.rst +setup.cfg +setup.py +sqlitedict.py +sqlitedict.egg-info/PKG-INFO +sqlitedict.egg-info/SOURCES.txt +sqlitedict.egg-info/dependency_links.txt +sqlitedict.egg-info/top_level.txt +tests/accessories.py +tests/autocommit.py +tests/test_autocommit.py +tests/test_core.py +tests/test_named_db.py +tests/test_onimport.py +tests/test_temp_db.py \ No newline at end of file diff --git a/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/dependency_links.txt b/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/dependency_links.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/installed-files.txt b/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/installed-files.txt new file mode 100644 index 0000000000000000000000000000000000000000..a5f6b11383c4e35473eda9d76bcd7a995b7e8ce4 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/installed-files.txt @@ -0,0 +1,6 @@ +../__pycache__/sqlitedict.cpython-310.pyc +../sqlitedict.py +PKG-INFO +SOURCES.txt +dependency_links.txt +top_level.txt diff --git a/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/top_level.txt b/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..6bb8973f26eb653aaa4a60f1c20541229ca028e0 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/sqlitedict-2.1.0.egg-info/top_level.txt @@ -0,0 +1 @@ +sqlitedict diff --git a/env-llmeval/lib/python3.10/site-packages/torch/_namedtensor_internals.py b/env-llmeval/lib/python3.10/site-packages/torch/_namedtensor_internals.py new file mode 100644 index 0000000000000000000000000000000000000000..cbc9de2de091d03295a0aca0011036e0a67a97eb --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/torch/_namedtensor_internals.py @@ -0,0 +1,157 @@ +from collections import OrderedDict + +""" +This file contains helper functions that implement experimental functionality +for named tensors in python. All of these are experimental, unstable, and +subject to change or deletion. +""" + + +def check_serializing_named_tensor(tensor): + if tensor.has_names(): + raise RuntimeError( + "NYI: Named tensors don't support serialization. Please drop " + "names via `tensor = tensor.rename(None)` before serialization." + ) + + +def build_dim_map(tensor): + """Returns a map of { dim: dim_name } where dim is a name if the dim is named + and the dim index otherwise.""" + return OrderedDict( + [(idx if name is None else name, name) for idx, name in enumerate(tensor.names)] + ) + + +def unzip_namedshape(namedshape): + if isinstance(namedshape, OrderedDict): + namedshape = namedshape.items() + if not hasattr(namedshape, "__iter__") and not isinstance(namedshape, tuple): + raise RuntimeError( + f"Expected namedshape to be OrderedDict or iterable of tuples, got: {type(namedshape)}" + ) + if len(namedshape) == 0: + raise RuntimeError("Expected namedshape to non-empty.") + return zip(*namedshape) + + +def namer_api_name(inplace): + if inplace: + return "rename_" + else: + return "rename" + + +def is_ellipsis(item): + return item == Ellipsis or item == "..." + + +def single_ellipsis_index(names, fn_name): + ellipsis_indices = [i for i, name in enumerate(names) if is_ellipsis(name)] + if len(ellipsis_indices) >= 2: + raise RuntimeError( + f"{fn_name}: More than one Ellipsis ('...') found in names (" + f"{names}). This function supports up to one Ellipsis." + ) + if len(ellipsis_indices) == 1: + return ellipsis_indices[0] + return None + + +def expand_single_ellipsis(numel_pre_glob, numel_post_glob, names): + return names[numel_pre_glob : len(names) - numel_post_glob] + + +def replace_ellipsis_by_position(ellipsis_idx, names, tensor_names): + globbed_names = expand_single_ellipsis( + ellipsis_idx, len(names) - ellipsis_idx - 1, tensor_names + ) + return names[:ellipsis_idx] + globbed_names + names[ellipsis_idx + 1 :] + + +def resolve_ellipsis(names, tensor_names, fn_name): + """ + Expands ... inside `names` to be equal to a list of names from `tensor_names`. + """ + ellipsis_idx = single_ellipsis_index(names, fn_name) + if ellipsis_idx is None: + return names + return replace_ellipsis_by_position(ellipsis_idx, names, tensor_names) + + +def update_names_with_list(tensor, names, inplace): + # Special case for tensor.rename(None) + if len(names) == 1 and names[0] is None: + return tensor._update_names(None, inplace) + + return tensor._update_names( + resolve_ellipsis(names, tensor.names, namer_api_name(inplace)), inplace + ) + + +def update_names_with_mapping(tensor, rename_map, inplace): + dim_map = build_dim_map(tensor) + for old_dim in rename_map.keys(): + new_dim = rename_map[old_dim] + if old_dim in dim_map.keys(): + dim_map[old_dim] = new_dim + else: + raise RuntimeError( + f"{namer_api_name(inplace)}: Tried to rename dim '{old_dim}' to dim " + f"{new_dim} in Tensor[{tensor.names}] but dim '{old_dim}' does not exist" + ) + return tensor._update_names(tuple(dim_map.values()), inplace) + + +def update_names(tensor, names, rename_map, inplace): + """There are two usages: + + tensor.rename(*names) returns a view on tensor with named dims `names`. + `names` must be of length `tensor.dim()`; otherwise, if '...' is in `names`, + then it is expanded greedily to be equal to the corresponding names from + `tensor.names`. + + For example, + ``` + >>> # xdoctest: +SKIP + >>> x = torch.empty(2, 3, 5, 7, names=('N', 'C', 'H', 'W')) + >>> x.rename('...', 'height', 'width').names + ('N', 'C', 'height', 'width') + + >>> # xdoctest: +SKIP + >>> x.rename('batch', '...', 'width').names + ('batch', 'C', 'H', 'width') + + ``` + + tensor.rename(**rename_map) returns a view on tensor that has rename dims + as specified in the mapping `rename_map`. + + For example, + ``` + >>> # xdoctest: +SKIP + >>> x = torch.empty(2, 3, 5, 7, names=('N', 'C', 'H', 'W')) + >>> x.rename(W='width', H='height').names + ('N', 'C', 'height', 'width') + + ``` + + Finally, tensor.rename has an in-place version called tensor.rename_. + """ + has_names = len(names) > 0 + has_rename_pairs = bool(rename_map) + if has_names and has_rename_pairs: + raise RuntimeError( + f"{namer_api_name(inplace)}: This function takes either positional " + f"args or keyword args, but not both. Use tensor.{namer_api_name(inplace)}(*names) " + f"to name dims and tensor.{namer_api_name(inplace)}(**rename_map) to rename " + "dims." + ) + + # Special case for tensor.rename(*[]), which is valid for a 0 dim tensor. + if not has_names and not has_rename_pairs: + return update_names_with_list(tensor, names, inplace) + + if has_names: + return update_names_with_list(tensor, names, inplace) + return update_names_with_mapping(tensor, rename_map, inplace) diff --git a/env-llmeval/lib/python3.10/site-packages/torch/_python_dispatcher.py b/env-llmeval/lib/python3.10/site-packages/torch/_python_dispatcher.py new file mode 100644 index 0000000000000000000000000000000000000000..bfd208eddb9e8c05938d149127c37df0249d9fd3 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/torch/_python_dispatcher.py @@ -0,0 +1,181 @@ +import re + +import torch._C as C + + +""" +PythonDispatcher class is a thin python-binding to C++ dispatcher and it +is designed to show how dispatcher precompute works. In particular, +it shows for a certain op `foo`, what the computed dispatch table looks +like after user register their kernels to certains dispatch keys. + +In the real C++ dispatcher we support many dispatch keys for different +functionalities. For simplicity PythonDispatcher only supports dispatch +keys for a single example of each use case. These use cases are listed below: + +- CPU/AutogradCPU: represents in-tree backends which we usually have dedicated inference & + autograd kernel in pytorch core library. + E.g. CPU, CUDA +- FPGA/AutogradOther: represents in-tree backends which we usually have backend specific + inference kernels, but they share the same autograd kernel specified in AutogradOther. + E.g. FPGA, SparseCsrCPU +- XLA/AutogradXLA: represents out-of-tree backends which we don't have either inference or autograd + kernel defined in pytorch core library. Backend owner is responsible for registering both + inference & autograd kernels in their extensions(e.g. torch-xla) for the operators they support. + E.g. XLA, XPU, MPS +- CompositeExplicitAutograd: alias key mapped to inference kernels of all backends like CPU, CUDA, XLA etc. + Kernels registered to this key MUST work for inference for all backends. +- Autograd: alias key mapped to autograd of all backends like AutogradCPU, AutogradXLA, AutogradOther. + Kernels registered to this key MUST work for autograd for all backends. +- CompositeImplicitAutograd: alias key CompositeImplicitAutograd = CompositeExplicitAutograd + Autograd + Kernels registered to this key MUST work for both inference + autograd for all backends. + +Note we only allow registrations to alias keys inside pytorch core library. E.g +you shouldn't register a CompositeImplicitAutograd or CompositeExplicitAutograd +kernel from torch-xla extension, instead you should upstream the kernel into +pytorch/pytorch repo so that it's available for all backends and continuously +tested even without the extension. + +Usage: + dispatcher = PythonDispatcher() + dispatcher.register(["CPU", "XLA", "CompositeImplicitAutograd"]) + print(dispatcher.dispatchTable()) # This tells you exactly which kernel is used for certain backend. + # For more debugging information + # print(dispatcher.keys()) + # print(dispatcher.registrations()) + # print(dispatcher.rawRegistrations()) + # print(dispatcher.rawDispatchTable()) +PythonDispatcher calls C++ dispatcher under the hood for to precompute dispatch table. +This file only provides the simplified API for developers, relevant test code is located in +test/test_dispatch.py +""" + + +class PythonDispatcher: + namespace = "__test__" + name = "foo" + # fmt: off + runtime_keys = [ + "CPU", "AutogradCPU", + "FPGA", "AutogradOther", + "XLA", "AutogradXLA", + "Lazy", "AutogradLazy", + ] + # fmt: on + alias_keys = [ + "CompositeExplicitAutograd", + "Autograd", + "CompositeImplicitAutograd", + ] + supported_keys = runtime_keys + alias_keys + + def __init__(self): + C._dispatch_check_invariants(self.name) # type: ignore[attr-defined] + self.ref = C._dispatch_library("FRAGMENT", self.namespace, "") + self.ref.def_("foo(Tensor x) -> Tensor") + + """ + Returns a list of dispatch keys supported by PythonDispatcher. + You can register kernels to these keys. + """ + + def keys(self): + return self.supported_keys + + """ + Register kernels to the target dispatchKeys. + dispatchKeys(list[str]): a list of dispatch keys that you want to register + your own kernel. Note that you don't need to write the kernel yourself in + this PythonDispatcher.E.g. for CPU key, a kernel(e.g fn_CPU for CPU) is + automatically generated and registered. + """ + + def register(self, dispatchKeys): + # Overriden is not supported and triggers a warning in C++ dispatcher. + if len(set(dispatchKeys)) != len(dispatchKeys): + raise RuntimeError( + f"Overriden is not allowed but found duplicates in {dispatchKeys}." + ) + # We currently forbid this in codegen instead of C++ dispatcher. + if ( + "CompositeImplicitAutograd" in dispatchKeys + and "CompositeExplicitAutograd" in dispatchKeys + ): + raise RuntimeError( + "Registration to both CompositeImplicitAutograd and CompositeExplicitAutograd is not allowed." + ) + for key in dispatchKeys: + if key not in self.supported_keys: + raise RuntimeError( + f"{key} is not supported, please select a dispatch key in {self.supported_keys}." + ) + self.ref.impl_t_t("foo", dispatch=key, debug="fn_" + key) + + """ + Helper function to format (key, kernel). + """ + + def _format_line(self, key, kernel): + return f"{key:<15} {kernel}\n" + + """ + Helper function to print a table header. + """ + + def _format_header(self, header): + s = f""" +{header} +""" + s += self._format_line("key", "kernel") + s += "---------------------------\n" + return s + + """ + Returns raw output of all registration info for debugging only. + Use registrations() for a simplified version. + """ + + def rawRegistrations(self): + return C._dispatch_dump(f"{self.namespace}::{self.name}") # type: ignore[attr-defined] + + """ + Returns raw output of computed dispatch table for debugging only. + Use dispatchTable() for a simplified version. + """ + + def rawDispatchTable(self): + return C._dispatch_dump_table(f"{self.namespace}::{self.name}") # type: ignore[attr-defined] + + """ + Returns a table(str) including all the registrations from users. + Note this includes registrations to both runtime keys and alias keys. + """ + + def registrations(self): + output = self._format_header("Registered Kernels") + state = self.rawRegistrations() + state_entries = state.split("\n") + for line in state_entries: + first = line.split(":")[0] + if any(first.startswith(k) for k in self.supported_keys): + kernel = line.split("::")[0].split(" ")[1] + output += self._format_line(first, kernel) + return output + + """ + Returns the computed dispatch table(str). Note this only include + runtime keys, registrations to alias keys have been decoded to their + mapped runtime keys. + """ + + def dispatchTable(self): + output = self._format_header("Computed Dispatch Table") + table = self.rawDispatchTable() + table_entries = table.split("\n") + regex = re.compile(r"registered at .*FallbackKernel\.cpp.*(\[)") + for line in table_entries: + k = line.split(":")[0] + if k in self.runtime_keys: + entry = regex.sub("[", line) + output += self._format_line(k, entry.split(": ")[1]) + return output diff --git a/env-llmeval/lib/python3.10/site-packages/torch/_utils_internal.py b/env-llmeval/lib/python3.10/site-packages/torch/_utils_internal.py new file mode 100644 index 0000000000000000000000000000000000000000..20c017c05b802a17494690b95cf835daa2f2d042 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/torch/_utils_internal.py @@ -0,0 +1,96 @@ +import logging +import os +import sys +import tempfile +from typing import Any, Dict + +import torch + +log = logging.getLogger(__name__) + + +# this arbitrary-looking assortment of functionality is provided here +# to have a central place for overrideable behavior. The motivating +# use is the FB build environment, where this source file is replaced +# by an equivalent. + +if torch._running_with_deploy(): + # __file__ is meaningless in the context of frozen torch used in torch deploy. + # setting empty torch_parent should allow below functions to operate without crashing, + # but it's unclear if there is a valid use case for them in the context of deploy. + torch_parent = "" +else: + if os.path.basename(os.path.dirname(__file__)) == "shared": + torch_parent = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + else: + torch_parent = os.path.dirname(os.path.dirname(__file__)) + + +def get_file_path(*path_components: str) -> str: + return os.path.join(torch_parent, *path_components) + + +def get_file_path_2(*path_components: str) -> str: + return os.path.join(*path_components) + + +def get_writable_path(path: str) -> str: + if os.access(path, os.W_OK): + return path + return tempfile.mkdtemp(suffix=os.path.basename(path)) + + +def prepare_multiprocessing_environment(path: str) -> None: + pass + + +def resolve_library_path(path: str) -> str: + return os.path.realpath(path) + + +def throw_abstract_impl_not_imported_error(opname, module, context): + if module in sys.modules: + raise NotImplementedError( + f"{opname}: We could not find the abstract impl for this operator. " + ) + else: + raise NotImplementedError( + f"{opname}: We could not find the abstract impl for this operator. " + f"The operator specified that you may need to import the '{module}' " + f"Python module to load the abstract impl. {context}" + ) + + +# Meta only, see +# https://www.internalfb.com/intern/wiki/ML_Workflow_Observability/User_Guides/Adding_instrumentation_to_your_code/ +# +# This will cause an event to get logged to Scuba via the signposts API. You +# can view samples on the API at https://fburl.com/scuba/workflow_signpost/zh9wmpqs +# we log to subsystem "torch", and the category and name you provide here. +# Each of the arguments translate into a Scuba column. We're still figuring +# out local conventions in PyTorch, but category should be something like +# "dynamo" or "inductor", and name should be a specific string describing what +# kind of event happened. +# +# Killswitch is at +# https://www.internalfb.com/intern/justknobs/?name=pytorch%2Fsignpost#event +def signpost_event(category: str, name: str, parameters: Dict[str, Any]): + log.info("%s %s: %r", category, name, parameters) + + +def log_compilation_event(metrics): + log.info("%s", metrics) + + +def print_graph(graph, msg: str): + pass + + +TEST_MASTER_ADDR = "127.0.0.1" +TEST_MASTER_PORT = 29500 +# USE_GLOBAL_DEPS controls whether __init__.py tries to load +# libtorch_global_deps, see Note [Global dependencies] +USE_GLOBAL_DEPS = True +# USE_RTLD_GLOBAL_WITH_LIBTORCH controls whether __init__.py tries to load +# _C.so with RTLD_GLOBAL during the call to dlopen. +USE_RTLD_GLOBAL_WITH_LIBTORCH = False diff --git a/env-llmeval/lib/python3.10/site-packages/torch/overrides.py b/env-llmeval/lib/python3.10/site-packages/torch/overrides.py new file mode 100644 index 0000000000000000000000000000000000000000..3084bf0668258ab625793db82a126c9d4573e36c --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/torch/overrides.py @@ -0,0 +1,1958 @@ +""" +Python implementation of ``__torch_function__`` + +While most of the torch API and handling for ``__torch_function__`` happens +at the C++ level, some of the torch API is written in Python so we need +python-level handling for ``__torch_function__`` overrides as well. The main +developer-facing functionality in this file are handle_torch_function and +has_torch_function. See torch/functional.py and test/test_overrides.py +for usage examples. + +Note +---- +heavily inspired by NumPy's ``__array_function__`` (see: +https://github.com/pytorch/pytorch/issues/24015 and +https://www.numpy.org/neps/nep-0018-array-function-protocol.html +) + +If changing this file in a way that can affect ``__torch_function__`` overhead, +please report the benchmarks in ``benchmarks/overrides_benchmark``. See the +instructions in the ``README.md`` in that directory. +""" + +import __future__ # noqa: F404 + +import collections +import functools +import types +import warnings +from typing import Dict, Set, List, Any, Callable, Iterable, Type, Tuple +from functools import wraps +import contextlib + +import torch +from torch._C import ( + _has_torch_function, _has_torch_function_unary, + _has_torch_function_variadic, _add_docstr, + _push_on_torch_function_stack, _pop_torch_function_stack, _get_function_stack_at, _len_torch_function_stack, + _is_torch_function_mode_enabled) + +__all__ = [ + "get_ignored_functions", + "get_overridable_functions", + "get_testing_overrides", + "handle_torch_function", + "has_torch_function", + "resolve_name", + "is_tensor_like", + "is_tensor_method_or_property", + "wrap_torch_function", + "enable_reentrant_dispatch", +] + + +def _disable_user_warnings( + func: Callable, regex: str = '.*is deprecated, please use.