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mavonic_private_repos/transformers/README_ko.md

<!--- Copyright 2020 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. --> <p align="center"> <br> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/> <br> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <b>한국어</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p> Jax, Pytorch, TensorFlow를 위한 최첨단 자연어처리</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 Transformers는 분류, 정보 추출, 질문 답변, 요약, 번역, 문장 생성 등을 100개 이상의 언어로 수행할 수 있는 수천개의 사전학습된 모델을 제공합니다. 우리의 목표는 모두가 최첨단의 NLP 기술을 쉽게 사용하는 것입니다. 🤗 Transformers는 이러한 사전학습 모델을 빠르게 다운로드해 특정 텍스트에 사용하고, 원하는 데이터로 fine-tuning해 커뮤니티나 우리의 [모델 허브](https://huggingface.co/models)에 공유할 수 있도록 API를 제공합니다. 또한, 모델 구조를 정의하는 각 파이썬 모듈은 완전히 독립적이여서 연구 실험을 위해 손쉽게 수정할 수 있습니다. 🤗 Transformers는 가장 유명한 3개의 딥러닝 라이브러리를 지원합니다. 이들은 서로 완벽히 연동됩니다 — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/). 간단하게 이 라이브러리 중 하나로 모델을 학습하고, 또 다른 라이브러리로 추론을 위해 모델을 불러올 수 있습니다. ## 온라인 데모 대부분의 모델을 [모델 허브](https://huggingface.co/models) 페이지에서 바로 테스트해볼 수 있습니다. 공개 및 비공개 모델을 위한 [비공개 모델 호스팅, 버전 관리, 추론 API](https://huggingface.co/pricing)도 제공합니다. 예시: - [BERT로 마스킹된 단어 완성하기](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [Electra를 이용한 개체명 인식](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [GPT-2로 텍스트 생성하기](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+) - [RoBERTa로 자연어 추론하기](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [BART를 이용한 요약](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [DistilBERT를 이용한 질문 답변](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [T5로 번역하기](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) **[Transformer와 글쓰기](https://transformer.huggingface.co)** 는 이 저장소의 텍스트 생성 능력에 관한 Hugging Face 팀의 공식 데모입니다. ## Hugging Face 팀의 커스텀 지원을 원한다면 <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## 퀵 투어 원하는 텍스트에 바로 모델을 사용할 수 있도록, 우리는 `pipeline` API를 제공합니다. Pipeline은 사전학습 모델과 그 모델을 학습할 때 적용한 전처리 방식을 하나로 합칩니다. 다음은 긍정적인 텍스트와 부정적인 텍스트를 분류하기 위해 pipeline을 사용한 간단한 예시입니다: ```python >>> from transformers import pipeline # Allocate a pipeline for sentiment-analysis >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` 코드의 두번째 줄은 pipeline이 사용하는 사전학습 모델을 다운로드하고 캐시로 저장합니다. 세번째 줄에선 그 모델이 주어진 텍스트를 평가합니다. 여기서 모델은 99.97%의 확률로 텍스트가 긍정적이라고 평가했습니다. 많은 NLP 과제들을 `pipeline`으로 바로 수행할 수 있습니다. 예를 들어, 질문과 문맥이 주어지면 손쉽게 답변을 추출할 수 있습니다: ``` python >>> from transformers import pipeline # Allocate a pipeline for question-answering >>> question_answerer = pipeline('question-answering') >>> question_answerer({ ... 'question': 'What is the name of the repository ?', ... 'context': 'Pipeline has been included in the huggingface/transformers repository' ... }) {'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'} ``` 답변뿐만 아니라, 여기에 사용된 사전학습 모델은 확신도와 토크나이즈된 문장 속 답변의 시작점, 끝점까지 반환합니다. [이 튜토리얼](https://huggingface.co/docs/transformers/task_summary)에서 `pipeline` API가 지원하는 다양한 과제를 확인할 수 있습니다. 코드 3줄로 원하는 과제에 맞게 사전학습 모델을 다운로드 받고 사용할 수 있습니다. 다음은 PyTorch 버전입니다: ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` 다음은 TensorFlow 버전입니다: ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` 토크나이저는 사전학습 모델의 모든 전처리를 책임집니다. 그리고 (위의 예시처럼) 1개의 스트링이나 리스트도 처리할 수 있습니다. 토크나이저는 딕셔너리를 반환하는데, 이는 다운스트림 코드에 사용하거나 언패킹 연산자 ** 를 이용해 모델에 바로 전달할 수도 있습니다. 모델 자체는 일반적으로 사용되는 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)나 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)입니다. [이 튜토리얼](https://huggingface.co/transformers/training.html)은 이러한 모델을 표준적인 PyTorch나 TensorFlow 학습 과정에서 사용하는 방법, 또는 새로운 데이터로 fine-tune하기 위해 `Trainer` API를 사용하는 방법을 설명해줍니다. ## 왜 transformers를 사용해야 할까요? 1. 손쉽게 사용할 수 있는 최첨단 모델: - NLU와 NLG 과제에서 뛰어난 성능을 보입니다. - 교육자 실무자에게 진입 장벽이 낮습니다. - 3개의 클래스만 배우면 바로 사용할 수 있습니다. - 하나의 API로 모든 사전학습 모델을 사용할 수 있습니다. 1. 더 적은 계산 비용, 더 적은 탄소 발자국: - 연구자들은 모델을 계속 다시 학습시키는 대신 학습된 모델을 공유할 수 있습니다. - 실무자들은 학습에 필요한 시간과 비용을 절약할 수 있습니다. - 수십개의 모델 구조, 2,000개 이상의 사전학습 모델, 100개 이상의 언어로 학습된 모델 등. 1. 모델의 각 생애주기에 적합한 프레임워크: - 코드 3줄로 최첨단 모델을 학습하세요. - 자유롭게 모델을 TF2.0나 PyTorch 프레임워크로 변환하세요. - 학습, 평가, 공개 등 각 단계에 맞는 프레임워크를 원하는대로 선택하세요. 1. 필요한 대로 모델이나 예시를 커스터마이즈하세요: - 우리는 저자가 공개한 결과를 재현하기 위해 각 모델 구조의 예시를 제공합니다. - 모델 내부 구조는 가능한 일관적으로 공개되어 있습니다. - 빠른 실험을 위해 모델 파일은 라이브러리와 독립적으로 사용될 수 있습니다. ## 왜 transformers를 사용하지 말아야 할까요? - 이 라이브러리는 신경망 블록을 만들기 위한 모듈이 아닙니다. 연구자들이 여러 파일을 살펴보지 않고 바로 각 모델을 사용할 수 있도록, 모델 파일 코드의 추상화 수준을 적정하게 유지했습니다. - 학습 API는 모든 모델에 적용할 수 있도록 만들어지진 않았지만, 라이브러리가 제공하는 모델들에 적용할 수 있도록 최적화되었습니다. 일반적인 머신 러닝을 위해선, 다른 라이브러리를 사용하세요. - 가능한 많은 사용 예시를 보여드리고 싶어서, [예시 폴더](https://github.com/huggingface/transformers/tree/main/examples)의 스크립트를 준비했습니다. 이 스크립트들을 수정 없이 특정한 문제에 바로 적용하지 못할 수 있습니다. 필요에 맞게 일부 코드를 수정해야 할 수 있습니다. ## 설치 ### pip로 설치하기 이 저장소는 Python 3.8+, Flax 0.4.1+, PyTorch 1.11+, TensorFlow 2.6+에서 테스트 되었습니다. [가상 환경](https://docs.python.org/3/library/venv.html)에 🤗 Transformers를 설치하세요. Python 가상 환경에 익숙하지 않다면, [사용자 가이드](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)를 확인하세요. 우선, 사용할 Python 버전으로 가상 환경을 만들고 실행하세요. 그 다음, Flax, PyTorch, TensorFlow 중 적어도 하나는 설치해야 합니다. 플랫폼에 맞는 설치 명령어를 확인하기 위해 [TensorFlow 설치 페이지](https://www.tensorflow.org/install/), [PyTorch 설치 페이지](https://pytorch.org/get-started/locally/#start-locally), [Flax 설치 페이지](https://github.com/google/flax#quick-install)를 확인하세요. 이들 중 적어도 하나가 설치되었다면, 🤗 Transformers는 다음과 같이 pip을 이용해 설치할 수 있습니다: ```bash pip install transformers ``` 예시들을 체험해보고 싶거나, 최최최첨단 코드를 원하거나, 새로운 버전이 나올 때까지 기다릴 수 없다면 [라이브러리를 소스에서 바로 설치](https://huggingface.co/docs/transformers/installation#installing-from-source)하셔야 합니다. ### conda로 설치하기 🤗 Transformers는 다음과 같이 conda로 설치할 수 있습니다: ```shell script conda install conda-forge::transformers ``` > **_노트:_** `huggingface` 채널에서 `transformers`를 설치하는 것은 사용이 중단되었습니다. Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는 방법을 확인하세요. ## 모델 구조 **🤗 Transformers가 제공하는 [모든 모델 체크포인트](https://huggingface.co/models)** 는 huggingface.co [모델 허브](https://huggingface.co)에 완벽히 연동되어 있습니다. [개인](https://huggingface.co/users)과 [기관](https://huggingface.co/organizations)이 모델 허브에 직접 업로드할 수 있습니다. 현재 사용 가능한 모델 체크포인트의 개수:  🤗 Transformers는 다음 모델들을 제공합니다: 각 모델의 요약은 [여기](https://huggingface.co/docs/transformers/model_summary)서 확인하세요. 각 모델이 Flax, PyTorch, TensorFlow으로 구현되었는지 또는 🤗 Tokenizers 라이브러리가 지원하는 토크나이저를 사용하는지 확인하려면, [이 표](https://huggingface.co/docs/transformers/index#supported-frameworks)를 확인하세요. 이 구현은 여러 데이터로 검증되었고 (예시 스크립트를 참고하세요) 오리지널 구현의 성능과 같아야 합니다. [도큐먼트](https://huggingface.co/docs/transformers/examples)의 Examples 섹션에서 성능에 대한 자세한 설명을 확인할 수 있습니다. ## 더 알아보기 | 섹션 | 설명 | |-|-| | [도큐먼트](https://huggingface.co/transformers/) | 전체 API 도큐먼트와 튜토리얼 | | [과제 요약](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers가 지원하는 과제들 | | [전처리 튜토리얼](https://huggingface.co/docs/transformers/preprocessing) | `Tokenizer` 클래스를 이용해 모델을 위한 데이터 준비하기 | | [학습과 fine-tuning](https://huggingface.co/docs/transformers/training) | 🤗 Transformers가 제공하는 모델 PyTorch/TensorFlow 학습 과정과 `Trainer` API에서 사용하기 | | [퀵 투어: Fine-tuning/사용 스크립트](https://github.com/huggingface/transformers/tree/main/examples) | 다양한 과제에서 모델 fine-tuning하는 예시 스크립트 | | [모델 공유 및 업로드](https://huggingface.co/docs/transformers/model_sharing) | 커뮤니티에 fine-tune된 모델을 업로드 및 공유하기 | | [마이그레이션](https://huggingface.co/docs/transformers/migration) | `pytorch-transformers`나 `pytorch-pretrained-bert`에서 🤗 Transformers로 이동하기| ## 인용 🤗 Transformers 라이브러리를 인용하고 싶다면, 이 [논문](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)을 인용해 주세요: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos

mavonic_private_repos/transformers/CONTRIBUTING.md

<!--- Copyright 2020 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. --> # Contribute to 🤗 Transformers Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable. It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the repository to say thank you. However you choose to contribute, please be mindful and respect our [code of conduct](https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md). **This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).** ## Ways to contribute There are several ways you can contribute to 🤗 Transformers: * Fix outstanding issues with the existing code. * Submit issues related to bugs or desired new features. * Implement new models. * Contribute to the examples or to the documentation. If you don't know where to start, there is a special [Good First Issue](https://github.com/huggingface/transformers/contribute) listing. It will give you a list of open issues that are beginner-friendly and help you start contributing to open-source. The best way to do that is to open a Pull Request and link it to the issue that you'd like to work on. We try to give priority to opened PRs as we can easily track the progress of the fix, and if the contributor does not have time anymore, someone else can take the PR over. For something slightly more challenging, you can also take a look at the [Good Second Issue](https://github.com/huggingface/transformers/labels/Good%20Second%20Issue) list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀 > All contributions are equally valuable to the community. 🥰 ## Fixing outstanding issues If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](#create-a-pull-request) and open a Pull Request! ## Submitting a bug-related issue or feature request Do your best to follow these guidelines when submitting a bug-related issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback. ### Did you find a bug? The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter. Before you report an issue, we would really appreciate it if you could **make sure the bug was not already reported** (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask in the [forum](https://discuss.huggingface.co/) first. This helps us respond quicker to fixing issues related to the library versus general questions. Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it: * Your **OS type and version** and **Python**, **PyTorch** and **TensorFlow** versions when applicable. * A short, self-contained, code snippet that allows us to reproduce the bug in less than 30s. * The *full* traceback if an exception is raised. * Attach any other additional information, like screenshots, you think may help. To get the OS and software versions automatically, run the following command: ```bash transformers-cli env ``` You can also run the same command from the root of the repository: ```bash python src/transformers/commands/transformers_cli.py env ``` ### Do you want a new feature? If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe: 1. What is the *motivation* behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community? Whatever it is, we'd love to hear about it! 2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you. 3. Provide a *code snippet* that demonstrates the features usage. 4. If the feature is related to a paper, please include a link. If your issue is well written we're already 80% of the way there by the time you create it. We have added [templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with your issue. ## Do you want to implement a new model? New models are constantly released and if you want to implement a new model, please provide the following information: * A short description of the model and a link to the paper. * Link to the implementation if it is open-sourced. * Link to the model weights if they are available. If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers! We have a technical guide for [how to add a model to 🤗 Transformers](https://huggingface.co/docs/transformers/add_new_model). ## Do you want to add documentation? We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested! For more details about how to generate, build, and write the documentation, take a look at the documentation [README](https://github.com/huggingface/transformers/tree/main/docs). ## Create a Pull Request Before writing any code, we strongly advise you to search through the existing PRs or issues to make sure nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback. You will need basic `git` proficiency to contribute to 🤗 Transformers. While `git` is not the easiest tool to use, it has the greatest manual. Type `git --help` in a shell and enjoy! If you prefer books, [Pro Git](https://git-scm.com/book/en/v2) is a very good reference. You'll need **[Python 3.8](https://github.com/huggingface/transformers/blob/main/setup.py#L426)** or above to contribute to 🤗 Transformers. Follow the steps below to start contributing: 1. Fork the [repository](https://github.com/huggingface/transformers) by clicking on the **[Fork](https://github.com/huggingface/transformers/fork)** button on the repository's page. This creates a copy of the code under your GitHub user account. 2. Clone your fork to your local disk, and add the base repository as a remote: ```bash git clone [email protected]:<your Github handle>/transformers.git cd transformers git remote add upstream https://github.com/huggingface/transformers.git ``` 3. Create a new branch to hold your development changes: ```bash git checkout -b a-descriptive-name-for-my-changes ``` 🚨 **Do not** work on the `main` branch! 4. Set up a development environment by running the following command in a virtual environment: ```bash pip install -e ".[dev]" ``` If 🤗 Transformers was already installed in the virtual environment, remove it with `pip uninstall transformers` before reinstalling it in editable mode with the `-e` flag. Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a failure with this command. If that's the case make sure to install the Deep Learning framework you are working with (PyTorch, TensorFlow and/or Flax) then do: ```bash pip install -e ".[quality]" ``` which should be enough for most use cases. 5. Develop the features in your branch. As you work on your code, you should make sure the test suite passes. Run the tests impacted by your changes like this: ```bash pytest tests/<TEST_TO_RUN>.py ``` For more information about tests, check out the [Testing](https://huggingface.co/docs/transformers/testing) guide. 🤗 Transformers relies on `black` and `ruff` to format its source code consistently. After you make changes, apply automatic style corrections and code verifications that can't be automated in one go with: ```bash make fixup ``` This target is also optimized to only work with files modified by the PR you're working on. If you prefer to run the checks one after the other, the following command applies the style corrections: ```bash make style ``` 🤗 Transformers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality controls are run by the CI, but you can run the same checks with: ```bash make quality ``` Finally, we have a lot of scripts to make sure we don't forget to update some files when adding a new model. You can run these scripts with: ```bash make repo-consistency ``` To learn more about those checks and how to fix any issues with them, check out the [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. If you're modifying documents under the `docs/source` directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check make sure you install the documentation builder: ```bash pip install ".[docs]" ``` Run the following command from the root of the repository: ```bash doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build ``` This will build the documentation in the `~/tmp/test-build` folder where you can inspect the generated Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request. Once you're happy with your changes, add the changed files with `git add` and record your changes locally with `git commit`: ```bash git add modified_file.py git commit ``` Please remember to write [good commit messages](https://chris.beams.io/posts/git-commit/) to clearly communicate the changes you made! To keep your copy of the code up to date with the original repository, rebase your branch on `upstream/branch` *before* you open a pull request or if requested by a maintainer: ```bash git fetch upstream git rebase upstream/main ``` Push your changes to your branch: ```bash git push -u origin a-descriptive-name-for-my-changes ``` If you've already opened a pull request, you'll need to force push with the `--force` flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally. 6. Now you can go to your fork of the repository on GitHub and click on **Pull Request** to open a pull request. Make sure you tick off all the boxes on our [checklist](#pull-request-checklist) below. When you're ready, you can send your changes to the project maintainers for review. 7. It's ok if maintainers request changes, it happens to our core contributors too! So everyone can see the changes in the pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request. ### Pull request checklist ☐ The pull request title should summarize your contribution.<br> ☐ If your pull request addresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people viewing the issue know you are working on it).<br> ☐ To indicate a work in progress please prefix the title with `[WIP]`. These are useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.<br> ☐ Make sure existing tests pass.<br> ☐ If adding a new feature, also add tests for it.<br> - If you are adding a new model, make sure you use `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` to trigger the common tests. - If you are adding new `@slow` tests, make sure they pass using `RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py`. - If you are adding a new tokenizer, write tests and make sure `RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` passes. - CircleCI does not run the slow tests, but GitHub Actions does every night!<br> ☐ All public methods must have informative docstrings (see [`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py) for an example).<br> ☐ Due to the rapidly growing repository, don't add any images, videos and other non-text files that'll significantly weigh down the repository. Instead, use a Hub repository such as [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) to host these files and reference them by URL. We recommend placing documentation related images in the following repository: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). You can open a PR on this dataset repository and ask a Hugging Face member to merge it. For more information about the checks run on a pull request, take a look at our [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. ### Tests An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests](https://github.com/huggingface/transformers/tree/main/tests) folder and examples tests in the [examples](https://github.com/huggingface/transformers/tree/main/examples) folder. We like `pytest` and `pytest-xdist` because it's faster. From the root of the repository, specify a *path to a subfolder or a test file* to run the test: ```bash python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model ``` Similarly, for the `examples` directory, specify a *path to a subfolder or test file* to run the test. For example, the following command tests the text classification subfolder in the PyTorch `examples` directory: ```bash pip install -r examples/xxx/requirements.txt # only needed the first time python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification ``` In fact, this is actually how our `make test` and `make test-examples` commands are implemented (not including the `pip install`)! You can also specify a smaller set of tests in order to test only the feature you're working on. By default, slow tests are skipped but you can set the `RUN_SLOW` environment variable to `yes` to run them. This will download many gigabytes of models so make sure you have enough disk space, a good internet connection or a lot of patience! <Tip warning={true}> Remember to specify a *path to a subfolder or a test file* to run the test. Otherwise, you'll run all the tests in the `tests` or `examples` folder, which will take a very long time! </Tip> ```bash RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification ``` Like the slow tests, there are other environment variables available which not enabled by default during testing: - `RUN_CUSTOM_TOKENIZERS`: Enables tests for custom tokenizers. - `RUN_PT_FLAX_CROSS_TESTS`: Enables tests for PyTorch + Flax integration. - `RUN_PT_TF_CROSS_TESTS`: Enables tests for TensorFlow + PyTorch integration. More environment variables and additional information can be found in the [testing_utils.py](src/transformers/testing_utils.py). 🤗 Transformers uses `pytest` as a test runner only. It doesn't use any `pytest`-specific features in the test suite itself. This means `unittest` is fully supported. Here's how to run tests with `unittest`: ```bash python -m unittest discover -s tests -t . -v python -m unittest discover -s examples -t examples -v ``` ### Style guide For documentation strings, 🤗 Transformers follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html). Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/main/docs#writing-documentation---specification) for more information. ### Develop on Windows On Windows (unless you're working in [Windows Subsystem for Linux](https://learn.microsoft.com/en-us/windows/wsl/) or WSL), you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings: ```bash git config core.autocrlf input ``` One way to run the `make` command on Windows is with MSYS2: 1. [Download MSYS2](https://www.msys2.org/), and we assume it's installed in `C:\msys64`. 2. Open the command line `C:\msys64\msys2.exe` (it should be available from the **Start** menu). 3. Run in the shell: `pacman -Syu` and install `make` with `pacman -S make`. 4. Add `C:\msys64\usr\bin` to your PATH environment variable. You can now use `make` from any terminal (PowerShell, cmd.exe, etc.)! 🎉 ### Sync a forked repository with upstream main (the Hugging Face repository) When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs. 1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. 2. If a PR is absolutely necessary, use the following steps after checking out your branch: ```bash git checkout -b your-branch-for-syncing git pull --squash --no-commit upstream main git commit -m '<your message without GitHub references>' git push --set-upstream origin your-branch-for-syncing ```

mavonic_private_repos

mavonic_private_repos/transformers/LICENSE

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mavonic_private_repos

mavonic_private_repos/transformers/CODE_OF_CONDUCT.md

# Contributor Covenant Code of Conduct ## Our Pledge We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation. We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. ## Our Standards Examples of behavior that contributes to a positive environment for our community include: * Demonstrating empathy and kindness toward other people * Being respectful of differing opinions, viewpoints, and experiences * Giving and gracefully accepting constructive feedback * Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience * Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: * The use of sexualized language or imagery, and sexual attention or advances of any kind * Trolling, insulting or derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or email address, without their explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Enforcement Responsibilities Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. ## Scope This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at [email protected]. All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the reporter of any incident. ## Enforcement Guidelines Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct: ### 1. Correction **Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. **Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested. ### 2. Warning **Community Impact**: A violation through a single incident or series of actions. **Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban. ### 3. Temporary Ban **Community Impact**: A serious violation of community standards, including sustained inappropriate behavior. **Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban. ### 4. Permanent Ban **Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. **Consequence**: A permanent ban from any sort of public interaction within the community. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.1, available at [https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder][Mozilla CoC]. For answers to common questions about this code of conduct, see the FAQ at [https://www.contributor-covenant.org/faq][FAQ]. Translations are available at [https://www.contributor-covenant.org/translations][translations]. [homepage]: https://www.contributor-covenant.org [v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html [Mozilla CoC]: https://github.com/mozilla/diversity [FAQ]: https://www.contributor-covenant.org/faq [translations]: https://www.contributor-covenant.org/translations

mavonic_private_repos

mavonic_private_repos/transformers/README_hd.md

<!--- Copyright 2020 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. --> <!--- A useful guide for English-Hindi translation of Hugging Face documentation - Add space around English words and numbers when they appear between Hindi characters. E.g., कुल मिलाकर 100 से अधिक भाषाएँ; ट्रांसफॉर्मर लाइब्रेरी का उपयोग करता है। - वर्गाकार उद्धरणों का प्रयोग करें, जैसे, "उद्धरण" Dictionary Hugging Face: गले लगाओ चेहरा token: शब्द (और मूल अंग्रेजी को कोष्ठक में चिह्नित करें） tokenize: टोकननाइज़ करें (और मूल अंग्रेज़ी को चिह्नित करने के लिए कोष्ठक का उपयोग करें) tokenizer: Tokenizer (मूल अंग्रेजी में कोष्ठक के साथ) transformer: transformer pipeline: समनुक्रम API: API (अनुवाद के बिना) inference: विचार Trainer: प्रशिक्षक। कक्षा के नाम के रूप में प्रस्तुत किए जाने पर अनुवादित नहीं किया गया। pretrained/pretrain: पूर्व प्रशिक्षण finetune: फ़ाइन ट्यूनिंग community: समुदाय example: जब विशिष्ट गोदाम example कैटलॉग करते समय "केस केस" के रूप में अनुवादित Python data structures (e.g., list, set, dict): मूल अंग्रेजी को चिह्नित करने के लिए सूचियों, सेटों, शब्दकोशों में अनुवाद करें और कोष्ठक का उपयोग करें NLP/Natural Language Processing: द्वारा NLP अनुवाद के बिना प्रकट होते हैं Natural Language Processing प्रस्तुत किए जाने पर प्राकृतिक भाषा संसाधन में अनुवाद करें checkpoint: जाँच बिंदु --> <p align="center"> <br> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/> <br> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <b>हिन्दी</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p>Jax, PyTorch और TensorFlow के लिए उन्नत मशीन लर्निंग</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 Transformers 100 से अधिक भाषाओं में पाठ वर्गीकरण, सूचना निष्कर्षण, प्रश्न उत्तर, सारांशीकरण, अनुवाद, पाठ निर्माण का समर्थन करने के लिए हजारों पूर्व-प्रशिक्षित मॉडल प्रदान करता है। इसका उद्देश्य सबसे उन्नत एनएलपी तकनीक को सभी के लिए सुलभ बनाना है। 🤗 Transformers त्वरित डाउनलोड और उपयोग के लिए एक एपीआई प्रदान करता है, जिससे आप किसी दिए गए पाठ पर एक पूर्व-प्रशिक्षित मॉडल ले सकते हैं, इसे अपने डेटासेट पर ठीक कर सकते हैं और इसे [मॉडल हब](https://huggingface.co/models) के माध्यम से समुदाय के साथ साझा कर सकते हैं। इसी समय, प्रत्येक परिभाषित पायथन मॉड्यूल पूरी तरह से स्वतंत्र है, जो संशोधन और तेजी से अनुसंधान प्रयोगों के लिए सुविधाजनक है। 🤗 Transformers तीन सबसे लोकप्रिय गहन शिक्षण पुस्तकालयों का समर्थन करता है： [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — और इसके साथ निर्बाध रूप से एकीकृत होता है। आप अपने मॉडल को सीधे एक ढांचे के साथ प्रशिक्षित कर सकते हैं और दूसरे के साथ लोड और अनुमान लगा सकते हैं। ## ऑनलाइन डेमो आप सबसे सीधे मॉडल पृष्ठ पर परीक्षण कर सकते हैं [model hub](https://huggingface.co/models) मॉडल पर। हम [निजी मॉडल होस्टिंग, मॉडल संस्करण, और अनुमान एपीआई](https://huggingface.co/pricing) भी प्रदान करते हैं।。 यहाँ कुछ उदाहरण हैं： - [शब्द को भरने के लिए मास्क के रूप में BERT का प्रयोग करें](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [इलेक्ट्रा के साथ नामित इकाई पहचान](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [जीपीटी-2 के साथ टेक्स्ट जनरेशन](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+) - [रॉबर्टा के साथ प्राकृतिक भाषा निष्कर्ष](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [बार्ट के साथ पाठ सारांश](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [डिस्टिलबर्ट के साथ प्रश्नोत्तर](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [अनुवाद के लिए T5 का प्रयोग करें](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) **[Write With Transformer](https://transformer.huggingface.co)**，हगिंग फेस टीम द्वारा बनाया गया, यह एक आधिकारिक पाठ पीढ़ी है demo。 ## यदि आप हगिंग फेस टीम से बीस्पोक समर्थन की तलाश कर रहे हैं <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## जल्दी शुरू करें हम त्वरित उपयोग के लिए मॉडल प्रदान करते हैं `pipeline` (पाइपलाइन) एपीआई। पाइपलाइन पूर्व-प्रशिक्षित मॉडल और संबंधित पाठ प्रीप्रोसेसिंग को एकत्रित करती है। सकारात्मक और नकारात्मक भावना को निर्धारित करने के लिए पाइपलाइनों का उपयोग करने का एक त्वरित उदाहरण यहां दिया गया है: ```python >>> from transformers import pipeline # भावना विश्लेषण पाइपलाइन का उपयोग करना >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` कोड की दूसरी पंक्ति पाइपलाइन द्वारा उपयोग किए गए पूर्व-प्रशिक्षित मॉडल को डाउनलोड और कैश करती है, जबकि कोड की तीसरी पंक्ति दिए गए पाठ पर मूल्यांकन करती है। यहां उत्तर 99 आत्मविश्वास के स्तर के साथ "सकारात्मक" है। कई एनएलपी कार्यों में आउट ऑफ़ द बॉक्स पाइपलाइनों का पूर्व-प्रशिक्षण होता है। उदाहरण के लिए, हम किसी दिए गए पाठ से किसी प्रश्न का उत्तर आसानी से निकाल सकते हैं: ``` python >>> from transformers import pipeline # प्रश्नोत्तर पाइपलाइन का उपयोग करना >>> question_answerer = pipeline('question-answering') >>> question_answerer({ ... 'question': 'What is the name of the repository ?', ... 'context': 'Pipeline has been included in the huggingface/transformers repository' ... }) {'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'} ``` उत्तर देने के अलावा, पूर्व-प्रशिक्षित मॉडल संगत आत्मविश्वास स्कोर भी देता है, जहां उत्तर टोकनयुक्त पाठ में शुरू और समाप्त होता है। आप [इस ट्यूटोरियल](https://huggingface.co/docs/transformers/task_summary) से पाइपलाइन एपीआई द्वारा समर्थित कार्यों के बारे में अधिक जान सकते हैं। अपने कार्य पर किसी भी पूर्व-प्रशिक्षित मॉडल को डाउनलोड करना और उसका उपयोग करना भी कोड की तीन पंक्तियों की तरह सरल है। यहाँ PyTorch संस्करण के लिए एक उदाहरण दिया गया है: ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` यहाँ समकक्ष है TensorFlow कोड: ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` टोकननाइज़र सभी पूर्व-प्रशिक्षित मॉडलों के लिए प्रीप्रोसेसिंग प्रदान करता है और इसे सीधे एक स्ट्रिंग (जैसे ऊपर दिए गए उदाहरण) या किसी सूची पर बुलाया जा सकता है। यह एक डिक्शनरी (तानाशाही) को आउटपुट करता है जिसे आप डाउनस्ट्रीम कोड में उपयोग कर सकते हैं या `**` अनपैकिंग एक्सप्रेशन के माध्यम से सीधे मॉडल को पास कर सकते हैं। मॉडल स्वयं एक नियमित [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) या [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (आपके बैकएंड के आधार पर), जो हो सकता है सामान्य तरीके से उपयोग किया जाता है। [यह ट्यूटोरियल](https://huggingface.co/transformers/training.html) बताता है कि इस तरह के मॉडल को क्लासिक PyTorch या TensorFlow प्रशिक्षण लूप में कैसे एकीकृत किया जाए, या हमारे `ट्रेनर` एपीआई का उपयोग कैसे करें ताकि इसे जल्दी से फ़ाइन ट्यून किया जा सके।एक नया डेटासेट पे। ## ट्रांसफार्मर का उपयोग क्यों करें? 1. उपयोग में आसानी के लिए उन्नत मॉडल: - एनएलयू और एनएलजी पर बेहतर प्रदर्शन - प्रवेश के लिए कम बाधाओं के साथ शिक्षण और अभ्यास के अनुकूल - उपयोगकर्ता-सामना करने वाले सार तत्व, केवल तीन वर्गों को जानने की जरूरत है - सभी मॉडलों के लिए एकीकृत एपीआई 1. कम कम्प्यूटेशनल ओवरहेड और कम कार्बन उत्सर्जन: - शोधकर्ता हर बार नए सिरे से प्रशिक्षण देने के बजाय प्रशिक्षित मॉडल साझा कर सकते हैं - इंजीनियर गणना समय और उत्पादन ओवरहेड को कम कर सकते हैं - दर्जनों मॉडल आर्किटेक्चर, 2,000 से अधिक पूर्व-प्रशिक्षित मॉडल, 100 से अधिक भाषाओं का समर्थन 1.मॉडल जीवनचक्र के हर हिस्से को शामिल करता है: - कोड की केवल 3 पंक्तियों में उन्नत मॉडलों को प्रशिक्षित करें - मॉडल को मनमाने ढंग से विभिन्न डीप लर्निंग फ्रेमवर्क के बीच स्थानांतरित किया जा सकता है, जैसा आप चाहते हैं - निर्बाध रूप से प्रशिक्षण, मूल्यांकन और उत्पादन के लिए सबसे उपयुक्त ढांचा चुनें 1. आसानी से अनन्य मॉडल को अनुकूलित करें और अपनी आवश्यकताओं के लिए मामलों का उपयोग करें: - हम मूल पेपर परिणामों को पुन: पेश करने के लिए प्रत्येक मॉडल आर्किटेक्चर के लिए कई उपयोग के मामले प्रदान करते हैं - मॉडल की आंतरिक संरचना पारदर्शी और सुसंगत रहती है - मॉडल फ़ाइल को अलग से इस्तेमाल किया जा सकता है, जो संशोधन और त्वरित प्रयोग के लिए सुविधाजनक है ## मुझे ट्रांसफॉर्मर का उपयोग कब नहीं करना चाहिए? - यह लाइब्रेरी मॉड्यूलर न्यूरल नेटवर्क टूलबॉक्स नहीं है। मॉडल फ़ाइल में कोड जानबूझकर अल्पविकसित है, बिना अतिरिक्त सार इनकैप्सुलेशन के, ताकि शोधकर्ता अमूर्तता और फ़ाइल जंपिंग में शामिल हुए जल्दी से पुनरावृति कर सकें। - `ट्रेनर` एपीआई किसी भी मॉडल के साथ संगत नहीं है, यह केवल इस पुस्तकालय के मॉडल के लिए अनुकूलित है। यदि आप सामान्य मशीन लर्निंग के लिए उपयुक्त प्रशिक्षण लूप कार्यान्वयन की तलाश में हैं, तो कहीं और देखें। - हमारे सर्वोत्तम प्रयासों के बावजूद, [उदाहरण निर्देशिका](https://github.com/huggingface/transformers/tree/main/examples) में स्क्रिप्ट केवल उपयोग के मामले हैं। आपकी विशिष्ट समस्या के लिए, वे जरूरी नहीं कि बॉक्स से बाहर काम करें, और आपको कोड की कुछ पंक्तियों को सूट करने की आवश्यकता हो सकती है। ## स्थापित करना ### पिप का उपयोग करना इस रिपॉजिटरी का परीक्षण Python 3.8+, Flax 0.4.1+, PyTorch 1.11+ और TensorFlow 2.6+ के तहत किया गया है। आप [वर्चुअल एनवायरनमेंट](https://docs.python.org/3/library/venv.html) में 🤗 ट्रांसफॉर्मर इंस्टॉल कर सकते हैं। यदि आप अभी तक पायथन के वर्चुअल एनवायरनमेंट से परिचित नहीं हैं, तो कृपया इसे [उपयोगकर्ता निर्देश](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) पढ़ें। सबसे पहले, पायथन के उस संस्करण के साथ एक आभासी वातावरण बनाएं जिसका आप उपयोग करने और उसे सक्रिय करने की योजना बना रहे हैं। फिर, आपको Flax, PyTorch या TensorFlow में से किसी एक को स्थापित करने की आवश्यकता है। अपने प्लेटफ़ॉर्म पर इन फ़्रेमवर्क को स्थापित करने के लिए, [TensorFlow स्थापना पृष्ठ](https://www.tensorflow.org/install/), [PyTorch स्थापना पृष्ठ](https://pytorch.org/get-started/locally) देखें start-locally या [Flax स्थापना पृष्ठ](https://github.com/google/flax#quick-install). जब इनमें से कोई एक बैकएंड सफलतापूर्वक स्थापित हो जाता है, तो ट्रांसफॉर्मर निम्नानुसार स्थापित किए जा सकते हैं: ```bash pip install transformers ``` यदि आप उपयोग के मामलों को आज़माना चाहते हैं या आधिकारिक रिलीज़ से पहले नवीनतम इन-डेवलपमेंट कोड का उपयोग करना चाहते हैं, तो आपको [सोर्स से इंस्टॉल करना होगा](https://huggingface.co/docs/transformers/installation#installing-from-) स्रोत। ### कोंडा का उपयोग करना ट्रांसफॉर्मर कोंडा के माध्यम से निम्नानुसार स्थापित किया जा सकता है: ```shell script conda install conda-forge::transformers ``` > **_नोट:_** `huggingface` चैनल से `transformers` इंस्टॉल करना पुराना पड़ चुका है। कोंडा के माध्यम से Flax, PyTorch, या TensorFlow में से किसी एक को स्थापित करने के लिए, निर्देशों के लिए उनके संबंधित स्थापना पृष्ठ देखें। ## मॉडल आर्किटेक्चर [उपयोगकर्ता](https://huggingface.co/users) और [organization](https://huggingface.co) द्वारा ट्रांसफॉर्मर समर्थित [**सभी मॉडल चौकियों**](https://huggingface.co/models/users) हगिंगफेस.को/ऑर्गनाइजेशन), सभी को बिना किसी बाधा के हगिंगफेस.को [मॉडल हब](https://huggingface.co) के साथ एकीकृत किया गया है। चौकियों की वर्तमान संख्या:  🤗 ट्रांसफॉर्मर वर्तमान में निम्नलिखित आर्किटेक्चर का समर्थन करते हैं: मॉडल के अवलोकन के लिए [यहां देखें](https://huggingface.co/docs/transformers/model_summary)： यह जांचने के लिए कि क्या किसी मॉडल में पहले से ही Flax, PyTorch या TensorFlow का कार्यान्वयन है, या यदि उसके पास Tokenizers लाइब्रेरी में संबंधित टोकन है, तो [यह तालिका](https://huggingface.co/docs/transformers/index#supported) देखें। -फ्रेमवर्क)। इन कार्यान्वयनों का परीक्षण कई डेटासेट पर किया गया है (देखें केस स्क्रिप्ट का उपयोग करें) और वैनिला कार्यान्वयन के लिए तुलनात्मक रूप से प्रदर्शन करना चाहिए। आप उपयोग के मामले के दस्तावेज़ [इस अनुभाग](https://huggingface.co/docs/transformers/examples) में व्यवहार का विवरण पढ़ सकते हैं। ## अधिक समझें |अध्याय | विवरण | |-|-| | [दस्तावेज़ीकरण](https://huggingface.co/transformers/) | पूरा एपीआई दस्तावेज़ीकरण और ट्यूटोरियल | | [कार्य सारांश](https://huggingface.co/docs/transformers/task_summary) | ट्रांसफॉर्मर समर्थित कार्य | | [प्रीप्रोसेसिंग ट्यूटोरियल](https://huggingface.co/docs/transformers/preprocessing) | मॉडल के लिए डेटा तैयार करने के लिए `टोकनाइज़र` का उपयोग करना | | [प्रशिक्षण और फाइन-ट्यूनिंग](https://huggingface.co/docs/transformers/training) | PyTorch/TensorFlow के ट्रेनिंग लूप या `ट्रेनर` API में ट्रांसफॉर्मर द्वारा दिए गए मॉडल का उपयोग करें | | [क्विक स्टार्ट: ट्वीकिंग एंड यूज़ केस स्क्रिप्ट्स](https://github.com/huggingface/transformers/tree/main/examples) | विभिन्न कार्यों के लिए केस स्क्रिप्ट का उपयोग करें | | [मॉडल साझा करना और अपलोड करना](https://huggingface.co/docs/transformers/model_sharing) | समुदाय के साथ अपने फाइन टूनड मॉडल अपलोड और साझा करें | | [माइग्रेशन](https://huggingface.co/docs/transformers/migration) | `पाइटोरच-ट्रांसफॉर्मर्स` या `पाइटोरच-प्रीट्रेनड-बर्ट` से ट्रांसफॉर्मर में माइग्रेट करना | ## उद्धरण हमने आधिकारिक तौर पर इस लाइब्रेरी का [पेपर](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) प्रकाशित किया है, अगर आप ट्रान्सफ़ॉर्मर्स लाइब्रेरी का उपयोग करते हैं, तो कृपया उद्धृत करें: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos

mavonic_private_repos/transformers/awesome-transformers.md

# Awesome projects built with Transformers This page lists awesome projects built on top of Transformers. Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone else to build their dream projects. In this list, we showcase incredibly impactful and novel projects that have pushed the field forward. We celebrate 100 of these projects as we reach the milestone of 100k stars as a community; but we're very open to pull requests adding other projects to the list. If you believe a project should be here and it's not, then please, open a PR to add it. ## [gpt4all](https://github.com/nomic-ai/gpt4all) [gpt4all](https://github.com/nomic-ai/gpt4all) is an ecosystem of open-source chatbots trained on massive collections of clean assistant data including code, stories and dialogue. It offers open-source, large language models such as LLaMA and GPT-J trained in an assistant-style. Keywords: Open-source, LLaMa, GPT-J, instruction, assistant ## [recommenders](https://github.com/microsoft/recommenders) This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It goes over several aspects required to build efficient recommendation systems: data preparation, modeling, evaluation, model selection & optimization, as well as operationalization Keywords: Recommender systems, AzureML ## [IOPaint](https://github.com/Sanster/IOPaint) Image inpainting tool powered by Stable Diffusion. Remove any unwanted object, defect, people from your pictures or erase and replace anything on your pictures. Keywords: inpainting, SD, Stable Diffusion ## [flair](https://github.com/flairNLP/flair) FLAIR is a powerful PyTorch NLP framework, convering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things. Keywords: NLP, text embedding, document embedding, biomedical, NER, PoS, sentiment-analysis ## [mindsdb](https://github.com/mindsdb/mindsdb) MindsDB is a low-code ML platform, which automates and integrates several ML frameworks into the data stack as "AI Tables" to streamline the integration of AI into applications, making it accessible to developers of all skill levels. Keywords: Database, low-code, AI table ## [langchain](https://github.com/hwchase17/langchain) [langchain](https://github.com/hwchase17/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools. Keywords: LLMs, Large Language Models, Agents, Chains ## [LlamaIndex](https://github.com/jerryjliu/llama_index) [LlamaIndex](https://github.com/jerryjliu/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retreival mechanisms to perform different LLM tasks and obtain knowledge-augmented results. Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation ## [ParlAI](https://github.com/facebookresearch/ParlAI) [ParlAI](https://github.com/facebookresearch/ParlAI) is a python framework for sharing, training and testing dialogue models, from open-domain chitchat, to task-oriented dialogue, to visual question answering. It provides more than 100 datasets under the same API, a large zoo of pretrained models, a set of agents, and has several integrations. Keywords: Dialogue, Chatbots, VQA, Datasets, Agents ## [sentence-transformers](https://github.com/UKPLab/sentence-transformers) This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity. Keywords: Dense vector representations, Text embeddings, Sentence embeddings ## [ludwig](https://github.com/ludwig-ai/ludwig) Ludwig is a declarative machine learning framework that makes it easy to define machine learning pipelines using a simple and flexible data-driven configuration system. Ludwig is targeted at a wide variety of AI tasks. It provides a data-driven configuration system, training, prediction, and evaluation scripts, as well as a programmatic API. Keywords: Declarative, Data-driven, ML Framework ## [InvokeAI](https://github.com/invoke-ai/InvokeAI) [InvokeAI](https://github.com/invoke-ai/InvokeAI) is an engine for Stable Diffusion models, aimed at professionals, artists, and enthusiasts. It leverages the latest AI-driven technologies through CLI as well as a WebUI. Keywords: Stable-Diffusion, WebUI, CLI ## [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) is an easy-to-use and powerful NLP library particularly targeted at the Chinese languages. It has support for multiple pre-trained model zoos, and supports a wide-range of NLP tasks from research to industrial applications. Keywords: NLP, Chinese, Research, Industry ## [stanza](https://github.com/stanfordnlp/stanza) The Stanford NLP Group's official Python NLP library. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. Keywords: NLP, Multilingual, CoreNLP ## [DeepPavlov](https://github.com/deeppavlov/DeepPavlov) [DeepPavlov](https://github.com/deeppavlov/DeepPavlov) is an open-source conversational AI library. It is designed for the development of production ready chat-bots and complex conversational systems, as well as research in the area of NLP and, particularly, of dialog systems. Keywords: Conversational, Chatbot, Dialog ## [alpaca-lora](https://github.com/tloen/alpaca-lora) Alpaca-lora contains code for reproducing the Stanford Alpaca results using low-rank adaptation (LoRA). The repository provides training (fine-tuning) as well as generation scripts. Keywords: LoRA, Parameter-efficient fine-tuning ## [imagen-pytorch](https://github.com/lucidrains/imagen-pytorch) An open-source Implementation of Imagen, Google's closed-source Text-to-Image Neural Network that beats DALL-E2. As of release, it is the new SOTA for text-to-image synthesis. Keywords: Imagen, Text-to-image ## [adapters](https://github.com/adapter-hub/adapters) [adapters](https://github.com/adapter-hub/adapters) is an extension of HuggingFace's Transformers library, integrating adapters into state-of-the-art language models by incorporating AdapterHub, a central repository for pre-trained adapter modules. It is a drop-in replacement for transformers, which is regularly updated to stay up-to-date with the developments of transformers. Keywords: Adapters, LoRA, Parameter-efficient fine-tuning, Hub ## [NeMo](https://github.com/NVIDIA/NeMo) NVIDIA [NeMo](https://github.com/NVIDIA/NeMo) is a conversational AI toolkit built for researchers working on automatic speech recognition (ASR), text-to-speech synthesis (TTS), large language models (LLMs), and natural language processing (NLP). The primary objective of [NeMo](https://github.com/NVIDIA/NeMo) is to help researchers from industry and academia to reuse prior work (code and pretrained models) and make it easier to create new https://developer.nvidia.com/conversational-ai#started. Keywords: Conversational, ASR, TTS, LLMs, NLP ## [Runhouse](https://github.com/run-house/runhouse) [Runhouse](https://github.com/run-house/runhouse) allows to send code and data to any of your compute or data infra, all in Python, and continue to interact with them normally from your existing code and environment. Runhouse developers mention: > Think of it as an expansion pack to your Python interpreter that lets it take detours to remote machines or manipulate remote data. Keywords: MLOps, Infrastructure, Data storage, Modeling ## [MONAI](https://github.com/Project-MONAI/MONAI) [MONAI](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Its ambitions are: - developing a community of academic, industrial and clinical researchers collaborating on a common foundation; - creating state-of-the-art, end-to-end training workflows for healthcare imaging; - providing researchers with the optimized and standardized way to create and evaluate deep learning models. Keywords: Healthcare imaging, Training, Evaluation ## [simpletransformers](https://github.com/ThilinaRajapakse/simpletransformers) Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize, train, and evaluate a model. It supports a wide variety of NLP tasks. Keywords: Framework, simplicity, NLP ## [JARVIS](https://github.com/microsoft/JARVIS) [JARVIS](https://github.com/microsoft/JARVIS) is a system attempting to merge LLMs such as GPT-4 with the rest of the open-source ML community: leveraging up to 60 downstream models in order to perform tasks identified by the LLM. Keywords: LLM, Agents, HF Hub ## [transformers.js](https://xenova.github.io/transformers.js/) [transformers.js](https://xenova.github.io/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser. Keywords: Transformers, JavaScript, browser ## [bumblebee](https://github.com/elixir-nx/bumblebee) Bumblebee provides pre-trained Neural Network models on top of Axon, a neural networks library for the Elixir language. It includes integration with 🤗 Models, allowing anyone to download and perform Machine Learning tasks with few lines of code. Keywords: Elixir, Axon ## [argilla](https://github.com/argilla-io/argilla) Argilla is an open-source platform providing advanced NLP labeling, monitoring, and workspaces. It is compatible with many open source ecosystems such as Hugging Face, Stanza, FLAIR, and others. Keywords: NLP, Labeling, Monitoring, Workspaces ## [haystack](https://github.com/deepset-ai/haystack) Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs. It offers production-ready tools to quickly build complex decision making, question answering, semantic search, text generation applications, and more. Keywords: NLP, Framework, LLM ## [spaCy](https://github.com/explosion/spaCy) [spaCy](https://github.com/explosion/spaCy) is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It offers support for transformers models through its third party package, spacy-transformers. Keywords: NLP, Framework ## [speechbrain](https://github.com/speechbrain/speechbrain) SpeechBrain is an open-source and all-in-one conversational AI toolkit based on PyTorch. The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, speech separation, language identification, multi-microphone signal processing, and many others. Keywords: Conversational, Speech ## [skorch](https://github.com/skorch-dev/skorch) Skorch is a scikit-learn compatible neural network library that wraps PyTorch. It has support for models within transformers, and tokenizers from tokenizers. Keywords: Scikit-Learn, PyTorch ## [bertviz](https://github.com/jessevig/bertviz) BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. Keywords: Visualization, Transformers ## [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) is a haiku library using the xmap/pjit operators in JAX for model parallelism of transformers. This library is designed for scalability up to approximately 40B parameters on TPUv3s. It was the library used to train the GPT-J model. Keywords: Haiku, Model parallelism, LLM, TPU ## [deepchem](https://github.com/deepchem/deepchem) DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, quantum chemistry, and biology. Keywords: Drug discovery, Materials Science, Quantum Chemistry, Biology ## [OpenNRE](https://github.com/thunlp/OpenNRE) An Open-Source Package for Neural Relation Extraction (NRE). It is targeted at a wide range of users, from newcomers to relation extraction, to developers, researchers, or students. Keywords: Neural Relation Extraction, Framework ## [pycorrector](https://github.com/shibing624/pycorrector) PyCorrector is a Chinese Text Error Correction Tool. It uses a language model to detect errors, pinyin feature and shape feature to correct Chinese text errors. it can be used for Chinese Pinyin and stroke input method. Keywords: Chinese, Error correction tool, Language model, Pinyin ## [nlpaug](https://github.com/makcedward/nlpaug) This python library helps you with augmenting nlp for machine learning projects. It is a lightweight library featuring synthetic data generation for improving model performance, support for audio and text, and compatibility with several ecosystems (scikit-learn, pytorch, tensorflow). Keywords: Data augmentation, Synthetic data generation, Audio, NLP ## [dream-textures](https://github.com/carson-katri/dream-textures) [dream-textures](https://github.com/carson-katri/dream-textures) is a library targeted at bringing stable-diffusion support within Blender. It supports several use-cases, such as image generation, texture projection, inpainting/outpainting, ControlNet, and upscaling. Keywords: Stable-Diffusion, Blender ## [seldon-core](https://github.com/SeldonIO/seldon-core) Seldon core converts your ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices. Seldon handles scaling to thousands of production machine learning models and provides advanced machine learning capabilities out of the box including Advanced Metrics, Request Logging, Explainers, Outlier Detectors, A/B Tests, Canaries and more. Keywords: Microservices, Modeling, Language wrappers ## [open_model_zoo](https://github.com/openvinotoolkit/open_model_zoo) This repository includes optimized deep learning models and a set of demos to expedite development of high-performance deep learning inference applications. Use these free pre-trained models instead of training your own models to speed-up the development and production deployment process. Keywords: Optimized models, Demos ## [ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion) ML-Stable-Diffusion is a repository by Apple bringing Stable Diffusion support to Core ML, on Apple Silicon devices. It supports stable diffusion checkpoints hosted on the Hugging Face Hub. Keywords: Stable Diffusion, Apple Silicon, Core ML ## [stable-dreamfusion](https://github.com/ashawkey/stable-dreamfusion) Stable-Dreamfusion is a pytorch implementation of the text-to-3D model Dreamfusion, powered by the Stable Diffusion text-to-2D model. Keywords: Text-to-3D, Stable Diffusion ## [txtai](https://github.com/neuml/txtai) [txtai](https://github.com/neuml/txtai) is an open-source platform for semantic search and workflows powered by language models. txtai builds embeddings databases, which are a union of vector indexes and relational databases enabling similarity search with SQL. Semantic workflows connect language models together into unified applications. Keywords: Semantic search, LLM ## [djl](https://github.com/deepjavalibrary/djl) Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for developers. DJL provides a native Java development experience and functions like any other regular Java library. DJL offers [a Java binding](https://github.com/deepjavalibrary/djl/tree/master/extensions/tokenizers) for HuggingFace Tokenizers and easy conversion toolkit for HuggingFace model to deploy in Java. Keywords: Java, Framework ## [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/) This project provides a unified framework to test generative language models on a large number of different evaluation tasks. It has support for more than 200 tasks, and supports different ecosystems: HF Transformers, GPT-NeoX, DeepSpeed, as well as the OpenAI API. Keywords: LLM, Evaluation, Few-shot ## [gpt-neox](https://github.com/EleutherAI/gpt-neox) This repository records EleutherAI's library for training large-scale language models on GPUs. The framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. It is focused on training multi-billion-parameter models. Keywords: Training, LLM, Megatron, DeepSpeed ## [muzic](https://github.com/microsoft/muzic) Muzic is a research project on AI music that empowers music understanding and generation with deep learning and artificial intelligence. Muzic was created by researchers from Microsoft Research Asia. Keywords: Music understanding, Music generation ## [dalle-flow](https://github.com/jina-ai/dalle-flow) DALL·E Flow is an interactive workflow for generating high-definition images from a text prompt. Itt leverages DALL·E-Mega, GLID-3 XL, and Stable Diffusion to generate image candidates, and then calls CLIP-as-service to rank the candidates w.r.t. the prompt. The preferred candidate is fed to GLID-3 XL for diffusion, which often enriches the texture and background. Finally, the candidate is upscaled to 1024x1024 via SwinIR. Keywords: High-definition image generation, Stable Diffusion, DALL-E Mega, GLID-3 XL, CLIP, SwinIR ## [lightseq](https://github.com/bytedance/lightseq) LightSeq is a high performance training and inference library for sequence processing and generation implemented in CUDA. It enables highly efficient computation of modern NLP and CV models such as BERT, GPT, Transformer, etc. It is therefore best useful for machine translation, text generation, image classification, and other sequence related tasks. Keywords: Training, Inference, Sequence Processing, Sequence Generation ## [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) The goal of this project is to create a learning based system that takes an image of a math formula and returns corresponding LaTeX code. Keywords: OCR, LaTeX, Math formula ## [open_clip](https://github.com/mlfoundations/open_clip) OpenCLIP is an open source implementation of OpenAI's CLIP. The goal of this repository is to enable training models with contrastive image-text supervision, and to investigate their properties such as robustness to distribution shift. The starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset. Specifically, a ResNet-50 model trained with this codebase on OpenAI's 15 million image subset of YFCC achieves 32.7% top-1 accuracy on ImageNet. Keywords: CLIP, Open-source, Contrastive, Image-text ## [dalle-playground](https://github.com/saharmor/dalle-playground) A playground to generate images from any text prompt using Stable Diffusion and Dall-E mini. Keywords: WebUI, Stable Diffusion, Dall-E mini ## [FedML](https://github.com/FedML-AI/FedML) [FedML](https://github.com/FedML-AI/FedML) is a federated learning and analytics library enabling secure and collaborative machine learning on decentralized data anywhere at any scale. It supports large-scale cross-silo federated learning, and cross-device federated learning on smartphones/IoTs, and research simulation. Keywords: Federated Learning, Analytics, Collaborative ML, Decentralized ## [gpt-code-clippy](https://github.com/CodedotAl/gpt-code-clippy) GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model -- based on GPT-3, called GPT-Codex -- that is fine-tuned on publicly available code from GitHub. Keywords: LLM, Code ## [TextAttack](https://github.com/QData/TextAttack) [TextAttack](https://github.com/QData/TextAttack) 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP. Keywords: Adversarial attacks, Data augmentation, NLP ## [OpenPrompt](https://github.com/thunlp/OpenPrompt) Prompt-learning is a paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modify the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. This library provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. [OpenPrompt](https://github.com/thunlp/OpenPrompt) supports loading PLMs directly from https://github.com/huggingface/transformers. ## [text-generation-webui](https://github.com/oobabooga/text-generation-webui/) [text-generation-webui](https://github.com/oobabooga/text-generation-webui/) is a Gradio Web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA. Keywords: LLM, WebUI ## [libra](https://github.com/Palashio/libra) An ergonomic machine learning [libra](https://github.com/Palashio/libra)ry for non-technical users. It focuses on ergonomics and on ensuring that training a model is as simple as it can be. Keywords: Ergonomic, Non-technical ## [alibi](https://github.com/SeldonIO/alibi) Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. Keywords: Model inspection, Model interpretation, Black-box, White-box ## [tortoise-tts](https://github.com/neonbjb/tortoise-tts) Tortoise is a text-to-speech program built with the following priorities: strong multi-voice capabilities, and highly realistic prosody and intonation. Keywords: Text-to-speech ## [flower](https://github.com/adap/flower) Flower (flwr) is a framework for building federated learning systems. The design of Flower is based on a few guiding principles: customizability, extendability, framework agnosticity, and ease-of-use. Keywords: Federated learning systems, Customizable, Extendable, Framework-agnostic, Simplicity ## [fast-bert](https://github.com/utterworks/fast-bert) Fast-Bert is a deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification. It is aimed at simplicity. Keywords: Deployment, BERT, XLNet ## [towhee](https://github.com/towhee-io/towhee) Towhee makes it easy to build neural data processing pipelines for AI applications. We provide hundreds of models, algorithms, and transformations that can be used as standard pipeline building blocks. Users can use Towhee's Pythonic API to build a prototype of their pipeline and automatically optimize it for production-ready environments. Keywords: Data processing pipeline, Optimization ## [alibi-detect](https://github.com/SeldonIO/alibi-detect) Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection. Keywords: Adversarial, Outlier, Drift detection ## [FARM](https://github.com/deepset-ai/FARM) [FARM](https://github.com/deepset-ai/FARM) makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. Keywords: Transfer Learning, Modular design, Multi-task learning, Experiment tracking ## [aitextgen](https://github.com/minimaxir/aitextgen) A robust Python tool for text-based AI training and generation using OpenAI's GPT-2 and EleutherAI's GPT Neo/GPT-3 architecture. [aitextgen](https://github.com/minimaxir/aitextgen) is a Python package that leverages PyTorch, Hugging Face Transformers and pytorch-lightning with specific optimizations for text generation using GPT-2, plus many added features. Keywords: Training, Generation ## [diffgram](https://github.com/diffgram/diffgram) Diffgram aims to integrate human supervision into platforms. We support your team programmatically changing the UI (Schema, layout, etc.) like in Streamlit. This means that you can collect and annotate timely data from users. In other words, we are the platform behind your platform, an integrated part of your application, to ship new & better AI products faster. Keywords: Human supervision, Platform ## [ecco](https://github.com/jalammar/ecco) Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0). Keywords: Model explainability ## [s3prl](https://github.com/s3prl/s3prl) [s3prl](https://github.com/s3prl/s3prl) stands for Self-Supervised Speech Pre-training and Representation Learning. Self-supervised speech pre-trained models are called upstream in this toolkit, and are utilized in various downstream tasks. Keywords: Speech, Training ## [ru-dalle](https://github.com/ai-forever/ru-dalle) RuDALL-E aims to be similar to DALL-E, targeted to Russian. Keywords: DALL-E, Russian ## [DeepKE](https://github.com/zjunlp/DeepKE) [DeepKE](https://github.com/zjunlp/DeepKE) is a knowledge extraction toolkit for knowledge graph construction supporting cnSchema，low-resource, document-level and multimodal scenarios for entity, relation and attribute extraction. Keywords: Knowledge Extraction, Knowledge Graphs ## [Nebuly](https://github.com/nebuly-ai/nebuly) Nebuly is the next-generation platform to monitor and optimize your AI costs in one place. The platform connects to all your AI cost sources (compute, API providers, AI software licenses, etc) and centralizes them in one place to give you full visibility on a model basis. The platform also provides optimization recommendations and a co-pilot model that can guide during the optimization process. The platform builds on top of the open-source tools allowing you to optimize the different steps of your AI stack to squeeze out the best possible cost performances. Keywords: Optimization, Performance, Monitoring ## [imaginAIry](https://github.com/brycedrennan/imaginAIry) Offers a CLI and a Python API to generate images with Stable Diffusion. It has support for many tools, like image structure control (controlnet), instruction-based image edits (InstructPix2Pix), prompt-based masking (clipseg), among others. Keywords: Stable Diffusion, CLI, Python API ## [sparseml](https://github.com/neuralmagic/sparseml) SparseML is an open-source model optimization toolkit that enables you to create inference-optimized sparse models using pruning, quantization, and distillation algorithms. Models optimized with SparseML can then be exported to the ONNX and deployed with DeepSparse for GPU-class performance on CPU hardware. Keywords: Model optimization, Pruning, Quantization, Distillation ## [opacus](https://github.com/pytorch/opacus) Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. Keywords: Differential privacy ## [LAVIS](https://github.com/salesforce/LAVIS) [LAVIS](https://github.com/salesforce/LAVIS) is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. This library aims to provide engineers and researchers with a one-stop solution to rapidly develop models for their specific multimodal scenarios, and benchmark them across standard and customized datasets. It features a unified interface design to access Keywords: Multimodal, NLP, Vision ## [buzz](https://github.com/chidiwilliams/buzz) Buzz transcribes and translates audio offline on your personal computer. Powered by OpenAI's Whisper. Keywords: Audio transcription, Translation ## [rust-bert](https://github.com/guillaume-be/rust-bert) Rust-native state-of-the-art Natural Language Processing models and pipelines. Port of Hugging Face's Transformers library, using the tch-rs crate and pre-processing from rust-tokenizers. Supports multi-threaded tokenization and GPU inference. This repository exposes the model base architecture, task-specific heads and ready-to-use pipelines. Keywords: Rust, BERT, Inference ## [EasyNLP](https://github.com/alibaba/EasyNLP) [EasyNLP](https://github.com/alibaba/EasyNLP) is an easy-to-use NLP development and application toolkit in PyTorch, first released inside Alibaba in 2021. It is built with scalable distributed training strategies and supports a comprehensive suite of NLP algorithms for various NLP applications. [EasyNLP](https://github.com/alibaba/EasyNLP) integrates knowledge distillation and few-shot learning for landing large pre-trained models, together with various popular multi-modality pre-trained models. It provides a unified framework of model training, inference, and deployment for real-world applications. Keywords: NLP, Knowledge distillation, Few-shot learning, Multi-modality, Training, Inference, Deployment ## [TurboTransformers](https://github.com/Tencent/TurboTransformers) A fast and user-friendly runtime for transformer inference (Bert, Albert, GPT2, Decoders, etc) on CPU and GPU. Keywords: Optimization, Performance ## [hivemind](https://github.com/learning-at-home/hivemind) Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers. Keywords: Decentralized training ## [docquery](https://github.com/impira/docquery) DocQuery is a library and command-line tool that makes it easy to analyze semi-structured and unstructured documents (PDFs, scanned images, etc.) using large language models (LLMs). You simply point DocQuery at one or more documents and specify a question you want to ask. DocQuery is created by the team at Impira. Keywords: Semi-structured documents, Unstructured documents, LLM, Document Question Answering ## [CodeGeeX](https://github.com/THUDM/CodeGeeX) [CodeGeeX](https://github.com/THUDM/CodeGeeX) is a large-scale multilingual code generation model with 13 billion parameters, pre-trained on a large code corpus of more than 20 programming languages. It has several unique features: - Multilingual code generation - Crosslingual code translation - Is a customizable programming assistant Keywords: Code Generation Model ## [ktrain](https://github.com/amaiya/ktrain) [ktrain](https://github.com/amaiya/ktrain) is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, [ktrain](https://github.com/amaiya/ktrain) is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. Keywords: Keras wrapper, Model building, Training, Deployment ## [FastDeploy](https://github.com/PaddlePaddle/FastDeploy) [FastDeploy](https://github.com/PaddlePaddle/FastDeploy) is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with packageout-of-the-box and unified experience, endend-to-end optimization for over fire160+ Text, Vision, Speech and Cross-modal AI models. Including image classification, object detection, OCR, face detection, matting, pp-tracking, NLP, stable diffusion, TTS and other tasks to meet developers' industrial deployment needs for multi-scenario, multi-hardware and multi-platform. Keywords: Model deployment, CLoud, Mobile, Edge ## [underthesea](https://github.com/undertheseanlp/underthesea) [underthesea](https://github.com/undertheseanlp/underthesea) is a Vietnamese NLP toolkit. Underthesea is a suite of open source Python modules data sets and tutorials supporting research and development in Vietnamese Natural Language Processing. We provides extremely easy API to quickly apply pretrained NLP models to your Vietnamese text, such as word segmentation, part-of-speech tagging (PoS), named entity recognition (NER), text classification and dependency parsing. Keywords: Vietnamese, NLP ## [hasktorch](https://github.com/hasktorch/hasktorch) Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the core C++ libraries shared by PyTorch. Keywords: Haskell, Neural Networks ## [donut](https://github.com/clovaai/donut) Donut, or Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model. Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, such as visual document classification or information extraction (a.k.a. document parsing). Keywords: Document Understanding ## [transformers-interpret](https://github.com/cdpierse/transformers-interpret) Transformers Interpret is a model explainability tool designed to work exclusively with the transformers package. In line with the philosophy of the Transformers package Transformers Interpret allows any transformers model to be explained in just two lines. Explainers are available for both text and computer vision models. Visualizations are also available in notebooks and as savable png and html files Keywords: Model interpretation, Visualization ## [mlrun](https://github.com/mlrun/mlrun) MLRun is an open MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. With MLRun, you can choose any IDE on your local machine or on the cloud. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements. Keywords: MLOps ## [FederatedScope](https://github.com/alibaba/FederatedScope) [FederatedScope](https://github.com/alibaba/FederatedScope) is a comprehensive federated learning platform that provides convenient usage and flexible customization for various federated learning tasks in both academia and industry. Based on an event-driven architecture, [FederatedScope](https://github.com/alibaba/FederatedScope) integrates rich collections of functionalities to satisfy the burgeoning demands from federated learning, and aims to build up an easy-to-use platform for promoting learning safely and effectively. Keywords: Federated learning, Event-driven ## [pythainlp](https://github.com/PyThaiNLP/pythainlp) PyThaiNLP is a Python package for text processing and linguistic analysis, similar to NLTK with focus on Thai language. Keywords: Thai, NLP, NLTK ## [FlagAI](https://github.com/FlagAI-Open/FlagAI) [FlagAI](https://github.com/FlagAI-Open/FlagAI) (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model. Our goal is to support training, fine-tuning, and deployment of large-scale models on various downstream tasks with multi-modality. Keywords: Large models, Training, Fine-tuning, Deployment, Multi-modal ## [pyserini](https://github.com/castorini/pyserini) [pyserini](https://github.com/castorini/pyserini) is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse representations is provided via integration with the group's Anserini IR toolkit. Retrieval using dense representations is provided via integration with Facebook's Faiss library. Keywords: IR, Information Retrieval, Dense, Sparse ## [baal](https://github.com/baal-org/baal) [baal](https://github.com/baal-org/baal) is an active learning library that supports both industrial applications and research usecases. [baal](https://github.com/baal-org/baal) currently supports Monte-Carlo Dropout, MCDropConnect, deep ensembles, and semi-supervised learning. Keywords: Active Learning, Research, Labeling ## [cleanlab](https://github.com/cleanlab/cleanlab) [cleanlab](https://github.com/cleanlab/cleanlab) is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels. For text, image, tabular, audio (among others) datasets, you can use cleanlab to automatically: detect data issues (outliers, label errors, near duplicates, etc), train robust ML models, infer consensus + annotator-quality for multi-annotator data, suggest data to (re)label next (active learning). Keywords: Data-Centric AI, Data Quality, Noisy Labels, Outlier Detection, Active Learning ## [BentoML](https://github.com/bentoml/BentoML) [BentoML](https://github.com/bentoml) is the unified framework for for building, shipping, and scaling production-ready AI applications incorporating traditional ML, pre-trained AI models, Generative and Large Language Models. All Hugging Face models and pipelines can be seamlessly integrated into BentoML applications, enabling the running of models on the most suitable hardware and independent scaling based on usage. Keywords: BentoML, Framework, Deployment, AI Applications ## [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) offers a user-friendly fine-tuning framework that incorporates PEFT. The repository includes training(fine-tuning) and inference examples for LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, and other LLMs. A ChatGLM version is also available in [ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning). Keywords: PEFT, fine-tuning, LLaMA-2, ChatGLM, Qwen

mavonic_private_repos

mavonic_private_repos/transformers/README_pt-br.md

<!--- Copyright 2023 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. --> <p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg"> <img alt="Hugging Face Transformers Library" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg" width="352" height="59" style="max-width: 100%;"> </picture> <br/> <br/> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <b>Рortuguês</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p>Aprendizado de máquina de última geração para JAX, PyTorch e TensorFlow</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> A biblioteca 🤗 Transformers oferece milhares de modelos pré-treinados para executar tarefas em diferentes modalidades, como texto, visão e áudio. Esses modelos podem ser aplicados a: * 📝 Texto, para tarefas como classificação de texto, extração de informações, resposta a perguntas, sumarização, tradução, geração de texto, em mais de 100 idiomas. * 🖼️ Imagens, para tarefas como classificação de imagens, detecção de objetos e segmentação. * 🗣️ Áudio, para tarefas como reconhecimento de fala e classificação de áudio. Os modelos Transformer também podem executar tarefas em diversas modalidades combinadas, como responder a perguntas em tabelas, reconhecimento óptico de caracteres, extração de informações de documentos digitalizados, classificação de vídeo e resposta a perguntas visuais. A biblioteca 🤗 Transformers oferece APIs para baixar e usar rapidamente esses modelos pré-treinados em um texto específico, ajustá-los em seus próprios conjuntos de dados e, em seguida, compartilhá-los com a comunidade em nosso [model hub](https://huggingface.co/models). Ao mesmo tempo, cada módulo Python que define uma arquitetura é totalmente independente e pode ser modificado para permitir experimentos de pesquisa rápidos. A biblioteca 🤗 Transformers é respaldada pelas três bibliotecas de aprendizado profundo mais populares — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) e [TensorFlow](https://www.tensorflow.org/) — com uma integração perfeita entre elas. É simples treinar seus modelos com uma delas antes de carregá-los para inferência com a outra ## Demonstração Online Você pode testar a maioria de nossos modelos diretamente em suas páginas a partir do [model hub](https://huggingface.co/models). Também oferecemos [hospedagem de modelos privados, versionamento e uma API de inferência](https://huggingface.co/pricing) para modelos públicos e privados. Aqui estão alguns exemplos: Em Processamento de Linguagem Natural: - [Completar palavra mascarada com BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [Reconhecimento de Entidades Nomeadas com Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [Geração de texto com GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C) - [Inferência de Linguagem Natural com RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [Sumarização com BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [Resposta a perguntas com DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [Tradução com T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) Em Visão Computacional: - [Classificação de Imagens com ViT](https://huggingface.co/google/vit-base-patch16-224) - [Detecção de Objetos com DETR](https://huggingface.co/facebook/detr-resnet-50) - [Segmentação Semântica com SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) - [Segmentação Panóptica com MaskFormer](https://huggingface.co/facebook/maskformer-swin-small-coco) - [Estimativa de Profundidade com DPT](https://huggingface.co/docs/transformers/model_doc/dpt) - [Classificação de Vídeo com VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae) - [Segmentação Universal com OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) Em Áudio: - [Reconhecimento Automático de Fala com Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h) - [Detecção de Palavras-Chave com Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) - [Classificação de Áudio com Transformer de Espectrograma de Áudio](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) Em Tarefas Multimodais: - [Respostas de Perguntas em Tabelas com TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq) - [Respostas de Perguntas Visuais com ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) - [Classificação de Imagens sem Anotação com CLIP](https://huggingface.co/openai/clip-vit-large-patch14) - [Respostas de Perguntas em Documentos com LayoutLM](https://huggingface.co/impira/layoutlm-document-qa) - [Classificação de Vídeo sem Anotação com X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip) ## 100 Projetos Usando Transformers Transformers é mais do que um conjunto de ferramentas para usar modelos pré-treinados: é uma comunidade de projetos construídos ao seu redor e o Hugging Face Hub. Queremos que o Transformers permita que desenvolvedores, pesquisadores, estudantes, professores, engenheiros e qualquer outra pessoa construa seus projetos dos sonhos. Para celebrar as 100.000 estrelas do Transformers, decidimos destacar a comunidade e criamos a página [awesome-transformers](./awesome-transformers.md), que lista 100 projetos incríveis construídos nas proximidades dos Transformers. Se você possui ou utiliza um projeto que acredita que deveria fazer parte da lista, abra um PR para adicioná-lo! ## Se você está procurando suporte personalizado da equipe Hugging Face <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## Tour Rápido Para usar imediatamente um modelo em uma entrada específica (texto, imagem, áudio, ...), oferecemos a API `pipeline`. Os pipelines agrupam um modelo pré-treinado com o pré-processamento que foi usado durante o treinamento desse modelo. Aqui está como usar rapidamente um pipeline para classificar textos como positivos ou negativos: ```python from transformers import pipeline # Carregue o pipeline de classificação de texto >>> classifier = pipeline("sentiment-analysis") # Classifique o texto como positivo ou negativo >>> classifier("Estamos muito felizes em apresentar o pipeline no repositório dos transformers.") [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` A segunda linha de código baixa e armazena em cache o modelo pré-treinado usado pelo pipeline, enquanto a terceira linha o avalia no texto fornecido. Neste exemplo, a resposta é "positiva" com uma confiança de 99,97%. Muitas tarefas têm um `pipeline` pré-treinado pronto para uso, não apenas em PNL, mas também em visão computacional e processamento de áudio. Por exemplo, podemos facilmente extrair objetos detectados em uma imagem: ``` python >>> import requests >>> from PIL import Image >>> from transformers import pipeline # Download an image with cute cats >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> image = Image.open(image_data) # Allocate a pipeline for object detection >>> object_detector = pipeline('object-detection') >>> object_detector(image) [{'score': 0.9982201457023621, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, {'score': 0.9960021376609802, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, {'score': 0.9954745173454285, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, {'score': 0.9988006353378296, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, {'score': 0.9986783862113953, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] ``` Aqui obtemos uma lista de objetos detectados na imagem, com uma caixa envolvendo o objeto e uma pontuação de confiança. Aqui está a imagem original à esquerda, com as previsões exibidas à direita: <h3 align="center"> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a> </h3> Você pode aprender mais sobre as tarefas suportadas pela API `pipeline` em [este tutorial](https://huggingface.co/docs/transformers/task_summary). Além do `pipeline`, para baixar e usar qualquer um dos modelos pré-treinados em sua tarefa específica, tudo o que é necessário são três linhas de código. Aqui está a versão em PyTorch: ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` E aqui está o código equivalente para TensorFlow: ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` O tokenizador é responsável por todo o pré-processamento que o modelo pré-treinado espera, e pode ser chamado diretamente em uma única string (como nos exemplos acima) ou em uma lista. Ele produzirá um dicionário que você pode usar no código subsequente ou simplesmente passar diretamente para o seu modelo usando o operador de descompactação de argumentos **. O modelo em si é um [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) ou um [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)(dependendo do seu back-end) que você pode usar como de costume. [Este tutorial](https://huggingface.co/docs/transformers/training) explica como integrar esse modelo em um ciclo de treinamento clássico do PyTorch ou TensorFlow, ou como usar nossa API `Trainer` para ajuste fino rápido em um novo conjunto de dados. ## Por que devo usar transformers? 1. Modelos state-of-the-art fáceis de usar: - Alto desempenho em compreensão e geração de linguagem natural, visão computacional e tarefas de áudio. - Barreira de entrada baixa para educadores e profissionais. - Poucas abstrações visíveis para o usuário, com apenas três classes para aprender. - Uma API unificada para usar todos os nossos modelos pré-treinados. 1. Menores custos de computação, menor pegada de carbono: - Pesquisadores podem compartilhar modelos treinados em vez de treinar sempre do zero. - Profissionais podem reduzir o tempo de computação e os custos de produção. - Dezenas de arquiteturas com mais de 60.000 modelos pré-treinados em todas as modalidades. 1. Escolha o framework certo para cada parte da vida de um modelo: - Treine modelos state-of-the-art em 3 linhas de código. - Mova um único modelo entre frameworks TF2.0/PyTorch/JAX à vontade. - Escolha o framework certo de forma contínua para treinamento, avaliação e produção. 1. Personalize facilmente um modelo ou um exemplo para atender às suas necessidades: - Fornecemos exemplos para cada arquitetura para reproduzir os resultados publicados pelos autores originais. - Os detalhes internos do modelo são expostos de maneira consistente. - Os arquivos do modelo podem ser usados de forma independente da biblioteca para experimentos rápidos. ## Por que não devo usar transformers? - Esta biblioteca não é uma caixa de ferramentas modular para construir redes neurais. O código nos arquivos do modelo não é refatorado com abstrações adicionais de propósito, para que os pesquisadores possam iterar rapidamente em cada um dos modelos sem se aprofundar em abstrações/arquivos adicionais. - A API de treinamento não é projetada para funcionar com qualquer modelo, mas é otimizada para funcionar com os modelos fornecidos pela biblioteca. Para loops de aprendizado de máquina genéricos, você deve usar outra biblioteca (possivelmente, [Accelerate](https://huggingface.co/docs/accelerate)). - Embora nos esforcemos para apresentar o maior número possível de casos de uso, os scripts em nossa [pasta de exemplos](https://github.com/huggingface/transformers/tree/main/examples) são apenas isso: exemplos. É esperado que eles não funcionem prontos para uso em seu problema específico e que seja necessário modificar algumas linhas de código para adaptá-los às suas necessidades. ### Com pip Este repositório é testado no Python 3.8+, Flax 0.4.1+, PyTorch 1.11+ e TensorFlow 2.6+. Você deve instalar o 🤗 Transformers em um [ambiente virtual](https://docs.python.org/3/library/venv.html). Se você não está familiarizado com ambientes virtuais em Python, confira o [guia do usuário](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Primeiro, crie um ambiente virtual com a versão do Python que você vai usar e ative-o. Em seguida, você precisará instalar pelo menos um dos back-ends Flax, PyTorch ou TensorFlow. Consulte a [página de instalação do TensorFlow](https://www.tensorflow.org/install/), a [página de instalação do PyTorch](https://pytorch.org/get-started/locally/#start-locally) e/ou [Flax](https://github.com/google/flax#quick-install) e [Jax](https://github.com/google/jax#installation) páginas de instalação para obter o comando de instalação específico para a sua plataforma. Quando um desses back-ends estiver instalado, o 🤗 Transformers pode ser instalado usando pip da seguinte forma: ```bash pip install transformers ``` Se você deseja experimentar com os exemplos ou precisa da versão mais recente do código e não pode esperar por um novo lançamento, você deve instalar a [biblioteca a partir do código-fonte](https://huggingface.co/docs/transformers/installation#installing-from-source). ### Com conda O 🤗 Transformers pode ser instalado com conda da seguinte forma: ```bash conda install conda-forge::transformers ``` > **_NOTA:_** Instalar `transformers` pelo canal `huggingface` está obsoleto. Siga as páginas de instalação do Flax, PyTorch ou TensorFlow para ver como instalá-los com conda. Siga as páginas de instalação do Flax, PyTorch ou TensorFlow para ver como instalá-los com o conda. > **_NOTA:_** No Windows, você pode ser solicitado a ativar o Modo de Desenvolvedor para aproveitar o cache. Se isso não for uma opção para você, por favor nos avise [neste problema](https://github.com/huggingface/huggingface_hub/issues/1062). ## Arquiteturas de Modelos **[Todos os pontos de verificação de modelo](https://huggingface.co/models)** fornecidos pelo 🤗 Transformers são integrados de forma transparente do [model hub](https://huggingface.co/models) do huggingface.co, onde são carregados diretamente por [usuários](https://huggingface.co/users) e [organizações](https://huggingface.co/organizations). Número atual de pontos de verificação:  🤗 Transformers atualmente fornece as seguintes arquiteturas: veja [aqui](https://huggingface.co/docs/transformers/model_summary) para um resumo de alto nível de cada uma delas. Para verificar se cada modelo tem uma implementação em Flax, PyTorch ou TensorFlow, ou possui um tokenizador associado com a biblioteca 🤗 Tokenizers, consulte [esta tabela](https://huggingface.co/docs/transformers/index#supported-frameworks). Essas implementações foram testadas em vários conjuntos de dados (veja os scripts de exemplo) e devem corresponder ao desempenho das implementações originais. Você pode encontrar mais detalhes sobre o desempenho na seção de Exemplos da [documentação](https://github.com/huggingface/transformers/tree/main/examples). ## Saiba mais | Seção | Descrição | |-|-| | [Documentação](https://huggingface.co/docs/transformers/) | Documentação completa da API e tutoriais | | [Resumo de Tarefas](https://huggingface.co/docs/transformers/task_summary) | Tarefas suportadas pelo 🤗 Transformers | | [Tutorial de Pré-processamento](https://huggingface.co/docs/transformers/preprocessing) | Usando a classe `Tokenizer` para preparar dados para os modelos | | [Treinamento e Ajuste Fino](https://huggingface.co/docs/transformers/training) | Usando os modelos fornecidos pelo 🤗 Transformers em um loop de treinamento PyTorch/TensorFlow e a API `Trainer` | | [Tour Rápido: Scripts de Ajuste Fino/Utilização](https://github.com/huggingface/transformers/tree/main/examples) | Scripts de exemplo para ajuste fino de modelos em uma ampla gama de tarefas | | [Compartilhamento e Envio de Modelos](https://huggingface.co/docs/transformers/model_sharing) | Envie e compartilhe seus modelos ajustados com a comunidade | ## Citação Agora temos um [artigo](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) que você pode citar para a biblioteca 🤗 Transformers: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = out, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

