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v5.0.0
setup.py
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Arthur
v5.0.0
26 янв 2026, 13:41
26 янв 2026, 13:41
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# Copyright 2021 The HuggingFace Team. All rights reserved. # # 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. """ Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/main/setup.py To create the package for pypi. 1. Create the release branch named: v<RELEASE>-release, for example v4.19-release. For a patch release checkout the current release branch. If releasing on a special branch, copy the updated README.md on the main branch for the commit you will make for the post-release and run `make fix-repo` on the main branch as well. 2. Run `make pre-release` (or `make pre-patch` for a patch release) and commit these changes with the message: "Release: <VERSION>" and push. 3. Go back to the main branch and run `make post-release` then `make fix-repo`. Commit these changes with the message "v<NEXT_VERSION>.dev.0" and push to main. # If you were just cutting the branch in preparation for a release, you can stop here for now. 4. Wait for the tests on the release branch to be completed and be green (otherwise revert and fix bugs) 5. On the release branch, add a tag in git to mark the release: "git tag v<VERSION> -m 'Adds tag v<VERSION> for pypi' " Push the tag to git: git push --tags origin v<RELEASE>-release 6. Have a core maintainer review and approve the deployment to pypi. """ import re import shutil from pathlib import Path from setuptools import Command, find_packages, setup # Remove stale transformers.egg-info directory to avoid https://github.com/pypa/pip/issues/5466 stale_egg_info = Path(__file__).parent / "transformers.egg-info" if stale_egg_info.exists(): print( f"Warning: {stale_egg_info} exists.\n\n" "If you recently updated transformers to 3.0 or later, this is expected,\n" "but it may prevent transformers from installing in editable mode.\n\n" "This directory is automatically generated by Python's packaging tools.\n" "I will remove it now.\n\n" "See https://github.com/pypa/pip/issues/5466 for details.\n" ) shutil.rmtree(stale_egg_info) # IMPORTANT: # 1. all dependencies should be listed here with their version requirements if any # 2. once modified, run: `make style` to update src/transformers/dependency_versions_table.py _deps = [ "Pillow>=10.0.1,<=15.0", "accelerate>=1.1.0", "av", "beautifulsoup4", "blobfile", "codecarbon>=2.8.1", "cookiecutter==1.7.3", "datasets>=2.15.0", # We need either this pin or pyarrow<21.0.0 "deepspeed>=0.9.3", "diffusers", "dill<0.3.5", "evaluate>=0.4.6", "faiss-cpu", "fastapi", "filelock", "ftfy", "fugashi>=1.0", "GitPython<3.1.19", "hf-doc-builder>=0.3.0", "hf_xet", "huggingface-hub>=1.3.0,<2.0", "importlib_metadata", "ipadic>=1.0.0,<2.0", "jinja2>=3.1.0", "jmespath>=1.0.1", "kenlm", "kernels>=0.10.2,<0.11", "librosa", "natten>=0.14.6,<0.15.0", "nltk<=3.8.1", "num2words", "numpy>=1.17", "openai>=1.98.0", "opencv-python", "optimum-benchmark>=0.3.0", "optuna", "pandas<2.3.0", # `datasets` requires `pandas` while `pandas==2.3.0` has issues with CircleCI on 2025/06/05 "packaging>=20.0", "parameterized>=0.9", # older version of parameterized cause pytest collection to fail on .expand "peft>=0.18.0", "phonemizer", "protobuf", "psutil", "pyyaml>=5.1", "pydantic>=2", "pytest>=7.2.0,<9.0.0", "pytest-asyncio>=1.2.0", "pytest-rerunfailures<16.0", "pytest-timeout", "pytest-xdist", "pytest-order", "python>=3.10.0", "ray[tune]>=2.7.0", "regex!=2019.12.17", "rhoknp>=1.1.0,<1.3.1", "rjieba", "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1", "ruff==0.14.10", # `sacrebleu` not used in `transformers`. However, it is needed in several tests, when a test calls # `evaluate.load("sacrebleu")`. This metric is used in the examples that we use to test the `Trainer` with, in the # `Trainer` tests (see references to `run_translation.py`). "sacrebleu>=1.4.12,<2.0.0", "sacremoses", "safetensors>=0.4.3", "sagemaker>=2.31.0", "schedulefree>=1.2.6", "scikit-learn", "scipy", "sentencepiece>=0.1.91,!