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+# This is a stub package designed to roughly emulate the _yaml
+# extension module, which previously existed as a standalone module
+# and has been moved into the `yaml` package namespace.
+# It does not perfectly mimic its old counterpart, but should get
+# close enough for anyone who's relying on it even when they shouldn't.
+import yaml
+
+# in some circumstances, the yaml module we imoprted may be from a different version, so we need
+# to tread carefully when poking at it here (it may not have the attributes we expect)
+if not getattr(yaml, '__with_libyaml__', False):
+    from sys import version_info
+
+    exc = ModuleNotFoundError if version_info >= (3, 6) else ImportError
+    raise exc("No module named '_yaml'")
+else:
+    from yaml._yaml import *
+    import warnings
+    warnings.warn(
+        'The _yaml extension module is now located at yaml._yaml'
+        ' and its location is subject to change.  To use the'
+        ' LibYAML-based parser and emitter, import from `yaml`:'
+        ' `from yaml import CLoader as Loader, CDumper as Dumper`.',
+        DeprecationWarning
+    )
+    del warnings
+    # Don't `del yaml` here because yaml is actually an existing
+    # namespace member of _yaml.
+
+__name__ = '_yaml'
+# If the module is top-level (i.e. not a part of any specific package)
+# then the attribute should be set to ''.
+# https://docs.python.org/3.8/library/types.html
+__package__ = ''

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+pip

lib/python3.12/site-packages/certifi-2026.1.4.dist-info/METADATA

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+Metadata-Version: 2.4
+Name: certifi
+Version: 2026.1.4
+Summary: Python package for providing Mozilla's CA Bundle.
+Home-page: https://github.com/certifi/python-certifi
+Author: Kenneth Reitz
+Author-email: me@kennethreitz.com
+License: MPL-2.0
+Project-URL: Source, https://github.com/certifi/python-certifi
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Requires-Python: >=3.7
+License-File: LICENSE
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: home-page
+Dynamic: license
+Dynamic: license-file
+Dynamic: project-url
+Dynamic: requires-python
+Dynamic: summary
+
+Certifi: Python SSL Certificates
+================================
+
+Certifi provides Mozilla's carefully curated collection of Root Certificates for
+validating the trustworthiness of SSL certificates while verifying the identity
+of TLS hosts. It has been extracted from the `Requests`_ project.
+
+Installation
+------------
+
+``certifi`` is available on PyPI. Simply install it with ``pip``::
+
+    $ pip install certifi
+
+Usage
+-----
+
+To reference the installed certificate authority (CA) bundle, you can use the
+built-in function::
+
+    >>> import certifi
+
+    >>> certifi.where()
+    '/usr/local/lib/python3.7/site-packages/certifi/cacert.pem'
+
+Or from the command line::
+
+    $ python -m certifi
+    /usr/local/lib/python3.7/site-packages/certifi/cacert.pem
+
+Enjoy!
+
+.. _`Requests`: https://requests.readthedocs.io/en/master/
+
+Addition/Removal of Certificates
+--------------------------------
+
+Certifi does not support any addition/removal or other modification of the
+CA trust store content. This project is intended to provide a reliable and
+highly portable root of trust to python deployments. Look to upstream projects
+for methods to use alternate trust.

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+certifi-2026.1.4.dist-info/licenses/LICENSE,sha256=6TcW2mucDVpKHfYP5pWzcPBpVgPSH2-D8FPkLPwQyvc,989
+certifi-2026.1.4.dist-info/top_level.txt,sha256=KMu4vUCfsjLrkPbSNdgdekS-pVJzBAJFO__nI8NF6-U,8
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+certifi/core.py,sha256=XFXycndG5pf37ayeF8N32HUuDafsyhkVMbO4BAPWHa0,3394
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+Wheel-Version: 1.0
+Generator: setuptools (80.9.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

lib/python3.12/site-packages/certifi-2026.1.4.dist-info/licenses/LICENSE

@@ -0,0 +1,20 @@
+This package contains a modified version of ca-bundle.crt:
+
+ca-bundle.crt -- Bundle of CA Root Certificates
+
+This is a bundle of X.509 certificates of public Certificate Authorities
+(CA). These were automatically extracted from Mozilla's root certificates
+file (certdata.txt).  This file can be found in the mozilla source tree:
+https://hg.mozilla.org/mozilla-central/file/tip/security/nss/lib/ckfw/builtins/certdata.txt
+It contains the certificates in PEM format and therefore
+can be directly used with curl / libcurl / php_curl, or with
+an Apache+mod_ssl webserver for SSL client authentication.
+Just configure this file as the SSLCACertificateFile.#
+
+***** BEGIN LICENSE BLOCK *****
+This Source Code Form is subject to the terms of the Mozilla Public License,
+v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain
+one at http://mozilla.org/MPL/2.0/.
+
+***** END LICENSE BLOCK *****
+@(#) $RCSfile: certdata.txt,v $ $Revision: 1.80 $ $Date: 2011/11/03 15:11:58 $