*', module: str = 'torch') -> Callable: + """ + Decorator that temporarily disables ``UserWarning``s for the given ``module`` if the warning message matches the + given ``regex`` pattern. + + Arguments + --------- + func : function + Function to disable the warnings for. + regex : str + A regex pattern compilable by ``re.compile``. This is used to match the ``UserWarning`` message. + module : str + The python module to which the filtering should be restricted. + + Returns + ------- + function + The wrapped function. + """ + + @wraps(func) + def wrapper(*args, **kwargs): + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=UserWarning, message=regex, module=module) + return func(*args, **kwargs) + return wrapper + + +@functools.lru_cache(None) +@_disable_user_warnings +def get_ignored_functions() -> Set[Callable]: + """ + Return public functions that cannot be overridden by ``__torch_function__``. + + Returns + ------- + Set[Callable] + A tuple of functions that are publicly available in the torch API but cannot + be overridden with ``__torch_function__``. Mostly this is because none of the + arguments of these functions are tensors or tensor-likes. + + Examples + -------- + >>> torch.Tensor.as_subclass in torch.overrides.get_ignored_functions() + True + >>> torch.add in torch.overrides.get_ignored_functions() + False + """ + Tensor = torch.Tensor + return { + torch.typename, + torch.is_tensor, + torch.is_storage, + torch.set_default_tensor_type, + torch.set_default_device, + torch.set_rng_state, + torch.get_rng_state, + torch.manual_seed, + torch.initial_seed, + torch.seed, + torch.save, + torch.load, + torch.set_printoptions, + torch.fork, + torch.get_default_dtype, + torch.get_num_interop_threads, + torch.get_num_threads, + torch.init_num_threads, + torch.import_ir_module, + torch.import_ir_module_from_buffer, + torch.is_anomaly_enabled, + torch.is_anomaly_check_nan_enabled, + torch.is_grad_enabled, + torch.merge_type_from_type_comment, + torch.parse_ir, + torch.parse_schema, + torch.parse_type_comment, + torch.set_anomaly_enabled, + torch.set_flush_denormal, + torch.set_num_interop_threads, + torch.set_num_threads, + torch.wait, + torch.as_tensor, + torch.from_numpy, + torch.get_device, + torch.tensor, + torch.default_generator, + torch.has_cuda, + torch.has_cudnn, + torch.has_lapack, + torch.device, + torch.dtype, + torch.finfo, + torch.has_mkl, + torch.has_mps, + torch.has_mkldnn, + torch.has_openmp, + torch.iinfo, + torch.memory_format, + torch.qscheme, + torch.set_grad_enabled, + torch.no_grad, + torch.enable_grad, + torch.inference_mode, + torch.is_inference_mode_enabled, + torch.layout, + torch.align_tensors, + torch.arange, + torch.as_strided, + torch.bartlett_window, + torch.blackman_window, + torch.broadcast_shapes, + torch.can_cast, + torch.compile, + torch.cudnn_affine_grid_generator, + torch.cudnn_batch_norm, + torch.cudnn_convolution, + torch.cudnn_convolution_transpose, + torch.cudnn_convolution_relu, + torch.cudnn_convolution_add_relu, + torch.cudnn_grid_sampler, + torch.cudnn_is_acceptable, + torch.empty, + torch.empty_permuted, + torch.empty_strided, + torch.empty_quantized, + torch.export.dynamic_dim, + torch.export.export, + torch.export.load, + torch.export.register_dataclass, + torch.export.save, + torch.eye, + torch.fft.fftfreq, + torch.fft.rfftfreq, + torch.from_file, + torch.full, + torch.fill, + torch.hamming_window, + torch.hann_window, + torch.kaiser_window, + torch.linspace, + torch.logspace, + torch.mkldnn_adaptive_avg_pool2d, + torch.mkldnn_convolution, + torch.mkldnn_max_pool2d, + torch.mkldnn_max_pool3d, + torch.mkldnn_linear_backward_weights, + torch.mkldnn_rnn_layer, + torch.normal, + torch.ones, + torch.promote_types, + torch.rand, + torch.randn, + torch.randint, + torch.randperm, + torch.range, + torch.result_type, + torch.scalar_tensor, + torch.sparse_coo_tensor, + torch.sparse_compressed_tensor, + torch.sparse_csr_tensor, + torch.sparse_csc_tensor, + torch.sparse_bsr_tensor, + torch.sparse_bsc_tensor, + torch.sym_constrain_range, + torch.sym_constrain_range_for_size, + torch.tril_indices, + torch.triu_indices, + torch.vander, + torch.zeros, + torch._jit_internal.boolean_dispatch, + torch.nn.functional.assert_int_or_pair, + torch.nn.functional.upsample, + torch.nn.functional.upsample_bilinear, + torch.nn.functional.upsample_nearest, + torch.nn.functional.has_torch_function, + torch.nn.functional.has_torch_function_unary, + torch.nn.functional.has_torch_function_variadic, + torch.nn.functional.handle_torch_function, + torch.nn.functional.sigmoid, + torch.nn.functional.hardsigmoid, + torch.nn.functional.tanh, + torch.nn.functional._canonical_mask, + torch.nn.functional._none_or_dtype, + # Doesn't actually take or return tensor arguments + torch.nn.init.calculate_gain, + # These are deprecated; don't test them + torch.nn.init.uniform, + torch.nn.init.normal, + torch.nn.init.constant, + torch.nn.init.eye, + torch.nn.init.dirac, + torch.nn.init.xavier_uniform, + torch.nn.init.xavier_normal, + torch.nn.init.kaiming_uniform, + torch.nn.init.kaiming_normal, + torch.nn.init.orthogonal, + torch.nn.init.sparse, + torch.nested.to_padded_tensor, + has_torch_function, + handle_torch_function, + torch.set_autocast_enabled, + torch.is_autocast_enabled, + torch.clear_autocast_cache, + torch.set_autocast_cpu_enabled, + torch.is_autocast_cpu_enabled, + torch.set_autocast_xla_enabled, + torch.is_autocast_xla_enabled, + torch.set_autocast_ipu_enabled, + torch.is_autocast_ipu_enabled, + torch.set_autocast_cpu_dtype, + torch.get_autocast_cpu_dtype, + torch.set_autocast_ipu_dtype, + torch.get_autocast_ipu_dtype, + torch.get_autocast_gpu_dtype, + torch.set_autocast_gpu_dtype, + torch.get_autocast_xla_dtype, + torch.set_autocast_xla_dtype, + torch.autocast_increment_nesting, + torch.autocast_decrement_nesting, + torch.is_autocast_cache_enabled, + torch.set_autocast_cache_enabled, + torch.nn.functional.hardswish, + torch.is_vulkan_available, + torch.are_deterministic_algorithms_enabled, + torch.use_deterministic_algorithms, + torch.is_deterministic_algorithms_warn_only_enabled, + torch.set_deterministic_debug_mode, + torch.get_deterministic_debug_mode, + torch.set_float32_matmul_precision, + torch.get_float32_matmul_precision, + torch.unify_type_list, + torch.is_warn_always_enabled, + torch.set_warn_always, + torch.vitals_enabled, + torch.set_vital, + torch.read_vitals, + torch.vmap, + torch.cond, + torch.frombuffer, + torch.asarray, + torch._functional_sym_constrain_range, + torch._make_dep_token, + Tensor.__delitem__, + Tensor.__dir__, + Tensor.__getattribute__, + Tensor.__init__, + Tensor.__iter__, + Tensor.__init_subclass__, + Tensor.__delattr__, + Tensor.__setattr__, + Tensor.__torch_function__, + Tensor.__torch_dispatch__, + Tensor.__new__, + Tensor.__class__, + Tensor.__subclasshook__, + Tensor.__hash__, + Tensor.as_subclass, + Tensor.eig, + Tensor.lstsq, + Tensor.reinforce, + Tensor.new, + Tensor.new_tensor, + Tensor.new_empty, + Tensor.new_empty_strided, + Tensor.new_zeros, + Tensor.new_ones, + Tensor.new_full, + Tensor._make_subclass, + Tensor.solve, + Tensor.symeig, + Tensor.stride, + Tensor.unflatten, + Tensor.to_sparse_coo, + Tensor.to_sparse_csr, + Tensor.to_sparse_csc, + Tensor.to_sparse_bsr, + Tensor.to_sparse_bsc, + Tensor._to_sparse, + Tensor._to_sparse_csr, + Tensor._to_sparse_csc, + Tensor._to_sparse_bsr, + Tensor._to_sparse_bsc, + Tensor._typed_storage, + Tensor._reduce_ex_internal, + Tensor._fix_weakref, + Tensor._view_func, + Tensor._view_func_unsafe, + Tensor._make_wrapper_subclass, + Tensor._python_dispatch.__get__, + Tensor._has_symbolic_sizes_strides.__get__, + Tensor._conj, + Tensor._conj_physical, + Tensor._neg_view, + Tensor._is_zerotensor, + Tensor._is_all_true, + Tensor._is_any_true, + Tensor._addmm_activation, + Tensor.to_padded_tensor, + } + + +@functools.lru_cache(None) +def get_default_nowrap_functions() -> Set[Callable]: + """ + Return public functions that do not wrap in a subclass when invoked by + the default ``Tensor.__torch_function__`` that preserves subclasses. Typically, + these functions represent field accesses (i.e., retrieving a Tensor that + is stored somewhere on the Tensor) as opposed to computation. Users of + these functions expect object identity to be preserved over multiple accesses + (e.g., ``a.grad is a.grad``) which cannot be upheld if we're wrapping on + the fly every time (furthermore, the tensor stored here might already be + the subclass, in which case wrapping really ought not to happen). + + Not ALL property accessors have this property; for example ``Tensor.T`` actually + just creates a new transposed tensor on the fly, and so we SHOULD interpose on + these calls (you need to check the implementation of the function to see if + this is the case or not). Additionally, if a property accessor doesn't return a Tensor, + it doesn't have to be on this list (though it is harmless if it is). + """ + Tensor = torch.Tensor + return { + Tensor._base.__get__, + Tensor.grad.__get__, + Tensor._grad.__get__, + } + + +@functools.lru_cache(None) +@_disable_user_warnings +def get_testing_overrides() -> Dict[Callable, Callable]: + """Return a dict containing dummy overrides for all overridable functions + + Returns + ------- + Dict[Callable, Callable] + A dictionary that maps overridable functions in the PyTorch API to + lambda functions that have the same signature as the real function + and unconditionally return -1. These lambda functions are useful + for testing API coverage for a type that defines ``__torch_function__``. + + Examples + -------- + >>> import inspect + >>> my_add = torch.overrides.get_testing_overrides()[torch.add] + >>> inspect.signature(my_add) + + """ + # Every function in the PyTorchAPI that can be overriden needs an entry + # in this dict. + # + # Optimally we would use inspect to get the function signature and define + # the lambda function procedurally but that is blocked by generating + # function signatures for native kernels that can be consumed by inspect. + # See Issue #28233. + Tensor = torch.Tensor + ret: Dict[Callable, Callable] = { + torch.abs: lambda input, out=None: -1, + torch.absolute: lambda input, out=None: -1, + torch.adaptive_avg_pool1d: lambda input, output_size: -1, + torch.adaptive_max_pool1d: lambda inputs, output_size: -1, + torch.acos: lambda input, out=None: -1, + torch.adjoint: lambda input: -1, + torch.arccos: lambda input, out=None: -1, + torch.acosh: lambda input, out=None: -1, + torch.arccosh: lambda input, out=None: -1, + torch.add: lambda input, other, out=None: -1, + torch.addbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1, + torch.addcdiv: lambda input, tensor1, tensor2, value=1, out=None: -1, + torch.addcmul: lambda input, tensor1, tensor2, value=1, out=None: -1, + torch.addmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1, + torch.addmv: lambda input, mat, vec, beta=1, alpha=1, out=None: -1, + torch.addr: lambda input, vec1, vec2, beta=1, alpha=1, out=None: -1, + torch.affine_grid_generator: lambda theta, size, align_corners: -1, + torch.all: lambda input, dim=None: -1, + torch.allclose: lambda input, other, trol=1e-05, atol=1e-08, equal_nan=False: -1, + torch.alpha_dropout: lambda input, p, train, inplace=False: -1, + torch.amax: lambda input, dim=None: -1, + torch.amin: lambda input, dim=None: -1, + torch.aminmax: lambda input, dim=None, keepdim=False, out=None: -1, + torch.angle: lambda input, out=None: -1, + torch.any: lambda input, dim=None, keepdim=False, out=None: -1, + torch.argmax: lambda input: -1, + torch.argmin: lambda input: -1, + torch.argsort: lambda input, dim=None: -1, + torch.asin: lambda input, out=None: -1, + torch._assert_async: lambda input, msg: -1, + torch.arcsin: lambda input, out=None: -1, + torch.asinh: lambda input, out=None: -1, + torch.arcsinh: lambda input, out=None: -1, + torch.atan: lambda input, out=None: -1, + torch.arctan: lambda input, out=None: -1, + torch.atan2: lambda input, other, out=None: -1, + torch.arctan2: lambda input, other, out=None: -1, + torch.atanh: lambda input, out=None: -1, + torch.arctanh: lambda input, out=None: -1, + torch.atleast_1d: lambda *tensors: -1, + torch.atleast_2d: lambda *tensors: -1, + torch.atleast_3d: lambda *tensors: -1, + torch.avg_pool1d: lambda input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True: -1, + torch.baddbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1, + torch.batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled: -1, + torch.batch_norm_backward_elemt: lambda grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count_tensor: -1, + torch.batch_norm_backward_reduce: lambda grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g: -1, + torch.batch_norm_elemt: lambda input, weight, bias, mean, invstd, eps: -1, + torch.batch_norm_gather_stats: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1, + torch.batch_norm_gather_stats_with_counts: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1, + torch.batch_norm_stats: lambda input, eps: -1, + torch.batch_norm_update_stats: lambda input, running_mean, running_var, momentum: -1, + torch.bernoulli: lambda input, generator=None, out=None: -1, + torch.bilinear: lambda input1, input2, weight, bias: -1, + torch.binary_cross_entropy_with_logits: (lambda input, target, weight=None, size_average=None, reduce=None, + reduction='mean', pos_weight=None: -1), + torch.bincount: lambda input, weights=None, minlength=0: -1, + torch.binomial: lambda count, prob, generator=None: -1, + torch.bitwise_and: lambda input, other, out=None: -1, + torch.bitwise_not: lambda input, out=None: -1, + torch.bitwise_or: lambda input, other, out=None: -1, + torch.bitwise_xor: lambda input, other, out=None: -1, + torch.bitwise_left_shift: lambda input, other, out=None: -1, + torch.bitwise_right_shift: lambda input, other, out=None: -1, + torch.block_diag: lambda *tensors: -1, + torch.bmm: lambda input, mat2, out=None: -1, + torch.broadcast_tensors: lambda *tensors: -1, + torch.broadcast_to: lambda self, size: -1, + torch.bucketize: lambda input, boundaries, out_int32=False, right=False, out=None: -1, + torch.cartesian_prod: lambda *tensors: -1, + torch.cat: lambda tensors, dim=0, out=None: -1, + torch.concat: lambda tensors, dim=0, out=None: -1, # alias for torch.cat + torch.concatenate: lambda tensors, dim=0, out=None: -1, # alias for torch.concatenate + torch.cdist: lambda x1, x2, p=2.0, compute_mode='use_mm_for_euclid_dist_if_necessary': -1, + torch.ceil: lambda input, out=None: -1, + torch.celu: lambda input, alpha=1., inplace=False: -1, + torch.chain_matmul: lambda *matrices, out=None: -1, + torch.channel_shuffle: lambda input, groups : -1, + torch.cholesky: lambda input, upper=False, out=None: -1, + torch.linalg.cholesky: lambda input, out=None: -1, + torch.linalg.cholesky_ex: lambda input, check_errors=False, out=None: -1, + torch.cholesky_inverse: lambda input, upper=False, out=None: -1, + torch.cholesky_solve: lambda input1, input2, upper=False, out=None: -1, + torch.choose_qparams_optimized: lambda input, numel, n_bins, ratio, bit_width: -1, + torch.chunk: lambda input, chunks, dim=0: -1, + torch.clamp: lambda input, min=None, max=None, out=None: -1, + torch.clip: lambda input, min=None, max=None, out=None: -1, + torch.clamp_min: lambda input, min, out=None: -1, + torch.clamp_max: lambda input, max, out=None: -1, + torch.column_stack: lambda tensors, out=None: -1, + torch.cov: lambda input, correction=1, fweights=None, aweights=None: -1, + torch.clone: lambda input: -1, + torch.combinations: lambda input, r=2, with_replacement=False: -1, + torch.complex: lambda real, imag: -1, + torch.copysign: lambda input, other, out=None: -1, + torch.polar: lambda abs, ang: -1, + torch.linalg.cond: lambda input, ord=None: -1, + torch.conj: lambda input, out=None: -1, + torch.conj_physical: lambda input, out=None: -1, + torch.resolve_conj: lambda input, out=None: -1, + torch.resolve_neg: lambda input, out=None: -1, + torch.constant_pad_nd: lambda input, pad, value=0: -1, + torch.conv1d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1, + torch.conv2d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1, + torch.conv3d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1, + torch.convolution: lambda input, weight, bias, stride, padding, dilation, transposed, output_adding, groups: -1, + torch.conv_tbc: lambda input, weight, bias, pad=0: -1, + torch.conv_transpose1d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1, + torch.conv_transpose2d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1, + torch.conv_transpose3d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1, + torch.corrcoef: lambda input: -1, + torch.cos: lambda input, out=None: -1, + torch.cosine_embedding_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean': -1, + torch.cosh: lambda input, out=None: -1, + torch.cosine_similarity: lambda x1, x2, dim=1, eps=1e-8: -1, + torch.count_nonzero: lambda input: -1, + torch.cross: lambda input, other, dim=None, out=None: -1, + torch.linalg.cross: lambda input, other, dim=-1, out=None: -1, + torch.ctc_loss: (lambda log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean', + zero_infinity=False: -1), + torch.cummax: lambda input, dim, out=None: -1, + torch.cummin: lambda input, dim, out=None: -1, + torch.cumprod: lambda input, dim, out=None, dtype=None: -1, + torch.cumsum: lambda input, dim, out=None, dtype=None: -1, + torch.cumulative_trapezoid: lambda y, x=None, dim=-1: -1, + torch.logcumsumexp: lambda input, dim, out=None: -1, + torch.deg2rad: lambda input, out=None: -1, + torch.dequantize: lambda input: -1, + torch.det: lambda input: -1, + torch.linalg.det: lambda input: -1, # alias for torch.det # type: ignore[attr-defined] + torch.detach: lambda input: -1, + torch.diag: lambda input, diagonal=0, out=None: -1, + torch.diag_embed: lambda input, diagonal=0, out=None: -1, + torch.diagflat: lambda input, offset=0: -1, + torch.diff: lambda input, n=1, dim=-1, prepend=None, append=None, out=None: -1, + torch.diagonal: lambda input, offset=0, dim1=0, dim2=1: -1, + torch.linalg.diagonal: lambda input, offset=0, dim1=-2, dim2=-1: -1, + torch.diagonal_scatter: lambda input, src, offset=0, dim1=0, dim2=1: -1, + torch.as_strided_scatter: lambda self, src, size, stride, storage_offset=None: -1, + torch.digamma: lambda input, out=None: -1, + torch.dist: lambda input, other, p=2: -1, + torch.div: lambda input, other, rounding_mode=None, out=None: -1, + torch.divide: lambda input, other, rounding_mode=None, out=None: -1, + torch.dot: lambda input, other, out=None: -1, + torch.dropout: lambda input, p, train, inplace=False: -1, + torch.dsmm: lambda input, mat2: -1, + torch.hsmm: lambda mat1, mat2: -1, + torch.dsplit: lambda input, indices_or_sections: -1, + torch.dstack: lambda tensors, out=None: -1, + torch.linalg.eig: lambda input, out=None: -1, + torch.linalg.eigvals: lambda input, out=None: -1, + torch.linalg.eigh: lambda input, UPLO="L", out=None: -1, + torch.linalg.eigvalsh: lambda input, UPLO="L", out=None: -1, + torch.einsum: lambda equation, *operands: -1, + torch.embedding: (lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, + sparse=False: -1), + torch.embedding_bag: (lambda input, weight, offsets, max_norm=None, norm_type=2, scale_grad_by_freq=False, + mode='mean', sparse=False, per_sample_weights=None, padding_idx=None: -1), + torch.empty_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1, + torch.eq: lambda input, other, out=None: -1, + torch.equal: lambda input, other: -1, + torch.erf: lambda input, out=None: -1, + torch.erfc: lambda input, out=None: -1, + torch.erfinv: lambda input, out=None: -1, + torch.exp: lambda input, out=None: -1, + torch.exp2: lambda input, out=None: -1, + torch.expm1: lambda input, out=None: -1, + torch.fake_quantize_per_channel_affine: lambda input, scale, zero_point, axis, quant_min, quant_max: -1, + torch.fake_quantize_per_tensor_affine: lambda input, scale, zero_point, quant_min, quant_max: -1, + torch.fused_moving_avg_obs_fake_quant: (lambda x, observer_on, fake_quant_on, averaging_const, running_min, + running_max, scale, zero_point, quant_min, quant_max, ch_axis, + per_row_fake_quant=False, symmetric_quant=False: -1), + torch.fbgemm_linear_fp16_weight: lambda input, packed_weight, bias: -1, + torch.fbgemm_linear_fp16_weight_fp32_activation: lambda input, packed_weight, bias: -1, + torch.fbgemm_linear_int8_weight: lambda input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias: -1, + torch.fbgemm_linear_int8_weight_fp32_activation: (lambda input, weight, packed, col_offsets, weight_scale, + weight_zero_point, bias: -1), + torch.fbgemm_linear_quantize_weight: lambda input: -1, + torch.fbgemm_pack_gemm_matrix_fp16: lambda input: -1, + torch.fbgemm_pack_quantized_matrix: lambda input, a, b: -1, + torch.feature_alpha_dropout: lambda input, p, train: -1, + torch.feature_dropout: lambda input, p, train: -1, + torch.fft.ifft: lambda input, n=None, dim=-1, norm=None: -1, + torch.fft.rfft: lambda input, n=None, dim=-1, norm=None: -1, + torch.fft.irfft: lambda input, n=None, dim=-1, norm=None: -1, + torch.fft.hfft: lambda input, n=None, dim=-1, norm=None: -1, + torch.fft.ihfft: lambda input, n=None, dim=-1, norm=None: -1, + torch.fft.hfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1, + torch.fft.ihfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1, + torch.fft.hfftn: lambda input, s=None, dim=-1, norm=None: -1, + torch.fft.ihfftn: lambda input, s=None, dim=-1, norm=None: -1, + torch.fft.fftn: lambda input, s=None, dim=None, norm=None: -1, + torch.fft.ifftn: lambda input, s=None, dim=None, norm=None: -1, + torch.fft.rfftn: lambda input, s=None, dim=None, norm=None: -1, + torch.fft.irfftn: lambda input, s=None, dim=None, norm=None: -1, + torch.fft.fft2: lambda input, s=None, dim=(-2, -1), norm=None: -1, + torch.fft.ifft2: lambda input, s=None, dim=(-2, -1), norm=None: -1, + torch.fft.rfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1, + torch.fft.irfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1, + torch.fft.fftshift: lambda input, dim=None: -1, + torch.fft.ifftshift: lambda input, dim=None: -1, + torch.fft.fft: lambda input, n=None, dim=-1, norm=None: -1, + torch.fix: lambda input, out=None: -1, + torch.flatten: lambda input, start_dim=0, end_dim=-1: -1, + torch.flip: lambda input, dims: -1, + torch.fliplr: lambda input: -1, + torch.flipud: lambda input: -1, + torch.frobenius_norm: lambda input, dim=None, keepdim=False, out=None: -1, + torch.floor: lambda input, out=None: -1, + torch.floor_divide: lambda input, other: -1, + torch.float_power: lambda input, exponent, out=None: -1, + torch.fmod: lambda input, other, out=None: -1, + torch.frac: lambda input, out=None: -1, + torch.frexp: lambda input, out=None: -1, + torch.full_like: lambda input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1, + torch._functional_assert_async: lambda input, msg, dep_token: -1, + torch.lu_unpack: lambda LU_data, LU_pivots, unpack_data=True, unpack_pivots=True: -1, + torch.gather: lambda input, dim, index, out=None, sparse_grad=False: -1, + torch.gcd: lambda input, other, out=None: -1, + torch.ge: lambda input, other, out=None: -1, + torch.greater_equal: lambda input, other, out=None: -1, + torch.geqrf: lambda input, out=None: -1, + torch.i0: lambda input, out=None: -1, + torch.inner: lambda input, other, out=None: -1, + torch.outer: lambda input, vec2, out=None: -1, + torch.ger: lambda input, vec2, out=None: -1, # alias for torch.outer + torch.gradient: lambda input, spacing=None, dim=None, edge_order=1: -1, + torch.grid_sampler: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1, + torch.grid_sampler_2d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1, + torch.grid_sampler_3d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1, + torch.