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mavonic_private_repos/transformers/SECURITY.md

# Security Policy ## Hugging Face Hub, remote artefacts, and remote code Transformers is open-source software that is tightly coupled to the Hugging Face Hub. While you have the ability to use it offline with pre-downloaded model weights, it provides a very simple way to download, use, and manage models locally. When downloading artefacts that have been uploaded by others on any platform, you expose yourself to risks. Please read below for the security recommendations in order to keep your runtime and local environment safe. ### Remote artefacts Models uploaded on the Hugging Face Hub come in different formats. We heavily recommend uploading and downloading models in the [`safetensors`](https://github.com/huggingface/safetensors) format (which is the default prioritized by the transformers library), as developed specifically to prevent arbitrary code execution on your system. To avoid loading models from unsafe formats(e.g. [pickle](https://docs.python.org/3/library/pickle.html), you should use the `use_safetenstors` parameter. If doing so, in the event that no .safetensors file is present, transformers will error when loading the model. ### Remote code #### Modeling Transformers supports many model architectures, but is also the bridge between your Python runtime and models that are stored in model repositories on the Hugging Face Hub. These models require the `trust_remote_code=True` parameter to be set when using them; please **always** verify the content of the modeling files when using this argument. We recommend setting a revision in order to ensure you protect yourself from updates on the repository. #### Tools Through the `Agent` framework, remote tools can be downloaded to be used by the Agent. You're to specify these tools yourself, but please keep in mind that their code will be run on your machine if the Agent chooses to run them. Please inspect the code of the tools before passing them to the Agent to protect your runtime and local setup. ## Reporting a Vulnerability 🤗 Please feel free to submit vulnerability reports to our private bug bounty program at https://hackerone.com/hugging_face. You'll need to request access to the program by emailing [email protected]. Note that you'll need to be invited to our program, so send us a quick email at [email protected] if you've found a vulnerability.

mavonic_private_repos

mavonic_private_repos/transformers/conftest.py

# Copyright 2020 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. # tests directory-specific settings - this file is run automatically # by pytest before any tests are run import doctest import sys import warnings from os.path import abspath, dirname, join import _pytest import pytest from transformers.testing_utils import HfDoctestModule, HfDocTestParser NOT_DEVICE_TESTS = { "test_tokenization", "test_processor", "test_processing", "test_beam_constraints", "test_configuration_utils", "test_data_collator", "test_trainer_callback", "test_trainer_utils", "test_feature_extraction", "test_image_processing", "test_image_processor", "test_image_transforms", "test_optimization", "test_retrieval", "test_config", "test_from_pretrained_no_checkpoint", "test_keep_in_fp32_modules", "test_gradient_checkpointing_backward_compatibility", "test_gradient_checkpointing_enable_disable", "test_save_load_fast_init_from_base", "test_fast_init_context_manager", "test_fast_init_tied_embeddings", "test_save_load_fast_init_to_base", "test_torch_save_load", "test_initialization", "test_forward_signature", "test_model_common_attributes", "test_model_main_input_name", "test_correct_missing_keys", "test_tie_model_weights", "test_can_use_safetensors", "test_load_save_without_tied_weights", "test_tied_weights_keys", "test_model_weights_reload_no_missing_tied_weights", "test_pt_tf_model_equivalence", "test_mismatched_shapes_have_properly_initialized_weights", "test_matched_shapes_have_loaded_weights_when_some_mismatched_shapes_exist", "test_model_is_small", "test_tf_from_pt_safetensors", "test_flax_from_pt_safetensors", "ModelTest::test_pipeline_", # None of the pipeline tests from PipelineTesterMixin (of which XxxModelTest inherits from) are running on device "ModelTester::test_pipeline_", "/repo_utils/", "/utils/", "/tools/", } # allow having multiple repository checkouts and not needing to remember to rerun # `pip install -e '.[dev]'` when switching between checkouts and running tests. git_repo_path = abspath(join(dirname(__file__), "src")) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action="ignore", category=FutureWarning) def pytest_configure(config): config.addinivalue_line( "markers", "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested" ) config.addinivalue_line( "markers", "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested" ) config.addinivalue_line("markers", "is_pipeline_test: mark test to run only when pipelines are tested") config.addinivalue_line("markers", "is_staging_test: mark test to run only in the staging environment") config.addinivalue_line("markers", "accelerate_tests: mark test that require accelerate") config.addinivalue_line("markers", "tool_tests: mark the tool tests that are run on their specific schedule") config.addinivalue_line("markers", "not_device_test: mark the tests always running on cpu") def pytest_collection_modifyitems(items): for item in items: if any(test_name in item.nodeid for test_name in NOT_DEVICE_TESTS): item.add_marker(pytest.mark.not_device_test) def pytest_addoption(parser): from transformers.testing_utils import pytest_addoption_shared pytest_addoption_shared(parser) def pytest_terminal_summary(terminalreporter): from transformers.testing_utils import pytest_terminal_summary_main make_reports = terminalreporter.config.getoption("--make-reports") if make_reports: pytest_terminal_summary_main(terminalreporter, id=make_reports) def pytest_sessionfinish(session, exitstatus): # If no tests are collected, pytest exists with code 5, which makes the CI fail. if exitstatus == 5: session.exitstatus = 0 # Doctest custom flag to ignore output. IGNORE_RESULT = doctest.register_optionflag("IGNORE_RESULT") OutputChecker = doctest.OutputChecker class CustomOutputChecker(OutputChecker): def check_output(self, want, got, optionflags): if IGNORE_RESULT & optionflags: return True return OutputChecker.check_output(self, want, got, optionflags) doctest.OutputChecker = CustomOutputChecker _pytest.doctest.DoctestModule = HfDoctestModule doctest.DocTestParser = HfDocTestParser

mavonic_private_repos

mavonic_private_repos/transformers/.coveragerc

[run] source=transformers omit = # skip convertion scripts from testing for now */convert_* */__main__.py [report] exclude_lines = pragma: no cover raise except register_parameter

mavonic_private_repos

mavonic_private_repos/transformers/README_vi.md

<!--- Copyright 2020 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. --> <p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg"> <img alt="Hugging Face Transformers Library" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg" width="352" height="59" style="max-width: 100%;"> </picture> <br/> <br/> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <b>Tiếng việt</b> | </p> </h4> <h3 align="center"> <p>Công nghệ Học máy tiên tiến cho JAX, PyTorch và TensorFlow</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 Transformers cung cấp hàng ngàn mô hình được huấn luyện trước để thực hiện các nhiệm vụ trên các modalities khác nhau như văn bản, hình ảnh và âm thanh. Các mô hình này có thể được áp dụng vào: * 📝 Văn bản, cho các nhiệm vụ như phân loại văn bản, trích xuất thông tin, trả lời câu hỏi, tóm tắt, dịch thuật và sinh văn bản, trong hơn 100 ngôn ngữ. * 🖼️ Hình ảnh, cho các nhiệm vụ như phân loại hình ảnh, nhận diện đối tượng và phân đoạn. * 🗣️ Âm thanh, cho các nhiệm vụ như nhận dạng giọng nói và phân loại âm thanh. Các mô hình Transformer cũng có thể thực hiện các nhiệm vụ trên **nhiều modalities kết hợp**, như trả lời câu hỏi về bảng, nhận dạng ký tự quang học, trích xuất thông tin từ tài liệu quét, phân loại video và trả lời câu hỏi hình ảnh. 🤗 Transformers cung cấp các API để tải xuống và sử dụng nhanh chóng các mô hình được huấn luyện trước đó trên văn bản cụ thể, điều chỉnh chúng trên tập dữ liệu của riêng bạn và sau đó chia sẻ chúng với cộng đồng trên [model hub](https://huggingface.co/models) của chúng tôi. Đồng thời, mỗi module python xác định một kiến trúc là hoàn toàn độc lập và có thể được sửa đổi để cho phép thực hiện nhanh các thí nghiệm nghiên cứu. 🤗 Transformers được hỗ trợ bởi ba thư viện học sâu phổ biến nhất — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) và [TensorFlow](https://www.tensorflow.org/) — với tích hợp mượt mà giữa chúng. Việc huấn luyện mô hình của bạn với một thư viện trước khi tải chúng để sử dụng trong suy luận với thư viện khác là rất dễ dàng. ## Các demo trực tuyến Bạn có thể kiểm tra hầu hết các mô hình của chúng tôi trực tiếp trên trang của chúng từ [model hub](https://huggingface.co/models). Chúng tôi cũng cung cấp [dịch vụ lưu trữ mô hình riêng tư, phiên bản và API suy luận](https://huggingface.co/pricing) cho các mô hình công khai và riêng tư. Dưới đây là một số ví dụ: Trong Xử lý Ngôn ngữ Tự nhiên: - [Hoàn thành từ vụng về từ với BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [Nhận dạng thực thể đặt tên với Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [Tạo văn bản tự nhiên với Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - [Suy luận Ngôn ngữ Tự nhiên với RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [Tóm tắt văn bản với BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [Trả lời câu hỏi với DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [Dịch văn bản với T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) Trong Thị giác Máy tính: - [Phân loại hình ảnh với ViT](https://huggingface.co/google/vit-base-patch16-224) - [Phát hiện đối tượng với DETR](https://huggingface.co/facebook/detr-resnet-50) - [Phân đoạn ngữ nghĩa với SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) - [Phân đoạn toàn diện với Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic) - [Ước lượng độ sâu với Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything) - [Phân loại video với VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae) - [Phân đoạn toàn cầu với OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) Trong âm thanh: - [Nhận dạng giọng nói tự động với Whisper](https://huggingface.co/openai/whisper-large-v3) - [Phát hiện từ khóa với Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) - [Phân loại âm thanh với Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) Trong các nhiệm vụ đa phương thức: - [Trả lời câu hỏi về bảng với TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq) - [Trả lời câu hỏi hình ảnh với ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) - [Mô tả hình ảnh với LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf) - [Phân loại hình ảnh không cần nhãn với SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) - [Trả lời câu hỏi văn bản tài liệu với LayoutLM](https://huggingface.co/impira/layoutlm-document-qa) - [Phân loại video không cần nhãn với X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip) - [Phát hiện đối tượng không cần nhãn với OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2) - [Phân đoạn hình ảnh không cần nhãn với CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg) - [Tạo mặt nạ tự động với SAM](https://huggingface.co/docs/transformers/model_doc/sam) ## 100 dự án sử dụng Transformers Transformers không chỉ là một bộ công cụ để sử dụng các mô hình được huấn luyện trước: đó là một cộng đồng các dự án xây dựng xung quanh nó và Hugging Face Hub. Chúng tôi muốn Transformers giúp các nhà phát triển, nhà nghiên cứu, sinh viên, giáo sư, kỹ sư và bất kỳ ai khác xây dựng những dự án mơ ước của họ. Để kỷ niệm 100.000 sao của transformers, chúng tôi đã quyết định tập trung vào cộng đồng và tạo ra trang [awesome-transformers](./awesome-transformers.md) liệt kê 100 dự án tuyệt vời được xây dựng xung quanh transformers. Nếu bạn sở hữu hoặc sử dụng một dự án mà bạn tin rằng nên được thêm vào danh sách, vui lòng mở một PR để thêm nó! ## Nếu bạn đang tìm kiếm hỗ trợ tùy chỉnh từ đội ngũ Hugging Face <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## Hành trình nhanh Để ngay lập tức sử dụng một mô hình trên một đầu vào cụ thể (văn bản, hình ảnh, âm thanh, ...), chúng tôi cung cấp API `pipeline`. Pipelines nhóm một mô hình được huấn luyện trước với quá trình tiền xử lý đã được sử dụng trong quá trình huấn luyện của mô hình đó. Dưới đây là cách sử dụng nhanh một pipeline để phân loại văn bản tích cực so với tiêu cực: ```python >>> from transformers import pipeline # Cấp phát một pipeline cho phân tích cảm xúc >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` Dòng code thứ hai tải xuống và lưu trữ bộ mô hình được huấn luyện được sử dụng bởi pipeline, trong khi dòng thứ ba đánh giá nó trên văn bản đã cho. Ở đây, câu trả lời là "tích cực" với độ tin cậy là 99,97%. Nhiều nhiệm vụ có sẵn một `pipeline` được huấn luyện trước, trong NLP nhưng cũng trong thị giác máy tính và giọng nói. Ví dụ, chúng ta có thể dễ dàng trích xuất các đối tượng được phát hiện trong một hình ảnh: ``` python >>> import requests >>> from PIL import Image >>> from transformers import pipeline # Tải xuống một hình ảnh với những con mèo dễ thương >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> image = Image.open(image_data) # Cấp phát một pipeline cho phát hiện đối tượng >>> object_detector = pipeline('object-detection') >>> object_detector(image) [{'score': 0.9982201457023621, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, {'score': 0.9960021376609802, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, {'score': 0.9954745173454285, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, {'score': 0.9988006353378296, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, {'score': 0.9986783862113953, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] ``` Ở đây, chúng ta nhận được một danh sách các đối tượng được phát hiện trong hình ảnh, với một hộp bao quanh đối tượng và một điểm đánh giá độ tin cậy. Đây là hình ảnh gốc ở bên trái, với các dự đoán hiển thị ở bên phải: <h3 align="center"> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a> </h3> Bạn có thể tìm hiểu thêm về các nhiệm vụ được hỗ trợ bởi API `pipeline` trong [hướng dẫn này](https://huggingface.co/docs/transformers/task_summary). Ngoài `pipeline`, để tải xuống và sử dụng bất kỳ mô hình được huấn luyện trước nào cho nhiệm vụ cụ thể của bạn, chỉ cần ba dòng code. Đây là phiên bản PyTorch: ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` Và đây là mã tương đương cho TensorFlow: ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` Tokenizer là thành phần chịu trách nhiệm cho việc tiền xử lý mà mô hình được huấn luyện trước mong đợi và có thể được gọi trực tiếp trên một chuỗi đơn (như trong các ví dụ trên) hoặc một danh sách. Nó sẽ xuất ra một từ điển mà bạn có thể sử dụng trong mã phụ thuộc hoặc đơn giản là truyền trực tiếp cho mô hình của bạn bằng cách sử dụng toán tử ** để giải nén đối số. Chính mô hình là một [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) thông thường hoặc một [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (tùy thuộc vào backend của bạn) mà bạn có thể sử dụng như bình thường. [Hướng dẫn này](https://huggingface.co/docs/transformers/training) giải thích cách tích hợp một mô hình như vậy vào một vòng lặp huấn luyện cổ điển PyTorch hoặc TensorFlow, hoặc cách sử dụng API `Trainer` của chúng tôi để tinh chỉnh nhanh chóng trên một bộ dữ liệu mới. ## Tại sao tôi nên sử dụng transformers? 1. Các mô hình tiên tiến dễ sử dụng: - Hiệu suất cao trong việc hiểu và tạo ra ngôn ngữ tự nhiên, thị giác máy tính và âm thanh. - Ngưỡng vào thấp cho giảng viên và người thực hành. - Ít trừu tượng dành cho người dùng với chỉ ba lớp học. - Một API thống nhất để sử dụng tất cả các mô hình được huấn luyện trước của chúng tôi. 2. Giảm chi phí tính toán, làm giảm lượng khí thải carbon: - Các nhà nghiên cứu có thể chia sẻ các mô hình đã được huấn luyện thay vì luôn luôn huấn luyện lại. - Người thực hành có thể giảm thời gian tính toán và chi phí sản xuất. - Hàng chục kiến trúc với hơn 400.000 mô hình được huấn luyện trước trên tất cả các phương pháp. 3. Lựa chọn framework phù hợp cho mọi giai đoạn của mô hình: - Huấn luyện các mô hình tiên tiến chỉ trong 3 dòng code. - Di chuyển một mô hình duy nhất giữa các framework TF2.0/PyTorch/JAX theo ý muốn. - Dễ dàng chọn framework phù hợp cho huấn luyện, đánh giá và sản xuất. 4. Dễ dàng tùy chỉnh một mô hình hoặc một ví dụ theo nhu cầu của bạn: - Chúng tôi cung cấp các ví dụ cho mỗi kiến trúc để tái tạo kết quả được công bố bởi các tác giả gốc. - Các thành phần nội tại của mô hình được tiết lộ một cách nhất quán nhất có thể. - Các tệp mô hình có thể được sử dụng độc lập với thư viện để thực hiện các thử nghiệm nhanh chóng. ## Tại sao tôi không nên sử dụng transformers? - Thư viện này không phải là một bộ công cụ modul cho các khối xây dựng mạng neural. Mã trong các tệp mô hình không được tái cấu trúc với các trừu tượng bổ sung một cách cố ý, để các nhà nghiên cứu có thể lặp nhanh trên từng mô hình mà không cần đào sâu vào các trừu tượng/tệp bổ sung. - API huấn luyện không được thiết kế để hoạt động trên bất kỳ mô hình nào, mà được tối ưu hóa để hoạt động với các mô hình được cung cấp bởi thư viện. Đối với vòng lặp học máy chung, bạn nên sử dụng một thư viện khác (có thể là [Accelerate](https://huggingface.co/docs/accelerate)). - Mặc dù chúng tôi cố gắng trình bày càng nhiều trường hợp sử dụng càng tốt, nhưng các tập lệnh trong thư mục [examples](https://github.com/huggingface/transformers/tree/main/examples) chỉ là ví dụ. Dự kiến rằng chúng sẽ không hoạt động ngay tức khắc trên vấn đề cụ thể của bạn và bạn sẽ phải thay đổi một số dòng mã để thích nghi với nhu cầu của bạn. ## Cài đặt ### Sử dụng pip Thư viện này được kiểm tra trên Python 3.8+, Flax 0.4.1+, PyTorch 1.11+ và TensorFlow 2.6+. Bạn nên cài đặt 🤗 Transformers trong một [môi trường ảo Python](https://docs.python.org/3/library/venv.html). Nếu bạn chưa quen với môi trường ảo Python, hãy xem [hướng dẫn sử dụng](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Trước tiên, tạo một môi trường ảo với phiên bản Python bạn sẽ sử dụng và kích hoạt nó. Sau đó, bạn sẽ cần cài đặt ít nhất một trong số các framework Flax, PyTorch hoặc TensorFlow. Vui lòng tham khảo [trang cài đặt TensorFlow](https://www.tensorflow.org/install/), [trang cài đặt PyTorch](https://pytorch.org/get-started/locally/#start-locally) và/hoặc [Flax](https://github.com/google/flax#quick-install) và [Jax](https://github.com/google/jax#installation) để biết lệnh cài đặt cụ thể cho nền tảng của bạn. Khi đã cài đặt một trong các backend đó, 🤗 Transformers có thể được cài đặt bằng pip như sau: ```bash pip install transformers ``` Nếu bạn muốn thực hiện các ví dụ hoặc cần phiên bản mới nhất của mã và không thể chờ đợi cho một phiên bản mới, bạn phải [cài đặt thư viện từ nguồn](https://huggingface.co/docs/transformers/installation#installing-from-source). ### Với conda 🤗 Transformers có thể được cài đặt bằng conda như sau: ```shell script conda install conda-forge::transformers ``` > **_GHI CHÚ:_** Cài đặt `transformers` từ kênh `huggingface` đã bị lỗi thời. Hãy làm theo trang cài đặt của Flax, PyTorch hoặc TensorFlow để xem cách cài đặt chúng bằng conda. > **_GHI CHÚ:_** Trên Windows, bạn có thể được yêu cầu kích hoạt Chế độ phát triển để tận dụng việc lưu cache. Nếu điều này không phải là một lựa chọn cho bạn, hãy cho chúng tôi biết trong [vấn đề này](https://github.com/huggingface/huggingface_hub/issues/1062). ## Kiến trúc mô hình **[Tất cả các điểm kiểm tra mô hình](https://huggingface.co/models)** được cung cấp bởi 🤗 Transformers được tích hợp một cách mượt mà từ trung tâm mô hình huggingface.co [model hub](https://huggingface.co/models), nơi chúng được tải lên trực tiếp bởi [người dùng](https://huggingface.co/users) và [tổ chức](https://huggingface.co/organizations). Số lượng điểm kiểm tra hiện tại:  🤗 Transformers hiện đang cung cấp các kiến trúc sau đây: xem [ở đây](https://huggingface.co/docs/transformers/model_summary) để có một tóm tắt tổng quan về mỗi kiến trúc. Để kiểm tra xem mỗi mô hình có một phiên bản thực hiện trong Flax, PyTorch hoặc TensorFlow, hoặc có một tokenizer liên quan được hỗ trợ bởi thư viện 🤗 Tokenizers, vui lòng tham khảo [bảng này](https://huggingface.co/docs/transformers/index#supported-frameworks). Những phiên bản này đã được kiểm tra trên một số tập dữ liệu (xem các tập lệnh ví dụ) và nên tương đương với hiệu suất của các phiên bản gốc. Bạn có thể tìm thấy thêm thông tin về hiệu suất trong phần Ví dụ của [tài liệu](https://github.com/huggingface/transformers/tree/main/examples). ## Tìm hiểu thêm | Phần | Mô tả | |-|-| | [Tài liệu](https://huggingface.co/docs/transformers/) | Toàn bộ tài liệu API và hướng dẫn | | [Tóm tắt nhiệm vụ](https://huggingface.co/docs/transformers/task_summary) | Các nhiệm vụ được hỗ trợ bởi 🤗 Transformers | | [Hướng dẫn tiền xử lý](https://huggingface.co/docs/transformers/preprocessing) | Sử dụng lớp `Tokenizer` để chuẩn bị dữ liệu cho các mô hình | | [Huấn luyện và điều chỉnh](https://huggingface.co/docs/transformers/training) | Sử dụng các mô hình được cung cấp bởi 🤗 Transformers trong vòng lặp huấn luyện PyTorch/TensorFlow và API `Trainer` | | [Hướng dẫn nhanh: Điều chỉnh/sử dụng các kịch bản](https://github.com/huggingface/transformers/tree/main/examples) | Các kịch bản ví dụ để điều chỉnh mô hình trên nhiều nhiệm vụ khác nhau | | [Chia sẻ và tải lên mô hình](https://huggingface.co/docs/transformers/model_sharing) | Tải lên và chia sẻ các mô hình đã điều chỉnh của bạn với cộng đồng | ## Trích dẫn Bây giờ chúng ta có một [bài báo](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) mà bạn có thể trích dẫn cho thư viện 🤗 Transformers: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos

mavonic_private_repos/transformers/README_ja.md

<!--- Copyright 2020 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. --> <!--- A useful guide for English-Traditional Japanese translation of Hugging Face documentation - Use square quotes, e.g.,「引用」 Dictionary API: API(翻訳しない) add: 追加 checkpoint: チェックポイント code: コード community: コミュニティ confidence: 信頼度 dataset: データセット documentation: ドキュメント example: 例 finetune: 微調整 Hugging Face: Hugging Face(翻訳しない) implementation: 実装 inference: 推論 library: ライブラリ module: モジュール NLP/Natural Language Processing: NLPと表示される場合は翻訳されず、Natural Language Processingと表示される場合は翻訳される online demos: オンラインデモ pipeline: pipeline(翻訳しない) pretrained/pretrain: 学習済み Python data structures (e.g., list, set, dict): リスト、セット、ディクショナリと訳され、括弧内は原文英語 repository: repository(翻訳しない) summary: 概要 token-: token-(翻訳しない) Trainer: Trainer(翻訳しない) transformer: transformer(翻訳しない) tutorial: チュートリアル user: ユーザ --> <p align="center"> <br> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/> <br> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <b>日本語</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p>JAX、PyTorch、TensorFlowのための最先端機械学習</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗Transformersは、テキスト、視覚、音声などの異なるモダリティに対してタスクを実行するために、事前に学習させた数千のモデルを提供します。 これらのモデルは次のような場合に適用できます: * 📝 テキストは、テキストの分類、情報抽出、質問応答、要約、翻訳、テキスト生成などのタスクのために、100以上の言語に対応しています。 * 🖼️ 画像分類、物体検出、セグメンテーションなどのタスクのための画像。 * 🗣️ 音声は、音声認識や音声分類などのタスクに使用します。 トランスフォーマーモデルは、テーブル質問応答、光学文字認識、スキャン文書からの情報抽出、ビデオ分類、視覚的質問応答など、**複数のモダリティを組み合わせた**タスクも実行可能です。 🤗Transformersは、与えられたテキストに対してそれらの事前学習されたモデルを素早くダウンロードして使用し、あなた自身のデータセットでそれらを微調整し、私たちの[model hub](https://huggingface.co/models)でコミュニティと共有するためのAPIを提供します。同時に、アーキテクチャを定義する各Pythonモジュールは完全にスタンドアロンであり、迅速な研究実験を可能にするために変更することができます。 🤗Transformersは[Jax](https://jax.readthedocs.io/en/latest/)、[PyTorch](https://pytorch.org/)、[TensorFlow](https://www.tensorflow.org/)という3大ディープラーニングライブラリーに支えられ、それぞれのライブラリをシームレスに統合しています。片方でモデルを学習してから、もう片方で推論用にロードするのは簡単なことです。 ## オンラインデモ [model hub](https://huggingface.co/models)から、ほとんどのモデルのページで直接テストすることができます。また、パブリックモデル、プライベートモデルに対して、[プライベートモデルのホスティング、バージョニング、推論API](https://huggingface.co/pricing)を提供しています。 以下はその一例です: 自然言語処理にて: - [BERTによるマスクドワード補完](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [Electraによる名前実体認識](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [GPT-2によるテキスト生成](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+) - [RoBERTaによる自然言語推論](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [BARTによる要約](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [DistilBERTによる質問応答](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [T5による翻訳](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) コンピュータビジョンにて: - [ViTによる画像分類](https://huggingface.co/google/vit-base-patch16-224) - [DETRによる物体検出](https://huggingface.co/facebook/detr-resnet-50) - [SegFormerによるセマンティックセグメンテーション](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) - [DETRによるパノプティックセグメンテーション](https://huggingface.co/facebook/detr-resnet-50-panoptic) オーディオにて: - [Wav2Vec2による自動音声認識](https://huggingface.co/facebook/wav2vec2-base-960h) - [Wav2Vec2によるキーワード検索](https://huggingface.co/superb/wav2vec2-base-superb-ks) マルチモーダルなタスクにて: - [ViLTによる視覚的質問応答](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) Hugging Faceチームによって作られた **[トランスフォーマーを使った書き込み](https://transformer.huggingface.co)** は、このリポジトリのテキスト生成機能の公式デモである。 ## Hugging Faceチームによるカスタム・サポートをご希望の場合 <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## クイックツアー 与えられた入力（テキスト、画像、音声、...）に対してすぐにモデルを使うために、我々は`pipeline`というAPIを提供しております。pipelineは、学習済みのモデルと、そのモデルの学習時に使用された前処理をグループ化したものです。以下は、肯定的なテキストと否定的なテキストを分類するためにpipelineを使用する方法です: ```python >>> from transformers import pipeline # Allocate a pipeline for sentiment-analysis >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` 2行目のコードでは、pipelineで使用される事前学習済みモデルをダウンロードしてキャッシュし、3行目では与えられたテキストに対してそのモデルを評価します。ここでは、答えは99.97%の信頼度で「ポジティブ」です。 自然言語処理だけでなく、コンピュータビジョンや音声処理においても、多くのタスクにはあらかじめ訓練された`pipeline`が用意されている。例えば、画像から検出された物体を簡単に抽出することができる: ``` python >>> import requests >>> from PIL import Image >>> from transformers import pipeline # Download an image with cute cats >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> image = Image.open(image_data) # Allocate a pipeline for object detection >>> object_detector = pipeline('object-detection') >>> object_detector(image) [{'score': 0.9982201457023621, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, {'score': 0.9960021376609802, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, {'score': 0.9954745173454285, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, {'score': 0.9988006353378296, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, {'score': 0.9986783862113953, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] ``` ここでは、画像から検出されたオブジェクトのリストが得られ、オブジェクトを囲むボックスと信頼度スコアが表示されます。左側が元画像、右側が予測結果を表示したものです: <h3 align="center"> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a> </h3> [このチュートリアル](https://huggingface.co/docs/transformers/task_summary)では、`pipeline`APIでサポートされているタスクについて詳しく説明しています。 `pipeline`に加えて、与えられたタスクに学習済みのモデルをダウンロードして使用するために必要なのは、3行のコードだけです。以下はPyTorchのバージョンです: ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` そしてこちらはTensorFlowと同等のコードとなります: ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` トークナイザは学習済みモデルが期待するすべての前処理を担当し、単一の文字列 (上記の例のように) またはリストに対して直接呼び出すことができます。これは下流のコードで使用できる辞書を出力します。また、単純に ** 引数展開演算子を使用してモデルに直接渡すこともできます。 モデル自体は通常の[Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) または [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (バックエンドによって異なる)で、通常通り使用することが可能です。[このチュートリアル](https://huggingface.co/docs/transformers/training)では、このようなモデルを従来のPyTorchやTensorFlowの学習ループに統合する方法や、私たちの`Trainer`APIを使って新しいデータセットで素早く微調整を行う方法について説明します。 ## なぜtransformersを使う必要があるのでしょうか？ 1. 使いやすい最新モデル: - 自然言語理解・生成、コンピュータビジョン、オーディオの各タスクで高いパフォーマンスを発揮します。 - 教育者、実務者にとっての低い参入障壁。 - 学習するクラスは3つだけで、ユーザが直面する抽象化はほとんどありません。 - 学習済みモデルを利用するための統一されたAPI。 1. 低い計算コスト、少ないカーボンフットプリント: - 研究者は、常に再トレーニングを行うのではなく、トレーニングされたモデルを共有することができます。 - 実務家は、計算時間や生産コストを削減することができます。 - すべてのモダリティにおいて、60,000以上の事前学習済みモデルを持つ数多くのアーキテクチャを提供します。 1. モデルのライフタイムのあらゆる部分で適切なフレームワークを選択可能: - 3行のコードで最先端のモデルをトレーニング。 - TF2.0/PyTorch/JAXフレームワーク間で1つのモデルを自在に移動させる。 - 学習、評価、生産に適したフレームワークをシームレスに選択できます。 1. モデルやサンプルをニーズに合わせて簡単にカスタマイズ可能: - 原著者が発表した結果を再現するために、各アーキテクチャの例を提供しています。 - モデル内部は可能な限り一貫して公開されています。 - モデルファイルはライブラリとは独立して利用することができ、迅速な実験が可能です。 ## なぜtransformersを使ってはいけないのでしょうか？ - このライブラリは、ニューラルネットのためのビルディングブロックのモジュール式ツールボックスではありません。モデルファイルのコードは、研究者が追加の抽象化/ファイルに飛び込むことなく、各モデルを素早く反復できるように、意図的に追加の抽象化でリファクタリングされていません。 - 学習APIはどのようなモデルでも動作するわけではなく、ライブラリが提供するモデルで動作するように最適化されています。一般的な機械学習のループには、別のライブラリ(おそらく[Accelerate](https://huggingface.co/docs/accelerate))を使用する必要があります。 - 私たちはできるだけ多くの使用例を紹介するよう努力していますが、[examples フォルダ](https://github.com/huggingface/transformers/tree/main/examples) にあるスクリプトはあくまで例です。あなたの特定の問題に対してすぐに動作するわけではなく、あなたのニーズに合わせるために数行のコードを変更する必要があることが予想されます。 ## インストール ### pipにて このリポジトリは、Python 3.8+, Flax 0.4.1+, PyTorch 1.11+, TensorFlow 2.6+ でテストされています。 🤗Transformersは[仮想環境](https://docs.python.org/3/library/venv.html)にインストールする必要があります。Pythonの仮想環境に慣れていない場合は、[ユーザーガイド](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)を確認してください。 まず、使用するバージョンのPythonで仮想環境を作成し、アクティベートします。 その後、Flax, PyTorch, TensorFlowのうち少なくとも1つをインストールする必要があります。 [TensorFlowインストールページ](https://www.tensorflow.org/install/)、[PyTorchインストールページ](https://pytorch.org/get-started/locally/#start-locally)、[Flax](https://github.com/google/flax#quick-install)、[Jax](https://github.com/google/jax#installation)インストールページで、お使いのプラットフォーム別のインストールコマンドを参照してください。 これらのバックエンドのいずれかがインストールされている場合、🤗Transformersは以下のようにpipを使用してインストールすることができます: ```bash pip install transformers ``` もしサンプルを試したい、またはコードの最先端が必要で、新しいリリースを待てない場合は、[ライブラリをソースからインストール](https://huggingface.co/docs/transformers/installation#installing-from-source)する必要があります。 ### condaにて 🤗Transformersは以下のようにcondaを使って設置することができます: ```shell script conda install conda-forge::transformers ``` > **_注意:_** `huggingface` チャンネルから `transformers` をインストールすることは非推奨です。 Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それぞれのインストールページに従ってください。 > **_注意:_** Windowsでは、キャッシュの恩恵を受けるために、デベロッパーモードを有効にするよう促されることがあります。このような場合は、[このissue](https://github.com/huggingface/huggingface_hub/issues/1062)でお知らせください。 ## モデルアーキテクチャ 🤗Transformersが提供する **[全モデルチェックポイント](https://huggingface.co/models)** は、[ユーザー](https://huggingface.co/users)や[組織](https://huggingface.co/organizations)によって直接アップロードされるhuggingface.co [model hub](https://huggingface.co)からシームレスに統合されています。 現在のチェックポイント数:  🤗Transformersは現在、以下のアーキテクチャを提供しています: それぞれのハイレベルな要約は[こちら](https://huggingface.co/docs/transformers/model_summary)を参照してください. 各モデルがFlax、PyTorch、TensorFlowで実装されているか、🤗Tokenizersライブラリに支えられた関連トークナイザを持っているかは、[この表](https://huggingface.co/docs/transformers/index#supported-frameworks)を参照してください。 これらの実装はいくつかのデータセットでテストされており(サンプルスクリプトを参照)、オリジナルの実装の性能と一致するはずである。性能の詳細は[documentation](https://github.com/huggingface/transformers/tree/main/examples)のExamplesセクションで見ることができます。 ## さらに詳しく | セクション | 概要 | |-|-| | [ドキュメント](https://huggingface.co/docs/transformers/) | 完全なAPIドキュメントとチュートリアル | | [タスク概要](https://huggingface.co/docs/transformers/task_summary) | 🤗Transformersがサポートするタスク | | [前処理チュートリアル](https://huggingface.co/docs/transformers/preprocessing) | モデル用のデータを準備するために`Tokenizer`クラスを使用 | | [トレーニングと微調整](https://huggingface.co/docs/transformers/training) | PyTorch/TensorFlowの学習ループと`Trainer`APIで🤗Transformersが提供するモデルを使用 | | [クイックツアー: 微調整/使用方法スクリプト](https://github.com/huggingface/transformers/tree/main/examples) | 様々なタスクでモデルの微調整を行うためのスクリプト例 | | [モデルの共有とアップロード](https://huggingface.co/docs/transformers/model_sharing) | 微調整したモデルをアップロードしてコミュニティで共有する | | [マイグレーション](https://huggingface.co/docs/transformers/migration) | `pytorch-transformers`または`pytorch-pretrained-bert`から🤗Transformers に移行する | ## 引用 🤗 トランスフォーマーライブラリに引用できる[論文](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)が出来ました: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos

mavonic_private_repos/transformers/README_es.md

<!--- Copyright 2020 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. --> <p align="center"> <br> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/> <br> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <b>Español</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p>Lo último de Machine Learning para JAX, PyTorch y TensorFlow</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 Transformers aporta miles de modelos preentrenados para realizar tareas en diferentes modalidades como texto, visión, y audio. Estos modelos pueden ser aplicados en: * 📝 Texto, para tareas como clasificación de texto, extracción de información, responder preguntas, resumir, traducir, generación de texto, en más de 100 idiomas. * 🖼️ Imágenes, para tareas como clasificación de imágenes, detección the objetos, y segmentación. * 🗣️ Audio, para tareas como reconocimiento de voz y clasificación de audio. Los modelos de Transformer también pueden realizar tareas en **muchas modalidades combinadas**, como responder preguntas, reconocimiento de carácteres ópticos,extracción de información de documentos escaneados, clasificación de video, y respuesta de preguntas visuales. 🤗 Transformers aporta APIs para descargar rápidamente y usar estos modelos preentrenados en un texto dado, afinarlos en tus propios sets de datos y compartirlos con la comunidad en nuestro [centro de modelos](https://huggingface.co/models). Al mismo tiempo, cada módulo de Python que define una arquitectura es completamente independiente y se puede modificar para permitir experimentos de investigación rápidos. 🤗 Transformers está respaldado por las tres bibliotecas de deep learning más populares — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) y [TensorFlow](https://www.tensorflow.org/) — con una perfecta integración entre ellos. Es sencillo entrenar sus modelos con uno antes de cargarlos para la inferencia con el otro. ## Demostraciones en línea Puedes probar la mayoría de nuestros modelos directamente en sus páginas desde el [centro de modelos](https://huggingface.co/models). También ofrecemos [alojamiento de modelos privados, control de versiones y una API de inferencia](https://huggingface.co/pricing) para modelos públicos y privados. Aquí hay algunos ejemplos: En procesamiento del lenguaje natural: - [Terminación de palabras enmascaradas con BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [Reconocimiento del nombre de la entidad con Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [Generación de texto con GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+) - [Inferencia del lenguaje natural con RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [Resumen con BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [Responder a preguntas con DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [Traducción con T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) En visión de ordenador: - [Clasificación de imágenes con ViT](https://huggingface.co/google/vit-base-patch16-224) - [Detección de objetos con DETR](https://huggingface.co/facebook/detr-resnet-50) - [Segmentación semántica con SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) - [Segmentación panóptica con DETR](https://huggingface.co/facebook/detr-resnet-50-panoptic) - [Segmentación Universal con OneFormer (Segmentación Semántica, de Instancia y Panóptica con un solo modelo)](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) En Audio: - [Reconocimiento de voz automático con Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h) - [Detección de palabras clave con Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) En tareas multimodales: - [Respuesta visual a preguntas con ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) **[Escribe con Transformer](https://transformer.huggingface.co)**, construido por el equipo de Hugging Face, es la demostración oficial de las capacidades de generación de texto de este repositorio. ## Si está buscando soporte personalizado del equipo de Hugging Face <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## Tour rápido Para usar inmediatamente un modelo en una entrada determinada (texto, imagen, audio, ...), proporcionamos la API de `pipeline`. Los pipelines agrupan un modelo previamente entrenado con el preprocesamiento que se usó durante el entrenamiento de ese modelo. Aquí se explica cómo usar rápidamente un pipeline para clasificar textos positivos frente a negativos: ```python >>> from transformers import pipeline # Allocate a pipeline for sentiment-analysis >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` La segunda línea de código descarga y almacena en caché el modelo previamente entrenado que usa la canalización, mientras que la tercera lo evalúa en el texto dado. Aquí la respuesta es "positiva" con una confianza del 99,97%. Muchas tareas tienen un `pipeline` preentrenado listo para funcionar, en NLP pero también en visión por ordenador y habla. Por ejemplo, podemos extraer fácilmente los objetos detectados en una imagen: ``` python >>> import requests >>> from PIL import Image >>> from transformers import pipeline # Download an image with cute cats >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> image = Image.open(image_data) # Allocate a pipeline for object detection >>> object_detector = pipeline('object_detection') >>> object_detector(image) [{'score': 0.9982201457023621, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, {'score': 0.9960021376609802, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, {'score': 0.9954745173454285, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, {'score': 0.9988006353378296, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, {'score': 0.9986783862113953, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] ``` Aquí obtenemos una lista de objetos detectados en la imagen, con un cuadro que rodea el objeto y una puntuación de confianza. Aquí está la imagen original a la derecha, con las predicciones mostradas a la izquierda: <h3 align="center"> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a> </h3> Puedes obtener más información sobre las tareas admitidas por la API de `pipeline` en [este tutorial](https://huggingface.co/docs/transformers/task_summary). Además de `pipeline`, para descargar y usar cualquiera de los modelos previamente entrenados en su tarea dada, todo lo que necesita son tres líneas de código. Aquí está la versión de PyTorch: ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` Y aquí está el código equivalente para TensorFlow: ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` El tokenizador es responsable de todo el preprocesamiento que espera el modelo preentrenado y se puede llamar directamente en una sola cadena (como en los ejemplos anteriores) o en una lista. Este dará como resultado un diccionario que puedes usar en el código descendente o simplemente pasarlo directamente a su modelo usando el operador de desempaquetado de argumento **. El modelo en si es un [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) normal o un [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (dependiendo De tu backend) que puedes usar de forma habitual. [Este tutorial](https://huggingface.co/docs/transformers/training) explica cómo integrar un modelo de este tipo en un ciclo de entrenamiento PyTorch o TensorFlow clásico, o como usar nuestra API `Trainer` para ajustar rápidamente un nuevo conjunto de datos. ## ¿Por qué debo usar transformers? 1. Modelos de última generación fáciles de usar: - Alto rendimiento en comprensión y generación de lenguaje natural, visión artificial y tareas de audio. - Baja barrera de entrada para educadores y profesionales. - Pocas abstracciones de cara al usuario con solo tres clases para aprender. - Una API unificada para usar todos nuestros modelos preentrenados. 1. Menores costes de cómputo, menor huella de carbono: - Los investigadores pueden compartir modelos entrenados en lugar de siempre volver a entrenar. - Los profesionales pueden reducir el tiempo de cómputo y los costos de producción. - Docenas de arquitecturas con más de 60 000 modelos preentrenados en todas las modalidades. 1. Elija el marco adecuado para cada parte de la vida útil de un modelo: - Entrene modelos de última generación en 3 líneas de código. - Mueva un solo modelo entre los marcos TF2.0/PyTorch/JAX a voluntad. - Elija sin problemas el marco adecuado para la formación, la evaluación y la producción. 1. Personalice fácilmente un modelo o un ejemplo según sus necesidades: - Proporcionamos ejemplos de cada arquitectura para reproducir los resultados publicados por sus autores originales.. - Los internos del modelo están expuestos lo más consistentemente posible.. - Los archivos modelo se pueden usar independientemente de la biblioteca para experimentos rápidos. ## ¿Por qué no debería usar transformers? - Esta biblioteca no es una caja de herramientas modular de bloques de construcción para redes neuronales. El código en los archivos del modelo no se refactoriza con abstracciones adicionales a propósito, de modo que los investigadores puedan iterar rápidamente en cada uno de los modelos sin sumergirse en abstracciones/archivos adicionales. - La API de entrenamiento no está diseñada para funcionar en ningún modelo, pero está optimizada para funcionar con los modelos proporcionados por la biblioteca. Para bucles genéricos de aprendizaje automático, debe usar otra biblioteca (posiblemente, [Accelerate](https://huggingface.co/docs/accelerate)). - Si bien nos esforzamos por presentar tantos casos de uso como sea posible, los scripts en nuestra [carpeta de ejemplos](https://github.com/huggingface/transformers/tree/main/examples) son solo eso: ejemplos. Se espera que no funcionen de forma inmediata en su problema específico y que deba cambiar algunas líneas de código para adaptarlas a sus necesidades. ## Instalación ### Con pip Este repositorio está probado en Python 3.8+, Flax 0.4.1+, PyTorch 1.11+ y TensorFlow 2.6+. Deberías instalar 🤗 Transformers en un [entorno virtual](https://docs.python.org/3/library/venv.html). Si no estas familiarizado con los entornos virtuales de Python, consulta la [guía de usuario](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Primero, crea un entorno virtual con la versión de Python que vas a usar y actívalo. Luego, deberás instalar al menos uno entre Flax, PyTorch o TensorFlow. Por favor, ve a la [página de instalación de TensorFlow](https://www.tensorflow.org/install/), [página de instalación de PyTorch](https://pytorch.org/get-started/locally/#start-locally) y/o las páginas de instalación de [Flax](https://github.com/google/flax#quick-install) y [Jax](https://github.com/google/jax#installation) con respecto al comando de instalación específico para tu plataforma. Cuando se ha instalado uno de esos backends, los 🤗 Transformers se pueden instalar usando pip de la siguiente manera: ```bash pip install transformers ``` Si deseas jugar con los ejemplos o necesitas la última versión del código y no puedes esperar a una nueva versión, tienes que [instalar la librería de la fuente](https://huggingface.co/docs/transformers/installation#installing-from-source). ### Con conda 🤗 Transformers se puede instalar usando conda de la siguiente manera: ```shell script conda install conda-forge::transformers ``` > **_NOTA:_** Instalar `transformers` desde el canal `huggingface` está obsoleto. Sigue las páginas de instalación de Flax, PyTorch o TensorFlow para ver cómo instalarlos con conda. > **_NOTA:_** En Windows, es posible que se le pida que active el modo de desarrollador para beneficiarse del almacenamiento en caché. Si esta no es una opción para usted, háganoslo saber en [esta issue](https://github.com/huggingface/huggingface_hub/issues/1062). ## Arquitecturas modelo **[Todos los puntos de control del modelo](https://huggingface.co/models)** aportados por 🤗 Transformers están perfectamente integrados desde huggingface.co [Centro de modelos](https://huggingface.co) donde son subidos directamente por los [usuarios](https://huggingface.co/users) y [organizaciones](https://huggingface.co/organizations). Número actual de puntos de control:  🤗 Transformers actualmente proporciona las siguientes arquitecturas: ver [aquí](https://huggingface.co/docs/transformers/model_summary) para un resumen de alto nivel de cada uno de ellas. Para comprobar si cada modelo tiene una implementación en Flax, PyTorch o TensorFlow, o tiene un tokenizador asociado respaldado por la librería 🤗 Tokenizers, ve a [esta tabla](https://huggingface.co/docs/transformers/index#supported-frameworks). Estas implementaciones se han probado en varios conjuntos de datos (consulte los scripts de ejemplo) y deberían coincidir con el rendimiento de las implementaciones originales. Puede encontrar más detalles sobre el rendimiento en la sección Examples de la [documentación](https://github.com/huggingface/transformers/tree/main/examples). ## Aprender más | Sección | Descripción | |-|-| | [Documentación](https://huggingface.co/docs/transformers/) | Toda la documentación de la API y tutoriales | | [Resumen de tareas](https://huggingface.co/docs/transformers/task_summary) | Tareas soportadas 🤗 Transformers | | [Tutorial de preprocesamiento](https://huggingface.co/docs/transformers/preprocessing) | Usando la clase `Tokenizer` para preparar datos para los modelos | | [Entrenamiento y puesta a punto](https://huggingface.co/docs/transformers/training) | Usando los modelos aportados por 🤗 Transformers en un bucle de entreno de PyTorch/TensorFlow y la API de `Trainer` | | [Recorrido rápido: secuencias de comandos de ajuste/uso](https://github.com/huggingface/transformers/tree/main/examples) | Scripts de ejemplo para ajustar modelos en una amplia gama de tareas | | [Compartir y subir modelos](https://huggingface.co/docs/transformers/model_sharing) | Carga y comparte tus modelos perfeccionados con la comunidad | | [Migración](https://huggingface.co/docs/transformers/migration) | Migra a 🤗 Transformers desde `pytorch-transformers` o `pytorch-pretrained-bert` | ## Citación Ahora nosotros tenemos un [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) que puedes citar para la librería de 🤗 Transformers: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos

mavonic_private_repos/transformers/README_te.md

<!--- Copyright 2020 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. --> <p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg"> <img alt="Hugging Face Transformers Library" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg" width="352" height="59" style="max-width: 100%;"> </picture> <br/> <br/> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <b>తెలుగు</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p>JAX, PyTorch మరియు TensorFlow కోసం అత్యాధునిక యంత్ర అభ్యాసం</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 ట్రాన్స్‌ఫార్మర్లు టెక్స్ట్, విజన్ మరియు ఆడియో వంటి విభిన్న పద్ధతులపై టాస్క్‌లను నిర్వహించడానికి వేలాది ముందుగా శిక్షణ పొందిన మోడల్‌లను అందిస్తాయి. ఈ నమూనాలు వర్తించవచ్చు: * 📝 టెక్స్ట్, 100కి పైగా భాషల్లో టెక్స్ట్ క్లాసిఫికేషన్, ఇన్ఫర్మేషన్ ఎక్స్‌ట్రాక్షన్, ప్రశ్నలకు సమాధానాలు, సారాంశం, అనువాదం, టెక్స్ట్ జనరేషన్ వంటి పనుల కోసం. * 🖼️ ఇమేజ్‌లు, ఇమేజ్ వర్గీకరణ, ఆబ్జెక్ట్ డిటెక్షన్ మరియు సెగ్మెంటేషన్ వంటి పనుల కోసం. * 🗣️ ఆడియో, స్పీచ్ రికగ్నిషన్ మరియు ఆడియో వర్గీకరణ వంటి పనుల కోసం. ట్రాన్స్‌ఫార్మర్ మోడల్‌లు టేబుల్ క్వశ్చన్ ఆన్సర్ చేయడం, ఆప్టికల్ క్యారెక్టర్ రికగ్నిషన్, స్కాన్ చేసిన డాక్యుమెంట్‌ల నుండి ఇన్ఫర్మేషన్ ఎక్స్‌ట్రాక్షన్, వీడియో క్లాసిఫికేషన్ మరియు విజువల్ క్వశ్చన్ ఆన్సర్ చేయడం వంటి **అనేక పద్ధతులతో కలిపి** పనులను కూడా చేయగలవు. 🤗 ట్రాన్స్‌ఫార్మర్లు అందించిన టెక్స్ట్‌లో ప్రీట్రైన్డ్ మోడల్‌లను త్వరగా డౌన్‌లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, వాటిని మీ స్వంత డేటాసెట్‌లలో ఫైన్-ట్యూన్ చేయడానికి మరియు వాటిని మా [మోడల్ హబ్](https://huggingface.co/models)లో సంఘంతో భాగస్వామ్యం చేయడానికి API లను అందిస్తుంది. అదే సమయంలో, ఆర్కిటెక్చర్‌ని నిర్వచించే ప్రతి పైథాన్ మాడ్యూల్ పూర్తిగా స్వతంత్రంగా ఉంటుంది మరియు త్వరిత పరిశోధన ప్రయోగాలను ప్రారంభించడానికి సవరించవచ్చు. 🤗 ట్రాన్స్‌ఫార్మర్‌లకు మూడు అత్యంత ప్రజాదరణ పొందిన డీప్ లెర్నింగ్ లైబ్రరీలు ఉన్నాయి — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) మరియు [TensorFlow](https://www.tensorflow.org/) — వాటి మధ్య అతుకులు లేని ఏకీకరణతో. మీ మోడల్‌లను ఒకదానితో మరొకదానితో అనుమితి కోసం లోడ్ చేసే ముందు వాటికి శిక్షణ ఇవ్వడం చాలా సులభం. ## ఆన్‌లైన్ డెమోలు మీరు [మోడల్ హబ్](https://huggingface.co/models) నుండి మా మోడళ్లలో చాలా వరకు వాటి పేజీలలో నేరుగా పరీక్షించవచ్చు. మేము పబ్లిక్ మరియు ప్రైవేట్ మోడల్‌ల కోసం [ప్రైవేట్ మోడల్ హోస్టింగ్, సంస్కరణ & అనుమితి API](https://huggingface.co/pricing)ని కూడా అందిస్తాము. ఇక్కడ కొన్ని ఉదాహరణలు ఉన్నాయి: సహజ భాషా ప్రాసెసింగ్‌లో: - [BERT తో మాస్క్‌డ్ వర్డ్ కంప్లీషన్](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [Electra తో పేరు ఎంటిటీ గుర్తింపు](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [GPT-2 తో టెక్స్ట్ జనరేషన్](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+) - [RoBERTa తో సహజ భాషా అనుమితి](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+Lost.+Nobody+lost+any+animal) - [BART తో సారాంశం](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [DistilBERT తో ప్రశ్న సమాధానం](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [T5 తో అనువాదం](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) కంప్యూటర్ దృష్టిలో: - [VIT తో చిత్ర వర్గీకరణ](https://huggingface.co/google/vit-base-patch16-224) - [DETR తో ఆబ్జెక్ట్ డిటెక్షన్](https://huggingface.co/facebook/detr-resnet-50) - [SegFormer తో సెమాంటిక్ సెగ్మెంటేషన్](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) - [MaskFormer తో పానోప్టిక్ సెగ్మెంటేషన్](https://huggingface.co/facebook/maskformer-swin-small-coco) - [DPT తో లోతు అంచనా](https://huggingface.co/docs/transformers/model_doc/dpt) - [VideoMAE తో వీడియో వర్గీకరణ](https://huggingface.co/docs/transformers/model_doc/videomae) - [OneFormer తో యూనివర్సల్ సెగ్మెంటేషన్](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) ఆడియోలో: - [Wav2Vec2 తో ఆటోమేటిక్ స్పీచ్ రికగ్నిషన్](https://huggingface.co/facebook/wav2vec2-base-960h) - [Wav2Vec2 తో కీవర్డ్ స్పాటింగ్](https://huggingface.co/superb/wav2vec2-base-superb-ks) - [ఆడియో స్పెక్ట్రోగ్రామ్ ట్రాన్స్‌ఫార్మర్‌తో ఆడియో వర్గీకరణ](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) మల్టీమోడల్ టాస్క్‌లలో: - [TAPAS తో టేబుల్ ప్రశ్న సమాధానాలు](https://huggingface.co/google/tapas-base-finetuned-wtq) - [ViLT తో దృశ్యమాన ప్రశ్నకు సమాధానం](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) - [CLIP తో జీరో-షాట్ ఇమేజ్ వర్గీకరణ](https://huggingface.co/openai/clip-vit-large-patch14) - [LayoutLM తో డాక్యుమెంట్ ప్రశ్నకు సమాధానం](https://huggingface.co/impira/layoutlm-document-qa) - [X-CLIP తో జీరో-షాట్ వీడియో వర్గీకరణ](https://huggingface.co/docs/transformers/model_doc/xclip) ## ట్రాన్స్‌ఫార్మర్‌లను ఉపయోగించి 100 ప్రాజెక్టులు ట్రాన్స్‌ఫార్మర్లు ప్రీట్రైన్డ్ మోడల్‌లను ఉపయోగించడానికి టూల్‌కిట్ కంటే ఎక్కువ: ఇది దాని చుట్టూ నిర్మించిన ప్రాజెక్ట్‌ల సంఘం మరియు హగ్గింగ్ ఫేస్ హబ్. డెవలపర్‌లు, పరిశోధకులు, విద్యార్థులు, ప్రొఫెసర్‌లు, ఇంజనీర్లు మరియు ఎవరినైనా అనుమతించేలా ట్రాన్స్‌ఫార్మర్‌లను మేము కోరుకుంటున్నాము వారి కలల ప్రాజెక్టులను నిర్మించడానికి. ట్రాన్స్‌ఫార్మర్‌ల 100,000 నక్షత్రాలను జరుపుకోవడానికి, మేము స్పాట్‌లైట్‌ని ఉంచాలని నిర్ణయించుకున్నాము సంఘం, మరియు మేము 100 జాబితాలను కలిగి ఉన్న [awesome-transformers](./awesome-transformers.md) పేజీని సృష్టించాము. ట్రాన్స్‌ఫార్మర్ల పరిసరాల్లో అద్భుతమైన ప్రాజెక్టులు నిర్మించబడ్డాయి. జాబితాలో భాగమని మీరు విశ్వసించే ప్రాజెక్ట్‌ను మీరు కలిగి ఉంటే లేదా ఉపయోగిస్తుంటే, దయచేసి దానిని జోడించడానికి PRని తెరవండి! ## మీరు హగ్గింగ్ ఫేస్ టీమ్ నుండి అనుకూల మద్దతు కోసం చూస్తున్నట్లయితే <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## త్వరిత పర్యటన ఇచ్చిన ఇన్‌పుట్ (టెక్స్ట్, ఇమేజ్, ఆడియో, ...)పై తక్షణమే మోడల్‌ను ఉపయోగించడానికి, మేము `pipeline` API ని అందిస్తాము. పైప్‌లైన్‌లు ఆ మోడల్ శిక్షణ సమయంలో ఉపయోగించిన ప్రీప్రాసెసింగ్‌తో కూడిన ప్రీట్రైన్డ్ మోడల్‌ను సమూహపరుస్తాయి. సానుకూల మరియు ప్రతికూల పాఠాలను వర్గీకరించడానికి పైప్‌లైన్‌ను త్వరగా ఎలా ఉపయోగించాలో ఇక్కడ ఉంది: ```python >>> from transformers import pipeline # Allocate a pipeline for sentiment-analysis >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` రెండవ లైన్ కోడ్ డౌన్‌లోడ్ మరియు పైప్‌లైన్ ఉపయోగించే ప్రీట్రైన్డ్ మోడల్‌ను కాష్ చేస్తుంది, మూడవది ఇచ్చిన టెక్స్ట్‌పై మూల్యాంకనం చేస్తుంది. ఇక్కడ సమాధానం 99.97% విశ్వాసంతో "పాజిటివ్". చాలా పనులు NLPలో కానీ కంప్యూటర్ విజన్ మరియు స్పీచ్‌లో కూడా ముందుగా శిక్షణ పొందిన `pipeline` సిద్ధంగా ఉన్నాయి. ఉదాహరణకు, మనం చిత్రంలో గుర్తించిన వస్తువులను సులభంగా సంగ్రహించవచ్చు: ``` python >>> import requests >>> from PIL import Image >>> from transformers import pipeline # Download an image with cute cats >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> image = Image.open(image_data) # Allocate a pipeline for object detection >>> object_detector = pipeline('object-detection') >>> object_detector(image) [{'score': 0.9982201457023621, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, {'score': 0.9960021376609802, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, {'score': 0.9954745173454285, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, {'score': 0.9988006353378296, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, {'score': 0.9986783862113953, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] ``` ఇక్కడ మనం ఆబ్జెక్ట్ చుట్టూ ఉన్న బాక్స్ మరియు కాన్ఫిడెన్స్ స్కోర్‌తో చిత్రంలో గుర్తించబడిన వస్తువుల జాబితాను పొందుతాము. ఇక్కడ ఎడమవైపున ఉన్న అసలు చిత్రం, కుడివైపున అంచనాలు ప్రదర్శించబడతాయి: <h3 align="center"> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a> </h3> మీరు [ఈ ట్యుటోరియల్](https://huggingface.co/docs/transformers/task_summary)లో `pipeline` API ద్వారా సపోర్ట్ చేసే టాస్క్‌ల గురించి మరింత తెలుసుకోవచ్చు. `pipeline`తో పాటు, మీరు ఇచ్చిన టాస్క్‌లో ఏదైనా ప్రీట్రైన్డ్ మోడల్‌లను డౌన్‌లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, దీనికి మూడు లైన్ల కోడ్ సరిపోతుంది. ఇక్కడ PyTorch వెర్షన్ ఉంది: ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` మరియు TensorFlow కి సమానమైన కోడ్ ఇక్కడ ఉంది: ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` ప్రిట్రైన్డ్ మోడల్ ఆశించే అన్ని ప్రీప్రాసెసింగ్‌లకు టోకెనైజర్ బాధ్యత వహిస్తుంది మరియు నేరుగా ఒకే స్ట్రింగ్ (పై ఉదాహరణలలో వలె) లేదా జాబితాపై కాల్ చేయవచ్చు. ఇది మీరు డౌన్‌స్ట్రీమ్ కోడ్‌లో ఉపయోగించగల నిఘంటువుని అవుట్‌పుట్ చేస్తుంది లేదా ** ఆర్గ్యుమెంట్ అన్‌ప్యాకింగ్ ఆపరేటర్‌ని ఉపయోగించి నేరుగా మీ మోడల్‌కి పంపుతుంది. మోడల్ కూడా సాధారణ [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) లేదా [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (మీ బ్యాకెండ్‌ని బట్టి) మీరు మామూలుగా ఉపయోగించవచ్చు. [ఈ ట్యుటోరియల్](https://huggingface.co/docs/transformers/training) అటువంటి మోడల్‌ని క్లాసిక్ PyTorch లేదా TensorFlow ట్రైనింగ్ లూప్‌లో ఎలా ఇంటిగ్రేట్ చేయాలో లేదా మా `Trainer` API ని ఎలా ఉపయోగించాలో వివరిస్తుంది కొత్త డేటాసెట్. ## నేను ట్రాన్స్‌ఫార్మర్‌లను ఎందుకు ఉపయోగించాలి? 1. ఉపయోగించడానికి సులభమైన స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్‌లు: - సహజ భాషా అవగాహన & ఉత్పత్తి, కంప్యూటర్ దృష్టి మరియు ఆడియో పనులపై అధిక పనితీరు. - విద్యావేత్తలు మరియు అభ్యాసకుల ప్రవేశానికి తక్కువ అవరోధం. - తెలుసుకోవడానికి కేవలం మూడు తరగతులతో కొన్ని వినియోగదారు-ముఖ సంగ్రహణలు. - మా అన్ని ప్రీట్రైన్డ్ మోడల్‌లను ఉపయోగించడం కోసం ఏకీకృత API. 2. తక్కువ గణన ఖర్చులు, చిన్న కార్బన్ పాదముద్ర: - పరిశోధకులు ఎల్లప్పుడూ మళ్లీ శిక్షణ పొందే బదులు శిక్షణ పొందిన నమూనాలను పంచుకోవచ్చు. - అభ్యాసకులు గణన సమయాన్ని మరియు ఉత్పత్తి ఖర్చులను తగ్గించగలరు. - అన్ని పద్ధతుల్లో 60,000 కంటే ఎక్కువ ప్రీట్రైన్డ్ మోడల్‌లతో డజన్ల కొద్దీ ఆర్కిటెక్చర్‌లు. 3. మోడల్ జీవితకాలంలో ప్రతి భాగానికి సరైన ఫ్రేమ్‌వర్క్‌ను ఎంచుకోండి: - 3 లైన్ల కోడ్‌లో స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్‌లకు శిక్షణ ఇవ్వండి. - TF2.0/PyTorch/JAX ఫ్రేమ్‌వర్క్‌ల మధ్య ఒకే మోడల్‌ను ఇష్టానుసారంగా తరలించండి. - శిక్షణ, మూల్యాంకనం మరియు ఉత్పత్తి కోసం సరైన ఫ్రేమ్‌వర్క్‌ను సజావుగా ఎంచుకోండి. 4. మీ అవసరాలకు అనుగుణంగా మోడల్ లేదా ఉదాహరణను సులభంగా అనుకూలీకరించండి: - ప్రతి ఆర్కిటెక్చర్ దాని అసలు రచయితలు ప్రచురించిన ఫలితాలను పునరుత్పత్తి చేయడానికి మేము ఉదాహరణలను అందిస్తాము. - మోడల్ ఇంటర్నల్‌లు వీలైనంత స్థిరంగా బహిర్గతమవుతాయి. - శీఘ్ర ప్రయోగాల కోసం లైబ్రరీ నుండి స్వతంత్రంగా మోడల్ ఫైల్‌లను ఉపయోగించవచ్చు. ## నేను ట్రాన్స్‌ఫార్మర్‌లను ఎందుకు ఉపయోగించకూడదు? - ఈ లైబ్రరీ న్యూరల్ నెట్‌ల కోసం బిల్డింగ్ బ్లాక్‌ల మాడ్యులర్ టూల్‌బాక్స్ కాదు. మోడల్ ఫైల్‌లలోని కోడ్ ఉద్దేశపూర్వకంగా అదనపు సంగ్రహణలతో రీఫ్యాక్టరింగ్ చేయబడదు, తద్వారా పరిశోధకులు అదనపు సంగ్రహణలు/ఫైళ్లలోకి ప్రవేశించకుండా ప్రతి మోడల్‌పై త్వరగా మళ్లించగలరు. - శిక్షణ API ఏ మోడల్‌లో పని చేయడానికి ఉద్దేశించబడలేదు కానీ లైబ్రరీ అందించిన మోడల్‌లతో పని చేయడానికి ఆప్టిమైజ్ చేయబడింది. సాధారణ మెషిన్ లెర్నింగ్ లూప్‌ల కోసం, మీరు మరొక లైబ్రరీని ఉపయోగించాలి (బహుశా, [Accelerate](https://huggingface.co/docs/accelerate)). - మేము వీలైనన్ని ఎక్కువ వినియోగ సందర్భాలను ప్రదర్శించడానికి ప్రయత్నిస్తున్నప్పుడు, మా [ఉదాహరణల ఫోల్డర్](https://github.com/huggingface/transformers/tree/main/examples)లోని స్క్రిప్ట్‌లు కేవలం: ఉదాహరణలు. మీ నిర్దిష్ట సమస్యపై అవి పని చేయవు మరియు వాటిని మీ అవసరాలకు అనుగుణంగా మార్చుకోవడానికి మీరు కొన్ని కోడ్ లైన్‌లను మార్చవలసి ఉంటుంది. ## సంస్థాపన ### పిప్ తో ఈ రిపోజిటరీ పైథాన్ 3.8+, ఫ్లాక్స్ 0.4.1+, PyTorch 1.11+ మరియు TensorFlow 2.6+లో పరీక్షించబడింది. మీరు [వర్చువల్ వాతావరణం](https://docs.python.org/3/library/venv.html)లో 🤗 ట్రాన్స్‌ఫార్మర్‌లను ఇన్‌స్టాల్ చేయాలి. మీకు పైథాన్ వర్చువల్ పరిసరాల గురించి తెలియకుంటే, [యూజర్ గైడ్](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) చూడండి. ముందుగా, మీరు ఉపయోగించబోతున్న పైథాన్ వెర్షన్‌తో వర్చువల్ వాతావరణాన్ని సృష్టించండి మరియు దానిని సక్రియం చేయండి. అప్పుడు, మీరు ఫ్లాక్స్, పైటార్చ్ లేదా టెన్సర్‌ఫ్లోలో కనీసం ఒకదానిని ఇన్‌స్టాల్ చేయాలి. దయచేసి [TensorFlow ఇన్‌స్టాలేషన్ పేజీ](https://www.tensorflow.org/install/), [PyTorch ఇన్‌స్టాలేషన్ పేజీ](https://pytorch.org/get-started/locally/#start-locally) మరియు/ని చూడండి లేదా మీ ప్లాట్‌ఫారమ్ కోసం నిర్దిష్ట ఇన్‌స్టాలేషన్ కమాండ్‌కు సంబంధించి [Flax](https://github.com/google/flax#quick-install) మరియు [Jax](https://github.com/google/jax#installation) ఇన్‌స్టాలేషన్ పేజీలు . ఆ బ్యాకెండ్‌లలో ఒకటి ఇన్‌స్టాల్ చేయబడినప్పుడు, 🤗 ట్రాన్స్‌ఫార్మర్‌లను ఈ క్రింది విధంగా పిప్‌ని ఉపయోగించి ఇన్‌స్టాల్ చేయవచ్చు: ```bash pip install transformers ``` మీరు ఉదాహరణలతో ప్లే చేయాలనుకుంటే లేదా కోడ్ యొక్క బ్లీడింగ్ ఎడ్జ్ అవసరం మరియు కొత్త విడుదల కోసం వేచి ఉండలేకపోతే, మీరు తప్పనిసరిగా [మూలం నుండి లైబ్రరీని ఇన్‌స్టాల్ చేయాలి](https://huggingface.co/docs/transformers/installation#installing-from-source). ### కొండా తో 🤗 కింది విధంగా కొండా ఉపయోగించి ట్రాన్స్‌ఫార్మర్‌లను ఇన్‌స్టాల్ చేయవచ్చు: ```shell script conda install conda-forge::transformers ``` > **_గమనిక:_** `huggingface` ఛానెల్ నుండి `transformers` ఇన్‌స్టాల్ చేయడం పురాతనంగా ఉంది. Flax, PyTorch లేదా TensorFlow యొక్క ఇన్‌స్టాలేషన్ పేజీలను కొండాతో ఎలా ఇన్‌స్టాల్ చేయాలో చూడటానికి వాటిని అనుసరించండి. > **_గమనిక:_** Windowsలో, కాషింగ్ నుండి ప్రయోజనం పొందేందుకు మీరు డెవలపర్ మోడ్‌ని సక్రియం చేయమని ప్రాంప్ట్ చేయబడవచ్చు. ఇది మీకు ఎంపిక కాకపోతే, దయచేసి [ఈ సంచిక](https://github.com/huggingface/huggingface_hub/issues/1062)లో మాకు తెలియజేయండి. ## మోడల్ ఆర్కిటెక్చర్లు **[అన్ని మోడల్ చెక్‌పాయింట్‌లు](https://huggingface.co/models)** 🤗 అందించిన ట్రాన్స్‌ఫార్మర్లు huggingface.co [model hub](https://huggingface.co/models) నుండి సజావుగా ఏకీకృతం చేయబడ్డాయి [users](https://huggingface.co/users) మరియు [organizations](https://huggingface.co/organizations) ద్వారా నేరుగా అప్‌లోడ్ చేయబడతాయి. ప్రస్తుత తనిఖీ కేంద్రాల సంఖ్య:  🤗 ట్రాన్స్‌ఫార్మర్లు ప్రస్తుతం కింది ఆర్కిటెక్చర్‌లను అందజేస్తున్నాయి: వాటిలో ప్రతి ఒక్కటి ఉన్నత స్థాయి సారాంశం కోసం [ఇక్కడ](https://huggingface.co/docs/transformers/model_summary) చూడండి. ఈ అమలులు అనేక డేటాసెట్‌లలో పరీక్షించబడ్డాయి (ఉదాహరణ స్క్రిప్ట్‌లను చూడండి) మరియు అసలైన అమలుల పనితీరుతో సరిపోలాలి. మీరు [డాక్యుమెంటేషన్](https://github.com/huggingface/transformers/tree/main/examples) యొక్క ఉదాహరణల విభాగంలో పనితీరుపై మరిన్ని వివరాలను కనుగొనవచ్చు. ## ఇంకా నేర్చుకో | విభాగం | వివరణ | |-|-| | [డాక్యుమెంటేషన్](https://huggingface.co/docs/transformers/) | పూర్తి API డాక్యుమెంటేషన్ మరియు ట్యుటోరియల్స్ | | [టాస్క్ సారాంశం](https://huggingface.co/docs/transformers/task_summary) | 🤗 ట్రాన్స్‌ఫార్మర్‌ల ద్వారా సపోర్ట్ చేయబడిన విధులు | | [ప్రీప్రాసెసింగ్ ట్యుటోరియల్](https://huggingface.co/docs/transformers/preprocessing) | మోడల్‌ల కోసం డేటాను సిద్ధం చేయడానికి `Tokenizer` క్లాస్‌ని ఉపయోగించడం | | [ట్రైనింగ్ మరియు ఫైన్-ట్యూనింగ్](https://huggingface.co/docs/transformers/training) | PyTorch/TensorFlow ట్రైనింగ్ లూప్ మరియు `Trainer` APIలో 🤗 ట్రాన్స్‌ఫార్మర్లు అందించిన మోడల్‌లను ఉపయోగించడం | | [త్వరిత పర్యటన: ఫైన్-ట్యూనింగ్/యూసేజ్ స్క్రిప్ట్‌లు](https://github.com/huggingface/transformers/tree/main/examples) | విస్తృత శ్రేణి టాస్క్‌లపై ఫైన్-ట్యూనింగ్ మోడల్స్ కోసం ఉదాహరణ స్క్రిప్ట్‌లు | | [మోడల్ భాగస్వామ్యం మరియు అప్‌లోడ్ చేయడం](https://huggingface.co/docs/transformers/model_sharing) | కమ్యూనిటీతో మీ ఫైన్-ట్యూన్డ్ మోడల్‌లను అప్‌లోడ్ చేయండి మరియు భాగస్వామ్యం చేయండి | ## అనులేఖనం 🤗 ట్రాన్స్‌ఫార్మర్స్ లైబ్రరీ కోసం మీరు ఉదహరించగల [పేపర్](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) ఇప్పుడు మా వద్ద ఉంది: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos