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+Name: deepspeed
+Version: 0.17.0
+Summary: DeepSpeed library
+Home-page: http://deepspeed.ai
+Author: DeepSpeed Team
+Author-email: deepspeed-info@microsoft.com
+License: Apache Software License 2.0
+Project-URL: Documentation, https://deepspeed.readthedocs.io
+Project-URL: Source, https://github.com/deepspeedai/DeepSpeed
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+Requires-Dist: pydantic>=2.0.0; extra == "all"
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+Requires-Dist: pytest-xdist; extra == "all"
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+Requires-Dist: accelerate; extra == "all"
+Requires-Dist: tabulate; extra == "all"
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+Requires-Dist: neural-compressor==2.1.0; extra == "all"
+Requires-Dist: torchvision; extra == "all"
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+Dynamic: author
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+Dynamic: home-page
+Dynamic: license
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+Dynamic: provides-extra
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+Dynamic: summary
+
+[![License Apache 2.0](https://badgen.net/badge/license/apache2.0/blue)](https://github.com/deepspeedai/DeepSpeed/blob/master/LICENSE)
+[![PyPI version](https://badge.fury.io/py/deepspeed.svg)](https://pypi.org/project/deepspeed/)
+[![Downloads](https://static.pepy.tech/badge/deepspeed)](https://pepy.tech/project/deepspeed)
+[![Build](https://badgen.net/badge/build/check-status/blue)](#build-pipeline-status)
+[![OpenSSF Best Practices](https://www.bestpractices.dev/projects/9530/badge)](https://www.bestpractices.dev/projects/9530)
+[![Twitter](https://img.shields.io/twitter/follow/DeepSpeedAI)](https://twitter.com/intent/follow?screen_name=DeepSpeedAI)
+[![Japanese Twitter](https://img.shields.io/badge/%E6%97%A5%E6%9C%AC%E8%AA%9ETwitter-%40DeepSpeedAI_JP-blue)](https://twitter.com/DeepSpeedAI_JP)
+[![Chinese Zhihu](https://img.shields.io/badge/%E7%9F%A5%E4%B9%8E-%E5%BE%AE%E8%BD%AFDeepSpeed-blue)](https://www.zhihu.com/people/deepspeed)
+
+
+<div align="center">
+ <img src="docs/assets/images/DeepSpeed_light.svg#gh-light-mode-only" width="400px">
+ <img src="docs/assets/images/DeepSpeed_dark_transparent.svg#gh-dark-mode-only" width="400px">
+</div>
+
+## Latest News
+<b> <span style="color:orange" > DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat)</span>.</b>
+* [2025/04] [DeepCompile: Unlocking Compiler Optimization for Distributed Training](https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepcompile/README.md)
+* [2025/03] [DeepSpeed-AutoTP: Automatic Tensor Parallel Training of Hugging Face models](https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/huggingface-tp/README.md)
+* [2024/12] [Ulysses-Offload: Democratizing Long Context LLM Training ](https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/ulysses-offload/README.md)
+* [2024/12] [DeepSpeed-Domino: Communication-Free LLM Training Engine](https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-domino/README.md)
+* [2024/08] [DeepSpeed on Windows](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/README.md) [[日本語](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/japanese/README.md)]  [[中文](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/chinese/README.md)]
+
+<!-- NOTE: we must use html for news items otherwise links will be broken in the 'more news' section -->
+<details>
+ <summary>More news</summary>
+ <ul>
+   <li> [2024/08] <a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-gds/README.md"> DeepNVMe: Improving DL Applications through I/O Optimizations</a> [<a href="ttps://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-gds/japanese/README.md"> 日本語 </a>] [<a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-gds/japanese/README.md"> 中文 </a>]</li>
+
+    <li> [2024/07] <a href="https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ucp/README.md"> DeepSpeed Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training</a> [<a href="https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ucp/japanese/README.md"> 日本語 </a>] </li>
+
+   <li> [2024/03] <a href="https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README.md"> DeepSpeed-FP6: The Power of FP6-Centric Serving for Large Language Models</a> [<a href="https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README-Chinese.md"> 中文 </a>] </li>
+
+ </ul>
+</details>
+
+---
+
+# Extreme Speed and Scale for DL Training and Inference
+
+***[DeepSpeed](https://www.deepspeed.ai/) enabled the world's most powerful language models (at the time of this writing) such as [MT-530B](https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/) and [BLOOM](https://huggingface.co/blog/bloom-megatron-deepspeed)***. It is an easy-to-use deep learning optimization software suite that powers unprecedented scale and speed for both training and inference. With DeepSpeed you can:
+
+* Train/Inference dense or sparse models with billions or trillions of parameters
+* Achieve excellent system throughput and efficiently scale to thousands of GPUs
+* Train/Inference on resource constrained GPU systems
+* Achieve unprecedented low latency and high throughput for inference
+* Achieve extreme compression for an unparalleled inference latency and model size reduction with low costs
+
+---
+
+# DeepSpeed's four innovation pillars
+
+<img src="docs/assets/images/DeepSpeed-pillars.png" width="800px">
+
+
+## DeepSpeed-Training
+
+DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infinity, etc. fall under the training pillar. Learn more: [DeepSpeed-Training](https://www.deepspeed.ai/training/)
+
+## DeepSpeed-Inference
+
+DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving unparalleled latency, throughput and cost reduction. This systematic composition of system technologies for inference falls under the inference pillar. Learn more: [DeepSpeed-Inference](https://www.deepspeed.ai/inference)
+
+
+## DeepSpeed-Compression
+
+To further increase the inference efficiency, DeepSpeed offers easy-to-use and flexible-to-compose compression techniques for researchers and practitioners to compress their models while delivering faster speed, smaller model size, and significantly reduced compression cost. Moreover, SoTA innovations on compression like ZeroQuant and XTC are included under the compression pillar. Learn more: [DeepSpeed-Compression](https://www.deepspeed.ai/compression)
+
+## DeepSpeed4Science
+
+In line with Microsoft's mission to solve humanity's most pressing challenges, the DeepSpeed team at Microsoft is responding to this opportunity by launching a new initiative called *DeepSpeed4Science*, aiming to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. Learn more: [tutorials](https://www.deepspeed.ai/deepspeed4science/)
+
+---
+
+# DeepSpeed Software Suite
+
+## DeepSpeed Library
+
+   The [DeepSpeed](https://github.com/deepspeedai/deepspeed) library (this repository) implements and packages the innovations and technologies in DeepSpeed Training, Inference and Compression Pillars into a single easy-to-use, open-sourced repository. It allows for easy composition of multitude of features within a single training, inference or compression pipeline. The DeepSpeed Library is heavily adopted by the DL community, and has been used to enable some of the most powerful models (see [DeepSpeed Adoption](#deepspeed-adoption)).
+
+## Model Implementations for Inference (MII)
+
+   [Model Implementations for Inference (MII)](https://github.com/deepspeedai/deepspeed-mii) is an open-sourced repository for making low-latency and high-throughput inference accessible to all data scientists by alleviating the need to apply complex system optimization techniques themselves. Out-of-box, MII offers support for thousands of widely used DL models, optimized using DeepSpeed-Inference, that can be deployed with a few lines of code, while achieving significant latency reduction compared to their vanilla open-sourced versions.
+
+## DeepSpeed on Azure
+
+   DeepSpeed users are diverse and have access to different environments. We recommend to try DeepSpeed on Azure as it is the simplest and easiest method. The recommended method to try DeepSpeed on Azure is through AzureML [recipes](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/workflows/train/deepspeed). The job submission and data preparation scripts have been made available [here](https://github.com/deepspeedai/Megatron-DeepSpeed/tree/main/examples_deepspeed/azureml). For more details on how to use DeepSpeed on Azure, please follow the [Azure tutorial](https://www.deepspeed.ai/tutorials/azure/).
+
+---
+
+# DeepSpeed Adoption
+
+DeepSpeed was an important part of Microsoft’s
+[AI at Scale](https://www.microsoft.com/en-us/research/project/ai-at-scale/)
+initiative to enable next-generation AI capabilities at scale, where you can find more
+information [here](https://innovation.microsoft.com/en-us/exploring-ai-at-scale).
+
+DeepSpeed has been used to train many different large-scale models, below is a list of several examples that we are aware of (if you'd like to include your model please submit a PR):
+
+  * [Megatron-Turing NLG (530B)](https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/)
+  * [Jurassic-1 (178B)](https://uploads-ssl.webflow.com/60fd4503684b466578c0d307/61138924626a6981ee09caf6_jurassic_tech_paper.pdf)
+  * [BLOOM (176B)](https://huggingface.co/blog/bloom-megatron-deepspeed)
+  * [GLM (130B)](https://github.com/THUDM/GLM-130B)
+  * [xTrimoPGLM (100B)](https://www.biorxiv.org/content/10.1101/2023.07.05.547496v2)
+  * [YaLM (100B)](https://github.com/yandex/YaLM-100B)
+  * [GPT-NeoX (20B)](https://github.com/EleutherAI/gpt-neox)
+  * [AlexaTM (20B)](https://www.amazon.science/blog/20b-parameter-alexa-model-sets-new-marks-in-few-shot-learning)
+  * [Turing NLG (17B)](https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/)
+  * [METRO-LM (5.4B)](https://arxiv.org/pdf/2204.06644.pdf)
+
+DeepSpeed has been integrated with several different popular open-source DL frameworks such as:
+
+|                                                                                                | Documentation                                |
+| ---------------------------------------------------------------------------------------------- | -------------------------------------------- |
+<img src="docs/assets/images/transformers-light.png#gh-light-mode-only" width="250px"><img src="docs/assets/images/transformers-dark.png#gh-dark-mode-only" width="250px"> | [Transformers with DeepSpeed](https://huggingface.co/docs/transformers/deepspeed) |
+| <img src="docs/assets/images/accelerate-light.png#gh-light-mode-only" width="250px"><img src="docs/assets/images/accelerate-dark.png#gh-dark-mode-only" width="250px"> | [Accelerate with DeepSpeed](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) |