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05, cudnn_enabled=True: -1, + torch.gru: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1, + torch.gru_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1, + torch.gt: lambda input, other, out=None: -1, + torch.greater: lambda input, other, out=None: -1, + torch.hardshrink: lambda input, lambd=0.5: -1, + torch.heaviside: lambda input, values, out=None: -1, + torch.hinge_embedding_loss: lambda input, target, margin=1.0, size_average=None, reduce=None, reduction='mean': -1, + torch.histc: lambda input, bins=100, min=0, max=0, out=None: -1, + torch.histogram: lambda input, bins=100, min=None, max=None, weight=None, density=False, out=None: -1, + torch.histogramdd: lambda input, bins, range=None, weight=None, density=False: -1, + torch.linalg.householder_product: lambda input, tau: -1, + torch.hspmm: lambda mat1, mat2, out=None: -1, + torch.hsplit: lambda input, indices_or_sections: -1, + torch.hstack: lambda tensors, out=None: -1, + torch.hypot: lambda input, other, out=None: -1, + torch.igamma: lambda input, other, out=None: -1, + torch.igammac: lambda input, other, out=None: -1, + torch.imag: lambda input, out=None: -1, + torch.index_add: lambda input, dim, index, source: -1, + torch.index_copy: lambda input, dim, index, source: -1, + torch.index_put: lambda input, indices, values, accumulate=False: -1, + torch.index_select: lambda input, dim, index, out=None: -1, + torch.index_fill: lambda input, dim, index, value: -1, + torch.index_reduce: lambda input, dim, index, source, reduce, include_input=True: -1, + torch.isfinite: lambda tensor: -1, + torch.isin: lambda e, te, assume_unique=False, invert=False: -1, + torch.isinf: lambda tensor: -1, + torch.isreal: lambda tensor: -1, + torch.isposinf: lambda input, out=None: -1, + torch.isneginf: lambda input, out=None: -1, + torch.instance_norm: (lambda input, running_mean, running_var, weight, bias, use_input_stats, momentum, eps, + cudnn_enabled: -1), + torch.int_repr: lambda input: -1, + torch.inverse: lambda input, out=None: -1, + torch.linalg.inv: lambda input, out=None: -1, + torch.linalg.inv_ex: lambda input, check_errors=False, out=None: -1, + torch.is_complex: lambda input: -1, + torch.is_conj: lambda input: -1, + torch.is_neg: lambda input: -1, + torch.is_distributed: lambda input: -1, + torch.is_inference: lambda input: -1, + torch.is_floating_point: lambda input: -1, + torch.is_nonzero: lambda input: -1, + torch.is_same_size: lambda input, other: -1, + torch.is_signed: lambda input: -1, + torch.isclose: lambda input, other, rtol=1e-05, atol=1e-08, equal_nan=False: -1, + torch.isnan: lambda input: -1, + torch.istft: (lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True, + normalized=False, onesided=None, length=None, return_complex=False: -1), + torch.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1, + torch.kron: lambda input, other: -1, + torch.kthvalue: lambda input, k, dim=None, keepdim=False, out=None: -1, + torch.linalg.ldl_factor_ex: lambda input, hermitian=False, check_errors=False, out=None: -1, + torch.linalg.ldl_factor: lambda input, hermitian=False, out=None: -1, + torch.linalg.ldl_solve: lambda LD, pivots, B, hermitian=False, out=None: -1, + torch.layer_norm: lambda input, normalized_shape, weight=None, bias=None, esp=1e-05, cudnn_enabled=True: -1, + torch.lcm: lambda input, other, out=None: -1, + torch.ldexp: lambda input, other, out=None: -1, + torch.le: lambda input, other, out=None: -1, + torch.less_equal: lambda input, other, out=None: -1, + torch.lerp: lambda input, end, weight, out=None: -1, + torch.lgamma: lambda input, out=None: -1, + torch.lobpcg: lambda input, k=None, B=None, X=None, n=None, iK=None, niter=None, tol=None, largest=None, method=None, + tracker=None, ortho_iparams=None, ortho_fparams=None, ortho_bparams=None: -1, + torch.log: lambda input, out=None: -1, + torch.log_softmax: lambda input, dim, dtype=None: -1, + torch.log10: lambda input, out=None: -1, + torch.log1p: lambda input, out=None: -1, + torch.log2: lambda input, out=None: -1, + torch.logaddexp: lambda input, other, out=None: -1, + torch.logaddexp2: lambda input, other, out=None: -1, + torch.logdet: lambda input: -1, + torch.xlogy: lambda x, y, out=None: -1, + torch.logical_and: lambda input, other, out=None: -1, + torch.logical_not: lambda input, out=None: -1, + torch.logical_or: lambda input, other, out=None: -1, + torch.logical_xor: lambda input, other, out=None: -1, + torch.logit: lambda input, eps=None: -1, + torch.logsumexp: lambda input, names, keepdim=False, out=None: -1, + torch.lstm: lambda data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional: -1, + torch.lstm_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1, + torch.lt: lambda input, other, out=None: -1, + torch.less: lambda input, other, out=None: -1, + torch.lu: lambda A, pivot=True, get_infos=False, out=None: -1, + torch.lu_solve: lambda b, LU_data, LU_pivots, out=None: -1, + torch.margin_ranking_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean': -1, # type: ignore[attr-defined] # noqa: B950 + torch.masked_fill: lambda input, mask, value: -1, + torch.masked_scatter: lambda input, mask, source: -1, + torch.masked_select: lambda input, mask, out=None: -1, + torch.matmul: lambda input, other, out=None: -1, + torch.linalg.lu: lambda input, pivot=True, out=None: -1, + torch.linalg.lu_factor: lambda input, pivot=True, out=None: -1, + torch.linalg.lu_factor_ex: lambda input, pivot=True, check_errors=False, out=None: -1, + torch.linalg.lu_solve: lambda LU, pivots, B, left=True, adjoint=False, out=None: -1, + torch.linalg.matmul: lambda input, other, out=None: -1, # alias for torch.matmul + torch.matrix_power: lambda input, n: -1, + torch.linalg.matrix_power: lambda input, n, out=None: -1, + torch.linalg.matrix_rank: lambda input, tol=None, hermitian=False: -1, + torch.linalg.multi_dot: lambda tensors, out=None: -1, + torch.matrix_exp: lambda input: -1, + torch.linalg.matrix_exp: lambda input: -1, + torch.max: lambda input, out=None: -1, + torch.maximum: lambda input, other, out=None: -1, + torch.fmax: lambda input, other, out=None: -1, + torch.max_pool1d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1, + torch.max_pool2d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1, + torch.max_pool3d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1, + torch.max_pool1d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1, + return_indices=False, ceil_mode=False: -1), + torch.mean: lambda input, dim=None: -1, + torch.nanmean: lambda input, dim=None, keepdim=False, dtype=None, out=None: -1, + torch.median: lambda input, dim=None: -1, + torch.nanmedian: lambda input, dim=None: -1, + torch.meshgrid: lambda *tensors, **kwargs: -1, + torch.min: lambda input, out=None: -1, + torch.minimum: lambda input, other, out=None: -1, + torch.fmin: lambda input, other, out=None: -1, + torch.miopen_batch_norm: (lambda input, weight, bias, running_mean, running_var, training, + exponential_average_factor, epsilon: -1), + torch.miopen_convolution: lambda input, weight, bias, padding, stride, dilation, groups, benchmark, deterministic: -1, + torch.miopen_convolution_add_relu: lambda input, weight, z, alpha, bias, stride, padding, dilation, groups: -1, + torch.miopen_convolution_relu: lambda input, weight, bias, stride, padding, dilation, groups: -1, + torch.miopen_convolution_transpose: (lambda input, weight, bias, padding, output_padding, stride, dilation, + groups, benchmark, deterministic: -1), + torch.miopen_depthwise_convolution: (lambda input, weight, bias, padding, stride, dilation, groups, benchmark, + deterministic: -1), + torch.miopen_rnn: (lambda input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, + dropout, train, bidirectional, batch_sizes, dropout_state: -1), + torch.mm: lambda input, mat2, out=None: -1, + torch.mode: lambda input, dim=-1, keepdim=False, out=None: -1, + torch.movedim: lambda input, source, destination: -1, + torch.moveaxis: lambda input, source, destination: -1, + torch.msort: lambda input, descending=False, out=None: -1, + torch.mul: lambda input, other, out=None: -1, + torch.multiply: lambda input, other, out=None: -1, + torch.multinomial: lambda input, num_samples, replacement=False, out=None: -1, + torch.mv: lambda input, vec, out=None: -1, + torch.mvlgamma: lambda input, p: -1, + torch.narrow: lambda input, dim, start, length: -1, + torch.nan_to_num: lambda input, nan=0.0, posinf=None, neginf=None, out=None: -1, + torch.native_batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps: -1, + torch._native_batch_norm_legit: lambda input, weight, bias, training, momentum, eps: -1, + torch.native_dropout: lambda input, p, train: -1, + torch.native_layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1, + torch.native_group_norm: lambda input, weight, bias, N, C, HxW, group, eps: -1, + torch.native_norm: lambda input, p=2, dim=None, keepdim=False, dtype=None: -1, + torch.native_channel_shuffle: lambda input, groups : -1, + torch.ne: lambda input, other, out=None: -1, + torch.not_equal: lambda input, other, out=None: -1, + torch.neg: lambda input, out=None: -1, + torch.negative: lambda input, out=None: -1, + torch.nextafter: lambda input, other, out=None: -1, + torch.nn.functional.adaptive_avg_pool2d: lambda input, output_size: -1, + torch.nn.functional.adaptive_avg_pool3d: lambda input, output_size: -1, + torch.nn.functional.adaptive_max_pool1d: lambda input, output_size, return_indices=False: -1, + torch.nn.functional.adaptive_max_pool1d_with_indices: lambda input, output_size, return_indices=False: -1, + torch.nn.functional.adaptive_max_pool2d: lambda input, output_size, return_indices=False: -1, + torch.nn.functional.adaptive_max_pool2d_with_indices: lambda input, output_size, return_indices=False: -1, + torch.nn.functional.adaptive_max_pool3d: lambda input, output_size, return_indices=False: -1, + torch.nn.functional.adaptive_max_pool3d_with_indices: lambda input, output_size, return_indices=False: -1, + torch.nn.functional.affine_grid: lambda theta, size, align_corners=None: -1, + torch.nn.functional.alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1, + torch.nn.functional.avg_pool2d: (lambda input, kernel_size, stride=None, padding=0, ceil_mode=False, + count_include_pad=True, divisor_override=None: -1), + torch.nn.functional.avg_pool3d: (lambda input, kernel_size, stride=None, padding=0, ceil_mode=False, + count_include_pad=True, divisor_override=None: -1), + torch.nn.functional.batch_norm: (lambda input, running_mean, running_var, weight=None, bias=None, training=False, + momentum=0.1, eps=1e-05: -1), + torch.nn.functional.bilinear: lambda input1, input2, weight, bias=None: -1, + torch.nn.functional.binary_cross_entropy: (lambda input, target, weight=None, size_average=None, reduce=None, + reduction="mean": -1), + torch.nn.functional.binary_cross_entropy_with_logits: (lambda input, target, weight=None, size_average=None, + reduce=None, reduction="mean", pos_weight=None: -1), + torch.nn.functional.celu: lambda input, alpha=1.0, inplace=False: -1, + torch.nn.functional.cosine_embedding_loss: (lambda input1, input2, target, margin=0, size_average=None, + reduce=None, reduction='mean': -1), + torch.nn.functional.cross_entropy: (lambda input, target, weight=None, size_average=None, ignore_index=-100, + reduce=None, reduction="mean", label_smoothing=0.0: -1), + torch.nn.functional.ctc_loss: (lambda log_probs, targets, input_lengths, target_lengths, blank=0, + reduction='mean', zero_infinity=False: -1), + torch.nn.functional.dropout: lambda input, p=0.5, training=True, inplace=False: -1, + torch.nn.functional.dropout1d: lambda input, p=0.5, training=True, inplace=False: -1, + torch.nn.functional.dropout2d: lambda input, p=0.5, training=True, inplace=False: -1, + torch.nn.functional.dropout3d: lambda input, p=0.5, training=True, inplace=False: -1, + torch.nn.functional.elu: lambda input, alpha=1.0, inplace=False: -1, + torch.nn.functional.embedding: (lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0, + scale_grad_by_freq=False, sparse=False: -1), + torch.nn.functional.embedding_bag: (lambda input, weight, offsets=None, max_norm=None, norm_type=2, + scale_grad_by_freq=False, mode='mean', sparse=False, per_sample_weights=None, + include_last_offset=False, padding_idx=None: -1), + torch.nn.functional.feature_alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1, + torch.nn.functional.fold: lambda input, output_size, kernel_size, dilation=1, padding=0, stride=1: -1, + torch.nn.functional.fractional_max_pool2d: (lambda input, kernel_size, output_size=None, output_ratio=None, + return_indices=False, _random_samples=None: -1), + torch.nn.functional.fractional_max_pool2d_with_indices: ( + lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False, + _random_samples=None: -1), + torch.nn.functional.fractional_max_pool3d: (lambda input, kernel_size, output_size=None, output_ratio=None, + return_indices=False, _random_samples=None: -1), + torch.nn.functional.fractional_max_pool3d_with_indices: ( + lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False, + _random_samples=None: -1), + torch.nn.functional.gaussian_nll_loss: lambda input, target, var, full=False, eps=1e-06, reduction='mean': -1, + torch.nn.functional.gelu: lambda input, approximate='none': -1, + torch.nn.functional.glu: lambda input, dim=-1: -1, + torch.nn.functional.grid_sample: lambda input, grid, mode='bilinear', padding_mode='zeros', align_corners=None: -1, + torch.nn.functional.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05: -1, + torch.nn.functional.gumbel_softmax: lambda logits, tau=1, hard=False, eps=1e-10, dim=-1: -1, + torch.nn.functional.hardshrink: lambda input, lambd=0.5: -1, + torch.nn.functional.hardtanh: lambda input, min_val=-1., max_val=1., inplace=False: -1, + torch.nn.functional.hinge_embedding_loss: (lambda input, target, margin=1.0, size_average=None, reduce=None, + reduction='mean': -1), + torch.nn.functional.instance_norm: (lambda input, running_mean=None, running_var=None, weight=None, bias=None, + use_input_stats=True, momentum=0.1, eps=1e-05: -1), + torch.nn.functional.interpolate: (lambda input, size=None, scale_factor=None, mode='nearest', align_corners=None, + recompute_scale_factor=None, antialias=False: -1), + torch.nn.functional.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1, + torch.nn.functional.l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1, + torch.nn.functional.layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1, + torch.nn.functional.leaky_relu: lambda input, negative_slope=0.01, inplace=False: -1, + torch.nn.functional.linear: lambda input, weight, bias=None: -1, + torch.nn.functional.local_response_norm: lambda input, size, alpha=0.0001, beta=0.75, k=1.0: -1, + torch.nn.functional.log_softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1, + torch.nn.functional.logsigmoid: lambda input: -1, + torch.nn.functional.lp_pool1d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1, + torch.nn.functional.lp_pool2d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1, + torch.nn.functional.margin_ranking_loss: (lambda input1, input2, target, margin=0, size_average=None, + reduce=None, reduction='mean': -1), + torch.nn.functional.max_pool1d: (lambda input, kernel_size, stride=None, padding=0, dilation=1, + ceil_mode=False, return_indices=False: -1), + torch.nn.functional.max_pool1d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1, + return_indices=False, ceil_mode=False: -1), + torch.nn.functional.max_pool2d: (lambda input, kernel_size, stride=None, padding=0, dilation=1, + ceil_mode=False, return_indices=False: -1), + torch.nn.functional.max_pool2d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1, + return_indices=False, ceil_mode=False: -1), + torch.nn.functional.max_pool3d: (lambda input, kernel_size, stride=None, padding=0, dilation=1, + return_indices=False, ceil_mode=False: -1), + torch.nn.functional.max_pool3d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1, + return_indices=False, ceil_mode=False: -1), + torch.nn.functional.max_unpool1d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1, + torch.nn.functional.max_unpool2d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1, + torch.nn.functional.max_unpool3d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1, + torch.nn.functional.mse_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1, + torch.nn.functional.multi_head_attention_forward: ( + lambda query, key, value, embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias, bias_k, bias_v, + add_zero_attn, dropout_p, out_proj_weight, out_proj_bias, training=True, key_padding_mask=None, + need_weights=True, attn_mask=None, use_separate_proj_weight=False, q_proj_weight=None, k_proj_weight=None, + v_proj_weight=None, static_k=None, static_v=None, average_attn_weights=None, is_causal=False: -1), + torch.nn.functional.multi_margin_loss: (lambda input, target, p=1, margin=1.0, weight=None, size_average=None, + reduce=None, reduction='mean': -1), + torch.nn.functional.multilabel_margin_loss: (lambda input, target, size_average=None, reduce=None, + reduction='mean': -1), + torch.nn.functional.multilabel_soft_margin_loss: (lambda input, target, weight=None, size_average=None, + reduce=None, reduction='mean': -1), + torch.nn.functional.nll_loss: (lambda input, target, weight=None, size_average=None, ignore_index=-100, + reduce=None, reduction='mean': -1), + torch.nn.functional.normalize: lambda input, p=2, dim=1, eps=1e-12, out=None: -1, + torch.nn.functional.one_hot: lambda tensor, num_classes=-1: -1, + torch.nn.functional.pad: lambda input, pad, mode='constant', value=0: -1, + torch.nn.functional.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1, + torch.nn.functional.poisson_nll_loss: (lambda input, target, log_input=True, full=False, size_average=None, + eps=1e-08, reduce=None, reduction='mean': -1), + torch.nn.functional.prelu: lambda input, weight: -1, + torch.nn.functional.relu: lambda input, inplace=False: -1, + torch.nn.functional.relu6: lambda input, inplace=False: -1, + torch.nn.functional.rrelu: lambda input, lower=0.125, upper=0.3333333333333333, training=False, inplace=False: -1, + torch.nn.functional.selu: lambda input, inplace=False: -1, + torch.nn.functional.silu: lambda input, inplace=False: -1, + torch.nn.functional.mish: lambda input, inplace=False: -1, + torch.nn.functional.scaled_dot_product_attention: lambda query, key, value, attn_mask=None, dropout_p=0.0: -1, + torch.nn.functional.smooth_l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean', beta=1.: -1, + torch.nn.functional.huber_loss: lambda input, target, reduction='mean', delta=1.: -1, + torch.nn.functional.soft_margin_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1, + torch.nn.functional.softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1, + torch.nn.functional.softmin: lambda input, dim=None, _stacklevel=3, dtype=None: -1, + torch.nn.functional.softplus: lambda input, beta=1, threshold=20: -1, + torch.nn.functional.softshrink: lambda input, lambd=0.5: -1, + torch.nn.functional.softsign: lambda input: -1, + torch.nn.functional.tanhshrink: lambda input: -1, + torch.nn.functional.threshold: lambda input, threshold, value, inplace=False: -1, + torch.nn.functional.triplet_margin_loss: (lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06, + swap=False, size_average=None, reduce=None, reduction='mean': -1), + torch.nn.functional.triplet_margin_with_distance_loss: (lambda anchor, positive, negative, *, + distance_function=None, margin=1.0, + swap=False, reduction='mean': -1), + torch.nn.functional.unfold: lambda input, kernel_size, dilation=1, padding=0, stride=1: -1, + torch.nn.init.uniform_: lambda tensor, a=0., b=1., generator=None: -1, + torch.nn.init.normal_: lambda tensor, mean=0., std=1., generator=None: -1, + torch.nn.init.constant_: lambda tensor, val: -1, + torch.nn.init.kaiming_uniform_: lambda tensor, a=0, mode='fan_in', nonlinearity='leaky_relu', generator=None: -1, + torch.nonzero: lambda input, as_tuple=False: -1, + torch.nonzero_static: lambda input, *, size, fill_value=-1: -1, + torch.argwhere: lambda input: -1, + torch.norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1, + torch.linalg.norm: lambda input, ord=None, dim=None, keepdim=False, out=None, dtype=None: -1, + torch.linalg.vector_norm: lambda input, ord=2, dim=None, keepdim=False, out=None, dtype=None: -1, + torch.linalg.matrix_norm: lambda input, ord='fro', dim=(-2, -1), keepdim=False, out=None, dtype=None: -1, + torch.norm_except_dim: lambda v, pow=2, dim=0: -1, + torch.nuclear_norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1, + torch.numel: lambda input: -1, + torch.orgqr: lambda input, tau: -1, + torch.ormqr: lambda input, input2, input3, left=True, transpose=False: -1, + torch.