=0.1.92", "starlette", "sudachipy>=0.6.6", "sudachidict_core>=20220729", "tensorboard", "timeout-decorator", "tiktoken", "timm>=1.0.23", "tokenizers>=0.22.0,<=0.23.0", "torch>=2.2", "torchaudio", "torchvision", "pyctcdecode>=0.4.0", "tqdm>=4.27", "typer-slim", "unidic>=1.0.2", "unidic_lite>=1.0.7", "urllib3<2.0.0", "uvicorn", "pytest-rich", "libcst", "rich", "opentelemetry-api", "mistral-common[image]>=1.8.8", ] # this is a lookup table with items like: # # tokenizers: "tokenizers==0.9.4" # packaging: "packaging" # # some of the values are versioned whereas others aren't. deps = {b: a for a, b in (re.findall(r"^(([^!=<>~ ]+)(?:[!=<>~ ].*)?$)", x)[0] for x in _deps)} # since we save this data in src/transformers/dependency_versions_table.py it can be easily accessed from # anywhere. If you need to quickly access the data from this table in a shell, you can do so easily with: # # python -c 'import sys; from transformers.dependency_versions_table import deps; \ # print(" ".join([ deps[x] for x in sys.argv[1:]]))' tokenizers datasets # # Just pass the desired package names to that script as it's shown with 2 packages above. # # If transformers is not yet installed and the work is done from the cloned repo remember to add `PYTHONPATH=src` to the script above # # You can then feed this for example to `pip`: # # pip install -U $(python -c 'import sys; from transformers.dependency_versions_table import deps; \ # print(" ".join([deps[x] for x in sys.argv[1:]]))' tokenizers datasets) # def deps_list(*pkgs): return [deps[pkg] for pkg in pkgs] class DepsTableUpdateCommand(Command): """ A custom distutils command that updates the dependency table. usage: python setup.py deps_table_update """ description = "build runtime dependency table" user_options = [ # format: (long option, short option, description). ("dep-table-update", None, "updates src/transformers/dependency_versions_table.py"), ] def initialize_options(self): pass def finalize_options(self): pass def run(self): entries = "\n".join([f' "{k}": "{v}",' for k, v in deps.items()]) content = [ "# THIS FILE HAS BEEN AUTOGENERATED. To update:", "# 1. modify the `_deps` dict in setup.py", "# 2. run `make fix-repo``", "deps = {", entries, "}", "", ] target = "src/transformers/dependency_versions_table.py" with open(target, "w", encoding="utf-8", newline="\n") as f: f.write("\n".join(content)) extras = {} extras["ja"] = deps_list("fugashi", "ipadic", "unidic_lite", "unidic", "sudachipy", "sudachidict_core", "rhoknp") extras["sklearn"] = deps_list("scikit-learn") extras["torch"] = deps_list("torch", "accelerate") extras["accelerate"] = deps_list("accelerate") extras["hf_xet"] = deps_list("hf_xet") extras["retrieval"] = deps_list("faiss-cpu", "datasets") extras["tokenizers"] = deps_list("tokenizers") extras["ftfy"] = deps_list("ftfy") extras["modelcreation"] = deps_list("cookiecutter") extras["sagemaker"] = deps_list("sagemaker") extras["deepspeed"] = deps_list("deepspeed") + extras["accelerate"] extras["optuna"] = deps_list("optuna") extras["ray"] = deps_list("ray[tune]") extras["hub-kernels"] = deps_list("kernels") extras["integrations"] = extras["hub-kernels"] + extras["optuna"] + extras["ray"] extras["serving"] = deps_list("openai", "pydantic", "uvicorn", "fastapi", "starlette", "rich") + extras["torch"] extras["audio"] = deps_list( "librosa", "pyctcdecode", "phonemizer", "kenlm", ) # `pip install ".[speech]"` is deprecated and `pip install ".[torch-speech]"` should be used instead extras["speech"] = deps_list("torchaudio") + extras["audio"] extras["torch-speech"] = deps_list("torchaudio") + extras["audio"] extras["vision"] = deps_list("Pillow") extras["timm"] = deps_list("timm") extras["torch-vision"] = deps_list("torchvision") + extras["vision"] extras["natten"] = deps_list("natten") extras["codecarbon"] = deps_list("codecarbon") extras["video"] = deps_list("av") extras["num2words"] = deps_list("num2words") extras["sentencepiece"] = deps_list("sentencepiece", "protobuf") extras["tiktoken"] = deps_list("tiktoken", "blobfile") extras["mistral-common"] = deps_list("mistral-common[image]") extras["chat_template"] = deps_list("jinja2", "jmespath") extras["testing"] = ( deps_list( "pytest", "pytest-asyncio", "pytest-rich", "pytest-xdist", "pytest-order", "pytest-rerunfailures", "timeout-decorator", "parameterized", "psutil", "datasets", "dill", "evaluate", "pytest-timeout", "ruff", "rouge-score", "nltk", "GitPython", "sacremoses", "rjieba", "beautifulsoup4", "tensorboard", "pydantic", "sentencepiece", "sacrebleu", # needed