mavonic_private_repos/transformers/CITATION.cff

cff-version: "1.2.0" date-released: 2020-10 message: "If you use this software, please cite it using these metadata." title: "Transformers: State-of-the-Art Natural Language Processing" url: "https://github.com/huggingface/transformers" authors: - family-names: Wolf given-names: Thomas - family-names: Debut given-names: Lysandre - family-names: Sanh given-names: Victor - family-names: Chaumond given-names: Julien - family-names: Delangue given-names: Clement - family-names: Moi given-names: Anthony - family-names: Cistac given-names: Perric - family-names: Ma given-names: Clara - family-names: Jernite given-names: Yacine - family-names: Plu given-names: Julien - family-names: Xu given-names: Canwen - family-names: "Le Scao" given-names: Teven - family-names: Gugger given-names: Sylvain - family-names: Drame given-names: Mariama - family-names: Lhoest given-names: Quentin - family-names: Rush given-names: "Alexander M." preferred-citation: type: conference-paper authors: - family-names: Wolf given-names: Thomas - family-names: Debut given-names: Lysandre - family-names: Sanh given-names: Victor - family-names: Chaumond given-names: Julien - family-names: Delangue given-names: Clement - family-names: Moi given-names: Anthony - family-names: Cistac given-names: Perric - family-names: Ma given-names: Clara - family-names: Jernite given-names: Yacine - family-names: Plu given-names: Julien - family-names: Xu given-names: Canwen - family-names: "Le Scao" given-names: Teven - family-names: Gugger given-names: Sylvain - family-names: Drame given-names: Mariama - family-names: Lhoest given-names: Quentin - family-names: Rush given-names: "Alexander M." booktitle: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations" month: 10 start: 38 end: 45 title: "Transformers: State-of-the-Art Natural Language Processing" year: 2020 publisher: "Association for Computational Linguistics" url: "https://www.aclweb.org/anthology/2020.emnlp-demos.6" address: "Online"

mavonic_private_repos

mavonic_private_repos/transformers/Makefile

.PHONY: deps_table_update modified_only_fixup extra_style_checks quality style fixup fix-copies test test-examples # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!) export PYTHONPATH = src check_dirs := examples tests src utils exclude_folders := examples/research_projects modified_only_fixup: $(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs))) @if test -n "$(modified_py_files)"; then \ echo "Checking/fixing $(modified_py_files)"; \ ruff check $(modified_py_files) --fix --exclude $(exclude_folders); \ ruff format $(modified_py_files) --exclude $(exclude_folders);\ else \ echo "No library .py files were modified"; \ fi # Update src/transformers/dependency_versions_table.py deps_table_update: @python setup.py deps_table_update deps_table_check_updated: @md5sum src/transformers/dependency_versions_table.py > md5sum.saved @python setup.py deps_table_update @md5sum -c --quiet md5sum.saved || (printf "\nError: the version dependency table is outdated.\nPlease run 'make fixup' or 'make style' and commit the changes.\n\n" && exit 1) @rm md5sum.saved # autogenerating code autogenerate_code: deps_table_update # Check that the repo is in a good state repo-consistency: python utils/check_copies.py python utils/check_table.py python utils/check_dummies.py python utils/check_repo.py python utils/check_inits.py python utils/check_config_docstrings.py python utils/check_config_attributes.py python utils/check_doctest_list.py python utils/update_metadata.py --check-only python utils/check_docstrings.py python utils/check_support_list.py # this target runs checks on all files quality: @python -c "from transformers import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) ruff check $(check_dirs) setup.py conftest.py ruff format --check $(check_dirs) setup.py conftest.py python utils/custom_init_isort.py --check_only python utils/sort_auto_mappings.py --check_only python utils/check_doc_toc.py # Format source code automatically and check is there are any problems left that need manual fixing extra_style_checks: python utils/custom_init_isort.py python utils/sort_auto_mappings.py python utils/check_doc_toc.py --fix_and_overwrite # this target runs checks on all files and potentially modifies some of them style: ruff check $(check_dirs) setup.py conftest.py --fix --exclude $(exclude_folders) ruff format $(check_dirs) setup.py conftest.py --exclude $(exclude_folders) ${MAKE} autogenerate_code ${MAKE} extra_style_checks # Super fast fix and check target that only works on relevant modified files since the branch was made fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency # Make marked copies of snippets of codes conform to the original fix-copies: python utils/check_copies.py --fix_and_overwrite python utils/check_table.py --fix_and_overwrite python utils/check_dummies.py --fix_and_overwrite python utils/check_doctest_list.py --fix_and_overwrite python utils/check_docstrings.py --fix_and_overwrite # Run tests for the library test: python -m pytest -n auto --dist=loadfile -s -v ./tests/ # Run tests for examples test-examples: python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/ # Run tests for SageMaker DLC release test-sagemaker: # install sagemaker dependencies in advance with pip install .[sagemaker] TEST_SAGEMAKER=True python -m pytest -n auto -s -v ./tests/sagemaker # Release stuff pre-release: python utils/release.py pre-patch: python utils/release.py --patch post-release: python utils/release.py --post_release post-patch: python utils/release.py --post_release --patch build-release: rm -rf dist rm -rf build python setup.py bdist_wheel python setup.py sdist python utils/check_build.py

mavonic_private_repos

mavonic_private_repos/transformers/README_ru.md

<!--- Copyright 2023 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. --> <p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg"> <img alt="Hugging Face Transformers Library" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg" width="352" height="59" style="max-width: 100%;"> </picture> <br/> <br/> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <b>Русский</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | <p> </h4> <h3 align="center"> <p>Современное машинное обучение для JAX, PyTorch и TensorFlow</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 Transformers предоставляет тысячи предварительно обученных моделей для выполнения различных задач, таких как текст, зрение и аудио. Эти модели могут быть применены к: * 📝 Тексту для таких задач, как классификация текстов, извлечение информации, ответы на вопросы, обобщение, перевод, генерация текстов на более чем 100 языках. * 🖼️ Изображениям для задач классификации изображений, обнаружения объектов и сегментации. * 🗣️ Аудио для задач распознавания речи и классификации аудио. Модели transformers также могут выполнять несколько задач, такие как ответы на табличные вопросы, распознавание оптических символов, извлечение информации из отсканированных документов, классификация видео и ответы на визуальные вопросы. 🤗 Transformers предоставляет API для быстрой загрузки и использования предварительно обученных моделей, их тонкой настройки на собственных датасетах и последующего взаимодействия ими с сообществом на нашем [сайте](https://huggingface.co/models). В то же время каждый python модуль, определяющий архитектуру, полностью автономен и может быть модифицирован для проведения быстрых исследовательских экспериментов. 🤗 Transformers опирается на три самые популярные библиотеки глубокого обучения - [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) и [TensorFlow](https://www.tensorflow.org/) - и легко интегрируется между ними. Это позволяет легко обучать модели с помощью одной из них, а затем загружать их для выводов с помощью другой. ## Онлайн демонстрация Большинство наших моделей можно протестировать непосредственно на их страницах с [сайта](https://huggingface.co/models). Мы также предлагаем [привтаный хостинг моделей, контроль версий и API для выводов](https://huggingface.co/pricing) для публичных и частных моделей. Вот несколько примеров: В области NLP ( Обработка текстов на естественном языке ): - [Маскированное заполнение слов с помощью BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [Распознавание сущностей с помощью Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [Генерация текста с помощью GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+) - [Выводы на естественном языке с помощью RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [Обобщение с помощью BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [Ответы на вопросы с помощью DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [Перевод с помощью T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) В области компьютерного зрения: - [Классификация изображений с помощью ViT](https://huggingface.co/google/vit-base-patch16-224) - [Обнаружение объектов с помощью DETR](https://huggingface.co/facebook/detr-resnet-50) - [Семантическая сегментация с помощью SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) - [Сегментация паноптикума с помощью MaskFormer](https://huggingface.co/facebook/maskformer-swin-small-coco) - [Оценка глубины с помощью DPT](https://huggingface.co/docs/transformers/model_doc/dpt) - [Классификация видео с помощью VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae) - [Универсальная сегментация с помощью OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) В области звука: - [Автоматическое распознавание речи с помощью Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h) - [Поиск ключевых слов с помощью Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) - [Классификация аудиоданных с помощью траснформера аудиоспектрограмм](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) В мультимодальных задачах: - [Ответы на вопросы по таблице с помощью TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq) - [Визуальные ответы на вопросы с помощью ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) - [Zero-shot классификация изображений с помощью CLIP](https://huggingface.co/openai/clip-vit-large-patch14) - [Ответы на вопросы по документам с помощью LayoutLM](https://huggingface.co/impira/layoutlm-document-qa) - [Zero-shot классификация видео с помощью X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip) ## 100 проектов, использующих Transformers Transformers - это не просто набор инструментов для использования предварительно обученных моделей: это сообщество проектов, созданное на его основе, и Hugging Face Hub. Мы хотим, чтобы Transformers позволил разработчикам, исследователям, студентам, профессорам, инженерам и всем желающим создавать проекты своей мечты. Чтобы отпраздновать 100 тысяч звезд Transformers, мы решили сделать акцент на сообществе, и создали страницу [awesome-transformers](./awesome-transformers.md), на которой перечислены 100 невероятных проектов, созданных с помощью transformers. Если вы являетесь владельцем или пользователем проекта, который, по вашему мнению, должен быть включен в этот список, пожалуйста, откройте PR для его добавления! ## Если вы хотите получить индивидуальную поддержку от команды Hugging Face <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## Быстрый гайд Для использования модели на заданном входе (текст, изображение, звук, ...) мы предоставляем API `pipeline`. Конвейеры объединяют предварительно обученную модель с препроцессингом, который использовался при ее обучении. Вот как можно быстро использовать конвейер для классификации положительных и отрицательных текстов: ```python >>> from transformers import pipeline # Выделение конвейера для анализа настроений >>> classifier = pipeline('sentiment-analysis') >>> classifier('Мы очень рады представить конвейер в transformers.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` Вторая строка кода загружает и кэширует предварительно обученную модель, используемую конвейером, а третья оценивает ее на заданном тексте. Здесь ответ "POSITIVE" с уверенностью 99,97%. Во многих задачах, как в НЛП, так и в компьютерном зрении и речи, уже есть готовый `pipeline`. Например, мы можем легко извлечь обнаруженные объекты на изображении: ``` python >>> import requests >>> from PIL import Image >>> from transformers import pipeline # Скачиваем изображение с милыми котиками >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> image = Image.open(image_data) # Выделение конвейера для обнаружения объектов >>> object_detector = pipeline('object-detection') >>> object_detector(image) [{'score': 0.9982201457023621, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, {'score': 0.9960021376609802, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, {'score': 0.9954745173454285, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, {'score': 0.9988006353378296, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, {'score': 0.9986783862113953, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] ``` Здесь мы получаем список объектов, обнаруженных на изображении, с рамкой вокруг объекта и оценкой достоверности. Слева - исходное изображение, справа прогнозы: <h3 align="center"> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a> </h3> Подробнее о задачах, поддерживаемых API `pipeline`, можно узнать в [этом учебном пособии](https://huggingface.co/docs/transformers/task_sum) В дополнение к `pipeline`, для загрузки и использования любой из предварительно обученных моделей в заданной задаче достаточно трех строк кода. Вот версия для PyTorch: ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Привет мир!", return_tensors="pt") >>> outputs = model(**inputs) ``` А вот эквивалентный код для TensorFlow: ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Привет мир!", return_tensors="tf") >>> outputs = model(**inputs) ``` Токенизатор отвечает за всю предварительную обработку, которую ожидает предварительно обученная модель, и может быть вызван непосредственно с помощью одной строки (как в приведенных выше примерах) или на списке. В результате будет получен словарь, который можно использовать в последующем коде или просто напрямую передать в модель с помощью оператора распаковки аргументов **. Сама модель представляет собой обычный [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) или [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (в зависимости от используемого бэкенда), который можно использовать как обычно. [В этом руководстве](https://huggingface.co/docs/transformers/training) рассказывается, как интегрировать такую модель в классический цикл обучения PyTorch или TensorFlow, или как использовать наш API `Trainer` для быстрой тонкой настройки на новом датасете. ## Почему необходимо использовать transformers? 1. Простые в использовании современные модели: - Высокая производительность в задачах понимания и генерации естественного языка, компьютерного зрения и аудио. - Низкий входной барьер для преподавателей и практиков. - Небольшое количество абстракций для пользователя и всего три класса для изучения. - Единый API для использования всех наших предварительно обученных моделей. 1. Более низкие вычислительные затраты, меньший "углеродный след": - Исследователи могут обмениваться обученными моделями вместо того, чтобы постоянно их переобучать. - Практики могут сократить время вычислений и производственные затраты. - Десятки архитектур с более чем 60 000 предварительно обученных моделей для всех модальностей. 1. Выбор подходящего фреймворка для каждого этапа жизни модели: - Обучение самых современных моделей за 3 строки кода. - Перемещайте одну модель между фреймворками TF2.0/PyTorch/JAX по своему усмотрению. - Беспрепятственный выбор подходящего фреймворка для обучения, оценки и производства. 1. Легко настроить модель или пример под свои нужды: - Мы предоставляем примеры для каждой архитектуры, чтобы воспроизвести результаты, опубликованные их авторами. - Внутренние компоненты модели раскрываются максимально последовательно. - Файлы моделей можно использовать независимо от библиотеки для проведения быстрых экспериментов. ## Почему я не должен использовать transformers? - Данная библиотека не является модульным набором строительных блоков для нейронных сетей. Код в файлах моделей специально не рефакторится дополнительными абстракциями, чтобы исследователи могли быстро итеративно работать с каждой из моделей, не погружаясь в дополнительные абстракции/файлы. - API обучения не предназначен для работы с любой моделью, а оптимизирован для работы с моделями, предоставляемыми библиотекой. Для работы с общими циклами машинного обучения следует использовать другую библиотеку (возможно, [Accelerate](https://huggingface.co/docs/accelerate)). - Несмотря на то, что мы стремимся представить как можно больше примеров использования, скрипты в нашей папке [примеров](https://github.com/huggingface/transformers/tree/main/examples) являются именно примерами. Предполагается, что они не будут работать "из коробки" для решения вашей конкретной задачи, и вам придется изменить несколько строк кода, чтобы адаптировать их под свои нужды. ## Установка ### С помощью pip Данный репозиторий протестирован на Python 3.8+, Flax 0.4.1+, PyTorch 1.11+ и TensorFlow 2.6+. Устанавливать 🤗 Transformers следует в [виртуальной среде](https://docs.python.org/3/library/venv.html). Если вы не знакомы с виртуальными средами Python, ознакомьтесь с [руководством пользователя](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Сначала создайте виртуальную среду с той версией Python, которую вы собираетесь использовать, и активируйте ее. Затем необходимо установить хотя бы один бекенд из Flax, PyTorch или TensorFlow. Пожалуйста, обратитесь к страницам [TensorFlow установочная страница](https://www.tensorflow.org/install/), [PyTorch установочная страница](https://pytorch.org/get-started/locally/#start-locally) и/или [Flax](https://github.com/google/flax#quick-install) и [Jax](https://github.com/google/jax#installation), где описаны команды установки для вашей платформы. После установки одного из этих бэкендов 🤗 Transformers может быть установлен с помощью pip следующим образом: ```bash pip install transformers ``` Если вы хотите поиграть с примерами или вам нужен самый современный код и вы не можете ждать нового релиза, вы должны [установить библиотеку из исходного кода](https://huggingface.co/docs/transformers/installation#installing-from-source). ### С помощью conda Установить Transformers с помощью conda можно следующим образом: ```bash conda install conda-forge::transformers ``` > **_ЗАМЕТКА:_** Установка `transformers` через канал `huggingface` устарела. О том, как установить Flax, PyTorch или TensorFlow с помощью conda, читайте на страницах, посвященных их установке. > **_ЗАМЕТКА:_** В операционной системе Windows вам может быть предложено активировать режим разработчика, чтобы воспользоваться преимуществами кэширования. Если для вас это невозможно, сообщите нам об этом [здесь](https://github.com/huggingface/huggingface_hub/issues/1062). ## Модельные архитектуры **[Все контрольные точки моделей](https://huggingface.co/models)**, предоставляемые 🤗 Transformers, беспрепятственно интегрируются с huggingface.co [model hub](https://huggingface.co/models), куда они загружаются непосредственно [пользователями](https://huggingface.co/users) и [организациями](https://huggingface.co/organizations). Текущее количество контрольных точек:  🤗 В настоящее время Transformers предоставляет следующие архитектуры: подробное описание каждой из них см. [здесь](https://huggingface.co/docs/transformers/model_summary). Чтобы проверить, есть ли у каждой модели реализация на Flax, PyTorch или TensorFlow, или связанный с ней токенизатор, поддерживаемый библиотекой 🤗 Tokenizers, обратитесь к [этой таблице](https://huggingface.co/docs/transformers/index#supported-frameworks). Эти реализации были протестированы на нескольких наборах данных (см. примеры скриптов) и должны соответствовать производительности оригинальных реализаций. Более подробную информацию о производительности можно найти в разделе "Примеры" [документации](https://github.com/huggingface/transformers/tree/main/examples). ## Изучи больше | Секция | Описание | |-|-| | [Документация](https://huggingface.co/docs/transformers/) | Полная документация по API и гайды | | [Краткие описания задач](https://huggingface.co/docs/transformers/task_summary) | Задачи поддерживаются 🤗 Transformers | | [Пособие по предварительной обработке](https://huggingface.co/docs/transformers/preprocessing) | Использование класса `Tokenizer` для подготовки данных для моделей | | [Обучение и доработка](https://huggingface.co/docs/transformers/training) | Использование моделей, предоставляемых 🤗 Transformers, в цикле обучения PyTorch/TensorFlow и API `Trainer`. | | [Быстрый тур: Тонкая настройка/скрипты использования](https://github.com/huggingface/transformers/tree/main/examples) | Примеры скриптов для тонкой настройки моделей на широком спектре задач | | [Совместное использование и загрузка моделей](https://huggingface.co/docs/transformers/model_sharing) | Загружайте и делитесь с сообществом своими доработанными моделями | ## Цитирование Теперь у нас есть [статья](https://www.aclweb.org/anthology/2020.emnlp-demos.6/), которую можно цитировать для библиотеки 🤗 Transformers: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos

mavonic_private_repos/transformers/README_zh-hant.md

<!--- Copyright 2020 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. --> <!--- A useful guide for English-Traditional Chinese translation of Hugging Face documentation - Add space around English words and numbers when they appear between Chinese characters. E.g., 共 100 多種語言; 使用 transformers 函式庫。 - Use square quotes, e.g.,「引用」 - Some of terms in the file can be found at National Academy for Educational Research (https://terms.naer.edu.tw/), an official website providing bilingual translations between English and Traditional Chinese. Dictionary API: API (不翻譯） add: 加入 checkpoint: 檢查點 code: 程式碼 community: 社群 confidence: 信賴度 dataset: 資料集 documentation: 文件 example: 基本翻譯為「範例」，或依語意翻為「例子」 finetune: 微調 Hugging Face: Hugging Face（不翻譯） implementation: 實作 inference: 推論 library: 函式庫 module: 模組 NLP/Natural Language Processing: 以 NLP 出現時不翻譯，以 Natural Language Processing 出現時翻譯為自然語言處理 online demos: 線上Demo pipeline: pipeline（不翻譯） pretrained/pretrain: 預訓練 Python data structures (e.g., list, set, dict): 翻譯為串列，集合，字典，並用括號標註原英文 repository: repository（不翻譯） summary: 概覽 token-: token-（不翻譯） Trainer: Trainer（不翻譯） transformer: transformer（不翻譯） tutorial: 教學 user: 使用者 --> <p align="center"> <br> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/> <br> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <b>繁體中文</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p>為 Jax、PyTorch 以及 TensorFlow 打造的先進自然語言處理函式庫</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 Transformers 提供了數以千計的預訓練模型，支援 100 多種語言的文本分類、資訊擷取、問答、摘要、翻譯、文本生成。它的宗旨是讓最先進的 NLP 技術人人易用。 🤗 Transformers 提供了便於快速下載和使用的API，讓你可以將預訓練模型用在給定文本、在你的資料集上微調然後經由 [model hub](https://huggingface.co/models) 與社群共享。同時，每個定義的 Python 模組架構均完全獨立，方便修改和快速研究實驗。 🤗 Transformers 支援三個最熱門的深度學習函式庫： [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) 以及 [TensorFlow](https://www.tensorflow.org/) — 並與之完美整合。你可以直接使用其中一個框架訓練你的模型，然後用另一個載入和推論。 ## 線上Demo 你可以直接在 [model hub](https://huggingface.co/models) 上測試大多數的模型。我們也提供了 [私有模型託管、模型版本管理以及推論API](https://huggingface.co/pricing)。 這裡是一些範例： - [用 BERT 做遮蓋填詞](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [用 Electra 做專有名詞辨識](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [用 GPT-2 做文本生成](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+) - [用 RoBERTa 做自然語言推論](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [用 BART 做文本摘要](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [用 DistilBERT 做問答](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [用 T5 做翻譯](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) **[Write With Transformer](https://transformer.huggingface.co)**，由 Hugging Face 團隊所打造，是一個文本生成的官方 demo。 ## 如果你在尋找由 Hugging Face 團隊所提供的客製化支援服務 <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## 快速上手 我們為快速使用模型提供了 `pipeline` API。 Pipeline 包含了預訓練模型和對應的文本預處理。下面是一個快速使用 pipeline 去判斷正負面情緒的例子： ```python >>> from transformers import pipeline # 使用情緒分析 pipeline >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` 第二行程式碼下載並快取 pipeline 使用的預訓練模型，而第三行程式碼則在給定的文本上進行了評估。這裡的答案“正面” (positive) 具有 99.97% 的信賴度。 許多的 NLP 任務都有隨選即用的預訓練 `pipeline`。例如，我們可以輕鬆地從給定文本中擷取問題答案： ``` python >>> from transformers import pipeline # 使用問答 pipeline >>> question_answerer = pipeline('question-answering') >>> question_answerer({ ... 'question': 'What is the name of the repository ?', ... 'context': 'Pipeline has been included in the huggingface/transformers repository' ... }) {'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'} ``` 除了提供問題解答，預訓練模型還提供了對應的信賴度分數以及解答在 tokenized 後的文本中開始和結束的位置。你可以從[這個教學](https://huggingface.co/docs/transformers/task_summary)了解更多 `pipeline` API支援的任務。 要在你的任務中下載和使用任何預訓練模型很簡單，只需三行程式碼。這裡是 PyTorch 版的範例： ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` 這裡是對應的 TensorFlow 程式碼： ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` Tokenizer 為所有的預訓練模型提供了預處理，並可以直接轉換單一字串（比如上面的例子）或串列 (list)。它會輸出一個的字典 (dict) 讓你可以在下游程式碼裡使用或直接藉由 `**` 運算式傳給模型。 模型本身是一個常規的 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) 或 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)（取決於你的後端），可依常規方式使用。 [這個教學](https://huggingface.co/transformers/training.html)解釋了如何將這樣的模型整合到一般的 PyTorch 或 TensorFlow 訓練迴圈中，或是如何使用我們的 `Trainer` API 在一個新的資料集上快速進行微調。 ## 為什麼要用 transformers？ 1. 便於使用的先進模型： - NLU 和 NLG 上性能卓越 - 對教學和實作友好且低門檻 - 高度抽象，使用者只須學習 3 個類別 - 對所有模型使用的制式化API 1. 更低的運算成本，更少的碳排放： - 研究人員可以分享已訓練的模型而非每次從頭開始訓練 - 工程師可以減少計算時間以及生產成本 - 數十種模型架構、兩千多個預訓練模型、100多種語言支援 1. 對於模型生命週期的每一個部分都面面俱到： - 訓練先進的模型，只需 3 行程式碼 - 模型可以在不同深度學習框架之間任意轉換 - 為訓練、評估和生產選擇最適合的框架，並完美銜接 1. 為你的需求輕鬆客製化專屬模型和範例： - 我們為每種模型架構提供了多個範例來重現原論文結果 - 一致的模型內部架構 - 模型檔案可單獨使用，便於修改和快速實驗 ## 什麼情況下我不該用 transformers？ - 本函式庫並不是模組化的神經網絡工具箱。模型文件中的程式碼並未做額外的抽象封裝，以便研究人員快速地翻閱及修改程式碼，而不會深陷複雜的類別包裝之中。 - `Trainer` API 並非相容任何模型，它只為本函式庫中的模型最佳化。對於一般的機器學習用途，請使用其他函式庫。 - 儘管我們已盡力而為，[examples 目錄](https://github.com/huggingface/transformers/tree/main/examples)中的腳本也僅為範例而已。對於特定問題，它們並不一定隨選即用，可能需要修改幾行程式碼以符合需求。 ## 安裝 ### 使用 pip 這個 Repository 已在 Python 3.8+、Flax 0.4.1+、PyTorch 1.11+ 和 TensorFlow 2.6+ 下經過測試。 你可以在[虛擬環境](https://docs.python.org/3/library/venv.html)中安裝 🤗 Transformers。如果你還不熟悉 Python 的虛擬環境，請閱此[使用者指引](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)。 首先，用你打算使用的版本的 Python 創建一個虛擬環境並進入。 然後，你需要安裝 Flax、PyTorch 或 TensorFlow 其中之一。對於該如何在你使用的平台上安裝這些框架，請參閱 [TensorFlow 安裝頁面](https://www.tensorflow.org/install/), [PyTorch 安裝頁面](https://pytorch.org/get-started/locally/#start-locally) 或 [Flax 安裝頁面](https://github.com/google/flax#quick-install)。 當其中一個後端安裝成功後，🤗 Transformers 可依此安裝： ```bash pip install transformers ``` 如果你想要試試範例或者想在正式發布前使用最新開發中的程式碼，你必須[從原始碼安裝](https://huggingface.co/docs/transformers/installation#installing-from-source)。 ### 使用 conda 🤗 Transformers 可以藉由 conda 依此安裝： ```shell script conda install conda-forge::transformers ``` > **_筆記:_** 從 `huggingface` 頻道安裝 `transformers` 已被淘汰。 要藉由 conda 安裝 Flax、PyTorch 或 TensorFlow 其中之一，請參閱它們各自安裝頁面的說明。 ## 模型架構 **🤗 Transformers 支援的[所有的模型檢查點](https://huggingface.co/models)**，由[使用者](https://huggingface.co/users)和[組織](https://huggingface.co/organizations)上傳，均與 huggingface.co [model hub](https://huggingface.co) 完美結合。 目前的檢查點數量：  🤗 Transformers 目前支援以下的架構: 模型概覽請參閱[這裡](https://huggingface.co/docs/transformers/model_summary). 要檢查某個模型是否已有 Flax、PyTorch 或 TensorFlow 的實作，或其是否在🤗 Tokenizers 函式庫中有對應的 tokenizer，敬請參閱[此表](https://huggingface.co/docs/transformers/index#supported-frameworks)。 這些實作均已於多個資料集測試（請參閱範例腳本）並應與原版實作表現相當。你可以在範例文件的[此節](https://huggingface.co/docs/transformers/examples)中了解實作的細節。 ## 了解更多 | 章節 | 描述 | |-|-| | [文件](https://huggingface.co/transformers/) | 完整的 API 文件和教學 | | [任務概覽](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers 支援的任務 | | [預處理教學](https://huggingface.co/docs/transformers/preprocessing) | 使用 `Tokenizer` 來為模型準備資料 | | [訓練和微調](https://huggingface.co/docs/transformers/training) | 使用 PyTorch/TensorFlow 的內建的訓練方式或於 `Trainer` API 中使用 🤗 Transformers 提供的模型 | | [快速上手：微調和範例腳本](https://github.com/huggingface/transformers/tree/main/examples) | 為各種任務提供的範例腳本 | | [模型分享和上傳](https://huggingface.co/docs/transformers/model_sharing) | 上傳並與社群分享你微調的模型 | | [遷移](https://huggingface.co/docs/transformers/migration) | 從 `pytorch-transformers` 或 `pytorch-pretrained-bert` 遷移到 🤗 Transformers | ## 引用 我們已將此函式庫的[論文](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)正式發表。如果你使用了 🤗 Transformers 函式庫，可以引用： ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos

mavonic_private_repos/transformers/ISSUES.md

<!--- Copyright 2020 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. --> # How To Request Support This is an Open Source Project so please be mindful that like in any other project of this kind there is no obligation to answer all requests for help. However, we want to encourage you to ask for help whenever you think it's needed! We are happy about every question we get because it allows us to better understand your needs, possible misunderstandings, and most importantly a way for you to help us make this library better. That being said, this document's main purpose is to provide guidelines at how you can formulate your requests to increase your chances to be understood and to get support. There are two main venues to receive support: [the forums](https://discuss.huggingface.co/) and [the GitHub issues](https://github.com/huggingface/transformers/issues). ## The Forums [The user forums](https://discuss.huggingface.co/) are supported by the wide community of the library users and backed up by developers when needed. If you have a difficulty with deploying this library or some questions, or you'd like to discuss a new feature, please first consider discussing those things at the forums. Only when you feel your subject matter has been crystalized and you still need support from the library developers do proceed to file an [issue](https://github.com/huggingface/transformers/issues). In particular all "Please explain" questions or objectively very user-specific feature requests belong to the forums. Here are some example of such questions: * "I would like to use a BertModel within a RL-Agent for a customer support service. How can I use a BertForMaskedLM in my ChatBotModel?" * "Could you please explain why T5 has no positional embedding matrix under T5Model?" * "How should I set my generation parameters for translation?" * "How to train T5 on De->En translation?" ## The GitHub Issues Everything which hints at a bug should be opened as an [issue](https://github.com/huggingface/transformers/issues). You are not required to read the following guidelines before opening an issue. However, if you notice that your issue doesn't get any replies, chances are that the developers have one or several difficulties with its quality. In this case, reading the following points and adjusting your issue accordingly could help. 1. Before posting an issue, first search for already posted issues, since chances are someone has already asked a similar question before you. If you use Google your search query should be: ``` "huggingface" "transformers" your query ``` The first two quoted words tell Google to limit the search to the context of the Huggingface Transformers. The remainder is your query - most commonly this would be the error message the software fails with. We will go deeper into details shortly. The results of such a query will typically match GitHub issues, Hugging Face forums, StackExchange, and blogs. If you find relevant hints, you may choose to continue the discussion there if you have follow up questions. If what you found is similar but doesn't quite answer your problem, please, post a new issue and do include links to similar issues or forum discussions you may have found. Let's look at some examples: The error message, often referred to as an assertion, tells us what went wrong. Here is an example of an assertion: ```python Traceback (most recent call last): File "<string>", line 1, in <module> File "/transformers/src/transformers/__init__.py", line 34, in <module> from . import dependency_versions_check File "/transformers/src/transformers/dependency_versions_check.py", line 34, in <module> from .utils import is_tokenizers_available File "/transformers/src/transformers/utils/import_utils.py", line 40, in <module> from tqdm.auto import tqdm ModuleNotFoundError: No module named 'tqdm.auto' ``` and it typically includes a traceback, so that we can see the full stack of calls the program made before it fails. This gives us the context to know why the program failed. Going back to the above example. If you received this error search, look at the very last line of the error which is: ```python ModuleNotFoundError: No module named 'tqdm.auto' ``` And now we can use it to do the searching on your favorite search engine: 1. first for `"huggingface" "transformers" "ModuleNotFoundError: No module named 'tqdm.auto'"` 2. if you don't find relevant results, then search for just `"ModuleNotFoundError: No module named 'tqdm.auto'"` 3. and finally if nothing still comes up, then remove the outside quotes: `ModuleNotFoundError: No module named 'tqdm.auto'` If the error includes any messages that include bits unique to your filesystem, always remove those in the search query since other users will not have the same filesystem as yours. For example: ```bash python -c 'open("/tmp/wrong_path.txt", "r")' Traceback (most recent call last): File "<string>", line 1, in <module> FileNotFoundError: [Errno 2] No such file or directory: '/tmp/wrong_path.txt' ``` Here you'd search for just: `"FileNotFoundError: [Errno 2] No such file or directory"` If the local information that you removed were inside the error message and you removed them you may need to remove double quotes since your query is no longer exact. So if the error message was something like: ```bash ValueError: '/tmp/wrong_path.txt' cannot be found ``` then you'd search for `"ValueError" "cannot be found"` As you search you will notice that when you don't use quotes often the search engines will return a variety of unrelated hits, which may or may not be what you want. Experiment with different ways and find which approach gives the most satisfactory results. 2. Keep the issue short, providing the information that you think will aid the developers to understand your situation. Put yourself in the shoes of the person who has never seen your code or knows anything about your custom setup. This mental exercise will help to develop an intuition to what/what not to share" 3. If there is a software failure, always provide the full traceback, for example: ```python $ python -c 'import transformers' Traceback (most recent call last): File "<string>", line 1, in <module> File "/transformers/src/transformers/__init__.py", line 34, in <module> from . import dependency_versions_check File "/transformers/src/transformers/dependency_versions_check.py", line 34, in <module> from .utils import is_tokenizers_available File "/transformers/src/transformers/utils/import_utils.py", line 40, in <module> from tqdm.auto import tqdm ModuleNotFoundError: No module named 'tqdm.auto' ``` As compared to providing just the last line of the error message, e.g.: ```python ModuleNotFoundError: No module named 'tqdm.auto' ``` which is not sufficient. If your application is running on more than one GPU (e.g. under `DistributedDataParallel`) and typically getting every log and traceback printed multiple times, please make sure that you paste only one copy of it. At times the traceback from parallel processes may get interleaved - so either disentangle these or change the loggers to log only for `local_rank==0` so that only one process logs things. 4. When quoting a traceback, command line instructions and any type of code always enclose it in triple backticks inside the editor window, that is: ```` ``` git clone https://github.com/huggingface/transformers cd transformers pip install . ``` ```` If it's a command line with a long argument list, please consider breaking it down using backslashes and new lines. Here is an example of a good command line quote: ```bash cd examples/seq2seq torchrun --nproc_per_node=2 ./finetune_trainer.py \ --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \ --output_dir output_dir --overwrite_output_dir \ --do_train --n_train 500 --num_train_epochs 1 \ --per_device_train_batch_size 1 --freeze_embeds \ --src_lang en_XX --tgt_lang ro_RO --task translation \ --fp16 ``` If you don't break it up, one has to scroll horizontally which often makes it quite difficult to quickly see what's happening. The backslashes allow us to copy the command directly into the console to run it, without needing to edit it. 5. Include only the important information that you think will help the developer to quickly identify the problem. For example applications often create huge amounts of logs. Ask yourself whether providing all or parts of the log is useful. Pasting a 100-1000 lines of log into the issue is an immediate turn off, since it will take a lot of time to figure out where the pertinent parts of the log are. Attaching a full log can be helpful if it's done as an attachment, if it's enclosed in the following html code in the comment editor window: ``` <details> <summary>Full log</summary> <pre> many lines go here </pre> </details> ``` which would result in the following entry, which can be opened if desired, but otherwise takes little space. <details> <summary>Full log</summary> <pre> many lines go here </pre> </details> You could also provide a link to a pastebin service, but this is less beneficial since those links tend to expire quickly and future readers of your issue might not be able to access that log file anymore and may lack some context. 6. If this is an issue in your code, do try to reduce that code to a minimal example that still demonstrates the problem. Please ask at the forums if you have a hard time figuring how to do that. Please realize that we don't have the luxury of having time to try and understand all of your custom code. If you really tried to make a short reproducible code but couldn't figure it out, it might be that having a traceback will give the developer enough information to know what's going on. But if it is not enough and we can't reproduce the problem, we can't really solve it. Do not despair if you can't figure it out from the beginning, just share what you can and perhaps someone else will be able to help you at the forums. If your setup involves any custom datasets, the best way to help us reproduce the problem is to create a [Google Colab notebook](https://colab.research.google.com/) that demonstrates the issue and once you verify that the issue still exists, include a link to that notebook in the Issue. Just make sure that you don't copy and paste the location bar url of the open notebook - as this is private and we won't be able to open it. Instead, you need to click on `Share` in the right upper corner of the notebook, select `Get Link` and then copy and paste the public link it will give to you. 7. If you forked off some of this project's code or example applications, please, do not ask us to go into your code repository and figure out what you may have done. The code is already very complex and unless there is an easy way to do a diff and it's a small diff, it won't be possible to find someone with time on their hands to make a lengthy investigation. Albeit, you might find someone at the forums who will be generous to do this for you. 8. Before reporting an issue, first, always try to update your environment to the latest official version of this library. We have no resources to go and debug older revisions, which could easily have bugs that have been fixed in the latest released version. We understand that this is not always possible, especially when APIs change, in which case file an issue against the highest library version your environment can support. Of course, if you upgrade the library, always retest that the problem is still there. 9. Please do not ask us to reproduce an issue with your custom data, since we don't have it. So, either you should use some existing dataset supported by HF datasets or you need to supply a code that generates a small sample on the fly, or some another quick and simple way to get it. Please do not send us any non-public domain data that may require a license or a permission to be used. 10. Do not tag multiple developers on the issue unless you know this is expected, either because you asked them and they gave you an explicit permission to tag them or the issue template instructs you to do so. The "who to tag for what domain" part of the issue template is there to help users direct their questions to the right developers who are designated maintainers of project's specific domains. They can then decide at their own discretion to tag other developers if they feel it'd help move the issue forward. We currently don't have a triage service and we trust your capacity to identify the right domain and thus the persons to tag in your issue. If you are not sure, please use the forums to ask for guidance. When in doubt, err on the side of not tagging a given person. If you tag multiple people out of context or permission don't be surprised if you get no response at all. Please remember that every time you tag someone, they get a notification and you're taking their time without their permission. Please be sensitive to that. If you got helped by one of the developers in the past please don't tag them in future issues, unless they are listed in the issue template for the domain you are asking about or that developer gave you an explicit permission to tag them in future issues. If you see a certain developer doing multiple and/or recent commits into a specific area of the project that you feel is relevant to your issue, it is not a good reason to tag them. Various developers may be fixing things that prevent them from moving forward, but often their work is focused on a totally different domain. And while they may or may not know how to help you with the problem at hand, it would benefit the whole community much more if they focus on the domain of their unique expertise. 11. Use the Edit button. Take your time, and re-read and improve the wording and formatting to make your posts and comments as easy to understand as possible. Avoid posting multiple comments in a row, as each comment generates a notification for the developers tagged in that issue. If you happened to post multiple comments in a row, and nobody followed up yet - consider merging those into one or a few comments while editing the combined content to be coherent. If you choose to edit your older comments after others posted follow up comments you need to be aware that your modifications might not be noticed, so if it's not a typo fixing, try to write a new comment flagging that something has been changed in the previous comments. For example, the very first comment is the most important one. If while the thread unfolds you realize that things aren't as they seemed to you originally you may want to edit the first post to reflect the up-to-date understanding of the issue at hand so that it helps those who read your issue in the future quickly understand what's going on and not need to sift through dozens of comments. It also helps to indicate that the post was edited. So, those reading the thread later can understand why there might be certain discontinuity in the information flow. Use bullets and items if you have lists of items and the outcome improves overall readability. Use backticks to refer to class and function names, e.g. `BartModel` and `generate` as these stand out and improve the speed of a reader's comprehension. Try not use italics and bold text too much as these often make the text more difficult to read. 12. If you are cross-referencing a specific comment in a given thread or another issue, always link to that specific comment, rather than using the issue link. If you do the latter it could be quite impossible to find which specific comment you're referring to. To get the link to the specific comment do not copy the url from the location bar of your browser, but instead, click the `...` icon in the upper right corner of the comment and then select "Copy Link". For example the first link is a link to an issue, and the second to a specific comment in the same issue: 1. https://github.com/huggingface/transformers/issues/9257 2. https://github.com/huggingface/transformers/issues/9257#issuecomment-749945162 13. If you are replying to a last comment, it's totally fine to make your reply with just your comment in it. The readers can follow the information flow here. But if you're replying to a comment that happened some comments back it's always a good practice to quote just the relevant lines you're replying it. The `>` is used for quoting, or you can always use the menu to do so. For example your editor box will look like: ``` > How big is your gpu cluster? Our cluster is made of 256 gpus. ``` If you are addressing multiple comments, quote the relevant parts of each before your answer. Some people use the same comment to do multiple replies, others separate them into separate comments. Either way works. The latter approach helps for linking to a specific comment. In general the best way to figure out what works the best is learn from issues posted by other people - see which issues get great responses and which get little to no response - observe what the posters who received great responses did differently from those who did not. Thank you for reading this somewhat lengthy document. We would like to conclude that these are not absolute rules, but a friendly advice that will help maximize the chances for us to understand what you are trying to communicate, reproduce the problem then resolve it to your satisfaction and the benefit of the whole community. If after reading this document there are remaining questions on how and why or there is a need for further elucidation, please, don't hesitate to ask your question in [this thread](https://discuss.huggingface.co/t/how-to-request-support/3128).

mavonic_private_repos

mavonic_private_repos/transformers/hubconf.py

# Copyright 2020 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. import os import sys SRC_DIR = os.path.join(os.path.dirname(__file__), "src") sys.path.append(SRC_DIR) from transformers import ( AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForMaskedLM, AutoModelForQuestionAnswering, AutoModelForSequenceClassification, AutoTokenizer, add_start_docstrings, ) dependencies = ["torch", "numpy", "tokenizers", "filelock", "requests", "tqdm", "regex", "sentencepiece", "sacremoses", "importlib_metadata", "huggingface_hub"] @add_start_docstrings(AutoConfig.__doc__) def config(*args, **kwargs): r""" # Using torch.hub ! import torch config = torch.hub.load('huggingface/transformers', 'config', 'google-bert/bert-base-uncased') # Download configuration from huggingface.co and cache. config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')` config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/my_configuration.json') config = torch.hub.load('huggingface/transformers', 'config', 'google-bert/bert-base-uncased', output_attentions=True, foo=False) assert config.output_attentions == True config, unused_kwargs = torch.hub.load('huggingface/transformers', 'config', 'google-bert/bert-base-uncased', output_attentions=True, foo=False, return_unused_kwargs=True) assert config.output_attentions == True assert unused_kwargs == {'foo': False} """ return AutoConfig.from_pretrained(*args, **kwargs) @add_start_docstrings(AutoTokenizer.__doc__) def tokenizer(*args, **kwargs): r""" # Using torch.hub ! import torch tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'google-bert/bert-base-uncased') # Download vocabulary from huggingface.co and cache. tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', './test/bert_saved_model/') # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')` """ return AutoTokenizer.from_pretrained(*args, **kwargs) @add_start_docstrings(AutoModel.__doc__) def model(*args, **kwargs): r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'model', 'google-bert/bert-base-uncased') # Download model and configuration from huggingface.co and cache. model = torch.hub.load('huggingface/transformers', 'model', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = torch.hub.load('huggingface/transformers', 'model', 'google-bert/bert-base-uncased', output_attentions=True) # Update configuration during loading assert model.config.output_attentions == True # Loading from a TF checkpoint file instead of a PyTorch model (slower) config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json') model = torch.hub.load('huggingface/transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ return AutoModel.from_pretrained(*args, **kwargs) @add_start_docstrings(AutoModelForCausalLM.__doc__) def modelForCausalLM(*args, **kwargs): r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', 'openai-community/gpt2') # Download model and configuration from huggingface.co and cache. model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', './test/saved_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', 'openai-community/gpt2', output_attentions=True) # Update configuration during loading assert model.config.output_attentions == True # Loading from a TF checkpoint file instead of a PyTorch model (slower) config = AutoConfig.from_pretrained('./tf_model/gpt_tf_model_config.json') model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', './tf_model/gpt_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ return AutoModelForCausalLM.from_pretrained(*args, **kwargs) @add_start_docstrings(AutoModelForMaskedLM.__doc__) def modelForMaskedLM(*args, **kwargs): r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', 'google-bert/bert-base-uncased') # Download model and configuration from huggingface.co and cache. model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', 'google-bert/bert-base-uncased', output_attentions=True) # Update configuration during loading assert model.config.output_attentions == True # Loading from a TF checkpoint file instead of a PyTorch model (slower) config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json') model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ return AutoModelForMaskedLM.from_pretrained(*args, **kwargs) @add_start_docstrings(AutoModelForSequenceClassification.__doc__) def modelForSequenceClassification(*args, **kwargs): r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'google-bert/bert-base-uncased') # Download model and configuration from huggingface.co and cache. model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'google-bert/bert-base-uncased', output_attentions=True) # Update configuration during loading assert model.config.output_attentions == True # Loading from a TF checkpoint file instead of a PyTorch model (slower) config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json') model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ return AutoModelForSequenceClassification.from_pretrained(*args, **kwargs) @add_start_docstrings(AutoModelForQuestionAnswering.__doc__) def modelForQuestionAnswering(*args, **kwargs): r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'google-bert/bert-base-uncased') # Download model and configuration from huggingface.co and cache. model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')` model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'google-bert/bert-base-uncased', output_attentions=True) # Update configuration during loading assert model.config.output_attentions == True # Loading from a TF checkpoint file instead of a PyTorch model (slower) config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json') model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config) """ return AutoModelForQuestionAnswering.from_pretrained(*args, **kwargs)

mavonic_private_repos

mavonic_private_repos/transformers/README_zh-hans.md

<!--- Copyright 2020 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. --> <!--- A useful guide for English-Chinese translation of Hugging Face documentation - Add space around English words and numbers when they appear between Chinese characters. E.g., 共 100 多种语言; 使用 transformers 库。 - Use square quotes, e.g.,「引用」 Dictionary Hugging Face: 抱抱脸 token: 词符（并用括号标注原英文） tokenize: 词符化（并用括号标注原英文） tokenizer: 词符化器（并用括号标注原英文） transformer: transformer（不翻译） pipeline: 流水线 API: API (不翻译） inference: 推理 Trainer: 训练器。当作为类名出现时不翻译。 pretrained/pretrain: 预训练 finetune: 微调 community: 社区 example: 当特指仓库中 example 目录时翻译为「用例」 Python data structures (e.g., list, set, dict): 翻译为列表，集合，词典，并用括号标注原英文 NLP/Natural Language Processing: 以 NLP 出现时不翻译，以 Natural Language Processing 出现时翻译为自然语言处理 checkpoint: 检查点 --> <p align="center"> <br> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/> <br> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <b>简体中文</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p>为 Jax、PyTorch 和 TensorFlow 打造的先进的自然语言处理</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 Transformers 提供了数以千计的预训练模型，支持 100 多种语言的文本分类、信息抽取、问答、摘要、翻译、文本生成。它的宗旨是让最先进的 NLP 技术人人易用。 🤗 Transformers 提供了便于快速下载和使用的API，让你可以把预训练模型用在给定文本、在你的数据集上微调然后通过 [model hub](https://huggingface.co/models) 与社区共享。同时，每个定义的 Python 模块均完全独立，方便修改和快速研究实验。 🤗 Transformers 支持三个最热门的深度学习库： [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) 以及 [TensorFlow](https://www.tensorflow.org/) — 并与之无缝整合。你可以直接使用一个框架训练你的模型然后用另一个加载和推理。 ## 在线演示 你可以直接在模型页面上测试大多数 [model hub](https://huggingface.co/models) 上的模型。 我们也提供了 [私有模型托管、模型版本管理以及推理API](https://huggingface.co/pricing)。 这里是一些例子： - [用 BERT 做掩码填词](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [用 Electra 做命名实体识别](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [用 GPT-2 做文本生成](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+) - [用 RoBERTa 做自然语言推理](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [用 BART 做文本摘要](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [用 DistilBERT 做问答](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [用 T5 做翻译](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) **[Write With Transformer](https://transformer.huggingface.co)**，由抱抱脸团队打造，是一个文本生成的官方 demo。 ## 如果你在寻找由抱抱脸团队提供的定制化支持服务 <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## 快速上手 我们为快速使用模型提供了 `pipeline` （流水线）API。流水线聚合了预训练模型和对应的文本预处理。下面是一个快速使用流水线去判断正负面情绪的例子： ```python >>> from transformers import pipeline # 使用情绪分析流水线 >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` 第二行代码下载并缓存了流水线使用的预训练模型，而第三行代码则在给定的文本上进行了评估。这里的答案“正面” (positive) 具有 99 的置信度。 许多的 NLP 任务都有开箱即用的预训练流水线。比如说，我们可以轻松的从给定文本中抽取问题答案： ``` python >>> from transformers import pipeline # 使用问答流水线 >>> question_answerer = pipeline('question-answering') >>> question_answerer({ ... 'question': 'What is the name of the repository ?', ... 'context': 'Pipeline has been included in the huggingface/transformers repository' ... }) {'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'} ``` 除了给出答案，预训练模型还给出了对应的置信度分数、答案在词符化 (tokenized) 后的文本中开始和结束的位置。你可以从[这个教程](https://huggingface.co/docs/transformers/task_summary)了解更多流水线API支持的任务。 要在你的任务上下载和使用任意预训练模型也很简单，只需三行代码。这里是 PyTorch 版的示例： ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` 这里是等效的 TensorFlow 代码： ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` 词符化器 (tokenizer) 为所有的预训练模型提供了预处理，并可以直接对单个字符串进行调用（比如上面的例子）或对列表 (list) 调用。它会输出一个你可以在下游代码里使用或直接通过 `**` 解包表达式传给模型的词典 (dict)。 模型本身是一个常规的 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) 或 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)（取决于你的后端），可以常规方式使用。 [这个教程](https://huggingface.co/transformers/training.html)解释了如何将这样的模型整合到经典的 PyTorch 或 TensorFlow 训练循环中，或是如何使用我们的 `Trainer` 训练器）API 来在一个新的数据集上快速微调。 ## 为什么要用 transformers？ 1. 便于使用的先进模型： - NLU 和 NLG 上表现优越 - 对教学和实践友好且低门槛 - 高级抽象，只需了解三个类 - 对所有模型统一的API 1. 更低计算开销，更少的碳排放： - 研究人员可以分享已训练的模型而非每次从头开始训练 - 工程师可以减少计算用时和生产环境开销 - 数十种模型架构、两千多个预训练模型、100多种语言支持 1. 对于模型生命周期的每一个部分都面面俱到： - 训练先进的模型，只需 3 行代码 - 模型在不同深度学习框架间任意转移，随你心意 - 为训练、评估和生产选择最适合的框架，衔接无缝 1. 为你的需求轻松定制专属模型和用例： - 我们为每种模型架构提供了多个用例来复现原论文结果 - 模型内部结构保持透明一致 - 模型文件可单独使用，方便魔改和快速实验 ## 什么情况下我不该用 transformers？ - 本库并不是模块化的神经网络工具箱。模型文件中的代码特意呈若璞玉，未经额外抽象封装，以便研究人员快速迭代魔改而不致溺于抽象和文件跳转之中。 - `Trainer` API 并非兼容任何模型，只为本库之模型优化。若是在寻找适用于通用机器学习的训练循环实现，请另觅他库。 - 尽管我们已尽力而为，[examples 目录](https://github.com/huggingface/transformers/tree/main/examples)中的脚本也仅为用例而已。对于你的特定问题，它们并不一定开箱即用，可能需要改几行代码以适之。 ## 安装 ### 使用 pip 这个仓库已在 Python 3.8+、Flax 0.4.1+、PyTorch 1.11+ 和 TensorFlow 2.6+ 下经过测试。 你可以在[虚拟环境](https://docs.python.org/3/library/venv.html)中安装 🤗 Transformers。如果你还不熟悉 Python 的虚拟环境，请阅此[用户说明](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)。 首先，用你打算使用的版本的 Python 创建一个虚拟环境并激活。 然后，你需要安装 Flax、PyTorch 或 TensorFlow 其中之一。关于在你使用的平台上安装这些框架，请参阅 [TensorFlow 安装页](https://www.tensorflow.org/install/), [PyTorch 安装页](https://pytorch.org/get-started/locally/#start-locally) 或 [Flax 安装页](https://github.com/google/flax#quick-install)。 当这些后端之一安装成功后， 🤗 Transformers 可依此安装： ```bash pip install transformers ``` 如果你想要试试用例或者想在正式发布前使用最新的开发中代码，你得[从源代码安装](https://huggingface.co/docs/transformers/installation#installing-from-source)。 ### 使用 conda 🤗 Transformers 可以通过 conda 依此安装： ```shell script conda install conda-forge::transformers ``` > **_笔记:_** 从 `huggingface` 渠道安装 `transformers` 已被废弃。 要通过 conda 安装 Flax、PyTorch 或 TensorFlow 其中之一，请参阅它们各自安装页的说明。 ## 模型架构 🤗 Transformers 支持的[**所有的模型检查点**](https://huggingface.co/models)由[用户](https://huggingface.co/users)和[组织](https://huggingface.co/organizations)上传，均与 huggingface.co [model hub](https://huggingface.co) 无缝整合。 目前的检查点数量：  🤗 Transformers 目前支持如下的架构: 模型概述请阅[这里](https://huggingface.co/docs/transformers/model_summary). 要检查某个模型是否已有 Flax、PyTorch 或 TensorFlow 的实现，或其是否在 🤗 Tokenizers 库中有对应词符化器（tokenizer），敬请参阅[此表](https://huggingface.co/docs/transformers/index#supported-frameworks)。 这些实现均已于多个数据集测试（请参看用例脚本）并应于原版实现表现相当。你可以在用例文档的[此节](https://huggingface.co/docs/transformers/examples)中了解表现的细节。 ## 了解更多 | 章节 | 描述 | |-|-| | [文档](https://huggingface.co/docs/transformers/) | 完整的 API 文档和教程 | | [任务总结](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers 支持的任务 | | [预处理教程](https://huggingface.co/docs/transformers/preprocessing) | 使用 `Tokenizer` 来为模型准备数据 | | [训练和微调](https://huggingface.co/docs/transformers/training) | 在 PyTorch/TensorFlow 的训练循环或 `Trainer` API 中使用 🤗 Transformers 提供的模型 | | [快速上手：微调和用例脚本](https://github.com/huggingface/transformers/tree/main/examples) | 为各种任务提供的用例脚本 | | [模型分享和上传](https://huggingface.co/docs/transformers/model_sharing) | 和社区上传和分享你微调的模型 | | [迁移](https://huggingface.co/docs/transformers/migration) | 从 `pytorch-transformers` 或 `pytorch-pretrained-bert` 迁移到 🤗 Transformers | ## 引用 我们已将此库的[论文](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)正式发表，如果你使用了 🤗 Transformers 库，请引用: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

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mavonic_private_repos/transformers/pyproject.toml

[tool.ruff] line-length = 119 [tool.ruff.lint] # Never enforce `E501` (line length violations). ignore = ["C901", "E501", "E741", "F402", "F823" ] select = ["C", "E", "F", "I", "W"] # Ignore import violations in all `__init__.py` files. [tool.ruff.lint.per-file-ignores] "__init__.py" = ["E402", "F401", "F403", "F811"] "src/transformers/file_utils.py" = ["F401"] "src/transformers/utils/dummy_*.py" = ["F401"] [tool.ruff.lint.isort] lines-after-imports = 2 known-first-party = ["transformers"] [tool.ruff.format] # Like Black, use double quotes for strings. quote-style = "double" # Like Black, indent with spaces, rather than tabs. indent-style = "space" # Like Black, respect magic trailing commas. skip-magic-trailing-comma = false # Like Black, automatically detect the appropriate line ending. line-ending = "auto" [tool.pytest.ini_options] doctest_optionflags="NUMBER NORMALIZE_WHITESPACE ELLIPSIS" doctest_glob="**/*.md" markers = [ "flash_attn_test: marks tests related to flash attention (deselect with '-m \"not flash_attn_test\"')", "bitsandbytes: select (or deselect with `not`) bitsandbytes integration tests", ]

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mavonic_private_repos/transformers/README_fr.md

<!--- Copyright 2020 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. --> <p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg"> <img alt="Bibliothèque Hugging Face Transformers" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg" width="352" height="59" style="max-width: 100%;"> </picture> <br/> <br/> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Construction" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="Version GitHub" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Pacte des contributeurs" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <b>Français</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p>Apprentissage automatique de pointe pour JAX, PyTorch et TensorFlow</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 Transformers fournit des milliers de modèles pré-entraînés pour effectuer des tâches sur différentes modalités telles que le texte, la vision et l'audio. Ces modèles peuvent être appliqués à : * 📝 Texte, pour des tâches telles que la classification de texte, l'extraction d'informations, la réponse aux questions, le résumé, la traduction et la génération de texte, dans plus de 100 langues. * 🖼️ Images, pour des tâches telles que la classification d'images, la détection d'objets et la segmentation. * 🗣️ Audio, pour des tâches telles que la reconnaissance vocale et la classification audio. Les modèles de transformer peuvent également effectuer des tâches sur **plusieurs modalités combinées**, telles que la réponse aux questions sur des tableaux, la reconnaissance optique de caractères, l'extraction d'informations à partir de documents numérisés, la classification vidéo et la réponse aux questions visuelles. 🤗 Transformers fournit des API pour télécharger et utiliser rapidement ces modèles pré-entraînés sur un texte donné, les affiner sur vos propres ensembles de données, puis les partager avec la communauté sur notre [hub de modèles](https://huggingface.co/models). En même temps, chaque module Python définissant une architecture est complètement indépendant et peut être modifié pour permettre des expériences de recherche rapides. 🤗 Transformers est soutenu par les trois bibliothèques d'apprentissage profond les plus populaires — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) et [TensorFlow](https://www.tensorflow.org/) — avec une intégration transparente entre eux. Il est facile de former vos modèles avec l'un avant de les charger pour l'inférence avec l'autre. ## Démos en ligne Vous pouvez tester la plupart de nos modèles directement sur leurs pages du [hub de modèles](https://huggingface.co/models). Nous proposons également [l'hébergement privé de modèles, le versionning et une API d'inférence](https://huggingface.co/pricing) pour des modèles publics et privés. Voici quelques exemples : En traitement du langage naturel : - [Complétion de mots masqués avec BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [Reconnaissance d'entités nommées avec Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [Génération de texte avec GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+) - [Inférence de langage naturel avec RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [Résumé avec BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [Réponse aux questions avec DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [Traduction avec T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) En vision par ordinateur : - [Classification d'images avec ViT](https://huggingface.co/google/vit-base-patch16-224) - [Détection d'objets avec DETR](https://huggingface.co/facebook/detr-resnet-50) - [Segmentation sémantique avec SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) - [Segmentation panoptique avec MaskFormer](https://huggingface.co/facebook/maskformer-swin-small-coco) - [Estimation de profondeur avec DPT](https://huggingface.co/docs/transformers/model_doc/dpt) - [Classification vidéo avec VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae) - [Segmentation universelle avec OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) En audio : - [Reconnaissance automatique de la parole avec Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h) - [Spotting de mots-clés avec Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) - [Classification audio avec Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) Dans les tâches multimodales : - [Réponses aux questions sur table avec TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq) - [Réponses aux questions visuelles avec ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) - [Classification d'images sans étiquette avec CLIP](https://huggingface.co/openai/clip-vit-large-patch14) - [Réponses aux questions sur les documents avec LayoutLM](https://huggingface.co/impira/layoutlm-document-qa) - [Classification vidéo sans étiquette avec X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip) ## 100 projets utilisant Transformers Transformers est plus qu'une boîte à outils pour utiliser des modèles pré-entraînés : c'est une communauté de projets construits autour de lui et du Hub Hugging Face. Nous voulons que Transformers permette aux développeurs, chercheurs, étudiants, professeurs, ingénieurs et à quiconque d'imaginer et de réaliser leurs projets de rêve. Afin de célébrer les 100 000 étoiles de transformers, nous avons décidé de mettre en avant la communauté et avons créé la page [awesome-transformers](./awesome-transformers.md) qui répertorie 100 projets incroyables construits autour de transformers. Si vous possédez ou utilisez un projet que vous pensez devoir figurer dans la liste, veuillez ouvrir une pull request pour l'ajouter ! ## Si vous recherchez un support personnalisé de la part de l'équipe Hugging Face <a target="_blank" href="https://huggingface.co/support"> <img alt="Programme d'accélération des experts HuggingFace" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## Tour rapide Pour utiliser immédiatement un modèle sur une entrée donnée (texte, image, audio,...), nous fournissons l'API `pipeline`. Les pipelines regroupent un modèle pré-entraîné avec la préparation des données qui a été utilisée lors de l'entraînement de ce modèle. Voici comment utiliser rapidement un pipeline pour classer des textes en positif ou négatif : ```python >>> from transformers import pipeline # Allouer un pipeline pour l'analyse de sentiment >>> classifieur = pipeline('sentiment-analysis') >>> classifieur("Nous sommes très heureux d'introduire le pipeline dans le référentiel transformers.") [{'label': 'POSITIF', 'score': 0.9996980428695679}] ``` La deuxième ligne de code télécharge et met en cache le modèle pré-entraîné utilisé par le pipeline, tandis que la troisième l'évalue sur le texte donné. Ici, la réponse est "positive" avec une confiance de 99,97%. De nombreuses tâches ont une pipeline pré-entraîné prêt à l'emploi, en NLP, mais aussi en vision par ordinateur et en parole. Par exemple, nous pouvons facilement extraire les objets détectés dans une image : ```python >>> import requests >>> from PIL import Image >>> from transformers import pipeline # Télécharger une image avec de jolis chats >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> donnees_image = requests.get(url, stream=True).raw >>> image = Image.open(donnees_image) # Allouer un pipeline pour la détection d'objets >>> detecteur_objets = pipeline('object-detection') >>> detecteur_objets(image) [{'score': 0.9982201457023621, 'label': 'télécommande', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, {'score': 0.9960021376609802, 'label': 'télécommande', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, {'score': 0.9954745173454285, 'label': 'canapé', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, {'score': 0.9988006353378296, 'label': 'chat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, {'score': 0.9986783862113953, 'label': 'chat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] ``` Ici, nous obtenons une liste d'objets détectés dans l'image, avec une boîte entourant l'objet et un score de confiance. Voici l'image originale à gauche, avec les prédictions affichées à droite : <h3 align="center"> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a> </h3> Vous pouvez en savoir plus sur les tâches supportées par l'API pipeline dans [ce tutoriel](https://huggingface.co/docs/transformers/task_summary). En plus de `pipeline`, pour télécharger et utiliser n'importe lequel des modèles pré-entraînés sur votre tâche donnée, il suffit de trois lignes de code. Voici la version PyTorch : ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") inputs = tokenizer("Bonjour le monde !", return_tensors="pt") outputs = model(**inputs) ``` Et voici le code équivalent pour TensorFlow : ```python from transformers import AutoTokenizer, TFAutoModel tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") inputs = tokenizer("Bonjour le monde !", return_tensors="tf") outputs = model(**inputs) ``` Le tokenizer est responsable de toutes les étapes de prétraitement que le modèle préentraîné attend et peut être appelé directement sur une seule chaîne de caractères (comme dans les exemples ci-dessus) ou sur une liste. Il produira un dictionnaire que vous pouvez utiliser dans votre code ou simplement passer directement à votre modèle en utilisant l'opérateur de déballage **. Le modèle lui-même est un module [`nn.Module` PyTorch](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) ou un modèle [`tf.keras.Model` TensorFlow](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (selon votre backend) que vous pouvez utiliser comme d'habitude. [Ce tutoriel](https://huggingface.co/docs/transformers/training) explique comment intégrer un tel modèle dans une boucle d'entraînement classique PyTorch ou TensorFlow, ou comment utiliser notre API `Trainer` pour affiner rapidement sur un nouvel ensemble de données. ## Pourquoi devrais-je utiliser transformers ? 1. Des modèles de pointe faciles à utiliser : - Hautes performances en compréhension et génération de langage naturel, en vision par ordinateur et en tâches audio. - Faible barrière à l'entrée pour les éducateurs et les praticiens. - Peu d'abstractions visibles pour l'utilisateur avec seulement trois classes à apprendre. - Une API unifiée pour utiliser tous nos modèles préentraînés. 1. Coûts informatiques réduits, empreinte carbone plus petite : - Les chercheurs peuvent partager des modèles entraînés au lieu de toujours les réentraîner. - Les praticiens peuvent réduire le temps de calcul et les coûts de production. - Des dizaines d'architectures avec plus de 400 000 modèles préentraînés dans toutes les modalités. 1. Choisissez le bon framework pour chaque partie de la vie d'un modèle : - Entraînez des modèles de pointe en 3 lignes de code. - Trasnférer un seul modèle entre les frameworks TF2.0/PyTorch/JAX à volonté. - Choisissez facilement le bon framework pour l'entraînement, l'évaluation et la production. 1. Personnalisez facilement un modèle ou un exemple selon vos besoins : - Nous fournissons des exemples pour chaque architecture afin de reproduire les résultats publiés par ses auteurs originaux. - Les détails internes du modèle sont exposés de manière aussi cohérente que possible. - Les fichiers de modèle peuvent être utilisés indépendamment de la bibliothèque pour des expériences rapides. ## Pourquoi ne devrais-je pas utiliser transformers ? - Cette bibliothèque n'est pas une boîte à outils modulaire de blocs de construction pour les réseaux neuronaux. Le code dans les fichiers de modèle n'est pas refactored avec des abstractions supplémentaires à dessein, afin que les chercheurs puissent itérer rapidement sur chacun des modèles sans plonger dans des abstractions/fichiers supplémentaires. - L'API d'entraînement n'est pas destinée à fonctionner avec n'importe quel modèle, mais elle est optimisée pour fonctionner avec les modèles fournis par la bibliothèque. Pour des boucles génériques d'apprentissage automatique, vous devriez utiliser une autre bibliothèque (éventuellement, [Accelerate](https://huggingface.co/docs/accelerate)). - Bien que nous nous efforcions de présenter autant de cas d'utilisation que possible, les scripts de notre [dossier d'exemples](https://github.com/huggingface/transformers/tree/main/examples) ne sont que cela : des exemples. Il est prévu qu'ils ne fonctionnent pas immédiatement sur votre problème spécifique et que vous devrez probablement modifier quelques lignes de code pour les adapter à vos besoins. ## Installation ### Avec pip Ce référentiel est testé sur Python 3.8+, Flax 0.4.1+, PyTorch 1.11+ et TensorFlow 2.6+. Vous devriez installer 🤗 Transformers dans un [environnement virtuel](https://docs.python.org/3/library/venv.html). Si vous n'êtes pas familier avec les environnements virtuels Python, consultez le [guide utilisateur](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). D'abord, créez un environnement virtuel avec la version de Python que vous allez utiliser et activez-le. Ensuite, vous devrez installer au moins l'un de Flax, PyTorch ou TensorFlow. Veuillez vous référer à la page d'installation de [TensorFlow](https://www.tensorflow.org/install/), de [PyTorch](https://pytorch.org/get-started/locally/#start-locally) et/ou de [Flax](https://github.com/google/flax#quick-install) et [Jax](https://github.com/google/jax#installation) pour connaître la commande d'installation spécifique à votre plateforme. Lorsqu'un de ces backends est installé, 🤗 Transformers peut être installé avec pip comme suit : ```bash pip install transformers ``` Si vous souhaitez jouer avec les exemples ou avez besoin de la dernière version du code et ne pouvez pas attendre une nouvelle version, vous devez [installer la bibliothèque à partir de la source](https://huggingface.co/docs/transformers/installation#installing-from-source). ### Avec conda 🤗 Transformers peut être installé avec conda comme suit : ```shell conda install conda-forge::transformers ``` > **_NOTE:_** L'installation de `transformers` depuis le canal `huggingface` est obsolète. Suivez les pages d'installation de Flax, PyTorch ou TensorFlow pour voir comment les installer avec conda. > **_NOTE:_** Sur Windows, on peut vous demander d'activer le mode développeur pour bénéficier de la mise en cache. Si ce n'est pas une option pour vous, veuillez nous le faire savoir dans [cette issue](https://github.com/huggingface/huggingface_hub/issues/1062). ## Architectures de modèles **[Tous les points de contrôle](https://huggingface.co/models)** de modèle fournis par 🤗 Transformers sont intégrés de manière transparente depuis le [hub de modèles](https://huggingface.co/models) huggingface.co, où ils sont téléchargés directement par les [utilisateurs](https://huggingface.co/users) et les [organisations](https://huggingface.co/organizations). Nombre actuel de points de contrôle :  🤗 Transformers fournit actuellement les architectures suivantes: consultez [ici](https://huggingface.co/docs/transformers/model_summary) pour un résumé global de chacune d'entre elles. Pour vérifier si chaque modèle a une implémentation en Flax, PyTorch ou TensorFlow, ou s'il a un tokenizer associé pris en charge par la bibliothèque 🤗 Tokenizers, consultez [ce tableau](https://huggingface.co/docs/transformers/index#supported-frameworks). Ces implémentations ont été testées sur plusieurs ensembles de données (voir les scripts d'exemple) et devraient correspondre aux performances des implémentations originales. Vous pouvez trouver plus de détails sur les performances dans la section Exemples de la [documentation](https://github.com/huggingface/transformers/tree/main/examples). ## En savoir plus | Section | Description | |-|-| | [Documentation](https://huggingface.co/docs/transformers/) | Documentation complète de l'API et tutoriels | | [Résumé des tâches](https://huggingface.co/docs/transformers/task_summary) | Tâches prises en charge par les 🤗 Transformers | | [Tutoriel de prétraitement](https://huggingface.co/docs/transformers/preprocessing) | Utilisation de la classe `Tokenizer` pour préparer les données pour les modèles | | [Entraînement et ajustement fin](https://huggingface.co/docs/transformers/training) | Utilisation des modèles fournis par les 🤗 Transformers dans une boucle d'entraînement PyTorch/TensorFlow et de l'API `Trainer` | | [Tour rapide : Scripts d'ajustement fin/d'utilisation](https://github.com/huggingface/transformers/tree/main/examples) | Scripts d'exemple pour ajuster finement les modèles sur une large gamme de tâches | | [Partage et téléversement de modèles](https://huggingface.co/docs/transformers/model_sharing) | Téléchargez et partagez vos modèles ajustés avec la communauté | ## Citation Nous disposons désormais d'un [article](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) que vous pouvez citer pour la bibliothèque 🤗 Transformers : ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos

mavonic_private_repos/transformers/setup.py

# 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 your the commit you will make for the post-release and run `make fix-copies` 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-copies`. 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. Build both the sources and the wheel. Do not change anything in setup.py between creating the wheel and the source distribution (obviously). Run `make build-release`. This will build the release and do some sanity checks for you. If this ends with an error message, you need to fix things before going further. You should now have a /dist directory with both .whl and .tar.gz source versions. 7. Check that everything looks correct by uploading the package to the pypi test server: twine upload dist/* -r testpypi (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 testpypi --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 *" python utils/check_build.py --check_lib If making a patch release, double check the bug you are patching is indeed resolved. 8. Upload the final version to actual pypi: twine upload dist/* -r pypi 9. Copy the release notes from RELEASE.md to the tag in github once everything is looking hunky-dory. """ import os 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( ( "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>=10.0.1,<=15.0", "accelerate>=0.21.0", "av==9.2.0", # Latest version of PyAV (10.0.0) has issues with audio stream. "beautifulsoup4", "codecarbon==1.2.0", "cookiecutter==1.7.3", "dataclasses", "datasets!=2.5.0", "decord==0.6.0", "deepspeed>=0.9.3", "diffusers", "dill<0.3.5", "evaluate>=0.2.0", "faiss-cpu", "fastapi", "filelock", "flax>=0.4.1,<=0.7.0", "fsspec<2023.10.0", "ftfy", "fugashi>=1.0", "GitPython<3.1.19", "huggingface-hub>=0.19.3,<1.0", "importlib_metadata", "ipadic>=1.0.0,<2.0", "isort>=5.5.4", "jax>=0.4.1,<=0.4.13", "jaxlib>=0.4.1,<=0.4.13", "jieba", "kenlm", # Keras pin - this is to make sure Keras 3 doesn't destroy us. Remove or change when we have proper support. "keras>2.9,<2.16", "keras-nlp>=0.3.1", "librosa", "nltk", "natten>=0.14.6,<0.15.0", "numpy>=1.17", "onnxconverter-common", "onnxruntime-tools>=1.4.2", "onnxruntime>=1.4.0", "opencv-python", "optuna", "optax>=0.0.8,<=0.1.4", "packaging>=20.0", "parameterized", "phonemizer", "protobuf", "psutil", "pyyaml>=5.1", "pydantic", "pytest>=7.2.0,<8.0.0", "pytest-timeout", "pytest-xdist", "python>=3.8.0", "ray[tune]>=2.7.0", "regex!=2019.12.17", "requests", "rhoknp>=1.1.0,<1.3.1", "rjieba", "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1", "ruff==0.1.5", "sacrebleu>=1.4.12,<2.0.0", "sacremoses", "safetensors>=0.4.1", "sagemaker>=2.31.0", "scikit-learn", "scipy<1.13.0", # SciPy >= 1.13.0 is not supported with the current jax pin (`jax>=0.4.1,<=0.4.13`) "sentencepiece>=0.1.91,!=0.1.92", "sigopt", "starlette", "sudachipy>=0.6.6", "sudachidict_core>=20220729", "tensorboard", # TensorFlow pin. When changing this value, update examples/tensorflow/_tests_requirements.txt accordingly "tensorflow-cpu>2.9,<2.16", "tensorflow>2.9,<2.16", "tensorflow-text<2.16", "tensorflow-probability<2.16", "tf2onnx", "timeout-decorator", "timm", "tokenizers>=0.19,<0.20", "torch", "torchaudio", "torchvision", "pyctcdecode>=0.4.0", "tqdm>=4.27", "unidic>=1.0.2", "unidic_lite>=1.0.7", "urllib3<2.0.0", "uvicorn", "pytest-rich", ] # 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", "rhoknp") extras["sklearn"] = deps_list("scikit-learn") extras["tf"] = deps_list("tensorflow", "onnxconverter-common", "tf2onnx", "tensorflow-text", "keras-nlp") extras["tf-cpu"] = deps_list("keras", "tensorflow-cpu", "onnxconverter-common", "tf2onnx", "tensorflow-text", "keras-nlp", "tensorflow-probability") extras["torch"] = deps_list("torch", "accelerate") 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", "scipy") 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["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["torch-vision"] = deps_list("torchvision") + extras["vision"] extras["natten"] = deps_list("natten") extras["codecarbon"] = deps_list("codecarbon") extras["video"] = deps_list("decord", "av") extras["sentencepiece"] = deps_list("sentencepiece", "protobuf") extras["testing"] = ( deps_list( "pytest", "pytest-rich", "pytest-xdist", "timeout-decorator", "parameterized", "psutil", "datasets", "dill", "evaluate", "pytest-timeout", "ruff", "sacrebleu", "rouge-score", "nltk", "GitPython", "sacremoses", "rjieba", "beautifulsoup4", "tensorboard", "pydantic", "sentencepiece", ) + extras["retrieval"] + extras["modelcreation"] ) extras["deepspeed-testing"] = extras["deepspeed"] + extras["testing"] + extras["optuna"] + extras["sentencepiece"] extras["quality"] = deps_list("datasets", "isort", "ruff", "GitPython", "urllib3") extras["all"] = ( extras["tf"] + extras["torch"] + extras["flax"] + extras["sentencepiece"] + extras["tokenizers"] + extras["torch-speech"] + extras["vision"] + extras["integrations"] + extras["timm"] + extras["torch-vision"] + extras["codecarbon"] + extras["accelerate"] + extras["video"] ) 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["onnxruntime"] ) extras["dev-tensorflow"] = ( extras["testing"] + extras["tf"] + extras["sentencepiece"] + extras["tokenizers"] + extras["vision"] + extras["quality"] + extras["sklearn"] + extras["modelcreation"] + extras["onnx"] + extras["tf-speech"] ) 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", "requests", "sentencepiece", "torch", "tokenizers", "tqdm", ) extras["agents"] = deps_list( "diffusers", "accelerate", "datasets", "torch", "sentencepiece", "opencv-python", "Pillow" ) # 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["requests"], # for downloading models over HTTPS deps["tokenizers"], deps["safetensors"], deps["tqdm"], # progress bars in model download and training scripts ] setup( name="transformers", version="4.41.0.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="[email protected]", 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 jax BERT GPT-2 Wav2Vec2 ViT", 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"]}, zip_safe=False, extras_require=extras, entry_points={"console_scripts": ["transformers-cli=transformers.commands.transformers_cli:main"]}, python_requires=">=3.8.0", install_requires=list(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 :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Topic :: Scientific/Engineering :: Artificial Intelligence", ], cmdclass={"deps_table_update": DepsTableUpdateCommand}, ) extras["tests_torch"] = deps_list() extras["tests_tf"] = deps_list() extras["tests_flax"] = deps_list() extras["tests_torch_and_tf"] = deps_list() extras["tests_torch_and_flax"] = deps_list() extras["tests_hub"] = deps_list() extras["tests_pipelines_torch"] = deps_list() extras["tests_pipelines_tf"] = deps_list() extras["tests_onnx"] = deps_list() extras["tests_examples_torch"] = deps_list() extras["tests_examples_tf"] = deps_list() extras["tests_custom_tokenizers"] = deps_list() extras["tests_exotic_models"] = deps_list() extras["consistency"] = deps_list()

mavonic_private_repos

mavonic_private_repos/transformers/README.md

<!--- Copyright 2020 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. --> <p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg"> <img alt="Hugging Face Transformers Library" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg" width="352" height="59" style="max-width: 100%;"> </picture> <br/> <br/> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <b>English</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_de.md">Deutsch</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p>State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. These models can be applied on: * 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages. * 🖼️ Images, for tasks like image classification, object detection, and segmentation. * 🗣️ Audio, for tasks like speech recognition and audio classification. Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering. 🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments. 🤗 Transformers is backed by the three most popular deep learning libraries — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other. ## Online demos You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) for public and private models. Here are a few examples: In Natural Language Processing: - [Masked word completion with BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [Named Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [Text generation with Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - [Natural Language Inference with RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [Summarization with BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [Question answering with DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [Translation with T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) In Computer Vision: - [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224) - [Object Detection with DETR](https://huggingface.co/facebook/detr-resnet-50) - [Semantic Segmentation with SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) - [Panoptic Segmentation with Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic) - [Depth Estimation with Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything) - [Video Classification with VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae) - [Universal Segmentation with OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) In Audio: - [Automatic Speech Recognition with Whisper](https://huggingface.co/openai/whisper-large-v3) - [Keyword Spotting with Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) - [Audio Classification with Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) In Multimodal tasks: - [Table Question Answering with TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq) - [Visual Question Answering with ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) - [Image captioning with LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf) - [Zero-shot Image Classification with SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) - [Document Question Answering with LayoutLM](https://huggingface.co/impira/layoutlm-document-qa) - [Zero-shot Video Classification with X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip) - [Zero-shot Object Detection with OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2) - [Zero-shot Image Segmentation with CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg) - [Automatic Mask Generation with SAM](https://huggingface.co/docs/transformers/model_doc/sam) ## 100 projects using Transformers Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone else to build their dream projects. In order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on the community, and we have created the [awesome-transformers](./awesome-transformers.md) page which lists 100 incredible projects built in the vicinity of transformers. If you own or use a project that you believe should be part of the list, please open a PR to add it! ## If you are looking for custom support from the Hugging Face team <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## Quick tour To immediately use a model on a given input (text, image, audio, ...), we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts: ```python >>> from transformers import pipeline # Allocate a pipeline for sentiment-analysis >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` The second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here, the answer is "positive" with a confidence of 99.97%. Many tasks have a pre-trained `pipeline` ready to go, in NLP but also in computer vision and speech. For example, we can easily extract detected objects in an image: ``` python >>> import requests >>> from PIL import Image >>> from transformers import pipeline # Download an image with cute cats >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> image = Image.open(image_data) # Allocate a pipeline for object detection >>> object_detector = pipeline('object-detection') >>> object_detector(image) [{'score': 0.9982201457023621, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, {'score': 0.9960021376609802, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, {'score': 0.9954745173454285, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, {'score': 0.9988006353378296, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, {'score': 0.9986783862113953, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] ``` Here, we get a list of objects detected in the image, with a box surrounding the object and a confidence score. Here is the original image on the left, with the predictions displayed on the right: <h3 align="center"> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a> </h3> You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/docs/transformers/task_summary). In addition to `pipeline`, to download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version: ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` And here is the equivalent code for TensorFlow: ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` The tokenizer is responsible for all the preprocessing the pretrained model expects and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator. The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use as usual. [This tutorial](https://huggingface.co/docs/transformers/training) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset. ## Why should I use transformers? 1. Easy-to-use state-of-the-art models: - High performance on natural language understanding & generation, computer vision, and audio tasks. - Low barrier to entry for educators and practitioners. - Few user-facing abstractions with just three classes to learn. - A unified API for using all our pretrained models. 1. Lower compute costs, smaller carbon footprint: - Researchers can share trained models instead of always retraining. - Practitioners can reduce compute time and production costs. - Dozens of architectures with over 400,000 pretrained models across all modalities. 1. Choose the right framework for every part of a model's lifetime: - Train state-of-the-art models in 3 lines of code. - Move a single model between TF2.0/PyTorch/JAX frameworks at will. - Seamlessly pick the right framework for training, evaluation, and production. 1. Easily customize a model or an example to your needs: - We provide examples for each architecture to reproduce the results published by its original authors. - Model internals are exposed as consistently as possible. - Model files can be used independently of the library for quick experiments. ## Why shouldn't I use transformers? - This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files. - The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library (possibly, [Accelerate](https://huggingface.co/docs/accelerate)). - While we strive to present as many use cases as possible, the scripts in our [examples folder](https://github.com/huggingface/transformers/tree/main/examples) are just that: examples. It is expected that they won't work out-of-the-box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. ## Installation ### With pip This repository is tested on Python 3.8+, Flax 0.4.1+, PyTorch 1.11+, and TensorFlow 2.6+. You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). First, create a virtual environment with the version of Python you're going to use and activate it. Then, you will need to install at least one of Flax, PyTorch, or TensorFlow. Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) installation pages regarding the specific installation command for your platform. When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows: ```bash pip install transformers ``` If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source). ### With conda 🤗 Transformers can be installed using conda as follows: ```shell script conda install conda-forge::transformers ``` > **_NOTE:_** Installing `transformers` from the `huggingface` channel is deprecated. Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda. > **_NOTE:_** On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062). ## Model architectures **[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co/models), where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations). Current number of checkpoints:  🤗 Transformers currently provides the following architectures: see [here](https://huggingface.co/docs/transformers/model_summary) for a high-level summary of each them. To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/docs/transformers/index#supported-frameworks). These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://github.com/huggingface/transformers/tree/main/examples). ## Learn more | Section | Description | |-|-| | [Documentation](https://huggingface.co/docs/transformers/) | Full API documentation and tutorials | | [Task summary](https://huggingface.co/docs/transformers/task_summary) | Tasks supported by 🤗 Transformers | | [Preprocessing tutorial](https://huggingface.co/docs/transformers/preprocessing) | Using the `Tokenizer` class to prepare data for the models | | [Training and fine-tuning](https://huggingface.co/docs/transformers/training) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API | | [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/main/examples) | Example scripts for fine-tuning models on a wide range of tasks | | [Model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing) | Upload and share your fine-tuned models with the community | ## Citation We now have a [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) you can cite for the 🤗 Transformers library: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos

mavonic_private_repos/transformers/README_de.md

<!--- Copyright 2024 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. --> <p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg"> <img alt="Hugging Face Transformers Library" src="https://huggingface.co/datasets/huggingface/documentation-images/raw/main/transformers-logo-light.svg" width="352" height="59" style="max-width: 100%;"> </picture> <br/> <br/> </p> <p align="center"> <a href="https://circleci.com/gh/huggingface/transformers"> <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"> </a> <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"> </a> <a href="https://huggingface.co/docs/transformers/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/transformers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg"> </a> <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"> </a> <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a> </p> <h4 align="center"> <p> <a href="https://github.com/huggingface/transformers/">English</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ja.md">日本語</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_hd.md">हिन्दी</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_ru.md">Русский</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_pt-br.md">Рortuguês</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_te.md">తెలుగు</a> | <a href="https://github.com/huggingface/transformers/blob/main/README_fr.md">Français</a> | <b>Deutsch</b> | <a href="https://github.com/huggingface/transformers/blob/main/README_vi.md">Tiếng Việt</a> | </p> </h4> <h3 align="center"> <p>Maschinelles Lernen auf dem neuesten Stand der Technik für JAX, PyTorch und TensorFlow</p> </h3> <h3 align="center"> <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a> </h3> 🤗 Transformers bietet Tausende von vortrainierten Modellen, um Aufgaben in verschiedenen Modalitäten wie Text, Bild und Audio durchzuführen. Diese Modelle können angewendet werden, auf: * 📝 Text - für Aufgaben wie Textklassifizierung, Informationsextraktion, Question Answering, automatische Textzusammenfassung, maschinelle Übersetzung und Textgenerierung in über 100 Sprachen. * 🖼️ Bilder - für Aufgaben wie Bildklassifizierung, Objekterkennung und Segmentierung. * 🗣️ Audio - für Aufgaben wie Spracherkennung und Audioklassifizierung. Transformer-Modelle können auch Aufgaben für **mehrere Modalitäten in Kombination** durchführen, z. B. tabellenbasiertes Question Answering, optische Zeichenerkennung, Informationsextraktion aus gescannten Dokumenten, Videoklassifizierung und visuelles Question Answering. 🤗 Transformers bietet APIs, um diese vortrainierten Modelle schnell herunterzuladen und für einen gegebenen Text zu verwenden, sie auf Ihren eigenen Datensätzen zu feintunen und dann mit der Community in unserem [Model Hub](https://huggingface.co/models) zu teilen. Gleichzeitig ist jedes Python-Modul, das eine Architektur definiert, komplett eigenständig und kann modifiziert werden, um schnelle Forschungsexperimente zu ermöglichen. 🤗 Transformers unterstützt die nahtlose Integration von drei der beliebtesten Deep-Learning-Bibliotheken: [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) und [TensorFlow](https://www.tensorflow.org/). Trainieren Sie Ihr Modell in einem Framework und laden Sie es zur Inferenz unkompliziert mit einem anderen. ## Online-Demos Sie können die meisten unserer Modelle direkt auf ihren Seiten im [Model Hub](https://huggingface.co/models) testen. Wir bieten auch [privates Modell-Hosting, Versionierung, & eine Inferenz-API](https://huggingface.co/pricing) für öffentliche und private Modelle an. Hier sind einige Beispiele: In der Computerlinguistik: - [Maskierte Wortvervollständigung mit BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) - [Eigennamenerkennung mit Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) - [Textgenerierung mit GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+) - [Natural Language Inference mit RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) - [Automatische Textzusammenfassung mit BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) - [Question Answering mit DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) - [Maschinelle Übersetzung mit T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) In der Computer Vision: - [Bildklassifizierung mit ViT](https://huggingface.co/google/vit-base-patch16-224) - [Objekterkennung mit DETR](https://huggingface.co/facebook/detr-resnet-50) - [Semantische Segmentierung mit SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) - [Panoptische Segmentierung mit MaskFormer](https://huggingface.co/facebook/maskformer-swin-small-coco) - [Depth Estimation mit DPT](https://huggingface.co/docs/transformers/model_doc/dpt) - [Videoklassifizierung mit VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae) - [Universelle Segmentierung mit OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) Im Audio-Bereich: - [Automatische Spracherkennung mit Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h) - [Keyword Spotting mit Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) - [Audioklassifizierung mit Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) In multimodalen Aufgaben: - [Tabellenbasiertes Question Answering mit TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq) - [Visuelles Question Answering mit ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) - [Zero-Shot-Bildklassifizierung mit CLIP](https://huggingface.co/openai/clip-vit-large-patch14) - [Dokumentenbasiertes Question Answering mit LayoutLM](https://huggingface.co/impira/layoutlm-document-qa) - [Zero-Shot-Videoklassifizierung mit X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip) ## 100 Projekte, die 🤗 Transformers verwenden 🤗 Transformers ist mehr als nur ein Toolkit zur Verwendung von vortrainierten Modellen: Es ist eine Gemeinschaft von Projekten, die darum herum und um den Hugging Face Hub aufgebaut sind. Wir möchten, dass 🤗 Transformers es Entwicklern, Forschern, Studenten, Professoren, Ingenieuren und jedem anderen ermöglicht, ihre Traumprojekte zu realisieren. Um die 100.000 Sterne von 🤗 Transformers zu feiern, haben wir beschlossen, die Gemeinschaft in den Mittelpunkt zu stellen und die Seite [awesome-transformers](./awesome-transformers.md) erstellt, die 100 unglaubliche Projekte auflistet, die zusammen mit 🤗 Transformers realisiert wurden. Wenn Sie ein Projekt besitzen oder nutzen, von dem Sie glauben, dass es Teil der Liste sein sollte, öffnen Sie bitte einen PR, um es hinzuzufügen! ## Wenn Sie individuelle Unterstützung vom Hugging Face-Team möchten <a target="_blank" href="https://huggingface.co/support"> <img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"> </a><br> ## Schnelleinstieg Um sofort ein Modell mit einer bestimmten Eingabe (Text, Bild, Audio ...) zu verwenden, bieten wir die `pipeline`-API an. Pipelines kombinieren ein vortrainiertes Modell mit der jeweiligen Vorverarbeitung, die während dessen Trainings verwendet wurde. Hier sehen Sie, wie man schnell eine Pipeline verwenden kann, um positive und negative Texte zu klassifizieren: ```python >>> from transformers import pipeline # Zuweisung einer Pipeline für die Sentiment-Analyse >>> classifier = pipeline('sentiment-analysis') >>> classifier('We are very happy to introduce pipeline to the transformers repository.') [{'label': 'POSITIVE', 'score': 0.9996980428695679}] ``` Die zweite Codezeile lädt und cacht das vortrainierte Modell, das von der Pipeline verwendet wird, während die dritte es an dem gegebenen Text evaluiert. Hier ist die Antwort "positiv" mit einer Konfidenz von 99,97 %. Viele Aufgaben, sowohl in der Computerlinguistik als auch in der Computer Vision und Sprachverarbeitung, haben eine vortrainierte `pipeline`, die sofort einsatzbereit ist. Z. B. können wir leicht erkannte Objekte in einem Bild extrahieren: ``` python >>> import requests >>> from PIL import Image >>> from transformers import pipeline # Download eines Bildes mit süßen Katzen >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" >>> image_data = requests.get(url, stream=True).raw >>> image = Image.open(image_data) # Zuweisung einer Pipeline für die Objekterkennung >>> object_detector = pipeline('object-detection') >>> object_detector(image) [{'score': 0.9982201457023621, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, {'score': 0.9960021376609802, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, {'score': 0.9954745173454285, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, {'score': 0.9988006353378296, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, {'score': 0.9986783862113953, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] ``` Hier erhalten wir eine Liste von Objekten, die im Bild erkannt wurden, mit einer Markierung, die das Objekt eingrenzt, und einem zugehörigen Konfidenzwert. Folgend ist das Originalbild links und die Vorhersagen rechts dargestellt: <h3 align="center"> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a> <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a> </h3> Sie können mehr über die von der `pipeline`-API unterstützten Aufgaben in [diesem Tutorial](https://huggingface.co/docs/transformers/task_summary) erfahren. Zusätzlich zur `pipeline` benötigt es nur drei Zeilen Code, um eines der vortrainierten Modelle für Ihre Aufgabe herunterzuladen und zu verwenden. Hier ist der Code für die PyTorch-Version: ```python >>> from transformers import AutoTokenizer, AutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="pt") >>> outputs = model(**inputs) ``` Und hier ist der entsprechende Code für TensorFlow: ```python >>> from transformers import AutoTokenizer, TFAutoModel >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello world!", return_tensors="tf") >>> outputs = model(**inputs) ``` Der Tokenizer ist für die gesamte Vorverarbeitung, die das vortrainierte Modell benötigt, verantwortlich und kann direkt auf einem einzelnen String (wie in den obigen Beispielen) oder einer Liste ausgeführt werden. Er gibt ein Dictionary aus, das Sie im darauffolgenden Code verwenden oder einfach direkt Ihrem Modell übergeben können, indem Sie den ** Operator zum Entpacken von Argumenten einsetzen. Das Modell selbst ist ein reguläres [PyTorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) oder ein [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (abhängig von Ihrem Backend), das Sie wie gewohnt verwenden können. [Dieses Tutorial](https://huggingface.co/docs/transformers/training) erklärt, wie man ein solches Modell in eine klassische PyTorch- oder TensorFlow-Trainingsschleife integrieren kann oder wie man unsere `Trainer`-API verwendet, um es schnell auf einem neuen Datensatz zu feintunen. ## Warum sollten Sie 🤗 Transformers verwenden? 1. Benutzerfreundliche Modelle auf dem neuesten Stand der Technik: - Hohe Leistung bei Aufgaben zu Natural Language Understanding & Generation, Computer Vision und Audio. - Niedrige Einstiegshürde für Bildungskräfte und Praktiker. - Wenige benutzerseitige Abstraktionen mit nur drei zu lernenden Klassen. - Eine einheitliche API für die Verwendung aller unserer vortrainierten Modelle. 1. Geringere Rechenkosten, kleinerer CO₂-Fußabdruck: - Forscher können trainierte Modelle teilen, anstatt sie immer wieder neu zu trainieren. - Praktiker können die Rechenzeit und Produktionskosten reduzieren. - Dutzende Architekturen mit über 400.000 vortrainierten Modellen über alle Modalitäten hinweg. 1. Wählen Sie das richtige Framework für jeden Lebensabschnitt eines Modells: - Trainieren Sie Modelle auf neustem Stand der Technik in nur drei Codezeilen. - Verwenden Sie ein einzelnes Modell nach Belieben mit TF2.0-/PyTorch-/JAX-Frameworks. - Wählen Sie nahtlos das richtige Framework für Training, Evaluation und Produktiveinsatz. 1. Passen Sie ein Modell oder Beispiel leicht an Ihre Bedürfnisse an: - Wir bieten Beispiele für jede Architektur an, um die von ihren ursprünglichen Autoren veröffentlichten Ergebnisse zu reproduzieren. - Modellinterna sind so einheitlich wie möglich verfügbar gemacht. - Modelldateien können unabhängig von der Bibliothek für schnelle Experimente verwendet werden. ## Warum sollten Sie 🤗 Transformers nicht verwenden? - Diese Bibliothek ist kein modularer Werkzeugkasten mit Bausteinen für neuronale Netze. Der Code in den Modelldateien ist absichtlich nicht mit zusätzlichen Abstraktionen refaktorisiert, sodass Forscher schnell mit jedem der Modelle iterieren können, ohne sich in zusätzliche Abstraktionen/Dateien vertiefen zu müssen. - Die Trainings-API ist nicht dafür gedacht, mit beliebigen Modellen zu funktionieren, sondern ist für die Verwendung mit den von der Bibliothek bereitgestellten Modellen optimiert. Für generische Trainingsschleifen von maschinellem Lernen sollten Sie eine andere Bibliothek verwenden (möglicherweise [Accelerate](https://huggingface.co/docs/accelerate)). - Auch wenn wir bestrebt sind, so viele Anwendungsfälle wie möglich zu veranschaulichen, sind die Beispielskripte in unserem [`examples`](./examples) Ordner genau das: Beispiele. Es ist davon auszugehen, dass sie nicht sofort auf Ihr spezielles Problem anwendbar sind und einige Codezeilen geändert werden müssen, um sie für Ihre Bedürfnisse anzupassen. ## Installation ### Mit pip Dieses Repository wurde mit Python 3.8+, Flax 0.4.1+, PyTorch 1.11+ und TensorFlow 2.6+ getestet. Sie sollten 🤗 Transformers in einer [virtuellen Umgebung](https://docs.python.org/3/library/venv.html) installieren. Wenn Sie mit virtuellen Python-Umgebungen nicht vertraut sind, schauen Sie sich den [Benutzerleitfaden](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) an. Erstellen und aktivieren Sie zuerst eine virtuelle Umgebung mit der Python-Version, die Sie verwenden möchten. Dann müssen Sie entweder Flax, PyTorch oder TensorFlow installieren. Bitte beziehe dich entsprechend auf die jeweiligen Installationsanleitungen für [TensorFlow](https://www.tensorflow.org/install/), [PyTorch](https://pytorch.org/get-started/locally/#start-locally), und/oder [Flax](https://github.com/google/flax#quick-install) und [Jax](https://github.com/google/jax#installation) für den spezifischen Installationsbefehl für Ihre Plattform. Wenn eines dieser Backends installiert ist, kann 🤗 Transformers wie folgt mit pip installiert werden: ```bash pip install transformers ``` Wenn Sie mit den Beispielen experimentieren möchten oder die neueste Version des Codes benötigen und nicht auf eine neue Veröffentlichung warten können, müssen Sie [die Bibliothek von der Quelle installieren](https://huggingface.co/docs/transformers/installation#installing-from-source). ### Mit conda 🤗 Transformers kann wie folgt mit conda installiert werden: ```shell script conda install conda-forge::transformers ``` > **_HINWEIS:_** Die Installation von `transformers` aus dem `huggingface`-Kanal ist veraltet. Folgen Sie den Installationsanleitungen von Flax, PyTorch oder TensorFlow, um zu sehen, wie sie mit conda installiert werden können. > **_HINWEIS:_** Auf Windows werden Sie möglicherweise aufgefordert, den Entwicklermodus zu aktivieren, um von Caching zu profitieren. Wenn das für Sie keine Option ist, lassen Sie es uns bitte in [diesem Issue](https://github.com/huggingface/huggingface_hub/issues/1062) wissen. ## Modellarchitekturen **[Alle Modell-Checkpoints](https://huggingface.co/models)**, die von 🤗 Transformers bereitgestellt werden, sind nahtlos aus dem huggingface.co [Model Hub](https://huggingface.co/models) integriert, wo sie direkt von [Benutzern](https://huggingface.co/users) und [Organisationen](https://huggingface.co/organizations) hochgeladen werden. Aktuelle Anzahl der Checkpoints:  🤗 Transformers bietet derzeit die folgenden Architekturen an: siehe [hier](https://huggingface.co/docs/transformers/model_summary) für eine jeweilige Übersicht. Um zu überprüfen, ob jedes Modell eine Implementierung in Flax, PyTorch oder TensorFlow hat oder über einen zugehörigen Tokenizer verfügt, der von der 🤗 Tokenizers-Bibliothek unterstützt wird, schauen Sie auf [diese Tabelle](https://huggingface.co/docs/transformers/index#supported-frameworks). Diese Implementierungen wurden mit mehreren Datensätzen getestet (siehe Beispielskripte) und sollten den Leistungen der ursprünglichen Implementierungen entsprechen. Weitere Details zur Leistung finden Sie im Abschnitt der Beispiele in der [Dokumentation](https://github.com/huggingface/transformers/tree/main/examples). ## Mehr erfahren | Abschnitt | Beschreibung | |-|-| | [Dokumentation](https://huggingface.co/docs/transformers/) | Vollständige API-Dokumentation und Tutorials | | [Zusammenfassung der Aufgaben](https://huggingface.co/docs/transformers/task_summary) | Von 🤗 Transformers unterstützte Aufgaben | | [Vorverarbeitungs-Tutorial](https://huggingface.co/docs/transformers/preprocessing) | Verwendung der `Tokenizer`-Klasse zur Vorverarbeitung der Daten für die Modelle | | [Training und Feintuning](https://huggingface.co/docs/transformers/training) | Verwendung der von 🤗 Transformers bereitgestellten Modelle in einer PyTorch-/TensorFlow-Trainingsschleife und der `Trainer`-API | | [Schnelleinstieg: Feintuning/Anwendungsskripte](https://github.com/huggingface/transformers/tree/main/examples) | Beispielskripte für das Feintuning von Modellen für eine breite Palette von Aufgaben | | [Modellfreigabe und -upload](https://huggingface.co/docs/transformers/model_sharing) | Laden Sie Ihre feingetunten Modelle hoch und teilen Sie sie mit der Community | ## Zitation Wir haben jetzt ein [Paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/), das Sie für die 🤗 Transformers-Bibliothek zitieren können: ```bibtex @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } ```

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/split_model_tests.py

# Copyright 2024 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. """ This script is used to get the list of folders under `tests/models` and split the list into `NUM_SLICES` splits. The main use case is a GitHub Actions workflow file calling this script to get the (nested) list of folders allowing it to split the list of jobs to run into multiple slices each containing a smaller number of jobs. This way, we can bypass the maximum of 256 jobs in a matrix. See the `setup` and `run_models_gpu` jobs defined in the workflow file `.github/workflows/self-scheduled.yml` for more details. Usage: This script is required to be run under `tests` folder of `transformers` root directory. Assume we are under `transformers` root directory: ```bash cd tests python ../utils/split_model_tests.py --num_splits 64 ``` """ import argparse import os if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--num_splits", type=int, default=1, help="the number of splits into which the (flat) list of folders will be split.", ) args = parser.parse_args() tests = os.getcwd() model_tests = os.listdir(os.path.join(tests, "models")) d1 = sorted(filter(os.path.isdir, os.listdir(tests))) d2 = sorted(filter(os.path.isdir, [f"models/{x}" for x in model_tests])) d1.remove("models") d = d2 + d1 num_jobs = len(d) num_jobs_per_splits = num_jobs // args.num_splits model_splits = [] end = 0 for idx in range(args.num_splits): start = end end = start + num_jobs_per_splits + (1 if idx < num_jobs % args.num_splits else 0) model_splits.append(d[start:end]) print(model_splits)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_docstrings.py

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """ Utility that checks all docstrings of public objects have an argument section matching their signature. Use from the root of the repo with: ```bash python utils/check_docstrings.py ``` for a check that will error in case of inconsistencies (used by `make repo-consistency`). To auto-fix issues run: ```bash python utils/check_docstrings.py --fix_and_overwrite ``` which is used by `make fix-copies` (note that this fills what it cans, you might have to manually fill information like argument descriptions). """ import argparse import ast import enum import inspect import operator as op import re from pathlib import Path from typing import Any, Optional, Tuple, Union from check_repo import ignore_undocumented from transformers.utils import direct_transformers_import PATH_TO_TRANSFORMERS = Path("src").resolve() / "transformers" # This is to make sure the transformers module imported is the one in the repo. transformers = direct_transformers_import(PATH_TO_TRANSFORMERS) OPTIONAL_KEYWORD = "*optional*" # Re pattern that catches args blocks in docstrings (with all variation around the name supported). _re_args = re.compile(r"^\s*(Args?|Arguments?|Attributes?|Params?|Parameters?):\s*$") # Re pattern that parses the start of an arg block: catches <name> (<description>) in those lines. _re_parse_arg = re.compile(r"^(\s*)(\S+)\s+\((.+)\)(?:\:|$)") # Re pattern that parses the end of a description of an arg (catches the default in *optional*, defaults to xxx). _re_parse_description = re.compile(r"\*optional\*, defaults to (.*)$") # This is a temporary list of objects to ignore while we progressively fix them. Do not add anything here, fix the # docstrings instead. If formatting should be ignored for the docstring, you can put a comment # no-format on the # line before the docstring. OBJECTS_TO_IGNORE = [ # Deprecated "InputExample", "InputFeatures", # Signature is *args/**kwargs # "PretrainedConfig", #ignored but could be fixed # "GenerationConfig", #ignored but could be fixed "TFSequenceSummary", "TFBertTokenizer", "TFGPT2Tokenizer", # Missing arguments in the docstring "ASTFeatureExtractor", "AlbertModel", "AlbertTokenizerFast", "AlignTextModel", "AlignVisionConfig", "AudioClassificationPipeline", "AutoformerConfig", "AutomaticSpeechRecognitionPipeline", "AzureOpenAiAgent", "BarkCoarseConfig", "BarkConfig", "BarkFineConfig", "BarkSemanticConfig", "BartConfig", "BartTokenizerFast", "BarthezTokenizerFast", "BeitModel", "BertConfig", "BertJapaneseTokenizer", "BertModel", "BertTokenizerFast", "BigBirdConfig", "BigBirdForQuestionAnswering", "BigBirdModel", "BigBirdPegasusConfig", "BigBirdTokenizerFast", "BitImageProcessor", "BlenderbotConfig", "BlenderbotSmallConfig", "BlenderbotSmallTokenizerFast", "BlenderbotTokenizerFast", "Blip2QFormerConfig", "Blip2VisionConfig", "BlipTextConfig", "BlipVisionConfig", "BloomConfig", "BloomTokenizerFast", "BridgeTowerTextConfig", "BridgeTowerVisionConfig", "BrosModel", "CamembertConfig", "CamembertModel", "CamembertTokenizerFast", "CanineModel", "CanineTokenizer", "ChineseCLIPTextModel", "ClapTextConfig", "ConditionalDetrConfig", "ConditionalDetrImageProcessor", "ConvBertConfig", "ConvBertTokenizerFast", "ConvNextConfig", "ConvNextV2Config", "ConversationalPipeline", "CpmAntTokenizer", "CvtConfig", "CvtModel", "DeiTImageProcessor", "DPRReaderTokenizer", "DPRReaderTokenizerFast", "DPTModel", "Data2VecAudioConfig", "Data2VecTextConfig", "Data2VecTextModel", "Data2VecVisionModel", "DataCollatorForLanguageModeling", "DebertaConfig", "DebertaV2Config", "DebertaV2Tokenizer", "DebertaV2TokenizerFast", "DecisionTransformerConfig", "DeformableDetrConfig", "DeformableDetrImageProcessor", "DeiTModel", "DepthEstimationPipeline", "DetaConfig", "DetaImageProcessor", "DetrConfig", "DetrImageProcessor", "DinatModel", "DistilBertConfig", "DistilBertTokenizerFast", "DocumentQuestionAnsweringPipeline", "DonutSwinModel", "EarlyStoppingCallback", "EfficientFormerConfig", "EfficientFormerImageProcessor", "EfficientNetConfig", "ElectraConfig", "ElectraTokenizerFast", "EncoderDecoderModel", "EncoderRepetitionPenaltyLogitsProcessor", "ErnieMModel", "ErnieModel", "ErnieMTokenizer", "EsmConfig", "EsmModel", "FlaxAlbertForMaskedLM", "FlaxAlbertForMultipleChoice", "FlaxAlbertForPreTraining", "FlaxAlbertForQuestionAnswering", "FlaxAlbertForSequenceClassification", "FlaxAlbertForTokenClassification", "FlaxAlbertModel", "FlaxBartForCausalLM", "FlaxBartForConditionalGeneration", "FlaxBartForQuestionAnswering", "FlaxBartForSequenceClassification", "FlaxBartModel", "FlaxBeitForImageClassification", "FlaxBeitForMaskedImageModeling", "FlaxBeitModel", "FlaxBertForCausalLM", "FlaxBertForMaskedLM", "FlaxBertForMultipleChoice", "FlaxBertForNextSentencePrediction", "FlaxBertForPreTraining", "FlaxBertForQuestionAnswering", "FlaxBertForSequenceClassification", "FlaxBertForTokenClassification", "FlaxBertModel", "FlaxBigBirdForCausalLM", "FlaxBigBirdForMaskedLM", "FlaxBigBirdForMultipleChoice", "FlaxBigBirdForPreTraining", "FlaxBigBirdForQuestionAnswering", "FlaxBigBirdForSequenceClassification", "FlaxBigBirdForTokenClassification", "FlaxBigBirdModel", "FlaxBlenderbotForConditionalGeneration", "FlaxBlenderbotModel", "FlaxBlenderbotSmallForConditionalGeneration", "FlaxBlenderbotSmallModel", "FlaxBloomForCausalLM", "FlaxBloomModel", "FlaxCLIPModel", "FlaxDistilBertForMaskedLM", "FlaxDistilBertForMultipleChoice", "FlaxDistilBertForQuestionAnswering", "FlaxDistilBertForSequenceClassification", "FlaxDistilBertForTokenClassification", "FlaxDistilBertModel", "FlaxElectraForCausalLM", "FlaxElectraForMaskedLM", "FlaxElectraForMultipleChoice", "FlaxElectraForPreTraining", "FlaxElectraForQuestionAnswering", "FlaxElectraForSequenceClassification", "FlaxElectraForTokenClassification", "FlaxElectraModel", "FlaxEncoderDecoderModel", "FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPTJForCausalLM", "FlaxGPTJModel", "FlaxGPTNeoForCausalLM", "FlaxGPTNeoModel", "FlaxLlamaForCausalLM", "FlaxLlamaModel", "FlaxGemmaForCausalLM", "FlaxGemmaModel", "FlaxMBartForConditionalGeneration", "FlaxMBartForQuestionAnswering", "FlaxMBartForSequenceClassification", "FlaxMBartModel", "FlaxMarianMTModel", "FlaxMarianModel", "FlaxMistralForCausalLM", "FlaxMistralModel", "FlaxOPTForCausalLM", "FlaxPegasusForConditionalGeneration", "FlaxPegasusModel", "FlaxRegNetForImageClassification", "FlaxRegNetModel", "FlaxResNetForImageClassification", "FlaxResNetModel", "FlaxRoFormerForMaskedLM", "FlaxRoFormerForMultipleChoice", "FlaxRoFormerForQuestionAnswering", "FlaxRoFormerForSequenceClassification", "FlaxRoFormerForTokenClassification", "FlaxRoFormerModel", "FlaxRobertaForCausalLM", "FlaxRobertaForMaskedLM", "FlaxRobertaForMultipleChoice", "FlaxRobertaForQuestionAnswering", "FlaxRobertaForSequenceClassification", "FlaxRobertaForTokenClassification", "FlaxRobertaModel", "FlaxRobertaPreLayerNormForCausalLM", "FlaxRobertaPreLayerNormForMaskedLM", "FlaxRobertaPreLayerNormForMultipleChoice", "FlaxRobertaPreLayerNormForQuestionAnswering", "FlaxRobertaPreLayerNormForSequenceClassification", "FlaxRobertaPreLayerNormForTokenClassification", "FlaxRobertaPreLayerNormModel", "FlaxSpeechEncoderDecoderModel", "FlaxViTForImageClassification", "FlaxViTModel", "FlaxVisionEncoderDecoderModel", "FlaxVisionTextDualEncoderModel", "FlaxWav2Vec2ForCTC", "FlaxWav2Vec2ForPreTraining", "FlaxWav2Vec2Model", "FlaxWhisperForAudioClassification", "FlaxWhisperForConditionalGeneration", "FlaxWhisperModel", "FlaxWhisperTimeStampLogitsProcessor", "FlaxXGLMForCausalLM", "FlaxXGLMModel", "FlaxXLMRobertaForCausalLM", "FlaxXLMRobertaForMaskedLM", "FlaxXLMRobertaForMultipleChoice", "FlaxXLMRobertaForQuestionAnswering", "FlaxXLMRobertaForSequenceClassification", "FlaxXLMRobertaForTokenClassification", "FlaxXLMRobertaModel", "FNetConfig", "FNetModel", "FNetTokenizerFast", "FSMTConfig", "FeatureExtractionPipeline", "FillMaskPipeline", "FlaubertConfig", "FlavaConfig", "FlavaForPreTraining", "FlavaImageModel", "FlavaImageProcessor", "FlavaMultimodalModel", "FlavaTextConfig", "FlavaTextModel", "FocalNetModel", "FunnelTokenizerFast", "GPTBigCodeConfig", "GPTJConfig", "GPTNeoXConfig", "GPTNeoXJapaneseConfig", "GPTNeoXTokenizerFast", "GPTSanJapaneseConfig", "GitConfig", "GitVisionConfig", "GraphormerConfig", "GroupViTTextConfig", "GroupViTVisionConfig", "HerbertTokenizerFast", "HubertConfig", "HubertForCTC", "IBertConfig", "IBertModel", "IdeficsConfig", "IdeficsProcessor", "ImageClassificationPipeline", "ImageFeatureExtractionPipeline", "ImageGPTConfig", "ImageSegmentationPipeline", "ImageToImagePipeline", "ImageToTextPipeline", "InformerConfig", "InstructBlipQFormerConfig", "JukeboxPriorConfig", "JukeboxTokenizer", "LEDConfig", "LEDTokenizerFast", "LayoutLMForQuestionAnswering", "LayoutLMTokenizerFast", "LayoutLMv2Config", "LayoutLMv2ForQuestionAnswering", "LayoutLMv2TokenizerFast", "LayoutLMv3Config", "LayoutLMv3ImageProcessor", "LayoutLMv3TokenizerFast", "LayoutXLMTokenizerFast", "LevitConfig", "LiltConfig", "LiltModel", "LongT5Config", "LongformerConfig", "LongformerModel", "LongformerTokenizerFast", "LukeModel", "LukeTokenizer", "LxmertTokenizerFast", "M2M100Config", "M2M100Tokenizer", "MarkupLMProcessor", "MaskGenerationPipeline", "MBart50TokenizerFast", "MBartConfig", "MCTCTFeatureExtractor", "MPNetConfig", "MPNetModel", "MPNetTokenizerFast", "MT5Config", "MT5TokenizerFast", "MarianConfig", "MarianTokenizer", "MarkupLMConfig", "MarkupLMModel", "MarkupLMTokenizer", "MarkupLMTokenizerFast", "Mask2FormerConfig", "MaskFormerConfig", "MaxTimeCriteria", "MegaConfig", "MegaModel", "MegatronBertConfig", "MegatronBertForPreTraining", "MegatronBertModel", "MobileBertConfig", "MobileBertModel", "MobileBertTokenizerFast", "MobileNetV1ImageProcessor", "MobileNetV1Model", "MobileNetV2ImageProcessor", "MobileNetV2Model", "MobileViTModel", "MobileViTV2Model", "MLukeTokenizer", "MraConfig", "MusicgenDecoderConfig", "MusicgenForConditionalGeneration", "MusicgenMelodyForConditionalGeneration", "MvpConfig", "MvpTokenizerFast", "MT5Tokenizer", "NatModel", "NerPipeline", "NezhaConfig", "NezhaModel", "NllbMoeConfig", "NllbTokenizer", "NllbTokenizerFast", "NystromformerConfig", "OPTConfig", "ObjectDetectionPipeline", "OneFormerProcessor", "OpenAIGPTTokenizerFast", "OpenLlamaConfig", "PLBartConfig", "PegasusConfig", "PegasusTokenizer", "PegasusTokenizerFast", "PegasusXConfig", "PerceiverImageProcessor", "PerceiverModel", "PerceiverTokenizer", "PersimmonConfig", "Pipeline", "Pix2StructConfig", "Pix2StructTextConfig", "PLBartTokenizer", "Pop2PianoConfig", "PreTrainedTokenizer", "PreTrainedTokenizerBase", "PreTrainedTokenizerFast", "PrefixConstrainedLogitsProcessor", "ProphetNetConfig", "QDQBertConfig", "QDQBertModel", "QuestionAnsweringPipeline", "RagConfig", "RagModel", "RagRetriever", "RagSequenceForGeneration", "RagTokenForGeneration", "RealmConfig", "RealmForOpenQA", "RealmScorer", "RealmTokenizerFast", "ReformerConfig", "ReformerTokenizerFast", "RegNetConfig", "RemBertConfig", "RemBertModel", "RemBertTokenizer", "RemBertTokenizerFast", "RepetitionPenaltyLogitsProcessor", "RetriBertConfig", "RetriBertTokenizerFast", "RoCBertConfig", "RoCBertModel", "RoCBertTokenizer", "RoFormerConfig", "RobertaConfig", "RobertaModel", "RobertaPreLayerNormConfig", "RobertaPreLayerNormModel", "RobertaTokenizerFast", "SEWConfig", "SEWDConfig", "SEWDForCTC", "SEWForCTC", "SamConfig", "SamPromptEncoderConfig", "SeamlessM4TConfig", # use of unconventional markdown "SeamlessM4Tv2Config", # use of unconventional markdown "Seq2SeqTrainingArguments", "SpecialTokensMixin", "Speech2Text2Config", "Speech2Text2Tokenizer", "Speech2TextTokenizer", "SpeechEncoderDecoderModel", "SpeechT5Config", "SpeechT5Model", "SplinterConfig", "SplinterTokenizerFast", "SqueezeBertTokenizerFast", "SummarizationPipeline", "Swin2SRImageProcessor", "Swinv2Model", "SwitchTransformersConfig", "T5Config", "T5Tokenizer", "T5TokenizerFast", "TableQuestionAnsweringPipeline", "TableTransformerConfig", "TapasConfig", "TapasModel", "TapasTokenizer", "Text2TextGenerationPipeline", "TextClassificationPipeline", "TextGenerationPipeline", "TFAlbertForMaskedLM", "TFAlbertForMultipleChoice", "TFAlbertForPreTraining", "TFAlbertForQuestionAnswering", "TFAlbertForSequenceClassification", "TFAlbertForTokenClassification", "TFAlbertModel", "TFBartForConditionalGeneration", "TFBartForSequenceClassification", "TFBartModel", "TFBertForMaskedLM", "TFBertForMultipleChoice", "TFBertForNextSentencePrediction", "TFBertForPreTraining", "TFBertForQuestionAnswering", "TFBertForSequenceClassification", "TFBertForTokenClassification", "TFBertModel", "TFBlenderbotForConditionalGeneration", "TFBlenderbotModel", "TFBlenderbotSmallForConditionalGeneration", "TFBlenderbotSmallModel", "TFBlipForConditionalGeneration", "TFBlipForImageTextRetrieval", "TFBlipForQuestionAnswering", "TFCLIPModel", "TFCTRLForSequenceClassification", "TFCTRLLMHeadModel", "TFCTRLModel", "TFCamembertForCausalLM", "TFCamembertForMaskedLM", "TFCamembertForMultipleChoice", "TFCamembertForQuestionAnswering", "TFCamembertForSequenceClassification", "TFCamembertForTokenClassification", "TFCamembertModel", "TFConvBertForMaskedLM", "TFConvBertForMultipleChoice", "TFConvBertForQuestionAnswering", "TFConvBertForSequenceClassification", "TFConvBertForTokenClassification", "TFConvBertModel", "TFConvNextForImageClassification", "TFConvNextModel", "TFConvNextV2Model", # Parsing issue. Equivalent to PT ConvNextV2Model, see PR #25558 "TFConvNextV2ForImageClassification", "TFCvtForImageClassification", "TFCvtModel", "TFDPRReader", "TFData2VecVisionForImageClassification", "TFData2VecVisionForSemanticSegmentation", "TFData2VecVisionModel", "TFDebertaForMaskedLM", "TFDebertaForQuestionAnswering", "TFDebertaForSequenceClassification", "TFDebertaForTokenClassification", "TFDebertaModel", "TFDebertaV2ForMaskedLM", "TFDebertaV2ForMultipleChoice", "TFDebertaV2ForQuestionAnswering", "TFDebertaV2ForSequenceClassification", "TFDebertaV2ForTokenClassification", "TFDebertaV2Model", "TFDeiTForImageClassification", "TFDeiTForImageClassificationWithTeacher", "TFDeiTForMaskedImageModeling", "TFDeiTModel", "TFDistilBertForMaskedLM", "TFDistilBertForMultipleChoice", "TFDistilBertForQuestionAnswering", "TFDistilBertForSequenceClassification", "TFDistilBertForTokenClassification", "TFDistilBertModel", "TFEfficientFormerForImageClassification", "TFEfficientFormerForImageClassificationWithTeacher", "TFEfficientFormerModel", "TFElectraForMaskedLM", "TFElectraForMultipleChoice", "TFElectraForPreTraining", "TFElectraForQuestionAnswering", "TFElectraForSequenceClassification", "TFElectraForTokenClassification", "TFElectraModel", "TFEncoderDecoderModel", "TFEsmForMaskedLM", "TFEsmForSequenceClassification", "TFEsmForTokenClassification", "TFEsmModel", "TFFlaubertForMultipleChoice", "TFFlaubertForQuestionAnsweringSimple", "TFFlaubertForSequenceClassification", "TFFlaubertForTokenClassification", "TFFlaubertModel", "TFFlaubertWithLMHeadModel", "TFFunnelBaseModel", "TFFunnelForMaskedLM", "TFFunnelForMultipleChoice", "TFFunnelForPreTraining", "TFFunnelForQuestionAnswering", "TFFunnelForSequenceClassification", "TFFunnelForTokenClassification", "TFFunnelModel", "TFGPT2DoubleHeadsModel", "TFGPT2ForSequenceClassification", "TFGPT2LMHeadModel", "TFGPT2Model", "TFGPTJForCausalLM", "TFGPTJForQuestionAnswering", "TFGPTJForSequenceClassification", "TFGPTJModel", "TFGroupViTModel", "TFHubertForCTC", "TFHubertModel", "TFLEDForConditionalGeneration", "TFLEDModel", "TFLayoutLMForMaskedLM", "TFLayoutLMForQuestionAnswering", "TFLayoutLMForSequenceClassification", "TFLayoutLMForTokenClassification", "TFLayoutLMModel", "TFLayoutLMv3ForQuestionAnswering", "TFLayoutLMv3ForSequenceClassification", "TFLayoutLMv3ForTokenClassification", "TFLayoutLMv3Model", "TFLongformerForMaskedLM", "TFLongformerForMultipleChoice", "TFLongformerForQuestionAnswering", "TFLongformerForSequenceClassification", "TFLongformerForTokenClassification", "TFLongformerModel", "TFLxmertForPreTraining", "TFLxmertModel", "TFMBartForConditionalGeneration", "TFMBartModel", "TFMPNetForMaskedLM", "TFMPNetForMultipleChoice", "TFMPNetForQuestionAnswering", "TFMPNetForSequenceClassification", "TFMPNetForTokenClassification", "TFMPNetModel", "TFMarianMTModel", "TFMarianModel", "TFMobileBertForMaskedLM", "TFMobileBertForMultipleChoice", "TFMobileBertForNextSentencePrediction", "TFMobileBertForPreTraining", "TFMobileBertForQuestionAnswering", "TFMobileBertForSequenceClassification", "TFMobileBertForTokenClassification", "TFMobileBertModel", "TFMobileViTForImageClassification", "TFMobileViTForSemanticSegmentation", "TFMobileViTModel", "TFOPTForCausalLM", "TFOPTModel", "TFOpenAIGPTDoubleHeadsModel", "TFOpenAIGPTForSequenceClassification", "TFOpenAIGPTLMHeadModel", "TFOpenAIGPTModel", "TFPegasusForConditionalGeneration", "TFPegasusModel", "TFRagModel", "TFRagSequenceForGeneration", "TFRagTokenForGeneration", "TFRegNetForImageClassification", "TFRegNetModel", "TFRemBertForCausalLM", "TFRemBertForMaskedLM", "TFRemBertForMultipleChoice", "TFRemBertForQuestionAnswering", "TFRemBertForSequenceClassification", "TFRemBertForTokenClassification", "TFRemBertModel", "TFRepetitionPenaltyLogitsProcessor", "TFResNetForImageClassification", "TFResNetModel", "TFRoFormerForCausalLM", "TFRoFormerForMaskedLM", "TFRoFormerForMultipleChoice", "TFRoFormerForQuestionAnswering", "TFRoFormerForSequenceClassification", "TFRoFormerForTokenClassification", "TFRoFormerModel", "TFRobertaForMaskedLM", "TFRobertaForMultipleChoice", "TFRobertaForQuestionAnswering", "TFRobertaForSequenceClassification", "TFRobertaForTokenClassification", "TFRobertaModel", "TFRobertaPreLayerNormForMaskedLM", "TFRobertaPreLayerNormForMultipleChoice", "TFRobertaPreLayerNormForQuestionAnswering", "TFRobertaPreLayerNormForSequenceClassification", "TFRobertaPreLayerNormForTokenClassification", "TFRobertaPreLayerNormModel", "TFSamModel", "TFSegformerForImageClassification", "TFSegformerForSemanticSegmentation", "TFSegformerModel", "TFSpeech2TextForConditionalGeneration", "TFSpeech2TextModel", "TFSwiftFormerForImageClassification", "TFSwiftFormerModel", "TFSwinForImageClassification", "TFSwinForMaskedImageModeling", "TFSwinModel", "TFT5EncoderModel", "TFT5ForConditionalGeneration", "TFT5Model", "TFTapasForMaskedLM", "TFTapasForQuestionAnswering", "TFTapasForSequenceClassification", "TFTapasModel", "TFTransfoXLForSequenceClassification", "TFTransfoXLLMHeadModel", "TFTransfoXLModel", "TFViTForImageClassification", "TFViTMAEForPreTraining", "TFViTMAEModel", "TFViTModel", "TFVisionEncoderDecoderModel", "TFVisionTextDualEncoderModel", "TFWav2Vec2ForCTC", "TFWav2Vec2Model", "TFWhisperForConditionalGeneration", "TFWhisperModel", "TFXGLMForCausalLM", "TFXGLMModel", "TFXLMForMultipleChoice", "TFXLMForQuestionAnsweringSimple", "TFXLMForSequenceClassification", "TFXLMForTokenClassification", "TFXLMModel", "TFXLMRobertaForCausalLM", "TFXLMRobertaForMaskedLM", "TFXLMRobertaForMultipleChoice", "TFXLMRobertaForQuestionAnswering", "TFXLMRobertaForSequenceClassification", "TFXLMRobertaForTokenClassification", "TFXLMRobertaModel", "TFXLMWithLMHeadModel", "TFXLNetForMultipleChoice", "TFXLNetForQuestionAnsweringSimple", "TFXLNetForSequenceClassification", "TFXLNetForTokenClassification", "TFXLNetLMHeadModel", "TFXLNetModel", "TimeSeriesTransformerConfig", "TokenClassificationPipeline", "TrOCRConfig", "TrainerState", "TrainingArguments", "TrajectoryTransformerConfig", "TranslationPipeline", "TvltImageProcessor", "UMT5Config", "UperNetConfig", "UperNetForSemanticSegmentation", "ViTHybridImageProcessor", "ViTHybridModel", "ViTMSNModel", "ViTModel", "VideoClassificationPipeline", "ViltConfig", "ViltForImagesAndTextClassification", "ViltModel", "VisionEncoderDecoderModel", "VisionTextDualEncoderModel", "VisualBertConfig", "VisualBertModel", "VisualQuestionAnsweringPipeline", "VitMatteForImageMatting", "VitsTokenizer", "VivitModel", "Wav2Vec2BertForCTC", "Wav2Vec2CTCTokenizer", "Wav2Vec2Config", "Wav2Vec2ConformerConfig", "Wav2Vec2ConformerForCTC", "Wav2Vec2FeatureExtractor", "Wav2Vec2PhonemeCTCTokenizer", "WavLMConfig", "WavLMForCTC", "WhisperConfig", "WhisperFeatureExtractor", "WhisperForAudioClassification", "XCLIPTextConfig", "XCLIPVisionConfig", "XGLMConfig", "XGLMModel", "XGLMTokenizerFast", "XLMConfig", "XLMProphetNetConfig", "XLMRobertaConfig", "XLMRobertaModel", "XLMRobertaTokenizerFast", "XLMRobertaXLConfig", "XLMRobertaXLModel", "XLNetConfig", "XLNetTokenizerFast", "XmodConfig", "XmodModel", "YolosImageProcessor", "YolosModel", "YosoConfig", "ZeroShotAudioClassificationPipeline", "ZeroShotClassificationPipeline", "ZeroShotImageClassificationPipeline", "ZeroShotObjectDetectionPipeline", ] # Supported math operations when interpreting the value of defaults. MATH_OPERATORS = { ast.Add: op.add, ast.Sub: op.sub, ast.Mult: op.mul, ast.Div: op.truediv, ast.Pow: op.pow, ast.BitXor: op.xor, ast.USub: op.neg, } def find_indent(line: str) -> int: """ Returns the number of spaces that start a line indent. """ search = re.search(r"^(\s*)(?:\S|$)", line) if search is None: return 0 return len(search.groups()[0]) def stringify_default(default: Any) -> str: """ Returns the string representation of a default value, as used in docstring: numbers are left as is, all other objects are in backtiks. Args: default (`Any`): The default value to process Returns: `str`: The string representation of that default. """ if isinstance(default, bool): # We need to test for bool first as a bool passes isinstance(xxx, (int, float)) return f"`{default}`" elif isinstance(default, enum.Enum): # We need to test for enum first as an enum with int values will pass isinstance(xxx, (int, float)) return f"`{str(default)}`" elif isinstance(default, int): return str(default) elif isinstance(default, float): result = str(default) return str(round(default, 2)) if len(result) > 6 else result elif isinstance(default, str): return str(default) if default.isnumeric() else f'`"{default}"`' elif isinstance(default, type): return f"`{default.__name__}`" else: return f"`{default}`" def eval_math_expression(expression: str) -> Optional[Union[float, int]]: # Mainly taken from the excellent https://stackoverflow.com/a/9558001 """ Evaluate (safely) a mathematial expression and returns its value. Args: expression (`str`): The expression to evaluate. Returns: `Optional[Union[float, int]]`: Returns `None` if the evaluation fails in any way and the value computed otherwise. Example: ```py >>> eval_expr('2^6') 4 >>> eval_expr('2**6') 64 >>> eval_expr('1 + 2*3**(4^5) / (6 + -7)') -5.0 ``` """ try: return eval_node(ast.parse(expression, mode="eval").body) except TypeError: return def eval_node(node): if isinstance(node, ast.Num): # <number> return node.n elif isinstance(node, ast.BinOp): # <left> <operator> <right> return MATH_OPERATORS[type(node.op)](eval_node(node.left), eval_node(node.right)) elif isinstance(node, ast.UnaryOp): # <operator> <operand> e.g., -1 return MATH_OPERATORS[type(node.op)](eval_node(node.operand)) else: raise TypeError(node) def replace_default_in_arg_description(description: str, default: Any) -> str: """ Catches the default value in the description of an argument inside a docstring and replaces it by the value passed. Args: description (`str`): The description of an argument in a docstring to process. default (`Any`): The default value that whould be in the docstring of that argument. Returns: `str`: The description updated with the new default value. """ # Lots of docstrings have `optional` or **opational** instead of *optional* so we do this fix here. description = description.replace("`optional`", OPTIONAL_KEYWORD) description = description.replace("**optional**", OPTIONAL_KEYWORD) if default is inspect._empty: # No default, make sure the description doesn't have any either idx = description.find(OPTIONAL_KEYWORD) if idx != -1: description = description[:idx].rstrip() if description.endswith(","): description = description[:-1].rstrip() elif default is None: # Default None are not written, we just set `*optional*`. If there is default that is not None specified in the # description, we do not erase it (as sometimes we set the default to `None` because the default is a mutable # object). idx = description.find(OPTIONAL_KEYWORD) if idx == -1: description = f"{description}, {OPTIONAL_KEYWORD}" elif re.search(r"defaults to `?None`?", description) is not None: len_optional = len(OPTIONAL_KEYWORD) description = description[: idx + len_optional] else: str_default = None # For numbers we may have a default that is given by a math operation (1/255 is really popular). We don't # want to replace those by their actual values. if isinstance(default, (int, float)) and re.search("defaults to `?(.*?)(?:`|$)", description) is not None: # Grab the default and evaluate it. current_default = re.search("defaults to `?(.*?)(?:`|$)", description).groups()[0] if default == eval_math_expression(current_default): try: # If it can be directly converted to the type of the default, it's a simple value str_default = str(type(default)(current_default)) except Exception: # Otherwise there is a math operator so we add a code block. str_default = f"`{current_default}`" elif isinstance(default, enum.Enum) and default.name == current_default.split(".")[-1]: # When the default is an Enum (this is often the case for PIL.Image.Resampling), and the docstring # matches the enum name, keep the existing docstring rather than clobbering it with the enum value. str_default = f"`{current_default}`" if str_default is None: str_default = stringify_default(default) # Make sure default match if OPTIONAL_KEYWORD not in description: description = f"{description}, {OPTIONAL_KEYWORD}, defaults to {str_default}" elif _re_parse_description.search(description) is None: idx = description.find(OPTIONAL_KEYWORD) len_optional = len(OPTIONAL_KEYWORD) description = f"{description[:idx + len_optional]}, defaults to {str_default}" else: description = _re_parse_description.sub(rf"*optional*, defaults to {str_default}", description) return description def get_default_description(arg: inspect.Parameter) -> str: """ Builds a default description for a parameter that was not documented. Args: arg (`inspect.Parameter`): The argument in the signature to generate a description for. Returns: `str`: The description. """ if arg.annotation is inspect._empty: arg_type = "<fill_type>" elif hasattr(arg.annotation, "__name__"): arg_type = arg.annotation.__name__ else: arg_type = str(arg.annotation) if arg.default is inspect._empty: return f"`{arg_type}`" elif arg.default is None: return f"`{arg_type}`, {OPTIONAL_KEYWORD}" else: str_default = stringify_default(arg.default) return f"`{arg_type}`, {OPTIONAL_KEYWORD}, defaults to {str_default}" def find_source_file(obj: Any) -> Path: """ Finds the source file of an object. Args: obj (`Any`): The object whose source file we are looking for. Returns: `Path`: The source file. """ module = obj.__module__ obj_file = PATH_TO_TRANSFORMERS for part in module.split(".")[1:]: obj_file = obj_file / part return obj_file.with_suffix(".py") def match_docstring_with_signature(obj: Any) -> Optional[Tuple[str, str]]: """ Matches the docstring of an object with its signature. Args: obj (`Any`): The object to process. Returns: `Optional[Tuple[str, str]]`: Returns `None` if there is no docstring or no parameters documented in the docstring, otherwise returns a tuple of two strings: the current documentation of the arguments in the docstring and the one matched with the signature. """ if len(getattr(obj, "__doc__", "")) == 0: # Nothing to do, there is no docstring. return # Read the docstring in the source code to see if there is a special command to ignore this object. try: source, _ = inspect.getsourcelines(obj) except OSError: source = [] idx = 0 while idx < len(source) and '"""' not in source[idx]: idx += 1 ignore_order = False if idx < len(source): line_before_docstring = source[idx - 1] if re.search(r"^\s*#\s*no-format\s*$", line_before_docstring): # This object is ignored return elif re.search(r"^\s*#\s*ignore-order\s*$", line_before_docstring): ignore_order = True # Read the signature signature = inspect.signature(obj).parameters obj_doc_lines = obj.__doc__.split("\n") # Get to the line where we start documenting arguments idx = 0 while idx < len(obj_doc_lines) and _re_args.search(obj_doc_lines[idx]) is None: idx += 1 if idx == len(obj_doc_lines): # Nothing to do, no parameters are documented. return indent = find_indent(obj_doc_lines[idx]) arguments = {} current_arg = None idx += 1 start_idx = idx # Keep going until the arg section is finished (nonempty line at the same indent level) or the end of the docstring. while idx < len(obj_doc_lines) and ( len(obj_doc_lines[idx].strip()) == 0 or find_indent(obj_doc_lines[idx]) > indent ): if find_indent(obj_doc_lines[idx]) == indent + 4: # New argument -> let's generate the proper doc for it re_search_arg = _re_parse_arg.search(obj_doc_lines[idx]) if re_search_arg is not None: _, name, description = re_search_arg.groups() current_arg = name if name in signature: default = signature[name].default if signature[name].kind is inspect._ParameterKind.VAR_KEYWORD: default = None new_description = replace_default_in_arg_description(description, default) else: new_description = description init_doc = _re_parse_arg.sub(rf"\1\2 ({new_description}):", obj_doc_lines[idx]) arguments[current_arg] = [init_doc] elif current_arg is not None: arguments[current_arg].append(obj_doc_lines[idx]) idx += 1 # We went too far by one (perhaps more if there are a lot of new lines) idx -= 1 while len(obj_doc_lines[idx].strip()) == 0: arguments[current_arg] = arguments[current_arg][:-1] idx -= 1 # And we went too far by one again. idx += 1 old_doc_arg = "\n".join(obj_doc_lines[start_idx:idx]) old_arguments = list(arguments.keys()) arguments = {name: "\n".join(doc) for name, doc in arguments.items()} # Add missing arguments with a template for name in set(signature.keys()) - set(arguments.keys()): arg = signature[name] # We ignore private arguments or *args/**kwargs (unless they are documented by the user) if name.startswith("_") or arg.kind in [ inspect._ParameterKind.VAR_KEYWORD, inspect._ParameterKind.VAR_POSITIONAL, ]: arguments[name] = "" else: arg_desc = get_default_description(arg) arguments[name] = " " * (indent + 4) + f"{name} ({arg_desc}): <fill_docstring>" # Arguments are sorted by the order in the signature unless a special comment is put. if ignore_order: new_param_docs = [arguments[name] for name in old_arguments if name in signature] missing = set(signature.keys()) - set(old_arguments) new_param_docs.extend([arguments[name] for name in missing if len(arguments[name]) > 0]) else: new_param_docs = [arguments[name] for name in signature.keys() if len(arguments[name]) > 0] new_doc_arg = "\n".join(new_param_docs) return old_doc_arg, new_doc_arg def fix_docstring(obj: Any, old_doc_args: str, new_doc_args: str): """ Fixes the docstring of an object by replacing its arguments documentaiton by the one matched with the signature. Args: obj (`Any`): The object whose dostring we are fixing. old_doc_args (`str`): The current documentation of the parameters of `obj` in the docstring (as returned by `match_docstring_with_signature`). new_doc_args (`str`): The documentation of the parameters of `obj` matched with its signature (as returned by `match_docstring_with_signature`). """ # Read the docstring in the source code and make sure we have the right part of the docstring source, line_number = inspect.getsourcelines(obj) # Get to the line where we start documenting arguments idx = 0 while idx < len(source) and _re_args.search(source[idx]) is None: idx += 1 if idx == len(source): # Args are not defined in the docstring of this object return # Get to the line where we stop documenting arguments indent = find_indent(source[idx]) idx += 1 start_idx = idx while idx < len(source) and (len(source[idx].strip()) == 0 or find_indent(source[idx]) > indent): idx += 1 idx -= 1 while len(source[idx].strip()) == 0: idx -= 1 idx += 1 if "".join(source[start_idx:idx])[:-1] != old_doc_args: # Args are not fully defined in the docstring of this object return obj_file = find_source_file(obj) with open(obj_file, "r", encoding="utf-8") as f: content = f.read() # Replace content lines = content.split("\n") lines = lines[: line_number + start_idx - 1] + [new_doc_args] + lines[line_number + idx - 1 :] print(f"Fixing the docstring of {obj.__name__} in {obj_file}.") with open(obj_file, "w", encoding="utf-8") as f: f.write("\n".join(lines)) def check_docstrings(overwrite: bool = False): """ Check docstrings of all public objects that are callables and are documented. Args: overwrite (`bool`, *optional*, defaults to `False`): Whether to fix inconsistencies or not. """ failures = [] hard_failures = [] to_clean = [] for name in dir(transformers): # Skip objects that are private or not documented. if name.startswith("_") or ignore_undocumented(name) or name in OBJECTS_TO_IGNORE: continue obj = getattr(transformers, name) if not callable(obj) or not isinstance(obj, type) or getattr(obj, "__doc__", None) is None: continue # Check docstring try: result = match_docstring_with_signature(obj) if result is not None: old_doc, new_doc = result else: old_doc, new_doc = None, None except Exception as e: print(e) hard_failures.append(name) continue if old_doc != new_doc: if overwrite: fix_docstring(obj, old_doc, new_doc) else: failures.append(name) elif not overwrite and new_doc is not None and ("<fill_type>" in new_doc or "<fill_docstring>" in new_doc): to_clean.append(name) # Deal with errors error_message = "" if len(hard_failures) > 0: error_message += ( "The argument part of the docstrings of the following objects could not be processed, check they are " "properly formatted." ) error_message += "\n" + "\n".join([f"- {name}" for name in hard_failures]) if len(failures) > 0: error_message += ( "The following objects docstrings do not match their signature. Run `make fix-copies` to fix this. " "In some cases, this error may be raised incorrectly by the docstring checker. If you think this is the " "case, you can manually check the docstrings and then add the object name to `OBJECTS_TO_IGNORE` in " "`utils/check_docstrings.py`." ) error_message += "\n" + "\n".join([f"- {name}" for name in failures]) if len(to_clean) > 0: error_message += ( "The following objects docstrings contain templates you need to fix: search for `<fill_type>` or " "`<fill_docstring>`." ) error_message += "\n" + "\n".join([f"- {name}" for name in to_clean]) if len(error_message) > 0: error_message = "There was at least one problem when checking docstrings of public objects.\n" + error_message raise ValueError(error_message) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() check_docstrings(overwrite=args.fix_and_overwrite)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/extract_warnings.py

import argparse import json import os import time import zipfile from get_ci_error_statistics import download_artifact, get_artifacts_links from transformers import logging logger = logging.get_logger(__name__) def extract_warnings_from_single_artifact(artifact_path, targets): """Extract warnings from a downloaded artifact (in .zip format)""" selected_warnings = set() buffer = [] def parse_line(fp): for line in fp: if isinstance(line, bytes): line = line.decode("UTF-8") if "warnings summary (final)" in line: continue # This means we are outside the body of a warning elif not line.startswith(" "): # process a single warning and move it to `selected_warnings`. if len(buffer) > 0: warning = "\n".join(buffer) # Only keep the warnings specified in `targets` if any(f": {x}: " in warning for x in targets): selected_warnings.add(warning) buffer.clear() continue else: line = line.strip() buffer.append(line) if from_gh: for filename in os.listdir(artifact_path): file_path = os.path.join(artifact_path, filename) if not os.path.isdir(file_path): # read the file if filename != "warnings.txt": continue with open(file_path) as fp: parse_line(fp) else: try: with zipfile.ZipFile(artifact_path) as z: for filename in z.namelist(): if not os.path.isdir(filename): # read the file if filename != "warnings.txt": continue with z.open(filename) as fp: parse_line(fp) except Exception: logger.warning( f"{artifact_path} is either an invalid zip file or something else wrong. This file is skipped." ) return selected_warnings def extract_warnings(artifact_dir, targets): """Extract warnings from all artifact files""" selected_warnings = set() paths = [os.path.join(artifact_dir, p) for p in os.listdir(artifact_dir) if (p.endswith(".zip") or from_gh)] for p in paths: selected_warnings.update(extract_warnings_from_single_artifact(p, targets)) return selected_warnings if __name__ == "__main__": def list_str(values): return values.split(",") parser = argparse.ArgumentParser() # Required parameters parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.") parser.add_argument( "--output_dir", type=str, required=True, help="Where to store the downloaded artifacts and other result files.", ) parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.") # optional parameters parser.add_argument( "--targets", default="DeprecationWarning,UserWarning,FutureWarning", type=list_str, help="Comma-separated list of target warning(s) which we want to extract.", ) parser.add_argument( "--from_gh", action="store_true", help="If running from a GitHub action workflow and collecting warnings from its artifacts.", ) args = parser.parse_args() from_gh = args.from_gh if from_gh: # The artifacts have to be downloaded using `actions/download-artifact@v4` pass else: os.makedirs(args.output_dir, exist_ok=True) # get download links artifacts = get_artifacts_links(args.workflow_run_id, token=args.token) with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp: json.dump(artifacts, fp, ensure_ascii=False, indent=4) # download artifacts for idx, (name, url) in enumerate(artifacts.items()): print(name) print(url) print("=" * 80) download_artifact(name, url, args.output_dir, args.token) # Be gentle to GitHub time.sleep(1) # extract warnings from artifacts selected_warnings = extract_warnings(args.output_dir, args.targets) selected_warnings = sorted(selected_warnings) with open(os.path.join(args.output_dir, "selected_warnings.json"), "w", encoding="UTF-8") as fp: json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/past_ci_versions.py

import argparse import os past_versions_testing = { "pytorch": { "1.13": { "torch": "1.13.1", "torchvision": "0.14.1", "torchaudio": "0.13.1", "python": 3.9, "cuda": "cu116", "install": ( "python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1" " --extra-index-url https://download.pytorch.org/whl/cu116" ), "base_image": "nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04", }, "1.12": { "torch": "1.12.1", "torchvision": "0.13.1", "torchaudio": "0.12.1", "python": 3.9, "cuda": "cu113", "install": ( "python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1" " --extra-index-url https://download.pytorch.org/whl/cu113" ), "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, "1.11": { "torch": "1.11.0", "torchvision": "0.12.0", "torchaudio": "0.11.0", "python": 3.9, "cuda": "cu113", "install": ( "python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0" " --extra-index-url https://download.pytorch.org/whl/cu113" ), "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, "1.10": { "torch": "1.10.2", "torchvision": "0.11.3", "torchaudio": "0.10.2", "python": 3.9, "cuda": "cu113", "install": ( "python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2" " --extra-index-url https://download.pytorch.org/whl/cu113" ), "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, # torchaudio < 0.10 has no CUDA-enabled binary distributions "1.9": { "torch": "1.9.1", "torchvision": "0.10.1", "torchaudio": "0.9.1", "python": 3.9, "cuda": "cu111", "install": ( "python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1" " --extra-index-url https://download.pytorch.org/whl/cu111" ), "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, }, "tensorflow": { "2.11": { "tensorflow": "2.11.1", "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.11.1", "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, "2.10": { "tensorflow": "2.10.1", "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.10.1", "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, "2.9": { "tensorflow": "2.9.3", "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.9.3", "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, "2.8": { "tensorflow": "2.8.2", "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.8.2", "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, "2.7": { "tensorflow": "2.7.3", "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.7.3", "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, "2.6": { "tensorflow": "2.6.5", "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.6.5", "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, "2.5": { "tensorflow": "2.5.3", "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.5.3", "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04", }, }, } if __name__ == "__main__": parser = argparse.ArgumentParser("Choose the framework and version to install") parser.add_argument( "--framework", help="The framework to install. Should be `torch` or `tensorflow`", type=str, required=True ) parser.add_argument("--version", help="The version of the framework to install.", type=str, required=True) args = parser.parse_args() info = past_versions_testing[args.framework][args.version] os.system(f'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile') print(f'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile') cuda = "" if args.framework == "pytorch": cuda = info["cuda"] os.system(f"echo \"export CUDA='{cuda}'\" >> ~/.profile") print(f"echo \"export CUDA='{cuda}'\" >> ~/.profile")