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1, + torch.permute: lambda self, dim: -1, + torch.pca_lowrank: lambda input, q=None, center=True, niter=2: -1, + torch.pdist: lambda input, p=2: -1, + torch.pinverse: lambda input, rcond=1e-15: -1, + torch.linalg.pinv: lambda input, rcond=1e-15, hermitian=False: -1, + torch.pixel_shuffle: lambda input, upscale_factor: -1, + torch.pixel_unshuffle: lambda input, downscale_factor: -1, + torch.poisson: lambda input, generator=None: -1, + torch.poisson_nll_loss: lambda input, target, log_input, full, eps, reduction: -1, + torch.polygamma: lambda input, n, out=None: -1, + torch.positive: lambda input, out=None: -1, + torch.prelu: lambda input, weight: -1, + torch.ones_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1, + torch.pow: lambda input, exponent, out=None: -1, + torch.prod: lambda input, dtype=None: -1, + torch.put: lambda input, index, source, accumulate=False: -1, + torch.q_per_channel_axis: lambda input: -1, + torch.q_per_channel_scales: lambda input: -1, + torch.q_per_channel_zero_points: lambda input: -1, + torch.q_scale: lambda input: -1, + torch.q_zero_point: lambda input: -1, + torch.qr: lambda input, some=True, out=None: -1, + torch.linalg.qr: lambda input, mode='reduced', out=None: -1, + torch.quantile: lambda input, q, dim=None, keepdim=False, interpolation='linear', out=None: -1, + torch.nanquantile: lambda input, q, dim=None, keepdim=False, interpolation='linear', out=None: -1, + torch.quantize_per_channel: lambda input, scales, zero_points, axis, dtype: -1, + torch.quantize_per_tensor: lambda input, scale, zero_point, dtype: -1, + torch.quantize_per_tensor_dynamic: lambda input, dtype, reduce_range: -1, + torch.quantized_batch_norm: lambda input, weight, bias, mean, var, eps, output_scale, output_zero_point: -1, + torch.quantized_gru_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, + col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1), + + torch.quantized_lstm_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, + col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1), + torch.quantized_max_pool1d: (lambda input, kernel_size, stride=tuple(), padding=(0,), + dilation=(1,), ceil_mode=False: -1), + torch.quantized_max_pool2d: (lambda input, kernel_size, stride=tuple(), padding=(0, 0), + dilation=(1, 1), ceil_mode=False: -1), + torch.quantized_max_pool3d: (lambda input, kernel_size, stride=tuple(), padding=(0, 0, 0), + dilation=(1, 1, 1), ceil_mode=False: -1), + torch.quantized_rnn_relu_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, + col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1), + torch.quantized_rnn_tanh_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, + col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1), + torch.rad2deg: lambda input, out=None: -1, + torch.rand_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1, + torch.randint_like: lambda input, high, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1, + torch.randn_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1, + torch.ravel: lambda input: -1, + torch.real: lambda input, out=None: -1, + torch.vdot: lambda input, other, out=None: -1, + torch.linalg.vecdot: lambda input, other, dim=-1, out=None: -1, + torch.view_as_real: lambda input: -1, + torch.view_as_complex: lambda input: -1, + torch.reciprocal: lambda input, out=None: -1, + torch.relu: lambda input, inplace=False: -1, + torch.remainder: lambda input, other, out=None: -1, + torch.renorm: lambda input, p, dim, maxnorm, out=None: -1, + torch.repeat_interleave: lambda input, dim=None: -1, + torch.reshape: lambda input, shape: -1, + torch.rnn_relu: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1, + torch.rnn_relu_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1, + torch.rnn_tanh: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1, + torch.rnn_tanh_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1, + torch.roll: lambda input, shifts, dims=None: -1, + torch.rot90: lambda input, k=1, dims=(0, 1): -1, + torch.round: lambda input, out=None: -1, + torch.row_stack: lambda tensors, out=None: -1, # alias for torch.vstack + torch._rowwise_prune: (lambda weight, mask, compressed_indices_dtype: -1), + torch.rrelu: lambda input, lower=1. / 8, upper=1. / 3, training=False, inplace=False: -1, + torch.rsqrt: lambda input, out=None: -1, + torch.rsub: lambda input, other, alpha=1: -1, + torch.saddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1, + torch.scatter: lambda input, dim, index, src: -1, + torch.scatter_add: lambda input, dim, index, src: -1, + torch.scatter_reduce: lambda input, dim, index, src, reduce, include_self=True: -1, + torch.searchsorted: lambda sorted_sequence, input, out_int32=False, right=False, out=None: -1, + torch._segment_reduce: lambda data, reduce="max", lengths=None, indices=None, offsets=None, axis=0, unsafe=False: -1, + torch.select: lambda input, dim, index: -1, + torch.select_scatter: lambda input, src, dim, index: -1, + torch.slice_scatter: lambda input, src, dim=0, start=None, end=None, step=1: -1, + torch.selu: lambda input, inplace=False: -1, + torch.sigmoid: lambda input, out=None: -1, + torch.sign: lambda input, out=None: -1, + torch.signbit: lambda input, out=None: -1, + torch.sgn: lambda input, out=None: -1, + torch.sin: lambda input, out=None: -1, + torch.sinc: lambda input, out=None: -1, + torch.sinh: lambda input, out=None: -1, + torch.slogdet: lambda input: -1, + torch.linalg.slogdet: lambda input: -1, + torch.smm: lambda input, mat2: -1, + torch.spmm: lambda input, mat2: -1, + torch.softmax: lambda input, dim, dtype=None: -1, + torch.linalg.solve: lambda A, B, left=True, out=None: -1, + torch.linalg.solve_ex: lambda A, B, left=True, check_errors=False, out=None: -1, + torch.sort: lambda input, dim=-1, descending=False, *, stable=False, out=None: -1, + torch.split: lambda tensor, split_size_or_sections, dim=0: -1, + torch.split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1, + torch.sqrt: lambda input, out=None: -1, + torch.square: lambda input, out=None: -1, + torch.squeeze: lambda input, dim=None, out=None: -1, + torch.sspaddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1, + torch.stack: lambda tensors, dim=0, out=None: -1, + torch.std: lambda input, dim=None: -1, + torch.std_mean: lambda input, dim=None: -1, + torch.stft: (lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True, + pad_mode='reflect', normalized=False, onesided=True, return_complex=None: -1), + torch.sub: lambda input, other, out=None: -1, + torch.subtract: lambda input, other, out=None: -1, + torch.sum: lambda input, dim=None: -1, + torch.sym_float: lambda input: -1, + torch.sym_int: lambda input: -1, + torch.sym_max: lambda a, b: -1, + torch.sym_min: lambda a, b: -1, + torch.sym_not: lambda input: -1, + torch.sym_ite: lambda a, b, c: -1, + torch.sym_sqrt: lambda input: -1, + torch.nansum: lambda input, dim=None: -1, + torch.svd: lambda input, some=True, compute_uv=True, out=None: -1, + torch.svd_lowrank: lambda input, q=6, niter=2, M=None: -1, + torch.linalg.svd: lambda input, full_matrices=True, out=None: -1, + torch.linalg.svdvals: lambda input, out=None: -1, + torch.swapaxes: lambda input, dim0, dim1: -1, + torch.swapdims: lambda input, axis0, axis1: -1, + torch.special.airy_ai: lambda input: -1, + torch.special.bessel_j0: lambda input: -1, + torch.special.bessel_j1: lambda input: -1, + torch.special.bessel_y0: lambda input: -1, + torch.special.bessel_y1: lambda input: -1, + torch.special.chebyshev_polynomial_t: lambda input, n, out=None: -1, + torch.special.chebyshev_polynomial_u: lambda input, n, out=None: -1, + torch.special.chebyshev_polynomial_v: lambda input, n, out=None: -1, + torch.special.chebyshev_polynomial_w: lambda input, n, out=None: -1, + torch.special.digamma: lambda input: -1, + torch.special.entr: lambda input: -1, + torch.special.erf: lambda input: -1, + torch.special.erfc: lambda input: -1, + torch.special.erfcx: lambda input: -1, + torch.special.erfinv: lambda input: -1, + torch.special.exp2: lambda input: -1, + torch.special.expit: lambda input: -1, + torch.special.expm1: lambda input: -1, + torch.special.gammainc: lambda input, other, out=None: -1, + torch.special.gammaincc: lambda input, other, out=None: -1, + torch.special.gammaln: lambda input: -1, + torch.special.hermite_polynomial_h: lambda input, n, out=None: -1, + torch.special.hermite_polynomial_he: lambda input, n, out=None: -1, + torch.special.i0: lambda input: -1, + torch.special.i0e: lambda input: -1, + torch.special.i1: lambda input: -1, + torch.special.i1e: lambda input: -1, + torch.special.laguerre_polynomial_l: lambda input, n, out=None: -1, + torch.special.legendre_polynomial_p: lambda input, n, out=None: -1, + torch.special.log1p: lambda input: -1, + torch.special.log_ndtr: lambda input: -1, + torch.special.log_softmax: lambda input, dim, dtype=None: -1, + torch.special.logit: lambda input: -1, + torch.special.logsumexp: lambda input, dim, keepdim=False, out=None: -1, + torch.special.modified_bessel_i0: lambda input: -1, + torch.special.modified_bessel_i1: lambda input: -1, + torch.special.modified_bessel_k0: lambda input: -1, + torch.special.modified_bessel_k1: lambda input: -1, + torch.special.multigammaln: lambda input, p: -1, + torch.special.ndtr: lambda input: -1, + torch.special.ndtri: lambda input: -1, + torch.special.polygamma: lambda input, n, out=None: -1, + torch.special.psi: lambda input: -1, + torch.special.round: lambda input: -1, + torch.special.scaled_modified_bessel_k0: lambda input: -1, + torch.special.scaled_modified_bessel_k1: lambda input: -1, + torch.special.shifted_chebyshev_polynomial_t: lambda input, n, out=None: -1, + torch.special.shifted_chebyshev_polynomial_u: lambda input, n, out=None: -1, + torch.special.shifted_chebyshev_polynomial_v: lambda input, n, out=None: -1, + torch.special.shifted_chebyshev_polynomial_w: lambda input, n, out=None: -1, + torch.special.sinc: lambda input: -1, + torch.special.softmax: lambda input, dim, dtype=None: -1, + torch.special.spherical_bessel_j0: lambda input: -1, + torch.special.xlog1py: lambda input, other, out=None: -1, + torch.special.xlogy: lambda input, other, out=None: -1, + torch.special.zeta: lambda self, other, out=None: -1, + torch.t: lambda input: -1, + torch.take: lambda input, index: -1, + torch.take_along_dim: lambda input, indices, dim=None, out=None: -1, + torch.tan: lambda input, out=None: -1, + torch.tanh: lambda input, out=None: -1, + torch.linalg.tensorinv: lambda a, ind=2: -1, + torch.linalg.tensorsolve: lambda a, b, dims=None: -1, + torch.tensordot: lambda a, b, dims=2, out=None: -1, + torch.tensor_split: lambda input, indices_or_sections, dim=0: -1, + torch.threshold: lambda input, threshold, value, inplace=False: -1, + torch.tile: lambda input, dims: -1, + torch.topk: lambda input, k, dim=-1, descending=False, out=None: -1, + torch.trace: lambda input: -1, + torch.transpose: lambda input, dim0, dim1: -1, + torch.trapz: lambda y, x=None, dim=-1: -1, + torch.trapezoid: lambda y, x=None, dim=-1: -1, + torch.triangular_solve: lambda input, A, upper=True, transpose=False, unitriangular=False: -1, + torch.linalg.solve_triangular: lambda input, B, upper, left=True, unitriangular=False: -1, + torch.tril: lambda input, diagonal=0, out=None: -1, + torch.triplet_margin_loss: (lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06, swap=False, + + size_average=None, reduce=None, reduction='mean': -1), + torch.triu: lambda input, diagonal=0, out=None: -1, + torch.true_divide: lambda input, other: -1, + torch.trunc: lambda input, out=None: -1, + torch.unbind: lambda input, dim=0: -1, + torch.unflatten: lambda input, dim, sizes, names: -1, + torch.unique: lambda input, sorted=True, return_inverse=False, return_counts=False, dim=None: -1, + torch.unique_consecutive: lambda input, return_inverse=False, return_counts=False, dim=None: -1, + torch.unravel_index: lambda indices, shape: -1, + torch.unsafe_chunk: lambda input, chunks, dim=0: -1, + torch.unsafe_split: lambda tensor, split_size_or_sections, dim=0: -1, + torch.unsafe_split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1, + torch.unsqueeze: lambda input, dim, out=None: -1, + torch.linalg.vander: lambda x, N=None: -1, + torch.var: lambda input, dim=None: -1, + torch.var_mean: lambda input, dim=None: -1, + torch.vsplit: lambda input, indices_or_sections: -1, + torch.vstack: lambda tensors, out=None: -1, + torch.where: lambda condition, x=None, y=None: -1, + torch.zeros_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1, + torch._fw_primal_copy: lambda self, level: -1, + torch._make_dual_copy: lambda primal, tangent, level: -1, + torch.view_as_real_copy: lambda self: -1, + torch.view_as_complex_copy: lambda self: -1, + torch._conj_copy: lambda self: -1, + torch._neg_view_copy: lambda self: -1, + torch.as_strided_copy: lambda self, size, stride, storage_offset=None: -1, + torch._sparse_broadcast_to_copy: lambda self, size: -1, + torch.diagonal_copy: lambda self, offset=0, dim1=0, dim2=1: -1, + torch.expand_copy: lambda self, size, *, implicit=False: -1, + torch.narrow_copy: lambda self, dim, start, length: -1, + torch.permute_copy: lambda self, dims: -1, + torch._reshape_alias_copy: lambda self, size, stride: -1, + torch.select_copy: lambda self, dim, index: -1, + torch.detach_copy: lambda self: -1, + torch.slice_copy: lambda self, dim=0, start=None, end=None, step=1: -1, + torch.split_copy: lambda self, split_size, dim=0: -1, + torch.split_with_sizes_copy: lambda self, split_sizes, dim=0: -1, + torch.squeeze_copy: lambda self, dim: -1, + torch.t_copy: lambda self: -1, + torch.transpose_copy: lambda self, dim0, dim1: -1, + torch.unsqueeze_copy: lambda self, dim: -1, + torch._indices_copy: lambda self: -1, + torch._values_copy: lambda self: -1, + torch.indices_copy: lambda self: -1, + torch.values_copy: lambda self: -1, + torch.crow_indices_copy: lambda self: -1, + torch.col_indices_copy: lambda self: -1, + torch.ccol_indices_copy: lambda self: -1, + torch.row_indices_copy: lambda self: -1, + torch.unbind_copy: lambda self, dim=0: -1, + torch.view_copy: lambda self, dtype: -1, + torch.unfold_copy: lambda self, dimension, size, step: -1, + torch.alias_copy: lambda self: -1, + Tensor.__floordiv__: lambda self, other: -1, + Tensor.__rfloordiv__: lambda self, other: -1, + Tensor.__ifloordiv__: lambda self, other: -1, + Tensor.__truediv__: lambda self, other: -1, + Tensor.__rtruediv__: lambda self, other: -1, + Tensor.__itruediv__: lambda self, other: -1, + Tensor.__lshift__: lambda self, other: -1, + Tensor.__rlshift__: lambda self, other: -1, + Tensor.__ilshift__: lambda self, other: -1, + Tensor.__rshift__: lambda self, other: -1, + Tensor.__rrshift__: lambda self, other: -1, + Tensor.__irshift__: lambda self, other: -1, + Tensor.__and__: lambda self, other: -1, + Tensor.__or__: lambda self, other: -1, + Tensor.__xor__: lambda self, other: -1, + Tensor.__float__: lambda self: -1, + Tensor.__complex__: lambda self: -1, + Tensor.__array__: lambda self, dtype: -1, + Tensor.__bool__: lambda self: -1, + Tensor.__contains__: lambda self, other: -1, + Tensor.__neg__: lambda self: -1, + Tensor.__invert__: lambda self: -1, + Tensor.__mod__: lambda self, other: -1, + Tensor.__rmod__: lambda self, other: -1, + Tensor.__imod__: lambda self, other: -1, + Tensor.__array_wrap__: lambda self, array: -1, + Tensor.__getitem__: lambda self, idx: -1, + Tensor.__deepcopy__: lambda self, memo: -1, + Tensor.__int__: lambda self: -1, + Tensor.__long__: lambda self: -1, + Tensor.__index__: lambda self: -1, + Tensor.__len__: lambda self: -1, + Tensor.__format__: lambda self, format_spec: -1, + Tensor.__reduce_ex__: lambda self, proto: -1, + Tensor.__reversed__: lambda self: -1, + Tensor.__repr__: lambda self, *, tensor_contents=None: -1, + Tensor.__setitem__: lambda self, k, v: -1, + Tensor.__setstate__: lambda self, d: -1, + Tensor.T.__get__: lambda self: -1, + Tensor.H.__get__: lambda self: -1, + Tensor.mT.__get__: lambda self: -1, + Tensor.mH.__get__: lambda self: -1, + Tensor._backward_hooks.__get__: lambda self: -1, + Tensor._post_accumulate_grad_hooks.__get__: lambda self: -1, + Tensor._base.__get__: lambda self: -1, + Tensor._cdata.__get__: lambda self: -1, + Tensor.grad.__get__: lambda self: -1, + Tensor._grad.__get__: lambda self: -1, + Tensor._grad_fn.__get__: lambda self: -1, + Tensor.grad_fn.__get__: lambda self: -1, + Tensor._version.__get__: lambda self: -1, + Tensor._autocast_to_reduced_precision: lambda self, cuda_enabled, cpu_enabled, cuda_dtype, cpu_dtype: -1, + Tensor._autocast_to_full_precision: lambda self, cuda_enabled, cpu_enabled: -1, + Tensor.data.__get__: lambda self: -1, + Tensor.device.__get__: lambda self: -1, + Tensor.dtype.__get__: lambda self: -1, + Tensor.is_cuda.__get__: lambda self: -1, + Tensor.is_cpu.__get__: lambda self: -1, + Tensor.is_xla.__get__: lambda self: -1, + Tensor.is_xpu.__get__: lambda self: -1, + Tensor.is_ipu.__get__: lambda self: -1, + Tensor.is_leaf.__get__: lambda self: -1, + Tensor.retains_grad.__get__: lambda self: -1, + Tensor.is_meta.__get__: lambda self: -1, + Tensor.is_mps.__get__: lambda self: -1, + Tensor.is_mtia.__get__: lambda self: -1, + Tensor.is_nested.__get__: lambda self: -1, + Tensor.is_ort.__get__: lambda self: -1, + Tensor.is_mkldnn.__get__: lambda self: -1, + Tensor.is_quantized.__get__: lambda self: -1, + Tensor.is_sparse.__get__: lambda self: -1, + Tensor.is_sparse_csr.__get__: lambda self: -1, + Tensor.is_vulkan.__get__: lambda self: -1, + Tensor.itemsize.__get__: lambda self: -1, + Tensor.layout.__get__: lambda self: -1, + Tensor.name.__get__: lambda self: -1, + Tensor.names.__get__: lambda self: -1, + Tensor.nbytes.__get__: lambda self: -1, + Tensor.ndim.__get__: lambda self: -1, + Tensor.output_nr.__get__: lambda self: -1, + Tensor.requires_grad.__get__: lambda self: -1, + Tensor.shape.__get__: lambda self: -1, + Tensor.volatile.__get__: lambda self: -1, + Tensor.real.__get__: lambda self: -1, + Tensor.imag.__get__: lambda self: -1, + Tensor.__cuda_array_interface__.__get__: lambda self: -1, + Tensor.type: lambda self, dtype=None, non_blocking=False, **kwargs: -1, + Tensor._dimI: lambda self: -1, + Tensor._dimV: lambda self: -1, + Tensor._indices: lambda self: -1, + Tensor._is_view: lambda self: -1, + Tensor._nnz: lambda self: -1, + Tensor.crow_indices: lambda self: -1, + Tensor.col_indices: lambda self: -1, + Tensor.ccol_indices: lambda self: -1, + Tensor.row_indices: lambda self: -1, + Tensor._update_names: lambda self, names, inplace: -1, + Tensor._values: lambda self: -1, + Tensor.adjoint: lambda self: -1, + Tensor.align_as: lambda self, other: -1, + Tensor.align_to: lambda self, order, ellipsis_idx: -1, + Tensor.apply_: lambda self, callable: -1, + Tensor.as_strided: lambda self, size, stride: -1, + Tensor.as_strided_: lambda self, size, stride: -1, + Tensor.backward: lambda self, gradient=None, retain_graph=None, create_graph=False, inputs=None: -1, + Tensor.bfloat16: lambda self, memory_format=torch.preserve_format: -1, + Tensor.bool: lambda self, memory_format=torch.preserve_format: -1, + Tensor.byte: lambda self, memory_format=torch.preserve_format: -1, + Tensor.char: lambda self, memory_format=torch.preserve_format: -1, + Tensor.cauchy_: lambda self, median=0, sigma=1, *, generator=None: -1, + Tensor.coalesce: lambda self: -1, + Tensor._coalesced_: lambda self, coalesced: -1, + Tensor.contiguous: lambda self, memory_format=torch.contiguous_format: -1, + Tensor.copy_: lambda self, src, non_blocking=False: -1, + Tensor.cpu: lambda self, memory_format=torch.preserve_format: -1, + Tensor.cuda: lambda self, memory_format=torch.preserve_format: -1, + Tensor.xpu: lambda self, memory_format=torch.preserve_format: -1, + Tensor.ipu: lambda self, memory_format=torch.preserve_format: -1, + Tensor.data_ptr: lambda self: -1, + Tensor.dense_dim: lambda self: -1, + Tensor.diagonal_scatter: lambda self, src, offset=0, dim1=0, dim2=1: -1, + Tensor.dim: lambda self: -1, + Tensor.dim_order: lambda self: -1, + Tensor.double: lambda self, memory_format=torch.preserve_format: -1, + Tensor.cdouble: lambda self, memory_format=torch.preserve_format: -1, + Tensor.element_size: lambda self: -1, + Tensor.expand: lambda self, size: -1, + Tensor.expand_as: lambda self, other: -1, + Tensor.exponential_: lambda self, lambd=1, *, generator=None: -1, + Tensor.fill_: lambda self, value: -1, + Tensor.fill_diagonal_: lambda self, value: -1, + Tensor.float: lambda self, memory_format=torch.preserve_format: -1, + Tensor.cfloat: lambda self, memory_format=torch.preserve_format: -1, + Tensor.geometric_: lambda self, p, *, generator=None: -1, + Tensor.get_device: lambda self: -1, + Tensor.half: lambda self, memory_format=torch.preserve_format: -1, + Tensor.chalf: lambda self, memory_format=torch.preserve_format: -1, + Tensor.has_names: lambda self: -1, + Tensor.indices: lambda self: -1, + Tensor.int: lambda self, memory_format=torch.preserve_format: -1, + Tensor.is_coalesced: lambda self: -1, + Tensor.is_contiguous: lambda self: -1, + Tensor.is_inference: lambda self: -1, + Tensor.is_pinned: lambda self: -1, + Tensor.is_set_to: lambda self, tensor: -1, + Tensor.is_shared: lambda self: -1, + Tensor.item: lambda self: -1, + Tensor.log_normal_: lambda self, mean=1, std=2, *, generator=None: -1, + Tensor.log_softmax: lambda self, dim: -1, + Tensor.long: lambda self, memory_format=torch.preserve_format: -1, + Tensor.map_: lambda self, tensor, callable: -1, + Tensor.map2_: lambda self, x, y, callable: -1, + Tensor.mm: lambda self, mat2: -1, + Tensor.narrow_copy: lambda self, dimension, start, length: -1, + Tensor.ndimension: lambda self: -1, + Tensor.nelement: lambda self: -1, + Tensor._nested_tensor_size: lambda self: -1, + Tensor._nested_tensor_storage_offsets: lambda self: -1, + Tensor._nested_tensor_strides: lambda self: -1, + Tensor.normal_: lambda self: -1, + Tensor.numpy: lambda self: -1, + Tensor.permute: lambda self, dim: -1, + Tensor.pin_memory: lambda self: -1, + Tensor.put_: lambda self, indices, tensor, accumulate=False: -1, + Tensor.qscheme: lambda self: -1, + Tensor.random_: lambda self, from_=0, to=None, *, generator=None: -1, + Tensor.record_stream: lambda self, stream: -1, + Tensor.refine_names: lambda self, names: -1, + Tensor.register_hook: lambda self, hook: -1, + Tensor.register_post_accumulate_grad_hook: lambda self, hook: -1, + Tensor.rename: lambda self, name: -1, + Tensor.repeat: lambda self, *size: -1, + Tensor.requires_grad_: lambda self, requires_grad=True: -1, + Tensor.reshape_as: lambda self, other: -1, + Tensor.resize: lambda self, *size: -1, + Tensor.resize_: lambda self, size: -1, + Tensor.resize_as: lambda self, other: -1, + Tensor.resize_as_sparse_: lambda self, other: -1, + Tensor.retain_grad: lambda self: -1, + Tensor.set_: lambda self, source=None, storage_offset=0, size=None, stride=None: -1, + Tensor.select_scatter: lambda self, src, dim, index: -1, + Tensor.share_memory_: lambda self: -1, + Tensor.short: lambda self, memory_format=torch.preserve_format: -1, + Tensor.size: lambda self: -1, + Tensor.slice_scatter: lambda self, src, dim=0, start=None, end=None, step=1: -1, + Tensor.sparse_dim: lambda self: -1, + Tensor.sparse_mask: lambda self, mask: -1, + Tensor._sparse_mask_projection: lambda self, mask, accumulate_matches=False: -1, + Tensor.sparse_resize_: lambda self, size1, size2, dense_dim: -1, + Tensor.sparse_resize_and_clear_: lambda self, size1, size2, dense_dim: -1, + Tensor.sspaddmm: lambda self, mat1, mat2, beta=1, alpha=1, out=None: -1, + Tensor.storage: lambda self: -1, + Tensor.untyped_storage: lambda self: -1, + Tensor.storage_offset: lambda self: -1, + Tensor.storage_type: lambda self: -1, + Tensor.sum_to_size: lambda self, size: -1, + Tensor.tile: lambda self, *reps: -1, + Tensor.to: lambda self, dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format: -1, + Tensor.to_dense: lambda self, dtype=None, *, masked_grad=None: -1, + Tensor._to_dense: lambda self, dtype=None, masked_grad=None: -1, + Tensor.to_sparse: lambda self: -1, + Tensor.tolist: lambda self: -1, + Tensor.to_mkldnn: lambda self: -1, + Tensor.type_as: lambda self, other: -1, + Tensor.unfold: lambda self, dimension, size, step: -1, + Tensor.uniform_: lambda self, from_=0, to=1: -1, + Tensor.values: lambda self: -1, + Tensor.view: lambda self, shape: -1, + Tensor.view_as: lambda self, other: -1, + Tensor.zero_: lambda self: -1, + Tensor.