+| <img src="docs/assets/images/lightning-light.svg#gh-light-mode-only" width="200px"><img src="docs/assets/images/lightning-dark.svg#gh-dark-mode-only" width="200px"> | [Lightning with DeepSpeed](https://lightning.ai/docs/pytorch/stable/advanced/model_parallel.html#deepspeed) |
+| <img src="docs/assets/images/mosaicml.svg" width="200px"> | [MosaicML with DeepSpeed](https://docs.mosaicml.com/projects/composer/en/latest/trainer/using_the_trainer.html?highlight=deepspeed#deepspeed-integration) |
+| <img src="docs/assets/images/determined.svg" width="225px"> | [Determined with DeepSpeed](https://docs.determined.ai/latest/training/apis-howto/deepspeed/overview.html) |
+| <img src="https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg" width=150> | [MMEngine with DeepSpeed](https://mmengine.readthedocs.io/en/latest/common_usage/large_model_training.html#deepspeed) |
+
+---
+
+# Build Pipeline Status
+
+| Description | Status |
+| ----------- | ------ |
+| NVIDIA | [![nv-torch110-p40](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-p40.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-p40.yml) [![nv-torch110-v100](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-v100.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-v100.yml) [![nv-torch-latest-v100](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-latest-v100.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-latest-v100.yml) [![nv-h100](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-h100.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-h100.yml) [![nv-inference](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-inference.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-inference.yml) [![nv-nightly](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-nightly.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-nightly.yml) |
+| AMD | [![amd-mi200](https://github.com/deepspeedai/DeepSpeed/actions/workflows/amd-mi200.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/amd-mi200.yml) |
+| CPU | [![torch-latest-cpu](https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-torch-latest.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-torch-latest.yml) [![cpu-inference](https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-inference.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-inference.yml) |
+| Intel Gaudi | [![hpu-gaudi2](https://github.com/deepspeedai/DeepSpeed/actions/workflows/hpu-gaudi2.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/hpu-gaudi2.yml) |
+| Intel XPU | [![xpu-max1100](https://github.com/deepspeedai/DeepSpeed/actions/workflows/xpu-max1100.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/xpu-max1100.yml) |
+| PyTorch Nightly | [![nv-torch-nightly-v100](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml) |
+| Integrations | [![nv-transformers-v100](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-transformers-v100.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-transformers-v100.yml) [![nv-lightning-v100](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-lightning-v100.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-lightning-v100.yml) [![nv-accelerate-v100](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-accelerate-v100.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-accelerate-v100.yml) [![nv-mii](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-mii.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-mii.yml) [![nv-ds-chat](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml) [![nv-sd](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-sd.yml/badge.svg)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-sd.yml) |
+| Misc | [![Formatting](https://github.com/deepspeedai/DeepSpeed/actions/workflows/formatting.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/formatting.yml) [![pages-build-deployment](https://github.com/deepspeedai/DeepSpeed/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/pages/pages-build-deployment) [![Documentation Status](https://readthedocs.org/projects/deepspeed/badge/?version=latest)](https://deepspeed.readthedocs.io/en/latest/?badge=latest)[![python](https://github.com/deepspeedai/DeepSpeed/actions/workflows/python.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/python.yml) |
+| Huawei Ascend NPU | [![Huawei Ascend NPU](https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml/badge.svg?branch=main)](https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml) |
+
+# Installation
+
+The quickest way to get started with DeepSpeed is via pip, this will install
+the latest release of DeepSpeed which is not tied to specific PyTorch or CUDA
+versions. DeepSpeed includes several C++/CUDA extensions that we commonly refer
+to as our 'ops'.  By default, all of these extensions/ops will be built
+just-in-time (JIT) using [torch's JIT C++ extension loader that relies on
+ninja](https://pytorch.org/docs/stable/cpp_extension.html) to build and
+dynamically link them at runtime.
+
+## Requirements
+* [PyTorch](https://pytorch.org/) must be installed _before_ installing DeepSpeed.
+* For full feature support we recommend a version of PyTorch that is >= 1.9 and ideally the latest PyTorch stable release.
+* A CUDA or ROCm compiler such as [nvcc](https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/#introduction) or [hipcc](https://github.com/ROCm-Developer-Tools/HIPCC) used to compile C++/CUDA/HIP extensions.
+* Specific GPUs we develop and test against are listed below, this doesn't mean your GPU will not work if it doesn't fall into this category it's just DeepSpeed is most well tested on the following:
+  * NVIDIA: Pascal, Volta, Ampere, and Hopper architectures
+  * AMD: MI100 and MI200
+
+## Contributed HW support
+* DeepSpeed now support various HW accelerators.
+
+| Contributor | Hardware                            | Accelerator Name | Contributor validated | Upstream validated |
+|-------------|-------------------------------------|------------------| --------------------- |--------------------|
+| Huawei      | Huawei Ascend NPU                   | npu              | Yes | No                 |
+| Intel       | Intel(R) Gaudi(R) 2 AI accelerator  | hpu              | Yes | Yes                |
+| Intel       | Intel(R) Xeon(R) Processors         | cpu              | Yes | Yes                |
+| Intel       | Intel(R) Data Center GPU Max series | xpu              | Yes | Yes                |
+| Tecorigin   | Scalable Data Analytics Accelerator | sdaa             | Yes | No                 |
+
+## PyPI
+We regularly push releases to [PyPI](https://pypi.org/project/deepspeed/) and encourage users to install from there in most cases.
+
+```bash
+pip install deepspeed
+```
+
+After installation, you can validate your install and see which extensions/ops
+your machine is compatible with via the DeepSpeed environment report.
+
+```bash
+ds_report
+```
+
+If you would like to pre-install any of the DeepSpeed extensions/ops (instead
+of JIT compiling) or install pre-compiled ops via PyPI please see our [advanced
+installation instructions](https://www.deepspeed.ai/tutorials/advanced-install/).
+
+## Windows
+Many DeepSpeed features are supported on Windows for both training and inference. You can read more about this in the original blog post [here](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/README.md). Among features that are currently not supported are async io (AIO) and GDS (which does not support Windows).
+1. Install PyTorch, such as pytorch 2.3+cu121.
+2. Install Visual C++ build tools, such as VS2022 C++ x64/x86 build tools.
+3. Launch Cmd console with Administrator permissions for creating required symlink folders and ensure MSVC tools are added to your PATH or launch the Developer Command Prompt for Visual Studio 2022 with administrator permissions.
+4. Run `build_win.bat` to build wheel in `dist` folder.
+
+# Features
+
+Please checkout [DeepSpeed-Training](https://www.deepspeed.ai/training), [DeepSpeed-Inference](https://www.deepspeed.ai/inference) and [DeepSpeed-Compression](https://www.deepspeed.ai/compression) pages for full set of features offered along each of these three pillars.
+
+# Further Reading
+
+All DeepSpeed documentation, tutorials, and blogs can be found on our website: [deepspeed.ai](https://www.deepspeed.ai/)
+
+
+|                                                                                                | Description                                  |
+| ---------------------------------------------------------------------------------------------- | -------------------------------------------- |
+| [Getting Started](https://www.deepspeed.ai/getting-started/)                                   |  First steps with DeepSpeed                  |
+| [DeepSpeed JSON Configuration](https://www.deepspeed.ai/docs/config-json/)                     |  Configuring DeepSpeed                       |
+| [API Documentation](https://deepspeed.readthedocs.io/en/latest/)                               |  Generated DeepSpeed API documentation       |
+| [Tutorials](https://www.deepspeed.ai/tutorials/)                                               |  Tutorials                                   |
+| [Blogs](https://www.deepspeed.ai/posts/)                                                       |  Blogs                                   |
+
+
+# Contributing
+DeepSpeed welcomes your contributions! Please see our
+[contributing](CONTRIBUTING.md) guide for more details on formatting, testing,
+etc.<br/>
+Thanks so much to all of our amazing contributors!
+
+<a href="https://github.com/deepspeedai/DeepSpeed/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=microsoft/DeepSpeed&r="  width="800px"/>
+</a>
+
+## Contributor License Agreement
+This project welcomes contributions and suggestions. Most contributions require you to
+agree to a Contributor License Agreement (CLA) declaring that you have the right to, and
+actually do, grant us the rights to use your contribution. For details, visit
+https://cla.opensource.microsoft.com.
+
+When you submit a pull request, a CLA bot will automatically determine whether you need
+to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply
+follow the instructions provided by the bot. You will only need to do this once across
+all repos using our CLA.
+
+## Code of Conduct
+This project has adopted the [Microsoft Open Source Code of
+Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the
+[Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact
+[opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+
+# Publications
+1. Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He. (2019) ZeRO: memory optimizations toward training trillion parameter models. [arXiv:1910.02054](https://arxiv.org/abs/1910.02054) and [In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '20)](https://dl.acm.org/doi/10.5555/3433701.3433727).