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_copies.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # 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. """ Utility that checks whether the copies defined in the library match the original or not. This includes: - All code commented with `# Copied from` comments, - The list of models in the main README.md matches the ones in the localized READMEs, - Files that are registered as full copies of one another in the `FULL_COPIES` constant of this script. This also checks the list of models in the README is complete (has all models) and add a line to complete if there is a model missing. Use from the root of the repo with: ```bash python utils/check_copies.py ``` for a check that will error in case of inconsistencies (used by `make repo-consistency`) or ```bash python utils/check_copies.py --fix_and_overwrite ``` for a check that will fix all inconsistencies automatically (used by `make fix-copies`). """ import argparse import glob import os import re import subprocess from collections import OrderedDict from typing import List, Optional, Tuple, Union from transformers.utils import direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_copies.py TRANSFORMERS_PATH = "src/transformers" MODEL_TEST_PATH = "tests/models" PATH_TO_DOCS = "docs/source/en" REPO_PATH = "." # Mapping for files that are full copies of others (keys are copies, values the file to keep them up to data with) FULL_COPIES = { "examples/tensorflow/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py", "examples/flax/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py", } LOCALIZED_READMES = { # If the introduction or the conclusion of the list change, the prompts may need to be updated. "README.md": { "start_prompt": "🤗 Transformers currently provides the following architectures", "end_prompt": "1. Want to contribute a new model?", "format_model_list": ( "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by" " {paper_authors}.{supplements}" ), }, "README_zh-hans.md": { "start_prompt": "🤗 Transformers 目前支持如下的架构", "end_prompt": "1. 想要贡献新的模型？", "format_model_list": ( "**[{title}]({model_link})** (来自 {paper_affiliations}) 伴随论文 {paper_title_link} 由 {paper_authors}" " 发布。{supplements}" ), }, "README_zh-hant.md": { "start_prompt": "🤗 Transformers 目前支援以下的架構", "end_prompt": "1. 想要貢獻新的模型？", "format_model_list": ( "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by" " {paper_authors}.{supplements}" ), }, "README_ko.md": { "start_prompt": "🤗 Transformers는 다음 모델들을 제공합니다", "end_prompt": "1. 새로운 모델을 올리고 싶나요?", "format_model_list": ( "**[{title}]({model_link})** ({paper_affiliations} 에서 제공)은 {paper_authors}.{supplements}의" " {paper_title_link}논문과 함께 발표했습니다." ), }, "README_es.md": { "start_prompt": "🤗 Transformers actualmente proporciona las siguientes arquitecturas", "end_prompt": "1. ¿Quieres aportar un nuevo modelo?", "format_model_list": ( "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by" " {paper_authors}.{supplements}" ), }, "README_ja.md": { "start_prompt": "🤗Transformersは現在、以下のアーキテクチャを提供しています", "end_prompt": "1. 新しいモデルを投稿したいですか？", "format_model_list": ( "**[{title}]({model_link})** ({paper_affiliations} から) {paper_authors}.{supplements} から公開された研究論文" " {paper_title_link}" ), }, "README_hd.md": { "start_prompt": "🤗 ट्रांसफॉर्मर वर्तमान में निम्नलिखित आर्किटेक्चर का समर्थन करते हैं", "end_prompt": "1. एक नए मॉडल में योगदान देना चाहते हैं?", "format_model_list": ( "**[{title}]({model_link})** ({paper_affiliations} से) {paper_authors}.{supplements} द्वारा" "अनुसंधान पत्र {paper_title_link} के साथ जारी किया गया" ), }, "README_ru.md": { "start_prompt": "🤗 В настоящее время Transformers предоставляет следующие архитектуры", "end_prompt": "1. Хотите внести новую модель?", "format_model_list": ( "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by" " {paper_authors}.{supplements}" ), }, "README_pt-br.md": { "start_prompt": "🤗 Transformers atualmente fornece as seguintes arquiteturas", "end_prompt": "1. Quer contribuir com um novo modelo?", "format_model_list": ( "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by" " {paper_authors}.{supplements}" ), }, "README_te.md": { "start_prompt": "🤗 ట్రాన్స్‌ఫార్మర్లు ప్రస్తుతం కింది ఆర్కిటెక్చర్‌లను అందజేస్తున్నాయి", "end_prompt": "1. కొత్త మోడల్‌ను అందించాలనుకుంటున్నారా?", "format_model_list": ( "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by" " {paper_authors}.{supplements}" ), }, "README_fr.md": { "start_prompt": "🤗 Transformers fournit actuellement les architectures suivantes", "end_prompt": "1. Vous souhaitez contribuer avec un nouveau modèle ?", "format_model_list": ( "**[{title}]({model_link})** (de {paper_affiliations}) publié dans l'article {paper_title_link} par" "{paper_authors}.{supplements}" ), }, "README_de.md": { "start_prompt": "🤗 Transformers bietet derzeit die folgenden Architekturen an", "end_prompt": "1. Möchten Sie ein neues Modell beitragen?", "format_model_list": ( "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by" " {paper_authors}.{supplements}" ), }, "README_vi.md": { "start_prompt": "🤗 Transformers hiện đang cung cấp các kiến trúc sau đây", "end_prompt": "1. Muốn đóng góp một mô hình mới?", "format_model_list": ( "**[{title}]({model_link})** (từ {paper_affiliations}) được phát hành với bài báo {paper_title_link} by" " {paper_authors}.{supplements}" ), }, } # This is to make sure the transformers module imported is the one in the repo. transformers_module = direct_transformers_import(TRANSFORMERS_PATH) def _is_definition_header_ending_line(line: str) -> bool: # Helper function. Returns `True` if `line` is the end parenthesis of a class/function definition return re.search(r"^\s*\)(\s*->.*:|:)\s*$", line) is not None def _should_continue(line: str, indent: str) -> bool: # Helper function. Returns `True` if `line` is empty, starts with the `indent` or is the end parenthesis of a # class/function definition return line.startswith(indent) or len(line.strip()) == 0 or _is_definition_header_ending_line(line) def _sanity_check_splits(splits_1, splits_2, is_class): """Check the two (inner) block structures of the corresponding code block given by `split_code_into_blocks` match. For the case of `class`, they must be of one of the following 3 cases: - a single block without name: class foo: a = 1 - a consecutive sequence of (1 or more) blocks with name class foo: def f(x): return x - a block without name, followed by a consecutive sequence of (1 or more) blocks with name class foo: a = 1 def f(x): return x def g(x): return None The 2 code snippets that give `splits_1` and `splits_2` have to be in the same case to pass this check, but the number of blocks with name in the consecutive sequence is not taken into account. For the case of `function or method`, we don't require it to be in one of the above 3 cases. However, the structure of`splits_1` and `splits_2` have to match exactly. In particular, the number of blocks with name in a consecutive sequence is taken into account. """ block_names_1 = [] block_names_2 = [] for block in splits_1[1:]: if block[0].startswith("_block_without_name_"): block_names_1.append("block_without_name") elif not block[0].startswith("_empty_block_") and ( not is_class or len(block_names_1) == 0 or block_names_1[-1].startswith("block_without_name") ): block_names_1.append("block_with_name") for block in splits_2[1:]: if block[0].startswith("_block_without_name_"): block_names_2.append("block_without_name") elif not block[0].startswith("_empty_block_") and ( not is_class or len(block_names_2) == 0 or block_names_2[-1].startswith("block_without_name") ): block_names_2.append("block_with_name") if is_class: if block_names_1 not in [ ["block_without_name"], ["block_with_name"], ["block_without_name", "block_with_name"], ]: raise ValueError( "For a class, it must have a specific structure. See the docstring of `_sanity_check_splits` in the file `utils/check_copies.py`" ) if block_names_1 != block_names_2: raise ValueError("The structures in the 2 code blocks differ.") def find_block_end(lines: List[str], start_index: int, indent: int) -> int: """ Find the end of the class/func block starting at `start_index` in a source code (defined by `lines`). Args: lines (`List[str]`): The source code, represented by a list of lines. start_index (`int`): The starting index of the target class/func block. indent (`int`): The indent of the class/func body. Returns: `int`: The index of the block's ending line plus by 1 (i.e. exclusive). """ indent = " " * indent # enter the block body line_index = start_index + 1 while line_index < len(lines) and _should_continue(lines[line_index], indent): line_index += 1 # Clean up empty lines at the end (if any). while len(lines[line_index - 1]) <= 1: line_index -= 1 return line_index def split_code_into_blocks( lines: List[str], start_index: int, end_index: int, indent: int, backtrace: bool = False ) -> List[Tuple[str, int, int]]: """ Split the class/func block starting at `start_index` in a source code (defined by `lines`) into *inner blocks*. The block's header is included as the first element. The contiguous regions (without empty lines) that are not inside any inner block are included as blocks. The contiguous regions of empty lines that are not inside any inner block are also included as (dummy) blocks. Args: lines (`List[str]`): The source code, represented by a list of lines. start_index (`int`): The starting index of the target class/func block. end_index (`int`): The ending index of the target class/func block. indent (`int`): The indent of the class/func body. backtrace (`bool`, *optional*, defaults to `False`): Whether or not to include the lines before the inner class/func block's header (e.g. comments, decorators, etc.) until an empty line is encountered. Returns: `List[Tuple[str, int, int]]`: A list of elements with the form `(block_name, start_index, end_index)`. """ splits = [] # `indent - 4` is the indent level of the target class/func header try: target_block_name = re.search( rf"^{' ' * (indent - 4)}((class|def)\s+\S+)(\(|\:)", lines[start_index] ).groups()[0] except Exception: start_context = min(start_index - 10, 0) end_context = min(end_index + 10, len(lines)) raise ValueError( f"Tried to split a class or function. It did not work. Error comes from line {start_index}: \n```\n" + "".join(lines[start_context:end_context]) + "```\n" ) # from now on, the `block` means inner blocks unless explicitly specified indent_str = " " * indent block_without_name_idx = 0 empty_block_idx = 0 # Find the lines for the definition header index = start_index if "(" in lines[start_index] and "):" not in lines[start_index] in lines[start_index]: while index < end_index: if _is_definition_header_ending_line(lines[index]): break index += 1 # the first line outside the definition header index += 1 splits.append((target_block_name, start_index, index)) block_start_index, prev_block_end_index = index, index while index < end_index: # if found, it will be an inner block block_found = re.search(rf"^{indent_str}((class|def)\s+\S+)(\(|\:)", lines[index]) if block_found: name = block_found.groups()[0] block_end_index = find_block_end(lines, index, indent + 4) # backtrace to include the lines before the found block's definition header (e.g. comments, decorators, # etc.) until an empty line is encountered. block_start_index = index if index > prev_block_end_index and backtrace: idx = index - 1 for idx in range(index - 1, prev_block_end_index - 2, -1): if not (len(lines[idx].strip()) > 0 and lines[idx].startswith(indent_str)): break idx += 1 if idx < index: block_start_index = idx # between the current found block and the previous found block if block_start_index > prev_block_end_index: # give it a dummy name if len("".join(lines[prev_block_end_index:block_start_index]).strip()) == 0: prev_block_name = f"_empty_block_{empty_block_idx}" empty_block_idx += 1 else: prev_block_name = f"_block_without_name_{block_without_name_idx}" block_without_name_idx += 1 # Add it as a block splits.append((prev_block_name, prev_block_end_index, block_start_index)) # Add the current found block splits.append((name, block_start_index, block_end_index)) prev_block_end_index = block_end_index index = block_end_index - 1 index += 1 if index > prev_block_end_index: if len("".join(lines[prev_block_end_index:index]).strip()) == 0: prev_block_name = f"_empty_block_{empty_block_idx}" else: prev_block_name = f"_block_without_name_{block_without_name_idx}" splits.append((prev_block_name, prev_block_end_index, index)) return splits def find_code_in_transformers( object_name: str, base_path: str = None, return_indices: bool = False ) -> Union[str, Tuple[List[str], int, int]]: """ Find and return the source code of an object. Args: object_name (`str`): The name of the object we want the source code of. base_path (`str`, *optional*): The path to the base folder where files are checked. If not set, it will be set to `TRANSFORMERS_PATH`. return_indices(`bool`, *optional*, defaults to `False`): If `False`, will only return the code (as a string), otherwise it will also return the whole lines of the file where the object specified by `object_name` is defined, together the start/end indices of the block in the file that defines the object. Returns: `Union[str, Tuple[List[str], int, int]]`: If `return_indices=False`, only the source code of the object will be returned. Otherwise, it also returns the whole lines of the file where the object specified by `object_name` is defined, together the start/end indices of the block in the file that defines the object. """ parts = object_name.split(".") i = 0 # We can't set this as the default value in the argument, otherwise `CopyCheckTester` will fail, as it uses a # patched temp directory. if base_path is None: base_path = TRANSFORMERS_PATH # Detail: the `Copied from` statement is originally designed to work with the last part of `TRANSFORMERS_PATH`, # (which is `transformers`). The same should be applied for `MODEL_TEST_PATH`. However, its last part is `models` # (to only check and search in it) which is a bit confusing. So we keep the copied statement staring with # `tests.models.` and change it to `tests` here. if base_path == MODEL_TEST_PATH: base_path = "tests" # First let's find the module where our object lives. module = parts[i] while i < len(parts) and not os.path.isfile(os.path.join(base_path, f"{module}.py")): i += 1 if i < len(parts): module = os.path.join(module, parts[i]) if i >= len(parts): raise ValueError( f"`object_name` should begin with the name of a module of transformers but got {object_name}." ) with open(os.path.join(base_path, f"{module}.py"), "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Now let's find the class / func in the code! indent = "" line_index = 0 for name in parts[i + 1 :]: while ( line_index < len(lines) and re.search(rf"^{indent}(class|def)\s+{name}(\(|\:)", lines[line_index]) is None ): line_index += 1 # find the target specified in the current level in `parts` -> increase `indent` so we can search the next indent += " " # the index of the first line in the (currently found) block *body* line_index += 1 if line_index >= len(lines): raise ValueError(f" {object_name} does not match any function or class in {module}.") # `indent` is already one level deeper than the (found) class/func block's definition header # We found the beginning of the class / func, now let's find the end (when the indent diminishes). # `start_index` is the index of the class/func block's definition header start_index = line_index - 1 end_index = find_block_end(lines, start_index, len(indent)) code = "".join(lines[start_index:end_index]) return (code, (lines, start_index, end_index)) if return_indices else code def replace_code(code: str, replace_pattern: str) -> str: """Replace `code` by a pattern of the form `with X1->X2,Y1->Y2,Z1->Z2`. Args: code (`str`): The code to be modified. replace_pattern (`str`): The pattern used to modify `code`. Returns: `str`: The modified code. """ if len(replace_pattern) > 0: patterns = replace_pattern.replace("with", "").split(",") patterns = [_re_replace_pattern.search(p) for p in patterns] for pattern in patterns: if pattern is None: continue obj1, obj2, option = pattern.groups() code = re.sub(obj1, obj2, code) if option.strip() == "all-casing": code = re.sub(obj1.lower(), obj2.lower(), code) code = re.sub(obj1.upper(), obj2.upper(), code) return code def find_code_and_splits(object_name: str, base_path: str, buffer: dict = None): """Find the code of an object (specified by `object_name`) and split it into blocks. Args: object_name (`str`): The name of the object, e.g. `transformers.models.bert.modeling_bert.BertAttention` or `tests.models.llama.test_modeling_llama.LlamaModelTest.test_config`. base_path (`str`): The path to the base directory within which the search will be performed. It could be either `TRANSFORMERS_PATH` or `MODEL_TEST_PATH`. buffer (`dict`, *optional*): The buffer used to store the previous results in order to speed up the process. Returns: lines (`List[str]`): The lines of the whole file where the object is defined. code (`str`): The object's code. code_splits (`List[Tuple[str, int, int]]`): `code` splitted into blocks. See `split_code_into_blocks`. """ if buffer is None: buffer = {} if (object_name, base_path) in buffer: lines, code, code_splits = buffer[(object_name, base_path)] else: code, (lines, target_start_index, target_end_index) = find_code_in_transformers( object_name, base_path=base_path, return_indices=True ) indent = get_indent(code) # Split the code into blocks # `indent` is the indent of the class/func definition header, but `code_splits` expects the indent level of the # block body. code_splits = split_code_into_blocks( lines, target_start_index, target_end_index, len(indent) + 4, backtrace=True ) buffer[(object_name, base_path)] = lines, code, code_splits return lines, code, code_splits _re_copy_warning = re.compile(r"^(\s*)#\s*Copied from\s+transformers\.(\S+\.\S+)\s*($|\S.*$)") _re_copy_warning_for_test_file = re.compile(r"^(\s*)#\s*Copied from\s+tests\.(\S+\.\S+)\s*($|\S.*$)") _re_replace_pattern = re.compile(r"^\s*(\S+)->(\S+)(\s+.*|$)") _re_fill_pattern = re.compile(r"<FILL\s+[^>]*>") def get_indent(code: str) -> str: """ Find the indent in the first non empty line in a code sample. Args: code (`str`): The code to inspect. Returns: `str`: The indent looked at (as string). """ lines = code.split("\n") idx = 0 while idx < len(lines) and len(lines[idx]) == 0: idx += 1 if idx < len(lines): return re.search(r"^(\s*)\S", lines[idx]).groups()[0] return "" def run_ruff(code): command = ["ruff", "format", "-", "--config", "pyproject.toml", "--silent"] process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) stdout, _ = process.communicate(input=code.encode()) return stdout.decode() def stylify(code: str) -> str: """ Applies the ruff part of our `make style` command to some code. This formats the code using `ruff format`. As `ruff` does not provide a python api this cannot be done on the fly. Args: code (`str`): The code to format. Returns: `str`: The formatted code. """ has_indent = len(get_indent(code)) > 0 if has_indent: code = f"class Bla:\n{code}" formatted_code = run_ruff(code) return formatted_code[len("class Bla:\n") :] if has_indent else formatted_code def check_codes_match(observed_code: str, theoretical_code: str) -> Optional[int]: """ Checks if two version of a code match with the exception of the class/function name. Args: observed_code (`str`): The code found. theoretical_code (`str`): The code to match. Returns: `Optional[int]`: The index of the first line where there is a difference (if any) and `None` if the codes match. """ observed_code_header = observed_code.split("\n")[0] theoretical_code_header = theoretical_code.split("\n")[0] # Catch the function/class name: it is expected that those do not match. _re_class_match = re.compile(r"class\s+([^\(:]+)(?:\(|:)") _re_func_match = re.compile(r"def\s+([^\(]+)\(") for re_pattern in [_re_class_match, _re_func_match]: if re_pattern.match(observed_code_header) is not None: try: observed_obj_name = re_pattern.search(observed_code_header).groups()[0] except Exception: raise ValueError( "Tried to split a class or function. It did not work. Error comes from: \n```\n" + observed_code_header + "\n```\n" ) try: theoretical_name = re_pattern.search(theoretical_code_header).groups()[0] except Exception: raise ValueError( "Tried to split a class or function. It did not work. Error comes from: \n```\n" + theoretical_code_header + "\n```\n" ) theoretical_code_header = theoretical_code_header.replace(theoretical_name, observed_obj_name) # Find the first diff. Line 0 is special since we need to compare with the function/class names ignored. diff_index = 0 if theoretical_code_header != observed_code_header: return 0 diff_index = 1 for observed_line, theoretical_line in zip(observed_code.split("\n")[1:], theoretical_code.split("\n")[1:]): if observed_line != theoretical_line: return diff_index diff_index += 1 def is_copy_consistent(filename: str, overwrite: bool = False, buffer: dict = None) -> Optional[List[Tuple[str, int]]]: """ Check if the code commented as a copy in a file matches the original. Args: filename (`str`): The name of the file to check. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the copies when they don't match. buffer (`dict`, *optional*): The buffer used to store the previous results in order to speed up the process. Returns: `Optional[List[Tuple[str, int]]]`: If `overwrite=False`, returns the list of differences as tuples `(str, int)` with the name of the object having a diff and the line number where theere is the first diff. """ base_path = TRANSFORMERS_PATH if not filename.startswith("tests") else MODEL_TEST_PATH with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() diffs = [] line_index = 0 # Not a for loop cause `lines` is going to change (if `overwrite=True`). while line_index < len(lines): search_re = _re_copy_warning if filename.startswith("tests"): search_re = _re_copy_warning_for_test_file search = search_re.search(lines[line_index]) if search is None: line_index += 1 continue # There is some copied code here, let's retrieve the original. indent, object_name, replace_pattern = search.groups() # Find the file lines, the object's code, and its blocks target_lines, theoretical_code, theoretical_code_splits = find_code_and_splits( object_name, base_path, buffer=buffer ) # code replaced by the patterns theoretical_code_blocks = OrderedDict() for name, start, end in theoretical_code_splits: name = replace_code(name, replace_pattern) code = "".join(target_lines[start:end]) code = replace_code(code, replace_pattern) theoretical_code_blocks[name] = code theoretical_indent = get_indent(theoretical_code) # `start_index` is the index of the first line (the definition header) after `# Copied from`. # (`indent != theoretical_indent` doesn't seem to occur so far, not sure what this case is for.) start_index = line_index + 1 if indent == theoretical_indent else line_index # enter the block body line_index = start_index + 1 subcode = "\n".join(theoretical_code.split("\n")[1:]) indent = get_indent(subcode) # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment. # We can't call `find_block_end` directly as there is sth. special `# End copy"` here. should_continue = True while line_index < len(lines) and should_continue: line_index += 1 if line_index >= len(lines): break line = lines[line_index] # There is a special pattern `# End copy` to stop early. It's not documented cause it shouldn't really be # used. should_continue = _should_continue(line, indent) and re.search(f"^{indent}# End copy", line) is None # `line_index` is outside the block # Clean up empty lines at the end (if any). while len(lines[line_index - 1]) <= 1: line_index -= 1 # Split the observed code into blocks observed_code_splits = split_code_into_blocks(lines, start_index, line_index, len(indent), backtrace=True) is_class = lines[start_index].startswith(f"{' ' * (len(indent) - 4)}class ") # sanity check _sanity_check_splits(theoretical_code_splits, observed_code_splits, is_class=is_class) # observed code in a structured way (a dict mapping block names to blocks' code) observed_code_blocks = OrderedDict() for name, start, end in observed_code_splits: code = "".join(lines[start:end]) observed_code_blocks[name] = code # Below, we change some names in `theoretical_code_blocks` and `observed_code_blocks`. These mappings map the # original names to the modified names: this is used to restore the original order of the code blocks. name_mappings_1 = {k: k for k in theoretical_code_blocks.keys()} name_mappings_2 = {k: k for k in observed_code_blocks.keys()} # Update code blocks' name and content: # If `"# Ignore copy"` is found in a block of the observed code: # 1. if it's a block only in the observed code --> add it to the theoretical code. # 2. if it's also in the theoretical code () --> put its content (body) to the corresponding block under the # same name in the theoretical code. # In both cases, we change the name to have a prefix `_ignored_` so we know if we can discard them during the # comparison. ignored_existing_block_index = 0 ignored_new_block_index = 0 for name in list(observed_code_blocks.keys()): code = observed_code_blocks[name] if "# Ignore copy" in code: if name in theoretical_code_blocks: # in the target --> just copy the content del theoretical_code_blocks[name] theoretical_code_blocks[f"_ignored_existing_block_{ignored_existing_block_index}"] = code name_mappings_1[name] = f"_ignored_existing_block_{ignored_existing_block_index}" del observed_code_blocks[name] observed_code_blocks[f"_ignored_existing_block_{ignored_existing_block_index}"] = code name_mappings_2[name] = f"_ignored_existing_block_{ignored_existing_block_index}" ignored_existing_block_index += 1 else: # not in the target --> add it theoretical_code_blocks[f"_ignored_new_block_{ignored_new_block_index}"] = code name_mappings_1[ f"_ignored_new_block_{ignored_new_block_index}" ] = f"_ignored_new_block_{ignored_new_block_index}" del observed_code_blocks[name] observed_code_blocks[f"_ignored_new_block_{ignored_new_block_index}"] = code name_mappings_2[name] = f"_ignored_new_block_{ignored_new_block_index}" ignored_new_block_index += 1 # Respect the original block order: # 1. in `theoretical_code_blocks`: the new blocks will follow the existing ones # 2. in `observed_code_blocks`: the original order are kept with names modified potentially. This is necessary # to compute the correct `diff_index` if `overwrite=True` and there is a diff. theoretical_code_blocks = { name_mappings_1[orig_name]: theoretical_code_blocks[name_mappings_1[orig_name]] for orig_name in name_mappings_1 } observed_code_blocks = { name_mappings_2[orig_name]: observed_code_blocks[name_mappings_2[orig_name]] for orig_name in name_mappings_2 } # Ignore the blocks specified to be ignored. This is the version used to check if there is a mismatch theoretical_code_blocks_clean = { k: v for k, v in theoretical_code_blocks.items() if not (k.startswith(("_ignored_existing_block_", "_ignored_new_block_"))) } theoretical_code = "".join(list(theoretical_code_blocks_clean.values())) # stylify `theoretical_code` before compare (this is needed only when `replace_pattern` is not empty) if replace_pattern: theoretical_code = stylify(theoretical_code) # Remove `\n\n` in `theoretical_code` before compare (so no empty line) while "\n\n" in theoretical_code: theoretical_code = theoretical_code.replace("\n\n", "\n") # Compute `observed_code` where we don't include any empty line + keep track the line index between the # original/processed `observed_code` so we can have the correct `diff_index`. idx_to_orig_idx_mapping_for_observed_code_lines = {} idx = -1 orig_idx = -1 observed_code = "" for name, code in observed_code_blocks.items(): if code.endswith("\n"): code = code[:-1] for code_line in code.split("\n"): orig_idx += 1 if code_line.strip() and not name.startswith(("_ignored_existing_block_", "_ignored_new_block_")): idx += 1 observed_code += code_line + "\n" idx_to_orig_idx_mapping_for_observed_code_lines[idx] = orig_idx # Test for a diff and act accordingly. diff_index = check_codes_match(observed_code, theoretical_code) if diff_index is not None: # switch to the index in the original `observed_code` (i.e. before removing empty lines) diff_index = idx_to_orig_idx_mapping_for_observed_code_lines[diff_index] diffs.append([object_name, diff_index + start_index + 1]) if overwrite: # `theoretical_code_to_write` is a single string but may have several lines. theoretical_code_to_write = stylify("".join(list(theoretical_code_blocks.values()))) lines = lines[:start_index] + [theoretical_code_to_write] + lines[line_index:] # Here we treat it as a single entry in `lines`. line_index = start_index + 1 if overwrite and len(diffs) > 0: # Warn the user a file has been modified. print(f"Detected changes, rewriting {filename}.") with open(filename, "w", encoding="utf-8", newline="\n") as f: f.writelines(lines) return diffs def check_copies(overwrite: bool = False, file: str = None): """ Check every file is copy-consistent with the original. Also check the model list in the main README and other READMEs are consistent. Args: overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the copies when they don't match. file (`bool`, *optional*): The path to a specific file to check and/or fix. """ buffer = {} if file is None: all_files = glob.glob(os.path.join(TRANSFORMERS_PATH, "**/*.py"), recursive=True) all_test_files = glob.glob(os.path.join(MODEL_TEST_PATH, "**/*.py"), recursive=True) all_files = list(all_files) + list(all_test_files) else: all_files = [file] diffs = [] for filename in all_files: new_diffs = is_copy_consistent(filename, overwrite, buffer) diffs += [f"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs] if not overwrite and len(diffs) > 0: diff = "\n".join(diffs) raise Exception( "Found the following copy inconsistencies:\n" + diff + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them." ) def check_full_copies(overwrite: bool = False): """ Check the files that are full copies of others (as indicated in `FULL_COPIES`) are copy-consistent. Args: overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the copies when they don't match. """ diffs = [] for target, source in FULL_COPIES.items(): with open(source, "r", encoding="utf-8") as f: source_code = f.read() with open(target, "r", encoding="utf-8") as f: target_code = f.read() if source_code != target_code: if overwrite: with open(target, "w", encoding="utf-8") as f: print(f"Replacing the content of {target} by the one of {source}.") f.write(source_code) else: diffs.append(f"- {target}: copy does not match {source}.") if not overwrite and len(diffs) > 0: diff = "\n".join(diffs) raise Exception( "Found the following copy inconsistencies:\n" + diff + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them." ) def get_model_list(filename: str, start_prompt: str, end_prompt: str) -> str: """ Extracts the model list from a README. Args: filename (`str`): The name of the README file to check. start_prompt (`str`): The string to look for that introduces the model list. end_prompt (`str`): The string to look for that ends the model list. Returns: `str`: The model list. """ with open(os.path.join(REPO_PATH, filename), "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Find the start of the list. start_index = 0 while not lines[start_index].startswith(start_prompt): start_index += 1 start_index += 1 result = [] current_line = "" end_index = start_index # Keep going until the end of the list. while not lines[end_index].startswith(end_prompt): if lines[end_index].startswith("1."): if len(current_line) > 1: result.append(current_line) current_line = lines[end_index] elif len(lines[end_index]) > 1: current_line = f"{current_line[:-1]} {lines[end_index].lstrip()}" end_index += 1 if len(current_line) > 1: result.append(current_line) return "".join(result) def convert_to_localized_md(model_list: str, localized_model_list: str, format_str: str) -> Tuple[bool, str]: """ Compare the model list from the main README to the one in a localized README. Args: model_list (`str`): The model list in the main README. localized_model_list (`str`): The model list in one of the localized README. format_str (`str`): The template for a model entry in the localized README (look at the `format_model_list` in the entries of `LOCALIZED_READMES` for examples). Returns: `Tuple[bool, str]`: A tuple where the first value indicates if the READMEs match or not, and the second value is the correct localized README. """ def _rep(match): title, model_link, paper_affiliations, paper_title_link, paper_authors, supplements = match.groups() return format_str.format( title=title, model_link=model_link, paper_affiliations=paper_affiliations, paper_title_link=paper_title_link, paper_authors=paper_authors, supplements=" " + supplements.strip() if len(supplements) != 0 else "", ) # This regex captures metadata from an English model description, including model title, model link, # affiliations of the paper, title of the paper, authors of the paper, and supplemental data (see DistilBERT for # example). _re_capture_meta = re.compile( r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\* \(from ([^)]*)\)[^\[]*([^\)]*\)).*?by (.*?[A-Za-z\*]{2,}?)\. (.*)$" ) # This regex is used to synchronize title link. _re_capture_title_link = re.compile(r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\*") # This regex is used to synchronize paper title and link. _re_capture_paper_link = re.compile(r" \[([^\]]*)\]\(([^\)]*)\)") if len(localized_model_list) == 0: localized_model_index = {} else: try: localized_model_index = { re.search(r"\*\*\[([^\]]*)", line).groups()[0]: line for line in localized_model_list.strip().split("\n") } except AttributeError: raise AttributeError("A model name in localized READMEs cannot be recognized.") model_keys = [re.search(r"\*\*\[([^\]]*)", line).groups()[0] for line in model_list.strip().split("\n")] # We exclude keys in localized README not in the main one. readmes_match = not any(k not in model_keys for k in localized_model_index) localized_model_index = {k: v for k, v in localized_model_index.items() if k in model_keys} for model in model_list.strip().split("\n"): title, model_link = _re_capture_title_link.search(model).groups() if title not in localized_model_index: readmes_match = False # Add an anchor white space behind a model description string for regex. # If metadata cannot be captured, the English version will be directly copied. localized_model_index[title] = _re_capture_meta.sub(_rep, model + " ") elif _re_fill_pattern.search(localized_model_index[title]) is not None: update = _re_capture_meta.sub(_rep, model + " ") if update != localized_model_index[title]: readmes_match = False localized_model_index[title] = update else: # Synchronize title link converted_model = _re_capture_title_link.sub( f"**[{title}]({model_link})**", localized_model_index[title], count=1 ) # Synchronize paper title and its link (if found) paper_title_link = _re_capture_paper_link.search(model) if paper_title_link is not None: paper_title, paper_link = paper_title_link.groups() converted_model = _re_capture_paper_link.sub( f" [{paper_title}]({paper_link})", converted_model, count=1 ) if converted_model != localized_model_index[title]: readmes_match = False localized_model_index[title] = converted_model sorted_index = sorted(localized_model_index.items(), key=lambda x: x[0].lower()) return readmes_match, "\n".join((x[1] for x in sorted_index)) + "\n" def _find_text_in_file(filename: str, start_prompt: str, end_prompt: str) -> Tuple[str, int, int, List[str]]: """ Find the text in a file between two prompts. Args: filename (`str`): The name of the file to look into. start_prompt (`str`): The string to look for that introduces the content looked for. end_prompt (`str`): The string to look for that ends the content looked for. Returns: Tuple[str, int, int, List[str]]: The content between the two prompts, the index of the start line in the original file, the index of the end line in the original file and the list of lines of that file. """ with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Find the start prompt. start_index = 0 while not lines[start_index].startswith(start_prompt): start_index += 1 start_index += 1 end_index = start_index while not lines[end_index].startswith(end_prompt): end_index += 1 end_index -= 1 while len(lines[start_index]) <= 1: start_index += 1 while len(lines[end_index]) <= 1: end_index -= 1 end_index += 1 return "".join(lines[start_index:end_index]), start_index, end_index, lines # Map a model name with the name it has in the README for the check_readme check SPECIAL_MODEL_NAMES = { "Bert Generation": "BERT For Sequence Generation", "BigBird": "BigBird-RoBERTa", "Data2VecAudio": "Data2Vec", "Data2VecText": "Data2Vec", "Data2VecVision": "Data2Vec", "DonutSwin": "Swin Transformer", "Marian": "MarianMT", "MaskFormerSwin": "Swin Transformer", "OpenAI GPT-2": "GPT-2", "OpenAI GPT": "GPT", "Perceiver": "Perceiver IO", "SAM": "Segment Anything", "ViT": "Vision Transformer (ViT)", } # Update this list with the models that shouldn't be in the README. This only concerns modular models or those who do # not have an associated paper. MODELS_NOT_IN_README = [ "BertJapanese", "Encoder decoder", "FairSeq Machine-Translation", "HerBERT", "RetriBERT", "Speech Encoder decoder", "Speech2Text", "Speech2Text2", "TimmBackbone", "Vision Encoder decoder", "VisionTextDualEncoder", "CLIPVisionModel", "SiglipVisionModel", "ChineseCLIPVisionModel", ] # Template for new entries to add in the main README when we have missing models. README_TEMPLATE = ( "1. **[{model_name}](https://huggingface.co/docs/main/transformers/model_doc/{model_type})** (from " "<FILL INSTITUTION>) released with the paper [<FILL PAPER TITLE>](<FILL ARKIV LINK>) by <FILL AUTHORS>." ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--file", type=str, default=None, help="A specific file to check and/or fix") parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() check_copies(args.fix_and_overwrite, args.file) check_full_copies(args.fix_and_overwrite)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/update_tiny_models.py

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """A script running `create_dummy_models.py` with a pre-defined set of arguments. This file is intended to be used in a CI workflow file without the need of specifying arguments. It creates and uploads tiny models for all model classes (if their tiny versions are not on the Hub yet), as well as produces an updated version of `tests/utils/tiny_model_summary.json`. That updated file should be merged into the `main` branch of `transformers` so the pipeline testing will use the latest created/updated tiny models. """ import argparse import copy import json import multiprocessing import os import time from create_dummy_models import COMPOSITE_MODELS, create_tiny_models from huggingface_hub import ModelFilter, hf_api import transformers from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer from transformers.image_processing_utils import BaseImageProcessor def get_all_model_names(): model_names = set() # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way. for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]: module = getattr(transformers.models.auto, module_name, None) if module is None: continue # all mappings in a single auto modeling file mapping_names = [ x for x in dir(module) if x.endswith("_MAPPING_NAMES") and (x.startswith("MODEL_") or x.startswith("TF_MODEL_") or x.startswith("FLAX_MODEL_")) ] for name in mapping_names: mapping = getattr(module, name) if mapping is not None: for v in mapping.values(): if isinstance(v, (list, tuple)): model_names.update(v) elif isinstance(v, str): model_names.add(v) return sorted(model_names) def get_tiny_model_names_from_repo(): # All model names defined in auto mappings model_names = set(get_all_model_names()) with open("tests/utils/tiny_model_summary.json") as fp: tiny_model_info = json.load(fp) tiny_models_names = set() for model_base_name in tiny_model_info: tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"]) # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub. not_on_hub = model_names.difference(tiny_models_names) for model_name in copy.copy(tiny_models_names): if not model_name.startswith("TF") and f"TF{model_name}" in not_on_hub: tiny_models_names.remove(model_name) elif model_name.startswith("TF") and model_name[2:] in not_on_hub: tiny_models_names.remove(model_name) return sorted(tiny_models_names) def get_tiny_model_summary_from_hub(output_path): special_models = COMPOSITE_MODELS.values() # All tiny model base names on Hub model_names = get_all_model_names() models = hf_api.list_models( filter=ModelFilter( author="hf-internal-testing", ) ) _models = set() for x in models: model = x.modelId org, model = model.split("/") if not model.startswith("tiny-random-"): continue model = model.replace("tiny-random-", "") if not model[0].isupper(): continue if model not in model_names and model not in special_models: continue _models.add(model) models = sorted(_models) # All tiny model names on Hub summary = {} for model in models: repo_id = f"hf-internal-testing/tiny-random-{model}" model = model.split("-")[0] try: repo_info = hf_api.repo_info(repo_id) content = { "tokenizer_classes": set(), "processor_classes": set(), "model_classes": set(), "sha": repo_info.sha, } except Exception: continue try: time.sleep(1) tokenizer_fast = AutoTokenizer.from_pretrained(repo_id) content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__) except Exception: pass try: time.sleep(1) tokenizer_slow = AutoTokenizer.from_pretrained(repo_id, use_fast=False) content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__) except Exception: pass try: time.sleep(1) img_p = AutoImageProcessor.from_pretrained(repo_id) content["processor_classes"].add(img_p.__class__.__name__) except Exception: pass try: time.sleep(1) feat_p = AutoFeatureExtractor.from_pretrained(repo_id) if not isinstance(feat_p, BaseImageProcessor): content["processor_classes"].add(feat_p.__class__.__name__) except Exception: pass try: time.sleep(1) model_class = getattr(transformers, model) m = model_class.from_pretrained(repo_id) content["model_classes"].add(m.__class__.__name__) except Exception: pass try: time.sleep(1) model_class = getattr(transformers, f"TF{model}") m = model_class.from_pretrained(repo_id) content["model_classes"].add(m.__class__.__name__) except Exception: pass content["tokenizer_classes"] = sorted(content["tokenizer_classes"]) content["processor_classes"] = sorted(content["processor_classes"]) content["model_classes"] = sorted(content["model_classes"]) summary[model] = content with open(os.path.join(output_path, "hub_tiny_model_summary.json"), "w") as fp: json.dump(summary, fp, ensure_ascii=False, indent=4) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.") args = parser.parse_args() # This has to be `spawn` to avoid hanging forever! multiprocessing.set_start_method("spawn") output_path = "tiny_models" all = True model_types = None models_to_skip = get_tiny_model_names_from_repo() no_check = True upload = True organization = "hf-internal-testing" create_tiny_models( output_path, all, model_types, models_to_skip, no_check, upload, organization, token=os.environ.get("TOKEN", None), num_workers=args.num_workers, )

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/notification_service.py

# Copyright 2020 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. import ast import collections import functools import json import operator import os import re import sys import time from typing import Dict, List, Optional, Union import requests from get_ci_error_statistics import get_jobs from get_previous_daily_ci import get_last_daily_ci_reports from slack_sdk import WebClient client = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"]) NON_MODEL_TEST_MODULES = [ "benchmark", "deepspeed", "extended", "fixtures", "generation", "onnx", "optimization", "pipelines", "sagemaker", "trainer", "utils", ] def handle_test_results(test_results): expressions = test_results.split(" ") failed = 0 success = 0 # When the output is short enough, the output is surrounded by = signs: "== OUTPUT ==" # When it is too long, those signs are not present. time_spent = expressions[-2] if "=" in expressions[-1] else expressions[-1] for i, expression in enumerate(expressions): if "failed" in expression: failed += int(expressions[i - 1]) if "passed" in expression: success += int(expressions[i - 1]) return failed, success, time_spent def handle_stacktraces(test_results): # These files should follow the following architecture: # === FAILURES === # <path>:<line>: Error ... # <path>:<line>: Error ... # <empty line> total_stacktraces = test_results.split("\n")[1:-1] stacktraces = [] for stacktrace in total_stacktraces: try: line = stacktrace[: stacktrace.index(" ")].split(":")[-2] error_message = stacktrace[stacktrace.index(" ") :] stacktraces.append(f"(line {line}) {error_message}") except Exception: stacktraces.append("Cannot retrieve error message.") return stacktraces def dicts_to_sum(objects: Union[Dict[str, Dict], List[dict]]): if isinstance(objects, dict): lists = objects.values() else: lists = objects # Convert each dictionary to counter counters = map(collections.Counter, lists) # Sum all the counters return functools.reduce(operator.add, counters) class Message: def __init__( self, title: str, ci_title: str, model_results: Dict, additional_results: Dict, selected_warnings: List = None, prev_ci_artifacts=None, ): self.title = title self.ci_title = ci_title # Failures and success of the modeling tests self.n_model_success = sum(r["success"] for r in model_results.values()) self.n_model_single_gpu_failures = sum(dicts_to_sum(r["failed"])["single"] for r in model_results.values()) self.n_model_multi_gpu_failures = sum(dicts_to_sum(r["failed"])["multi"] for r in model_results.values()) # Some suites do not have a distinction between single and multi GPU. self.n_model_unknown_failures = sum(dicts_to_sum(r["failed"])["unclassified"] for r in model_results.values()) self.n_model_failures = ( self.n_model_single_gpu_failures + self.n_model_multi_gpu_failures + self.n_model_unknown_failures ) # Failures and success of the additional tests self.n_additional_success = sum(r["success"] for r in additional_results.values()) if len(additional_results) > 0: # `dicts_to_sum` uses `dicts_to_sum` which requires a non empty dictionary. Let's just add an empty entry. all_additional_failures = dicts_to_sum([r["failed"] for r in additional_results.values()]) self.n_additional_single_gpu_failures = all_additional_failures["single"] self.n_additional_multi_gpu_failures = all_additional_failures["multi"] self.n_additional_unknown_gpu_failures = all_additional_failures["unclassified"] else: self.n_additional_single_gpu_failures = 0 self.n_additional_multi_gpu_failures = 0 self.n_additional_unknown_gpu_failures = 0 self.n_additional_failures = ( self.n_additional_single_gpu_failures + self.n_additional_multi_gpu_failures + self.n_additional_unknown_gpu_failures ) # Results self.n_failures = self.n_model_failures + self.n_additional_failures self.n_success = self.n_model_success + self.n_additional_success self.n_tests = self.n_failures + self.n_success self.model_results = model_results self.additional_results = additional_results self.thread_ts = None if selected_warnings is None: selected_warnings = [] self.selected_warnings = selected_warnings self.prev_ci_artifacts = prev_ci_artifacts @property def time(self) -> str: all_results = [*self.model_results.values(), *self.additional_results.values()] time_spent = [r["time_spent"].split(", ")[0] for r in all_results if len(r["time_spent"])] total_secs = 0 for time in time_spent: time_parts = time.split(":") # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute. if len(time_parts) == 1: time_parts = [0, 0, time_parts[0]] hours, minutes, seconds = int(time_parts[0]), int(time_parts[1]), float(time_parts[2]) total_secs += hours * 3600 + minutes * 60 + seconds hours, minutes, seconds = total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60 return f"{int(hours)}h{int(minutes)}m{int(seconds)}s" @property def header(self) -> Dict: return {"type": "header", "text": {"type": "plain_text", "text": self.title}} @property def ci_title_section(self) -> Dict: return {"type": "section", "text": {"type": "mrkdwn", "text": self.ci_title}} @property def no_failures(self) -> Dict: return { "type": "section", "text": { "type": "plain_text", "text": f"🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.", "emoji": True, }, "accessory": { "type": "button", "text": {"type": "plain_text", "text": "Check Action results", "emoji": True}, "url": f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}", }, } @property def failures(self) -> Dict: return { "type": "section", "text": { "type": "plain_text", "text": ( f"There were {self.n_failures} failures, out of {self.n_tests} tests.\n" f"Number of model failures: {self.n_model_failures}.\n" f"The suite ran in {self.time}." ), "emoji": True, }, "accessory": { "type": "button", "text": {"type": "plain_text", "text": "Check Action results", "emoji": True}, "url": f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}", }, } @property def warnings(self) -> Dict: # If something goes wrong, let's avoid the CI report failing to be sent. button_text = "Check warnings (Link not found)" # Use the workflow run link job_link = f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}" for job in github_actions_jobs: if "Extract warnings in CI artifacts" in job["name"] and job["conclusion"] == "success": button_text = "Check warnings" # Use the actual job link job_link = job["html_url"] break huggingface_hub_warnings = [x for x in self.selected_warnings if "huggingface_hub" in x] text = f"There are {len(self.selected_warnings)} warnings being selected." text += f"\n{len(huggingface_hub_warnings)} of them are from `huggingface_hub`." return { "type": "section", "text": { "type": "plain_text", "text": text, "emoji": True, }, "accessory": { "type": "button", "text": {"type": "plain_text", "text": button_text, "emoji": True}, "url": job_link, }, } @staticmethod def get_device_report(report, rjust=6): if "single" in report and "multi" in report: return f"{str(report['single']).rjust(rjust)} | {str(report['multi']).rjust(rjust)} | " elif "single" in report: return f"{str(report['single']).rjust(rjust)} | {'0'.rjust(rjust)} | " elif "multi" in report: return f"{'0'.rjust(rjust)} | {str(report['multi']).rjust(rjust)} | " @property def category_failures(self) -> Dict: model_failures = [v["failed"] for v in self.model_results.values()] category_failures = {} for model_failure in model_failures: for key, value in model_failure.items(): if key not in category_failures: category_failures[key] = dict(value) else: category_failures[key]["unclassified"] += value["unclassified"] category_failures[key]["single"] += value["single"] category_failures[key]["multi"] += value["multi"] individual_reports = [] for key, value in category_failures.items(): device_report = self.get_device_report(value) if sum(value.values()): if device_report: individual_reports.append(f"{device_report}{key}") else: individual_reports.append(key) header = "Single | Multi | Category\n" category_failures_report = prepare_reports( title="The following modeling categories had failures", header=header, reports=individual_reports ) return {"type": "section", "text": {"type": "mrkdwn", "text": category_failures_report}} def compute_diff_for_failure_reports(self, curr_failure_report, prev_failure_report): # noqa # Remove the leading and training parts that don't contain failure count information. model_failures = curr_failure_report.split("\n")[3:-2] prev_model_failures = prev_failure_report.split("\n")[3:-2] entries_changed = set(model_failures).difference(prev_model_failures) prev_map = {} for f in prev_model_failures: items = [x.strip() for x in f.split("| ")] prev_map[items[-1]] = [int(x) for x in items[:-1]] curr_map = {} for f in entries_changed: items = [x.strip() for x in f.split("| ")] curr_map[items[-1]] = [int(x) for x in items[:-1]] diff_map = {} for k, v in curr_map.items(): if k not in prev_map: diff_map[k] = v else: diff = [x - y for x, y in zip(v, prev_map[k])] if max(diff) > 0: diff_map[k] = diff entries_changed = [] for model_name, diff_values in diff_map.items(): diff = [str(x) for x in diff_values] diff = [f"+{x}" if (x != "0" and not x.startswith("-")) else x for x in diff] diff = [x.rjust(9) for x in diff] device_report = " | ".join(diff) + " | " report = f"{device_report}{model_name}" entries_changed.append(report) entries_changed = sorted(entries_changed, key=lambda s: s.split("| ")[-1]) return entries_changed @property def model_failures(self) -> List[Dict]: # Obtain per-model failures def per_model_sum(model_category_dict): return dicts_to_sum(model_category_dict["failed"].values()) failures = {} non_model_failures = { k: per_model_sum(v) for k, v in self.model_results.items() if sum(per_model_sum(v).values()) } for k, v in self.model_results.items(): if k in NON_MODEL_TEST_MODULES: pass if sum(per_model_sum(v).values()): dict_failed = dict(v["failed"]) pytorch_specific_failures = dict_failed.pop("PyTorch") tensorflow_specific_failures = dict_failed.pop("TensorFlow") other_failures = dicts_to_sum(dict_failed.values()) failures[k] = { "PyTorch": pytorch_specific_failures, "TensorFlow": tensorflow_specific_failures, "other": other_failures, } model_reports = [] other_module_reports = [] for key, value in non_model_failures.items(): if key in NON_MODEL_TEST_MODULES: device_report = self.get_device_report(value) if sum(value.values()): if device_report: report = f"{device_report}{key}" else: report = key other_module_reports.append(report) for key, value in failures.items(): device_report_values = [ value["PyTorch"]["single"], value["PyTorch"]["multi"], value["TensorFlow"]["single"], value["TensorFlow"]["multi"], sum(value["other"].values()), ] if sum(device_report_values): device_report = " | ".join([str(x).rjust(9) for x in device_report_values]) + " | " report = f"{device_report}{key}" model_reports.append(report) # (Possibly truncated) reports for the current workflow run - to be sent to Slack channels model_header = "Single PT | Multi PT | Single TF | Multi TF | Other | Category\n" sorted_model_reports = sorted(model_reports, key=lambda s: s.split("| ")[-1]) model_failures_report = prepare_reports( title="These following model modules had failures", header=model_header, reports=sorted_model_reports ) module_header = "Single | Multi | Category\n" sorted_module_reports = sorted(other_module_reports, key=lambda s: s.split("| ")[-1]) module_failures_report = prepare_reports( title="The following non-model modules had failures", header=module_header, reports=sorted_module_reports ) # To be sent to Slack channels model_failure_sections = [ {"type": "section", "text": {"type": "mrkdwn", "text": model_failures_report}}, {"type": "section", "text": {"type": "mrkdwn", "text": module_failures_report}}, ] # Save the complete (i.e. no truncation) failure tables (of the current workflow run) # (to be uploaded as artifacts) model_failures_report = prepare_reports( title="These following model modules had failures", header=model_header, reports=sorted_model_reports, to_truncate=False, ) file_path = os.path.join(os.getcwd(), "prev_ci_results/model_failures_report.txt") with open(file_path, "w", encoding="UTF-8") as fp: fp.write(model_failures_report) module_failures_report = prepare_reports( title="The following non-model modules had failures", header=module_header, reports=sorted_module_reports, to_truncate=False, ) file_path = os.path.join(os.getcwd(), "prev_ci_results/module_failures_report.txt") with open(file_path, "w", encoding="UTF-8") as fp: fp.write(module_failures_report) if self.prev_ci_artifacts is not None: # if the last run produces artifact named `prev_ci_results` if ( "prev_ci_results" in self.prev_ci_artifacts and "model_failures_report.txt" in self.prev_ci_artifacts["prev_ci_results"] ): # Compute the difference of the previous/current (model failure) table prev_model_failures = self.prev_ci_artifacts["prev_ci_results"]["model_failures_report.txt"] entries_changed = self.compute_diff_for_failure_reports(model_failures_report, prev_model_failures) if len(entries_changed) > 0: # Save the complete difference diff_report = prepare_reports( title="Changed model modules failures", header=model_header, reports=entries_changed, to_truncate=False, ) file_path = os.path.join(os.getcwd(), "prev_ci_results/changed_model_failures_report.txt") with open(file_path, "w", encoding="UTF-8") as fp: fp.write(diff_report) # To be sent to Slack channels diff_report = prepare_reports( title="*Changed model modules failures*", header=model_header, reports=entries_changed, ) model_failure_sections.append( {"type": "section", "text": {"type": "mrkdwn", "text": diff_report}}, ) return model_failure_sections @property def additional_failures(self) -> Dict: failures = {k: v["failed"] for k, v in self.additional_results.items()} errors = {k: v["error"] for k, v in self.additional_results.items()} individual_reports = [] for key, value in failures.items(): device_report = self.get_device_report(value) if sum(value.values()) or errors[key]: report = f"{key}" if errors[key]: report = f"[Errored out] {report}" if device_report: report = f"{device_report}{report}" individual_reports.append(report) header = "Single | Multi | Category\n" failures_report = prepare_reports( title="The following non-modeling tests had failures", header=header, reports=individual_reports ) return {"type": "section", "text": {"type": "mrkdwn", "text": failures_report}} @property def payload(self) -> str: blocks = [self.header] if self.ci_title: blocks.append(self.ci_title_section) if self.n_model_failures > 0 or self.n_additional_failures > 0: blocks.append(self.failures) if self.n_model_failures > 0: blocks.append(self.category_failures) for block in self.model_failures: if block["text"]["text"]: blocks.append(block) if self.n_additional_failures > 0: blocks.append(self.additional_failures) if self.n_model_failures == 0 and self.n_additional_failures == 0: blocks.append(self.no_failures) if len(self.selected_warnings) > 0: blocks.append(self.warnings) new_failure_blocks = self.get_new_model_failure_blocks(with_header=False) if len(new_failure_blocks) > 0: blocks.extend(new_failure_blocks) return json.dumps(blocks) @staticmethod def error_out(title, ci_title="", runner_not_available=False, runner_failed=False, setup_failed=False): blocks = [] title_block = {"type": "header", "text": {"type": "plain_text", "text": title}} blocks.append(title_block) if ci_title: ci_title_block = {"type": "section", "text": {"type": "mrkdwn", "text": ci_title}} blocks.append(ci_title_block) offline_runners = [] if runner_not_available: text = "💔 CI runners are not available! Tests are not run. 😭" result = os.environ.get("OFFLINE_RUNNERS") if result is not None: offline_runners = json.loads(result) elif runner_failed: text = "💔 CI runners have problems! Tests are not run. 😭" elif setup_failed: text = "💔 Setup job failed. Tests are not run. 😭" else: text = "💔 There was an issue running the tests. 😭" error_block_1 = { "type": "header", "text": { "type": "plain_text", "text": text, }, } text = "" if len(offline_runners) > 0: text = "\n • " + "\n • ".join(offline_runners) text = f"The following runners are offline:\n{text}\n\n" text += "🙏 Let's fix it ASAP! 🙏" error_block_2 = { "type": "section", "text": { "type": "plain_text", "text": text, }, "accessory": { "type": "button", "text": {"type": "plain_text", "text": "Check Action results", "emoji": True}, "url": f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}", }, } blocks.extend([error_block_1, error_block_2]) payload = json.dumps(blocks) print("Sending the following payload") print(json.dumps({"blocks": blocks})) client.chat_postMessage( channel=SLACK_REPORT_CHANNEL_ID, text=text, blocks=payload, ) def post(self): payload = self.payload print("Sending the following payload") print(json.dumps({"blocks": json.loads(payload)})) text = f"{self.n_failures} failures out of {self.n_tests} tests," if self.n_failures else "All tests passed." self.thread_ts = client.chat_postMessage( channel=SLACK_REPORT_CHANNEL_ID, blocks=payload, text=text, ) def get_reply_blocks(self, job_name, job_result, failures, device, text): """ failures: A list with elements of the form {"line": full test name, "trace": error trace} """ # `text` must be less than 3001 characters in Slack SDK # keep some room for adding "[Truncated]" when necessary MAX_ERROR_TEXT = 3000 - len("[Truncated]") failure_text = "" for idx, error in enumerate(failures): new_text = failure_text + f'*{error["line"]}*\n_{error["trace"]}_\n\n' if len(new_text) > MAX_ERROR_TEXT: # `failure_text` here has length <= 3000 failure_text = failure_text + "[Truncated]" break # `failure_text` here has length <= MAX_ERROR_TEXT failure_text = new_text title = job_name if device is not None: title += f" ({device}-gpu)" content = {"type": "section", "text": {"type": "mrkdwn", "text": text}} # TODO: Make sure we always have a valid job link (or at least a way not to break the report sending) # Currently we get the device from a job's artifact name. # If a device is found, the job name should contain the device type, for example, `XXX (single-gpu)`. # This could be done by adding `machine_type` in a job's `strategy`. # (If `job_result["job_link"][device]` is `None`, we get an error: `... [ERROR] must provide a string ...`) if job_result["job_link"] is not None and job_result["job_link"][device] is not None: content["accessory"] = { "type": "button", "text": {"type": "plain_text", "text": "GitHub Action job", "emoji": True}, "url": job_result["job_link"][device], } return [ {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}}, content, {"type": "section", "text": {"type": "mrkdwn", "text": failure_text}}, ] def get_new_model_failure_blocks(self, with_header=True): if self.prev_ci_artifacts is None: return {} sorted_dict = sorted(self.model_results.items(), key=lambda t: t[0]) prev_model_results = {} if ( "prev_ci_results" in self.prev_ci_artifacts and "model_results.json" in self.prev_ci_artifacts["prev_ci_results"] ): prev_model_results = json.loads(self.prev_ci_artifacts["prev_ci_results"]["model_results.json"]) all_failure_lines = {} for job, job_result in sorted_dict: if len(job_result["failures"]): devices = sorted(job_result["failures"].keys(), reverse=True) for device in devices: failures = job_result["failures"][device] prev_error_lines = {} if job in prev_model_results and device in prev_model_results[job]["failures"]: prev_error_lines = {error["line"] for error in prev_model_results[job]["failures"][device]} url = None if job_result["job_link"] is not None and job_result["job_link"][device] is not None: url = job_result["job_link"][device] for idx, error in enumerate(failures): if error["line"] in prev_error_lines: continue new_text = f'{error["line"]}\n\n' if new_text not in all_failure_lines: all_failure_lines[new_text] = [] all_failure_lines[new_text].append(f"<{url}|{device}>" if url is not None else device) MAX_ERROR_TEXT = 3000 - len("[Truncated]") - len("```New model failures```\n\n") failure_text = "" for line, devices in all_failure_lines.items(): new_text = failure_text + f"{'|'.join(devices)} gpu\n{line}" if len(new_text) > MAX_ERROR_TEXT: # `failure_text` here has length <= 3000 failure_text = failure_text + "[Truncated]" break # `failure_text` here has length <= MAX_ERROR_TEXT failure_text = new_text blocks = [] if failure_text: if with_header: blocks.append( {"type": "header", "text": {"type": "plain_text", "text": "New model failures", "emoji": True}} ) else: failure_text = f"*New model failures*\n\n{failure_text}" blocks.append({"type": "section", "text": {"type": "mrkdwn", "text": failure_text}}) return blocks def post_reply(self): if self.thread_ts is None: raise ValueError("Can only post reply if a post has been made.") sorted_dict = sorted(self.model_results.items(), key=lambda t: t[0]) for job, job_result in sorted_dict: if len(job_result["failures"]): for device, failures in job_result["failures"].items(): text = "\n".join( sorted([f"*{k}*: {v[device]}" for k, v in job_result["failed"].items() if v[device]]) ) blocks = self.get_reply_blocks(job, job_result, failures, device, text=text) print("Sending the following reply") print(json.dumps({"blocks": blocks})) client.chat_postMessage( channel=SLACK_REPORT_CHANNEL_ID, text=f"Results for {job}", blocks=blocks, thread_ts=self.thread_ts["ts"], ) time.sleep(1) for job, job_result in self.additional_results.items(): if len(job_result["failures"]): for device, failures in job_result["failures"].items(): blocks = self.get_reply_blocks( job, job_result, failures, device, text=f'Number of failures: {job_result["failed"][device]}', ) print("Sending the following reply") print(json.dumps({"blocks": blocks})) client.chat_postMessage( channel=SLACK_REPORT_CHANNEL_ID, text=f"Results for {job}", blocks=blocks, thread_ts=self.thread_ts["ts"], ) time.sleep(1) blocks = self.get_new_model_failure_blocks() if blocks: print("Sending the following reply") print(json.dumps({"blocks": blocks})) client.chat_postMessage( channel=SLACK_REPORT_CHANNEL_ID, text="Results for new failures", blocks=blocks, thread_ts=self.thread_ts["ts"], ) time.sleep(1) def retrieve_artifact(artifact_path: str, gpu: Optional[str]): if gpu not in [None, "single", "multi"]: raise ValueError(f"Invalid GPU for artifact. Passed GPU: `{gpu}`.") _artifact = {} if os.path.exists(artifact_path): files = os.listdir(artifact_path) for file in files: try: with open(os.path.join(artifact_path, file)) as f: _artifact[file.split(".")[0]] = f.read() except UnicodeDecodeError as e: raise ValueError(f"Could not open {os.path.join(artifact_path, file)}.") from e return _artifact def retrieve_available_artifacts(): class Artifact: def __init__(self, name: str, single_gpu: bool = False, multi_gpu: bool = False): self.name = name self.single_gpu = single_gpu self.multi_gpu = multi_gpu self.paths = [] def __str__(self): return self.name def add_path(self, path: str, gpu: str = None): self.paths.append({"name": self.name, "path": path, "gpu": gpu}) _available_artifacts: Dict[str, Artifact] = {} directories = filter(os.path.isdir, os.listdir()) for directory in directories: artifact_name = directory name_parts = artifact_name.split("_postfix_") if len(name_parts) > 1: artifact_name = name_parts[0] if artifact_name.startswith("single-gpu"): artifact_name = artifact_name[len("single-gpu") + 1 :] if artifact_name in _available_artifacts: _available_artifacts[artifact_name].single_gpu = True else: _available_artifacts[artifact_name] = Artifact(artifact_name, single_gpu=True) _available_artifacts[artifact_name].add_path(directory, gpu="single") elif artifact_name.startswith("multi-gpu"): artifact_name = artifact_name[len("multi-gpu") + 1 :] if artifact_name in _available_artifacts: _available_artifacts[artifact_name].multi_gpu = True else: _available_artifacts[artifact_name] = Artifact(artifact_name, multi_gpu=True) _available_artifacts[artifact_name].add_path(directory, gpu="multi") else: if artifact_name not in _available_artifacts: _available_artifacts[artifact_name] = Artifact(artifact_name) _available_artifacts[artifact_name].add_path(directory) return _available_artifacts def prepare_reports(title, header, reports, to_truncate=True): report = "" MAX_ERROR_TEXT = 3000 - len("[Truncated]") if not to_truncate: MAX_ERROR_TEXT = float("inf") if len(reports) > 0: # `text` must be less than 3001 characters in Slack SDK # keep some room for adding "[Truncated]" when necessary for idx in range(len(reports)): _report = header + "\n".join(reports[: idx + 1]) new_report = f"{title}:\n```\n{_report}\n```\n" if len(new_report) > MAX_ERROR_TEXT: # `report` here has length <= 3000 report = report + "[Truncated]" break report = new_report return report if __name__ == "__main__": SLACK_REPORT_CHANNEL_ID = os.environ["SLACK_REPORT_CHANNEL"] # runner_status = os.environ.get("RUNNER_STATUS") # runner_env_status = os.environ.get("RUNNER_ENV_STATUS") setup_status = os.environ.get("SETUP_STATUS") # runner_not_available = True if runner_status is not None and runner_status != "success" else False # runner_failed = True if runner_env_status is not None and runner_env_status != "success" else False # Let's keep the lines regardig runners' status (we might be able to use them again in the future) runner_not_available = False runner_failed = False # Some jobs don't depend (`needs`) on the job `setup`: in this case, the status of the job `setup` is `skipped`. setup_failed = False if setup_status in ["skipped", "success"] else True org = "huggingface" repo = "transformers" repository_full_name = f"{org}/{repo}" # This env. variable is set in workflow file (under the job `send_results`). ci_event = os.environ["CI_EVENT"] # To find the PR number in a commit title, for example, `Add AwesomeFormer model (#99999)` pr_number_re = re.compile(r"\(#(\d+)\)$") title = f"🤗 Results of the {ci_event} tests." # Add Commit/PR title with a link for push CI # (check the title in 2 env. variables - depending on the CI is triggered via `push` or `workflow_run` event) ci_title_push = os.environ.get("CI_TITLE_PUSH") ci_title_workflow_run = os.environ.get("CI_TITLE_WORKFLOW_RUN") ci_title = ci_title_push if ci_title_push else ci_title_workflow_run ci_sha = os.environ.get("CI_SHA") ci_url = None if ci_sha: ci_url = f"https://github.com/{repository_full_name}/commit/{ci_sha}" if ci_title is not None: if ci_url is None: raise ValueError( "When a title is found (`ci_title`), it means a `push` event or a `workflow_run` even (triggered by " "another `push` event), and the commit SHA has to be provided in order to create the URL to the " "commit page." ) ci_title = ci_title.strip().split("\n")[0].strip() # Retrieve the PR title and author login to complete the report commit_number = ci_url.split("/")[-1] ci_detail_url = f"https://api.github.com/repos/{repository_full_name}/commits/{commit_number}" ci_details = requests.get(ci_detail_url).json() ci_author = ci_details["author"]["login"] merged_by = None # Find the PR number (if any) and change the url to the actual PR page. numbers = pr_number_re.findall(ci_title) if len(numbers) > 0: pr_number = numbers[0] ci_detail_url = f"https://api.github.com/repos/{repository_full_name}/pulls/{pr_number}" ci_details = requests.get(ci_detail_url).json() ci_author = ci_details["user"]["login"] ci_url = f"https://github.com/{repository_full_name}/pull/{pr_number}" merged_by = ci_details["merged_by"]["login"] if merged_by is None: ci_title = f"<{ci_url}|{ci_title}>\nAuthor: {ci_author}" else: ci_title = f"<{ci_url}|{ci_title}>\nAuthor: {ci_author} | Merged by: {merged_by}" elif ci_sha: ci_title = f"<{ci_url}|commit: {ci_sha}>" else: ci_title = "" if runner_not_available or runner_failed or setup_failed: Message.error_out(title, ci_title, runner_not_available, runner_failed, setup_failed) exit(0) # sys.argv[0] is always `utils/notification_service.py`. arguments = sys.argv[1:] # In our usage in `.github/workflows/slack-report.yml`, we always pass an argument when calling this script. # The argument could be an empty string `""` if a job doesn't depend on the job `setup`. if arguments[0] == "": models = [] else: model_list_as_str = arguments[0] try: folder_slices = ast.literal_eval(model_list_as_str) # Need to change from elements like `models/bert` to `models_bert` (the ones used as artifact names). models = [x.replace("models/", "models_") for folders in folder_slices for x in folders] except Exception: Message.error_out(title, ci_title) raise ValueError("Errored out.") github_actions_jobs = get_jobs( workflow_run_id=os.environ["GITHUB_RUN_ID"], token=os.environ["ACCESS_REPO_INFO_TOKEN"] ) github_actions_job_links = {job["name"]: job["html_url"] for job in github_actions_jobs} artifact_name_to_job_map = {} for job in github_actions_jobs: for step in job["steps"]: if step["name"].startswith("Test suite reports artifacts: "): artifact_name = step["name"][len("Test suite reports artifacts: ") :] artifact_name_to_job_map[artifact_name] = job break available_artifacts = retrieve_available_artifacts() modeling_categories = [ "PyTorch", "TensorFlow", "Flax", "Tokenizers", "Pipelines", "Trainer", "ONNX", "Auto", "Unclassified", ] # This dict will contain all the information relative to each model: # - Failures: the total, as well as the number of failures per-category defined above # - Success: total # - Time spent: as a comma-separated list of elapsed time # - Failures: as a line-break separated list of errors model_results = { model: { "failed": {m: {"unclassified": 0, "single": 0, "multi": 0} for m in modeling_categories}, "success": 0, "time_spent": "", "failures": {}, "job_link": {}, } for model in models if f"run_models_gpu_{model}_test_reports" in available_artifacts } unclassified_model_failures = [] for model in model_results.keys(): for artifact_path in available_artifacts[f"run_models_gpu_{model}_test_reports"].paths: artifact = retrieve_artifact(artifact_path["path"], artifact_path["gpu"]) if "stats" in artifact: # Link to the GitHub Action job job = artifact_name_to_job_map[artifact_path["path"]] model_results[model]["job_link"][artifact_path["gpu"]] = job["html_url"] failed, success, time_spent = handle_test_results(artifact["stats"]) model_results[model]["success"] += success model_results[model]["time_spent"] += time_spent[1:-1] + ", " stacktraces = handle_stacktraces(artifact["failures_line"]) for line in artifact["summary_short"].split("\n"): if line.startswith("FAILED "): line = line[len("FAILED ") :] line = line.split()[0].replace("\n", "") if artifact_path["gpu"] not in model_results[model]["failures"]: model_results[model]["failures"][artifact_path["gpu"]] = [] model_results[model]["failures"][artifact_path["gpu"]].append( {"line": line, "trace": stacktraces.pop(0)} ) if re.search("test_modeling_tf_", line): model_results[model]["failed"]["TensorFlow"][artifact_path["gpu"]] += 1 elif re.search("test_modeling_flax_", line): model_results[model]["failed"]["Flax"][artifact_path["gpu"]] += 1 elif re.search("test_modeling", line): model_results[model]["failed"]["PyTorch"][artifact_path["gpu"]] += 1 elif re.search("test_tokenization", line): model_results[model]["failed"]["Tokenizers"][artifact_path["gpu"]] += 1 elif re.search("test_pipelines", line): model_results[model]["failed"]["Pipelines"][artifact_path["gpu"]] += 1 elif re.search("test_trainer", line): model_results[model]["failed"]["Trainer"][artifact_path["gpu"]] += 1 elif re.search("onnx", line): model_results[model]["failed"]["ONNX"][artifact_path["gpu"]] += 1 elif re.search("auto", line): model_results[model]["failed"]["Auto"][artifact_path["gpu"]] += 1 else: model_results[model]["failed"]["Unclassified"][artifact_path["gpu"]] += 1 unclassified_model_failures.append(line) # Additional runs additional_files = { "PyTorch pipelines": "run_pipelines_torch_gpu_test_reports", "TensorFlow pipelines": "run_pipelines_tf_gpu_test_reports", "Examples directory": "run_examples_gpu_test_reports", "Torch CUDA extension tests": "run_torch_cuda_extensions_gpu_test_reports", } if ci_event in ["push", "Nightly CI"] or ci_event.startswith("Past CI"): del additional_files["Examples directory"] del additional_files["PyTorch pipelines"] del additional_files["TensorFlow pipelines"] elif ci_event.startswith("Scheduled CI (AMD)"): del additional_files["TensorFlow pipelines"] del additional_files["Torch CUDA extension tests"] elif ci_event.startswith("Push CI (AMD)"): additional_files = {} # A map associating the job names (specified by `inputs.job` in a workflow file) with the keys of # `additional_files`. This is used to remove some entries in `additional_files` that are not concerned by a # specific job. See below. job_to_test_map = { "run_pipelines_torch_gpu": "PyTorch pipelines", "run_pipelines_tf_gpu": "TensorFlow pipelines", "run_examples_gpu": "Examples directory", "run_torch_cuda_extensions_gpu": "Torch CUDA extension tests", } # Remove some entries in `additional_files` if they are not concerned. test_name = None job_name = os.getenv("CI_TEST_JOB") if job_name in job_to_test_map: test_name = job_to_test_map[job_name] additional_files = {k: v for k, v in additional_files.items() if k == test_name} additional_results = { key: { "failed": {"unclassified": 0, "single": 0, "multi": 0}, "success": 0, "time_spent": "", "error": False, "failures": {}, "job_link": {}, } for key in additional_files.keys() } for key in additional_results.keys(): # If a whole suite of test fails, the artifact isn't available. if additional_files[key] not in available_artifacts: additional_results[key]["error"] = True continue for artifact_path in available_artifacts[additional_files[key]].paths: # Link to the GitHub Action job job = artifact_name_to_job_map[artifact_path["path"]] additional_results[key]["job_link"][artifact_path["gpu"]] = job["html_url"] artifact = retrieve_artifact(artifact_path["path"], artifact_path["gpu"]) stacktraces = handle_stacktraces(artifact["failures_line"]) failed, success, time_spent = handle_test_results(artifact["stats"]) additional_results[key]["failed"][artifact_path["gpu"] or "unclassified"] += failed additional_results[key]["success"] += success additional_results[key]["time_spent"] += time_spent[1:-1] + ", " if len(artifact["errors"]): additional_results[key]["error"] = True if failed: for line in artifact["summary_short"].split("\n"): if line.startswith("FAILED "): line = line[len("FAILED ") :] line = line.split()[0].replace("\n", "") if artifact_path["gpu"] not in additional_results[key]["failures"]: additional_results[key]["failures"][artifact_path["gpu"]] = [] additional_results[key]["failures"][artifact_path["gpu"]].append( {"line": line, "trace": stacktraces.pop(0)} ) # Let's only check the warning for the model testing job. Currently, the job `run_extract_warnings` is only run # when `inputs.job` (in the workflow file) is `run_models_gpu`. The reason is: otherwise we need to save several # artifacts with different names which complicates the logic for an insignificant part of the CI workflow reporting. selected_warnings = [] if job_name == "run_models_gpu": if "warnings_in_ci" in available_artifacts: directory = available_artifacts["warnings_in_ci"].paths[0]["path"] with open(os.path.join(directory, "selected_warnings.json")) as fp: selected_warnings = json.load(fp) if not os.path.isdir(os.path.join(os.getcwd(), "prev_ci_results")): os.makedirs(os.path.join(os.getcwd(), "prev_ci_results")) # Only the model testing job is concerned: this condition is to avoid other jobs to upload the empty list as # results. if job_name == "run_models_gpu": with open("prev_ci_results/model_results.json", "w", encoding="UTF-8") as fp: json.dump(model_results, fp, indent=4, ensure_ascii=False) prev_ci_artifacts = None target_workflow = "huggingface/transformers/.github/workflows/self-scheduled.yml@refs/heads/main" if os.environ.get("CI_WORKFLOW_REF") == target_workflow: # Get the last previously completed CI's failure tables artifact_names = ["prev_ci_results"] output_dir = os.path.join(os.getcwd(), "previous_reports") os.makedirs(output_dir, exist_ok=True) prev_ci_artifacts = get_last_daily_ci_reports( artifact_names=artifact_names, output_dir=output_dir, token=os.environ["ACCESS_REPO_INFO_TOKEN"] ) message = Message( title, ci_title, model_results, additional_results, selected_warnings=selected_warnings, prev_ci_artifacts=prev_ci_artifacts, ) # send report only if there is any failure (for push CI) if message.n_failures or (ci_event != "push" and not ci_event.startswith("Push CI (AMD)")): message.post() message.post_reply()