__dlpack__: lambda self, stream=None: -1, + Tensor.__dlpack_device__: lambda self: -1, + torch.linalg.lstsq: lambda self, b, cond=None, driver=None: -1, + } + + ret2 = {} + ignored = get_ignored_functions() + + for k, v in ret.items(): + # Generate methods like __add__ and add_ by default from add + names = [ + k.__name__, # Default method + k.__name__ + "_", # Inplace variant + "__" + k.__name__ + "__", # Dunder method + "__i" + k.__name__ + "__", # Inplace dunder method + "__r" + k.__name__ + "__", # Reverse dunder method + ] + + if k.__name__.startswith("bitwise_"): + # bitwise_ have dunder methods of the form ____ + # And so on. + subname = k.__name__[len("bitwise_"):] + names.extend([ + "__" + subname + "__", + "__i" + subname + "__", + "__r" + subname + "__" + ]) + + for name in names: + func = getattr(Tensor, name, None) + if callable(func) and func not in ret and func not in ignored: + ret2[func] = v + + ret.update(ret2) + return ret + +def wrap_torch_function(dispatcher: Callable): + """Wraps a given function with ``__torch_function__`` -related functionality. + + Parameters + ---------- + dispatcher: Callable + A callable that returns an iterable of Tensor-likes passed into the function. + + Note + ---- + This decorator may reduce the performance of your code. Generally, it's enough to express + your code as a series of functions that, themselves, support __torch_function__. If you + find yourself in the rare situation where this is not the case, e.g. if you're wrapping a + low-level library and you also need it to work for Tensor-likes, then this function is available. + + Examples + -------- + >>> def dispatcher(a): # Must have the same signature as func + ... return (a,) + >>> @torch.overrides.wrap_torch_function(dispatcher) + >>> def func(a): # This will make func dispatchable by __torch_function__ + ... return a + 0 + """ + def inner(func): + @functools.wraps(func) + def wrapped(*args, **kwargs): + relevant_args = dispatcher(*args, **kwargs) + if has_torch_function(relevant_args): + return handle_torch_function(wrapped, relevant_args, *args, **kwargs) + + return func(*args, **kwargs) + + return wrapped + + return inner + +def _get_overloaded_args(relevant_args: Iterable[Any], get_type_fn: Callable[[Any], Type] = None) -> List[Any]: + """Returns a list of arguments on which to call __torch_function__. + + Checks arguments in relevant_args for __torch_function__ implementations, + storing references to the arguments and their types in overloaded_args and + overloaded_types in order of calling precedence. Only distinct types are + considered. If a type is a subclass of another type it will have higher + precedence, otherwise the precedence order is the same as the order of + arguments in relevant_args, that is, from left-to-right in the argument list. + + The precedence-determining algorithm implemented in this function is + described in `NEP-0018`_. + + See torch::append_overloaded_arg for the equivalent function in the C++ + implementation. + + Parameters + ---------- + relevant_args : iterable of array-like + Iterable of array-like arguments to check for __torch_function__ + methods. + + get_type_fn : callable, optional + Function to call on each argument in relevant_args to get its type. + + Returns + ------- + overloaded_args : list + Arguments from relevant_args on which to call __torch_function__ + methods, in the order in which they should be called. + + .. _NEP-0018: + https://numpy.org/neps/nep-0018-array-function-protocol.html + """ + if get_type_fn is None: + get_type_fn = type + + # If torch function is not enabled, there are no overloaded types + if not torch._C._is_torch_function_enabled(): + return [] + # Runtime is O(num_arguments * num_unique_types) + overloaded_types: Set[Type] = set() + overloaded_args: List[Any] = [] + for arg in relevant_args: + arg_type = get_type_fn(arg) + # We only collect arguments if they have a unique type, which ensures + # reasonable performance even with a long list of possibly overloaded + # arguments. + # + # NB: Important to exclude _disabled_torch_function_impl, otherwise + # https://github.com/pytorch/pytorch/issues/64687 + if (arg_type not in overloaded_types and hasattr(arg_type, '__torch_function__') and + arg_type.__torch_function__ != torch._C._disabled_torch_function_impl): + # Create lists explicitly for the first type (usually the only one + # done) to avoid setting up the iterator for overloaded_args. + if overloaded_types: + overloaded_types.add(arg_type) + # By default, insert argument at the end, but if it is + # subclass of another argument, insert it before that argument. + # This ensures "subclasses before superclasses". + index = len(overloaded_args) + for i, old_arg in enumerate(overloaded_args): + if issubclass(arg_type, get_type_fn(old_arg)): + index = i + break + overloaded_args.insert(index, arg) + else: + overloaded_types = {arg_type} + overloaded_args = [arg] + return overloaded_args + + +def handle_torch_function( + public_api: Callable, relevant_args: Iterable[Any], *args, **kwargs) -> Any: + """Implement a function with checks for ``__torch_function__`` overrides. + + See torch::autograd::handle_torch_function for the equivalent of this + function in the C++ implementation. + + Arguments + --------- + public_api : function + Function exposed by the public torch API originally called like + ``public_api(*args, **kwargs)`` on which arguments are now being + checked. + relevant_args : iterable + Iterable of arguments to check for __torch_function__ methods. + args : tuple + Arbitrary positional arguments originally passed into ``public_api``. + kwargs : tuple + Arbitrary keyword arguments originally passed into ``public_api``. + + Returns + ------- + object + Result from calling ``implementation`` or an ``__torch_function__`` + method, as appropriate. + + Raises + ------ + TypeError : if no implementation is found. + + Example + ------- + >>> def func(a): + ... if has_torch_function_unary(a): + ... return handle_torch_function(func, (a,), a) + ... return a + 0 + """ + # Check for __torch_function__ methods. + overloaded_args = _get_overloaded_args(relevant_args) + # overloaded_args already have unique types. + types = tuple(map(type, overloaded_args)) + + # Check for __torch_function__ mode. + if _is_torch_function_mode_enabled(): + # if we're here, the mode must be set to a TorchFunctionStackMode + # this unsets it and calls directly into TorchFunctionStackMode's torch function + with _pop_mode_temporarily() as mode: + result = mode.__torch_function__(public_api, types, args, kwargs) + if result is not NotImplemented: + return result + + # Call overrides + for overloaded_arg in overloaded_args: + # This call needs to become a classmethod call in the future. + # See https://github.com/pytorch/pytorch/issues/63767 + torch_func_method = overloaded_arg.__torch_function__ + if hasattr(torch_func_method, "__self__") and torch_func_method.__self__ is overloaded_arg and \ + torch_func_method is not torch._C._disabled_torch_function_impl: + warnings.warn("Defining your `__torch_function__ as a plain method is deprecated and " + "will be an error in future, please define it as a classmethod.", + DeprecationWarning) + + # Use `public_api` instead of `implementation` so __torch_function__ + # implementations can do equality/identity comparisons. + result = torch_func_method(public_api, types, args, kwargs) + + if result is not NotImplemented: + return result + + func_name = f'{public_api.__module__}.{public_api.__name__}' + msg = ( + f"no implementation found for '{func_name}' on types that implement " + f'__torch_function__: {[type(arg) for arg in overloaded_args]}' + ) + if _is_torch_function_mode_enabled(): + msg += f" nor in mode {_get_current_function_mode()}" + raise TypeError(msg) + +has_torch_function = _add_docstr( + _has_torch_function, + r"""Check for __torch_function__ implementations in the elements of an iterable + or if a __torch_function__ mode is enabled. Considers exact ``Tensor`` s + and ``Parameter`` s non-dispatchable. Use this to guard a call to + :func:`handle_torch_function`; don't use it to test if something + is Tensor-like, use :func:`is_tensor_like` instead. + Arguments + --------- + relevant_args : iterable + Iterable or arguments to check for __torch_function__ methods. + Returns + ------- + bool + True if any of the elements of relevant_args have __torch_function__ + implementations, False otherwise. + See Also + ________ + torch.is_tensor_like + Checks if something is a Tensor-like, including an exact ``Tensor``. + """ +) + +has_torch_function_unary = _add_docstr( + _has_torch_function_unary, + r"""Special case of `has_torch_function` for single inputs. + Instead of: + `has_torch_function((t,))` + call: + `has_torch_function_unary(t)` + which skips unnecessary packing and unpacking work. + """ +) + +has_torch_function_variadic = _add_docstr( + _has_torch_function_variadic, + r"""Special case of `has_torch_function` that skips tuple creation. + + This uses the METH_FASTCALL protocol introduced in Python 3.7 + + Instead of: + `has_torch_function((a, b))` + call: + `has_torch_function_variadic(a, b)` + which skips unnecessary packing and unpacking work. + """ +) + +@functools.lru_cache(None) +def _get_overridable_functions() -> Tuple[Dict[Any, List[Callable]], Dict[Callable, str]]: + overridable_funcs = collections.defaultdict(list) + index = {} + tested_namespaces = [ + ("torch", torch, torch.__all__), + ("torch.functional", torch.functional, torch.functional.__all__), + ("torch.nn.functional", torch.nn.functional, dir(torch.nn.functional)), + ("torch.nn.init", torch.nn.init, dir(torch.nn.init)), + ("torch.Tensor", torch.Tensor, dir(torch.Tensor)), + ("torch.linalg", torch.linalg, dir(torch.linalg)), + ("torch.fft", torch.fft, dir(torch.fft)), + ("torch.special", torch.special, dir(torch.special)), + ] + for namespace_str, namespace, ns_funcs in tested_namespaces: + for func_name in ns_funcs: + ignore = False + # ignore private functions or functions that are deleted in torch.__init__ + if namespace is not torch.Tensor: + if func_name.startswith('__'): + continue + elif func_name.startswith('_'): + ignore = True + elif func_name.endswith('_'): + ignore = True + elif not func_name[0].islower(): + ignore = True + elif func_name == 'unique_dim': + continue + else: + func = getattr(namespace, func_name) + if getattr(object, func_name, None) == func: + continue + if func_name == '__weakref__': + continue + func = getattr(namespace, func_name) + if namespace is torch.Tensor and getattr(object, func_name, None) == func: + continue + # ignore re-exported modules + if isinstance(func, types.ModuleType): + continue + # ignore __future__ imports + if isinstance(func, __future__._Feature): + continue + + if not callable(func) and hasattr(func, "__get__"): + index[func.__get__] = f"{namespace_str}.{func_name}.__get__" + index[func.__set__] = f"{namespace_str}.{func_name}.__set__" + if ignore: + continue + if func.__get__ in get_ignored_functions(): + msg = ("{}.{} is in the tuple returned by torch._overrides.get_ignored_functions " + "but still has an explicit override") + assert func.__get__ not in get_testing_overrides(), msg.format(namespace, func.__name__) + continue + else: + overridable_funcs[func].append(func.__get__) + continue + + if not callable(func): + continue + + index[func] = f"{namespace_str}.{func_name}" + + if ignore: + continue + + # cannot be overriden by __torch_function__ + if func in get_ignored_functions(): + msg = ("{}.{} is in the tuple returned by torch._overrides.get_ignored_functions " + "but still has an explicit override") + assert func not in get_testing_overrides(), msg.format(namespace, func.__name__) + continue + overridable_funcs[namespace].append(func) + return overridable_funcs, index + +@_disable_user_warnings +def get_overridable_functions() -> Dict[Any, List[Callable]]: + """List functions that are overridable via __torch_function__ + + Returns + ------- + Dict[Any, List[Callable]] + A dictionary that maps namespaces that contain overridable functions + to functions in that namespace that can be overridden. + """ + return _get_overridable_functions()[0] + +@_disable_user_warnings +def resolve_name(f): + """Get a human readable string name for a function passed to + __torch_function__ + + Arguments + --------- + f : Callable + Function to resolve the name of. + + Returns + ------- + str + Name of the function; if eval'ed it should give back the input + function. + """ + if isinstance(f, (torch._ops.OpOverload, torch._ops.OpOverloadPacket)): + return str(f) + return _get_overridable_functions()[1].get(f) + +@functools.lru_cache(None) +def _get_tensor_methods() -> Set[Callable]: + """ Returns a set of the overridable methods on ``torch.Tensor`` """ + overridable_funcs = get_overridable_functions() + methods = set(overridable_funcs[torch.Tensor]) + return methods + +@_disable_user_warnings +def is_tensor_method_or_property(func: Callable) -> bool: + """ + Returns True if the function passed in is a handler for a + method or property belonging to ``torch.Tensor``, as passed + into ``__torch_function__``. + + .. note:: + For properties, their ``__get__`` method must be passed in. + + This may be needed, in particular, for the following reasons: + + 1. Methods/properties sometimes don't contain a `__module__` slot. + 2. They require that the first passed-in argument is an instance + of ``torch.Tensor``. + + Examples + -------- + >>> is_tensor_method_or_property(torch.Tensor.add) + True + >>> is_tensor_method_or_property(torch.add) + False + """ + return func in _get_tensor_methods() or func.__name__ == "__get__" + +def is_tensor_like(inp): + """ + Returns ``True`` if the passed-in input is a Tensor-like. + + Currently, this occurs whenever there's a ``__torch_function__`` + attribute on the type of the input. + + Examples + -------- + A subclass of tensor is generally a Tensor-like. + + >>> class SubTensor(torch.Tensor): ... + >>> is_tensor_like(SubTensor([0])) + True + + Built-in or user types aren't usually Tensor-like. + + >>> is_tensor_like(6) + False + >>> is_tensor_like(None) + False + >>> class NotATensor: ... + >>> is_tensor_like(NotATensor()) + False + + But, they can be made Tensor-like by implementing __torch_function__. + + >>> class TensorLike: + ... @classmethod + ... def __torch_function__(cls, func, types, args, kwargs): + ... return -1 + >>> is_tensor_like(TensorLike()) + True + """ + return type(inp) is torch.Tensor or hasattr(inp, "__torch_function__") + +class TorchFunctionMode: + """ + A ``TorchFunctionMode`` allows you to override the meaning of all + ``__torch_function__`` overrideable functions within a dynamic scope, + without having to actually create a tensor subclass or manually + monkey-patch functions in the PyTorch API. Some common situations + where you should use a mode: + + * You want to override the meaning of factory functions, or other + functions that do not otherwise take a tensor as an argument + (these cannot be overridden with tensor subclasses). + + * You want to override the behavior of all functions without needing + to wrap your inputs in tensor subclasses; e.g., if you are just + interested in logging intermediate computations. + + * You want to control the order of execution of various tensor + subclasses explicitly, rather than implicitly via the return of + ``NotImplemented``. + + Independent subclasses of :class:`TorchFunctionMode` are compositional: + modes can be pushed onto a stack using ``with MyMode():``. + When you call functions in the PyTorch API inside your + ``__torch_function__`` implementation, by default, they will forward on to + the next mode on the mode stack. If you want recursively call back into + your current ``__torch_function__`` implementation, either explicitly + invoke ``self.__torch_function__(...)``, or use the context manager + ``enable_torch_function_mode(self, replace=self.inner)`` to make PyTorch + API self-referential (beware of infinite loops, in this case!) + """ + inner: "TorchFunctionMode" + + # Force metaclass to generate constructor at the base of the hierarchy + def __init__(self): + pass + + def __torch_function__(self, func, types, args=(), kwargs=None): + raise NotImplementedError() + + def __enter__(self): + _push_mode(self) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + _pop_mode() + + @classmethod + def push(cls, *args, **kwargs): + warnings.warn("`Mode.push()` is no longer necessary and can be replaced with just `with Mode()`") + instance = cls(*args, **kwargs) + return instance + + +def _get_current_function_mode(): + stack_len = _len_torch_function_stack() + return _get_function_stack_at(stack_len - 1) if stack_len > 0 else None + + +def _get_current_function_mode_stack(): + stack_len = _len_torch_function_stack() + return [_get_function_stack_at(i) for i in range(stack_len)] + +def _push_mode(mode): + _push_on_torch_function_stack(mode) + + +def _pop_mode(): + old = _pop_torch_function_stack() + return old + + +@contextlib.contextmanager +def _pop_mode_temporarily(): + old = _pop_mode() + try: + yield old + finally: + _push_mode(old) + +class BaseTorchFunctionMode(TorchFunctionMode): + def __torch_function__(self, func, types, args=(), kwargs=None): + if kwargs is None: + kwargs = {} + return func(*args, **kwargs) + + +@contextlib.contextmanager +def enable_reentrant_dispatch(): + # NB: this can't simply be + # `enable_reentrant_dispatch = torch._C._RestorePythonTLSSnapshot` + # because: + # 1. torch._C._RestorePythonTLSSnapshot is unavailable when this file + # initially gets imported. Probably an import order thing. + # 2. enable_reentrant_dispatch is technically public API; assigning + # it the object would change the __module__ to look private. + with torch._C._RestorePythonTLSSnapshot(): + try: + yield + finally: + pass diff --git a/env-llmeval/lib/python3.10/site-packages/torch/version.py b/env-llmeval/lib/python3.10/site-packages/torch/version.py new file mode 100644 index 0000000000000000000000000000000000000000..af40d2da785b6423c15a89cda84a478f0fe2bbd8 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/torch/version.py @@ -0,0 +1,8 @@ +from typing import Optional + +__all__ = ['__version__', 'debug', 'cuda', 'git_version', 'hip'] +__version__ = '2.2.2+cu121' +debug = False +cuda: Optional[str] = '12.1' +git_version = '39901f229520a5256505ec24782f716ee7ddc843' +hip: Optional[str] = None diff --git a/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/INSTALLER b/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/LICENSE b/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..c84a47288f8442851788d20022245894452bdeab --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 EleutherAI + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..43936014fd5ea9256869918b3576c3583974270f --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/METADATA @@ -0,0 +1,118 @@ +Metadata-Version: 2.1 +Name: tqdm-multiprocess +Version: 0.0.11 +Summary: Easy multiprocessing with tqdm and logging redirected to main process. +Home-page: https://github.com/EleutherAI/tqdm-multiprocess +Author: researcher2 +Author-email: 2researcher2@gmail.com +License: UNKNOWN +Platform: UNKNOWN +Classifier: Programming Language :: Python :: 3 +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: OS Independent +Requires-Python: >=3.6 +Description-Content-Type: text/markdown +Requires-Dist: tqdm +Requires-Dist: colorama +Provides-Extra: dev +Requires-Dist: twine ; extra == 'dev' + +# tqdm-multiprocess +Using queues, tqdm-multiprocess supports multiple worker processes, each with multiple tqdm progress bars, displaying them cleanly