+2. Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. (2020) DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters. [In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '20, Tutorial)](https://dl.acm.org/doi/10.1145/3394486.3406703).
+3. Minjia Zhang, Yuxiong He. (2020) Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping. [arXiv:2010.13369](https://arxiv.org/abs/2010.13369) and [NeurIPS 2020](https://proceedings.neurips.cc/paper/2020/hash/a1140a3d0df1c81e24ae954d935e8926-Abstract.html).
+4. Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, Yuxiong He. (2021) ZeRO-Offload: Democratizing Billion-Scale Model Training. [arXiv:2101.06840](https://arxiv.org/abs/2101.06840) and [USENIX ATC 2021](https://www.usenix.org/conference/atc21/presentation/ren-jie). [[paper]](https://arxiv.org/abs/2101.06840) [[slides]](https://www.usenix.org/system/files/atc21_slides_ren-jie.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/)
+5. Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He. (2021) 1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed. [arXiv:2102.02888](https://arxiv.org/abs/2102.02888) and [ICML 2021](http://proceedings.mlr.press/v139/tang21a.html).
+6. Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, Yuxiong He. (2021) ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning. [arXiv:2104.07857](https://arxiv.org/abs/2104.07857) and [SC 2021](https://dl.acm.org/doi/abs/10.1145/3458817.3476205). [[paper]](https://arxiv.org/abs/2104.07857) [[slides]](docs/assets/files/SC21-ZeRO-Infinity.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/zero-infinity-and-deepspeed-unlocking-unprecedented-model-scale-for-deep-learning-training/)
+7. Conglong Li, Ammar Ahmad Awan, Hanlin Tang, Samyam Rajbhandari, Yuxiong He. (2021) 1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed. [arXiv:2104.06069](https://arxiv.org/abs/2104.06069) and [HiPC 2022](https://hipc.org/advance-program/).
+8. Conglong Li, Minjia Zhang, Yuxiong He. (2021) The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models. [arXiv:2108.06084](https://arxiv.org/abs/2108.06084) and [NeurIPS 2022](https://openreview.net/forum?id=JpZ5du_Kdh).
+9. Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He. (2022) Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. [arXiv:2202.06009](https://arxiv.org/abs/2202.06009).
+10. Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He. (2022) DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale [arXiv:2201.05596](https://arxiv.org/abs/2201.05596) and [ICML 2022](https://proceedings.mlr.press/v162/rajbhandari22a.html). [[pdf]](https://arxiv.org/abs/2201.05596) [[slides]](docs/assets/files/ICML-5mins.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-advancing-moe-inference-and-training-to-power-next-generation-ai-scale/)
+11. Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, Bryan Catanzaro. (2022) Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model [arXiv:2201.11990](https://arxiv.org/abs/2201.11990).
+12. Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He. (2022) Extreme Compression for Pre-trained Transformers Made Simple and Efficient. [arXiv:2206.01859](https://arxiv.org/abs/2206.01859) and [NeurIPS 2022](https://openreview.net/forum?id=xNeAhc2CNAl).
+13. Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He. (2022) ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. [arXiv:2206.01861](https://arxiv.org/abs/2206.01861) and [NeurIPS 2022](https://openreview.net/forum?id=f-fVCElZ-G1) [[slides]](docs/assets/files/zeroquant_series.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/)
+14. Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He. (2022) DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale. [arXiv:2207.00032](https://arxiv.org/abs/2207.00032) and [SC 2022](https://dl.acm.org/doi/abs/10.5555/3571885.3571946). [[paper]](https://arxiv.org/abs/2207.00032) [[slides]](docs/assets/files/sc22-ds-inference.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-accelerating-large-scale-model-inference-and-training-via-system-optimizations-and-compression/)
+15. Zhewei Yao, Xiaoxia Wu, Conglong Li, Connor Holmes, Minjia Zhang, Cheng Li, Yuxiong He. (2022) Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers. [arXiv:2211.11586](https://arxiv.org/abs/2211.11586).
+16. Conglong Li, Zhewei Yao, Xiaoxia Wu, Minjia Zhang, Yuxiong He. (2022) DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing. [arXiv:2212.03597](https://arxiv.org/abs/2212.03597) [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/)
+17. Xiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi, Zhewei Yao, Yuxiong He. (2023) Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases. [arXiv:2301.12017](https://arxiv.org/abs/2301.12017) and [ICML2023](https://icml.cc/Conferences/2023).
+18. Syed Zawad, Cheng Li, Zhewei Yao, Elton Zheng, Yuxiong He, Feng Yan. (2023) DySR: Adaptive Super-Resolution via Algorithm and System Co-design. [ICLR:2023](https://openreview.net/forum?id=Pgtn4l6eKjv).
+19. Sheng Shen, Zhewei Yao, Chunyuan Li, Trevor Darrell, Kurt Keutzer, Yuxiong He. (2023) Scaling Vision-Language Models with Sparse Mixture of Experts. [arXiv:2303.07226](https://arxiv.org/abs/2303.07226) and [Finding at EMNLP2023](https://2023.emnlp.org/).
+20. Quentin Anthony, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He, Aamir Shafi, Mustafa Abduljabbar, Hari Subramoni, Dhabaleswar Panda. (2023) MCR-DL: Mix-and-Match Communication Runtime for Deep Learning [arXiv:2303.08374](https://arxiv.org/abs/2303.08374) and will appear at IPDPS 2023.
+21. Siddharth Singh, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He, Abhinav Bhatele. (2023) A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training [arXiv:2303.06318](https://arxiv.org/abs/2303.06318) and [ICS 2023](https://dl.acm.org/doi/10.1145/3577193.3593704).
+22. Guanhua Wang, Heyang Qin, Sam Ade Jacobs, Xiaoxia Wu, Connor Holmes, Zhewei Yao, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He. (2023) ZeRO++: Extremely Efficient Collective Communication for Giant Model Training [arXiv:2306.10209](https://arxiv.org/abs/2306.10209) and [ML for Sys Workshop at NeurIPS2023](http://mlforsystems.org/) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)
+23. Zhewei Yao, Xiaoxia Wu, Cheng Li, Stephen Youn, Yuxiong He. (2023) ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation [arXiv:2303.08302](https://arxiv.org/abs/2303.08302) and [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/) [[slides]](docs/assets/files/zeroquant_series.pdf)
+24. Pareesa Ameneh Golnari, Zhewei Yao, Yuxiong He. (2023) Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important? [arXiv:2305.09847](https://arxiv.org/abs/2305.09847)
+25. Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, Zhongzhu Zhou, Michael Wyatt, Molly Smith, Lev Kurilenko, Heyang Qin, Masahiro Tanaka, Shuai Che, Shuaiwen Leon Song, Yuxiong He. (2023) DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales [arXiv:2308.01320](https://arxiv.org/abs/2308.01320).
+26. Xiaoxia Wu, Zhewei Yao, Yuxiong He. (2023) ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats [arXiv:2307.09782](https://arxiv.org/abs/2307.09782) and [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/) [[slides]](docs/assets/files/zeroquant_series.pdf)
+27. Zhewei Yao, Xiaoxia Wu, Conglong Li, Minjia Zhang, Heyang Qin, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He. (2023) DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention [arXiv:2309.14327](https://arxiv.org/pdf/2309.14327.pdf)
+28. Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, et al. (2023) DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies [arXiv:2310.04610](https://arxiv.org/abs/2310.04610) [[blog]](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)
+29. Zhewei Yao, Reza Yazdani Aminabadi, Stephen Youn, Xiaoxia Wu, Elton Zheng, Yuxiong He. (2023) ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers [arXiv:2310.17723](https://arxiv.org/abs/2310.17723)
+
+30. Xiaoxia Wu, Haojun Xia, Stephen Youn, Zhen Zheng, Shiyang Chen, Arash Bakhtiari, Michael Wyatt, Reza Yazdani Aminabadi, Yuxiong He, Olatunji Ruwase, Leon Song, Zhewei Yao (2023) ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks [arXiv:2312.08583](https://arxiv.org/abs/2312.08583)
+
+31. Haojun Xia, Zhen Zheng, Xiaoxia Wu, Shiyang Chen, Zhewei Yao, Stephen Youn, Arash Bakhtiari, Michael Wyatt, Donglin Zhuang, Zhongzhu Zhou, Olatunji Ruwase, Yuxiong He, Shuaiwen Leon Song. (2024) FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design  [arXiv:2401.14112](https://arxiv.org/abs/2401.14112)
+32. Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, Minjia Zhang, Reza Yazdani Aminadabi, Shuaiwen Leon Song, Samyam Rajbhandari, Yuxiong He. (2024) [System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://dl.acm.org/doi/10.1145/3662158.3662806)
+33. Xinyu Lian, Sam Ade Jacobs, Lev Kurilenko, Masahiro Tanaka, Stas Bekman, Olatunji Ruwase, Minjia Zhang. (2024) Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training [arXiv:2406.18820](https://arxiv.org/abs/2406.18820)
+
+
+
+
+# Videos
+1. DeepSpeed KDD 2020 Tutorial
+    1. [Overview](https://www.youtube.com/watch?v=CaseqC45DNc&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=29)
+    2. [ZeRO + large model training](https://www.youtube.com/watch?v=y4_bCiAsIAk&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=28)
+    3. [17B T-NLG demo](https://www.youtube.com/watch?v=9V-ZbP92drg&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=27)
+    4. [Fastest BERT training + RScan tuning](https://www.youtube.com/watch?v=o1K-ZG9F6u0&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=26)
+    5. DeepSpeed hands on deep dive: [part 1](https://www.youtube.com/watch?v=_NOk-mBwDYg&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=92), [part 2](https://www.youtube.com/watch?v=sG6_c4VXLww&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=94), [part 3](https://www.youtube.com/watch?v=k9yPkBTayos&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=93)
+    6. [FAQ](https://www.youtube.com/watch?v=nsHu6vEgPew&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=24)
+2. Microsoft Research Webinar
+    * Registration is free and all videos are available on-demand.
+    * [ZeRO & Fastest BERT: Increasing the scale and speed of deep learning training in DeepSpeed](https://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-On-Demand.html).
+3. [DeepSpeed on AzureML](https://youtu.be/yBVXR8G8Bg8)
+4. [Large Model Training and Inference with DeepSpeed // Samyam Rajbhandari // LLMs in Prod Conference](https://www.youtube.com/watch?v=cntxC3g22oU) [[slides]](docs/assets/files/presentation-mlops.pdf)
+5. Community Tutorials
+    * [DeepSpeed: All the tricks to scale to gigantic models (Mark Saroufim)](https://www.youtube.com/watch?v=pDGI668pNg0)
+    * [Turing-NLG, DeepSpeed and the ZeRO optimizer (Yannic Kilcher)](https://www.youtube.com/watch?v=tC01FRB0M7w)
+    * [Ultimate Guide To Scaling ML Models (The AI Epiphany)](https://www.youtube.com/watch?v=hc0u4avAkuM)

