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Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/main/setup.py










To create the package for pypi.










1. Run `make pre-release` (or `make pre-patch` for a patch release) then run `make fix-copies` to fix the index of the




documentation.










If releasing on a special branch, copy the updated README.md on the main branch for your the commit you will make




for the post-release and run `make fix-copies` on the main branch as well.










2. Run Tests for Amazon Sagemaker. The documentation is located in `./tests/sagemaker/README.md`, otherwise @philschmid.










3. Unpin specific versions from setup.py that use a git install.










4. Checkout the release branch (v-release, for example v4.19-release), and commit these changes with the




message: "Release: " and push.










5. Wait for the tests on main to be completed and be green (otherwise revert and fix bugs)










6. Add a tag in git to mark the release: "git tag v -m 'Adds tag v for pypi' "




Push the tag to git: git push --tags origin v-release










7. Build both the sources and the wheel. Do not change anything in setup.py between




creating the wheel and the source distribution (obviously).










For the wheel, run: "python setup.py bdist_wheel" in the top level directory.




(this will build a wheel for the python version you use to build it).










For the sources, run: "python setup.py sdist"




You should now have a /dist directory with both .whl and .tar.gz source versions.










8. Check that everything looks correct by uploading the package to the pypi test server:










twine upload dist/* -r pypitest




(pypi suggest using twine as other methods upload files via plaintext.)




You may have to specify the repository url, use the following command then:




twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/










Check that you can install it in a virtualenv by running:




pip install -i https://testpypi.python.org/pypi transformers










Check you can run the following commands:




python -c "from transformers import pipeline; classifier = pipeline('text-classification'); print(classifier('What a nice release'))"




python -c "from transformers import *"










9. Upload the final version to actual pypi:




twine upload dist/* -r pypi










10. Copy the release notes from RELEASE.md to the tag in github once everything is looking hunky-dory.










11. Run `make post-release` then run `make fix-copies`. If you were on a branch for the release,




you need to go back to main before executing this.




"""










import os




import re




import shutil




from distutils.core import Command




from pathlib import Path










from setuptools import 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(




(




"Warning: {} 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"




).format(stale_egg_info)




)




shutil.rmtree(stale_egg_info)
















# IMPORTANT:




# 1. all dependencies should be listed here with their version requirements if any




# 2. once modified, run: `make deps_table_update` to update src/transformers/dependency_versions_table.py




_deps = [




"Pillow",




"accelerate>=0.10.0",




"black==22.3", # after updating to black 2023, also update Python version in pyproject.toml to 3.7




"codecarbon==1.2.0",




"cookiecutter==1.7.3",




"dataclasses",




"datasets!=2.5.0",




"decord==0.6.0",




"deepspeed>=0.6.5",




"dill<0.3.5",




"evaluate>=0.2.0",




"fairscale>0.3",




"faiss-cpu",




"fastapi",




"filelock",




"flake8>=3.8.3",




"flax>=0.4.1",




"ftfy",




"fugashi>=1.0",




"GitPython<3.1.19",




"hf-doc-builder>=0.3.0",




"huggingface-hub>=0.10.0,<1.0",




"importlib_metadata",




"ipadic>=1.0.0,<2.0",




"isort>=5.5.4",




"jax>=0.2.8,!=0.3.2,<=0.3.6",




"jaxlib>=0.1.65,<=0.3.6",




"jieba",




"kenlm",




"keras-nlp>=0.3.1",




"nltk",




"natten>=0.14.4",




"numpy>=1.17",




"onnxconverter-common",




"onnxruntime-tools>=1.4.2",




"onnxruntime>=1.4.0",




"optuna",




"optax>=0.0.8",




"packaging>=20.0",




"parameterized",




"phonemizer",




"protobuf<=3.20.2",




"psutil",




"pyyaml>=5.1",




"pydantic",




"pytest",




"pytest-timeout",




"pytest-xdist",




"python>=3.7.0",




"ray[tune]",




"regex!=2019.12.17",




"requests",




"rjieba",




"rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",




"sacrebleu>=1.4.12,<2.0.0",




"sacremoses",




"safetensors>=0.2.1",




"sagemaker>=2.31.0",




"scikit-learn",




"sentencepiece>=0.1.91,!=0.1.92",




"sigopt",




"librosa",




"starlette",




"tensorflow-cpu>=2.4,<2.12",




"tensorflow>=2.4,<2.12",




"tensorflow-text",




"tf2onnx",




"timeout-decorator",




"timm",




"tokenizers>=0.11.1,!=0.11.3,<0.14",




"torch>=1.7,!=1.12.0",




"torchaudio",




"pyctcdecode>=0.4.0",




"tqdm>=4.27",




"unidic>=1.0.2",




"unidic_lite>=1.0.7",




"uvicorn",




"beautifulsoup4",




"sudachipy>=0.6.6",




"sudachidict_core>=20220729",




"pyknp>=0.6.1",




]
