through the main process. The worker processes also have access to a single global tqdm for aggregate progress monitoring. + +Logging is also redirected from the subprocesses to the root logger in the main process. + +Currently doesn't support tqdm(iterator), you will need to intialize your worker tqdms with a total and update manually. + +Due to the performance limits of the default Python multiprocess queue you need to update your global and worker process tqdms infrequently to avoid flooding the main process. I will attempt to implement a lock free ringbuffer at some point to see if things can be improved. + +## Installation + +```bash +pip install tqdm-multiprocess +``` + +## Usage + +*TqdmMultiProcessPool* creates a standard python multiprocessing pool with the desired number of processes. Under the hood it uses async_apply with an event loop to monitor a tqdm and logging queue, allowing the worker processes to redirect both their tqdm objects and logging messages to your main process. There is also a queue for the workers to update the single global tqdm. + +As shown below, you create a list of tasks containing their function and a tuple with your parameters. The functions you pass in will need the extra arguments on the end "tqdm_func, global_tqdm". You must use tqdm_func when initializing your tqdms for the redirection to work. As mentioned above, passing iterators into the tqdm function is currently not supported, so set total=total_steps when setting up your tqdm, and then update the progress manually with the update() method. All other arguments to tqdm should work fine. + +Once you have your task list, call the map() method on your pool, passing in the process count, global_tqdm (or None), task list, as well as error and done callback functions. The error callback will be trigerred if your task functions return anything evaluating as False (if not task_result in the source code). The done callback will be called when the task succesfully completes. + +The map method returns a list containing the returned results for all your tasks in original order. + +### examples/basic_example.py + +```python +from time import sleep +import multiprocessing +import tqdm + +import logging +from tqdm_multiprocess.logger import setup_logger_tqdm +logger = logging.getLogger(__name__) + +from tqdm_multiprocess import TqdmMultiProcessPool + +iterations1 = 100 +iterations2 = 5 +iterations3 = 2 +def some_other_function(tqdm_func, global_tqdm): + + total_iterations = iterations1 * iterations2 * iterations3 + with tqdm_func(total=total_iterations, dynamic_ncols=True) as progress3: + progress3.set_description("outer") + for i in range(iterations3): + logger.info("outer") + total_iterations = iterations1 * iterations2 + with tqdm_func(total=total_iterations, dynamic_ncols=True) as progress2: + progress2.set_description("middle") + for j in range(iterations2): + logger.info("middle") + #for k in tqdm_func(range(iterations1), dynamic_ncols=True, desc="inner"): + with tqdm_func(total=iterations1, dynamic_ncols=True) as progress1: + for j in range(iterations1): + # logger.info("inner") # Spam slows down tqdm too much + progress1.set_description("innert") + sleep(0.01) + progress1.update() + progress2.update() + progress3.update() + global_tqdm.update() + + logger.warning(f"Warning test message. {multiprocessing.current_process().name}") + logger.error(f"Error test message. {multiprocessing.current_process().name}") + + +# Multiprocessed +def example_multiprocessing_function(some_input, tqdm_func, global_tqdm): + logger.debug(f"Debug test message - I won't show up in console. {multiprocessing.current_process().name}") + logger.info(f"Info test message. {multiprocessing.current_process().name}") + some_other_function(tqdm_func, global_tqdm) + return True + +def error_callback(result): + print("Error!") + +def done_callback(result): + print("Done. Result: ", result) + +def example(): + pool = TqdmMultiProcessPool() + process_count = 4 + task_count = 10 + initial_tasks = [(example_multiprocessing_function, (i,)) for i in range(task_count)] + total_iterations = iterations1 * iterations2 * iterations3 * task_count + with tqdm.tqdm(total=total_iterations, dynamic_ncols=True) as global_progress: + global_progress.set_description("global") + results = pool.map(process_count, global_progress, initial_tasks, error_callback, done_callback) + print(results) + +if __name__ == '__main__': + logfile_path = "tqdm_multiprocessing_example.log" + setup_logger_tqdm(logfile_path) # Logger will write messages using tqdm.write + example() +``` + + diff --git a/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/RECORD b/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..cfa85245b1b2db3d2f2f995a783c0fdb6f84864c --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/RECORD @@ -0,0 +1,18 @@ +examples/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +examples/__pycache__/__init__.cpython-310.pyc,, +examples/__pycache__/basic_example.cpython-310.pyc,, +examples/basic_example.py,sha256=BXEyjPzHVxUVtD2g5A-xeZOtzRY10jiyuITTRqI67n8,2463 +tqdm_multiprocess-0.0.11.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +tqdm_multiprocess-0.0.11.dist-info/LICENSE,sha256=TLAo1IEUDiwrQ9q89Bt-tJmmCon8eSsGrAZAzpwLguo,1088 +tqdm_multiprocess-0.0.11.dist-info/METADATA,sha256=BlWIKFVIP1eqTciC9JJO15VSoGMf70M9nWAWu8Oiq64,5679 +tqdm_multiprocess-0.0.11.dist-info/RECORD,, +tqdm_multiprocess-0.0.11.dist-info/WHEEL,sha256=D1Wh14kWDxPnrM-5t_6UCB-UuQNrEODtRa3vF4OsvQY,97 +tqdm_multiprocess-0.0.11.dist-info/top_level.txt,sha256=84yOpK8vPnmQ1iMs3w7vmZuUFMI2t62KewLi_i7CniE,18 +tqdm_multiprocess/__init__.py,sha256=Iv5LxLnGnJfTkeg12f6EveHdejESd6Y15AW1iLi3Z5U,39 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diff --git a/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/top_level.txt b/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..da91afe6090b269413fa65ecce26becf0dc8a003 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/tqdm_multiprocess-0.0.11.dist-info/top_level.txt @@ -0,0 +1 @@ +tqdm_multiprocess diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/__init__.py b/env-llmeval/lib/python3.10/site-packages/yarl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..127721ad0963cf9f54b1d53212278c9085f1328e --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/yarl/__init__.py @@ -0,0 +1,5 @@ +from ._url import URL, cache_clear, cache_configure, cache_info + +__version__ = "1.9.4" + +__all__ = ("URL", "cache_clear", "cache_configure", "cache_info") diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/__init__.pyi b/env-llmeval/lib/python3.10/site-packages/yarl/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..5fd4bd0d122cb5329ba7e62d9cfa566939961551 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/yarl/__init__.pyi @@ -0,0 +1,121 @@ +import sys +from functools import _CacheInfo +from typing import Any, Mapping, Optional, Sequence, Tuple, Type, Union, overload + +import multidict + +if sys.version_info >= (3, 8): + from typing import Final, TypedDict, final +else: + from typing_extensions import Final, TypedDict, final + +_SimpleQuery = Union[str, int, float] +_QueryVariable = Union[_SimpleQuery, Sequence[_SimpleQuery]] +_Query = Union[ + None, str, Mapping[str, _QueryVariable], Sequence[Tuple[str, _QueryVariable]] +] + +@final +class URL: + scheme: Final[str] + raw_user: Final[str] + user: Final[Optional[str]] + raw_password: Final[Optional[str]] + password: Final[Optional[str]] + raw_host: Final[Optional[str]] + host: Final[Optional[str]] + port: Final[Optional[int]] + explicit_port: Final[Optional[int]] + raw_authority: Final[str] + authority: Final[str] + raw_path: Final[str] + path: Final[str] + raw_query_string: Final[str] + query_string: Final[str] + path_qs: Final[str] + raw_path_qs: Final[str] + raw_fragment: Final[str] + fragment: Final[str] + query: Final[multidict.MultiDict[str]] + raw_name: Final[str] + name: Final[str] + raw_suffix: Final[str] + suffix: Final[str] + raw_suffixes: Final[Tuple[str, ...]] + suffixes: Final[Tuple[str, ...]] + raw_parts: Final[Tuple[str, ...]] + parts: Final[Tuple[str, ...]] + parent: Final[URL] + def __init__( + self, val: Union[str, "URL"] = ..., *, encoded: bool = ... + ) -> None: ... + @classmethod + def build( + cls, + *, + scheme: str = ..., + authority: str = ..., + user: Optional[str] = ..., + password: Optional[str] = ..., + host: str = ..., + port: Optional[int] = ..., + path: str = ..., + query: Optional[_Query] = ..., + query_string: str = ..., + fragment: str = ..., + encoded: bool = ... + ) -> URL: ... + def __str__(self) -> str: ... + def __repr__(self) -> str: ... + def __bytes__(self) -> bytes: ... + def __eq__(self, other: Any) -> bool: ... + def __le__(self, other: Any) -> bool: ... + def __lt__(self, other: Any) -> bool: ... + def __ge__(self, other: Any) -> bool: ... + def __gt__(self, other: Any) -> bool: ... + def __hash__(self) -> int: ... + def __truediv__(self, name: str) -> URL: ... + def __mod__(self, query: _Query) -> URL: ... + def is_absolute(self) -> bool: ... + def is_default_port(self) -> bool: ... + def origin(self) -> URL: ... + def relative(self) -> URL: ... + def with_scheme(self, scheme: str) -> URL: ... + def with_user(self, user: Optional[str]) -> URL: ... + def with_password(self, password: Optional[str]) -> URL: ... + def with_host(self, host: str) -> URL: ... + def with_port(self, port: Optional[int]) -> URL: ... + def with_path(self, path: str, *, encoded: bool = ...) -> URL: ... + @overload + def with_query(self, query: _Query) -> URL: ... + @overload + def with_query(self, **kwargs: _QueryVariable) -> URL: ... + @overload + def update_query(self, query: _Query) -> URL: ... + @overload + def update_query(self, **kwargs: _QueryVariable) -> URL: ... + def with_fragment(self, fragment: Optional[str]) -> URL: ... + def with_name(self, name: str) -> URL: ... + def with_suffix(self, suffix: str) -> URL: ... + def join(self, url: URL) -> URL: ... + def joinpath(self, *url: str, encoded: bool = ...) -> URL: ... + def human_repr(self) -> str: ... + # private API + @classmethod + def _normalize_path(cls, path: str) -> str: ... + +@final +class cached_property: + def __init__(self, wrapped: Any) -> None: ... + def __get__(self, inst: URL, owner: Type[URL]) -> Any: ... + def __set__(self, inst: URL, value: Any) -> None: ... + +class CacheInfo(TypedDict): + idna_encode: _CacheInfo + idna_decode: _CacheInfo + +def cache_clear() -> None: ... +def cache_info() -> CacheInfo: ... +def cache_configure( + *, idna_encode_size: Optional[int] = ..., idna_decode_size: Optional[int] = ... +) -> None: ... diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/__init__.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e9f6bf7b2f8c275912f680a33259e90dc09a2799 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/__init__.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/_quoting.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/_quoting.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0d96cd5936b99d06b501363863cdbc9b80e65e33 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/_quoting.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/_quoting_py.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/_quoting_py.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..441d5bf84b2574df47fa5e45decd1cba18faf1a9 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/_quoting_py.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/_url.cpython-310.pyc b/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/_url.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..44e6b8b5ef8a180a45504765cded99eb84f36e20 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/yarl/__pycache__/_url.cpython-310.pyc differ diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/_quoting.py b/env-llmeval/lib/python3.10/site-packages/yarl/_quoting.py new file mode 100644 index 0000000000000000000000000000000000000000..8d1c705ff253f7dec76a3e2e5dce97fd9a81abcd --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/yarl/_quoting.py @@ -0,0 +1,18 @@ +import os +import sys + +__all__ = ("_Quoter", "_Unquoter") + + +NO_EXTENSIONS = bool(os.environ.get("YARL_NO_EXTENSIONS")) # type: bool +if sys.implementation.name != "cpython": + NO_EXTENSIONS = True + + +if not NO_EXTENSIONS: # pragma: no branch + try: + from ._quoting_c import _Quoter, _Unquoter # type: ignore[assignment] + except ImportError: # pragma: no cover + from ._quoting_py import _Quoter, _Unquoter # type: ignore[assignment] +else: + from ._quoting_py import _Quoter, _Unquoter # type: ignore[assignment] diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_c.cpython-310-x86_64-linux-gnu.so b/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_c.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..69e72a8ab5297d7b3f13c9592ff863376ee5fd51 Binary files /dev/null and b/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_c.cpython-310-x86_64-linux-gnu.so differ diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_c.pyi b/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_c.pyi new file mode 100644 index 0000000000000000000000000000000000000000..1c8fc24ec7ecc12cdbf9b0432aac4dd5b7aa32ca --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_c.pyi @@ -0,0 +1,16 @@ +from typing import Optional + +class _Quoter: + def __init__( + self, + *, + safe: str = ..., + protected: str = ..., + qs: bool = ..., + requote: bool = ... + ) -> None: ... + def __call__(self, val: Optional[str] = ...) -> Optional[str]: ... + +class _Unquoter: + def __init__(self, *, unsafe: str = ..., qs: bool = ...) -> None: ... + def __call__(self, val: Optional[str] = ...) -> Optional[str]: ... diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_c.pyx b/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_c.pyx new file mode 100644 index 0000000000000000000000000000000000000000..96f69c14e2bbc356bd16587c7d480150d842fde7 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_c.pyx @@ -0,0 +1,371 @@ +# cython: language_level=3 + +from cpython.exc cimport PyErr_NoMemory +from cpython.mem cimport PyMem_Free, PyMem_Malloc, PyMem_Realloc +from cpython.unicode cimport PyUnicode_DecodeASCII, PyUnicode_DecodeUTF8Stateful +from libc.stdint cimport uint8_t, uint64_t +from libc.string cimport memcpy, memset + +from string import ascii_letters, digits + + +cdef str GEN_DELIMS = ":/?#[]@" +cdef str SUB_DELIMS_WITHOUT_QS = "!$'()*," +cdef str SUB_DELIMS = SUB_DELIMS_WITHOUT_QS + '+?=;' +cdef str RESERVED = GEN_DELIMS + SUB_DELIMS +cdef str UNRESERVED = ascii_letters + digits + '-._~' +cdef str ALLOWED = UNRESERVED + SUB_DELIMS_WITHOUT_QS +cdef str QS = '+&=;' + +DEF BUF_SIZE = 8 * 1024 # 8KiB +cdef char BUFFER[BUF_SIZE] + +cdef inline Py_UCS4 _to_hex(uint8_t v): + if v < 10: + return (v+0x30) # ord('0') == 0x30 + else: + return (v+0x41-10) # ord('A') == 0x41 + + +cdef inline int _from_hex(Py_UCS4 v): + if '0' <= v <= '9': + return (v) - 0x30 # ord('0') == 0x30 + elif 'A' <= v <= 'F': + return (v) - 0x41 + 10 # ord('A') == 0x41 + elif 'a' <= v <= 'f': + return (v) - 0x61 + 10 # ord('a') == 0x61 + else: + return -1 + + +cdef inline int _is_lower_hex(Py_UCS4 v): + return 'a' <= v <= 'f' + + +cdef inline Py_UCS4 _restore_ch(Py_UCS4 d1, Py_UCS4 d2): + cdef int digit1 = _from_hex(d1) + if digit1 < 0: + return -1 + cdef int digit2 = _from_hex(d2) + if digit2 < 0: + return -1 + return (digit1 << 4 | digit2) + + +cdef uint8_t ALLOWED_TABLE[16] +cdef uint8_t ALLOWED_NOTQS_TABLE[16] + + +cdef inline bint bit_at(uint8_t array[], uint64_t ch): + return array[ch >> 3] & (1 << (ch & 7)) + + +cdef inline void set_bit(uint8_t array[], uint64_t ch): + array[ch >> 3] |= (1 << (ch & 7)) + + +memset(ALLOWED_TABLE, 0, sizeof(ALLOWED_TABLE)) +memset(ALLOWED_NOTQS_TABLE, 0, sizeof(ALLOWED_NOTQS_TABLE)) + +for i in range(128): + if chr(i) in ALLOWED: + set_bit(ALLOWED_TABLE, i) + set_bit(ALLOWED_NOTQS_TABLE, i) + if chr(i) in QS: + set_bit(ALLOWED_NOTQS_TABLE, i) + +# ----------------- writer --------------------------- + +cdef struct Writer: + char *buf + Py_ssize_t size + Py_ssize_t pos + bint changed + + +cdef inline void _init_writer(Writer* writer): + writer.buf = &BUFFER[0] + writer.size = BUF_SIZE + writer.pos = 0 + writer.changed = 0 + + +cdef inline void _release_writer(Writer* writer): + if writer.buf != BUFFER: + PyMem_Free(writer.buf) + + +cdef inline int _write_char(Writer* writer, Py_UCS4 ch, bint changed): + cdef char * buf + cdef Py_ssize_t size + + if writer.pos == writer.size: + # reallocate + size = writer.size + BUF_SIZE + if writer.buf == BUFFER: + buf = PyMem_Malloc(size) + if buf == NULL: + PyErr_NoMemory() + return -1 + memcpy(buf, writer.buf, writer.size) + else: + buf = PyMem_Realloc(writer.buf, size) + if buf == NULL: + PyErr_NoMemory() + return -1 + writer.buf = buf + writer.size = size + writer.buf[writer.pos] = ch + writer.pos += 1 + writer.changed |= changed + return 0 + + +cdef inline int _write_pct(Writer* writer, uint8_t ch, bint changed): + if _write_char(writer, '%', changed) < 0: + return -1 + if _write_char(writer, _to_hex(ch >> 4), changed) < 0: + return -1 + return _write_char(writer, _to_hex(ch & 0x0f), changed) + + +cdef inline int _write_utf8(Writer* writer, Py_UCS4 symbol): + cdef uint64_t utf = symbol + + if utf < 0x80: + return _write_pct(writer, utf, True) + elif utf < 0x800: + if _write_pct(writer, (0xc0 | (utf >> 6)), True) < 0: + return -1 + return _write_pct(writer, (0x80 | (utf & 0x3f)), True) + elif 0xD800 <= utf <= 0xDFFF: + # surogate pair, ignored + return 0 + elif utf < 0x10000: + if _write_pct(writer, (0xe0 | (utf >> 12)), True) < 0: + return -1 + if _write_pct(writer, (0x80 | ((utf >> 6) & 0x3f)), + True) < 0: + return -1 + return _write_pct(writer, (0x80 | (utf & 0x3f)), True) + elif utf > 0x10FFFF: + # symbol is too large + return 0 + else: + if _write_pct(writer, (0xf0 | (utf >> 18)), True) < 0: + return -1 + if _write_pct(writer, (0x80 | ((utf >> 12) & 0x3f)), + True) < 0: + return -1 + if _write_pct(writer, (0x80 | ((utf >> 6) & 0x3f)), + True) < 0: + return -1 + return _write_pct(writer, (0x80 | (utf & 0x3f)), True) + + +# --------------------- end writer -------------------------- + + +cdef class _Quoter: + cdef bint _qs + cdef bint _requote + + cdef uint8_t _safe_table[16] + cdef uint8_t _protected_table[16] + + def __init__( + self, *, str safe='', str protected='', bint qs=False, bint requote=True, + ): + cdef Py_UCS4 ch + + self._qs = qs + self._requote = requote + + if not self._qs: + memcpy(self._safe_table, + ALLOWED_NOTQS_TABLE, + sizeof(self._safe_table)) + else: + memcpy(self._safe_table, + ALLOWED_TABLE, + sizeof(self._safe_table)) + for ch in safe: + if ord(ch) > 127: + raise ValueError("Only safe symbols with ORD < 128 are allowed") + set_bit(self._safe_table, ch) + + memset(self._protected_table, 0, sizeof(self._protected_table)) + for ch in protected: + if ord(ch) > 127: + raise ValueError("Only safe symbols with ORD < 128 are allowed") + set_bit(self._safe_table, ch) + set_bit(self._protected_table, ch) + + def __call__(self, val): + cdef Writer writer + if val is None: + return None + if type(val) is not str: + if isinstance(val, str): + # derived from str + val = str(val) + else: + raise TypeError("Argument should be str") + _init_writer(&writer) + try: + return self._do_quote(val, &writer) + finally: + _release_writer(&writer) + + cdef str _do_quote(self, str val, Writer *writer): + cdef Py_UCS4 ch + cdef int changed + cdef int idx = 0 + cdef int length = len(val) + + while idx < length: + ch = val[idx] + idx += 1 + if ch == '%' and self._requote and idx <= length - 2: + ch = _restore_ch(val[idx], val[idx + 1]) + if ch != -1: + idx += 2 + if ch < 128: + if bit_at(self._protected_table, ch): + if _write_pct(writer, ch, True) < 0: + raise + continue + + if bit_at(self._safe_table, ch): + if _write_char(writer, ch, True) < 0: + raise + continue + + changed = (_is_lower_hex(val[idx - 2]) or + _is_lower_hex(val[idx - 1])) + if _write_pct(writer, ch, changed) < 0: + raise + continue + else: + ch = '%' + + if self._write(writer, ch) < 0: + raise + + if not writer.changed: + return val + else: + return PyUnicode_DecodeASCII(writer.buf, writer.pos, "strict") + + cdef inline int _write(self, Writer *writer, Py_UCS4 ch): + if self._qs: + if ch == ' ': + return _write_char(writer, '+', True) + + if ch < 128 and bit_at(self._safe_table, ch): + return _write_char(writer, ch, False) + + return _write_utf8(writer, ch) + + +cdef class _Unquoter: + cdef str _unsafe + cdef bint _qs + cdef _Quoter _quoter + cdef _Quoter _qs_quoter + + def __init__(self, *, unsafe='', qs=False): + self._unsafe = unsafe + self._qs = qs + self._quoter = _Quoter() + self._qs_quoter = _Quoter(qs=True) + + def __call__(self, val): + if val is None: + return None + if type(val) is not str: + if isinstance(val, str): + # derived from str + val = str(val) + else: + raise TypeError("Argument should be str") + return self._do_unquote(val) + + cdef str _do_unquote(self, str val): + if len(val) == 0: + return val + cdef list ret = [] + cdef char buffer[4] + cdef Py_ssize_t buflen = 0 + cdef Py_ssize_t consumed + cdef str unquoted + cdef Py_UCS4 ch = 0 + cdef Py_ssize_t idx = 0 + cdef Py_ssize_t length = len(val) + cdef Py_ssize_t start_pct + + while idx < length: + ch = val[idx] + idx += 1 + if ch == '%' and idx <= length - 2: + ch = _restore_ch(val[idx], val[idx + 1]) + if ch != -1: + idx += 2 + assert buflen < 4 + buffer[buflen] = ch + buflen += 1 + try: + unquoted = PyUnicode_DecodeUTF8Stateful(buffer, buflen, + NULL, &consumed) + except UnicodeDecodeError: + start_pct = idx - buflen * 3 + buffer[0] = ch + buflen = 1 + ret.append(val[start_pct : idx - 3]) + try: + unquoted = PyUnicode_DecodeUTF8Stateful(buffer, buflen, + NULL, &consumed) + except UnicodeDecodeError: + buflen = 0 + ret.append(val[idx - 3 : idx]) + continue + if not unquoted: + assert consumed == 0 + continue + assert consumed == buflen + buflen = 0 + if self._qs and unquoted in '+=&;': + ret.append(self._qs_quoter(unquoted)) + elif unquoted in self._unsafe: + ret.append(self._quoter(unquoted)) + else: + ret.append(unquoted) + continue + else: + ch = '%' + + if buflen: + start_pct = idx - 1 - buflen * 3 + ret.append(val[start_pct : idx - 1]) + buflen = 0 + + if ch == '+': + if not self._qs or ch in self._unsafe: + ret.append('+') + else: + ret.append(' ') + continue + + if ch in self._unsafe: + ret.append('%') + h = hex(ord(ch)).upper()[2:] + for ch in h: + ret.append(ch) + continue + + ret.append(ch) + + if buflen: + ret.append(val[length - buflen * 3 : length]) + + return ''.join(ret) diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_py.py b/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_py.py new file mode 100644 index 0000000000000000000000000000000000000000..585a1da804027636310d5abd1ed24806771425ba --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/yarl/_quoting_py.py @@ -0,0 +1,197 @@ +import codecs +import re +from string import ascii_letters, ascii_lowercase, digits +from typing import Optional, cast + +BASCII_LOWERCASE = ascii_lowercase.encode("ascii") +BPCT_ALLOWED = {f"%{i:02X}".encode("ascii") for i in range(256)} +GEN_DELIMS = ":/?