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/notification_service_doc_tests.py

# Copyright 2022 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. import json import os import re import time from typing import Dict, List from get_ci_error_statistics import get_jobs from slack_sdk import WebClient client = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"]) def handle_test_results(test_results): expressions = test_results.split(" ") failed = 0 success = 0 # When the output is short enough, the output is surrounded by = signs: "== OUTPUT ==" # When it is too long, those signs are not present. time_spent = expressions[-2] if "=" in expressions[-1] else expressions[-1] for i, expression in enumerate(expressions): if "failed" in expression: failed += int(expressions[i - 1]) if "passed" in expression: success += int(expressions[i - 1]) return failed, success, time_spent def extract_first_line_failure(failures_short_lines): failures = {} file = None in_error = False for line in failures_short_lines.split("\n"): if re.search(r"_ \[doctest\]", line): in_error = True file = line.split(" ")[2] elif in_error and not line.split(" ")[0].isdigit(): failures[file] = line in_error = False return failures class Message: def __init__(self, title: str, doc_test_results: Dict): self.title = title self.n_success = sum(job_result["n_success"] for job_result in doc_test_results.values()) self.n_failures = sum(job_result["n_failures"] for job_result in doc_test_results.values()) self.n_tests = self.n_success + self.n_failures # Failures and success of the modeling tests self.doc_test_results = doc_test_results @property def time(self) -> str: all_results = [*self.doc_test_results.values()] time_spent = [r["time_spent"].split(", ")[0] for r in all_results if len(r["time_spent"])] total_secs = 0 for time in time_spent: time_parts = time.split(":") # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute. if len(time_parts) == 1: time_parts = [0, 0, time_parts[0]] hours, minutes, seconds = int(time_parts[0]), int(time_parts[1]), float(time_parts[2]) total_secs += hours * 3600 + minutes * 60 + seconds hours, minutes, seconds = total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60 return f"{int(hours)}h{int(minutes)}m{int(seconds)}s" @property def header(self) -> Dict: return {"type": "header", "text": {"type": "plain_text", "text": self.title}} @property def no_failures(self) -> Dict: return { "type": "section", "text": { "type": "plain_text", "text": f"🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.", "emoji": True, }, "accessory": { "type": "button", "text": {"type": "plain_text", "text": "Check Action results", "emoji": True}, "url": f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}", }, } @property def failures(self) -> Dict: return { "type": "section", "text": { "type": "plain_text", "text": ( f"There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in" f" {self.time}." ), "emoji": True, }, "accessory": { "type": "button", "text": {"type": "plain_text", "text": "Check Action results", "emoji": True}, "url": f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}", }, } @property def category_failures(self) -> List[Dict]: failure_blocks = [] MAX_ERROR_TEXT = 3000 - len("The following examples had failures:\n\n\n\n") - len("[Truncated]\n") line_length = 40 category_failures = {k: v["failed"] for k, v in doc_test_results.items() if isinstance(v, dict)} def single_category_failures(category, failures): text = "" if len(failures) == 0: return "" text += f"*{category} failures*:".ljust(line_length // 2).rjust(line_length // 2) + "\n" for idx, failure in enumerate(failures): new_text = text + f"`{failure}`\n" if len(new_text) > MAX_ERROR_TEXT: text = text + "[Truncated]\n" break text = new_text return text for category, failures in category_failures.items(): report = single_category_failures(category, failures) if len(report) == 0: continue block = { "type": "section", "text": { "type": "mrkdwn", "text": f"The following examples had failures:\n\n\n{report}\n", }, } failure_blocks.append(block) return failure_blocks @property def payload(self) -> str: blocks = [self.header] if self.n_failures > 0: blocks.append(self.failures) if self.n_failures > 0: blocks.extend(self.category_failures) if self.n_failures == 0: blocks.append(self.no_failures) return json.dumps(blocks) @staticmethod def error_out(): payload = [ { "type": "section", "text": { "type": "plain_text", "text": "There was an issue running the tests.", }, "accessory": { "type": "button", "text": {"type": "plain_text", "text": "Check Action results", "emoji": True}, "url": f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}", }, } ] print("Sending the following payload") print(json.dumps({"blocks": json.loads(payload)})) client.chat_postMessage( channel=SLACK_REPORT_CHANNEL_ID, text="There was an issue running the tests.", blocks=payload, ) def post(self): print("Sending the following payload") print(json.dumps({"blocks": json.loads(self.payload)})) text = f"{self.n_failures} failures out of {self.n_tests} tests," if self.n_failures else "All tests passed." self.thread_ts = client.chat_postMessage( channel=SLACK_REPORT_CHANNEL_ID, blocks=self.payload, text=text, ) def get_reply_blocks(self, job_name, job_link, failures, text): # `text` must be less than 3001 characters in Slack SDK # keep some room for adding "[Truncated]" when necessary MAX_ERROR_TEXT = 3000 - len("[Truncated]") failure_text = "" for key, value in failures.items(): new_text = failure_text + f"*{key}*\n_{value}_\n\n" if len(new_text) > MAX_ERROR_TEXT: # `failure_text` here has length <= 3000 failure_text = failure_text + "[Truncated]" break # `failure_text` here has length <= MAX_ERROR_TEXT failure_text = new_text title = job_name content = {"type": "section", "text": {"type": "mrkdwn", "text": text}} if job_link is not None: content["accessory"] = { "type": "button", "text": {"type": "plain_text", "text": "GitHub Action job", "emoji": True}, "url": job_link, } return [ {"type": "header", "text": {"type": "plain_text", "text": title, "emoji": True}}, content, {"type": "section", "text": {"type": "mrkdwn", "text": failure_text}}, ] def post_reply(self): if self.thread_ts is None: raise ValueError("Can only post reply if a post has been made.") sorted_dict = sorted(self.doc_test_results.items(), key=lambda t: t[0]) for job_name, job_result in sorted_dict: if len(job_result["failures"]) > 0: text = f"*Num failures* :{len(job_result['failed'])} \n" failures = job_result["failures"] blocks = self.get_reply_blocks(job_name, job_result["job_link"], failures, text=text) print("Sending the following reply") print(json.dumps({"blocks": blocks})) client.chat_postMessage( channel=SLACK_REPORT_CHANNEL_ID, text=f"Results for {job_name}", blocks=blocks, thread_ts=self.thread_ts["ts"], ) time.sleep(1) def retrieve_artifact(name: str): _artifact = {} if os.path.exists(name): files = os.listdir(name) for file in files: try: with open(os.path.join(name, file), encoding="utf-8") as f: _artifact[file.split(".")[0]] = f.read() except UnicodeDecodeError as e: raise ValueError(f"Could not open {os.path.join(name, file)}.") from e return _artifact def retrieve_available_artifacts(): class Artifact: def __init__(self, name: str): self.name = name self.paths = [] def __str__(self): return self.name def add_path(self, path: str): self.paths.append({"name": self.name, "path": path}) _available_artifacts: Dict[str, Artifact] = {} directories = filter(os.path.isdir, os.listdir()) for directory in directories: artifact_name = directory if artifact_name not in _available_artifacts: _available_artifacts[artifact_name] = Artifact(artifact_name) _available_artifacts[artifact_name].add_path(directory) return _available_artifacts if __name__ == "__main__": SLACK_REPORT_CHANNEL_ID = os.environ["SLACK_REPORT_CHANNEL"] github_actions_jobs = get_jobs( workflow_run_id=os.environ["GITHUB_RUN_ID"], token=os.environ["ACCESS_REPO_INFO_TOKEN"] ) artifact_name_to_job_map = {} for job in github_actions_jobs: for step in job["steps"]: if step["name"].startswith("Test suite reports artifacts: "): artifact_name = step["name"][len("Test suite reports artifacts: ") :] artifact_name_to_job_map[artifact_name] = job break available_artifacts = retrieve_available_artifacts() doc_test_results = {} # `artifact_key` is the artifact path for artifact_key, artifact_obj in available_artifacts.items(): artifact_path = artifact_obj.paths[0] if not artifact_path["path"].startswith("doc_tests_gpu_test_reports_"): continue # change "_" back to "/" (to show the job name as path) job_name = artifact_path["path"].replace("doc_tests_gpu_test_reports_", "").replace("_", "/") # This dict (for each job) will contain all the information relative to each doc test job, in particular: # - failed: list of failed tests # - failures: dict in the format 'test': 'error_message' job_result = {} doc_test_results[job_name] = job_result job = artifact_name_to_job_map[artifact_path["path"]] job_result["job_link"] = job["html_url"] job_result["category"] = "Python Examples" if job_name.startswith("src/") else "MD Examples" artifact = retrieve_artifact(artifact_path["path"]) if "stats" in artifact: failed, success, time_spent = handle_test_results(artifact["stats"]) job_result["n_failures"] = failed job_result["n_success"] = success job_result["time_spent"] = time_spent[1:-1] + ", " job_result["failed"] = [] job_result["failures"] = {} all_failures = extract_first_line_failure(artifact["failures_short"]) for line in artifact["summary_short"].split("\n"): if re.search("FAILED", line): line = line.replace("FAILED ", "") line = line.split()[0].replace("\n", "") if "::" in line: file_path, test = line.split("::") else: file_path, test = line, line job_result["failed"].append(test) failure = all_failures[test] if test in all_failures else "N/A" job_result["failures"][test] = failure # Save and to be uploaded as artifact os.makedirs("doc_test_results", exist_ok=True) with open("doc_test_results/doc_test_results.json", "w", encoding="UTF-8") as fp: json.dump(doc_test_results, fp, ensure_ascii=False, indent=4) message = Message("🤗 Results of the doc tests.", doc_test_results) message.post() message.post_reply()

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/get_previous_daily_ci.py

import os import zipfile import requests from get_ci_error_statistics import download_artifact, get_artifacts_links def get_daily_ci_runs(token, num_runs=7): """Get the workflow runs of the scheduled (daily) CI. This only selects the runs triggered by the `schedule` event on the `main` branch. """ headers = None if token is not None: headers = {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"} # The id of a workflow (not of a workflow run) workflow_id = "636036" url = f"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs" # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results url += f"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}" result = requests.get(url, headers=headers).json() return result["workflow_runs"] def get_last_daily_ci_runs(token): """Get the last completed workflow run id of the scheduled (daily) CI.""" workflow_runs = get_daily_ci_runs(token) workflow_run_id = None for workflow_run in workflow_runs: if workflow_run["status"] == "completed": workflow_run_id = workflow_run["id"] break return workflow_run_id def get_last_daily_ci_artifacts(artifact_names, output_dir, token): """Get the artifacts of last completed workflow run id of the scheduled (daily) CI.""" workflow_run_id = get_last_daily_ci_runs(token) if workflow_run_id is not None: artifacts_links = get_artifacts_links(worflow_run_id=workflow_run_id, token=token) for artifact_name in artifact_names: if artifact_name in artifacts_links: artifact_url = artifacts_links[artifact_name] download_artifact( artifact_name=artifact_name, artifact_url=artifact_url, output_dir=output_dir, token=token ) def get_last_daily_ci_reports(artifact_names, output_dir, token): """Get the artifacts' content of the last completed workflow run id of the scheduled (daily) CI.""" get_last_daily_ci_artifacts(artifact_names, output_dir, token) results = {} for artifact_name in artifact_names: artifact_zip_path = os.path.join(output_dir, f"{artifact_name}.zip") if os.path.isfile(artifact_zip_path): results[artifact_name] = {} with zipfile.ZipFile(artifact_zip_path) as z: for filename in z.namelist(): if not os.path.isdir(filename): # read the file with z.open(filename) as f: results[artifact_name][filename] = f.read().decode("UTF-8") return results

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_support_list.py

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """ Utility that checks the supports of 3rd party libraries are listed in the documentation file. Currently, this includes: - flash attention support - SDPA support Use from the root of the repo with (as used in `make repo-consistency`): ```bash python utils/check_support_list.py ``` It has no auto-fix mode. """ import os from glob import glob # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_doctest_list.py REPO_PATH = "." def check_flash_support_list(): with open(os.path.join(REPO_PATH, "docs/source/en/perf_infer_gpu_one.md"), "r") as f: doctext = f.read() doctext = doctext.split("FlashAttention-2 is currently supported for the following architectures:")[1] doctext = doctext.split("You can request to add FlashAttention-2 support")[0] patterns = glob(os.path.join(REPO_PATH, "src/transformers/models/**/modeling_*.py")) patterns_tf = glob(os.path.join(REPO_PATH, "src/transformers/models/**/modeling_tf_*.py")) patterns_flax = glob(os.path.join(REPO_PATH, "src/transformers/models/**/modeling_flax_*.py")) patterns = list(set(patterns) - set(patterns_tf) - set(patterns_flax)) archs_supporting_fa2 = [] for filename in patterns: with open(filename, "r") as f: text = f.read() if "_supports_flash_attn_2 = True" in text: model_name = os.path.basename(filename).replace(".py", "").replace("modeling_", "") archs_supporting_fa2.append(model_name) for arch in archs_supporting_fa2: if arch not in doctext: raise ValueError( f"{arch} should be in listed in the flash attention documentation but is not. Please update the documentation." ) def check_sdpa_support_list(): with open(os.path.join(REPO_PATH, "docs/source/en/perf_infer_gpu_one.md"), "r") as f: doctext = f.read() doctext = doctext.split( "For now, Transformers supports SDPA inference and training for the following architectures:" )[1] doctext = doctext.split("Note that FlashAttention can only be used for models using the")[0] patterns = glob(os.path.join(REPO_PATH, "src/transformers/models/**/modeling_*.py")) patterns_tf = glob(os.path.join(REPO_PATH, "src/transformers/models/**/modeling_tf_*.py")) patterns_flax = glob(os.path.join(REPO_PATH, "src/transformers/models/**/modeling_flax_*.py")) patterns = list(set(patterns) - set(patterns_tf) - set(patterns_flax)) archs_supporting_sdpa = [] for filename in patterns: with open(filename, "r") as f: text = f.read() if "_supports_sdpa = True" in text: model_name = os.path.basename(filename).replace(".py", "").replace("modeling_", "") archs_supporting_sdpa.append(model_name) for arch in archs_supporting_sdpa: if arch not in doctext: raise ValueError( f"{arch} should be in listed in the SDPA documentation but is not. Please update the documentation." ) if __name__ == "__main__": check_flash_support_list() check_sdpa_support_list()

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/pr_slow_ci_models.py

# Copyright 2024 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. """ This script is used to get the models for which to run slow CI. A new model added in a pull request will be included, as well as models specified in a commit message with a prefix `[run-slow]`, `[run_slow]` or `[run slow]`. For example, the commit message `[run_slow]bert, gpt2` will give `bert` and `gpt2`. Usage: ```bash python utils/pr_slow_ci_models.py.py ``` """ import argparse import re from pathlib import Path from typing import List from git import Repo PATH_TO_REPO = Path(__file__).parent.parent.resolve() def get_new_python_files_between_commits(base_commit: str, commits: List[str]) -> List[str]: """ Get the list of added python files between a base commit and one or several commits. Args: repo (`git.Repo`): A git repository (for instance the Transformers repo). base_commit (`str`): The commit reference of where to compare for the diff. This is the current commit, not the branching point! commits (`List[str]`): The list of commits with which to compare the repo at `base_commit` (so the branching point). Returns: `List[str]`: The list of python files added between a base commit and one or several commits. """ code_diff = [] for commit in commits: for diff_obj in commit.diff(base_commit): # We always add new python files if diff_obj.change_type == "A" and diff_obj.b_path.endswith(".py"): code_diff.append(diff_obj.b_path) return code_diff def get_new_python_files() -> List[str]: """ Return a list of python files that have been added between the current head and the main branch. Returns: `List[str]`: The list of python files added. """ repo = Repo(PATH_TO_REPO) try: # For the cases where the main branch exists locally main = repo.refs.main except AttributeError: # On GitHub Actions runners, it doesn't have local main branch main = repo.remotes.origin.refs.main print(f"main is at {main.commit}") print(f"Current head is at {repo.head.commit}") branching_commits = repo.merge_base(main, repo.head) for commit in branching_commits: print(f"Branching commit: {commit}") return get_new_python_files_between_commits(repo.head.commit, branching_commits) def get_new_model(): new_files = get_new_python_files() reg = re.compile(r"src/transformers/(models/.*)/modeling_.*\.py") new_model = "" for x in new_files: find_new_model = reg.findall(x) if len(find_new_model) > 0: new_model = find_new_model[0] # It's unlikely we have 2 new modeling files in a pull request. break return new_model def parse_commit_message(commit_message: str) -> str: """ Parses the commit message to find the models specified in it to run slow CI. Args: commit_message (`str`): The commit message of the current commit. Returns: `str`: The substring in `commit_message` after `[run-slow]`, [run_slow]` or [run slow]`. If no such prefix is found, the empty string is returned. """ if commit_message is None: return "" command_search = re.search(r"\[([^\]]*)\](.*)", commit_message) if command_search is None: return "" command = command_search.groups()[0] command = command.lower().replace("-", " ").replace("_", " ") run_slow = command == "run slow" if run_slow: models = command_search.groups()[1].strip() return models else: return "" def get_models(commit_message: str): models = parse_commit_message(commit_message) return [f"models/{x}" for x in models.replace(",", " ").split()] if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--commit_message", type=str, default="", help="The commit message.") args = parser.parse_args() new_model = get_new_model() specified_models = get_models(args.commit_message) models = ([] if new_model == "" else [new_model]) + specified_models print(sorted(set(models)))

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_self_hosted_runner.py

import argparse import json import subprocess def get_runner_status(target_runners, token): offline_runners = [] cmd = ( f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"' " https://api.github.com/repos/huggingface/transformers/actions/runners" ) output = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE) o = output.stdout.decode("utf-8") status = json.loads(o) runners = status["runners"] for runner in runners: if runner["name"] in target_runners: if runner["status"] == "offline": offline_runners.append(runner) # save the result so we can report them on Slack with open("offline_runners.txt", "w") as fp: fp.write(json.dumps(offline_runners)) if len(offline_runners) > 0: failed = "\n".join([x["name"] for x in offline_runners]) raise ValueError(f"The following runners are offline:\n{failed}") if __name__ == "__main__": def list_str(values): return values.split(",") parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--target_runners", default=None, type=list_str, required=True, help="Comma-separated list of runners to check status.", ) parser.add_argument( "--token", default=None, type=str, required=True, help="A token that has actions:read permission." ) args = parser.parse_args() get_runner_status(args.target_runners, args.token)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/important_models.txt

models/llama models/mistral models/mixtral models/gemma

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/print_env.py

#!/usr/bin/env python3 # coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # 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. # this script dumps information about the environment import os import sys import transformers os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" print("Python version:", sys.version) print("transformers version:", transformers.__version__) try: import torch print("Torch version:", torch.__version__) print("Cuda available:", torch.cuda.is_available()) print("Cuda version:", torch.version.cuda) print("CuDNN version:", torch.backends.cudnn.version()) print("Number of GPUs available:", torch.cuda.device_count()) print("NCCL version:", torch.cuda.nccl.version()) except ImportError: print("Torch version:", None) try: import deepspeed print("DeepSpeed version:", deepspeed.__version__) except ImportError: print("DeepSpeed version:", None) try: import tensorflow as tf print("TensorFlow version:", tf.__version__) print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU"))) print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU"))) except ImportError: print("TensorFlow version:", None)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_doc_toc.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # 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. """ This script is responsible for cleaning the model section of the table of content by removing duplicates and sorting the entries in alphabetical order. Usage (from the root of the repo): Check that the table of content is properly sorted (used in `make quality`): ```bash python utils/check_doc_toc.py ``` Auto-sort the table of content if it is not properly sorted (used in `make style`): ```bash python utils/check_doc_toc.py --fix_and_overwrite ``` """ import argparse from collections import defaultdict from typing import List import yaml PATH_TO_TOC = "docs/source/en/_toctree.yml" def clean_model_doc_toc(model_doc: List[dict]) -> List[dict]: """ Cleans a section of the table of content of the model documentation (one specific modality) by removing duplicates and sorting models alphabetically. Args: model_doc (`List[dict]`): The list of dictionaries extracted from the `_toctree.yml` file for this specific modality. Returns: `List[dict]`: List of dictionaries like the input, but cleaned up and sorted. """ counts = defaultdict(int) for doc in model_doc: counts[doc["local"]] += 1 duplicates = [key for key, value in counts.items() if value > 1] new_doc = [] for duplicate_key in duplicates: titles = list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}) if len(titles) > 1: raise ValueError( f"{duplicate_key} is present several times in the documentation table of content at " "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the " "others." ) # Only add this once new_doc.append({"local": duplicate_key, "title": titles[0]}) # Add none duplicate-keys new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]) # Sort return sorted(new_doc, key=lambda s: s["title"].lower()) def check_model_doc(overwrite: bool = False): """ Check that the content of the table of content in `_toctree.yml` is clean (no duplicates and sorted for the model API doc) and potentially auto-cleans it. Args: overwrite (`bool`, *optional*, defaults to `False`): Whether to just check if the TOC is clean or to auto-clean it (when `overwrite=True`). """ with open(PATH_TO_TOC, encoding="utf-8") as f: content = yaml.safe_load(f.read()) # Get to the API doc api_idx = 0 while content[api_idx]["title"] != "API": api_idx += 1 api_doc = content[api_idx]["sections"] # Then to the model doc model_idx = 0 while api_doc[model_idx]["title"] != "Models": model_idx += 1 model_doc = api_doc[model_idx]["sections"] # Extract the modalities and clean them one by one. modalities_docs = [(idx, section) for idx, section in enumerate(model_doc) if "sections" in section] diff = False for idx, modality_doc in modalities_docs: old_modality_doc = modality_doc["sections"] new_modality_doc = clean_model_doc_toc(old_modality_doc) if old_modality_doc != new_modality_doc: diff = True if overwrite: model_doc[idx]["sections"] = new_modality_doc if diff: if overwrite: api_doc[model_idx]["sections"] = model_doc content[api_idx]["sections"] = api_doc with open(PATH_TO_TOC, "w", encoding="utf-8") as f: f.write(yaml.dump(content, allow_unicode=True)) else: raise ValueError( "The model doc part of the table of content is not properly sorted, run `make style` to fix this." ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() check_model_doc(args.fix_and_overwrite)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/get_ci_error_statistics.py

import argparse import json import math import os import time import traceback import zipfile from collections import Counter import requests def get_jobs(workflow_run_id, token=None): """Extract jobs in a GitHub Actions workflow run""" headers = None if token is not None: headers = {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"} url = f"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100" result = requests.get(url, headers=headers).json() jobs = [] try: jobs.extend(result["jobs"]) pages_to_iterate_over = math.ceil((result["total_count"] - 100) / 100) for i in range(pages_to_iterate_over): result = requests.get(url + f"&page={i + 2}", headers=headers).json() jobs.extend(result["jobs"]) return jobs except Exception: print(f"Unknown error, could not fetch links:\n{traceback.format_exc()}") return [] def get_job_links(workflow_run_id, token=None): """Extract job names and their job links in a GitHub Actions workflow run""" headers = None if token is not None: headers = {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"} url = f"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100" result = requests.get(url, headers=headers).json() job_links = {} try: job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}) pages_to_iterate_over = math.ceil((result["total_count"] - 100) / 100) for i in range(pages_to_iterate_over): result = requests.get(url + f"&page={i + 2}", headers=headers).json() job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}) return job_links except Exception: print(f"Unknown error, could not fetch links:\n{traceback.format_exc()}") return {} def get_artifacts_links(worflow_run_id, token=None): """Get all artifact links from a workflow run""" headers = None if token is not None: headers = {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"} url = f"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100" result = requests.get(url, headers=headers).json() artifacts = {} try: artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}) pages_to_iterate_over = math.ceil((result["total_count"] - 100) / 100) for i in range(pages_to_iterate_over): result = requests.get(url + f"&page={i + 2}", headers=headers).json() artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}) return artifacts except Exception: print(f"Unknown error, could not fetch links:\n{traceback.format_exc()}") return {} def download_artifact(artifact_name, artifact_url, output_dir, token): """Download a GitHub Action artifact from a URL. The URL is of the form `https://api.github.com/repos/huggingface/transformers/actions/artifacts/{ARTIFACT_ID}/zip`, but it can't be used to download directly. We need to get a redirect URL first. See https://docs.github.com/en/rest/actions/artifacts#download-an-artifact """ headers = None if token is not None: headers = {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"} result = requests.get(artifact_url, headers=headers, allow_redirects=False) download_url = result.headers["Location"] response = requests.get(download_url, allow_redirects=True) file_path = os.path.join(output_dir, f"{artifact_name}.zip") with open(file_path, "wb") as fp: fp.write(response.content) def get_errors_from_single_artifact(artifact_zip_path, job_links=None): """Extract errors from a downloaded artifact (in .zip format)""" errors = [] failed_tests = [] job_name = None with zipfile.ZipFile(artifact_zip_path) as z: for filename in z.namelist(): if not os.path.isdir(filename): # read the file if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]: with z.open(filename) as f: for line in f: line = line.decode("UTF-8").strip() if filename == "failures_line.txt": try: # `error_line` is the place where `error` occurs error_line = line[: line.index(": ")] error = line[line.index(": ") + len(": ") :] errors.append([error_line, error]) except Exception: # skip un-related lines pass elif filename == "summary_short.txt" and line.startswith("FAILED "): # `test` is the test method that failed test = line[len("FAILED ") :] failed_tests.append(test) elif filename == "job_name.txt": job_name = line if len(errors) != len(failed_tests): raise ValueError( f"`errors` and `failed_tests` should have the same number of elements. Got {len(errors)} for `errors` " f"and {len(failed_tests)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some" " problem." ) job_link = None if job_name and job_links: job_link = job_links.get(job_name, None) # A list with elements of the form (line of error, error, failed test) result = [x + [y] + [job_link] for x, y in zip(errors, failed_tests)] return result def get_all_errors(artifact_dir, job_links=None): """Extract errors from all artifact files""" errors = [] paths = [os.path.join(artifact_dir, p) for p in os.listdir(artifact_dir) if p.endswith(".zip")] for p in paths: errors.extend(get_errors_from_single_artifact(p, job_links=job_links)) return errors def reduce_by_error(logs, error_filter=None): """count each error""" counter = Counter() counter.update([x[1] for x in logs]) counts = counter.most_common() r = {} for error, count in counts: if error_filter is None or error not in error_filter: r[error] = {"count": count, "failed_tests": [(x[2], x[0]) for x in logs if x[1] == error]} r = dict(sorted(r.items(), key=lambda item: item[1]["count"], reverse=True)) return r def get_model(test): """Get the model name from a test method""" test = test.split("::")[0] if test.startswith("tests/models/"): test = test.split("/")[2] else: test = None return test def reduce_by_model(logs, error_filter=None): """count each error per model""" logs = [(x[0], x[1], get_model(x[2])) for x in logs] logs = [x for x in logs if x[2] is not None] tests = {x[2] for x in logs} r = {} for test in tests: counter = Counter() # count by errors in `test` counter.update([x[1] for x in logs if x[2] == test]) counts = counter.most_common() error_counts = {error: count for error, count in counts if (error_filter is None or error not in error_filter)} n_errors = sum(error_counts.values()) if n_errors > 0: r[test] = {"count": n_errors, "errors": error_counts} r = dict(sorted(r.items(), key=lambda item: item[1]["count"], reverse=True)) return r def make_github_table(reduced_by_error): header = "| no. | error | status |" sep = "|-:|:-|:-|" lines = [header, sep] for error in reduced_by_error: count = reduced_by_error[error]["count"] line = f"| {count} | {error[:100]} | |" lines.append(line) return "\n".join(lines) def make_github_table_per_model(reduced_by_model): header = "| model | no. of errors | major error | count |" sep = "|-:|-:|-:|-:|" lines = [header, sep] for model in reduced_by_model: count = reduced_by_model[model]["count"] error, _count = list(reduced_by_model[model]["errors"].items())[0] line = f"| {model} | {count} | {error[:60]} | {_count} |" lines.append(line) return "\n".join(lines) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.") parser.add_argument( "--output_dir", type=str, required=True, help="Where to store the downloaded artifacts and other result files.", ) parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.") args = parser.parse_args() os.makedirs(args.output_dir, exist_ok=True) _job_links = get_job_links(args.workflow_run_id, token=args.token) job_links = {} # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee. # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`. if _job_links: for k, v in _job_links.items(): # This is how GitHub actions combine job names. if " / " in k: index = k.find(" / ") k = k[index + len(" / ") :] job_links[k] = v with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp: json.dump(job_links, fp, ensure_ascii=False, indent=4) artifacts = get_artifacts_links(args.workflow_run_id, token=args.token) with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp: json.dump(artifacts, fp, ensure_ascii=False, indent=4) for idx, (name, url) in enumerate(artifacts.items()): download_artifact(name, url, args.output_dir, args.token) # Be gentle to GitHub time.sleep(1) errors = get_all_errors(args.output_dir, job_links=job_links) # `e[1]` is the error counter = Counter() counter.update([e[1] for e in errors]) # print the top 30 most common test errors most_common = counter.most_common(30) for item in most_common: print(item) with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp: json.dump(errors, fp, ensure_ascii=False, indent=4) reduced_by_error = reduce_by_error(errors) reduced_by_model = reduce_by_model(errors) s1 = make_github_table(reduced_by_error) s2 = make_github_table_per_model(reduced_by_model) with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp: fp.write(s1) with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp: fp.write(s2)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/create_dummy_models.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. 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. import argparse import collections.abc import copy import inspect import json import multiprocessing import os import shutil import tempfile import traceback from pathlib import Path from check_config_docstrings import get_checkpoint_from_config_class from datasets import load_dataset from get_test_info import get_model_to_tester_mapping, get_tester_classes_for_model from huggingface_hub import Repository, create_repo, hf_api, upload_folder from transformers import ( CONFIG_MAPPING, FEATURE_EXTRACTOR_MAPPING, IMAGE_PROCESSOR_MAPPING, PROCESSOR_MAPPING, TOKENIZER_MAPPING, AutoTokenizer, LayoutLMv3TokenizerFast, PreTrainedTokenizer, PreTrainedTokenizerFast, logging, ) from transformers.feature_extraction_utils import FeatureExtractionMixin from transformers.file_utils import is_tf_available, is_torch_available from transformers.image_processing_utils import BaseImageProcessor from transformers.models.auto.configuration_auto import AutoConfig, model_type_to_module_name from transformers.models.fsmt import configuration_fsmt from transformers.processing_utils import ProcessorMixin, transformers_module from transformers.tokenization_utils_base import PreTrainedTokenizerBase # make sure tokenizer plays nice with multiprocessing os.environ["TOKENIZERS_PARALLELISM"] = "false" logging.set_verbosity_error() logging.disable_progress_bar() logger = logging.get_logger(__name__) os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" if not is_torch_available(): raise ValueError("Please install PyTorch.") if not is_tf_available(): raise ValueError("Please install TensorFlow.") FRAMEWORKS = ["pytorch", "tensorflow"] INVALID_ARCH = [] TARGET_VOCAB_SIZE = 1024 data = {"training_ds": None, "testing_ds": None} COMPOSITE_MODELS = { "EncoderDecoderModel": "EncoderDecoderModel-bert-bert", "SpeechEncoderDecoderModel": "SpeechEncoderDecoderModel-wav2vec2-bert", "VisionEncoderDecoderModel": "VisionEncoderDecoderModel-vit-gpt2", "VisionTextDualEncoderModel": "VisionTextDualEncoderModel-vit-bert", } # This list contains the model architectures for which a tiny version could not be created. # Avoid to add new architectures here - unless we have verified carefully that it's (almost) impossible to create them. # One such case is: no model tester class is implemented for a model type (like `MT5`) because its architecture is # identical to another one (`MT5` is based on `T5`), but trained on different datasets or with different techniques. UNCONVERTIBLE_MODEL_ARCHITECTURES = { "BertGenerationEncoder", "BertGenerationDecoder", "CamembertForSequenceClassification", "CamembertForMultipleChoice", "CamembertForMaskedLM", "CamembertForCausalLM", "CamembertForTokenClassification", "CamembertForQuestionAnswering", "CamembertModel", "TFCamembertForMultipleChoice", "TFCamembertForTokenClassification", "TFCamembertForQuestionAnswering", "TFCamembertForSequenceClassification", "TFCamembertForMaskedLM", "TFCamembertModel", "TFCamembertForCausalLM", "DecisionTransformerModel", "GraphormerModel", "InformerModel", "JukeboxModel", "MarianForCausalLM", "MaskFormerSwinModel", "MaskFormerSwinBackbone", "MT5Model", "MT5ForConditionalGeneration", "UMT5ForConditionalGeneration", "TFMT5ForConditionalGeneration", "TFMT5Model", "QDQBertForSequenceClassification", "QDQBertForMaskedLM", "QDQBertModel", "QDQBertForTokenClassification", "QDQBertLMHeadModel", "QDQBertForMultipleChoice", "QDQBertForQuestionAnswering", "QDQBertForNextSentencePrediction", "ReformerModelWithLMHead", "RetriBertModel", "Speech2Text2ForCausalLM", "TimeSeriesTransformerModel", "TrajectoryTransformerModel", "TrOCRForCausalLM", "XLMProphetNetForConditionalGeneration", "XLMProphetNetForCausalLM", "XLMProphetNetModel", "XLMRobertaModel", "XLMRobertaForTokenClassification", "XLMRobertaForMultipleChoice", "XLMRobertaForMaskedLM", "XLMRobertaForCausalLM", "XLMRobertaForSequenceClassification", "XLMRobertaForQuestionAnswering", "TFXLMRobertaForSequenceClassification", "TFXLMRobertaForMaskedLM", "TFXLMRobertaForCausalLM", "TFXLMRobertaForQuestionAnswering", "TFXLMRobertaModel", "TFXLMRobertaForMultipleChoice", "TFXLMRobertaForTokenClassification", } def get_processor_types_from_config_class(config_class, allowed_mappings=None): """Return a tuple of processors for `config_class`. We use `tuple` here to include (potentially) both slow & fast tokenizers. """ # To make a uniform return type def _to_tuple(x): if not isinstance(x, collections.abc.Sequence): x = (x,) else: x = tuple(x) return x if allowed_mappings is None: allowed_mappings = ["processor", "tokenizer", "image_processor", "feature_extractor"] processor_types = () # Check first if a model has `ProcessorMixin`. Otherwise, check if it has tokenizers, and/or an image processor or # a feature extractor if config_class in PROCESSOR_MAPPING and "processor" in allowed_mappings: processor_types = _to_tuple(PROCESSOR_MAPPING[config_class]) else: if config_class in TOKENIZER_MAPPING and "tokenizer" in allowed_mappings: processor_types = TOKENIZER_MAPPING[config_class] if config_class in IMAGE_PROCESSOR_MAPPING and "image_processor" in allowed_mappings: processor_types += _to_tuple(IMAGE_PROCESSOR_MAPPING[config_class]) elif config_class in FEATURE_EXTRACTOR_MAPPING and "feature_extractor" in allowed_mappings: processor_types += _to_tuple(FEATURE_EXTRACTOR_MAPPING[config_class]) # Remark: some configurations have no processor at all. For example, generic composite models like # `EncoderDecoderModel` is used for any (compatible) text models. Also, `DecisionTransformer` doesn't # require any processor. # We might get `None` for some tokenizers - remove them here. processor_types = tuple(p for p in processor_types if p is not None) return processor_types def get_architectures_from_config_class(config_class, arch_mappings, models_to_skip=None): """Return a tuple of all possible architectures attributed to a configuration class `config_class`. For example, BertConfig -> [BertModel, BertForMaskedLM, ..., BertForQuestionAnswering]. """ # A model architecture could appear in several mappings. For example, `BartForConditionalGeneration` is in # - MODEL_FOR_PRETRAINING_MAPPING_NAMES # - MODEL_WITH_LM_HEAD_MAPPING_NAMES # - MODEL_FOR_MASKED_LM_MAPPING_NAMES # - MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES # We avoid the duplication. architectures = set() if models_to_skip is None: models_to_skip = [] models_to_skip = UNCONVERTIBLE_MODEL_ARCHITECTURES.union(models_to_skip) for mapping in arch_mappings: if config_class in mapping: models = mapping[config_class] models = tuple(models) if isinstance(models, collections.abc.Sequence) else (models,) for model in models: if model.__name__ not in models_to_skip: architectures.add(model) architectures = tuple(architectures) return architectures def get_config_class_from_processor_class(processor_class): """Get the config class from a processor class. Some config/model classes use tokenizers/feature_extractors from other models. For example, `GPT-J` uses `GPT2Tokenizer`. If no checkpoint is found for a config class, or a checkpoint is found without necessary file(s) to create the processor for `processor_class`, we get the config class that corresponds to `processor_class` and use it to find a checkpoint in order to create the processor. """ processor_prefix = processor_class.__name__ for postfix in ["TokenizerFast", "Tokenizer", "ImageProcessor", "FeatureExtractor", "Processor"]: processor_prefix = processor_prefix.replace(postfix, "") # `Wav2Vec2CTCTokenizer` -> `Wav2Vec2Config` if processor_prefix == "Wav2Vec2CTC": processor_prefix = "Wav2Vec2" # Find the new configuration class new_config_name = f"{processor_prefix}Config" new_config_class = getattr(transformers_module, new_config_name) return new_config_class def build_processor(config_class, processor_class, allow_no_checkpoint=False): """Create a processor for `processor_class`. If a processor is not able to be built with the original arguments, this method tries to change the arguments and call itself recursively, by inferring a new `config_class` or a new `processor_class` from another one, in order to find a checkpoint containing the necessary files to build a processor. The processor is not saved here. Instead, it will be saved in `convert_processors` after further changes in `convert_processors`. For each model architecture`, a copy will be created and saved along the built model. """ # Currently, this solely uses the docstring in the source file of `config_class` to find a checkpoint. checkpoint = get_checkpoint_from_config_class(config_class) if checkpoint is None: # try to get the checkpoint from the config class for `processor_class`. # This helps cases like `XCLIPConfig` and `VideoMAEFeatureExtractor` to find a checkpoint from `VideoMAEConfig`. config_class_from_processor_class = get_config_class_from_processor_class(processor_class) checkpoint = get_checkpoint_from_config_class(config_class_from_processor_class) processor = None try: processor = processor_class.from_pretrained(checkpoint) except Exception as e: logger.error(f"{e.__class__.__name__}: {e}") # Try to get a new processor class from checkpoint. This is helpful for a checkpoint without necessary file to load # processor while `processor_class` is an Auto class. For example, `sew` has `Wav2Vec2Processor` in # `PROCESSOR_MAPPING_NAMES`, its `tokenizer_class` is `AutoTokenizer`, and the checkpoint # `https://huggingface.co/asapp/sew-tiny-100k` has no tokenizer file, but we can get # `tokenizer_class: Wav2Vec2CTCTokenizer` from the config file. (The new processor class won't be able to load from # `checkpoint`, but it helps this recursive method to find a way to build a processor). if ( processor is None and checkpoint is not None and issubclass(processor_class, (PreTrainedTokenizerBase, AutoTokenizer)) ): try: config = AutoConfig.from_pretrained(checkpoint) except Exception as e: logger.error(f"{e.__class__.__name__}: {e}") config = None if config is not None: if not isinstance(config, config_class): raise ValueError( f"`config` (which is of type {config.__class__.__name__}) should be an instance of `config_class`" f" ({config_class.__name__})!" ) tokenizer_class = config.tokenizer_class new_processor_class = None if tokenizer_class is not None: new_processor_class = getattr(transformers_module, tokenizer_class) if new_processor_class != processor_class: processor = build_processor(config_class, new_processor_class) # If `tokenizer_class` is not specified in `config`, let's use `config` to get the process class via auto # mappings, but only allow the tokenizer mapping being used. This is to make `Wav2Vec2Conformer` build if processor is None: new_processor_classes = get_processor_types_from_config_class( config.__class__, allowed_mappings=["tokenizer"] ) # Used to avoid infinite recursion between a pair of fast/slow tokenizer types names = [ x.__name__.replace("Fast", "") for x in [processor_class, new_processor_class] if x is not None ] new_processor_classes = [ x for x in new_processor_classes if x is not None and x.__name__.replace("Fast", "") not in names ] if len(new_processor_classes) > 0: new_processor_class = new_processor_classes[0] # Let's use fast tokenizer if there is any for x in new_processor_classes: if x.__name__.endswith("Fast"): new_processor_class = x break processor = build_processor(config_class, new_processor_class) if processor is None: # Try to build each component (tokenizer & feature extractor) of a `ProcessorMixin`. if issubclass(processor_class, ProcessorMixin): attrs = {} for attr_name in processor_class.attributes: attrs[attr_name] = [] # This could be a tuple (for tokenizers). For example, `CLIPProcessor` has # - feature_extractor_class = "CLIPFeatureExtractor" # - tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast") attr_class_names = getattr(processor_class, f"{attr_name}_class") if not isinstance(attr_class_names, tuple): attr_class_names = (attr_class_names,) for name in attr_class_names: attr_class = getattr(transformers_module, name) attr = build_processor(config_class, attr_class) if attr is not None: attrs[attr_name].append(attr) # try to build a `ProcessorMixin`, so we can return a single value if all(len(v) > 0 for v in attrs.values()): try: processor = processor_class(**{k: v[0] for k, v in attrs.items()}) except Exception as e: logger.error(f"{e.__class__.__name__}: {e}") else: # `checkpoint` might lack some file(s) to load a processor. For example, `facebook/hubert-base-ls960` # has no tokenizer file to load `Wav2Vec2CTCTokenizer`. In this case, we try to build a processor # with the configuration class (for example, `Wav2Vec2Config`) corresponding to `processor_class`. config_class_from_processor_class = get_config_class_from_processor_class(processor_class) if config_class_from_processor_class != config_class: processor = build_processor(config_class_from_processor_class, processor_class) # Try to create an image processor or a feature extractor without any checkpoint if ( processor is None and allow_no_checkpoint and (issubclass(processor_class, BaseImageProcessor) or issubclass(processor_class, FeatureExtractionMixin)) ): try: processor = processor_class() except Exception as e: logger.error(f"{e.__class__.__name__}: {e}") # validation if processor is not None: if not (isinstance(processor, processor_class) or processor_class.__name__.startswith("Auto")): raise ValueError( f"`processor` (which is of type {processor.__class__.__name__}) should be an instance of" f" {processor_class.__name__} or an Auto class!" ) return processor def get_tiny_config(config_class, model_class=None, **model_tester_kwargs): """Retrieve a tiny configuration from `config_class` using each model's `ModelTester`. Args: config_class: Subclass of `PreTrainedConfig`. Returns: An instance of `config_class` with tiny hyperparameters """ model_type = config_class.model_type # For model type like `data2vec-vision` and `donut-swin`, we can't get the config/model file name directly via # `model_type` as it would be sth. like `configuration_data2vec_vision.py`. # A simple way is to use `inspect.getsourcefile(config_class)`. config_source_file = inspect.getsourcefile(config_class) # The modeling file name without prefix (`modeling_`) and postfix (`.py`) modeling_name = config_source_file.split(os.path.sep)[-1].replace("configuration_", "").replace(".py", "") try: print("Importing", model_type_to_module_name(model_type)) module_name = model_type_to_module_name(model_type) if not modeling_name.startswith(module_name): raise ValueError(f"{modeling_name} doesn't start with {module_name}!") test_file = os.path.join("tests", "models", module_name, f"test_modeling_{modeling_name}.py") models_to_model_testers = get_model_to_tester_mapping(test_file) # Find the model tester class model_tester_class = None tester_classes = [] if model_class is not None: tester_classes = get_tester_classes_for_model(test_file, model_class) else: for _tester_classes in models_to_model_testers.values(): tester_classes.extend(_tester_classes) if len(tester_classes) > 0: # sort with the length of the class names first, then the alphabetical order # This is to avoid `T5EncoderOnlyModelTest` is used instead of `T5ModelTest`, which has # `is_encoder_decoder=False` and causes some pipeline tests failing (also failures in `Optimum` CI). # TODO: More fine grained control of the desired tester class. model_tester_class = sorted(tester_classes, key=lambda x: (len(x.__name__), x.__name__))[0] except ModuleNotFoundError: error = f"Tiny config not created for {model_type} - cannot find the testing module from the model name." raise ValueError(error) if model_tester_class is None: error = f"Tiny config not created for {model_type} - no model tester is found in the testing module." raise ValueError(error) # CLIP-like models have `text_model_tester` and `vision_model_tester`, and we need to pass `vocab_size` to # `text_model_tester` via `text_kwargs`. The same trick is also necessary for `Flava`. if "vocab_size" in model_tester_kwargs: if "text_kwargs" in inspect.signature(model_tester_class.__init__).parameters.keys(): vocab_size = model_tester_kwargs.pop("vocab_size") model_tester_kwargs["text_kwargs"] = {"vocab_size": vocab_size} # `parent` is an instance of `unittest.TestCase`, but we don't need it here. model_tester = model_tester_class(parent=None, **model_tester_kwargs) if hasattr(model_tester, "get_pipeline_config"): config = model_tester.get_pipeline_config() elif hasattr(model_tester, "prepare_config_and_inputs"): # `PoolFormer` has no `get_config` defined. Furthermore, it's better to use `prepare_config_and_inputs` even if # `get_config` is defined, since there might be some extra changes in `prepare_config_and_inputs`. config = model_tester.prepare_config_and_inputs()[0] elif hasattr(model_tester, "get_config"): config = model_tester.get_config() else: error = ( f"Tiny config not created for {model_type} - the model tester {model_tester_class.__name__} lacks" " necessary method to create config." ) raise ValueError(error) # make sure this is long enough (some model tester has `20` for this attr.) to pass `text-generation` # pipeline tests. max_positions = [] for key in ["max_position_embeddings", "max_source_positions", "max_target_positions"]: if getattr(config, key, 0) > 0: max_positions.append(getattr(config, key)) if getattr(config, "text_config", None) is not None: if getattr(config.text_config, key, None) is not None: max_positions.append(getattr(config.text_config, key)) if len(max_positions) > 0: max_position = max(200, min(max_positions)) for key in ["max_position_embeddings", "max_source_positions", "max_target_positions"]: if getattr(config, key, 0) > 0: setattr(config, key, max_position) if getattr(config, "text_config", None) is not None: if getattr(config.text_config, key, None) is not None: setattr(config.text_config, key, max_position) return config def convert_tokenizer(tokenizer_fast: PreTrainedTokenizerFast): new_tokenizer = tokenizer_fast.train_new_from_iterator( data["training_ds"]["text"], TARGET_VOCAB_SIZE, show_progress=False ) # Make sure it at least runs if not isinstance(new_tokenizer, LayoutLMv3TokenizerFast): new_tokenizer(data["testing_ds"]["text"]) return new_tokenizer def convert_feature_extractor(feature_extractor, tiny_config): to_convert = False kwargs = {} if hasattr(tiny_config, "image_size"): kwargs["size"] = tiny_config.image_size kwargs["crop_size"] = tiny_config.image_size to_convert = True elif ( hasattr(tiny_config, "vision_config") and tiny_config.vision_config is not None and hasattr(tiny_config.vision_config, "image_size") ): kwargs["size"] = tiny_config.vision_config.image_size kwargs["crop_size"] = tiny_config.vision_config.image_size to_convert = True # Speech2TextModel specific. if hasattr(tiny_config, "input_feat_per_channel"): kwargs["feature_size"] = tiny_config.input_feat_per_channel kwargs["num_mel_bins"] = tiny_config.input_feat_per_channel to_convert = True if to_convert: feature_extractor = feature_extractor.__class__(**kwargs) return feature_extractor def convert_processors(processors, tiny_config, output_folder, result): """Change a processor to work with smaller inputs. For tokenizers, we try to reduce their vocabulary size. For feature extractor, we use smaller image size or change other attributes using the values from `tiny_config`. See `convert_feature_extractor`. This method should not fail: we catch the errors and put them in `result["warnings"]` with descriptive messages. """ def _sanity_check(fast_tokenizer, slow_tokenizer, keep_fast_tokenizer=False): """Set tokenizer(s) to `None` if the fast/slow tokenizers have different values for `vocab_size` or `length`. If `keep_fast_tokenizer=True`, the fast tokenizer will be kept. """ # sanity check 1: fast and slow tokenizers should be compatible (vocab_size) if fast_tokenizer is not None and slow_tokenizer is not None: if fast_tokenizer.vocab_size != slow_tokenizer.vocab_size: warning_messagae = ( "The fast/slow tokenizers " f"({fast_tokenizer.__class__.__name__}/{slow_tokenizer.__class__.__name__}) have different " "vocabulary size: " f"fast_tokenizer.vocab_size = {fast_tokenizer.vocab_size} and " f"slow_tokenizer.vocab_size = {slow_tokenizer.vocab_size}." ) result["warnings"].append(warning_messagae) if not keep_fast_tokenizer: fast_tokenizer = None slow_tokenizer = None # sanity check 2: fast and slow tokenizers should be compatible (length) if fast_tokenizer is not None and slow_tokenizer is not None: if len(fast_tokenizer) != len(slow_tokenizer): warning_messagae = ( f"The fast/slow tokenizers () have different length: " f"len(fast_tokenizer) = {len(fast_tokenizer)} and " f"len(slow_tokenizer) = {len(slow_tokenizer)}." ) result["warnings"].append(warning_messagae) if not keep_fast_tokenizer: fast_tokenizer = None slow_tokenizer = None return fast_tokenizer, slow_tokenizer tokenizers = [] feature_extractors = [] for processor in processors: if isinstance(processor, PreTrainedTokenizerBase): if processor.__class__.__name__ not in {x.__class__.__name__ for x in tokenizers}: tokenizers.append(processor) elif isinstance(processor, BaseImageProcessor): if processor.__class__.__name__ not in {x.__class__.__name__ for x in feature_extractors}: feature_extractors.append(processor) elif isinstance(processor, FeatureExtractionMixin): if processor.__class__.__name__ not in {x.__class__.__name__ for x in feature_extractors}: feature_extractors.append(processor) elif isinstance(processor, ProcessorMixin): if hasattr(processor, "tokenizer"): if processor.tokenizer.__class__.__name__ not in {x.__class__.__name__ for x in tokenizers}: tokenizers.append(processor.tokenizer) # Currently, we only have these 2 possibilities if hasattr(processor, "image_processor"): if processor.image_processor.__class__.__name__ not in { x.__class__.__name__ for x in feature_extractors }: feature_extractors.append(processor.image_processor) elif hasattr(processor, "feature_extractor"): if processor.feature_extractor.__class__.__name__ not in { x.__class__.__name__ for x in feature_extractors }: feature_extractors.append(processor.feature_extractor) # check the built processors have the unique type num_types = len({x.__class__.__name__ for x in feature_extractors}) if num_types >= 2: raise ValueError(f"`feature_extractors` should contain at most 1 type, but it contains {num_types} types!") num_types = len({x.__class__.__name__.replace("Fast", "") for x in tokenizers}) if num_types >= 2: raise ValueError(f"`tokenizers` should contain at most 1 tokenizer type, but it contains {num_types} types!") fast_tokenizer = None slow_tokenizer = None for tokenizer in tokenizers: if isinstance(tokenizer, PreTrainedTokenizerFast): fast_tokenizer = tokenizer else: slow_tokenizer = tokenizer # If the (original) fast/slow tokenizers don't correspond, keep only the fast tokenizer. # This doesn't necessarily imply the fast/slow tokenizers in a single Hub repo. has issues. # It's more of an issue in `build_processor` which tries to get a checkpoint with as much effort as possible. # For `YosoModel` (which uses `AlbertTokenizer(Fast)`), its real (Hub) checkpoint doesn't contain valid files to # load the slower tokenizer (`AlbertTokenizer`), and it ends up finding the (canonical) checkpoint of `AlbertModel`, # which has different vocabulary. # TODO: Try to improve `build_processor`'s definition and/or usage to avoid the above situation in the first place. fast_tokenizer, slow_tokenizer = _sanity_check(fast_tokenizer, slow_tokenizer, keep_fast_tokenizer=True) original_fast_tokenizer, original_slow_tokenizer = fast_tokenizer, slow_tokenizer if fast_tokenizer: try: # Wav2Vec2ForCTC , ByT5Tokenizer etc. all are already small enough and have no fast version that can # be retrained if fast_tokenizer.vocab_size > TARGET_VOCAB_SIZE: fast_tokenizer = convert_tokenizer(fast_tokenizer) except Exception: result["warnings"].append( ( f"Failed to convert the fast tokenizer for {fast_tokenizer.__class__.__name__}.", traceback.format_exc(), ) ) # If `fast_tokenizer` exists, `slow_tokenizer` should correspond to it. if fast_tokenizer: # Make sure the fast tokenizer can be saved try: # We don't save it to `output_folder` at this moment - only at the end of this function. with tempfile.TemporaryDirectory() as tmpdir: fast_tokenizer.save_pretrained(tmpdir) try: slow_tokenizer = AutoTokenizer.from_pretrained(tmpdir, use_fast=False) except Exception: result["warnings"].append( ( f"Failed to load the slow tokenizer saved from {fast_tokenizer.__class__.__name__}.", traceback.format_exc(), ) ) # Let's just keep the fast version slow_tokenizer = None except Exception: result["warnings"].append( ( f"Failed to save the fast tokenizer for {fast_tokenizer.__class__.__name__}.", traceback.format_exc(), ) ) fast_tokenizer = None # If the (possibly converted) fast/slow tokenizers don't correspond, set them to `None`, and use the original # tokenizers. fast_tokenizer, slow_tokenizer = _sanity_check(fast_tokenizer, slow_tokenizer, keep_fast_tokenizer=False) # If there is any conversion failed, we keep the original tokenizers. if (original_fast_tokenizer is not None and fast_tokenizer is None) or ( original_slow_tokenizer is not None and slow_tokenizer is None ): warning_messagae = ( "There are some issues when converting the fast/slow tokenizers. The original tokenizers from the Hub " " will be used instead." ) result["warnings"].append(warning_messagae) # Let's use the original version at the end (`original_fast_tokenizer` and `original_slow_tokenizer`) fast_tokenizer = original_fast_tokenizer slow_tokenizer = original_slow_tokenizer # Make sure the fast tokenizer can be saved if fast_tokenizer: # We don't save it to `output_folder` at this moment - only at the end of this function. with tempfile.TemporaryDirectory() as tmpdir: try: fast_tokenizer.save_pretrained(tmpdir) except Exception: result["warnings"].append( ( f"Failed to save the fast tokenizer for {fast_tokenizer.__class__.__name__}.", traceback.format_exc(), ) ) fast_tokenizer = None # Make sure the slow tokenizer can be saved if slow_tokenizer: # We don't save it to `output_folder` at this moment - only at the end of this function. with tempfile.TemporaryDirectory() as tmpdir: try: slow_tokenizer.save_pretrained(tmpdir) except Exception: result["warnings"].append( ( f"Failed to save the slow tokenizer for {slow_tokenizer.__class__.__name__}.", traceback.format_exc(), ) ) slow_tokenizer = None # update feature extractors using the tiny config try: feature_extractors = [convert_feature_extractor(p, tiny_config) for p in feature_extractors] except Exception: result["warnings"].append( ( "Failed to convert feature extractors.", traceback.format_exc(), ) ) feature_extractors = [] if hasattr(tiny_config, "max_position_embeddings") and tiny_config.max_position_embeddings > 0: if fast_tokenizer is not None: if fast_tokenizer.__class__.__name__ in [ "RobertaTokenizerFast", "XLMRobertaTokenizerFast", "LongformerTokenizerFast", "MPNetTokenizerFast", ]: fast_tokenizer.model_max_length = tiny_config.max_position_embeddings - 2 else: fast_tokenizer.model_max_length = tiny_config.max_position_embeddings if slow_tokenizer is not None: if slow_tokenizer.__class__.__name__ in [ "RobertaTokenizer", "XLMRobertaTokenizer", "LongformerTokenizer", "MPNetTokenizer", ]: slow_tokenizer.model_max_length = tiny_config.max_position_embeddings - 2 else: slow_tokenizer.model_max_length = tiny_config.max_position_embeddings processors = [fast_tokenizer, slow_tokenizer] + feature_extractors processors = [p for p in processors if p is not None] for p in processors: p.save_pretrained(output_folder) return processors def get_checkpoint_dir(output_dir, model_arch): """Get framework-agnostic architecture name. Used to save all PT/TF/Flax models into the same directory.""" arch_name = model_arch.__name__ if arch_name.startswith("TF"): arch_name = arch_name[2:] elif arch_name.startswith("Flax"): arch_name = arch_name[4:] return os.path.join(output_dir, arch_name) def build_model(model_arch, tiny_config, output_dir): """Create and save a model for `model_arch`. Also copy the set of processors to each model (under the same model type) output folder. """ checkpoint_dir = get_checkpoint_dir(output_dir, model_arch) processor_output_dir = os.path.join(output_dir, "processors") # copy the (same set of) processors (for a model type) to the model arch. specific folder if os.path.isdir(processor_output_dir): shutil.copytree(processor_output_dir, checkpoint_dir, dirs_exist_ok=True) tiny_config = copy.deepcopy(tiny_config) if any(model_arch.__name__.endswith(x) for x in ["ForCausalLM", "LMHeadModel"]): tiny_config.is_encoder_decoder = False tiny_config.is_decoder = True model = model_arch(config=tiny_config) model.save_pretrained(checkpoint_dir) model.from_pretrained(checkpoint_dir) return model def fill_result_with_error(result, error, trace, models_to_create): """Fill `result` with errors for all target model arch if we can't build processor""" error = (error, trace) result["error"] = error for framework in FRAMEWORKS: if framework in models_to_create: result[framework] = {} for model_arch in models_to_create[framework]: result[framework][model_arch.__name__] = {"model": None, "checkpoint": None, "error": error} result["processor"] = {p.__class__.__name__: p.__class__.__name__ for p in result["processor"].values()} def upload_model(model_dir, organization, token): """Upload the tiny models""" arch_name = model_dir.split(os.path.sep)[-1] repo_name = f"tiny-random-{arch_name}" repo_id = f"{organization}/{repo_name}" repo_exist = False error = None try: create_repo(repo_id=repo_id, exist_ok=False, repo_type="model", token=token) except Exception as e: error = e if "You already created" in str(e): error = None logger.warning("Remote repository exists and will be cloned.") repo_exist = True try: create_repo(repo_id=repo_id, exist_ok=True, repo_type="model", token=token) except Exception as e: error = e if error is not None: raise error with tempfile.TemporaryDirectory() as tmpdir: repo = Repository(local_dir=tmpdir, clone_from=repo_id, token=token) repo.git_pull() shutil.copytree(model_dir, tmpdir, dirs_exist_ok=True) if repo_exist: # Open a PR on the existing Hub repo. hub_pr_url = upload_folder( folder_path=model_dir, repo_id=repo_id, repo_type="model", commit_message=f"Update tiny models for {arch_name}", commit_description=f"Upload tiny models for {arch_name}", create_pr=True, token=token, ) logger.warning(f"PR open in {hub_pr_url}.") # TODO: We need this information? else: # Push to Hub repo directly repo.git_add(auto_lfs_track=True) repo.git_commit(f"Upload tiny models for {arch_name}") repo.git_push(blocking=True) # this prints a progress bar with the upload logger.warning(f"Tiny models {arch_name} pushed to {repo_id}.") def build_composite_models(config_class, output_dir): import tempfile from transformers import ( BertConfig, BertLMHeadModel, BertModel, BertTokenizer, BertTokenizerFast, EncoderDecoderModel, GPT2Config, GPT2LMHeadModel, GPT2Tokenizer, GPT2TokenizerFast, SpeechEncoderDecoderModel, TFEncoderDecoderModel, TFVisionEncoderDecoderModel, TFVisionTextDualEncoderModel, VisionEncoderDecoderModel, VisionTextDualEncoderModel, ViTConfig, ViTFeatureExtractor, ViTModel, Wav2Vec2Config, Wav2Vec2Model, Wav2Vec2Processor, ) # These will be removed at the end if they are empty result = {"error": None, "warnings": []} if config_class.model_type == "encoder-decoder": encoder_config_class = BertConfig decoder_config_class = BertConfig encoder_processor = (BertTokenizerFast, BertTokenizer) decoder_processor = (BertTokenizerFast, BertTokenizer) encoder_class = BertModel decoder_class = BertLMHeadModel model_class = EncoderDecoderModel tf_model_class = TFEncoderDecoderModel elif config_class.model_type == "vision-encoder-decoder": encoder_config_class = ViTConfig decoder_config_class = GPT2Config encoder_processor = (ViTFeatureExtractor,) decoder_processor = (GPT2TokenizerFast, GPT2Tokenizer) encoder_class = ViTModel decoder_class = GPT2LMHeadModel model_class = VisionEncoderDecoderModel tf_model_class = TFVisionEncoderDecoderModel elif config_class.model_type == "speech-encoder-decoder": encoder_config_class = Wav2Vec2Config decoder_config_class = BertConfig encoder_processor = (Wav2Vec2Processor,) decoder_processor = (BertTokenizerFast, BertTokenizer) encoder_class = Wav2Vec2Model decoder_class = BertLMHeadModel model_class = SpeechEncoderDecoderModel tf_model_class = None elif config_class.model_type == "vision-text-dual-encoder": # Not encoder-decoder, but encoder-encoder. We just keep the same name as above to make code easier encoder_config_class = ViTConfig decoder_config_class = BertConfig encoder_processor = (ViTFeatureExtractor,) decoder_processor = (BertTokenizerFast, BertTokenizer) encoder_class = ViTModel decoder_class = BertModel model_class = VisionTextDualEncoderModel tf_model_class = TFVisionTextDualEncoderModel with tempfile.TemporaryDirectory() as tmpdir: try: # build encoder models_to_create = {"processor": encoder_processor, "pytorch": (encoder_class,), "tensorflow": []} encoder_output_dir = os.path.join(tmpdir, "encoder") build(encoder_config_class, models_to_create, encoder_output_dir) # build decoder models_to_create = {"processor": decoder_processor, "pytorch": (decoder_class,), "tensorflow": []} decoder_output_dir = os.path.join(tmpdir, "decoder") build(decoder_config_class, models_to_create, decoder_output_dir) # build encoder-decoder encoder_path = os.path.join(encoder_output_dir, encoder_class.__name__) decoder_path = os.path.join(decoder_output_dir, decoder_class.__name__) if config_class.model_type != "vision-text-dual-encoder": # Specify these explicitly for encoder-decoder like models, but not for `vision-text-dual-encoder` as it # has no decoder. decoder_config = decoder_config_class.from_pretrained(decoder_path) decoder_config.is_decoder = True decoder_config.add_cross_attention = True model = model_class.from_encoder_decoder_pretrained( encoder_path, decoder_path, decoder_config=decoder_config, ) elif config_class.model_type == "vision-text-dual-encoder": model = model_class.from_vision_text_pretrained(encoder_path, decoder_path) model_path = os.path.join( output_dir, f"{model_class.__name__}-{encoder_config_class.model_type}-{decoder_config_class.model_type}", ) model.save_pretrained(model_path) if tf_model_class is not None: model = tf_model_class.from_pretrained(model_path) model.save_pretrained(model_path) # copy the processors encoder_processor_path = os.path.join(encoder_output_dir, "processors") decoder_processor_path = os.path.join(decoder_output_dir, "processors") if os.path.isdir(encoder_processor_path): shutil.copytree(encoder_processor_path, model_path, dirs_exist_ok=True) if os.path.isdir(decoder_processor_path): shutil.copytree(decoder_processor_path, model_path, dirs_exist_ok=True) # fill `result` result["processor"] = {x.__name__: x.__name__ for x in encoder_processor + decoder_processor} result["pytorch"] = {model_class.__name__: {"model": model_class.__name__, "checkpoint": model_path}} result["tensorflow"] = {} if tf_model_class is not None: result["tensorflow"] = { tf_model_class.__name__: {"model": tf_model_class.__name__, "checkpoint": model_path} } except Exception: result["error"] = ( f"Failed to build models for {config_class.__name__}.", traceback.format_exc(), ) if not result["error"]: del result["error"] if not result["warnings"]: del result["warnings"] return result def get_token_id_from_tokenizer(token_id_name, tokenizer, original_token_id): """Use `tokenizer` to get the values of `bos_token_id`, `eos_token_ids`, etc. The argument `token_id_name` should be a string ending with `_token_id`, and `original_token_id` should be an integer that will be return if `tokenizer` has no token corresponding to `token_id_name`. """ token_id = original_token_id if not token_id_name.endswith("_token_id"): raise ValueError(f"`token_id_name` is {token_id_name}, which doesn't end with `_token_id`!") token = getattr(tokenizer, token_id_name.replace("_token_id", "_token"), None) if token is not None: if isinstance(tokenizer, PreTrainedTokenizerFast): token_id = tokenizer._convert_token_to_id_with_added_voc(token) else: token_id = tokenizer._convert_token_to_id(token) return token_id def get_config_overrides(config_class, processors): # `Bark` configuration is too special. Let's just not handle this for now. if config_class.__name__ == "BarkConfig": return {} config_overrides = {} # Check if there is any tokenizer (prefer fast version if any) tokenizer = None for processor in processors: if isinstance(processor, PreTrainedTokenizerFast): tokenizer = processor break elif isinstance(processor, PreTrainedTokenizer): tokenizer = processor if tokenizer is None: return config_overrides # Get some properties of the (already converted) tokenizer (smaller vocab size, special token ids, etc.) # We use `len(tokenizer)` instead of `tokenizer.vocab_size` to avoid potential issues for tokenizers with non-empty # `added_tokens_encoder`. One example is the `DebertaV2Tokenizer` where the mask token is the extra token. vocab_size = len(tokenizer) # The original checkpoint has length `35998`, but it doesn't have ids `30400` and `30514` but instead `35998` and # `35999`. if config_class.__name__ == "GPTSanJapaneseConfig": vocab_size += 2 config_overrides["vocab_size"] = vocab_size # Used to create a new model tester with `tokenizer.vocab_size` in order to get the (updated) special token ids. model_tester_kwargs = {"vocab_size": vocab_size} # `FSMTModelTester` accepts `src_vocab_size` and `tgt_vocab_size` but not `vocab_size`. if config_class.__name__ == "FSMTConfig": del model_tester_kwargs["vocab_size"] model_tester_kwargs["src_vocab_size"] = tokenizer.src_vocab_size model_tester_kwargs["tgt_vocab_size"] = tokenizer.tgt_vocab_size _tiny_config = get_tiny_config(config_class, **model_tester_kwargs) # handle the possibility of `text_config` inside `_tiny_config` for clip-like models (`owlvit`, `groupvit`, etc.) if hasattr(_tiny_config, "text_config"): _tiny_config = _tiny_config.text_config # Collect values of some special token ids for attr in dir(_tiny_config): if attr.endswith("_token_id"): token_id = getattr(_tiny_config, attr) if token_id is not None: # Using the token id values from `tokenizer` instead of from `_tiny_config`. token_id = get_token_id_from_tokenizer(attr, tokenizer, original_token_id=token_id) config_overrides[attr] = token_id if config_class.__name__ == "FSMTConfig": config_overrides["src_vocab_size"] = tokenizer.src_vocab_size config_overrides["tgt_vocab_size"] = tokenizer.tgt_vocab_size # `FSMTConfig` has `DecoderConfig` as `decoder` attribute. config_overrides["decoder"] = configuration_fsmt.DecoderConfig( vocab_size=tokenizer.tgt_vocab_size, bos_token_id=config_overrides["eos_token_id"] ) return config_overrides def build(config_class, models_to_create, output_dir): """Create all models for a certain model type. Args: config_class (`PretrainedConfig`): A subclass of `PretrainedConfig` that is used to determine `models_to_create`. models_to_create (`dict`): A dictionary containing the processor/model classes that we want to create the instances. These models are of the same model type which is associated to `config_class`. output_dir (`str`): The directory to save all the checkpoints. Each model architecture will be saved in a subdirectory under it. Models in different frameworks with the same architecture will be saved in the same subdirectory. """ if data["training_ds"] is None or data["testing_ds"] is None: ds = load_dataset("wikitext", "wikitext-2-raw-v1") data["training_ds"] = ds["train"] data["testing_ds"] = ds["test"] if config_class.model_type in [ "encoder-decoder", "vision-encoder-decoder", "speech-encoder-decoder", "vision-text-dual-encoder", ]: return build_composite_models(config_class, output_dir) result = {k: {} for k in models_to_create} # These will be removed at the end if they are empty result["error"] = None result["warnings"] = [] # Build processors processor_classes = models_to_create["processor"] if len(processor_classes) == 0: error = f"No processor class could be found in {config_class.__name__}." fill_result_with_error(result, error, None, models_to_create) logger.error(result["error"][0]) return result for processor_class in processor_classes: try: processor = build_processor(config_class, processor_class, allow_no_checkpoint=True) if processor is not None: result["processor"][processor_class] = processor except Exception: error = f"Failed to build processor for {processor_class.__name__}." trace = traceback.format_exc() fill_result_with_error(result, error, trace, models_to_create) logger.error(result["error"][0]) return result if len(result["processor"]) == 0: error = f"No processor could be built for {config_class.__name__}." fill_result_with_error(result, error, None, models_to_create) logger.error(result["error"][0]) return result try: tiny_config = get_tiny_config(config_class) except Exception as e: error = f"Failed to get tiny config for {config_class.__name__}: {e}" trace = traceback.format_exc() fill_result_with_error(result, error, trace, models_to_create) logger.error(result["error"][0]) return result # Convert the processors (reduce vocabulary size, smaller image size, etc.) processors = list(result["processor"].values()) processor_output_folder = os.path.join(output_dir, "processors") try: processors = convert_processors(processors, tiny_config, processor_output_folder, result) except Exception: error = "Failed to convert the processors." trace = traceback.format_exc() result["warnings"].append((error, trace)) if len(processors) == 0: error = f"No processor is returned by `convert_processors` for {config_class.__name__}." fill_result_with_error(result, error, None, models_to_create) logger.error(result["error"][0]) return result try: config_overrides = get_config_overrides(config_class, processors) except Exception as e: error = f"Failure occurs while calling `get_config_overrides`: {e}" trace = traceback.format_exc() fill_result_with_error(result, error, trace, models_to_create) logger.error(result["error"][0]) return result # Just for us to see this easily in the report if "vocab_size" in config_overrides: result["vocab_size"] = config_overrides["vocab_size"] # Update attributes that `vocab_size` involves for k, v in config_overrides.items(): if hasattr(tiny_config, k): setattr(tiny_config, k, v) # So far, we only have to deal with `text_config`, as `config_overrides` contains text-related attributes only. # `FuyuConfig` saves data under both FuyuConfig and its `text_config`. This is not good, but let's just update # every involved fields to avoid potential failure. if ( hasattr(tiny_config, "text_config") and tiny_config.text_config is not None and hasattr(tiny_config.text_config, k) ): setattr(tiny_config.text_config, k, v) # If `text_config_dict` exists, we need to update its value here too in order to # make # `save_pretrained -> from_pretrained` work. if hasattr(tiny_config, "text_config_dict"): tiny_config.text_config_dict[k] = v if result["warnings"]: logger.warning(result["warnings"][0][0]) # update `result["processor"]` result["processor"] = {type(p).__name__: p.__class__.__name__ for p in processors} for pytorch_arch in models_to_create["pytorch"]: result["pytorch"][pytorch_arch.__name__] = {} error = None try: model = build_model(pytorch_arch, tiny_config, output_dir=output_dir) except Exception as e: model = None error = f"Failed to create the pytorch model for {pytorch_arch}: {e}" trace = traceback.format_exc() result["pytorch"][pytorch_arch.__name__]["model"] = model.__class__.__name__ if model is not None else None result["pytorch"][pytorch_arch.__name__]["checkpoint"] = ( get_checkpoint_dir(output_dir, pytorch_arch) if model is not None else None ) if error is not None: result["pytorch"][pytorch_arch.__name__]["error"] = (error, trace) logger.error(f"{pytorch_arch.__name__}: {error}") for tensorflow_arch in models_to_create["tensorflow"]: # Make PT/TF weights compatible pt_arch_name = tensorflow_arch.__name__[2:] # Remove `TF` pt_arch = getattr(transformers_module, pt_arch_name) result["tensorflow"][tensorflow_arch.__name__] = {} error = None if pt_arch.__name__ in result["pytorch"] and result["pytorch"][pt_arch.__name__]["checkpoint"] is not None: ckpt = get_checkpoint_dir(output_dir, pt_arch) # Use the same weights from PyTorch. try: model = tensorflow_arch.from_pretrained(ckpt) model.save_pretrained(ckpt) except Exception as e: # Conversion may fail. Let's not create a model with different weights to avoid confusion (for now). model = None error = f"Failed to convert the pytorch model to the tensorflow model for {pt_arch}: {e}" trace = traceback.format_exc() else: try: model = build_model(tensorflow_arch, tiny_config, output_dir=output_dir) except Exception as e: model = None error = f"Failed to create the tensorflow model for {tensorflow_arch}: {e}" trace = traceback.format_exc() result["tensorflow"][tensorflow_arch.__name__]["model"] = ( model.__class__.__name__ if model is not None else None ) result["tensorflow"][tensorflow_arch.__name__]["checkpoint"] = ( get_checkpoint_dir(output_dir, tensorflow_arch) if model is not None else None ) if error is not None: result["tensorflow"][tensorflow_arch.__name__]["error"] = (error, trace) logger.error(f"{tensorflow_arch.__name__}: {error}") if not result["error"]: del result["error"] if not result["warnings"]: del result["warnings"] return result def build_tiny_model_summary(results, organization=None, token=None): """Build a summary: a dictionary of the form { model architecture name: { "tokenizer_classes": [...], "processor_classes": [...], "model_classes": [...], } .. } """ tiny_model_summary = {} for config_name in results: processors = [key for key, value in results[config_name]["processor"].items()] tokenizer_classes = sorted([x for x in processors if x.endswith("TokenizerFast") or x.endswith("Tokenizer")]) processor_classes = sorted([x for x in processors if x not in tokenizer_classes]) for framework in FRAMEWORKS: if framework not in results[config_name]: continue for arch_name in results[config_name][framework]: model_classes = [arch_name] base_arch_name = arch_name[2:] if arch_name.startswith("TF") else arch_name # tiny model is not created for `arch_name` if results[config_name][framework][arch_name]["model"] is None: model_classes = [] if base_arch_name not in tiny_model_summary: tiny_model_summary[base_arch_name] = {} tiny_model_summary[base_arch_name].update( { "tokenizer_classes": tokenizer_classes, "processor_classes": processor_classes, } ) tiny_model_summary[base_arch_name]["model_classes"] = sorted( tiny_model_summary[base_arch_name].get("model_classes", []) + model_classes ) if organization is not None: repo_name = f"tiny-random-{base_arch_name}" # composite models' checkpoints have more precise repo. names on the Hub. if base_arch_name in COMPOSITE_MODELS: repo_name = f"tiny-random-{COMPOSITE_MODELS[base_arch_name]}" repo_id = f"{organization}/{repo_name}" try: commit_hash = hf_api.repo_info(repo_id, token=token).sha except Exception: # The directory is not created, but processor(s) is/are included in `results`. logger.warning(f"Failed to get information for {repo_id}.\n{traceback.format_exc()}") del tiny_model_summary[base_arch_name] continue tiny_model_summary[base_arch_name]["sha"] = commit_hash return tiny_model_summary def build_failed_report(results, include_warning=True): failed_results = {} for config_name in results: if "error" in results[config_name]: if config_name not in failed_results: failed_results[config_name] = {} failed_results[config_name] = {"error": results[config_name]["error"]} if include_warning and "warnings" in results[config_name]: if config_name not in failed_results: failed_results[config_name] = {} failed_results[config_name]["warnings"] = results[config_name]["warnings"] for framework in FRAMEWORKS: if framework not in results[config_name]: continue for arch_name in results[config_name][framework]: if "error" in results[config_name][framework][arch_name]: if config_name not in failed_results: failed_results[config_name] = {} if framework not in failed_results[config_name]: failed_results[config_name][framework] = {} if arch_name not in failed_results[config_name][framework]: failed_results[config_name][framework][arch_name] = {} error = results[config_name][framework][arch_name]["error"] failed_results[config_name][framework][arch_name]["error"] = error return failed_results def build_simple_report(results): text = "" failed_text = "" for config_name in results: for framework in FRAMEWORKS: if framework not in results[config_name]: continue for arch_name in results[config_name][framework]: if "error" in results[config_name][framework][arch_name]: result = results[config_name][framework][arch_name]["error"] failed_text += f"{arch_name}: {result[0]}\n" else: result = ("OK",) text += f"{arch_name}: {result[0]}\n" return text, failed_text def update_tiny_model_summary_file(report_path): with open(os.path.join(report_path, "tiny_model_summary.json")) as fp: new_data = json.load(fp) with open("tests/utils/tiny_model_summary.json") as fp: data = json.load(fp) for key, value in new_data.items(): if key not in data: data[key] = value else: for attr in ["tokenizer_classes", "processor_classes", "model_classes"]: # we might get duplication here. We will remove them below when creating `updated_data`. data[key][attr].extend(value[attr]) new_sha = value.get("sha", None) if new_sha is not None: data[key]["sha"] = new_sha updated_data = {} for key in sorted(data.keys()): updated_data[key] = {} for attr, value in data[key].items(): # deduplication and sort updated_data[key][attr] = sorted(set(value)) if attr != "sha" else value with open(os.path.join(report_path, "updated_tiny_model_summary.json"), "w") as fp: json.dump(updated_data, fp, indent=4, ensure_ascii=False) def create_tiny_models( output_path, all, model_types, models_to_skip, no_check, upload, organization, token, num_workers=1, ): clone_path = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) if os.getcwd() != clone_path: raise ValueError(f"This script should be run from the root of the clone of `transformers` {clone_path}") report_path = os.path.join(output_path, "reports") os.makedirs(report_path) _pytorch_arch_mappings = [ x for x in dir(transformers_module) if x.startswith("MODEL_") and x.endswith("_MAPPING") and x != "MODEL_NAMES_MAPPING" ] _tensorflow_arch_mappings = [ x for x in dir(transformers_module) if x.startswith("TF_MODEL_") and x.endswith("_MAPPING") ] pytorch_arch_mappings = [getattr(transformers_module, x) for x in _pytorch_arch_mappings] tensorflow_arch_mappings = [getattr(transformers_module, x) for x in _tensorflow_arch_mappings] config_classes = CONFIG_MAPPING.values() if not all: config_classes = [CONFIG_MAPPING[model_type] for model_type in model_types] # A map from config classes to tuples of processors (tokenizer, feature extractor, processor) classes processor_type_map = {c: get_processor_types_from_config_class(c) for c in config_classes} to_create = {} for c in config_classes: processors = processor_type_map[c] models = get_architectures_from_config_class(c, pytorch_arch_mappings, models_to_skip) tf_models = get_architectures_from_config_class(c, tensorflow_arch_mappings, models_to_skip) if len(models) + len(tf_models) > 0: to_create[c] = {"processor": processors, "pytorch": models, "tensorflow": tf_models} results = {} if num_workers <= 1: for c, models_to_create in list(to_create.items()): print(f"Create models for {c.__name__} ...") result = build(c, models_to_create, output_dir=os.path.join(output_path, c.model_type)) results[c.__name__] = result print("=" * 40) else: all_build_args = [] for c, models_to_create in list(to_create.items()): all_build_args.append((c, models_to_create, os.path.join(output_path, c.model_type))) with multiprocessing.Pool() as pool: results = pool.starmap(build, all_build_args) results = {buid_args[0].__name__: result for buid_args, result in zip(all_build_args, results)} if upload: if organization is None: raise ValueError("The argument `organization` could not be `None`. No model is uploaded") to_upload = [] for model_type in os.listdir(output_path): # This is the directory containing the reports if model_type == "reports": continue for arch in os.listdir(os.path.join(output_path, model_type)): if arch == "processors": continue to_upload.append(os.path.join(output_path, model_type, arch)) to_upload = sorted(to_upload) upload_results = {} if len(to_upload) > 0: for model_dir in to_upload: try: upload_model(model_dir, organization, token) except Exception as e: error = f"Failed to upload {model_dir}. {e.__class__.__name__}: {e}" logger.error(error) upload_results[model_dir] = error with open(os.path.join(report_path, "failed_uploads.json"), "w") as fp: json.dump(upload_results, fp, indent=4) # Build the tiny model summary file. The `tokenizer_classes` and `processor_classes` could be both empty lists. # When using the items in this file to update the file `tests/utils/tiny_model_summary.json`, the model # architectures with `tokenizer_classes` and `processor_classes` being both empty should **NOT** be added to # `tests/utils/tiny_model_summary.json`. tiny_model_summary = build_tiny_model_summary(results, organization=organization, token=token) with open(os.path.join(report_path, "tiny_model_summary.json"), "w") as fp: json.dump(tiny_model_summary, fp, indent=4) with open(os.path.join(report_path, "tiny_model_creation_report.json"), "w") as fp: json.dump(results, fp, indent=4) # Build the warning/failure report (json format): same format as the complete `results` except this contains only # warnings or errors. failed_results = build_failed_report(results) with open(os.path.join(report_path, "failed_report.json"), "w") as fp: json.dump(failed_results, fp, indent=4) simple_report, failed_report = build_simple_report(results) # The simplified report: a .txt file with each line of format: # {model architecture name}: {OK or error message} with open(os.path.join(report_path, "simple_report.txt"), "w") as fp: fp.write(simple_report) # The simplified failure report: same above except this only contains line with errors with open(os.path.join(report_path, "simple_failed_report.txt"), "w") as fp: fp.write(failed_report) update_tiny_model_summary_file(report_path=os.path.join(output_path, "reports")) if __name__ == "__main__": # This has to be `spawn` to avoid hanging forever! multiprocessing.set_start_method("spawn") def list_str(values): return values.split(",") parser = argparse.ArgumentParser() parser.add_argument("--all", action="store_true", help="Will create all tiny models.") parser.add_argument( "--no_check", action="store_true", help="If set, will not check the validity of architectures. Use with caution.", ) parser.add_argument( "-m", "--model_types", type=list_str, help="Comma-separated list of model type(s) from which the tiny models will be created.", ) parser.add_argument( "--models_to_skip", type=list_str, help=( "Comma-separated list of model class names(s) from which the tiny models won't be created.\nThis is usually " "the list of model classes that have their tiny versions already uploaded to the Hub." ), ) parser.add_argument("--upload", action="store_true", help="If to upload the created tiny models to the Hub.") parser.add_argument( "--organization", default=None, type=str, help="The organization on the Hub to which the tiny models will be uploaded.", ) parser.add_argument( "--token", default=None, type=str, help="A valid authentication token for HuggingFace Hub with write access." ) parser.add_argument("output_path", type=Path, help="Path indicating where to store generated model.") parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.") args = parser.parse_args() if not args.all and not args.model_types: raise ValueError("Please provide at least one model type or pass `--all` to export all architectures.") create_tiny_models( args.output_path, args.all, args.model_types, args.models_to_skip, args.no_check, args.upload, args.organization, args.token, args.num_workers, )