# 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 deps_table_update``",




"deps = {",




entries,




"}",




"",




]




target = "src/transformers/dependency_versions_table.py"




print(f"updating {target}")




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", "pyknp")




extras["sklearn"] = deps_list("scikit-learn")










extras["tf"] = deps_list("tensorflow", "onnxconverter-common", "tf2onnx", "tensorflow-text", "keras-nlp")




extras["tf-cpu"] = deps_list("tensorflow-cpu", "onnxconverter-common", "tf2onnx", "tensorflow-text", "keras-nlp")










extras["torch"] = deps_list("torch")




extras["accelerate"] = deps_list("accelerate")










if os.name == "nt": # windows




extras["retrieval"] = deps_list("datasets") # faiss is not supported on windows




extras["flax"] = [] # jax is not supported on windows




else:




extras["retrieval"] = deps_list("faiss-cpu", "datasets")




extras["flax"] = deps_list("jax", "jaxlib", "flax", "optax")










extras["tokenizers"] = deps_list("tokenizers")




extras["ftfy"] = deps_list("ftfy")




extras["onnxruntime"] = deps_list("onnxruntime", "onnxruntime-tools")




extras["onnx"] = deps_list("onnxconverter-common", "tf2onnx") + extras["onnxruntime"]




extras["modelcreation"] = deps_list("cookiecutter")










extras["sagemaker"] = deps_list("sagemaker")




extras["deepspeed"] = deps_list("deepspeed") + extras["accelerate"]




extras["fairscale"] = deps_list("fairscale")




extras["optuna"] = deps_list("optuna")




extras["ray"] = deps_list("ray[tune]")




extras["sigopt"] = deps_list("sigopt")










extras["integrations"] = extras["optuna"] + extras["ray"] + extras["sigopt"]










extras["serving"] = deps_list("pydantic", "uvicorn", "fastapi", "starlette")




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["tf-speech"] = extras["audio"]




extras["flax-speech"] = extras["audio"]




extras["vision"] = deps_list("Pillow")




extras["timm"] = deps_list("timm")




extras["natten"] = deps_list("natten")




extras["codecarbon"] = deps_list("codecarbon")




extras["video"] = deps_list("decord")










extras["sentencepiece"] = deps_list("sentencepiece", "protobuf")




extras["testing"] = (




deps_list(




"pytest",




"pytest-xdist",




"timeout-decorator",




"parameterized",




"psutil",




"datasets",




"dill",




"evaluate",




"pytest-timeout",




"black",




"sacrebleu",




"rouge-score",




"nltk",




"GitPython",




"hf-doc-builder",




"protobuf", # Can be removed once we can unpin protobuf




"sacremoses",




"rjieba",




"safetensors",




"beautifulsoup4",




)




+ extras["retrieval"]




+ extras["modelcreation"]




)










extras["deepspeed-testing"] = extras["deepspeed"] + extras["testing"] + extras["optuna"] + extras["sentencepiece"]










extras["quality"] = deps_list("black", "datasets", "isort", "flake8", "GitPython", "hf-doc-builder")










extras["all"] = (




extras["tf"]




+ extras["torch"]




+ extras["flax"]




+ extras["sentencepiece"]




+ extras["tokenizers"]




+ extras["torch-speech"]




+ extras["vision"]




+ extras["integrations"]




+ extras["timm"]




+ extras["codecarbon"]




+ extras["accelerate"]




+ extras["video"]




)










# Might need to add doc-builder and some specific deps in the future




extras["docs_specific"] = ["hf-doc-builder"]










# "docs" needs "all" to resolve all the references




extras["docs"] = extras["all"] + extras["docs_specific"]










extras["dev-torch"] = (




extras["testing"]




+ extras["torch"]




+ extras["sentencepiece"]




+ extras["tokenizers"]




+ extras["torch-speech"]




+ extras["vision"]




+ extras["integrations"]




+ extras["timm"]




+ extras["codecarbon"]




+ extras["quality"]




+ extras["ja"]




+ extras["docs_specific"]




+ extras["sklearn"]




+ extras["modelcreation"]




+ extras["onnxruntime"]




)




extras["dev-tensorflow"] = (




extras["testing"]




+ extras["tf"]




+ extras["sentencepiece"]




+ extras["tokenizers"]




+ extras["vision"]




+ extras["quality"]




+ extras["docs_specific"]




+ extras["sklearn"]