#[]@" +SUB_DELIMS_WITHOUT_QS = "!$'()*," +SUB_DELIMS = SUB_DELIMS_WITHOUT_QS + "+&=;" +RESERVED = GEN_DELIMS + SUB_DELIMS +UNRESERVED = ascii_letters + digits + "-._~" +ALLOWED = UNRESERVED + SUB_DELIMS_WITHOUT_QS + + +_IS_HEX = re.compile(b"[A-Z0-9][A-Z0-9]") +_IS_HEX_STR = re.compile("[A-Fa-f0-9][A-Fa-f0-9]") + +utf8_decoder = codecs.getincrementaldecoder("utf-8") + + +class _Quoter: + def __init__( + self, + *, + safe: str = "", + protected: str = "", + qs: bool = False, + requote: bool = True, + ) -> None: + self._safe = safe + self._protected = protected + self._qs = qs + self._requote = requote + + def __call__(self, val: Optional[str]) -> Optional[str]: + if val is None: + return None + if not isinstance(val, str): + raise TypeError("Argument should be str") + if not val: + return "" + bval = cast(str, val).encode("utf8", errors="ignore") + ret = bytearray() + pct = bytearray() + safe = self._safe + safe += ALLOWED + if not self._qs: + safe += "+&=;" + safe += self._protected + bsafe = safe.encode("ascii") + idx = 0 + while idx < len(bval): + ch = bval[idx] + idx += 1 + + if pct: + if ch in BASCII_LOWERCASE: + ch = ch - 32 # convert to uppercase + pct.append(ch) + if len(pct) == 3: # pragma: no branch # peephole optimizer + buf = pct[1:] + if not _IS_HEX.match(buf): + ret.extend(b"%25") + pct.clear() + idx -= 2 + continue + try: + unquoted = chr(int(pct[1:].decode("ascii"), base=16)) + except ValueError: + ret.extend(b"%25") + pct.clear() + idx -= 2 + continue + + if unquoted in self._protected: + ret.extend(pct) + elif unquoted in safe: + ret.append(ord(unquoted)) + else: + ret.extend(pct) + pct.clear() + + # special case, if we have only one char after "%" + elif len(pct) == 2 and idx == len(bval): + ret.extend(b"%25") + pct.clear() + idx -= 1 + + continue + + elif ch == ord("%") and self._requote: + pct.clear() + pct.append(ch) + + # special case if "%" is last char + if idx == len(bval): + ret.extend(b"%25") + + continue + + if self._qs: + if ch == ord(" "): + ret.append(ord("+")) + continue + if ch in bsafe: + ret.append(ch) + continue + + ret.extend((f"%{ch:02X}").encode("ascii")) + + ret2 = ret.decode("ascii") + if ret2 == val: + return val + return ret2 + + +class _Unquoter: + def __init__(self, *, unsafe: str = "", qs: bool = False) -> None: + self._unsafe = unsafe + self._qs = qs + self._quoter = _Quoter() + self._qs_quoter = _Quoter(qs=True) + + def __call__(self, val: Optional[str]) -> Optional[str]: + if val is None: + return None + if not isinstance(val, str): + raise TypeError("Argument should be str") + if not val: + return "" + decoder = cast(codecs.BufferedIncrementalDecoder, utf8_decoder()) + ret = [] + idx = 0 + while idx < len(val): + ch = val[idx] + idx += 1 + if ch == "%" and idx <= len(val) - 2: + pct = val[idx : idx + 2] + if _IS_HEX_STR.fullmatch(pct): + b = bytes([int(pct, base=16)]) + idx += 2 + try: + unquoted = decoder.decode(b) + except UnicodeDecodeError: + start_pct = idx - 3 - len(decoder.buffer) * 3 + ret.append(val[start_pct : idx - 3]) + decoder.reset() + try: + unquoted = decoder.decode(b) + except UnicodeDecodeError: + ret.append(val[idx - 3 : idx]) + continue + if not unquoted: + continue + if self._qs and unquoted in "+=&;": + to_add = self._qs_quoter(unquoted) + if to_add is None: # pragma: no cover + raise RuntimeError("Cannot quote None") + ret.append(to_add) + elif unquoted in self._unsafe: + to_add = self._quoter(unquoted) + if to_add is None: # pragma: no cover + raise RuntimeError("Cannot quote None") + ret.append(to_add) + else: + ret.append(unquoted) + continue + + if decoder.buffer: + start_pct = idx - 1 - len(decoder.buffer) * 3 + ret.append(val[start_pct : idx - 1]) + decoder.reset() + + if ch == "+": + if not self._qs or ch in self._unsafe: + ret.append("+") + else: + ret.append(" ") + continue + + if ch in self._unsafe: + ret.append("%") + h = hex(ord(ch)).upper()[2:] + for ch in h: + ret.append(ch) + continue + + ret.append(ch) + + if decoder.buffer: + ret.append(val[-len(decoder.buffer) * 3 :]) + + ret2 = "".join(ret) + if ret2 == val: + return val + return ret2 diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/_url.py b/env-llmeval/lib/python3.10/site-packages/yarl/_url.py new file mode 100644 index 0000000000000000000000000000000000000000..9cca27ef86c69fa903da38b2c9d1e5b5337c4c60 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/yarl/_url.py @@ -0,0 +1,1200 @@ +import functools +import math +import warnings +from collections.abc import Mapping, Sequence +from contextlib import suppress +from ipaddress import ip_address +from urllib.parse import SplitResult, parse_qsl, quote, urljoin, urlsplit, urlunsplit + +import idna +from multidict import MultiDict, MultiDictProxy + +from ._quoting import _Quoter, _Unquoter + +DEFAULT_PORTS = {"http": 80, "https": 443, "ws": 80, "wss": 443} + +sentinel = object() + + +def rewrite_module(obj: object) -> object: + obj.__module__ = "yarl" + return obj + + +class cached_property: + """Use as a class method decorator. It operates almost exactly like + the Python `@property` decorator, but it puts the result of the + method it decorates into the instance dict after the first call, + effectively replacing the function it decorates with an instance + variable. It is, in Python parlance, a data descriptor. + + """ + + def __init__(self, wrapped): + self.wrapped = wrapped + try: + self.__doc__ = wrapped.__doc__ + except AttributeError: # pragma: no cover + self.__doc__ = "" + self.name = wrapped.__name__ + + def __get__(self, inst, owner, _sentinel=sentinel): + if inst is None: + return self + val = inst._cache.get(self.name, _sentinel) + if val is not _sentinel: + return val + val = self.wrapped(inst) + inst._cache[self.name] = val + return val + + def __set__(self, inst, value): + raise AttributeError("cached property is read-only") + + +def _normalize_path_segments(segments): + """Drop '.' and '..' from a sequence of str segments""" + + resolved_path = [] + + for seg in segments: + if seg == "..": + # ignore any .. segments that would otherwise cause an + # IndexError when popped from resolved_path if + # resolving for rfc3986 + with suppress(IndexError): + resolved_path.pop() + elif seg != ".": + resolved_path.append(seg) + + if segments and segments[-1] in (".", ".."): + # do some post-processing here. + # if the last segment was a relative dir, + # then we need to append the trailing '/' + resolved_path.append("") + + return resolved_path + + +@rewrite_module +class URL: + # Don't derive from str + # follow pathlib.Path design + # probably URL will not suffer from pathlib problems: + # it's intended for libraries like aiohttp, + # not to be passed into standard library functions like os.open etc. + + # URL grammar (RFC 3986) + # pct-encoded = "%" HEXDIG HEXDIG + # reserved = gen-delims / sub-delims + # gen-delims = ":" / "/" / "?" / "#" / "[" / "]" / "@" + # sub-delims = "!" / "$" / "&" / "'" / "(" / ")" + # / "*" / "+" / "," / ";" / "=" + # unreserved = ALPHA / DIGIT / "-" / "." / "_" / "~" + # URI = scheme ":" hier-part [ "?" query ] [ "#" fragment ] + # hier-part = "//" authority path-abempty + # / path-absolute + # / path-rootless + # / path-empty + # scheme = ALPHA *( ALPHA / DIGIT / "+" / "-" / "." ) + # authority = [ userinfo "@" ] host [ ":" port ] + # userinfo = *( unreserved / pct-encoded / sub-delims / ":" ) + # host = IP-literal / IPv4address / reg-name + # IP-literal = "[" ( IPv6address / IPvFuture ) "]" + # IPvFuture = "v" 1*HEXDIG "." 1*( unreserved / sub-delims / ":" ) + # IPv6address = 6( h16 ":" ) ls32 + # / "::" 5( h16 ":" ) ls32 + # / [ h16 ] "::" 4( h16 ":" ) ls32 + # / [ *1( h16 ":" ) h16 ] "::" 3( h16 ":" ) ls32 + # / [ *2( h16 ":" ) h16 ] "::" 2( h16 ":" ) ls32 + # / [ *3( h16 ":" ) h16 ] "::" h16 ":" ls32 + # / [ *4( h16 ":" ) h16 ] "::" ls32 + # / [ *5( h16 ":" ) h16 ] "::" h16 + # / [ *6( h16 ":" ) h16 ] "::" + # ls32 = ( h16 ":" h16 ) / IPv4address + # ; least-significant 32 bits of address + # h16 = 1*4HEXDIG + # ; 16 bits of address represented in hexadecimal + # IPv4address = dec-octet "." dec-octet "." dec-octet "." dec-octet + # dec-octet = DIGIT ; 0-9 + # / %x31-39 DIGIT ; 10-99 + # / "1" 2DIGIT ; 100-199 + # / "2" %x30-34 DIGIT ; 200-249 + # / "25" %x30-35 ; 250-255 + # reg-name = *( unreserved / pct-encoded / sub-delims ) + # port = *DIGIT + # path = path-abempty ; begins with "/" or is empty + # / path-absolute ; begins with "/" but not "//" + # / path-noscheme ; begins with a non-colon segment + # / path-rootless ; begins with a segment + # / path-empty ; zero characters + # path-abempty = *( "/" segment ) + # path-absolute = "/" [ segment-nz *( "/" segment ) ] + # path-noscheme = segment-nz-nc *( "/" segment ) + # path-rootless = segment-nz *( "/" segment ) + # path-empty = 0 + # segment = *pchar + # segment-nz = 1*pchar + # segment-nz-nc = 1*( unreserved / pct-encoded / sub-delims / "@" ) + # ; non-zero-length segment without any colon ":" + # pchar = unreserved / pct-encoded / sub-delims / ":" / "@" + # query = *( pchar / "/" / "?" ) + # fragment = *( pchar / "/" / "?" ) + # URI-reference = URI / relative-ref + # relative-ref = relative-part [ "?" query ] [ "#" fragment ] + # relative-part = "//" authority path-abempty + # / path-absolute + # / path-noscheme + # / path-empty + # absolute-URI = scheme ":" hier-part [ "?" query ] + __slots__ = ("_cache", "_val") + + _QUOTER = _Quoter(requote=False) + _REQUOTER = _Quoter() + _PATH_QUOTER = _Quoter(safe="@:", protected="/+", requote=False) + _PATH_REQUOTER = _Quoter(safe="@:", protected="/+") + _QUERY_QUOTER = _Quoter(safe="?/:@", protected="=+&;", qs=True, requote=False) + _QUERY_REQUOTER = _Quoter(safe="?/:@", protected="=+&;", qs=True) + _QUERY_PART_QUOTER = _Quoter(safe="?/:@", qs=True, requote=False) + _FRAGMENT_QUOTER = _Quoter(safe="?/:@", requote=False) + _FRAGMENT_REQUOTER = _Quoter(safe="?/:@") + + _UNQUOTER = _Unquoter() + _PATH_UNQUOTER = _Unquoter(unsafe="+") + _QS_UNQUOTER = _Unquoter(qs=True) + + def __new__(cls, val="", *, encoded=False, strict=None): + if strict is not None: # pragma: no cover + warnings.warn("strict parameter is ignored") + if type(val) is cls: + return val + if type(val) is str: + val = urlsplit(val) + elif type(val) is SplitResult: + if not encoded: + raise ValueError("Cannot apply decoding to SplitResult") + elif isinstance(val, str): + val = urlsplit(str(val)) + else: + raise TypeError("Constructor parameter should be str") + + if not encoded: + if not val[1]: # netloc + netloc = "" + host = "" + else: + host = val.hostname + if host is None: + raise ValueError("Invalid URL: host is required for absolute urls") + + try: + port = val.port + except ValueError as e: + raise ValueError( + "Invalid URL: port can't be converted to integer" + ) from e + + netloc = cls._make_netloc( + val.username, val.password, host, port, encode=True, requote=True + ) + path = cls._PATH_REQUOTER(val[2]) + if netloc: + path = cls._normalize_path(path) + + cls._validate_authority_uri_abs_path(host=host, path=path) + query = cls._QUERY_REQUOTER(val[3]) + fragment = cls._FRAGMENT_REQUOTER(val[4]) + val = SplitResult(val[0], netloc, path, query, fragment) + + self = object.__new__(cls) + self._val = val + self._cache = {} + return self + + @classmethod + def build( + cls, + *, + scheme="", + authority="", + user=None, + password=None, + host="", + port=None, + path="", + query=None, + query_string="", + fragment="", + encoded=False, + ): + """Creates and returns a new URL""" + + if authority and (user or password or host or port): + raise ValueError( + 'Can\'t mix "authority" with "user", "password", "host" or "port".' + ) + if not isinstance(port, (int, type(None))): + raise TypeError("The port is required to be int.") + if port and not host: + raise ValueError('Can\'t build URL with "port" but without "host".') + if query and query_string: + raise ValueError('Only one of "query" or "query_string" should be passed') + if ( + scheme is None + or authority is None + or host is None + or path is None + or query_string is None + or fragment is None + ): + raise TypeError( + 'NoneType is illegal for "scheme", "authority", "host", "path", ' + '"query_string", and "fragment" args, use empty string instead.' + ) + + if authority: + if encoded: + netloc = authority + else: + tmp = SplitResult("", authority, "", "", "") + netloc = cls._make_netloc( + tmp.username, tmp.password, tmp.hostname, tmp.port, encode=True + ) + elif not user and not password and not host and not port: + netloc = "" + else: + netloc = cls._make_netloc( + user, password, host, port, encode=not encoded, encode_host=not encoded + ) + if not encoded: + path = cls._PATH_QUOTER(path) + if netloc: + path = cls._normalize_path(path) + + cls._validate_authority_uri_abs_path(host=host, path=path) + query_string = cls._QUERY_QUOTER(query_string) + fragment = cls._FRAGMENT_QUOTER(fragment) + + url = cls( + SplitResult(scheme, netloc, path, query_string, fragment), encoded=True + ) + + if query: + return url.with_query(query) + else: + return url + + def __init_subclass__(cls): + raise TypeError(f"Inheriting a class {cls!r} from URL is forbidden") + + def __str__(self): + val = self._val + if not val.path and self.is_absolute() and (val.query or val.fragment): + val = val._replace(path="/") + return urlunsplit(val) + + def __repr__(self): + return f"{self.__class__.__name__}('{str(self)}')" + + def __bytes__(self): + return str(self).encode("ascii") + + def __eq__(self, other): + if not type(other) is URL: + return NotImplemented + + val1 = self._val + if not val1.path and self.is_absolute(): + val1 = val1._replace(path="/") + + val2 = other._val + if not val2.path and other.is_absolute(): + val2 = val2._replace(path="/") + + return val1 == val2 + + def __hash__(self): + ret = self._cache.get("hash") + if ret is None: + val = self._val + if not val.path and self.is_absolute(): + val = val._replace(path="/") + ret = self._cache["hash"] = hash(val) + return ret + + def __le__(self, other): + if not type(other) is URL: + return NotImplemented + return self._val <= other._val + + def __lt__(self, other): + if not type(other) is URL: + return NotImplemented + return self._val < other._val + + def __ge__(self, other): + if not type(other) is URL: + return NotImplemented + return self._val >= other._val + + def __gt__(self, other): + if not type(other) is URL: + return NotImplemented + return self._val > other._val + + def __truediv__(self, name): + if not isinstance(name, str): + return NotImplemented + return self._make_child((str(name),)) + + def __mod__(self, query): + return self.update_query(query) + + def __bool__(self) -> bool: + return bool( + self._val.netloc or self._val.path or self._val.query or self._val.fragment + ) + + def __getstate__(self): + return (self._val,) + + def __setstate__(self, state): + if state[0] is None and isinstance(state[1], dict): + # default style pickle + self._val = state[1]["_val"] + else: + self._val, *unused = state + self._cache = {} + + def is_absolute(self): + """A check for absolute URLs. + + Return True for absolute ones (having scheme or starting + with //), False otherwise. + + """ + return self.raw_host is not None + + def is_default_port(self): + """A check for default port. + + Return True if port is default for specified scheme, + e.g. 'http://python.org' or 'http://python.org:80', False + otherwise. + + """ + if self.port is None: + return False + default = DEFAULT_PORTS.get(self.scheme) + if default is None: + return False + return self.port == default + + def origin(self): + """Return an URL with scheme, host and port parts only. + + user, password, path, query and fragment are removed. + + """ + # TODO: add a keyword-only option for keeping user/pass maybe? + if not self.is_absolute(): + raise ValueError("URL should be absolute") + if not self._val.scheme: + raise ValueError("URL should have scheme") + v = self._val + netloc = self._make_netloc(None, None, v.hostname, v.port) + val = v._replace(netloc=netloc, path="", query="", fragment="") + return URL(val, encoded=True) + + def relative(self): + """Return a relative part of the URL. + + scheme, user, password, host and port are removed. + + """ + if not self.is_absolute(): + raise ValueError("URL should be absolute") + val = self._val._replace(scheme="", netloc="") + return URL(val, encoded=True) + + @property + def scheme(self): + """Scheme for absolute URLs. + + Empty string for relative URLs or URLs starting with // + + """ + return self._val.scheme + + @property + def raw_authority(self): + """Encoded authority part of URL. + + Empty string for relative URLs. + + """ + return self._val.netloc + + @cached_property + def authority(self): + """Decoded authority part of URL. + + Empty string for relative URLs. + + """ + return self._make_netloc( + self.user, self.password, self.host, self.port, encode_host=False + ) + + @property + def raw_user(self): + """Encoded user part of URL. + + None if user is missing. + + """ + # not .username + ret = self._val.username + if not ret: + return None + return ret + + @cached_property + def user(self): + """Decoded user part of URL. + + None if user is missing. + + """ + return self._UNQUOTER(self.raw_user) + + @property + def raw_password(self): + """Encoded password part of URL. + + None if password is missing. + + """ + return self._val.password + + @cached_property + def password(self): + """Decoded password part of URL. + + None if password is missing. + + """ + return self._UNQUOTER(self.raw_password) + + @property + def raw_host(self): + """Encoded host part of URL. + + None for relative URLs. + + """ + # Use host instead of hostname for sake of shortness + # May add .hostname prop later + return self._val.hostname + + @cached_property + def host(self): + """Decoded host part of URL. + + None for relative URLs. + + """ + raw = self.raw_host + if raw is None: + return None + if "%" in raw: + # Hack for scoped IPv6 addresses like + # fe80::2%Перевірка + # presence of '%' sign means only IPv6 address, so idna is useless. + return raw + return _idna_decode(raw) + + @property + def port(self): + """Port part of URL, with scheme-based fallback. + + None for relative URLs or URLs without explicit port and + scheme without default port substitution. + + """ + return self._val.port or DEFAULT_PORTS.get(self._val.scheme) + + @property + def explicit_port(self): + """Port part of URL, without scheme-based fallback. + + None for relative URLs or URLs without explicit port. + + """ + return self._val.port + + @property + def raw_path(self): + """Encoded path of URL. + + / for absolute URLs without path part. + + """ + ret = self._val.path + if not ret and self.is_absolute(): + ret = "/" + return ret + + @cached_property + def path(self): + """Decoded path of URL. + + / for absolute URLs without path part. + + """ + return self._PATH_UNQUOTER(self.raw_path) + + @cached_property + def query(self): + """A MultiDictProxy representing parsed query parameters in decoded + representation. + + Empty value if URL has no query part. + + """ + ret = MultiDict(parse_qsl(self.raw_query_string, keep_blank_values=True)) + return MultiDictProxy(ret) + + @property + def raw_query_string(self): + """Encoded query part of URL. + + Empty string if query is missing. + + """ + return self._val.query + + @cached_property + def query_string(self): + """Decoded query part of URL. + + Empty string if query is missing. + + """ + return self._QS_UNQUOTER(self.raw_query_string) + + @cached_property + def path_qs(self): + """Decoded path of URL with query.""" + if not self.query_string: + return self.path + return f"{self.path}?{self.query_string}" + + @cached_property + def raw_path_qs(self): + """Encoded path of URL with query.""" + if not self.raw_query_string: + return self.raw_path + return f"{self.raw_path}?