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_model_tester.py

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. import glob import os from get_test_info import get_tester_classes if __name__ == "__main__": failures = [] pattern = os.path.join("tests", "models", "**", "test_modeling_*.py") test_files = glob.glob(pattern) # TODO: deal with TF/Flax too test_files = [ x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_")) ] for test_file in test_files: tester_classes = get_tester_classes(test_file) for tester_class in tester_classes: # A few tester classes don't have `parent` parameter in `__init__`. # TODO: deal this better try: tester = tester_class(parent=None) except Exception: continue if hasattr(tester, "get_config"): config = tester.get_config() for k, v in config.to_dict().items(): if isinstance(v, int): target = None if k in ["vocab_size"]: target = 100 elif k in ["max_position_embeddings"]: target = 128 elif k in ["hidden_size", "d_model"]: target = 40 elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]: target = 5 if target is not None and v > target: failures.append( f"{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`" f' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller' f" than {target}." ) if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures))

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_table.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # 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. """ Utility that checks the big table in the file docs/source/en/index.md and potentially updates it. Use from the root of the repo with: ```bash python utils/check_inits.py ``` for a check that will error in case of inconsistencies (used by `make repo-consistency`). To auto-fix issues run: ```bash python utils/check_inits.py --fix_and_overwrite ``` which is used by `make fix-copies`. """ import argparse import collections import os import re from typing import List from transformers.utils import direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_table.py TRANSFORMERS_PATH = "src/transformers" PATH_TO_DOCS = "docs/source/en" REPO_PATH = "." def _find_text_in_file(filename: str, start_prompt: str, end_prompt: str) -> str: """ Find the text in filename between two prompts. Args: filename (`str`): The file to search into. start_prompt (`str`): A string to look for at the start of the content searched. end_prompt (`str`): A string that will mark the end of the content to look for. Returns: `str`: The content between the prompts. """ with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Find the start prompt. start_index = 0 while not lines[start_index].startswith(start_prompt): start_index += 1 start_index += 1 # Now go until the end prompt. end_index = start_index while not lines[end_index].startswith(end_prompt): end_index += 1 end_index -= 1 while len(lines[start_index]) <= 1: start_index += 1 while len(lines[end_index]) <= 1: end_index -= 1 end_index += 1 return "".join(lines[start_index:end_index]), start_index, end_index, lines # Regexes that match TF/Flax/PT model names. Add here suffixes that are used to identify models, separated by | _re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") _re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") # Will match any TF or Flax model too so need to be in an else branch after the two previous regexes. _re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") # This is to make sure the transformers module imported is the one in the repo. transformers_module = direct_transformers_import(TRANSFORMERS_PATH) def camel_case_split(identifier: str) -> List[str]: """ Split a camel-cased name into words. Args: identifier (`str`): The camel-cased name to parse. Returns: `List[str]`: The list of words in the identifier (as seprated by capital letters). Example: ```py >>> camel_case_split("CamelCasedClass") ["Camel", "Cased", "Class"] ``` """ # Regex thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier) return [m.group(0) for m in matches] def _center_text(text: str, width: int) -> str: """ Utility that will add spaces on the left and right of a text to make it centered for a given width. Args: text (`str`): The text to center. width (`int`): The desired length of the result. Returns: `str`: A text of length `width` with the original `text` in the middle. """ text_length = 2 if text == "✅" or text == "❌" else len(text) left_indent = (width - text_length) // 2 right_indent = width - text_length - left_indent return " " * left_indent + text + " " * right_indent SPECIAL_MODEL_NAME_LINK_MAPPING = { "Data2VecAudio": "[Data2VecAudio](model_doc/data2vec)", "Data2VecText": "[Data2VecText](model_doc/data2vec)", "Data2VecVision": "[Data2VecVision](model_doc/data2vec)", "DonutSwin": "[DonutSwin](model_doc/donut)", } MODEL_NAMES_WITH_SAME_CONFIG = { "BARThez": "BART", "BARTpho": "BART", "BertJapanese": "BERT", "BERTweet": "BERT", "BORT": "BERT", "ByT5": "T5", "CPM": "OpenAI GPT-2", "DePlot": "Pix2Struct", "DialoGPT": "OpenAI GPT-2", "DiT": "BEiT", "FLAN-T5": "T5", "FLAN-UL2": "T5", "HerBERT": "BERT", "LayoutXLM": "LayoutLMv2", "Llama2": "LLaMA", "Llama3": "LLaMA", "MADLAD-400": "T5", "MatCha": "Pix2Struct", "mBART-50": "mBART", "Megatron-GPT2": "OpenAI GPT-2", "mLUKE": "LUKE", "MMS": "Wav2Vec2", "NLLB": "M2M100", "PhoBERT": "BERT", "T5v1.1": "T5", "TAPEX": "BART", "UL2": "T5", "Wav2Vec2Phoneme": "Wav2Vec2", "XLM-V": "XLM-RoBERTa", "XLS-R": "Wav2Vec2", "XLSR-Wav2Vec2": "Wav2Vec2", } MODEL_NAMES_TO_IGNORE = ["CLIPVisionModel", "SiglipVisionModel", "ChineseCLIPVisionModel"] def get_model_table_from_auto_modules() -> str: """ Generates an up-to-date model table from the content of the auto modules. """ # Dictionary model names to config. config_maping_names = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES model_name_to_config = { name: config_maping_names[code] for code, name in transformers_module.MODEL_NAMES_MAPPING.items() if code in config_maping_names } model_name_to_prefix = {name: config.replace("Config", "") for name, config in model_name_to_config.items()} # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax. pt_models = collections.defaultdict(bool) tf_models = collections.defaultdict(bool) flax_models = collections.defaultdict(bool) # Let's lookup through all transformers object (once). for attr_name in dir(transformers_module): lookup_dict = None if _re_tf_models.match(attr_name) is not None: lookup_dict = tf_models attr_name = _re_tf_models.match(attr_name).groups()[0] elif _re_flax_models.match(attr_name) is not None: lookup_dict = flax_models attr_name = _re_flax_models.match(attr_name).groups()[0] elif _re_pt_models.match(attr_name) is not None: lookup_dict = pt_models attr_name = _re_pt_models.match(attr_name).groups()[0] if lookup_dict is not None: while len(attr_name) > 0: if attr_name in model_name_to_prefix.values(): lookup_dict[attr_name] = True break # Try again after removing the last word in the name attr_name = "".join(camel_case_split(attr_name)[:-1]) # Let's build that table! model_names = list(model_name_to_config.keys()) + list(MODEL_NAMES_WITH_SAME_CONFIG.keys()) # model name to doc link mapping model_names_mapping = transformers_module.models.auto.configuration_auto.MODEL_NAMES_MAPPING model_name_to_link_mapping = {value: f"[{value}](model_doc/{key})" for key, value in model_names_mapping.items()} # update mapping with special model names model_name_to_link_mapping = { k: SPECIAL_MODEL_NAME_LINK_MAPPING[k] if k in SPECIAL_MODEL_NAME_LINK_MAPPING else v for k, v in model_name_to_link_mapping.items() } # MaskFormerSwin and TimmBackbone are backbones and so not meant to be loaded and used on their own. Instead, they define architectures which can be loaded using the AutoBackbone API. names_to_exclude = ["MaskFormerSwin", "TimmBackbone", "Speech2Text2"] model_names = [name for name in model_names if name not in names_to_exclude] model_names.sort(key=str.lower) columns = ["Model", "PyTorch support", "TensorFlow support", "Flax Support"] # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side). widths = [len(c) + 2 for c in columns] widths[0] = max([len(doc_link) for doc_link in model_name_to_link_mapping.values()]) + 2 # Build the table per se table = "|" + "|".join([_center_text(c, w) for c, w in zip(columns, widths)]) + "|\n" # Use ":-----:" format to center-aligned table cell texts table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]) + "|\n" check = {True: "✅", False: "❌"} for name in model_names: if name in MODEL_NAMES_TO_IGNORE: continue if name in MODEL_NAMES_WITH_SAME_CONFIG.keys(): prefix = model_name_to_prefix[MODEL_NAMES_WITH_SAME_CONFIG[name]] else: prefix = model_name_to_prefix[name] line = [ model_name_to_link_mapping[name], check[pt_models[prefix]], check[tf_models[prefix]], check[flax_models[prefix]], ] table += "|" + "|".join([_center_text(l, w) for l, w in zip(line, widths)]) + "|\n" return table def check_model_table(overwrite=False): """ Check the model table in the index.md is consistent with the state of the lib and potentially fix it. Args: overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the table when it's not up to date. """ current_table, start_index, end_index, lines = _find_text_in_file( filename=os.path.join(PATH_TO_DOCS, "index.md"), start_prompt="<!--This table is updated automatically from the auto modules", end_prompt="<!-- End table-->", ) new_table = get_model_table_from_auto_modules() if current_table != new_table: if overwrite: with open(os.path.join(PATH_TO_DOCS, "index.md"), "w", encoding="utf-8", newline="\n") as f: f.writelines(lines[:start_index] + [new_table] + lines[end_index:]) else: raise ValueError( "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this." ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() check_model_table(args.fix_and_overwrite)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/split_doctest_jobs.py

# Copyright 2024 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. """ This script is used to get the files against which we will run doc testing. This uses `tests_fetcher.get_all_doctest_files` then groups the test files by their directory paths. The files in `docs/source/en/model_doc` or `docs/source/en/tasks` are **NOT** grouped together with other files in the same directory: the objective is to run doctest against them in independent GitHub Actions jobs. Assume we are under `transformers` root directory: To get a map (dictionary) between directory (or file) paths and the corresponding files ```bash python utils/split_doctest_jobs.py ``` or to get a list of lists of directory (or file) paths ```bash python utils/split_doctest_jobs.py --only_return_keys --num_splits 4 ``` (this is used to allow GitHub Actions to generate more than 256 jobs using matrix) """ import argparse from collections import defaultdict from pathlib import Path from tests_fetcher import get_all_doctest_files if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--only_return_keys", action="store_true", help="if to only return the keys (which is a list of list of files' directory or file paths).", ) parser.add_argument( "--num_splits", type=int, default=1, help="the number of splits into which the (flat) list of direcotry/file paths will be split. This has effect only if `only_return_keys` is `True`.", ) args = parser.parse_args() all_doctest_files = get_all_doctest_files() raw_test_collection_map = defaultdict(list) for file in all_doctest_files: file_dir = "/".join(Path(file).parents[0].parts) raw_test_collection_map[file_dir].append(file) refined_test_collection_map = {} for file_dir in raw_test_collection_map.keys(): if file_dir in ["docs/source/en/model_doc", "docs/source/en/tasks"]: for file in raw_test_collection_map[file_dir]: refined_test_collection_map[file] = file else: refined_test_collection_map[file_dir] = " ".join(sorted(raw_test_collection_map[file_dir])) sorted_file_dirs = sorted(refined_test_collection_map.keys()) test_collection_map = {} for file_dir in sorted_file_dirs: test_collection_map[file_dir] = refined_test_collection_map[file_dir] num_jobs = len(test_collection_map) num_jobs_per_splits = num_jobs // args.num_splits file_directory_splits = [] end = 0 for idx in range(args.num_splits): start = end end = start + num_jobs_per_splits + (1 if idx < num_jobs % args.num_splits else 0) file_directory_splits.append(sorted_file_dirs[start:end]) if args.only_return_keys: print(file_directory_splits) else: print(dict(test_collection_map))

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/slow_documentation_tests.txt

docs/source/en/generation_strategies.md docs/source/en/model_doc/code_llama.md docs/source/en/model_doc/ctrl.md docs/source/en/model_doc/kosmos-2.md docs/source/en/model_doc/seamless_m4t.md docs/source/en/model_doc/seamless_m4t_v2.md docs/source/en/task_summary.md docs/source/en/tasks/prompting.md src/transformers/models/blip_2/modeling_blip_2.py src/transformers/models/ctrl/modeling_ctrl.py src/transformers/models/fuyu/modeling_fuyu.py src/transformers/models/idefics2/modeling_idefics2.py src/transformers/models/kosmos2/modeling_kosmos2.py src/transformers/models/musicgen_melody/modeling_musicgen_melody.py src/transformers/models/musicgen_melody/processing_musicgen_melody.py

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/get_test_info.py

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. import importlib import os import sys # This is required to make the module import works (when the python process is running from the root of the repo) sys.path.append(".") r""" The argument `test_file` in this file refers to a model test file. This should be a string of the from `tests/models/*/test_modeling_*.py`. """ def get_module_path(test_file): """Return the module path of a model test file.""" components = test_file.split(os.path.sep) if components[0:2] != ["tests", "models"]: raise ValueError( "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got " f"{test_file} instead." ) test_fn = components[-1] if not test_fn.endswith("py"): raise ValueError(f"`test_file` should be a python file. Got {test_fn} instead.") if not test_fn.startswith("test_modeling_"): raise ValueError( f"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead." ) components = components[:-1] + [test_fn.replace(".py", "")] test_module_path = ".".join(components) return test_module_path def get_test_module(test_file): """Get the module of a model test file.""" test_module_path = get_module_path(test_file) test_module = importlib.import_module(test_module_path) return test_module def get_tester_classes(test_file): """Get all classes in a model test file whose names ends with `ModelTester`.""" tester_classes = [] test_module = get_test_module(test_file) for attr in dir(test_module): if attr.endswith("ModelTester"): tester_classes.append(getattr(test_module, attr)) # sort with class names return sorted(tester_classes, key=lambda x: x.__name__) def get_test_classes(test_file): """Get all [test] classes in a model test file with attribute `all_model_classes` that are non-empty. These are usually the (model) test classes containing the (non-slow) tests to run and are subclasses of one of the classes `ModelTesterMixin`, `TFModelTesterMixin` or `FlaxModelTesterMixin`, as well as a subclass of `unittest.TestCase`. Exceptions include `RagTestMixin` (and its subclasses). """ test_classes = [] test_module = get_test_module(test_file) for attr in dir(test_module): attr_value = getattr(test_module, attr) # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking # `all_model_classes` is not empty (which also excludes other special classes). model_classes = getattr(attr_value, "all_model_classes", []) if len(model_classes) > 0: test_classes.append(attr_value) # sort with class names return sorted(test_classes, key=lambda x: x.__name__) def get_model_classes(test_file): """Get all model classes that appear in `all_model_classes` attributes in a model test file.""" test_classes = get_test_classes(test_file) model_classes = set() for test_class in test_classes: model_classes.update(test_class.all_model_classes) # sort with class names return sorted(model_classes, key=lambda x: x.__name__) def get_model_tester_from_test_class(test_class): """Get the model tester class of a model test class.""" test = test_class() if hasattr(test, "setUp"): test.setUp() model_tester = None if hasattr(test, "model_tester"): # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case. if test.model_tester is not None: model_tester = test.model_tester.__class__ return model_tester def get_test_classes_for_model(test_file, model_class): """Get all [test] classes in `test_file` that have `model_class` in their `all_model_classes`.""" test_classes = get_test_classes(test_file) target_test_classes = [] for test_class in test_classes: if model_class in test_class.all_model_classes: target_test_classes.append(test_class) # sort with class names return sorted(target_test_classes, key=lambda x: x.__name__) def get_tester_classes_for_model(test_file, model_class): """Get all model tester classes in `test_file` that are associated to `model_class`.""" test_classes = get_test_classes_for_model(test_file, model_class) tester_classes = [] for test_class in test_classes: tester_class = get_model_tester_from_test_class(test_class) if tester_class is not None: tester_classes.append(tester_class) # sort with class names return sorted(tester_classes, key=lambda x: x.__name__) def get_test_to_tester_mapping(test_file): """Get a mapping from [test] classes to model tester classes in `test_file`. This uses `get_test_classes` which may return classes that are NOT subclasses of `unittest.TestCase`. """ test_classes = get_test_classes(test_file) test_tester_mapping = {test_class: get_model_tester_from_test_class(test_class) for test_class in test_classes} return test_tester_mapping def get_model_to_test_mapping(test_file): """Get a mapping from model classes to test classes in `test_file`.""" model_classes = get_model_classes(test_file) model_test_mapping = { model_class: get_test_classes_for_model(test_file, model_class) for model_class in model_classes } return model_test_mapping def get_model_to_tester_mapping(test_file): """Get a mapping from model classes to model tester classes in `test_file`.""" model_classes = get_model_classes(test_file) model_to_tester_mapping = { model_class: get_tester_classes_for_model(test_file, model_class) for model_class in model_classes } return model_to_tester_mapping def to_json(o): """Make the information succinct and easy to read. Avoid the full class representation like `<class 'transformers.models.bert.modeling_bert.BertForMaskedLM'>` when displaying the results. Instead, we use class name (`BertForMaskedLM`) for the readability. """ if isinstance(o, str): return o elif isinstance(o, type): return o.__name__ elif isinstance(o, (list, tuple)): return [to_json(x) for x in o] elif isinstance(o, dict): return {to_json(k): to_json(v) for k, v in o.items()} else: return o

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/get_modified_files.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # 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. # this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.: # python ./utils/get_modified_files.py utils src tests examples # # it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered # since the output of this script is fed into Makefile commands it doesn't print a newline after the results import re import subprocess import sys fork_point_sha = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8") modified_files = ( subprocess.check_output(f"git diff --diff-filter=d --name-only {fork_point_sha}".split()).decode("utf-8").split() ) joined_dirs = "|".join(sys.argv[1:]) regex = re.compile(rf"^({joined_dirs}).*?\.py$") relevant_modified_files = [x for x in modified_files if regex.match(x)] print(" ".join(relevant_modified_files), end="")

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_dummies.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # 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. """ This script is responsible for making sure the dummies in utils/dummies_xxx.py are up to date with the main init. Why dummies? This is to make sure that a user can always import all objects from `transformers`, even if they don't have the necessary extra libs installed. Those objects will then raise helpful error message whenever the user tries to access one of their methods. Usage (from the root of the repo): Check that the dummy files are up to date (used in `make repo-consistency`): ```bash python utils/check_dummies.py ``` Update the dummy files if needed (used in `make fix-copies`): ```bash python utils/check_dummies.py --fix_and_overwrite ``` """ import argparse import os import re from typing import Dict, List, Optional # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_dummies.py PATH_TO_TRANSFORMERS = "src/transformers" # Matches is_xxx_available() _re_backend = re.compile(r"is\_([a-z_]*)_available()") # Matches from xxx import bla _re_single_line_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n") # Matches if not is_xxx_available() _re_test_backend = re.compile(r"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)") # Template for the dummy objects. DUMMY_CONSTANT = """ {0} = None """ DUMMY_CLASS = """ class {0}(metaclass=DummyObject): _backends = {1} def __init__(self, *args, **kwargs): requires_backends(self, {1}) """ DUMMY_FUNCTION = """ def {0}(*args, **kwargs): requires_backends({0}, {1}) """ def find_backend(line: str) -> Optional[str]: """ Find one (or multiple) backend in a code line of the init. Args: line (`str`): A code line in an init file. Returns: Optional[`str`]: If one (or several) backend is found, returns it. In the case of multiple backends (the line contains `if is_xxx_available() and `is_yyy_available()`) returns all backends joined on `_and_` (so `xxx_and_yyy` for instance). """ if _re_test_backend.search(line) is None: return None backends = [b[0] for b in _re_backend.findall(line)] backends.sort() return "_and_".join(backends) def read_init() -> Dict[str, List[str]]: """ Read the init and extract backend-specific objects. Returns: Dict[str, List[str]]: A dictionary mapping backend name to the list of object names requiring that backend. """ with open(os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Get to the point we do the actual imports for type checking line_index = 0 while not lines[line_index].startswith("if TYPE_CHECKING"): line_index += 1 backend_specific_objects = {} # Go through the end of the file while line_index < len(lines): # If the line is an if is_backend_available, we grab all objects associated. backend = find_backend(lines[line_index]) if backend is not None: while not lines[line_index].startswith(" else:"): line_index += 1 line_index += 1 objects = [] # Until we unindent, add backend objects to the list while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8): line = lines[line_index] single_line_import_search = _re_single_line_import.search(line) if single_line_import_search is not None: # Single-line imports objects.extend(single_line_import_search.groups()[0].split(", ")) elif line.startswith(" " * 12): # Multiple-line imports (with 3 indent level) objects.append(line[12:-2]) line_index += 1 backend_specific_objects[backend] = objects else: line_index += 1 return backend_specific_objects def create_dummy_object(name: str, backend_name: str) -> str: """ Create the code for a dummy object. Args: name (`str`): The name of the object. backend_name (`str`): The name of the backend required for that object. Returns: `str`: The code of the dummy object. """ if name.isupper(): return DUMMY_CONSTANT.format(name) elif name.islower(): return DUMMY_FUNCTION.format(name, backend_name) else: return DUMMY_CLASS.format(name, backend_name) def create_dummy_files(backend_specific_objects: Optional[Dict[str, List[str]]] = None) -> Dict[str, str]: """ Create the content of the dummy files. Args: backend_specific_objects (`Dict[str, List[str]]`, *optional*): The mapping backend name to list of backend-specific objects. If not passed, will be obtained by calling `read_init()`. Returns: `Dict[str, str]`: A dictionary mapping backend name to code of the corresponding backend file. """ if backend_specific_objects is None: backend_specific_objects = read_init() dummy_files = {} for backend, objects in backend_specific_objects.items(): backend_name = "[" + ", ".join(f'"{b}"' for b in backend.split("_and_")) + "]" dummy_file = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n" dummy_file += "from ..utils import DummyObject, requires_backends\n\n" dummy_file += "\n".join([create_dummy_object(o, backend_name) for o in objects]) dummy_files[backend] = dummy_file return dummy_files def check_dummies(overwrite: bool = False): """ Check if the dummy files are up to date and maybe `overwrite` with the right content. Args: overwrite (`bool`, *optional*, default to `False`): Whether or not to overwrite the content of the dummy files. Will raise an error if they are not up to date when `overwrite=False`. """ dummy_files = create_dummy_files() # For special correspondence backend name to shortcut as used in utils/dummy_xxx_objects.py short_names = {"torch": "pt"} # Locate actual dummy modules and read their content. path = os.path.join(PATH_TO_TRANSFORMERS, "utils") dummy_file_paths = { backend: os.path.join(path, f"dummy_{short_names.get(backend, backend)}_objects.py") for backend in dummy_files.keys() } actual_dummies = {} for backend, file_path in dummy_file_paths.items(): if os.path.isfile(file_path): with open(file_path, "r", encoding="utf-8", newline="\n") as f: actual_dummies[backend] = f.read() else: actual_dummies[backend] = "" # Compare actual with what they should be. for backend in dummy_files.keys(): if dummy_files[backend] != actual_dummies[backend]: if overwrite: print( f"Updating transformers.utils.dummy_{short_names.get(backend, backend)}_objects.py as the main " "__init__ has new objects." ) with open(dummy_file_paths[backend], "w", encoding="utf-8", newline="\n") as f: f.write(dummy_files[backend]) else: raise ValueError( "The main __init__ has objects that are not present in " f"transformers.utils.dummy_{short_names.get(backend, backend)}_objects.py. Run `make fix-copies` " "to fix this." ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() check_dummies(args.fix_and_overwrite)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_config_attributes.py

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. import inspect import os import re from transformers.configuration_utils import PretrainedConfig from transformers.utils import direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_config_docstrings.py PATH_TO_TRANSFORMERS = "src/transformers" # This is to make sure the transformers module imported is the one in the repo. transformers = direct_transformers_import(PATH_TO_TRANSFORMERS) CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING SPECIAL_CASES_TO_ALLOW = { # 'max_position_embeddings' is not used in modeling file, but needed for eval frameworks like Huggingface's lighteval (https://github.com/huggingface/lighteval/blob/af24080ea4f16eaf1683e353042a2dfc9099f038/src/lighteval/models/base_model.py#L264). # periods and offsers are not used in modeling file, but used in the configuration file to define `layers_block_type` and `layers_num_experts`. "JambaConfig": [ "max_position_embeddings", "attn_layer_offset", "attn_layer_period", "expert_layer_offset", "expert_layer_period", ], # used to compute the property `self.chunk_length` "EncodecConfig": ["overlap"], # used to compute the property `self.layers_block_type` "RecurrentGemmaConfig": ["block_types"], # used as in the config to define `intermediate_size` "MambaConfig": ["expand"], # used as `self.bert_model = BertModel(config, ...)` "DPRConfig": True, "FuyuConfig": True, # not used in modeling files, but it's an important information "FSMTConfig": ["langs"], # used internally in the configuration class file "GPTNeoConfig": ["attention_types"], # used internally in the configuration class file "EsmConfig": ["is_folding_model"], # used during training (despite we don't have training script for these models yet) "Mask2FormerConfig": ["ignore_value"], # `ignore_value` used during training (despite we don't have training script for these models yet) # `norm` used in conversion script (despite not using in the modeling file) "OneFormerConfig": ["ignore_value", "norm"], # used during preprocessing and collation, see `collating_graphormer.py` "GraphormerConfig": ["spatial_pos_max"], # used internally in the configuration class file "T5Config": ["feed_forward_proj"], # used internally in the configuration class file # `tokenizer_class` get default value `T5Tokenizer` intentionally "MT5Config": ["feed_forward_proj", "tokenizer_class"], "UMT5Config": ["feed_forward_proj", "tokenizer_class"], # used internally in the configuration class file "LongT5Config": ["feed_forward_proj"], # used internally in the configuration class file "Pop2PianoConfig": ["feed_forward_proj"], # used internally in the configuration class file "SwitchTransformersConfig": ["feed_forward_proj"], # having default values other than `1e-5` - we can't fix them without breaking "BioGptConfig": ["layer_norm_eps"], # having default values other than `1e-5` - we can't fix them without breaking "GLPNConfig": ["layer_norm_eps"], # having default values other than `1e-5` - we can't fix them without breaking "SegformerConfig": ["layer_norm_eps"], # having default values other than `1e-5` - we can't fix them without breaking "CvtConfig": ["layer_norm_eps"], # having default values other than `1e-5` - we can't fix them without breaking "PerceiverConfig": ["layer_norm_eps"], # used internally to calculate the feature size "InformerConfig": ["num_static_real_features", "num_time_features"], # used internally to calculate the feature size "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"], # used internally to calculate the feature size "AutoformerConfig": ["num_static_real_features", "num_time_features"], # used internally to calculate `mlp_dim` "SamVisionConfig": ["mlp_ratio"], # For (head) training, but so far not implemented "ClapAudioConfig": ["num_classes"], # Not used, but providing useful information to users "SpeechT5HifiGanConfig": ["sampling_rate"], # used internally in the configuration class file "UdopConfig": ["feed_forward_proj"], # Actually used in the config or generation config, in that case necessary for the sub-components generation "SeamlessM4TConfig": [ "max_new_tokens", "t2u_max_new_tokens", "t2u_decoder_attention_heads", "t2u_decoder_ffn_dim", "t2u_decoder_layers", "t2u_encoder_attention_heads", "t2u_encoder_ffn_dim", "t2u_encoder_layers", "t2u_max_position_embeddings", ], # Actually used in the config or generation config, in that case necessary for the sub-components generation "SeamlessM4Tv2Config": [ "max_new_tokens", "t2u_decoder_attention_heads", "t2u_decoder_ffn_dim", "t2u_decoder_layers", "t2u_encoder_attention_heads", "t2u_encoder_ffn_dim", "t2u_encoder_layers", "t2u_max_position_embeddings", "t2u_variance_pred_dropout", "t2u_variance_predictor_embed_dim", "t2u_variance_predictor_hidden_dim", "t2u_variance_predictor_kernel_size", ], } # TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure SPECIAL_CASES_TO_ALLOW.update( { "CLIPSegConfig": True, "DeformableDetrConfig": True, "DetaConfig": True, "DinatConfig": True, "DonutSwinConfig": True, "EfficientFormerConfig": True, "FastSpeech2ConformerConfig": True, "FSMTConfig": True, "JukeboxConfig": True, "LayoutLMv2Config": True, "MaskFormerSwinConfig": True, "MT5Config": True, # For backward compatibility with trust remote code models "MptConfig": True, "MptAttentionConfig": True, "NatConfig": True, "OneFormerConfig": True, "PerceiverConfig": True, "RagConfig": True, "SpeechT5Config": True, "SwinConfig": True, "Swin2SRConfig": True, "Swinv2Config": True, "SwitchTransformersConfig": True, "TableTransformerConfig": True, "TapasConfig": True, "UniSpeechConfig": True, "UniSpeechSatConfig": True, "WavLMConfig": True, "WhisperConfig": True, # TODO: @Arthur (for `alignment_head` and `alignment_layer`) "JukeboxPriorConfig": True, # TODO: @Younes (for `is_decoder`) "Pix2StructTextConfig": True, "IdeficsConfig": True, "IdeficsVisionConfig": True, "IdeficsPerceiverConfig": True, } ) def check_attribute_being_used(config_class, attributes, default_value, source_strings): """Check if any name in `attributes` is used in one of the strings in `source_strings` Args: config_class (`type`): The configuration class for which the arguments in its `__init__` will be checked. attributes (`List[str]`): The name of an argument (or attribute) and its variant names if any. default_value (`Any`): A default value for the attribute in `attributes` assigned in the `__init__` of `config_class`. source_strings (`List[str]`): The python source code strings in the same modeling directory where `config_class` is defined. The file containing the definition of `config_class` should be excluded. """ attribute_used = False for attribute in attributes: for modeling_source in source_strings: # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)` if ( f"config.{attribute}" in modeling_source or f'getattr(config, "{attribute}"' in modeling_source or f'getattr(self.config, "{attribute}"' in modeling_source ): attribute_used = True # Deal with multi-line cases elif ( re.search( rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"', modeling_source, ) is not None ): attribute_used = True # `SequenceSummary` is called with `SequenceSummary(config)` elif attribute in [ "summary_type", "summary_use_proj", "summary_activation", "summary_last_dropout", "summary_proj_to_labels", "summary_first_dropout", ]: if "SequenceSummary" in modeling_source: attribute_used = True if attribute_used: break if attribute_used: break # common and important attributes, even if they do not always appear in the modeling files attributes_to_allow = [ "bos_index", "eos_index", "pad_index", "unk_index", "mask_index", "image_size", "use_cache", "out_features", "out_indices", "sampling_rate", # backbone related arguments passed to load_backbone "use_pretrained_backbone", "backbone", "backbone_config", "use_timm_backbone", "backbone_kwargs", ] attributes_used_in_generation = ["encoder_no_repeat_ngram_size"] # Special cases to be allowed case_allowed = True if not attribute_used: case_allowed = False for attribute in attributes: # Allow if the default value in the configuration class is different from the one in `PretrainedConfig` if attribute in ["is_encoder_decoder"] and default_value is True: case_allowed = True elif attribute in ["tie_word_embeddings"] and default_value is False: case_allowed = True # Allow cases without checking the default value in the configuration class elif attribute in attributes_to_allow + attributes_used_in_generation: case_allowed = True elif attribute.endswith("_token_id"): case_allowed = True # configuration class specific cases if not case_allowed: allowed_cases = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__, []) case_allowed = allowed_cases is True or attribute in allowed_cases return attribute_used or case_allowed def check_config_attributes_being_used(config_class): """Check the arguments in `__init__` of `config_class` are used in the modeling files in the same directory Args: config_class (`type`): The configuration class for which the arguments in its `__init__` will be checked. """ # Get the parameters in `__init__` of the configuration class, and the default values if any signature = dict(inspect.signature(config_class.__init__).parameters) parameter_names = [x for x in list(signature.keys()) if x not in ["self", "kwargs"]] parameter_defaults = [signature[param].default for param in parameter_names] # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long # as one variant is used, the test should pass reversed_attribute_map = {} if len(config_class.attribute_map) > 0: reversed_attribute_map = {v: k for k, v in config_class.attribute_map.items()} # Get the path to modeling source files config_source_file = inspect.getsourcefile(config_class) model_dir = os.path.dirname(config_source_file) # Let's check against all frameworks: as long as one framework uses an attribute, we are good. modeling_paths = [os.path.join(model_dir, fn) for fn in os.listdir(model_dir) if fn.startswith("modeling_")] # Get the source code strings modeling_sources = [] for path in modeling_paths: if os.path.isfile(path): with open(path, encoding="utf8") as fp: modeling_sources.append(fp.read()) unused_attributes = [] for config_param, default_value in zip(parameter_names, parameter_defaults): # `attributes` here is all the variant names for `config_param` attributes = [config_param] # some configuration classes have non-empty `attribute_map`, and both names could be used in the # corresponding modeling files. As long as one of them appears, it is fine. if config_param in reversed_attribute_map: attributes.append(reversed_attribute_map[config_param]) if not check_attribute_being_used(config_class, attributes, default_value, modeling_sources): unused_attributes.append(attributes[0]) return sorted(unused_attributes) def check_config_attributes(): """Check the arguments in `__init__` of all configuration classes are used in python files""" configs_with_unused_attributes = {} for _config_class in list(CONFIG_MAPPING.values()): # Skip deprecated models if "models.deprecated" in _config_class.__module__: continue # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.) config_classes_in_module = [ cls for name, cls in inspect.getmembers( inspect.getmodule(_config_class), lambda x: inspect.isclass(x) and issubclass(x, PretrainedConfig) and inspect.getmodule(x) == inspect.getmodule(_config_class), ) ] for config_class in config_classes_in_module: unused_attributes = check_config_attributes_being_used(config_class) if len(unused_attributes) > 0: configs_with_unused_attributes[config_class.__name__] = unused_attributes if len(configs_with_unused_attributes) > 0: error = "The following configuration classes contain unused attributes in the corresponding modeling files:\n" for name, attributes in configs_with_unused_attributes.items(): error += f"{name}: {attributes}\n" raise ValueError(error) if __name__ == "__main__": check_config_attributes()

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/download_glue_data.py

""" Script for downloading all GLUE data. Original source: https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e Note: for legal reasons, we are unable to host MRPC. You can either use the version hosted by the SentEval team, which is already tokenized, or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually. For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example). You should then rename and place specific files in a folder (see below for an example). mkdir MRPC cabextract MSRParaphraseCorpus.msi -d MRPC cat MRPC/_2DEC3DBE877E4DB192D17C0256E90F1D | tr -d $'\r' > MRPC/msr_paraphrase_train.txt cat MRPC/_D7B391F9EAFF4B1B8BCE8F21B20B1B61 | tr -d $'\r' > MRPC/msr_paraphrase_test.txt rm MRPC/_* rm MSRParaphraseCorpus.msi 1/30/19: It looks like SentEval is no longer hosting their extracted and tokenized MRPC data, so you'll need to download the data from the original source for now. 2/11/19: It looks like SentEval actually *is* hosting the extracted data. Hooray! """ import argparse import os import sys import urllib.request import zipfile TASKS = ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"] TASK2PATH = { "CoLA": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4", "SST": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8", "MRPC": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc", "QQP": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5", "STS": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5", "MNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce", "SNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df", "QNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601", "RTE": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb", "WNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf", "diagnostic": "https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D", } MRPC_TRAIN = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt" MRPC_TEST = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt" def download_and_extract(task, data_dir): print(f"Downloading and extracting {task}...") data_file = f"{task}.zip" urllib.request.urlretrieve(TASK2PATH[task], data_file) with zipfile.ZipFile(data_file) as zip_ref: zip_ref.extractall(data_dir) os.remove(data_file) print("\tCompleted!") def format_mrpc(data_dir, path_to_data): print("Processing MRPC...") mrpc_dir = os.path.join(data_dir, "MRPC") if not os.path.isdir(mrpc_dir): os.mkdir(mrpc_dir) if path_to_data: mrpc_train_file = os.path.join(path_to_data, "msr_paraphrase_train.txt") mrpc_test_file = os.path.join(path_to_data, "msr_paraphrase_test.txt") else: print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN) mrpc_train_file = os.path.join(mrpc_dir, "msr_paraphrase_train.txt") mrpc_test_file = os.path.join(mrpc_dir, "msr_paraphrase_test.txt") urllib.request.urlretrieve(MRPC_TRAIN, mrpc_train_file) urllib.request.urlretrieve(MRPC_TEST, mrpc_test_file) if not os.path.isfile(mrpc_train_file): raise ValueError(f"Train data not found at {mrpc_train_file}") if not os.path.isfile(mrpc_test_file): raise ValueError(f"Test data not found at {mrpc_test_file}") urllib.request.urlretrieve(TASK2PATH["MRPC"], os.path.join(mrpc_dir, "dev_ids.tsv")) dev_ids = [] with open(os.path.join(mrpc_dir, "dev_ids.tsv"), encoding="utf8") as ids_fh: for row in ids_fh: dev_ids.append(row.strip().split("\t")) with open(mrpc_train_file, encoding="utf8") as data_fh, open( os.path.join(mrpc_dir, "train.tsv"), "w", encoding="utf8" ) as train_fh, open(os.path.join(mrpc_dir, "dev.tsv"), "w", encoding="utf8") as dev_fh: header = data_fh.readline() train_fh.write(header) dev_fh.write(header) for row in data_fh: label, id1, id2, s1, s2 = row.strip().split("\t") if [id1, id2] in dev_ids: dev_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) else: train_fh.write("%s\t%s\t%s\t%s\t%s\n" % (label, id1, id2, s1, s2)) with open(mrpc_test_file, encoding="utf8") as data_fh, open( os.path.join(mrpc_dir, "test.tsv"), "w", encoding="utf8" ) as test_fh: header = data_fh.readline() test_fh.write("index\t#1 ID\t#2 ID\t#1 String\t#2 String\n") for idx, row in enumerate(data_fh): label, id1, id2, s1, s2 = row.strip().split("\t") test_fh.write("%d\t%s\t%s\t%s\t%s\n" % (idx, id1, id2, s1, s2)) print("\tCompleted!") def download_diagnostic(data_dir): print("Downloading and extracting diagnostic...") if not os.path.isdir(os.path.join(data_dir, "diagnostic")): os.mkdir(os.path.join(data_dir, "diagnostic")) data_file = os.path.join(data_dir, "diagnostic", "diagnostic.tsv") urllib.request.urlretrieve(TASK2PATH["diagnostic"], data_file) print("\tCompleted!") return def get_tasks(task_names): task_names = task_names.split(",") if "all" in task_names: tasks = TASKS else: tasks = [] for task_name in task_names: if task_name not in TASKS: raise ValueError(f"Task {task_name} not found!") tasks.append(task_name) return tasks def main(arguments): parser = argparse.ArgumentParser() parser.add_argument("--data_dir", help="directory to save data to", type=str, default="glue_data") parser.add_argument( "--tasks", help="tasks to download data for as a comma separated string", type=str, default="all" ) parser.add_argument( "--path_to_mrpc", help="path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt", type=str, default="", ) args = parser.parse_args(arguments) if not os.path.isdir(args.data_dir): os.mkdir(args.data_dir) tasks = get_tasks(args.tasks) for task in tasks: if task == "MRPC": format_mrpc(args.data_dir, args.path_to_mrpc) elif task == "diagnostic": download_diagnostic(args.data_dir) else: download_and_extract(task, args.data_dir) if __name__ == "__main__": sys.exit(main(sys.argv[1:]))

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/sort_auto_mappings.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # 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. """ Utility that sorts the names in the auto mappings defines in the auto modules in alphabetical order. Use from the root of the repo with: ```bash python utils/sort_auto_mappings.py ``` to auto-fix all the auto mappings (used in `make style`). To only check if the mappings are properly sorted (as used in `make quality`), do: ```bash python utils/sort_auto_mappings.py --check_only ``` """ import argparse import os import re from typing import Optional # Path are set with the intent you should run this script from the root of the repo. PATH_TO_AUTO_MODULE = "src/transformers/models/auto" # re pattern that matches mapping introductions: # SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict _re_intro_mapping = re.compile(r"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict") # re pattern that matches identifiers in mappings _re_identifier = re.compile(r'\s*\(\s*"(\S[^"]+)"') def sort_auto_mapping(fname: str, overwrite: bool = False) -> Optional[bool]: """ Sort all auto mappings in a file. Args: fname (`str`): The name of the file where we want to sort auto-mappings. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to fix and overwrite the file. Returns: `Optional[bool]`: Returns `None` if `overwrite=True`. Otherwise returns `True` if the file has an auto-mapping improperly sorted, `False` if the file is okay. """ with open(fname, "r", encoding="utf-8") as f: content = f.read() lines = content.split("\n") new_lines = [] line_idx = 0 while line_idx < len(lines): if _re_intro_mapping.search(lines[line_idx]) is not None: # Start of a new mapping! indent = len(re.search(r"^(\s*)\S", lines[line_idx]).groups()[0]) + 8 while not lines[line_idx].startswith(" " * indent + "("): new_lines.append(lines[line_idx]) line_idx += 1 blocks = [] while lines[line_idx].strip() != "]": # Blocks either fit in one line or not if lines[line_idx].strip() == "(": start_idx = line_idx while not lines[line_idx].startswith(" " * indent + ")"): line_idx += 1 blocks.append("\n".join(lines[start_idx : line_idx + 1])) else: blocks.append(lines[line_idx]) line_idx += 1 # Sort blocks by their identifiers blocks = sorted(blocks, key=lambda x: _re_identifier.search(x).groups()[0]) new_lines += blocks else: new_lines.append(lines[line_idx]) line_idx += 1 if overwrite: with open(fname, "w", encoding="utf-8") as f: f.write("\n".join(new_lines)) else: return "\n".join(new_lines) != content def sort_all_auto_mappings(overwrite: bool = False): """ Sort all auto mappings in the library. Args: overwrite (`bool`, *optional*, defaults to `False`): Whether or not to fix and overwrite the file. """ fnames = [os.path.join(PATH_TO_AUTO_MODULE, f) for f in os.listdir(PATH_TO_AUTO_MODULE) if f.endswith(".py")] diffs = [sort_auto_mapping(fname, overwrite=overwrite) for fname in fnames] if not overwrite and any(diffs): failures = [f for f, d in zip(fnames, diffs) if d] raise ValueError( f"The following files have auto mappings that need sorting: {', '.join(failures)}. Run `make style` to fix" " this." ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.") args = parser.parse_args() sort_all_auto_mappings(not args.check_only)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_inits.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # 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. """ Utility that checks the custom inits of Transformers are well-defined: Transformers uses init files that delay the import of an object to when it's actually needed. This is to avoid the main init importing all models, which would make the line `import transformers` very slow when the user has all optional dependencies installed. The inits with delayed imports have two halves: one definining a dictionary `_import_structure` which maps modules to the name of the objects in each module, and one in `TYPE_CHECKING` which looks like a normal init for type-checkers. The goal of this script is to check the objects defined in both halves are the same. This also checks the main init properly references all submodules, even if it doesn't import anything from them: every submodule should be defined as a key of `_import_structure`, with an empty list as value potentially, or the submodule won't be importable. Use from the root of the repo with: ```bash python utils/check_inits.py ``` for a check that will error in case of inconsistencies (used by `make repo-consistency`). There is no auto-fix possible here sadly :-( """ import collections import os import re from pathlib import Path from typing import Dict, List, Optional, Tuple # Path is set with the intent you should run this script from the root of the repo. PATH_TO_TRANSFORMERS = "src/transformers" # Matches is_xxx_available() _re_backend = re.compile(r"is\_([a-z_]*)_available()") # Catches a one-line _import_struct = {xxx} _re_one_line_import_struct = re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}") # Catches a line with a key-values pattern: "bla": ["foo", "bar"] _re_import_struct_key_value = re.compile(r'\s+"\S*":\s+\[([^\]]*)\]') # Catches a line if not is_foo_available _re_test_backend = re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)") # Catches a line _import_struct["bla"].append("foo") _re_import_struct_add_one = re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)') # Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"] _re_import_struct_add_many = re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]") # Catches a line with an object between quotes and a comma: "MyModel", _re_quote_object = re.compile(r'^\s+"([^"]+)",') # Catches a line with objects between brackets only: ["foo", "bar"], _re_between_brackets = re.compile(r"^\s+\[([^\]]+)\]") # Catches a line with from foo import bar, bla, boo _re_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n") # Catches a line with try: _re_try = re.compile(r"^\s*try:") # Catches a line with else: _re_else = re.compile(r"^\s*else:") def find_backend(line: str) -> Optional[str]: """ Find one (or multiple) backend in a code line of the init. Args: line (`str`): A code line of the main init. Returns: Optional[`str`]: If one (or several) backend is found, returns it. In the case of multiple backends (the line contains `if is_xxx_available() and `is_yyy_available()`) returns all backends joined on `_and_` (so `xxx_and_yyy` for instance). """ if _re_test_backend.search(line) is None: return None backends = [b[0] for b in _re_backend.findall(line)] backends.sort() return "_and_".join(backends) def parse_init(init_file) -> Optional[Tuple[Dict[str, List[str]], Dict[str, List[str]]]]: """ Read an init_file and parse (per backend) the `_import_structure` objects defined and the `TYPE_CHECKING` objects defined. Args: init_file (`str`): Path to the init file to inspect. Returns: `Optional[Tuple[Dict[str, List[str]], Dict[str, List[str]]]]`: A tuple of two dictionaries mapping backends to list of imported objects, one for the `_import_structure` part of the init and one for the `TYPE_CHECKING` part of the init. Returns `None` if the init is not a custom init. """ with open(init_file, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Get the to `_import_structure` definition. line_index = 0 while line_index < len(lines) and not lines[line_index].startswith("_import_structure = {"): line_index += 1 # If this is a traditional init, just return. if line_index >= len(lines): return None # First grab the objects without a specific backend in _import_structure objects = [] while not lines[line_index].startswith("if TYPE_CHECKING") and find_backend(lines[line_index]) is None: line = lines[line_index] # If we have everything on a single line, let's deal with it. if _re_one_line_import_struct.search(line): content = _re_one_line_import_struct.search(line).groups()[0] imports = re.findall(r"\[([^\]]+)\]", content) for imp in imports: objects.extend([obj[1:-1] for obj in imp.split(", ")]) line_index += 1 continue single_line_import_search = _re_import_struct_key_value.search(line) if single_line_import_search is not None: imports = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", ") if len(obj) > 0] objects.extend(imports) elif line.startswith(" " * 8 + '"'): objects.append(line[9:-3]) line_index += 1 # Those are stored with the key "none". import_dict_objects = {"none": objects} # Let's continue with backend-specific objects in _import_structure while not lines[line_index].startswith("if TYPE_CHECKING"): # If the line is an if not is_backend_available, we grab all objects associated. backend = find_backend(lines[line_index]) # Check if the backend declaration is inside a try block: if _re_try.search(lines[line_index - 1]) is None: backend = None if backend is not None: line_index += 1 # Scroll until we hit the else block of try-except-else while _re_else.search(lines[line_index]) is None: line_index += 1 line_index += 1 objects = [] # Until we unindent, add backend objects to the list while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 4): line = lines[line_index] if _re_import_struct_add_one.search(line) is not None: objects.append(_re_import_struct_add_one.search(line).groups()[0]) elif _re_import_struct_add_many.search(line) is not None: imports = _re_import_struct_add_many.search(line).groups()[0].split(", ") imports = [obj[1:-1] for obj in imports if len(obj) > 0] objects.extend(imports) elif _re_between_brackets.search(line) is not None: imports = _re_between_brackets.search(line).groups()[0].split(", ") imports = [obj[1:-1] for obj in imports if len(obj) > 0] objects.extend(imports) elif _re_quote_object.search(line) is not None: objects.append(_re_quote_object.search(line).groups()[0]) elif line.startswith(" " * 8 + '"'): objects.append(line[9:-3]) elif line.startswith(" " * 12 + '"'): objects.append(line[13:-3]) line_index += 1 import_dict_objects[backend] = objects else: line_index += 1 # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend objects = [] while ( line_index < len(lines) and find_backend(lines[line_index]) is None and not lines[line_index].startswith("else") ): line = lines[line_index] single_line_import_search = _re_import.search(line) if single_line_import_search is not None: objects.extend(single_line_import_search.groups()[0].split(", ")) elif line.startswith(" " * 8): objects.append(line[8:-2]) line_index += 1 type_hint_objects = {"none": objects} # Let's continue with backend-specific objects while line_index < len(lines): # If the line is an if is_backend_available, we grab all objects associated. backend = find_backend(lines[line_index]) # Check if the backend declaration is inside a try block: if _re_try.search(lines[line_index - 1]) is None: backend = None if backend is not None: line_index += 1 # Scroll until we hit the else block of try-except-else while _re_else.search(lines[line_index]) is None: line_index += 1 line_index += 1 objects = [] # Until we unindent, add backend objects to the list while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8): line = lines[line_index] single_line_import_search = _re_import.search(line) if single_line_import_search is not None: objects.extend(single_line_import_search.groups()[0].split(", ")) elif line.startswith(" " * 12): objects.append(line[12:-2]) line_index += 1 type_hint_objects[backend] = objects else: line_index += 1 return import_dict_objects, type_hint_objects def analyze_results(import_dict_objects: Dict[str, List[str]], type_hint_objects: Dict[str, List[str]]) -> List[str]: """ Analyze the differences between _import_structure objects and TYPE_CHECKING objects found in an init. Args: import_dict_objects (`Dict[str, List[str]]`): A dictionary mapping backend names (`"none"` for the objects independent of any specific backend) to list of imported objects. type_hint_objects (`Dict[str, List[str]]`): A dictionary mapping backend names (`"none"` for the objects independent of any specific backend) to list of imported objects. Returns: `List[str]`: The list of errors corresponding to mismatches. """ def find_duplicates(seq): return [k for k, v in collections.Counter(seq).items() if v > 1] # If one backend is missing from the other part of the init, error early. if list(import_dict_objects.keys()) != list(type_hint_objects.keys()): return ["Both sides of the init do not have the same backends!"] errors = [] # Find all errors. for key in import_dict_objects.keys(): # Duplicate imports in any half. duplicate_imports = find_duplicates(import_dict_objects[key]) if duplicate_imports: errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}") duplicate_type_hints = find_duplicates(type_hint_objects[key]) if duplicate_type_hints: errors.append(f"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}") # Missing imports in either part of the init. if sorted(set(import_dict_objects[key])) != sorted(set(type_hint_objects[key])): name = "base imports" if key == "none" else f"{key} backend" errors.append(f"Differences for {name}:") for a in type_hint_objects[key]: if a not in import_dict_objects[key]: errors.append(f" {a} in TYPE_HINT but not in _import_structure.") for a in import_dict_objects[key]: if a not in type_hint_objects[key]: errors.append(f" {a} in _import_structure but not in TYPE_HINT.") return errors def check_all_inits(): """ Check all inits in the transformers repo and raise an error if at least one does not define the same objects in both halves. """ failures = [] for root, _, files in os.walk(PATH_TO_TRANSFORMERS): if "__init__.py" in files: fname = os.path.join(root, "__init__.py") objects = parse_init(fname) if objects is not None: errors = analyze_results(*objects) if len(errors) > 0: errors[0] = f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}" failures.append("\n".join(errors)) if len(failures) > 0: raise ValueError("\n\n".join(failures)) def get_transformers_submodules() -> List[str]: """ Returns the list of Transformers submodules. """ submodules = [] for path, directories, files in os.walk(PATH_TO_TRANSFORMERS): for folder in directories: # Ignore private modules if folder.startswith("_"): directories.remove(folder) continue # Ignore leftovers from branches (empty folders apart from pycache) if len(list((Path(path) / folder).glob("*.py"))) == 0: continue short_path = str((Path(path) / folder).relative_to(PATH_TO_TRANSFORMERS)) submodule = short_path.replace(os.path.sep, ".") submodules.append(submodule) for fname in files: if fname == "__init__.py": continue short_path = str((Path(path) / fname).relative_to(PATH_TO_TRANSFORMERS)) submodule = short_path.replace(".py", "").replace(os.path.sep, ".") if len(submodule.split(".")) == 1: submodules.append(submodule) return submodules IGNORE_SUBMODULES = [ "convert_pytorch_checkpoint_to_tf2", "modeling_flax_pytorch_utils", "models.esm.openfold_utils", "modeling_attn_mask_utils", "safetensors_conversion", ] def check_submodules(): """ Check all submodules of Transformers are properly registered in the main init. Error otherwise. """ # This is to make sure the transformers module imported is the one in the repo. from transformers.utils import direct_transformers_import transformers = direct_transformers_import(PATH_TO_TRANSFORMERS) import_structure_keys = set(transformers._import_structure.keys()) # This contains all the base keys of the _import_structure object defined in the init, but if the user is missing # some optional dependencies, they may not have all of them. Thus we read the init to read all additions and # (potentiall re-) add them. with open(os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), "r") as f: init_content = f.read() import_structure_keys.update(set(re.findall(r"import_structure\[\"([^\"]*)\"\]", init_content))) module_not_registered = [ module for module in get_transformers_submodules() if module not in IGNORE_SUBMODULES and module not in import_structure_keys ] if len(module_not_registered) > 0: list_of_modules = "\n".join(f"- {module}" for module in module_not_registered) raise ValueError( "The following submodules are not properly registed in the main init of Transformers:\n" f"{list_of_modules}\n" "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value." ) if __name__ == "__main__": check_all_inits() check_submodules()

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_doctest_list.py

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """ This script is responsible for cleaning the list of doctests by making sure the entries all exist and are in alphabetical order. Usage (from the root of the repo): Check that the doctest list is properly sorted and all files exist (used in `make repo-consistency`): ```bash python utils/check_doctest_list.py ``` Auto-sort the doctest list if it is not properly sorted (used in `make fix-copies`): ```bash python utils/check_doctest_list.py --fix_and_overwrite ``` """ import argparse import os # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_doctest_list.py REPO_PATH = "." DOCTEST_FILE_PATHS = ["not_doctested.txt", "slow_documentation_tests.txt"] def clean_doctest_list(doctest_file: str, overwrite: bool = False): """ Cleans the doctest in a given file. Args: doctest_file (`str`): The path to the doctest file to check or clean. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to fix problems. If `False`, will error when the file is not clean. """ non_existent_paths = [] all_paths = [] with open(doctest_file, "r", encoding="utf-8") as f: for line in f: line = line.strip().split(" ")[0] path = os.path.join(REPO_PATH, line) if not (os.path.isfile(path) or os.path.isdir(path)): non_existent_paths.append(line) all_paths.append(line) if len(non_existent_paths) > 0: non_existent_paths = "\n".join([f"- {f}" for f in non_existent_paths]) raise ValueError(f"`{doctest_file}` contains non-existent paths:\n{non_existent_paths}") sorted_paths = sorted(all_paths) if all_paths != sorted_paths: if not overwrite: raise ValueError( f"Files in `{doctest_file}` are not in alphabetical order, run `make fix-copies` to fix " "this automatically." ) with open(doctest_file, "w", encoding="utf-8") as f: f.write("\n".join(sorted_paths) + "\n") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() for doctest_file in DOCTEST_FILE_PATHS: doctest_file = os.path.join(REPO_PATH, "utils", doctest_file) clean_doctest_list(doctest_file, args.fix_and_overwrite)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_config_docstrings.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # 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. import inspect import re from transformers.utils import direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_config_docstrings.py PATH_TO_TRANSFORMERS = "src/transformers" # This is to make sure the transformers module imported is the one in the repo. transformers = direct_transformers_import(PATH_TO_TRANSFORMERS) CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING # Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`. # For example, `[google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)` _re_checkpoint = re.compile(r"\[(.+?)\]\((https://huggingface\.co/.+?)\)") CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = { "DecisionTransformerConfig", "EncoderDecoderConfig", "MusicgenConfig", "RagConfig", "SpeechEncoderDecoderConfig", "TimmBackboneConfig", "VisionEncoderDecoderConfig", "VisionTextDualEncoderConfig", "LlamaConfig", } def get_checkpoint_from_config_class(config_class): checkpoint = None # source code of `config_class` config_source = inspect.getsource(config_class) checkpoints = _re_checkpoint.findall(config_source) # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link. # For example, `('google-bert/bert-base-uncased', 'https://huggingface.co/google-bert/bert-base-uncased')` for ckpt_name, ckpt_link in checkpoints: # allow the link to end with `/` if ckpt_link.endswith("/"): ckpt_link = ckpt_link[:-1] # verify the checkpoint name corresponds to the checkpoint link ckpt_link_from_name = f"https://huggingface.co/{ckpt_name}" if ckpt_link == ckpt_link_from_name: checkpoint = ckpt_name break return checkpoint def check_config_docstrings_have_checkpoints(): configs_without_checkpoint = [] for config_class in list(CONFIG_MAPPING.values()): # Skip deprecated models if "models.deprecated" in config_class.__module__: continue checkpoint = get_checkpoint_from_config_class(config_class) name = config_class.__name__ if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK: configs_without_checkpoint.append(name) if len(configs_without_checkpoint) > 0: message = "\n".join(sorted(configs_without_checkpoint)) raise ValueError( f"The following configurations don't contain any valid checkpoint:\n{message}\n\n" "The requirement is to include a link pointing to one of the models of this architecture in the " "docstring of the config classes listed above. The link should have be a markdown format like " "[myorg/mymodel](https://huggingface.co/myorg/mymodel)." ) if __name__ == "__main__": check_config_docstrings_have_checkpoints()

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/notification_service_quantization.py

# Copyright 2024 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. import ast import json import os import sys import time from typing import Dict from get_ci_error_statistics import get_jobs from notification_service import ( Message, handle_stacktraces, handle_test_results, prepare_reports, retrieve_artifact, retrieve_available_artifacts, ) from slack_sdk import WebClient client = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"]) class QuantizationMessage(Message): def __init__( self, title: str, results: Dict, ): self.title = title # Failures and success of the modeling tests self.n_success = sum(r["success"] for r in results.values()) self.single_gpu_failures = sum(r["failed"]["single"] for r in results.values()) self.multi_gpu_failures = sum(r["failed"]["multi"] for r in results.values()) self.n_failures = self.single_gpu_failures + self.multi_gpu_failures self.n_tests = self.n_failures + self.n_success self.results = results self.thread_ts = None @property def payload(self) -> str: blocks = [self.header] if self.n_failures > 0: blocks.append(self.failures_overwiew) blocks.append(self.failures_detailed) if self.n_failures == 0: blocks.append(self.no_failures) return json.dumps(blocks) @property def time(self) -> str: all_results = self.results.values() time_spent = [] for r in all_results: if len(r["time_spent"]): time_spent.extend([x for x in r["time_spent"].split(", ") if len(x.strip())]) total_secs = 0 for time in time_spent: time_parts = time.split(":") # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute. if len(time_parts) == 1: time_parts = [0, 0, time_parts[0]] hours, minutes, seconds = int(time_parts[0]), int(time_parts[1]), float(time_parts[2]) total_secs += hours * 3600 + minutes * 60 + seconds hours, minutes, seconds = total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60 return f"{int(hours)}h{int(minutes)}m{int(seconds)}s" @property def failures_overwiew(self) -> Dict: return { "type": "section", "text": { "type": "plain_text", "text": ( f"There were {self.n_failures} failures, out of {self.n_tests} tests.\n" f"The suite ran in {self.time}." ), "emoji": True, }, "accessory": { "type": "button", "text": {"type": "plain_text", "text": "Check Action results", "emoji": True}, "url": f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}", }, } @property def failures_detailed(self) -> Dict: failures = {k: v["failed"] for k, v in self.results.items()} individual_reports = [] for key, value in failures.items(): device_report = self.get_device_report(value) if sum(value.values()): report = f"{device_report}{key}" individual_reports.append(report) header = "Single | Multi | Category\n" failures_report = prepare_reports( title="The following quantization tests had failures", header=header, reports=individual_reports ) return {"type": "section", "text": {"type": "mrkdwn", "text": failures_report}} def post(self): payload = self.payload print("Sending the following payload") print(json.dumps({"blocks": json.loads(payload)})) text = f"{self.n_failures} failures out of {self.n_tests} tests," if self.n_failures else "All tests passed." self.thread_ts = client.chat_postMessage( channel=SLACK_REPORT_CHANNEL_ID, blocks=payload, text=text, ) def post_reply(self): if self.thread_ts is None: raise ValueError("Can only post reply if a post has been made.") for job, job_result in self.results.items(): if len(job_result["failures"]): for device, failures in job_result["failures"].items(): blocks = self.get_reply_blocks( job, job_result, failures, device, text=f'Number of failures: {job_result["failed"][device]}', ) print("Sending the following reply") print(json.dumps({"blocks": blocks})) client.chat_postMessage( channel="#transformers-ci-daily-quantization", text=f"Results for {job}", blocks=blocks, thread_ts=self.thread_ts["ts"], ) time.sleep(1) if __name__ == "__main__": setup_status = os.environ.get("SETUP_STATUS") SLACK_REPORT_CHANNEL_ID = os.environ["SLACK_REPORT_CHANNEL"] setup_failed = True if setup_status is not None and setup_status != "success" else False # This env. variable is set in workflow file (under the job `send_results`). ci_event = os.environ["CI_EVENT"] title = f"🤗 Results of the {ci_event} tests." if setup_failed: Message.error_out( title, ci_title="", runner_not_available=False, runner_failed=False, setup_failed=setup_failed ) exit(0) arguments = sys.argv[1:][0] try: quantization_matrix = ast.literal_eval(arguments) # Need to change from elements like `quantization/bnb` to `quantization_bnb` (the ones used as artifact names). quantization_matrix = [x.replace("quantization/", "quantization_") for x in quantization_matrix] except SyntaxError: Message.error_out(title, ci_title="") raise ValueError("Errored out.") available_artifacts = retrieve_available_artifacts() quantization_results = { quant: { "failed": {"single": 0, "multi": 0}, "success": 0, "time_spent": "", "failures": {}, "job_link": {}, } for quant in quantization_matrix if f"run_quantization_torch_gpu_{ quant }_test_reports" in available_artifacts } github_actions_jobs = get_jobs( workflow_run_id=os.environ["GITHUB_RUN_ID"], token=os.environ["ACCESS_REPO_INFO_TOKEN"] ) github_actions_job_links = {job["name"]: job["html_url"] for job in github_actions_jobs} artifact_name_to_job_map = {} for job in github_actions_jobs: for step in job["steps"]: if step["name"].startswith("Test suite reports artifacts: "): artifact_name = step["name"][len("Test suite reports artifacts: ") :] artifact_name_to_job_map[artifact_name] = job break for quant in quantization_results.keys(): for artifact_path in available_artifacts[f"run_quantization_torch_gpu_{ quant }_test_reports"].paths: artifact = retrieve_artifact(artifact_path["path"], artifact_path["gpu"]) if "stats" in artifact: # Link to the GitHub Action job job = artifact_name_to_job_map[artifact_path["path"]] quantization_results[quant]["job_link"][artifact_path["gpu"]] = job["html_url"] failed, success, time_spent = handle_test_results(artifact["stats"]) quantization_results[quant]["failed"][artifact_path["gpu"]] += failed quantization_results[quant]["success"] += success quantization_results[quant]["time_spent"] += time_spent[1:-1] + ", " stacktraces = handle_stacktraces(artifact["failures_line"]) for line in artifact["summary_short"].split("\n"): if line.startswith("FAILED "): line = line[len("FAILED ") :] line = line.split()[0].replace("\n", "") if artifact_path["gpu"] not in quantization_results[quant]["failures"]: quantization_results[quant]["failures"][artifact_path["gpu"]] = [] quantization_results[quant]["failures"][artifact_path["gpu"]].append( {"line": line, "trace": stacktraces.pop(0)} ) message = QuantizationMessage( title, results=quantization_results, ) message.post() message.post_reply()