lib/python3.12/site-packages/deepspeed-0.17.0.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (80.10.2)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

lib/python3.12/site-packages/deepspeed-0.17.0.dist-info/entry_points.txt

@@ -0,0 +1,2 @@
+[pytest_randomly.random_seeder]
+deepspeed = deepspeed.runtime.utils:set_random_seed

lib/python3.12/site-packages/deepspeed-0.17.0.dist-info/licenses/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
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lib/python3.12/site-packages/deepspeed-0.17.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+deepspeed

lib/python3.12/site-packages/distutils-precedence.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2638ce9e2500e572a5e0de7faed6661eb569d1b696fcba07b0dd223da5f5d224
+size 151

lib/python3.12/site-packages/filelock/__init__.py

@@ -0,0 +1,70 @@
+"""
+A platform independent file lock that supports the with-statement.
+
+.. autodata:: filelock.__version__
+   :no-value:
+
+"""
+
+from __future__ import annotations
+
+import sys
+import warnings
+from typing import TYPE_CHECKING
+
+from ._api import AcquireReturnProxy, BaseFileLock
+from ._error import Timeout
+from ._soft import SoftFileLock
+from ._unix import UnixFileLock, has_fcntl
+from ._windows import WindowsFileLock
+from .asyncio import (
+    AsyncAcquireReturnProxy,
+    AsyncSoftFileLock,
+    AsyncUnixFileLock,
+    AsyncWindowsFileLock,
+    BaseAsyncFileLock,
+)
+from .version import version
+
+#: version of the project as a string
+__version__: str = version
+
+
+if sys.platform == "win32":  # pragma: win32 cover
+    _FileLock: type[BaseFileLock] = WindowsFileLock
+    _AsyncFileLock: type[BaseAsyncFileLock] = AsyncWindowsFileLock
+else:  # pragma: win32 no cover # noqa: PLR5501
+    if has_fcntl:
+        _FileLock: type[BaseFileLock] = UnixFileLock
+        _AsyncFileLock: type[BaseAsyncFileLock] = AsyncUnixFileLock
+    else:
+        _FileLock = SoftFileLock
+        _AsyncFileLock = AsyncSoftFileLock
+        if warnings is not None:
+            warnings.warn("only soft file lock is available", stacklevel=2)
+
+if TYPE_CHECKING:
+    FileLock = SoftFileLock
+    AsyncFileLock = AsyncSoftFileLock
+else:
+    #: Alias for the lock, which should be used for the current platform.
+    FileLock = _FileLock
+    AsyncFileLock = _AsyncFileLock
+
+
+__all__ = [
+    "AcquireReturnProxy",
+    "AsyncAcquireReturnProxy",
+    "AsyncFileLock",
+    "AsyncSoftFileLock",
+    "AsyncUnixFileLock",
+    "AsyncWindowsFileLock",
+    "BaseAsyncFileLock",
+    "BaseFileLock",
+    "FileLock",
+    "SoftFileLock",
+    "Timeout",
+    "UnixFileLock",
+    "WindowsFileLock",
+    "__version__",
+]

lib/python3.12/site-packages/filelock/_api.py

@@ -0,0 +1,403 @@
+from __future__ import annotations
+
+import contextlib
+import inspect
+import logging
+import os
+import time
+import warnings
+from abc import ABCMeta, abstractmethod
+from dataclasses import dataclass
+from threading import local
+from typing import TYPE_CHECKING, Any, cast
+from weakref import WeakValueDictionary
+
+from ._error import Timeout
+
+if TYPE_CHECKING:
+    import sys
+    from types import TracebackType
+
+    if sys.version_info >= (3, 11):  # pragma: no cover (py311+)
+        from typing import Self
+    else:  # pragma: no cover (<py311)
+        from typing_extensions import Self
+
+
+_LOGGER = logging.getLogger("filelock")
+
+
+# This is a helper class which is returned by :meth:`BaseFileLock.acquire` and wraps the lock to make sure __enter__
+# is not called twice when entering the with statement. If we would simply return *self*, the lock would be acquired
+# again in the *__enter__* method of the BaseFileLock, but not released again automatically. issue #37 (memory leak)
+class AcquireReturnProxy:
+    """A context-aware object that will release the lock file when exiting."""
+
+    def __init__(self, lock: BaseFileLock) -> None:
+        self.lock = lock
+
+    def __enter__(self) -> BaseFileLock:
+        return self.lock
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.lock.release()
+
+
+@dataclass
+class FileLockContext:
+    """A dataclass which holds the context for a ``BaseFileLock`` object."""
+
+    # The context is held in a separate class to allow optional use of thread local storage via the
+    # ThreadLocalFileContext class.
+
+    #: The path to the lock file.
+    lock_file: str
+
+    #: The default timeout value.
+    timeout: float
+
+    #: The mode for the lock files
+    mode: int
+
+    #: Whether the lock should be blocking or not
+    blocking: bool
+
+    #: The file descriptor for the *_lock_file* as it is returned by the os.open() function, not None when lock held
+    lock_file_fd: int | None = None
+
+    #: The lock counter is used for implementing the nested locking mechanism.
+    lock_counter: int = 0  # When the lock is acquired is increased and the lock is only released, when this value is 0
+
+
+class ThreadLocalFileContext(FileLockContext, local):
+    """A thread local version of the ``FileLockContext`` class."""
+
+
+class FileLockMeta(ABCMeta):
+    def __call__(  # noqa: PLR0913
+        cls,
+        lock_file: str | os.PathLike[str],
+        timeout: float = -1,
+        mode: int = 0o644,
+        thread_local: bool = True,  # noqa: FBT001, FBT002
+        *,
+        blocking: bool = True,
+        is_singleton: bool = False,
+        **kwargs: Any,  # capture remaining kwargs for subclasses  # noqa: ANN401
+    ) -> BaseFileLock:
+        if is_singleton:
+            instance = cls._instances.get(str(lock_file))  # type: ignore[attr-defined]
+            if instance:
+                params_to_check = {
+                    "thread_local": (thread_local, instance.is_thread_local()),
+                    "timeout": (timeout, instance.timeout),
+                    "mode": (mode, instance.mode),
+                    "blocking": (blocking, instance.blocking),
+                }
+
+                non_matching_params = {
+                    name: (passed_param, set_param)
+                    for name, (passed_param, set_param) in params_to_check.items()
+                    if passed_param != set_param
+                }
+                if not non_matching_params:
+                    return cast("BaseFileLock", instance)
+
+                # parameters do not match; raise error
+                msg = "Singleton lock instances cannot be initialized with differing arguments"
+                msg += "\nNon-matching arguments: "
+                for param_name, (passed_param, set_param) in non_matching_params.items():
+                    msg += f"\n\t{param_name} (existing lock has {set_param} but {passed_param} was passed)"
+                raise ValueError(msg)
+
+        # Workaround to make `__init__`'s params optional in subclasses
+        # E.g. virtualenv changes the signature of the `__init__` method in the `BaseFileLock` class descendant
+        # (https://github.com/tox-dev/filelock/pull/340)
+
+        all_params = {
+            "timeout": timeout,
+            "mode": mode,
+            "thread_local": thread_local,
+            "blocking": blocking,
+            "is_singleton": is_singleton,
+            **kwargs,
+        }
+
+        present_params = inspect.signature(cls.__init__).parameters  # type: ignore[misc]
+        init_params = {key: value for key, value in all_params.items() if key in present_params}
+
+        instance = super().__call__(lock_file, **init_params)
+
+        if is_singleton:
+            cls._instances[str(lock_file)] = instance  # type: ignore[attr-defined]
+
+        return cast("BaseFileLock", instance)
+
+
+class BaseFileLock(contextlib.ContextDecorator, metaclass=FileLockMeta):
+    """Abstract base class for a file lock object."""
+
+    _instances: WeakValueDictionary[str, BaseFileLock]
+
+    def __init_subclass__(cls, **kwargs: dict[str, Any]) -> None:
+        """Setup unique state for lock subclasses."""
+        super().__init_subclass__(**kwargs)
+        cls._instances = WeakValueDictionary()
+
+    def __init__(  # noqa: PLR0913
+        self,
+        lock_file: str | os.PathLike[str],
+        timeout: float = -1,
+        mode: int = 0o644,
+        thread_local: bool = True,  # noqa: FBT001, FBT002
+        *,
+        blocking: bool = True,
+        is_singleton: bool = False,
+    ) -> None:
+        """
+        Create a new lock object.
+
+        :param lock_file: path to the file
+        :param timeout: default timeout when acquiring the lock, in seconds. It will be used as fallback value in \
+            the acquire method, if no timeout value (``None``) is given. If you want to disable the timeout, set it \
+            to a negative value. A timeout of 0 means that there is exactly one attempt to acquire the file lock.
+        :param mode: file permissions for the lockfile
+        :param thread_local: Whether this object's internal context should be thread local or not. If this is set to \
+            ``False`` then the lock will be reentrant across threads.
+        :param blocking: whether the lock should be blocking or not
+        :param is_singleton: If this is set to ``True`` then only one instance of this class will be created \
+            per lock file. This is useful if you want to use the lock object for reentrant locking without needing \
+            to pass the same object around.
+
+        """
+        self._is_thread_local = thread_local
+        self._is_singleton = is_singleton
+
+        # Create the context. Note that external code should not work with the context directly and should instead use
+        # properties of this class.
+        kwargs: dict[str, Any] = {
+            "lock_file": os.fspath(lock_file),
+            "timeout": timeout,
+            "mode": mode,
+            "blocking": blocking,
+        }
+        self._context: FileLockContext = (ThreadLocalFileContext if thread_local else FileLockContext)(**kwargs)
+
+    def is_thread_local(self) -> bool:
+        """:return: a flag indicating if this lock is thread local or not"""
+        return self._is_thread_local
+
+    @property
+    def is_singleton(self) -> bool:
+        """:return: a flag indicating if this lock is singleton or not"""
+        return self._is_singleton
+
+    @property
+    def lock_file(self) -> str:
+        """:return: path to the lock file"""
+        return self._context.lock_file
+
+    @property
+    def timeout(self) -> float:
+        """
+        :return: the default timeout value, in seconds
+
+        .. versionadded:: 2.0.0
+        """
+        return self._context.timeout
+
+    @timeout.setter
+    def timeout(self, value: float | str) -> None:
+        """
+        Change the default timeout value.
+
+        :param value: the new value, in seconds
+
+        """
+        self._context.timeout = float(value)
+
+    @property
+    def blocking(self) -> bool:
+        """:return: whether the locking is blocking or not"""
+        return self._context.blocking
+
+    @blocking.setter
+    def blocking(self, value: bool) -> None:
+        """
+        Change the default blocking value.
+
+        :param value: the new value as bool
+
+        """
+        self._context.blocking = value
+
+    @property
+    def mode(self) -> int:
+        """:return: the file permissions for the lockfile"""
+        return self._context.mode
+
+    @abstractmethod
+    def _acquire(self) -> None:
+        """If the file lock could be acquired, self._context.lock_file_fd holds the file descriptor of the lock file."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def _release(self) -> None:
+        """Releases the lock and sets self._context.lock_file_fd to None."""
+        raise NotImplementedError
+
+    @property
+    def is_locked(self) -> bool:
+        """
+
+        :return: A boolean indicating if the lock file is holding the lock currently.
+
+        .. versionchanged:: 2.0.0
+
+            This was previously a method and is now a property.
+        """
+        return self._context.lock_file_fd is not None
+
+    @property
+    def lock_counter(self) -> int:
+        """:return: The number of times this lock has been acquired (but not yet released)."""
+        return self._context.lock_counter
+
+    def acquire(
+        self,
+        timeout: float | None = None,
+        poll_interval: float = 0.05,
+        *,
+        poll_intervall: float | None = None,
+        blocking: bool | None = None,
+    ) -> AcquireReturnProxy:
+        """
+        Try to acquire the file lock.
+
+        :param timeout: maximum wait time for acquiring the lock, ``None`` means use the default :attr:`~timeout` is and
+         if ``timeout < 0``, there is no timeout and this method will block until the lock could be acquired
+        :param poll_interval: interval of trying to acquire the lock file
+        :param poll_intervall: deprecated, kept for backwards compatibility, use ``poll_interval`` instead
+        :param blocking: defaults to True. If False, function will return immediately if it cannot obtain a lock on the
+         first attempt. Otherwise, this method will block until the timeout expires or the lock is acquired.
+        :raises Timeout: if fails to acquire lock within the timeout period
+        :return: a context object that will unlock the file when the context is exited
+
+        .. code-block:: python
+
+            # You can use this method in the context manager (recommended)
+            with lock.acquire():
+                pass
+
+            # Or use an equivalent try-finally construct:
+            lock.acquire()
+            try:
+                pass
+            finally:
+                lock.release()
+
+        .. versionchanged:: 2.0.0
+
+            This method returns now a *proxy* object instead of *self*,
+            so that it can be used in a with statement without side effects.
+
+        """
+        # Use the default timeout, if no timeout is provided.
+        if timeout is None:
+            timeout = self._context.timeout
+
+        if blocking is None:
+            blocking = self._context.blocking
+
+        if poll_intervall is not None:
+            msg = "use poll_interval instead of poll_intervall"
+            warnings.warn(msg, DeprecationWarning, stacklevel=2)
+            poll_interval = poll_intervall
+
+        # Increment the number right at the beginning. We can still undo it, if something fails.
+        self._context.lock_counter += 1
+
+        lock_id = id(self)
+        lock_filename = self.lock_file
+        start_time = time.perf_counter()
+        try:
+            while True:
+                if not self.is_locked:
+                    _LOGGER.debug("Attempting to acquire lock %s on %s", lock_id, lock_filename)
+                    self._acquire()
+                if self.is_locked:
+                    _LOGGER.debug("Lock %s acquired on %s", lock_id, lock_filename)
+                    break
+                if blocking is False:
+                    _LOGGER.debug("Failed to immediately acquire lock %s on %s", lock_id, lock_filename)
+                    raise Timeout(lock_filename)  # noqa: TRY301
+                if 0 <= timeout < time.perf_counter() - start_time:
+                    _LOGGER.debug("Timeout on acquiring lock %s on %s", lock_id, lock_filename)
+                    raise Timeout(lock_filename)  # noqa: TRY301
+                msg = "Lock %s not acquired on %s, waiting %s seconds ..."
+                _LOGGER.debug(msg, lock_id, lock_filename, poll_interval)
+                time.sleep(poll_interval)
+        except BaseException:  # Something did go wrong, so decrement the counter.
+            self._context.lock_counter = max(0, self._context.lock_counter - 1)
+            raise
+        return AcquireReturnProxy(lock=self)
+
+    def release(self, force: bool = False) -> None:  # noqa: FBT001, FBT002
+        """
+        Releases the file lock. Please note, that the lock is only completely released, if the lock counter is 0.
+        Also note, that the lock file itself is not automatically deleted.
+
+        :param force: If true, the lock counter is ignored and the lock is released in every case/
+
+        """
+        if self.is_locked:
+            self._context.lock_counter -= 1
+
+            if self._context.lock_counter == 0 or force:
+                lock_id, lock_filename = id(self), self.lock_file
+
+                _LOGGER.debug("Attempting to release lock %s on %s", lock_id, lock_filename)
+                self._release()
+                self._context.lock_counter = 0
+                _LOGGER.debug("Lock %s released on %s", lock_id, lock_filename)
+
+    def __enter__(self) -> Self:
+        """
+        Acquire the lock.
+
+        :return: the lock object
+
+        """
+        self.acquire()
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        """
+        Release the lock.
+
+        :param exc_type: the exception type if raised
+        :param exc_value: the exception value if raised
+        :param traceback: the exception traceback if raised
+
+        """
+        self.release()
+
+    def __del__(self) -> None:
+        """Called when the lock object is deleted."""
+        self.release(force=True)
+
+
+__all__ = [
+    "AcquireReturnProxy",
+    "BaseFileLock",
+]