+ extras["modelcreation"]




+ extras["onnx"]




+ extras["tf-speech"]




)




extras["dev"] = (




extras["all"]




+ extras["testing"]




+ extras["quality"]




+ extras["ja"]




+ extras["docs_specific"]




+ extras["sklearn"]




+ extras["modelcreation"]




)










extras["torchhub"] = deps_list(




"filelock",




"huggingface-hub",




"importlib_metadata",




"numpy",




"packaging",




"protobuf",




"regex",




"requests",




"sentencepiece",




"torch",




"tokenizers",




"tqdm",




)










# when modifying the following list, make sure to update src/transformers/dependency_versions_check.py




install_requires = [




deps["importlib_metadata"] + ";python_version<'3.8'", # importlib_metadata for Python versions that don't have it




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["requests"], # for downloading models over HTTPS




deps["tokenizers"],




deps["tqdm"], # progress bars in model download and training scripts




]










setup(




name="transformers",




version="4.26.0.dev0", # 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="State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow",




long_description=open("README.md", "r", encoding="utf-8").read(),




long_description_content_type="text/markdown",




keywords="NLP vision speech deep learning transformer pytorch tensorflow BERT GPT-2 Wav2Vec2 ViT",




license="Apache",




url="https://github.com/huggingface/transformers",




package_dir={"": "src"},




packages=find_packages("src"),




package_data={"transformers": ["py.typed", "*.cu", "*.cpp", "*.cuh", "*.h"]},




zip_safe=False,




extras_require=extras,




entry_points={"console_scripts": ["transformers-cli=transformers.commands.transformers_cli:main"]},




python_requires=">=3.7.0",




install_requires=install_requires,




classifiers=[




"Development Status :: 5 - Production/Stable",




"Intended Audience :: Developers",




"Intended Audience :: Education",




"Intended Audience :: Science/Research",




"License :: OSI Approved :: Apache Software License",




"Operating System :: OS Independent",




"Programming Language :: Python :: 3",




"Programming Language :: Python :: 3.7",




"Programming Language :: Python :: 3.8",




"Programming Language :: Python :: 3.9",




"Topic :: Scientific/Engineering :: Artificial Intelligence",




],




cmdclass={"deps_table_update": DepsTableUpdateCommand},




)


Xulosa
Nutqni aniqlash texnologiyasi aloqani o'zgartirdi va vazifalarni bajarish uchun ketadigan vaqtni qisqartirdi. Shuningdek, u odamlarga texnologiyadan ilgari imkoni bo'lmagan usullarda foydalanishga imkon berdi. Quyida nutqni tanib olishning baʼzi afzalliklari va ovozni aniqlashning afzalliklari keltirilgan. Eng yaqqol foydasi shundaki, nutqni aniqlash texnologiyasi nogiron kishilarga oʻz ovozlarini kiritish sifatida kompyuterda yozish va boshqarish imkonini berdi. Ushbu texnologiyadan oldin, ma'lum jismoniy nuqsonlari bo'lgan ko'plab odamlar kompyuterlardan unumli foydalana olmas edilar. Nutqni tan olish bu odamlarga tenglik keltirdi va ularga yuqori texnologik jamiyatimizda ishtirok etish imkonini berdi. Murakkab algoritmlar va tabiiy tilni qayta ishlashdan foydalangan holda, nutqni aniqlash ilovalari har doim to'g'ri imlo va so'zlardan to'g'ri foydalanishimizni ta'minlaydi. Ovozni aniqlash aniqlik va ravshanlik bilan yozish imkonini berdi. Bu ko'p vaqtni tejaydi, ayniqsa ish joyida. Matn yozish qobiliyatiga ega bo'lmagan yoki sekin yozadigan odamlar uchun ovozni aniqlash o'yinni o'zgartiradi. Ma'lumki, matn terish uchun uzoq vaqt sarflangan vaqt mushak-skelet tizimining sog'lig'iga salbiy ta'sir qiladi. Shunday qilib, nutqni aniqlash fikringizni chop etishning xavfsizroq va tezroq alternatividir. Nutqni aniqlash ko'plab sohalarda ham inqilob qilmoqda. Misol uchun, tibbiyot sohasida shifokorlar endi hech narsa yozmasdan yoki yozmasdan to'g'ridan-to'g'ri bemorning fayllariga tibbiy yozuvlarni qo'shishlari mumkin. Ular oddiygina ovoz yozuvchisi bilan gaplashadi va bemorning elektron sog'liqni saqlash yozuvi avtomatik ravishda yangilanadi. Bu shifokorlarga davolanish va hayotni saqlab qolish uchun ko'proq vaqt sarflash imkonini berdi.


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