{self.raw_query_string}" + + @property + def raw_fragment(self): + """Encoded fragment part of URL. + + Empty string if fragment is missing. + + """ + return self._val.fragment + + @cached_property + def fragment(self): + """Decoded fragment part of URL. + + Empty string if fragment is missing. + + """ + return self._UNQUOTER(self.raw_fragment) + + @cached_property + def raw_parts(self): + """A tuple containing encoded *path* parts. + + ('/',) for absolute URLs if *path* is missing. + + """ + path = self._val.path + if self.is_absolute(): + if not path: + parts = ["/"] + else: + parts = ["/"] + path[1:].split("/") + else: + if path.startswith("/"): + parts = ["/"] + path[1:].split("/") + else: + parts = path.split("/") + return tuple(parts) + + @cached_property + def parts(self): + """A tuple containing decoded *path* parts. + + ('/',) for absolute URLs if *path* is missing. + + """ + return tuple(self._UNQUOTER(part) for part in self.raw_parts) + + @cached_property + def parent(self): + """A new URL with last part of path removed and cleaned up query and + fragment. + + """ + path = self.raw_path + if not path or path == "/": + if self.raw_fragment or self.raw_query_string: + return URL(self._val._replace(query="", fragment=""), encoded=True) + return self + parts = path.split("/") + val = self._val._replace(path="/".join(parts[:-1]), query="", fragment="") + return URL(val, encoded=True) + + @cached_property + def raw_name(self): + """The last part of raw_parts.""" + parts = self.raw_parts + if self.is_absolute(): + parts = parts[1:] + if not parts: + return "" + else: + return parts[-1] + else: + return parts[-1] + + @cached_property + def name(self): + """The last part of parts.""" + return self._UNQUOTER(self.raw_name) + + @cached_property + def raw_suffix(self): + name = self.raw_name + i = name.rfind(".") + if 0 < i < len(name) - 1: + return name[i:] + else: + return "" + + @cached_property + def suffix(self): + return self._UNQUOTER(self.raw_suffix) + + @cached_property + def raw_suffixes(self): + name = self.raw_name + if name.endswith("."): + return () + name = name.lstrip(".") + return tuple("." + suffix for suffix in name.split(".")[1:]) + + @cached_property + def suffixes(self): + return tuple(self._UNQUOTER(suffix) for suffix in self.raw_suffixes) + + @staticmethod + def _validate_authority_uri_abs_path(host, path): + """Ensure that path in URL with authority starts with a leading slash. + + Raise ValueError if not. + """ + if len(host) > 0 and len(path) > 0 and not path.startswith("/"): + raise ValueError( + "Path in a URL with authority should start with a slash ('/') if set" + ) + + def _make_child(self, segments, encoded=False): + """add segments to self._val.path, accounting for absolute vs relative paths""" + # keep the trailing slash if the last segment ends with / + parsed = [""] if segments and segments[-1][-1:] == "/" else [] + for seg in reversed(segments): + if not seg: + continue + if seg[0] == "/": + raise ValueError( + f"Appending path {seg!r} starting from slash is forbidden" + ) + seg = seg if encoded else self._PATH_QUOTER(seg) + if "/" in seg: + parsed += ( + sub for sub in reversed(seg.split("/")) if sub and sub != "." + ) + elif seg != ".": + parsed.append(seg) + parsed.reverse() + old_path = self._val.path + if old_path: + parsed = [*old_path.rstrip("/").split("/"), *parsed] + if self.is_absolute(): + parsed = _normalize_path_segments(parsed) + if parsed and parsed[0] != "": + # inject a leading slash when adding a path to an absolute URL + # where there was none before + parsed = ["", *parsed] + new_path = "/".join(parsed) + return URL( + self._val._replace(path=new_path, query="", fragment=""), encoded=True + ) + + @classmethod + def _normalize_path(cls, path): + # Drop '.' and '..' from str path + + prefix = "" + if path.startswith("/"): + # preserve the "/" root element of absolute paths, copying it to the + # normalised output as per sections 5.2.4 and 6.2.2.3 of rfc3986. + prefix = "/" + path = path[1:] + + segments = path.split("/") + return prefix + "/".join(_normalize_path_segments(segments)) + + @classmethod + def _encode_host(cls, host, human=False): + try: + ip, sep, zone = host.partition("%") + ip = ip_address(ip) + except ValueError: + host = host.lower() + # IDNA encoding is slow, + # skip it for ASCII-only strings + # Don't move the check into _idna_encode() helper + # to reduce the cache size + if human or host.isascii(): + return host + host = _idna_encode(host) + else: + host = ip.compressed + if sep: + host += "%" + zone + if ip.version == 6: + host = "[" + host + "]" + return host + + @classmethod + def _make_netloc( + cls, user, password, host, port, encode=False, encode_host=True, requote=False + ): + quoter = cls._REQUOTER if requote else cls._QUOTER + if encode_host: + ret = cls._encode_host(host) + else: + ret = host + if port is not None: + ret = ret + ":" + str(port) + if password is not None: + if not user: + user = "" + else: + if encode: + user = quoter(user) + if encode: + password = quoter(password) + user = user + ":" + password + elif user and encode: + user = quoter(user) + if user: + ret = user + "@" + ret + return ret + + def with_scheme(self, scheme): + """Return a new URL with scheme replaced.""" + # N.B. doesn't cleanup query/fragment + if not isinstance(scheme, str): + raise TypeError("Invalid scheme type") + if not self.is_absolute(): + raise ValueError("scheme replacement is not allowed for relative URLs") + return URL(self._val._replace(scheme=scheme.lower()), encoded=True) + + def with_user(self, user): + """Return a new URL with user replaced. + + Autoencode user if needed. + + Clear user/password if user is None. + + """ + # N.B. doesn't cleanup query/fragment + val = self._val + if user is None: + password = None + elif isinstance(user, str): + user = self._QUOTER(user) + password = val.password + else: + raise TypeError("Invalid user type") + if not self.is_absolute(): + raise ValueError("user replacement is not allowed for relative URLs") + return URL( + self._val._replace( + netloc=self._make_netloc(user, password, val.hostname, val.port) + ), + encoded=True, + ) + + def with_password(self, password): + """Return a new URL with password replaced. + + Autoencode password if needed. + + Clear password if argument is None. + + """ + # N.B. doesn't cleanup query/fragment + if password is None: + pass + elif isinstance(password, str): + password = self._QUOTER(password) + else: + raise TypeError("Invalid password type") + if not self.is_absolute(): + raise ValueError("password replacement is not allowed for relative URLs") + val = self._val + return URL( + self._val._replace( + netloc=self._make_netloc(val.username, password, val.hostname, val.port) + ), + encoded=True, + ) + + def with_host(self, host): + """Return a new URL with host replaced. + + Autoencode host if needed. + + Changing host for relative URLs is not allowed, use .join() + instead. + + """ + # N.B. doesn't cleanup query/fragment + if not isinstance(host, str): + raise TypeError("Invalid host type") + if not self.is_absolute(): + raise ValueError("host replacement is not allowed for relative URLs") + if not host: + raise ValueError("host removing is not allowed") + val = self._val + return URL( + self._val._replace( + netloc=self._make_netloc(val.username, val.password, host, val.port) + ), + encoded=True, + ) + + def with_port(self, port): + """Return a new URL with port replaced. + + Clear port to default if None is passed. + + """ + # N.B. doesn't cleanup query/fragment + if port is not None: + if isinstance(port, bool) or not isinstance(port, int): + raise TypeError(f"port should be int or None, got {type(port)}") + if port < 0 or port > 65535: + raise ValueError(f"port must be between 0 and 65535, got {port}") + if not self.is_absolute(): + raise ValueError("port replacement is not allowed for relative URLs") + val = self._val + return URL( + self._val._replace( + netloc=self._make_netloc(val.username, val.password, val.hostname, port) + ), + encoded=True, + ) + + def with_path(self, path, *, encoded=False): + """Return a new URL with path replaced.""" + if not encoded: + path = self._PATH_QUOTER(path) + if self.is_absolute(): + path = self._normalize_path(path) + if len(path) > 0 and path[0] != "/": + path = "/" + path + return URL(self._val._replace(path=path, query="", fragment=""), encoded=True) + + @classmethod + def _query_seq_pairs(cls, quoter, pairs): + for key, val in pairs: + if isinstance(val, (list, tuple)): + for v in val: + yield quoter(key) + "=" + quoter(cls._query_var(v)) + else: + yield quoter(key) + "=" + quoter(cls._query_var(val)) + + @staticmethod + def _query_var(v): + cls = type(v) + if issubclass(cls, str): + return v + if issubclass(cls, float): + if math.isinf(v): + raise ValueError("float('inf') is not supported") + if math.isnan(v): + raise ValueError("float('nan') is not supported") + return str(float(v)) + if issubclass(cls, int) and cls is not bool: + return str(int(v)) + raise TypeError( + "Invalid variable type: value " + "should be str, int or float, got {!r} " + "of type {}".format(v, cls) + ) + + def _get_str_query(self, *args, **kwargs): + if kwargs: + if len(args) > 0: + raise ValueError( + "Either kwargs or single query parameter must be present" + ) + query = kwargs + elif len(args) == 1: + query = args[0] + else: + raise ValueError("Either kwargs or single query parameter must be present") + + if query is None: + query = None + elif isinstance(query, Mapping): + quoter = self._QUERY_PART_QUOTER + query = "&".join(self._query_seq_pairs(quoter, query.items())) + elif isinstance(query, str): + query = self._QUERY_QUOTER(query) + elif isinstance(query, (bytes, bytearray, memoryview)): + raise TypeError( + "Invalid query type: bytes, bytearray and memoryview are forbidden" + ) + elif isinstance(query, Sequence): + quoter = self._QUERY_PART_QUOTER + # We don't expect sequence values if we're given a list of pairs + # already; only mappings like builtin `dict` which can't have the + # same key pointing to multiple values are allowed to use + # `_query_seq_pairs`. + query = "&".join( + quoter(k) + "=" + quoter(self._query_var(v)) for k, v in query + ) + else: + raise TypeError( + "Invalid query type: only str, mapping or " + "sequence of (key, value) pairs is allowed" + ) + + return query + + def with_query(self, *args, **kwargs): + """Return a new URL with query part replaced. + + Accepts any Mapping (e.g. dict, multidict.MultiDict instances) + or str, autoencode the argument if needed. + + A sequence of (key, value) pairs is supported as well. + + It also can take an arbitrary number of keyword arguments. + + Clear query if None is passed. + + """ + # N.B. doesn't cleanup query/fragment + + new_query = self._get_str_query(*args, **kwargs) or "" + return URL( + self._val._replace(path=self._val.path, query=new_query), encoded=True + ) + + def update_query(self, *args, **kwargs): + """Return a new URL with query part updated.""" + s = self._get_str_query(*args, **kwargs) + query = None + if s is not None: + new_query = MultiDict(parse_qsl(s, keep_blank_values=True)) + query = MultiDict(self.query) + query.update(new_query) + + return URL( + self._val._replace(query=self._get_str_query(query) or ""), encoded=True + ) + + def with_fragment(self, fragment): + """Return a new URL with fragment replaced. + + Autoencode fragment if needed. + + Clear fragment to default if None is passed. + + """ + # N.B. doesn't cleanup query/fragment + if fragment is None: + raw_fragment = "" + elif not isinstance(fragment, str): + raise TypeError("Invalid fragment type") + else: + raw_fragment = self._FRAGMENT_QUOTER(fragment) + if self.raw_fragment == raw_fragment: + return self + return URL(self._val._replace(fragment=raw_fragment), encoded=True) + + def with_name(self, name): + """Return a new URL with name (last part of path) replaced. + + Query and fragment parts are cleaned up. + + Name is encoded if needed. + + """ + # N.B. DOES cleanup query/fragment + if not isinstance(name, str): + raise TypeError("Invalid name type") + if "/" in name: + raise ValueError("Slash in name is not allowed") + name = self._PATH_QUOTER(name) + if name in (".", ".."): + raise ValueError(". and .. values are forbidden") + parts = list(self.raw_parts) + if self.is_absolute(): + if len(parts) == 1: + parts.append(name) + else: + parts[-1] = name + parts[0] = "" # replace leading '/' + else: + parts[-1] = name + if parts[0] == "/": + parts[0] = "" # replace leading '/' + return URL( + self._val._replace(path="/".join(parts), query="", fragment=""), + encoded=True, + ) + + def with_suffix(self, suffix): + """Return a new URL with suffix (file extension of name) replaced. + + Query and fragment parts are cleaned up. + + suffix is encoded if needed. + """ + if not isinstance(suffix, str): + raise TypeError("Invalid suffix type") + if suffix and not suffix.startswith(".") or suffix == ".": + raise ValueError(f"Invalid suffix {suffix!r}") + name = self.raw_name + if not name: + raise ValueError(f"{self!r} has an empty name") + old_suffix = self.raw_suffix + if not old_suffix: + name = name + suffix + else: + name = name[: -len(old_suffix)] + suffix + return self.with_name(name) + + def join(self, url): + """Join URLs + + Construct a full (“absolute”) URL by combining a “base URL” + (self) with another URL (url). + + Informally, this uses components of the base URL, in + particular the addressing scheme, the network location and + (part of) the path, to provide missing components in the + relative URL. + + """ + # See docs for urllib.parse.urljoin + if not isinstance(url, URL): + raise TypeError("url should be URL") + return URL(urljoin(str(self), str(url)), encoded=True) + + def joinpath(self, *other, encoded=False): + """Return a new URL with the elements in other appended to the path.""" + return self._make_child(other, encoded=encoded) + + def human_repr(self): + """Return decoded human readable string for URL representation.""" + user = _human_quote(self.user, "#/:?@[]") + password = _human_quote(self.password, "#/:?@[]") + host = self.host + if host: + host = self._encode_host(self.host, human=True) + path = _human_quote(self.path, "#?") + query_string = "&".join( + "{}={}".format(_human_quote(k, "#&+;="), _human_quote(v, "#&+;=")) + for k, v in self.query.items() + ) + fragment = _human_quote(self.fragment, "") + return urlunsplit( + SplitResult( + self.scheme, + self._make_netloc( + user, + password, + host, + self._val.port, + encode_host=False, + ), + path, + query_string, + fragment, + ) + ) + + +def _human_quote(s, unsafe): + if not s: + return s + for c in "%" + unsafe: + if c in s: + s = s.replace(c, f"%{ord(c):02X}") + if s.isprintable(): + return s + return "".join(c if c.isprintable() else quote(c) for c in s) + + +_MAXCACHE = 256 + + +@functools.lru_cache(_MAXCACHE) +def _idna_decode(raw): + try: + return idna.decode(raw.encode("ascii")) + except UnicodeError: # e.g. '::1' + return raw.encode("ascii").decode("idna") + + +@functools.lru_cache(_MAXCACHE) +def _idna_encode(host): + try: + return idna.encode(host, uts46=True).decode("ascii") + except UnicodeError: + return host.encode("idna").decode("ascii") + + +@rewrite_module +def cache_clear(): + _idna_decode.cache_clear() + _idna_encode.cache_clear() + + +@rewrite_module +def cache_info(): + return { + "idna_encode": _idna_encode.cache_info(), + "idna_decode": _idna_decode.cache_info(), + } + + +@rewrite_module +def cache_configure(*, idna_encode_size=_MAXCACHE, idna_decode_size=_MAXCACHE): + global _idna_decode, _idna_encode + + _idna_encode = functools.lru_cache(idna_encode_size)(_idna_encode.__wrapped__) + _idna_decode = functools.lru_cache(idna_decode_size)(_idna_decode.__wrapped__) diff --git a/env-llmeval/lib/python3.10/site-packages/yarl/py.typed b/env-llmeval/lib/python3.10/site-packages/yarl/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..dcf2c804da5e19d617a03a6c68aa128d1d1f89a0 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/yarl/py.typed @@ -0,0 +1 @@ +# Placeholder diff --git a/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/INSTALLER b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/LICENSE b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..dcec4760996be2143d28a12718adc8f9e050bf29 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/LICENSE @@ -0,0 +1,27 @@ +Copyright (c) 2016, Gregory Szorc +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this +list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, +this list of conditions and the following disclaimer in the documentation +and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its contributors +may be used to endorse or promote products derived from this software without +specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/METADATA b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..d7be705bd9e275884a7c1ead91178b093b16afe5 --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/METADATA @@ -0,0 +1,61 @@ +Metadata-Version: 2.1 +Name: zstandard +Version: 0.22.0 +Summary: Zstandard bindings for Python +Home-page: https://github.com/indygreg/python-zstandard +Author: Gregory Szorc +Author-email: gregory.szorc@gmail.com +License: BSD +Keywords: zstandard,zstd,compression +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: C +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 +Requires-Python: >=3.8 +License-File: LICENSE +Requires-Dist: cffi >=1.11 ; platform_python_implementation == "PyPy" +Provides-Extra: cffi +Requires-Dist: cffi >=1.11 ; extra == 'cffi' + +================ +python-zstandard +================ + +| |ci-test| |ci-wheel| |ci-typing| |ci-sdist| |ci-anaconda| |ci-sphinx| + +This project provides Python bindings for interfacing with the +`Zstandard `_ compression library. A C extension +and CFFI interface are provided. + +The primary goal of the project is to provide a rich interface to the +underlying C API through a Pythonic interface while not sacrificing +performance. This means exposing most of the features and flexibility +of the C API while not sacrificing usability or safety that Python provides. + +The canonical home for this project is +https://github.com/indygreg/python-zstandard. + +For usage documentation, see https://python-zstandard.readthedocs.org/. + +.. |ci-test| image:: https://github.com/indygreg/python-zstandard/workflows/.github/workflows/test.yml/badge.svg + :target: https://github.com/indygreg/python-zstandard/blob/main/.github/workflows/test.yml + +.. |ci-wheel| image:: https://github.com/indygreg/python-zstandard/workflows/.github/workflows/wheel.yml/badge.svg + :target: https://github.com/indygreg/python-zstandard/blob/main/.github/workflows/wheel.yml + +.. |ci-typing| image:: https://github.com/indygreg/python-zstandard/workflows/.github/workflows/typing.yml/badge.svg + :target: https://github.com/indygreg/python-zstandard/blob/main/.github/workflows/typing.yml + +.. |ci-sdist| image:: https://github.com/indygreg/python-zstandard/workflows/.github/workflows/sdist.yml/badge.svg + :target: https://github.com/indygreg/python-zstandard/blob/main/.github/workflows/sdist.yml + +.. |ci-anaconda| image:: https://github.com/indygreg/python-zstandard/workflows/.github/workflows/anaconda.yml/badge.svg + :target: https://github.com/indygreg/python-zstandard/blob/main/.github/workflows/anaconda.yml + +.. |ci-sphinx| image:: https://github.com/indygreg/python-zstandard/workflows/.github/workflows/sphinx.yml/badge.svg + :target: https://github.com/indygreg/python-zstandard/blob/main/.github/workflows/sphinx.yml diff --git a/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/RECORD b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..26b60f0eddc30ceff11a25901732cb7a7152cf0d --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/RECORD @@ -0,0 +1,14 @@ +zstandard-0.22.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +zstandard-0.22.0.dist-info/LICENSE,sha256=eI_oXlNySkCymQUcSryc901Csn55aDJbgIkcJfea20s,1484 +zstandard-0.22.0.dist-info/METADATA,sha256=ioMv0Bi7ECdt9Ll9mP93LfpR3Jm8KRzaXS2mE_w4S8I,2909 +zstandard-0.22.0.dist-info/RECORD,, +zstandard-0.22.0.dist-info/WHEEL,sha256=cD39NF6a3hkhaWoPQJng7gnGZRIfQsUCtwcedITCPtg,152 +zstandard-0.22.0.dist-info/top_level.txt,sha256=J-wj94pPadY4ipFaanrYBlrMblOSegEYS8o_LdogrpU,10 +zstandard/__init__.py,sha256=7CalB28kCVwqwflhLUHneyPK_Mt2xp_Ogt24ISHlQoQ,7102 +zstandard/__init__.pyi,sha256=kvP6Us9IPX4Ssfg5BwaE3_E7uuE1YzVUo9hTzYUcNHA,13938 +zstandard/__pycache__/__init__.cpython-310.pyc,, +zstandard/__pycache__/backend_cffi.cpython-310.pyc,, +zstandard/_cffi.cpython-310-x86_64-linux-gnu.so,sha256=uS7_ZwqYNCxo5PrUdagck_arC70Bt9BH1zj2U6FUKIY,11882792 +zstandard/backend_c.cpython-310-x86_64-linux-gnu.so,sha256=t3A7at18BBMylNMbf4DwJSD7IzAozR_rb7dUd7bQKXE,11287920 +zstandard/backend_cffi.py,sha256=hMQlr0fkN-E2ZUBqUszANzeYV9Y7ditGmiasKjfbAMg,152430 +zstandard/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 diff --git a/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/WHEEL b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..7b52c3f3e667e03ca4b2a8b53a94655a796beafc --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/WHEEL @@ -0,0 +1,6 @@ +Wheel-Version: 1.0 +Generator: bdist_wheel (0.41.2) +Root-Is-Purelib: false +Tag: cp310-cp310-manylinux_2_17_x86_64 +Tag: cp310-cp310-manylinux2014_x86_64 + diff --git a/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/top_level.txt b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..864700d2b3e63509d1e25eff308c0a99386bb4ac --- /dev/null +++ b/env-llmeval/lib/python3.10/site-packages/zstandard-0.22.0.dist-info/top_level.txt @@ -0,0 +1 @@ +zstandard