lib/python3.12/site-packages/filelock/_error.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from typing import Any
+
+
+class Timeout(TimeoutError):  # noqa: N818
+    """Raised when the lock could not be acquired in *timeout* seconds."""
+
+    def __init__(self, lock_file: str) -> None:
+        super().__init__()
+        self._lock_file = lock_file
+
+    def __reduce__(self) -> str | tuple[Any, ...]:
+        return self.__class__, (self._lock_file,)  # Properly pickle the exception
+
+    def __str__(self) -> str:
+        return f"The file lock '{self._lock_file}' could not be acquired."
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({self.lock_file!r})"
+
+    @property
+    def lock_file(self) -> str:
+        """:return: The path of the file lock."""
+        return self._lock_file
+
+
+__all__ = [
+    "Timeout",
+]

lib/python3.12/site-packages/filelock/_soft.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+
+import os
+import sys
+from contextlib import suppress
+from errno import EACCES, EEXIST
+from pathlib import Path
+
+from ._api import BaseFileLock
+from ._util import ensure_directory_exists, raise_on_not_writable_file
+
+
+class SoftFileLock(BaseFileLock):
+    """Simply watches the existence of the lock file."""
+
+    def _acquire(self) -> None:
+        raise_on_not_writable_file(self.lock_file)
+        ensure_directory_exists(self.lock_file)
+        # first check for exists and read-only mode as the open will mask this case as EEXIST
+        flags = (
+            os.O_WRONLY  # open for writing only
+            | os.O_CREAT
+            | os.O_EXCL  # together with above raise EEXIST if the file specified by filename exists
+            | os.O_TRUNC  # truncate the file to zero byte
+        )
+        try:
+            file_handler = os.open(self.lock_file, flags, self._context.mode)
+        except OSError as exception:  # re-raise unless expected exception
+            if not (
+                exception.errno == EEXIST  # lock already exist
+                or (exception.errno == EACCES and sys.platform == "win32")  # has no access to this lock
+            ):  # pragma: win32 no cover
+                raise
+        else:
+            self._context.lock_file_fd = file_handler
+
+    def _release(self) -> None:
+        assert self._context.lock_file_fd is not None  # noqa: S101
+        os.close(self._context.lock_file_fd)  # the lock file is definitely not None
+        self._context.lock_file_fd = None
+        with suppress(OSError):  # the file is already deleted and that's what we want
+            Path(self.lock_file).unlink()
+
+
+__all__ = [
+    "SoftFileLock",
+]