in trainer tests, see references to `run_translation.py` "libcst", "urllib3", ) + extras["retrieval"] + extras["modelcreation"] + extras["mistral-common"] + extras["serving"] ) extras["deepspeed-testing"] = extras["deepspeed"] + extras["testing"] + extras["optuna"] + extras["sentencepiece"] extras["ruff"] = deps_list("ruff") extras["quality"] = deps_list("datasets", "ruff", "GitPython", "urllib3", "libcst", "rich", "pandas") extras["all"] = ( extras["torch"] + extras["sentencepiece"] + extras["tokenizers"] + extras["torch-speech"] + extras["vision"] + extras["integrations"] + extras["timm"] + extras["torch-vision"] + extras["codecarbon"] + extras["accelerate"] + extras["video"] + extras["num2words"] + extras["mistral-common"] + extras["chat_template"] ) extras["dev-torch"] = ( extras["testing"] + extras["torch"] + extras["sentencepiece"] + extras["tokenizers"] + extras["torch-speech"] + extras["vision"] + extras["integrations"] + extras["timm"] + extras["torch-vision"] + extras["codecarbon"] + extras["quality"] + extras["ja"] + extras["sklearn"] + extras["modelcreation"] + extras["num2words"] ) extras["dev"] = ( extras["all"] + extras["testing"] + extras["quality"] + extras["ja"] + extras["sklearn"] + extras["modelcreation"] ) extras["torchhub"] = deps_list( "filelock", "huggingface-hub", "importlib_metadata", "numpy", "packaging", "protobuf", "regex", "sentencepiece", "torch", "tokenizers", "tqdm", ) extras["benchmark"] = deps_list("optimum-benchmark") # OpenTelemetry dependencies for metrics collection in continuous batching # TODO: refactor this to split API and SDK; SDK and exporter should only be needed to run code that collects metrics whereas API is what people will need to instrument their code and handle exporter themselves extras["open-telemetry"] = deps_list("opentelemetry-api") + ["opentelemetry-exporter-otlp", "opentelemetry-sdk"] # when modifying the following list, make sure to update src/transformers/dependency_versions_check.py install_requires = [ deps["filelock"], # filesystem locks, e.g., to prevent parallel downloads deps["huggingface-hub"], deps["numpy"], deps["packaging"], # utilities from PyPA to e.g., compare versions deps["pyyaml"], # used for the model cards metadata deps["regex"], # for OpenAI GPT deps["tokenizers"], deps["typer-slim"], # CLI utilities. In practice, already a dependency of huggingface_hub deps["safetensors"], deps["tqdm"], # progress bars in model download and training scripts ] setup( name="transformers", version="5.0.0", # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots) author="The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)", author_email="transformers@huggingface.co", description="Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.", long_description=open("README.md", "r", encoding="utf-8").read(), long_description_content_type="text/markdown", keywords="machine-learning nlp python pytorch transformer llm vlm deep-learning inference training model-hub pretrained-models llama gemma qwen", license="Apache 2.0 License", url="https://github.com/huggingface/transformers", package_dir={"": "src"}, packages=find_packages("src"), include_package_data=True, package_data={"": ["**/*.cu", "**/*.cpp", "**/*.cuh", "**/*.h", "**/*.pyx", "py.typed"]}, zip_safe=False, extras_require=extras, entry_points={"console_scripts": ["transformers=transformers.cli.transformers:main"]}, python_requires=">=3.10.0", install_requires=list(install_requires), classifiers=[ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Topic :: Scientific/Engineering :: Artificial Intelligence", ], cmdclass={"deps_table_update": DepsTableUpdateCommand}, ) extras["tests_torch"] = deps_list() extras["tests_hub"] = deps_list() extras["tests_pipelines_torch"] = deps_list() extras["tests_examples_torch"] = deps_list() extras["tests_custom_tokenizers"] = deps_list() extras["tests_exotic_models"] = deps_list() extras["consistency"] = deps_list()