lib/python3.12/site-packages/filelock/_unix.py

@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+import os
+import sys
+from contextlib import suppress
+from errno import ENOSYS
+from pathlib import Path
+from typing import cast
+
+from ._api import BaseFileLock
+from ._util import ensure_directory_exists
+
+#: a flag to indicate if the fcntl API is available
+has_fcntl = False
+if sys.platform == "win32":  # pragma: win32 cover
+
+    class UnixFileLock(BaseFileLock):
+        """Uses the :func:`fcntl.flock` to hard lock the lock file on unix systems."""
+
+        def _acquire(self) -> None:
+            raise NotImplementedError
+
+        def _release(self) -> None:
+            raise NotImplementedError
+
+else:  # pragma: win32 no cover
+    try:
+        import fcntl
+
+        _ = (fcntl.flock, fcntl.LOCK_EX, fcntl.LOCK_NB, fcntl.LOCK_UN)
+    except (ImportError, AttributeError):
+        pass
+    else:
+        has_fcntl = True
+
+    class UnixFileLock(BaseFileLock):
+        """Uses the :func:`fcntl.flock` to hard lock the lock file on unix systems."""
+
+        def _acquire(self) -> None:
+            ensure_directory_exists(self.lock_file)
+            open_flags = os.O_RDWR | os.O_TRUNC
+            if not Path(self.lock_file).exists():
+                open_flags |= os.O_CREAT
+            fd = os.open(self.lock_file, open_flags, self._context.mode)
+            with suppress(PermissionError):  # This locked is not owned by this UID
+                os.fchmod(fd, self._context.mode)
+            try:
+                fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
+            except OSError as exception:
+                os.close(fd)
+                if exception.errno == ENOSYS:  # NotImplemented error
+                    msg = "FileSystem does not appear to support flock; use SoftFileLock instead"
+                    raise NotImplementedError(msg) from exception
+            else:
+                self._context.lock_file_fd = fd
+
+        def _release(self) -> None:
+            # Do not remove the lockfile:
+            #   https://github.com/tox-dev/py-filelock/issues/31
+            #   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
+            fd = cast("int", self._context.lock_file_fd)
+            self._context.lock_file_fd = None
+            fcntl.flock(fd, fcntl.LOCK_UN)
+            os.close(fd)
+
+
+__all__ = [
+    "UnixFileLock",
+    "has_fcntl",
+]

lib/python3.12/site-packages/filelock/_util.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+import os
+import stat
+import sys
+from errno import EACCES, EISDIR
+from pathlib import Path
+
+
+def raise_on_not_writable_file(filename: str) -> None:
+    """
+    Raise an exception if attempting to open the file for writing would fail.
+
+    This is done so files that will never be writable can be separated from files that are writable but currently
+    locked.
+
+    :param filename: file to check
+    :raises OSError: as if the file was opened for writing.
+
+    """
+    try:  # use stat to do exists + can write to check without race condition
+        file_stat = os.stat(filename)  # noqa: PTH116
+    except OSError:
+        return  # swallow does not exist or other errors
+
+    if file_stat.st_mtime != 0:  # if os.stat returns but modification is zero that's an invalid os.stat - ignore it
+        if not (file_stat.st_mode & stat.S_IWUSR):
+            raise PermissionError(EACCES, "Permission denied", filename)
+
+        if stat.S_ISDIR(file_stat.st_mode):
+            if sys.platform == "win32":  # pragma: win32 cover
+                # On Windows, this is PermissionError
+                raise PermissionError(EACCES, "Permission denied", filename)
+            else:  # pragma: win32 no cover # noqa: RET506
+                # On linux / macOS, this is IsADirectoryError
+                raise IsADirectoryError(EISDIR, "Is a directory", filename)
+
+
+def ensure_directory_exists(filename: Path | str) -> None:
+    """
+    Ensure the directory containing the file exists (create it if necessary).
+
+    :param filename: file.
+
+    """
+    Path(filename).parent.mkdir(parents=True, exist_ok=True)
+
+
+__all__ = [
+    "ensure_directory_exists",
+    "raise_on_not_writable_file",
+]

lib/python3.12/site-packages/filelock/_windows.py

@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+import os
+import sys
+from contextlib import suppress
+from errno import EACCES
+from pathlib import Path
+from typing import cast
+
+from ._api import BaseFileLock
+from ._util import ensure_directory_exists, raise_on_not_writable_file
+
+if sys.platform == "win32":  # pragma: win32 cover
+    import msvcrt
+
+    class WindowsFileLock(BaseFileLock):
+        """Uses the :func:`msvcrt.locking` function to hard lock the lock file on Windows systems."""
+
+        def _acquire(self) -> None:
+            raise_on_not_writable_file(self.lock_file)
+            ensure_directory_exists(self.lock_file)
+            flags = (
+                os.O_RDWR  # open for read and write
+                | os.O_CREAT  # create file if not exists
+                | os.O_TRUNC  # truncate file if not empty
+            )
+            try:
+                fd = os.open(self.lock_file, flags, self._context.mode)
+            except OSError as exception:
+                if exception.errno != EACCES:  # has no access to this lock
+                    raise
+            else:
+                try:
+                    msvcrt.locking(fd, msvcrt.LK_NBLCK, 1)
+                except OSError as exception:
+                    os.close(fd)  # close file first
+                    if exception.errno != EACCES:  # file is already locked
+                        raise
+                else:
+                    self._context.lock_file_fd = fd
+
+        def _release(self) -> None:
+            fd = cast("int", self._context.lock_file_fd)
+            self._context.lock_file_fd = None
+            msvcrt.locking(fd, msvcrt.LK_UNLCK, 1)
+            os.close(fd)
+
+            with suppress(OSError):  # Probably another instance of the application hat acquired the file lock.
+                Path(self.lock_file).unlink()
+
+else:  # pragma: win32 no cover
+
+    class WindowsFileLock(BaseFileLock):
+        """Uses the :func:`msvcrt.locking` function to hard lock the lock file on Windows systems."""
+
+        def _acquire(self) -> None:
+            raise NotImplementedError
+
+        def _release(self) -> None:
+            raise NotImplementedError
+
+
+__all__ = [
+    "WindowsFileLock",
+]

lib/python3.12/site-packages/filelock/asyncio.py

@@ -0,0 +1,342 @@
+"""An asyncio-based implementation of the file lock."""
+
+from __future__ import annotations
+
+import asyncio
+import contextlib
+import logging
+import os
+import time
+from dataclasses import dataclass
+from threading import local
+from typing import TYPE_CHECKING, Any, Callable, NoReturn, cast
+
+from ._api import BaseFileLock, FileLockContext, FileLockMeta
+from ._error import Timeout
+from ._soft import SoftFileLock
+from ._unix import UnixFileLock
+from ._windows import WindowsFileLock
+
+if TYPE_CHECKING:
+    import sys
+    from concurrent import futures
+    from types import TracebackType
+
+    if sys.version_info >= (3, 11):  # pragma: no cover (py311+)
+        from typing import Self
+    else:  # pragma: no cover (<py311)
+        from typing_extensions import Self
+
+
+_LOGGER = logging.getLogger("filelock")
+
+
+@dataclass
+class AsyncFileLockContext(FileLockContext):
+    """A dataclass which holds the context for a ``BaseAsyncFileLock`` object."""
+
+    #: Whether run in executor
+    run_in_executor: bool = True
+
+    #: The executor
+    executor: futures.Executor | None = None
+
+    #: The loop
+    loop: asyncio.AbstractEventLoop | None = None
+
+
+class AsyncThreadLocalFileContext(AsyncFileLockContext, local):
+    """A thread local version of the ``FileLockContext`` class."""
+
+
+class AsyncAcquireReturnProxy:
+    """A context-aware object that will release the lock file when exiting."""
+
+    def __init__(self, lock: BaseAsyncFileLock) -> None:  # noqa: D107
+        self.lock = lock
+
+    async def __aenter__(self) -> BaseAsyncFileLock:  # noqa: D105
+        return self.lock
+
+    async def __aexit__(  # noqa: D105
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        await self.lock.release()
+
+
+class AsyncFileLockMeta(FileLockMeta):
+    def __call__(  # type: ignore[override] # noqa: PLR0913
+        cls,  # noqa: N805
+        lock_file: str | os.PathLike[str],
+        timeout: float = -1,
+        mode: int = 0o644,
+        thread_local: bool = False,  # noqa: FBT001, FBT002
+        *,
+        blocking: bool = True,
+        is_singleton: bool = False,
+        loop: asyncio.AbstractEventLoop | None = None,
+        run_in_executor: bool = True,
+        executor: futures.Executor | None = None,
+    ) -> BaseAsyncFileLock:
+        if thread_local and run_in_executor:
+            msg = "run_in_executor is not supported when thread_local is True"
+            raise ValueError(msg)
+        instance = super().__call__(
+            lock_file=lock_file,
+            timeout=timeout,
+            mode=mode,
+            thread_local=thread_local,
+            blocking=blocking,
+            is_singleton=is_singleton,
+            loop=loop,
+            run_in_executor=run_in_executor,
+            executor=executor,
+        )
+        return cast("BaseAsyncFileLock", instance)
+
+
+class BaseAsyncFileLock(BaseFileLock, metaclass=AsyncFileLockMeta):
+    """Base class for asynchronous file locks."""
+
+    def __init__(  # noqa: PLR0913
+        self,
+        lock_file: str | os.PathLike[str],
+        timeout: float = -1,
+        mode: int = 0o644,
+        thread_local: bool = False,  # noqa: FBT001, FBT002
+        *,
+        blocking: bool = True,
+        is_singleton: bool = False,
+        loop: asyncio.AbstractEventLoop | None = None,
+        run_in_executor: bool = True,
+        executor: futures.Executor | None = None,
+    ) -> None:
+        """
+        Create a new lock object.
+
+        :param lock_file: path to the file
+        :param timeout: default timeout when acquiring the lock, in seconds. It will be used as fallback value in \
+            the acquire method, if no timeout value (``None``) is given. If you want to disable the timeout, set it \
+            to a negative value. A timeout of 0 means that there is exactly one attempt to acquire the file lock.
+        :param mode: file permissions for the lockfile
+        :param thread_local: Whether this object's internal context should be thread local or not. If this is set to \
+            ``False`` then the lock will be reentrant across threads.
+        :param blocking: whether the lock should be blocking or not
+        :param is_singleton: If this is set to ``True`` then only one instance of this class will be created \
+            per lock file. This is useful if you want to use the lock object for reentrant locking without needing \
+            to pass the same object around.
+        :param loop: The event loop to use. If not specified, the running event loop will be used.
+        :param run_in_executor: If this is set to ``True`` then the lock will be acquired in an executor.
+        :param executor: The executor to use. If not specified, the default executor will be used.
+
+        """
+        self._is_thread_local = thread_local
+        self._is_singleton = is_singleton
+
+        # Create the context. Note that external code should not work with the context directly and should instead use
+        # properties of this class.
+        kwargs: dict[str, Any] = {
+            "lock_file": os.fspath(lock_file),
+            "timeout": timeout,
+            "mode": mode,
+            "blocking": blocking,
+            "loop": loop,
+            "run_in_executor": run_in_executor,
+            "executor": executor,
+        }
+        self._context: AsyncFileLockContext = (AsyncThreadLocalFileContext if thread_local else AsyncFileLockContext)(
+            **kwargs
+        )
+
+    @property
+    def run_in_executor(self) -> bool:
+        """::return: whether run in executor."""
+        return self._context.run_in_executor
+
+    @property
+    def executor(self) -> futures.Executor | None:
+        """::return: the executor."""
+        return self._context.executor
+
+    @executor.setter
+    def executor(self, value: futures.Executor | None) -> None:  # pragma: no cover
+        """
+        Change the executor.
+
+        :param value: the new executor or ``None``
+        :type value: futures.Executor | None
+
+        """
+        self._context.executor = value
+
+    @property
+    def loop(self) -> asyncio.AbstractEventLoop | None:
+        """::return: the event loop."""
+        return self._context.loop
+
+    async def acquire(  # type: ignore[override]
+        self,
+        timeout: float | None = None,
+        poll_interval: float = 0.05,
+        *,
+        blocking: bool | None = None,
+    ) -> AsyncAcquireReturnProxy:
+        """
+        Try to acquire the file lock.
+
+        :param timeout: maximum wait time for acquiring the lock, ``None`` means use the default
+            :attr:`~BaseFileLock.timeout` is and if ``timeout < 0``, there is no timeout and
+            this method will block until the lock could be acquired
+        :param poll_interval: interval of trying to acquire the lock file
+        :param blocking: defaults to True. If False, function will return immediately if it cannot obtain a lock on the
+         first attempt. Otherwise, this method will block until the timeout expires or the lock is acquired.
+        :raises Timeout: if fails to acquire lock within the timeout period
+        :return: a context object that will unlock the file when the context is exited
+
+        .. code-block:: python
+
+            # You can use this method in the context manager (recommended)
+            with lock.acquire():
+                pass
+
+            # Or use an equivalent try-finally construct:
+            lock.acquire()
+            try:
+                pass
+            finally:
+                lock.release()
+
+        """
+        # Use the default timeout, if no timeout is provided.
+        if timeout is None:
+            timeout = self._context.timeout
+
+        if blocking is None:
+            blocking = self._context.blocking
+
+        # Increment the number right at the beginning. We can still undo it, if something fails.
+        self._context.lock_counter += 1
+
+        lock_id = id(self)
+        lock_filename = self.lock_file
+        start_time = time.perf_counter()
+        try:
+            while True:
+                if not self.is_locked:
+                    _LOGGER.debug("Attempting to acquire lock %s on %s", lock_id, lock_filename)
+                    await self._run_internal_method(self._acquire)
+                if self.is_locked:
+                    _LOGGER.debug("Lock %s acquired on %s", lock_id, lock_filename)
+                    break
+                if blocking is False:
+                    _LOGGER.debug("Failed to immediately acquire lock %s on %s", lock_id, lock_filename)
+                    raise Timeout(lock_filename)  # noqa: TRY301
+                if 0 <= timeout < time.perf_counter() - start_time:
+                    _LOGGER.debug("Timeout on acquiring lock %s on %s", lock_id, lock_filename)
+                    raise Timeout(lock_filename)  # noqa: TRY301
+                msg = "Lock %s not acquired on %s, waiting %s seconds ..."
+                _LOGGER.debug(msg, lock_id, lock_filename, poll_interval)
+                await asyncio.sleep(poll_interval)
+        except BaseException:  # Something did go wrong, so decrement the counter.
+            self._context.lock_counter = max(0, self._context.lock_counter - 1)
+            raise
+        return AsyncAcquireReturnProxy(lock=self)
+
+    async def release(self, force: bool = False) -> None:  # type: ignore[override]  # noqa: FBT001, FBT002
+        """
+        Releases the file lock. Please note, that the lock is only completely released, if the lock counter is 0.
+        Also note, that the lock file itself is not automatically deleted.
+
+        :param force: If true, the lock counter is ignored and the lock is released in every case/
+
+        """
+        if self.is_locked:
+            self._context.lock_counter -= 1
+
+            if self._context.lock_counter == 0 or force:
+                lock_id, lock_filename = id(self), self.lock_file
+
+                _LOGGER.debug("Attempting to release lock %s on %s", lock_id, lock_filename)
+                await self._run_internal_method(self._release)
+                self._context.lock_counter = 0
+                _LOGGER.debug("Lock %s released on %s", lock_id, lock_filename)
+
+    async def _run_internal_method(self, method: Callable[[], Any]) -> None:
+        if asyncio.iscoroutinefunction(method):
+            await method()
+        elif self.run_in_executor:
+            loop = self.loop or asyncio.get_running_loop()
+            await loop.run_in_executor(self.executor, method)
+        else:
+            method()
+
+    def __enter__(self) -> NoReturn:
+        """
+        Replace old __enter__ method to avoid using it.
+
+        NOTE: DO NOT USE `with` FOR ASYNCIO LOCKS, USE `async with` INSTEAD.
+
+        :return: none
+        :rtype: NoReturn
+        """
+        msg = "Do not use `with` for asyncio locks, use `async with` instead."
+        raise NotImplementedError(msg)
+
+    async def __aenter__(self) -> Self:
+        """
+        Acquire the lock.
+
+        :return: the lock object
+
+        """
+        await self.acquire()
+        return self
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        """
+        Release the lock.
+
+        :param exc_type: the exception type if raised
+        :param exc_value: the exception value if raised
+        :param traceback: the exception traceback if raised
+
+        """
+        await self.release()
+
+    def __del__(self) -> None:
+        """Called when the lock object is deleted."""
+        with contextlib.suppress(RuntimeError):
+            loop = self.loop or asyncio.get_running_loop()
+            if not loop.is_running():  # pragma: no cover
+                loop.run_until_complete(self.release(force=True))
+            else:
+                loop.create_task(self.release(force=True))
+
+
+class AsyncSoftFileLock(SoftFileLock, BaseAsyncFileLock):
+    """Simply watches the existence of the lock file."""
+
+
+class AsyncUnixFileLock(UnixFileLock, BaseAsyncFileLock):
+    """Uses the :func:`fcntl.flock` to hard lock the lock file on unix systems."""
+
+
+class AsyncWindowsFileLock(WindowsFileLock, BaseAsyncFileLock):
+    """Uses the :func:`msvcrt.locking` to hard lock the lock file on windows systems."""
+
+
+__all__ = [
+    "AsyncAcquireReturnProxy",
+    "AsyncSoftFileLock",
+    "AsyncUnixFileLock",
+    "AsyncWindowsFileLock",
+    "BaseAsyncFileLock",
+]

lib/python3.12/site-packages/filelock/version.py

@@ -0,0 +1,21 @@
+# file generated by setuptools-scm
+# don't change, don't track in version control
+
+__all__ = ["__version__", "__version_tuple__", "version", "version_tuple"]
+
+TYPE_CHECKING = False
+if TYPE_CHECKING:
+    from typing import Tuple
+    from typing import Union
+
+    VERSION_TUPLE = Tuple[Union[int, str], ...]
+else:
+    VERSION_TUPLE = object
+
+version: str
+__version__: str
+__version_tuple__: VERSION_TUPLE
+version_tuple: VERSION_TUPLE
+
+__version__ = version = '3.18.0'
+__version_tuple__ = version_tuple = (3, 18, 0)