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/not_doctested.txt

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src/transformers/models/rwkv/configuration_rwkv.py src/transformers/models/rwkv/convert_rwkv_checkpoint_to_hf.py src/transformers/models/rwkv/modeling_rwkv.py src/transformers/models/sam/configuration_sam.py src/transformers/models/sam/convert_sam_to_hf.py src/transformers/models/sam/image_processing_sam.py src/transformers/models/sam/modeling_sam.py src/transformers/models/sam/modeling_tf_sam.py src/transformers/models/sam/processing_sam.py src/transformers/models/seamless_m4t/convert_fairseq2_to_hf.py src/transformers/models/seamless_m4t_v2/convert_fairseq2_to_hf.py src/transformers/models/segformer/configuration_segformer.py src/transformers/models/segformer/convert_segformer_original_to_pytorch.py src/transformers/models/sew/convert_sew_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/sew_d/convert_sew_d_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/speech_encoder_decoder/configuration_speech_encoder_decoder.py src/transformers/models/speech_encoder_decoder/convert_mbart_wav2vec2_seq2seq_original_to_pytorch.py src/transformers/models/speech_encoder_decoder/convert_speech_to_text_wav2vec2_seq2seq_original_to_pytorch.py src/transformers/models/speech_encoder_decoder/modeling_flax_speech_encoder_decoder.py src/transformers/models/speech_to_text/convert_s2t_fairseq_to_tfms.py src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py src/transformers/models/speecht5/configuration_speecht5.py src/transformers/models/speecht5/convert_hifigan.py src/transformers/models/speecht5/convert_speecht5_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/speecht5/number_normalizer.py src/transformers/models/splinter/configuration_splinter.py src/transformers/models/splinter/modeling_splinter.py src/transformers/models/squeezebert/modeling_squeezebert.py src/transformers/models/stablelm/modeling_stablelm.py src/transformers/models/starcoder2/modeling_starcoder2.py src/transformers/models/swiftformer/configuration_swiftformer.py src/transformers/models/swiftformer/convert_swiftformer_original_to_hf.py src/transformers/models/swiftformer/modeling_swiftformer.py src/transformers/models/swin/convert_swin_simmim_to_pytorch.py src/transformers/models/swin/convert_swin_timm_to_pytorch.py src/transformers/models/swin/modeling_tf_swin.py src/transformers/models/swin2sr/configuration_swin2sr.py src/transformers/models/swin2sr/convert_swin2sr_original_to_pytorch.py src/transformers/models/swinv2/convert_swinv2_timm_to_pytorch.py src/transformers/models/swinv2/modeling_swinv2.py src/transformers/models/switch_transformers/configuration_switch_transformers.py src/transformers/models/switch_transformers/convert_big_switch.py src/transformers/models/switch_transformers/convert_switch_transformers_original_flax_checkpoint_to_pytorch.py src/transformers/models/switch_transformers/modeling_switch_transformers.py src/transformers/models/t5/configuration_t5.py src/transformers/models/t5/convert_t5_original_tf_checkpoint_to_pytorch.py src/transformers/models/t5/convert_t5x_checkpoint_to_flax.py src/transformers/models/t5/convert_t5x_checkpoint_to_pytorch.py src/transformers/models/t5/modeling_flax_t5.py src/transformers/models/t5/modeling_t5.py src/transformers/models/t5/modeling_tf_t5.py src/transformers/models/table_transformer/configuration_table_transformer.py src/transformers/models/table_transformer/convert_table_transformer_to_hf.py src/transformers/models/table_transformer/convert_table_transformer_to_hf_no_timm.py src/transformers/models/tapas/configuration_tapas.py src/transformers/models/tapas/convert_tapas_original_tf_checkpoint_to_pytorch.py src/transformers/models/tapas/modeling_tapas.py src/transformers/models/tapas/modeling_tf_tapas.py src/transformers/models/timesformer/convert_timesformer_to_pytorch.py src/transformers/models/timm_backbone/configuration_timm_backbone.py src/transformers/models/timm_backbone/modeling_timm_backbone.py src/transformers/models/trocr/convert_trocr_unilm_to_pytorch.py src/transformers/models/tvlt/configuration_tvlt.py src/transformers/models/tvlt/modeling_tvlt.py src/transformers/models/umt5/configuration_umt5.py src/transformers/models/umt5/convert_umt5_checkpoint_to_pytorch.py src/transformers/models/umt5/modeling_umt5.py src/transformers/models/unispeech/convert_unispeech_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/unispeech_sat/configuration_unispeech_sat.py src/transformers/models/unispeech_sat/convert_unispeech_original_s3prl_checkpoint_to_pytorch.py src/transformers/models/unispeech_sat/convert_unispeech_sat_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/upernet/configuration_upernet.py src/transformers/models/upernet/convert_convnext_upernet_to_pytorch.py src/transformers/models/upernet/convert_swin_upernet_to_pytorch.py src/transformers/models/videomae/configuration_videomae.py src/transformers/models/videomae/convert_videomae_to_pytorch.py src/transformers/models/vilt/configuration_vilt.py src/transformers/models/vilt/convert_vilt_original_to_pytorch.py src/transformers/models/vipllava/configuration_vipllava.py src/transformers/models/vipllava/modeling_vipllava.py src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py src/transformers/models/visual_bert/convert_visual_bert_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/visual_bert/modeling_visual_bert.py src/transformers/models/vit/convert_dino_to_pytorch.py src/transformers/models/vit/convert_vit_timm_to_pytorch.py src/transformers/models/vit/modeling_flax_vit.py src/transformers/models/vit_hybrid/configuration_vit_hybrid.py src/transformers/models/vit_hybrid/convert_vit_hybrid_timm_to_pytorch.py src/transformers/models/vit_hybrid/modeling_vit_hybrid.py src/transformers/models/vit_mae/convert_vit_mae_to_pytorch.py src/transformers/models/vit_mae/modeling_tf_vit_mae.py src/transformers/models/vit_msn/configuration_vit_msn.py src/transformers/models/vit_msn/convert_msn_to_pytorch.py src/transformers/models/vivit/configuration_vivit.py src/transformers/models/vivit/convert_vivit_flax_to_pytorch.py src/transformers/models/vivit/image_processing_vivit.py src/transformers/models/vivit/modeling_vivit.py src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/wav2vec2/convert_wav2vec2_original_s3prl_checkpoint_to_pytorch.py src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py src/transformers/models/wav2vec2_bert/convert_wav2vec2_seamless_checkpoint.py src/transformers/models/wav2vec2_conformer/convert_wav2vec2_conformer_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/wavlm/convert_wavlm_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/wavlm/convert_wavlm_original_s3prl_checkpoint_to_pytorch.py src/transformers/models/whisper/convert_openai_to_hf.py src/transformers/models/whisper/english_normalizer.py src/transformers/models/whisper/modeling_flax_whisper.py src/transformers/models/x_clip/configuration_x_clip.py src/transformers/models/x_clip/convert_x_clip_original_pytorch_to_hf.py src/transformers/models/xglm/configuration_xglm.py src/transformers/models/xglm/convert_xglm_original_ckpt_to_trfms.py src/transformers/models/xglm/modeling_flax_xglm.py src/transformers/models/xglm/modeling_tf_xglm.py src/transformers/models/xglm/modeling_xglm.py src/transformers/models/xlm/convert_xlm_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/xlm/modeling_tf_xlm.py src/transformers/models/xlm/modeling_xlm.py src/transformers/models/xlm_prophetnet/configuration_xlm_prophetnet.py src/transformers/models/xlm_prophetnet/modeling_xlm_prophetnet.py src/transformers/models/xlm_roberta/modeling_flax_xlm_roberta.py src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py src/transformers/models/xlm_roberta/modeling_xlm_roberta.py src/transformers/models/xlm_roberta_xl/convert_xlm_roberta_xl_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py src/transformers/models/xlnet/convert_xlnet_original_tf_checkpoint_to_pytorch.py src/transformers/models/xlnet/modeling_tf_xlnet.py src/transformers/models/xlnet/modeling_xlnet.py src/transformers/models/xmod/convert_xmod_original_pytorch_checkpoint_to_pytorch.py src/transformers/models/yolos/convert_yolos_to_pytorch.py src/transformers/models/yoso/convert_yoso_pytorch_to_pytorch.py src/transformers/models/yoso/modeling_yoso.py src/transformers/onnx/__main__.py src/transformers/onnx/config.py src/transformers/onnx/convert.py src/transformers/onnx/features.py src/transformers/onnx/utils.py src/transformers/optimization.py src/transformers/optimization_tf.py src/transformers/pipelines/audio_classification.py src/transformers/pipelines/audio_utils.py src/transformers/pipelines/automatic_speech_recognition.py src/transformers/pipelines/base.py src/transformers/pipelines/conversational.py src/transformers/pipelines/depth_estimation.py src/transformers/pipelines/document_question_answering.py src/transformers/pipelines/feature_extraction.py src/transformers/pipelines/fill_mask.py src/transformers/pipelines/image_classification.py src/transformers/pipelines/image_segmentation.py src/transformers/pipelines/image_to_text.py src/transformers/pipelines/mask_generation.py src/transformers/pipelines/object_detection.py src/transformers/pipelines/pt_utils.py src/transformers/pipelines/question_answering.py src/transformers/pipelines/table_question_answering.py src/transformers/pipelines/text_classification.py src/transformers/pipelines/token_classification.py src/transformers/pipelines/video_classification.py src/transformers/pipelines/visual_question_answering.py src/transformers/pipelines/zero_shot_audio_classification.py src/transformers/pipelines/zero_shot_classification.py src/transformers/pipelines/zero_shot_image_classification.py src/transformers/pipelines/zero_shot_object_detection.py src/transformers/processing_utils.py src/transformers/pytorch_utils.py src/transformers/quantizers/auto.py src/transformers/quantizers/base.py src/transformers/quantizers/quantizer_awq.py src/transformers/quantizers/quantizer_bnb_4bit.py src/transformers/quantizers/quantizer_bnb_8bit.py src/transformers/quantizers/quantizer_gptq.py src/transformers/quantizers/quantizers_utils.py src/transformers/sagemaker/trainer_sm.py src/transformers/sagemaker/training_args_sm.py src/transformers/testing_utils.py src/transformers/tf_utils.py src/transformers/time_series_utils.py src/transformers/tokenization_utils.py src/transformers/tokenization_utils_base.py src/transformers/tokenization_utils_fast.py src/transformers/tools/agent_types.py src/transformers/tools/agents.py src/transformers/tools/base.py src/transformers/tools/document_question_answering.py src/transformers/tools/evaluate_agent.py src/transformers/tools/image_captioning.py src/transformers/tools/image_question_answering.py src/transformers/tools/image_segmentation.py src/transformers/tools/prompts.py src/transformers/tools/python_interpreter.py src/transformers/tools/speech_to_text.py src/transformers/tools/text_classification.py src/transformers/tools/text_question_answering.py src/transformers/tools/text_summarization.py src/transformers/tools/text_to_speech.py src/transformers/tools/translation.py src/transformers/trainer.py src/transformers/trainer_callback.py src/transformers/trainer_pt_utils.py src/transformers/trainer_seq2seq.py src/transformers/trainer_utils.py src/transformers/training_args.py src/transformers/training_args_seq2seq.py src/transformers/training_args_tf.py src/transformers/utils/backbone_utils.py src/transformers/utils/bitsandbytes.py src/transformers/utils/constants.py src/transformers/utils/doc.py src/transformers/utils/dummy_detectron2_objects.py src/transformers/utils/dummy_essentia_and_librosa_and_pretty_midi_and_scipy_and_torch_objects.py src/transformers/utils/dummy_flax_objects.py src/transformers/utils/dummy_keras_nlp_objects.py src/transformers/utils/dummy_music_objects.py src/transformers/utils/dummy_pt_objects.py src/transformers/utils/dummy_sentencepiece_and_tokenizers_objects.py src/transformers/utils/dummy_sentencepiece_objects.py src/transformers/utils/dummy_speech_objects.py src/transformers/utils/dummy_tensorflow_text_objects.py src/transformers/utils/dummy_tf_objects.py src/transformers/utils/dummy_tokenizers_objects.py src/transformers/utils/dummy_vision_objects.py src/transformers/utils/fx.py src/transformers/utils/generic.py src/transformers/utils/hp_naming.py src/transformers/utils/hub.py src/transformers/utils/import_utils.py src/transformers/utils/logging.py src/transformers/utils/model_parallel_utils.py src/transformers/utils/notebook.py src/transformers/utils/peft_utils.py src/transformers/utils/quantization_config.py src/transformers/utils/sentencepiece_model_pb2.py src/transformers/utils/sentencepiece_model_pb2_new.py src/transformers/utils/versions.py

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_build.py

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. import argparse import importlib from pathlib import Path # Test all the extensions added in the setup FILES_TO_FIND = [ "kernels/rwkv/wkv_cuda.cu", "kernels/rwkv/wkv_op.cpp", "kernels/deformable_detr/ms_deform_attn.h", "kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh", "models/graphormer/algos_graphormer.pyx", ] def test_custom_files_are_present(transformers_path): # Test all the extensions added in the setup for file in FILES_TO_FIND: if not (transformers_path / file).exists(): return False return True if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--check_lib", action="store_true", help="Whether to check the build or the actual package.") args = parser.parse_args() if args.check_lib: transformers_module = importlib.import_module("transformers") transformers_path = Path(transformers_module.__file__).parent else: transformers_path = Path.cwd() / "build/lib/transformers" if not test_custom_files_are_present(transformers_path): raise ValueError("The built release does not contain the custom files. Fix this before going further!")

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/release.py

# coding=utf-8 # 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. """ Utility that prepares the repository for releases (or patches) by updating all versions in the relevant places. It also performs some post-release cleanup, by updating the links in the main README to respective model doc pages (from main to stable). To prepare for a release, use from the root of the repo on the release branch with: ```bash python release.py ``` or use `make pre-release`. To prepare for a patch release, use from the root of the repo on the release branch with: ```bash python release.py --patch ``` or use `make pre-patch`. To do the post-release cleanup, use from the root of the repo on the main branch with: ```bash python release.py --post_release ``` or use `make post-release`. """ import argparse import os import re import packaging.version # All paths are defined with the intent that this script should be run from the root of the repo. PATH_TO_EXAMPLES = "examples/" # This maps a type of file to the pattern to look for when searching where the version is defined, as well as the # template to follow when replacing it with the new version. REPLACE_PATTERNS = { "examples": (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'), "init": (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'), "setup": (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'), } # This maps a type of file to its path in Transformers REPLACE_FILES = { "init": "src/transformers/__init__.py", "setup": "setup.py", } README_FILE = "README.md" def update_version_in_file(fname: str, version: str, file_type: str): """ Update the version of Transformers in one file. Args: fname (`str`): The path to the file where we want to update the version. version (`str`): The new version to set in the file. file_type (`str`): The type of the file (should be a key in `REPLACE_PATTERNS`). """ with open(fname, "r", encoding="utf-8", newline="\n") as f: code = f.read() re_pattern, replace = REPLACE_PATTERNS[file_type] replace = replace.replace("VERSION", version) code = re_pattern.sub(replace, code) with open(fname, "w", encoding="utf-8", newline="\n") as f: f.write(code) def update_version_in_examples(version: str): """ Update the version in all examples files. Args: version (`str`): The new version to set in the examples. """ for folder, directories, fnames in os.walk(PATH_TO_EXAMPLES): # Removing some of the folders with non-actively maintained examples from the walk if "research_projects" in directories: directories.remove("research_projects") if "legacy" in directories: directories.remove("legacy") for fname in fnames: if fname.endswith(".py"): update_version_in_file(os.path.join(folder, fname), version, file_type="examples") def global_version_update(version: str, patch: bool = False): """ Update the version in all needed files. Args: version (`str`): The new version to set everywhere. patch (`bool`, *optional*, defaults to `False`): Whether or not this is a patch release. """ for pattern, fname in REPLACE_FILES.items(): update_version_in_file(fname, version, pattern) if not patch: # We don't update the version in the examples for patch releases. update_version_in_examples(version) def clean_main_ref_in_model_list(): """ Replace the links from main doc to stable doc in the model list of the README. """ # If the introduction or the conclusion of the list change, the prompts may need to be updated. _start_prompt = "🤗 Transformers currently provides the following architectures" _end_prompt = "1. Want to contribute a new model?" with open(README_FILE, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Find the start of the list. start_index = 0 while not lines[start_index].startswith(_start_prompt): start_index += 1 start_index += 1 index = start_index # Update the lines in the model list. while not lines[index].startswith(_end_prompt): if lines[index].startswith("1."): lines[index] = lines[index].replace( "https://huggingface.co/docs/transformers/main/model_doc", "https://huggingface.co/docs/transformers/model_doc", ) index += 1 with open(README_FILE, "w", encoding="utf-8", newline="\n") as f: f.writelines(lines) def get_version() -> packaging.version.Version: """ Reads the current version in the main __init__. """ with open(REPLACE_FILES["init"], "r") as f: code = f.read() default_version = REPLACE_PATTERNS["init"][0].search(code).groups()[0] return packaging.version.parse(default_version) def pre_release_work(patch: bool = False): """ Do all the necessary pre-release steps: - figure out the next minor release version and ask confirmation - update the version eveywhere - clean-up the model list in the main README Args: patch (`bool`, *optional*, defaults to `False`): Whether or not this is a patch release. """ # First let's get the default version: base version if we are in dev, bump minor otherwise. default_version = get_version() if patch and default_version.is_devrelease: raise ValueError("Can't create a patch version from the dev branch, checkout a released version!") if default_version.is_devrelease: default_version = default_version.base_version elif patch: default_version = f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}" else: default_version = f"{default_version.major}.{default_version.minor + 1}.0" # Now let's ask nicely if we have found the right version. version = input(f"Which version are you releasing? [{default_version}]") if len(version) == 0: version = default_version print(f"Updating version to {version}.") global_version_update(version, patch=patch) if not patch: print("Cleaning main README, don't forget to run `make fix-copies`.") clean_main_ref_in_model_list() def post_release_work(): """ Do all the necesarry post-release steps: - figure out the next dev version and ask confirmation - update the version eveywhere - clean-up the model list in the main README """ # First let's get the current version current_version = get_version() dev_version = f"{current_version.major}.{current_version.minor + 1}.0.dev0" current_version = current_version.base_version # Check with the user we got that right. version = input(f"Which version are we developing now? [{dev_version}]") if len(version) == 0: version = dev_version print(f"Updating version to {version}.") global_version_update(version) print("Cleaning main README, don't forget to run `make fix-copies`.") clean_main_ref_in_model_list() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.") parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.") args = parser.parse_args() if not args.post_release: pre_release_work(patch=args.patch) elif args.patch: print("Nothing to do after a patch :-)") else: post_release_work()

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/add_pipeline_model_mapping_to_test.py

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # 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. """A script to add and/or update the attribute `pipeline_model_mapping` in model test files. This script will be (mostly) used in the following 2 situations: - run within a (scheduled) CI job to: - check if model test files in the library have updated `pipeline_model_mapping`, - and/or update test files and (possibly) open a GitHub pull request automatically - being run by a `transformers` member to quickly check and update some particular test file(s) This script is **NOT** intended to be run (manually) by community contributors. """ import argparse import glob import inspect import os import re import unittest from get_test_info import get_test_classes from tests.test_pipeline_mixin import pipeline_test_mapping PIPELINE_TEST_MAPPING = {} for task, _ in pipeline_test_mapping.items(): PIPELINE_TEST_MAPPING[task] = {"pt": None, "tf": None} # DO **NOT** add item to this set (unless the reason is approved) TEST_FILE_TO_IGNORE = { "tests/models/esm/test_modeling_esmfold.py", # The pipeline test mapping is added to `test_modeling_esm.py` } def get_framework(test_class): """Infer the framework from the test class `test_class`.""" if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]: return "pt" elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]: return "tf" elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]: return "flax" else: return None def get_mapping_for_task(task, framework): """Get mappings defined in `XXXPipelineTests` for the task `task`.""" # Use the cached results if PIPELINE_TEST_MAPPING[task].get(framework, None) is not None: return PIPELINE_TEST_MAPPING[task][framework] pipeline_test_class = pipeline_test_mapping[task]["test"] mapping = None if framework == "pt": mapping = getattr(pipeline_test_class, "model_mapping", None) elif framework == "tf": mapping = getattr(pipeline_test_class, "tf_model_mapping", None) if mapping is not None: mapping = dict(mapping.items()) # cache the results PIPELINE_TEST_MAPPING[task][framework] = mapping return mapping def get_model_for_pipeline_test(test_class, task): """Get the model architecture(s) related to the test class `test_class` for a pipeline `task`.""" framework = get_framework(test_class) if framework is None: return None mapping = get_mapping_for_task(task, framework) if mapping is None: return None config_classes = list({model_class.config_class for model_class in test_class.all_model_classes}) if len(config_classes) != 1: raise ValueError("There should be exactly one configuration class from `test_class.all_model_classes`.") # This could be a list/tuple of model classes, but it's rare. model_class = mapping.get(config_classes[0], None) if isinstance(model_class, (tuple, list)): model_class = sorted(model_class, key=lambda x: x.__name__) return model_class def get_pipeline_model_mapping(test_class): """Get `pipeline_model_mapping` for `test_class`.""" mapping = [(task, get_model_for_pipeline_test(test_class, task)) for task in pipeline_test_mapping] mapping = sorted([(task, model) for task, model in mapping if model is not None], key=lambda x: x[0]) return dict(mapping) def get_pipeline_model_mapping_string(test_class): """Get `pipeline_model_mapping` for `test_class` as a string (to be added to the test file). This will be a 1-line string. After this is added to a test file, `make style` will format it beautifully. """ framework = get_framework(test_class) if framework == "pt": framework = "torch" default_value = "{}" mapping = get_pipeline_model_mapping(test_class) if len(mapping) == 0: return "" texts = [] for task, model_classes in mapping.items(): if isinstance(model_classes, (tuple, list)): # A list/tuple of model classes value = "(" + ", ".join([x.__name__ for x in model_classes]) + ")" else: # A single model class value = model_classes.__name__ texts.append(f'"{task}": {value}') text = "{" + ", ".join(texts) + "}" text = f"pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}" return text def is_valid_test_class(test_class): """Restrict to `XXXModelTesterMixin` and should be a subclass of `unittest.TestCase`.""" base_class_names = {"ModelTesterMixin", "TFModelTesterMixin", "FlaxModelTesterMixin"} if not issubclass(test_class, unittest.TestCase): return False return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__])) > 0 def find_test_class(test_file): """Find a test class in `test_file` to which we will add `pipeline_model_mapping`.""" test_classes = [x for x in get_test_classes(test_file) if is_valid_test_class(x)] target_test_class = None for test_class in test_classes: # If a test class has defined `pipeline_model_mapping`, let's take it if getattr(test_class, "pipeline_model_mapping", None) is not None: target_test_class = test_class break # Take the test class with the shortest name (just a heuristic) if target_test_class is None and len(test_classes) > 0: target_test_class = sorted(test_classes, key=lambda x: (len(x.__name__), x.__name__))[0] return target_test_class def find_block_ending(lines, start_idx, indent_level): end_idx = start_idx for idx, line in enumerate(lines[start_idx:]): indent = len(line) - len(line.lstrip()) if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"): end_idx = start_idx + idx elif idx > 0 and indent <= indent_level: # Outside the definition block of `pipeline_model_mapping` break return end_idx def add_pipeline_model_mapping(test_class, overwrite=False): """Add `pipeline_model_mapping` to `test_class`.""" if getattr(test_class, "pipeline_model_mapping", None) is not None: if not overwrite: return "", -1 line_to_add = get_pipeline_model_mapping_string(test_class) if len(line_to_add) == 0: return "", -1 line_to_add = line_to_add + "\n" # The code defined the class `test_class` class_lines, class_start_line_no = inspect.getsourcelines(test_class) # `inspect` gives the code for an object, including decorator(s) if any. # We (only) need the exact line of the class definition. for idx, line in enumerate(class_lines): if line.lstrip().startswith("class "): class_lines = class_lines[idx:] class_start_line_no += idx break class_end_line_no = class_start_line_no + len(class_lines) - 1 # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last # block that appears in a `test_class`. start_idx = None # The indent level of the line at `class_lines[start_idx]` (if defined) indent_level = 0 # To record if `pipeline_model_mapping` is found in `test_class`. def_line = None for idx, line in enumerate(class_lines): if line.strip().startswith("all_model_classes = "): indent_level = len(line) - len(line.lstrip()) start_idx = idx elif line.strip().startswith("all_generative_model_classes = "): indent_level = len(line) - len(line.lstrip()) start_idx = idx elif line.strip().startswith("pipeline_model_mapping = "): indent_level = len(line) - len(line.lstrip()) start_idx = idx def_line = line break if start_idx is None: return "", -1 # Find the ending index (inclusive) of the above found block. end_idx = find_block_ending(class_lines, start_idx, indent_level) # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use # `is_timm_available()` instead of `is_torch_available()`. # Keep leading and trailing whitespaces r = re.compile(r"\s(is_\S+?_available\(\))\s") for line in class_lines[start_idx : end_idx + 1]: backend_condition = r.search(line) if backend_condition is not None: # replace the leading and trailing whitespaces to the space character " ". target = " " + backend_condition[0][1:-1] + " " line_to_add = r.sub(target, line_to_add) break if def_line is None: # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of # `all_model_classes` or `all_generative_model_classes`. target_idx = end_idx else: # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index. target_idx = start_idx - 1 # mark the lines of the currently existing `pipeline_model_mapping` to be removed. for idx in range(start_idx, end_idx + 1): # These lines are going to be removed before writing to the test file. class_lines[idx] = None # noqa # Make sure the test class is a subclass of `PipelineTesterMixin`. parent_classes = [x.__name__ for x in test_class.__bases__] if "PipelineTesterMixin" not in parent_classes: # Put `PipelineTesterMixin` just before `unittest.TestCase` _parent_classes = [x for x in parent_classes if x != "TestCase"] + ["PipelineTesterMixin"] if "TestCase" in parent_classes: # Here we **assume** the original string is always with `unittest.TestCase`. _parent_classes.append("unittest.TestCase") parent_classes = ", ".join(_parent_classes) for idx, line in enumerate(class_lines): # Find the ending of the declaration of `test_class` if line.strip().endswith("):"): # mark the lines of the declaration of `test_class` to be removed for _idx in range(idx + 1): class_lines[_idx] = None # noqa break # Add the new, one-line, class declaration for `test_class` class_lines[0] = f"class {test_class.__name__}({parent_classes}):\n" # Add indentation line_to_add = " " * indent_level + line_to_add # Insert `pipeline_model_mapping` to `class_lines`. # (The line at `target_idx` should be kept by definition!) class_lines = class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :] # Remove the lines that are marked to be removed class_lines = [x for x in class_lines if x is not None] # Move from test class to module (in order to write to the test file) module_lines = inspect.getsourcelines(inspect.getmodule(test_class))[0] # Be careful with the 1-off between line numbers and array indices module_lines = module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:] code = "".join(module_lines) moddule_file = inspect.getsourcefile(test_class) with open(moddule_file, "w", encoding="UTF-8", newline="\n") as fp: fp.write(code) return line_to_add def add_pipeline_model_mapping_to_test_file(test_file, overwrite=False): """Add `pipeline_model_mapping` to `test_file`.""" test_class = find_test_class(test_file) if test_class: add_pipeline_model_mapping(test_class, overwrite=overwrite) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--test_file", type=str, help="A path to the test file, starting with the repository's `tests` directory." ) parser.add_argument( "--all", action="store_true", help="If to check and modify all test files.", ) parser.add_argument( "--overwrite", action="store_true", help="If to overwrite a test class if it has already defined `pipeline_model_mapping`.", ) args = parser.parse_args() if not args.all and not args.test_file: raise ValueError("Please specify either `test_file` or pass `--all` to check/modify all test files.") elif args.all and args.test_file: raise ValueError("Only one of `--test_file` and `--all` could be specified.") test_files = [] if args.test_file: test_files = [args.test_file] else: pattern = os.path.join("tests", "models", "**", "test_modeling_*.py") for test_file in glob.glob(pattern): # `Flax` is not concerned at this moment if not test_file.startswith("test_modeling_flax_"): test_files.append(test_file) for test_file in test_files: if test_file in TEST_FILE_TO_IGNORE: print(f"[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.") continue add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/update_metadata.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # 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. """ Utility that updates the metadata of the Transformers library in the repository `huggingface/transformers-metadata`. Usage for an update (as used by the GitHub action `update_metadata`): ```bash python utils/update_metadata.py --token <token> --commit_sha <commit_sha> ``` Usage to check all pipelines are properly defined in the constant `PIPELINE_TAGS_AND_AUTO_MODELS` of this script, so that new pipelines are properly added as metadata (as used in `make repo-consistency`): ```bash python utils/update_metadata.py --check-only ``` """ import argparse import collections import os import re import tempfile from typing import Dict, List, Tuple import pandas as pd from datasets import Dataset from huggingface_hub import hf_hub_download, upload_folder from transformers.utils import direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/update_metadata.py TRANSFORMERS_PATH = "src/transformers" # This is to make sure the transformers module imported is the one in the repo. transformers_module = direct_transformers_import(TRANSFORMERS_PATH) # Regexes that match TF/Flax/PT model names. _re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") _re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") # Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes. _re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") # Fill this with tuples (pipeline_tag, model_mapping, auto_model) PIPELINE_TAGS_AND_AUTO_MODELS = [ ("pretraining", "MODEL_FOR_PRETRAINING_MAPPING_NAMES", "AutoModelForPreTraining"), ("feature-extraction", "MODEL_MAPPING_NAMES", "AutoModel"), ("image-feature-extraction", "MODEL_FOR_IMAGE_MAPPING_NAMES", "AutoModel"), ("audio-classification", "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioClassification"), ("text-generation", "MODEL_FOR_CAUSAL_LM_MAPPING_NAMES", "AutoModelForCausalLM"), ("automatic-speech-recognition", "MODEL_FOR_CTC_MAPPING_NAMES", "AutoModelForCTC"), ("image-classification", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForImageClassification"), ("image-segmentation", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES", "AutoModelForImageSegmentation"), ("image-to-image", "MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES", "AutoModelForImageToImage"), ("fill-mask", "MODEL_FOR_MASKED_LM_MAPPING_NAMES", "AutoModelForMaskedLM"), ("object-detection", "MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForObjectDetection"), ( "zero-shot-object-detection", "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForZeroShotObjectDetection", ), ("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"), ("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"), ("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"), ("automatic-speech-recognition", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES", "AutoModelForSpeechSeq2Seq"), ( "table-question-answering", "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForTableQuestionAnswering", ), ("token-classification", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES", "AutoModelForTokenClassification"), ("multiple-choice", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES", "AutoModelForMultipleChoice"), ( "next-sentence-prediction", "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES", "AutoModelForNextSentencePrediction", ), ( "audio-frame-classification", "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioFrameClassification", ), ("audio-xvector", "MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES", "AutoModelForAudioXVector"), ( "document-question-answering", "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForDocumentQuestionAnswering", ), ( "visual-question-answering", "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForVisualQuestionAnswering", ), ("image-to-text", "MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES", "AutoModelForVision2Seq"), ( "zero-shot-image-classification", "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForZeroShotImageClassification", ), ("depth-estimation", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES", "AutoModelForDepthEstimation"), ("video-classification", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForVideoClassification"), ("mask-generation", "MODEL_FOR_MASK_GENERATION_MAPPING_NAMES", "AutoModelForMaskGeneration"), ("text-to-audio", "MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES", "AutoModelForTextToSpectrogram"), ("text-to-audio", "MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES", "AutoModelForTextToWaveform"), ] def camel_case_split(identifier: str) -> List[str]: """ Split a camel-cased name into words. Args: identifier (`str`): The camel-cased name to parse. Returns: `List[str]`: The list of words in the identifier (as seprated by capital letters). Example: ```py >>> camel_case_split("CamelCasedClass") ["Camel", "Cased", "Class"] ``` """ # Regex thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier) return [m.group(0) for m in matches] def get_frameworks_table() -> pd.DataFrame: """ Generates a dataframe containing the supported auto classes for each model type, using the content of the auto modules. """ # Dictionary model names to config. config_maping_names = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES model_prefix_to_model_type = { config.replace("Config", ""): model_type for model_type, config in config_maping_names.items() } # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax. pt_models = collections.defaultdict(bool) tf_models = collections.defaultdict(bool) flax_models = collections.defaultdict(bool) # Let's lookup through all transformers object (once) and find if models are supported by a given backend. for attr_name in dir(transformers_module): lookup_dict = None if _re_tf_models.match(attr_name) is not None: lookup_dict = tf_models attr_name = _re_tf_models.match(attr_name).groups()[0] elif _re_flax_models.match(attr_name) is not None: lookup_dict = flax_models attr_name = _re_flax_models.match(attr_name).groups()[0] elif _re_pt_models.match(attr_name) is not None: lookup_dict = pt_models attr_name = _re_pt_models.match(attr_name).groups()[0] if lookup_dict is not None: while len(attr_name) > 0: if attr_name in model_prefix_to_model_type: lookup_dict[model_prefix_to_model_type[attr_name]] = True break # Try again after removing the last word in the name attr_name = "".join(camel_case_split(attr_name)[:-1]) all_models = set(list(pt_models.keys()) + list(tf_models.keys()) + list(flax_models.keys())) all_models = list(all_models) all_models.sort() data = {"model_type": all_models} data["pytorch"] = [pt_models[t] for t in all_models] data["tensorflow"] = [tf_models[t] for t in all_models] data["flax"] = [flax_models[t] for t in all_models] # Now let's find the right processing class for each model. In order we check if there is a Processor, then a # Tokenizer, then a FeatureExtractor, then an ImageProcessor processors = {} for t in all_models: if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES: processors[t] = "AutoProcessor" elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES: processors[t] = "AutoTokenizer" elif t in transformers_module.models.auto.image_processing_auto.IMAGE_PROCESSOR_MAPPING_NAMES: processors[t] = "AutoImageProcessor" elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES: processors[t] = "AutoFeatureExtractor" else: # Default to AutoTokenizer if a model has nothing, for backward compatibility. processors[t] = "AutoTokenizer" data["processor"] = [processors[t] for t in all_models] return pd.DataFrame(data) def update_pipeline_and_auto_class_table(table: Dict[str, Tuple[str, str]]) -> Dict[str, Tuple[str, str]]: """ Update the table maping models to pipelines and auto classes without removing old keys if they don't exist anymore. Args: table (`Dict[str, Tuple[str, str]]`): The existing table mapping model names to a tuple containing the pipeline tag and the auto-class name with which they should be used. Returns: `Dict[str, Tuple[str, str]]`: The updated table in the same format. """ auto_modules = [ transformers_module.models.auto.modeling_auto, transformers_module.models.auto.modeling_tf_auto, transformers_module.models.auto.modeling_flax_auto, ] for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS: model_mappings = [model_mapping, f"TF_{model_mapping}", f"FLAX_{model_mapping}"] auto_classes = [auto_class, f"TF_{auto_class}", f"Flax_{auto_class}"] # Loop through all three frameworks for module, cls, mapping in zip(auto_modules, auto_classes, model_mappings): # The type of pipeline may not exist in this framework if not hasattr(module, mapping): continue # First extract all model_names model_names = [] for name in getattr(module, mapping).values(): if isinstance(name, str): model_names.append(name) else: model_names.extend(list(name)) # Add pipeline tag and auto model class for those models table.update({model_name: (pipeline_tag, cls) for model_name in model_names}) return table def update_metadata(token: str, commit_sha: str): """ Update the metadata for the Transformers repo in `huggingface/transformers-metadata`. Args: token (`str`): A valid token giving write access to `huggingface/transformers-metadata`. commit_sha (`str`): The commit SHA on Transformers corresponding to this update. """ frameworks_table = get_frameworks_table() frameworks_dataset = Dataset.from_pandas(frameworks_table) resolved_tags_file = hf_hub_download( "huggingface/transformers-metadata", "pipeline_tags.json", repo_type="dataset", token=token ) tags_dataset = Dataset.from_json(resolved_tags_file) table = { tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"]) for i in range(len(tags_dataset)) } table = update_pipeline_and_auto_class_table(table) # Sort the model classes to avoid some nondeterministic updates to create false update commits. model_classes = sorted(table.keys()) tags_table = pd.DataFrame( { "model_class": model_classes, "pipeline_tag": [table[m][0] for m in model_classes], "auto_class": [table[m][1] for m in model_classes], } ) tags_dataset = Dataset.from_pandas(tags_table) hub_frameworks_json = hf_hub_download( repo_id="huggingface/transformers-metadata", filename="frameworks.json", repo_type="dataset", token=token, ) with open(hub_frameworks_json) as f: hub_frameworks_json = f.read() hub_pipeline_tags_json = hf_hub_download( repo_id="huggingface/transformers-metadata", filename="pipeline_tags.json", repo_type="dataset", token=token, ) with open(hub_pipeline_tags_json) as f: hub_pipeline_tags_json = f.read() with tempfile.TemporaryDirectory() as tmp_dir: frameworks_dataset.to_json(os.path.join(tmp_dir, "frameworks.json")) tags_dataset.to_json(os.path.join(tmp_dir, "pipeline_tags.json")) with open(os.path.join(tmp_dir, "frameworks.json")) as f: frameworks_json = f.read() with open(os.path.join(tmp_dir, "pipeline_tags.json")) as f: pipeline_tags_json = f.read() frameworks_equal = hub_frameworks_json == frameworks_json hub_pipeline_tags_equal = hub_pipeline_tags_json == pipeline_tags_json if frameworks_equal and hub_pipeline_tags_equal: print("No updates on the Hub, not pushing the metadata files.") return if commit_sha is not None: commit_message = ( f"Update with commit {commit_sha}\n\nSee: " f"https://github.com/huggingface/transformers/commit/{commit_sha}" ) else: commit_message = "Update" upload_folder( repo_id="huggingface/transformers-metadata", folder_path=tmp_dir, repo_type="dataset", token=token, commit_message=commit_message, ) def check_pipeline_tags(): """ Check all pipeline tags are properly defined in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant of this script. """ in_table = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS} pipeline_tasks = transformers_module.pipelines.SUPPORTED_TASKS missing = [] for key in pipeline_tasks: if key not in in_table: model = pipeline_tasks[key]["pt"] if isinstance(model, (list, tuple)): model = model[0] model = model.__name__ if model not in in_table.values(): missing.append(key) if len(missing) > 0: msg = ", ".join(missing) raise ValueError( "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside " f"`utils/update_metadata.py`: {msg}. Please add them!" ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.") parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.") parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.") args = parser.parse_args() if args.check_only: check_pipeline_tags() else: update_metadata(args.token, args.commit_sha)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/get_github_job_time.py

import argparse import math import traceback import dateutil.parser as date_parser import requests def extract_time_from_single_job(job): """Extract time info from a single job in a GitHub Actions workflow run""" job_info = {} start = job["started_at"] end = job["completed_at"] start_datetime = date_parser.parse(start) end_datetime = date_parser.parse(end) duration_in_min = round((end_datetime - start_datetime).total_seconds() / 60.0) job_info["started_at"] = start job_info["completed_at"] = end job_info["duration"] = duration_in_min return job_info def get_job_time(workflow_run_id, token=None): """Extract time info for all jobs in a GitHub Actions workflow run""" headers = None if token is not None: headers = {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"} url = f"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100" result = requests.get(url, headers=headers).json() job_time = {} try: job_time.update({job["name"]: extract_time_from_single_job(job) for job in result["jobs"]}) pages_to_iterate_over = math.ceil((result["total_count"] - 100) / 100) for i in range(pages_to_iterate_over): result = requests.get(url + f"&page={i + 2}", headers=headers).json() job_time.update({job["name"]: extract_time_from_single_job(job) for job in result["jobs"]}) return job_time except Exception: print(f"Unknown error, could not fetch links:\n{traceback.format_exc()}") return {} if __name__ == "__main__": r""" Example: python get_github_job_time.py --workflow_run_id 2945609517 """ parser = argparse.ArgumentParser() # Required parameters parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.") args = parser.parse_args() job_time = get_job_time(args.workflow_run_id) job_time = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True)) for k, v in job_time.items(): print(f'{k}: {v["duration"]}')

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_tf_ops.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # 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. import argparse import json import os from tensorflow.core.protobuf.saved_model_pb2 import SavedModel # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_copies.py REPO_PATH = "." # Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model) INTERNAL_OPS = [ "Assert", "AssignVariableOp", "EmptyTensorList", "MergeV2Checkpoints", "ReadVariableOp", "ResourceGather", "RestoreV2", "SaveV2", "ShardedFilename", "StatefulPartitionedCall", "StaticRegexFullMatch", "VarHandleOp", ] def onnx_compliancy(saved_model_path, strict, opset): saved_model = SavedModel() onnx_ops = [] with open(os.path.join(REPO_PATH, "utils", "tf_ops", "onnx.json")) as f: onnx_opsets = json.load(f)["opsets"] for i in range(1, opset + 1): onnx_ops.extend(onnx_opsets[str(i)]) with open(saved_model_path, "rb") as f: saved_model.ParseFromString(f.read()) model_op_names = set() # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs) for meta_graph in saved_model.meta_graphs: # Add operations in the graph definition model_op_names.update(node.op for node in meta_graph.graph_def.node) # Go through the functions in the graph definition for func in meta_graph.graph_def.library.function: # Add operations in each function model_op_names.update(node.op for node in func.node_def) # Convert to list, sorted if you want model_op_names = sorted(model_op_names) incompatible_ops = [] for op in model_op_names: if op not in onnx_ops and op not in INTERNAL_OPS: incompatible_ops.append(op) if strict and len(incompatible_ops) > 0: raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops) elif len(incompatible_ops) > 0: print(f"Found the following incompatible ops for the opset {opset}:") print(*incompatible_ops, sep="\n") else: print(f"The saved model {saved_model_path} can properly be converted with ONNX.") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).") parser.add_argument( "--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested." ) parser.add_argument( "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model." ) parser.add_argument( "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)" ) args = parser.parse_args() if args.framework == "onnx": onnx_compliancy(args.saved_model_path, args.strict, args.opset)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/check_repo.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # 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. """ Utility that performs several consistency checks on the repo. This includes: - checking all models are properly defined in the __init__ of models/ - checking all models are in the main __init__ - checking all models are properly tested - checking all object in the main __init__ are documented - checking all models are in at least one auto class - checking all the auto mapping are properly defined (no typos, importable) - checking the list of deprecated models is up to date Use from the root of the repo with (as used in `make repo-consistency`): ```bash python utils/check_repo.py ``` It has no auto-fix mode. """ import inspect import os import re import sys import types import warnings from collections import OrderedDict from difflib import get_close_matches from pathlib import Path from typing import List, Tuple from transformers import is_flax_available, is_tf_available, is_torch_available from transformers.models.auto import get_values from transformers.models.auto.configuration_auto import CONFIG_MAPPING_NAMES from transformers.models.auto.feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING_NAMES from transformers.models.auto.image_processing_auto import IMAGE_PROCESSOR_MAPPING_NAMES from transformers.models.auto.processing_auto import PROCESSOR_MAPPING_NAMES from transformers.models.auto.tokenization_auto import TOKENIZER_MAPPING_NAMES from transformers.utils import ENV_VARS_TRUE_VALUES, direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_repo.py PATH_TO_TRANSFORMERS = "src/transformers" PATH_TO_TESTS = "tests" PATH_TO_DOC = "docs/source/en" # Update this list with models that are supposed to be private. PRIVATE_MODELS = [ "AltRobertaModel", "DPRSpanPredictor", "UdopStack", "LongT5Stack", "RealmBertModel", "T5Stack", "MT5Stack", "UMT5Stack", "Pop2PianoStack", "SwitchTransformersStack", "TFDPRSpanPredictor", "MaskFormerSwinModel", "MaskFormerSwinPreTrainedModel", "BridgeTowerTextModel", "BridgeTowerVisionModel", "Kosmos2TextModel", "Kosmos2TextForCausalLM", "Kosmos2VisionModel", "SeamlessM4Tv2TextToUnitModel", "SeamlessM4Tv2CodeHifiGan", "SeamlessM4Tv2TextToUnitForConditionalGeneration", ] # Update this list for models that are not tested with a comment explaining the reason it should not be. # Being in this list is an exception and should **not** be the rule. IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [ # models to ignore for not tested "RecurrentGemmaModel", # Building part of bigger (tested) model. "FuyuForCausalLM", # Not tested fort now "InstructBlipQFormerModel", # Building part of bigger (tested) model. "UMT5EncoderModel", # Building part of bigger (tested) model. "Blip2QFormerModel", # Building part of bigger (tested) model. "ErnieMForInformationExtraction", "FastSpeech2ConformerHifiGan", # Already tested by SpeechT5HifiGan (# Copied from) "FastSpeech2ConformerWithHifiGan", # Built with two smaller (tested) models. "GraphormerDecoderHead", # Building part of bigger (tested) model. "JukeboxVQVAE", # Building part of bigger (tested) model. "JukeboxPrior", # Building part of bigger (tested) model. "DecisionTransformerGPT2Model", # Building part of bigger (tested) model. "SegformerDecodeHead", # Building part of bigger (tested) model. "MgpstrModel", # Building part of bigger (tested) model. "BertLMHeadModel", # Needs to be setup as decoder. "MegatronBertLMHeadModel", # Building part of bigger (tested) model. "RealmBertModel", # Building part of bigger (tested) model. "RealmReader", # Not regular model. "RealmScorer", # Not regular model. "RealmForOpenQA", # Not regular model. "ReformerForMaskedLM", # Needs to be setup as decoder. "TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?) "TFRobertaForMultipleChoice", # TODO: fix "TFRobertaPreLayerNormForMultipleChoice", # TODO: fix "SeparableConv1D", # Building part of bigger (tested) model. "FlaxBartForCausalLM", # Building part of bigger (tested) model. "FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM. "OPTDecoderWrapper", "TFSegformerDecodeHead", # Not a regular model. "AltRobertaModel", # Building part of bigger (tested) model. "BlipTextLMHeadModel", # No need to test it as it is tested by BlipTextVision models "TFBlipTextLMHeadModel", # No need to test it as it is tested by BlipTextVision models "BridgeTowerTextModel", # No need to test it as it is tested by BridgeTowerModel model. "BridgeTowerVisionModel", # No need to test it as it is tested by BridgeTowerModel model. "BarkCausalModel", # Building part of bigger (tested) model. "BarkModel", # Does not have a forward signature - generation tested with integration tests. "SeamlessM4TTextToUnitModel", # Building part of bigger (tested) model. "SeamlessM4TCodeHifiGan", # Building part of bigger (tested) model. "SeamlessM4TTextToUnitForConditionalGeneration", # Building part of bigger (tested) model. ] # Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't # trigger the common tests. TEST_FILES_WITH_NO_COMMON_TESTS = [ "models/decision_transformer/test_modeling_decision_transformer.py", "models/camembert/test_modeling_camembert.py", "models/mt5/test_modeling_flax_mt5.py", "models/mbart/test_modeling_mbart.py", "models/mt5/test_modeling_mt5.py", "models/pegasus/test_modeling_pegasus.py", "models/camembert/test_modeling_tf_camembert.py", "models/mt5/test_modeling_tf_mt5.py", "models/xlm_roberta/test_modeling_tf_xlm_roberta.py", "models/xlm_roberta/test_modeling_flax_xlm_roberta.py", "models/xlm_prophetnet/test_modeling_xlm_prophetnet.py", "models/xlm_roberta/test_modeling_xlm_roberta.py", "models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py", "models/vision_text_dual_encoder/test_modeling_tf_vision_text_dual_encoder.py", "models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py", "models/decision_transformer/test_modeling_decision_transformer.py", "models/bark/test_modeling_bark.py", ] # Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and # should **not** be the rule. IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [ # models to ignore for model xxx mapping "AlignTextModel", "AlignVisionModel", "ClapTextModel", "ClapTextModelWithProjection", "ClapAudioModel", "ClapAudioModelWithProjection", "Blip2ForConditionalGeneration", "Blip2QFormerModel", "Blip2VisionModel", "ErnieMForInformationExtraction", "FastSpeech2ConformerHifiGan", "FastSpeech2ConformerWithHifiGan", "GitVisionModel", "GraphormerModel", "GraphormerForGraphClassification", "BlipForConditionalGeneration", "BlipForImageTextRetrieval", "BlipForQuestionAnswering", "BlipVisionModel", "BlipTextLMHeadModel", "BlipTextModel", "BrosSpadeEEForTokenClassification", "BrosSpadeELForTokenClassification", "TFBlipForConditionalGeneration", "TFBlipForImageTextRetrieval", "TFBlipForQuestionAnswering", "TFBlipVisionModel", "TFBlipTextLMHeadModel", "TFBlipTextModel", "Swin2SRForImageSuperResolution", "BridgeTowerForImageAndTextRetrieval", "BridgeTowerForMaskedLM", "BridgeTowerForContrastiveLearning", "CLIPSegForImageSegmentation", "CLIPSegVisionModel", "CLIPSegTextModel", "EsmForProteinFolding", "GPTSanJapaneseModel", "TimeSeriesTransformerForPrediction", "InformerForPrediction", "AutoformerForPrediction", "PatchTSTForPretraining", "PatchTSTForPrediction", "JukeboxVQVAE", "JukeboxPrior", "SamModel", "DPTForDepthEstimation", "DecisionTransformerGPT2Model", "GLPNForDepthEstimation", "ViltForImagesAndTextClassification", "ViltForImageAndTextRetrieval", "ViltForTokenClassification", "ViltForMaskedLM", "PerceiverForMultimodalAutoencoding", "PerceiverForOpticalFlow", "SegformerDecodeHead", "TFSegformerDecodeHead", "FlaxBeitForMaskedImageModeling", "BeitForMaskedImageModeling", "ChineseCLIPTextModel", "ChineseCLIPVisionModel", "CLIPTextModel", "CLIPTextModelWithProjection", "CLIPVisionModelWithProjection", "ClvpForCausalLM", "ClvpModel", "GroupViTTextModel", "GroupViTVisionModel", "TFCLIPTextModel", "TFCLIPVisionModel", "TFGroupViTTextModel", "TFGroupViTVisionModel", "FlaxCLIPTextModel", "FlaxCLIPTextModelWithProjection", "FlaxCLIPVisionModel", "FlaxWav2Vec2ForCTC", "DetrForSegmentation", "Pix2StructVisionModel", "Pix2StructTextModel", "Pix2StructForConditionalGeneration", "ConditionalDetrForSegmentation", "DPRReader", "FlaubertForQuestionAnswering", "FlavaImageCodebook", "FlavaTextModel", "FlavaImageModel", "FlavaMultimodalModel", "GPT2DoubleHeadsModel", "GPTSw3DoubleHeadsModel", "InstructBlipVisionModel", "InstructBlipQFormerModel", "LayoutLMForQuestionAnswering", "LukeForMaskedLM", "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", "MgpstrModel", "OpenAIGPTDoubleHeadsModel", "OwlViTTextModel", "OwlViTVisionModel", "Owlv2TextModel", "Owlv2VisionModel", "OwlViTForObjectDetection", "PatchTSMixerForPrediction", "PatchTSMixerForPretraining", "RagModel", "RagSequenceForGeneration", "RagTokenForGeneration", "RealmEmbedder", "RealmForOpenQA", "RealmScorer", "RealmReader", "TFDPRReader", "TFGPT2DoubleHeadsModel", "TFLayoutLMForQuestionAnswering", "TFOpenAIGPTDoubleHeadsModel", "TFRagModel", "TFRagSequenceForGeneration", "TFRagTokenForGeneration", "Wav2Vec2ForCTC", "HubertForCTC", "SEWForCTC", "SEWDForCTC", "XLMForQuestionAnswering", "XLNetForQuestionAnswering", "SeparableConv1D", "VisualBertForRegionToPhraseAlignment", "VisualBertForVisualReasoning", "VisualBertForQuestionAnswering", "VisualBertForMultipleChoice", "TFWav2Vec2ForCTC", "TFHubertForCTC", "XCLIPVisionModel", "XCLIPTextModel", "AltCLIPTextModel", "AltCLIPVisionModel", "AltRobertaModel", "TvltForAudioVisualClassification", "BarkCausalModel", "BarkCoarseModel", "BarkFineModel", "BarkSemanticModel", "MusicgenMelodyModel", "MusicgenModel", "MusicgenForConditionalGeneration", "SpeechT5ForSpeechToSpeech", "SpeechT5ForTextToSpeech", "SpeechT5HifiGan", "VitMatteForImageMatting", "SeamlessM4TTextToUnitModel", "SeamlessM4TTextToUnitForConditionalGeneration", "SeamlessM4TCodeHifiGan", "SeamlessM4TForSpeechToSpeech", # no auto class for speech-to-speech "TvpForVideoGrounding", "UdopForConditionalGeneration", "SeamlessM4Tv2NARTextToUnitModel", "SeamlessM4Tv2NARTextToUnitForConditionalGeneration", "SeamlessM4Tv2CodeHifiGan", "SeamlessM4Tv2ForSpeechToSpeech", # no auto class for speech-to-speech "SegGptForImageSegmentation", "SiglipVisionModel", "SiglipTextModel", ] # DO NOT edit this list! # (The corresponding pytorch objects should never have been in the main `__init__`, but it's too late to remove) OBJECT_TO_SKIP_IN_MAIN_INIT_CHECK = [ "FlaxBertLayer", "FlaxBigBirdLayer", "FlaxRoFormerLayer", "TFBertLayer", "TFLxmertEncoder", "TFLxmertXLayer", "TFMPNetLayer", "TFMobileBertLayer", "TFSegformerLayer", "TFViTMAELayer", ] # Update this list for models that have multiple model types for the same model doc. MODEL_TYPE_TO_DOC_MAPPING = OrderedDict( [ ("data2vec-text", "data2vec"), ("data2vec-audio", "data2vec"), ("data2vec-vision", "data2vec"), ("donut-swin", "donut"), ] ) # This is to make sure the transformers module imported is the one in the repo. transformers = direct_transformers_import(PATH_TO_TRANSFORMERS) def check_missing_backends(): """ Checks if all backends are installed (otherwise the check of this script is incomplete). Will error in the CI if that's not the case but only throw a warning for users running this. """ missing_backends = [] if not is_torch_available(): missing_backends.append("PyTorch") if not is_tf_available(): missing_backends.append("TensorFlow") if not is_flax_available(): missing_backends.append("Flax") if len(missing_backends) > 0: missing = ", ".join(missing_backends) if os.getenv("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES: raise Exception( "Full repo consistency checks require all backends to be installed (with `pip install -e '.[dev]'` in the " f"Transformers repo, the following are missing: {missing}." ) else: warnings.warn( "Full repo consistency checks require all backends to be installed (with `pip install -e '.[dev]'` in the " f"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you " "didn't make any change in one of those backends modeling files, you should probably execute the " "command above to be on the safe side." ) def check_model_list(): """ Checks the model listed as subfolders of `models` match the models available in `transformers.models`. """ # Get the models from the directory structure of `src/transformers/models/` models_dir = os.path.join(PATH_TO_TRANSFORMERS, "models") _models = [] for model in os.listdir(models_dir): if model == "deprecated": continue model_dir = os.path.join(models_dir, model) if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir): _models.append(model) # Get the models in the submodule `transformers.models` models = [model for model in dir(transformers.models) if not model.startswith("__")] missing_models = sorted(set(_models).difference(models)) if missing_models: raise Exception( f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}." ) # If some modeling modules should be ignored for all checks, they should be added in the nested list # _ignore_modules of this function. def get_model_modules() -> List[str]: """Get all the model modules inside the transformers library (except deprecated models).""" _ignore_modules = [ "modeling_auto", "modeling_encoder_decoder", "modeling_marian", "modeling_mmbt", "modeling_outputs", "modeling_retribert", "modeling_utils", "modeling_flax_auto", "modeling_flax_encoder_decoder", "modeling_flax_utils", "modeling_speech_encoder_decoder", "modeling_flax_speech_encoder_decoder", "modeling_flax_vision_encoder_decoder", "modeling_timm_backbone", "modeling_tf_auto", "modeling_tf_encoder_decoder", "modeling_tf_outputs", "modeling_tf_pytorch_utils", "modeling_tf_utils", "modeling_tf_vision_encoder_decoder", "modeling_vision_encoder_decoder", ] modules = [] for model in dir(transformers.models): # There are some magic dunder attributes in the dir, we ignore them if model == "deprecated" or model.startswith("__"): continue model_module = getattr(transformers.models, model) for submodule in dir(model_module): if submodule.startswith("modeling") and submodule not in _ignore_modules: modeling_module = getattr(model_module, submodule) if inspect.ismodule(modeling_module): modules.append(modeling_module) return modules def get_models(module: types.ModuleType, include_pretrained: bool = False) -> List[Tuple[str, type]]: """ Get the objects in a module that are models. Args: module (`types.ModuleType`): The module from which we are extracting models. include_pretrained (`bool`, *optional*, defaults to `False`): Whether or not to include the `PreTrainedModel` subclass (like `BertPreTrainedModel`) or not. Returns: List[Tuple[str, type]]: List of models as tuples (class name, actual class). """ models = [] model_classes = (transformers.PreTrainedModel, transformers.TFPreTrainedModel, transformers.FlaxPreTrainedModel) for attr_name in dir(module): if not include_pretrained and ("Pretrained" in attr_name or "PreTrained" in attr_name): continue attr = getattr(module, attr_name) if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__: models.append((attr_name, attr)) return models def is_building_block(model: str) -> bool: """ Returns `True` if a model is a building block part of a bigger model. """ if model.endswith("Wrapper"): return True if model.endswith("Encoder"): return True if model.endswith("Decoder"): return True if model.endswith("Prenet"): return True def is_a_private_model(model: str) -> bool: """Returns `True` if the model should not be in the main init.""" if model in PRIVATE_MODELS: return True return is_building_block(model) def check_models_are_in_init(): """Checks all models defined in the library are in the main init.""" models_not_in_init = [] dir_transformers = dir(transformers) for module in get_model_modules(): models_not_in_init += [ model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers ] # Remove private models models_not_in_init = [model for model in models_not_in_init if not is_a_private_model(model)] if len(models_not_in_init) > 0: raise Exception(f"The following models should be in the main init: {','.join(models_not_in_init)}.") # If some test_modeling files should be ignored when checking models are all tested, they should be added in the # nested list _ignore_files of this function. def get_model_test_files() -> List[str]: """ Get the model test files. Returns: `List[str]`: The list of test files. The returned files will NOT contain the `tests` (i.e. `PATH_TO_TESTS` defined in this script). They will be considered as paths relative to `tests`. A caller has to use `os.path.join(PATH_TO_TESTS, ...)` to access the files. """ _ignore_files = [ "test_modeling_common", "test_modeling_encoder_decoder", "test_modeling_flax_encoder_decoder", "test_modeling_flax_speech_encoder_decoder", "test_modeling_marian", "test_modeling_tf_common", "test_modeling_tf_encoder_decoder", ] test_files = [] model_test_root = os.path.join(PATH_TO_TESTS, "models") model_test_dirs = [] for x in os.listdir(model_test_root): x = os.path.join(model_test_root, x) if os.path.isdir(x): model_test_dirs.append(x) for target_dir in [PATH_TO_TESTS] + model_test_dirs: for file_or_dir in os.listdir(target_dir): path = os.path.join(target_dir, file_or_dir) if os.path.isfile(path): filename = os.path.split(path)[-1] if "test_modeling" in filename and os.path.splitext(filename)[0] not in _ignore_files: file = os.path.join(*path.split(os.sep)[1:]) test_files.append(file) return test_files # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class # for the all_model_classes variable. def find_tested_models(test_file: str) -> List[str]: """ Parse the content of test_file to detect what's in `all_model_classes`. This detects the models that inherit from the common test class. Args: test_file (`str`): The path to the test file to check Returns: `List[str]`: The list of models tested in that file. """ with open(os.path.join(PATH_TO_TESTS, test_file), "r", encoding="utf-8", newline="\n") as f: content = f.read() all_models = re.findall(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content) # Check with one less parenthesis as well all_models += re.findall(r"all_model_classes\s+=\s+\(([^\)]*)\)", content) if len(all_models) > 0: model_tested = [] for entry in all_models: for line in entry.split(","): name = line.strip() if len(name) > 0: model_tested.append(name) return model_tested def should_be_tested(model_name: str) -> bool: """ Whether or not a model should be tested. """ if model_name in IGNORE_NON_TESTED: return False return not is_building_block(model_name) def check_models_are_tested(module: types.ModuleType, test_file: str) -> List[str]: """Check models defined in a module are all tested in a given file. Args: module (`types.ModuleType`): The module in which we get the models. test_file (`str`): The path to the file where the module is tested. Returns: `List[str]`: The list of error messages corresponding to models not tested. """ # XxxPreTrainedModel are not tested defined_models = get_models(module) tested_models = find_tested_models(test_file) if tested_models is None: if test_file.replace(os.path.sep, "/") in TEST_FILES_WITH_NO_COMMON_TESTS: return return [ f"{test_file} should define `all_model_classes` to apply common tests to the models it tests. " + "If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file " + "`utils/check_repo.py`." ] failures = [] for model_name, _ in defined_models: if model_name not in tested_models and should_be_tested(model_name): failures.append( f"{model_name} is defined in {module.__name__} but is not tested in " + f"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file." + "If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`" + "in the file `utils/check_repo.py`." ) return failures def check_all_models_are_tested(): """Check all models are properly tested.""" modules = get_model_modules() test_files = get_model_test_files() failures = [] for module in modules: # Matches a module to its test file. test_file = [file for file in test_files if f"test_{module.__name__.split('.')[-1]}.py" in file] if len(test_file) == 0: failures.append(f"{module.__name__} does not have its corresponding test file {test_file}.") elif len(test_file) > 1: failures.append(f"{module.__name__} has several test files: {test_file}.") else: test_file = test_file[0] new_failures = check_models_are_tested(module, test_file) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def get_all_auto_configured_models() -> List[str]: """Return the list of all models in at least one auto class.""" result = set() # To avoid duplicates we concatenate all model classes in a set. if is_torch_available(): for attr_name in dir(transformers.models.auto.modeling_auto): if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_auto, attr_name))) if is_tf_available(): for attr_name in dir(transformers.models.auto.modeling_tf_auto): if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_tf_auto, attr_name))) if is_flax_available(): for attr_name in dir(transformers.models.auto.modeling_flax_auto): if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_flax_auto, attr_name))) return list(result) def ignore_unautoclassed(model_name: str) -> bool: """Rules to determine if a model should be in an auto class.""" # Special white list if model_name in IGNORE_NON_AUTO_CONFIGURED: return True # Encoder and Decoder should be ignored if "Encoder" in model_name or "Decoder" in model_name: return True return False def check_models_are_auto_configured(module: types.ModuleType, all_auto_models: List[str]) -> List[str]: """ Check models defined in module are each in an auto class. Args: module (`types.ModuleType`): The module in which we get the models. all_auto_models (`List[str]`): The list of all models in an auto class (as obtained with `get_all_auto_configured_models()`). Returns: `List[str]`: The list of error messages corresponding to models not tested. """ defined_models = get_models(module) failures = [] for model_name, _ in defined_models: if model_name not in all_auto_models and not ignore_unautoclassed(model_name): failures.append( f"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. " "If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file " "`utils/check_repo.py`." ) return failures def check_all_models_are_auto_configured(): """Check all models are each in an auto class.""" # This is where we need to check we have all backends or the check is incomplete. check_missing_backends() modules = get_model_modules() all_auto_models = get_all_auto_configured_models() failures = [] for module in modules: new_failures = check_models_are_auto_configured(module, all_auto_models) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def check_all_auto_object_names_being_defined(): """Check all names defined in auto (name) mappings exist in the library.""" # This is where we need to check we have all backends or the check is incomplete. check_missing_backends() failures = [] mappings_to_check = { "TOKENIZER_MAPPING_NAMES": TOKENIZER_MAPPING_NAMES, "IMAGE_PROCESSOR_MAPPING_NAMES": IMAGE_PROCESSOR_MAPPING_NAMES, "FEATURE_EXTRACTOR_MAPPING_NAMES": FEATURE_EXTRACTOR_MAPPING_NAMES, "PROCESSOR_MAPPING_NAMES": PROCESSOR_MAPPING_NAMES, } # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way. for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]: module = getattr(transformers.models.auto, module_name, None) if module is None: continue # all mappings in a single auto modeling file mapping_names = [x for x in dir(module) if x.endswith("_MAPPING_NAMES")] mappings_to_check.update({name: getattr(module, name) for name in mapping_names}) for name, mapping in mappings_to_check.items(): for _, class_names in mapping.items(): if not isinstance(class_names, tuple): class_names = (class_names,) for class_name in class_names: if class_name is None: continue # dummy object is accepted if not hasattr(transformers, class_name): # If the class name is in a model name mapping, let's not check if there is a definition in any modeling # module, if it's a private model defined in this file. if name.endswith("MODEL_MAPPING_NAMES") and is_a_private_model(class_name): continue if name.endswith("MODEL_FOR_IMAGE_MAPPING_NAMES") and is_a_private_model(class_name): continue failures.append( f"`{class_name}` appears in the mapping `{name}` but it is not defined in the library." ) if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def check_all_auto_mapping_names_in_config_mapping_names(): """Check all keys defined in auto mappings (mappings of names) appear in `CONFIG_MAPPING_NAMES`.""" # This is where we need to check we have all backends or the check is incomplete. check_missing_backends() failures = [] # `TOKENIZER_PROCESSOR_MAPPING_NAMES` and `AutoTokenizer` is special, and don't need to follow the rule. mappings_to_check = { "IMAGE_PROCESSOR_MAPPING_NAMES": IMAGE_PROCESSOR_MAPPING_NAMES, "FEATURE_EXTRACTOR_MAPPING_NAMES": FEATURE_EXTRACTOR_MAPPING_NAMES, "PROCESSOR_MAPPING_NAMES": PROCESSOR_MAPPING_NAMES, } # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way. for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]: module = getattr(transformers.models.auto, module_name, None) if module is None: continue # all mappings in a single auto modeling file mapping_names = [x for x in dir(module) if x.endswith("_MAPPING_NAMES")] mappings_to_check.update({name: getattr(module, name) for name in mapping_names}) for name, mapping in mappings_to_check.items(): for model_type in mapping: if model_type not in CONFIG_MAPPING_NAMES: failures.append( f"`{model_type}` appears in the mapping `{name}` but it is not defined in the keys of " "`CONFIG_MAPPING_NAMES`." ) if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def check_all_auto_mappings_importable(): """Check all auto mappings can be imported.""" # This is where we need to check we have all backends or the check is incomplete. check_missing_backends() failures = [] mappings_to_check = {} # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way. for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]: module = getattr(transformers.models.auto, module_name, None) if module is None: continue # all mappings in a single auto modeling file mapping_names = [x for x in dir(module) if x.endswith("_MAPPING_NAMES")] mappings_to_check.update({name: getattr(module, name) for name in mapping_names}) for name in mappings_to_check: name = name.replace("_MAPPING_NAMES", "_MAPPING") if not hasattr(transformers, name): failures.append(f"`{name}`") if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def check_objects_being_equally_in_main_init(): """ Check if a (TensorFlow or Flax) object is in the main __init__ iif its counterpart in PyTorch is. """ attrs = dir(transformers) failures = [] for attr in attrs: obj = getattr(transformers, attr) if not hasattr(obj, "__module__") or "models.deprecated" in obj.__module__: continue module_path = obj.__module__ module_name = module_path.split(".")[-1] module_dir = ".".join(module_path.split(".")[:-1]) if ( module_name.startswith("modeling_") and not module_name.startswith("modeling_tf_") and not module_name.startswith("modeling_flax_") ): parent_module = sys.modules[module_dir] frameworks = [] if is_tf_available(): frameworks.append("TF") if is_flax_available(): frameworks.append("Flax") for framework in frameworks: other_module_path = module_path.replace("modeling_", f"modeling_{framework.lower()}_") if os.path.isfile("src/" + other_module_path.replace(".", "/") + ".py"): other_module_name = module_name.replace("modeling_", f"modeling_{framework.lower()}_") other_module = getattr(parent_module, other_module_name) if hasattr(other_module, f"{framework}{attr}"): if not hasattr(transformers, f"{framework}{attr}"): if f"{framework}{attr}" not in OBJECT_TO_SKIP_IN_MAIN_INIT_CHECK: failures.append(f"{framework}{attr}") if hasattr(other_module, f"{framework}_{attr}"): if not hasattr(transformers, f"{framework}_{attr}"): if f"{framework}_{attr}" not in OBJECT_TO_SKIP_IN_MAIN_INIT_CHECK: failures.append(f"{framework}_{attr}") if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) _re_decorator = re.compile(r"^\s*@(\S+)\s+$") def check_decorator_order(filename: str) -> List[int]: """ Check that in a given test file, the slow decorator is always last. Args: filename (`str`): The path to a test file to check. Returns: `List[int]`: The list of failures as a list of indices where there are problems. """ with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() decorator_before = None errors = [] for i, line in enumerate(lines): search = _re_decorator.search(line) if search is not None: decorator_name = search.groups()[0] if decorator_before is not None and decorator_name.startswith("parameterized"): errors.append(i) decorator_before = decorator_name elif decorator_before is not None: decorator_before = None return errors def check_all_decorator_order(): """Check that in all test files, the slow decorator is always last.""" errors = [] for fname in os.listdir(PATH_TO_TESTS): if fname.endswith(".py"): filename = os.path.join(PATH_TO_TESTS, fname) new_errors = check_decorator_order(filename) errors += [f"- {filename}, line {i}" for i in new_errors] if len(errors) > 0: msg = "\n".join(errors) raise ValueError( "The parameterized decorator (and its variants) should always be first, but this is not the case in the" f" following files:\n{msg}" ) def find_all_documented_objects() -> List[str]: """ Parse the content of all doc files to detect which classes and functions it documents. Returns: `List[str]`: The list of all object names being documented. """ documented_obj = [] for doc_file in Path(PATH_TO_DOC).glob("**/*.rst"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] for doc_file in Path(PATH_TO_DOC).glob("**/*.md"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall(r"\[\[autodoc\]\]\s+(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] return documented_obj # One good reason for not being documented is to be deprecated. Put in this list deprecated objects. DEPRECATED_OBJECTS = [ "AutoModelWithLMHead", "BartPretrainedModel", "DataCollator", "DataCollatorForSOP", "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "NerPipeline", "PretrainedBartModel", "PretrainedFSMTModel", "SingleSentenceClassificationProcessor", "SquadDataTrainingArguments", "SquadDataset", "SquadExample", "SquadFeatures", "SquadV1Processor", "SquadV2Processor", "TFAutoModelWithLMHead", "TFBartPretrainedModel", "TextDataset", "TextDatasetForNextSentencePrediction", "Wav2Vec2ForMaskedLM", "Wav2Vec2Tokenizer", "glue_compute_metrics", "glue_convert_examples_to_features", "glue_output_modes", "glue_processors", "glue_tasks_num_labels", "squad_convert_examples_to_features", "xnli_compute_metrics", "xnli_output_modes", "xnli_processors", "xnli_tasks_num_labels", "TFTrainingArguments", ] # Exceptionally, some objects should not be documented after all rules passed. # ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT! UNDOCUMENTED_OBJECTS = [ "AddedToken", # This is a tokenizers class. "BasicTokenizer", # Internal, should never have been in the main init. "CharacterTokenizer", # Internal, should never have been in the main init. "DPRPretrainedReader", # Like an Encoder. "DummyObject", # Just picked by mistake sometimes. "MecabTokenizer", # Internal, should never have been in the main init. "ModelCard", # Internal type. "SqueezeBertModule", # Internal building block (should have been called SqueezeBertLayer) "TFDPRPretrainedReader", # Like an Encoder. "TransfoXLCorpus", # Internal type. "WordpieceTokenizer", # Internal, should never have been in the main init. "absl", # External module "add_end_docstrings", # Internal, should never have been in the main init. "add_start_docstrings", # Internal, should never have been in the main init. "convert_tf_weight_name_to_pt_weight_name", # Internal used to convert model weights "logger", # Internal logger "logging", # External module "requires_backends", # Internal function "AltRobertaModel", # Internal module ] # This list should be empty. Objects in it should get their own doc page. SHOULD_HAVE_THEIR_OWN_PAGE = [ # Benchmarks "PyTorchBenchmark", "PyTorchBenchmarkArguments", "TensorFlowBenchmark", "TensorFlowBenchmarkArguments", "AutoBackbone", "BeitBackbone", "BitBackbone", "ConvNextBackbone", "ConvNextV2Backbone", "DinatBackbone", "Dinov2Backbone", "FocalNetBackbone", "MaskFormerSwinBackbone", "MaskFormerSwinConfig", "MaskFormerSwinModel", "NatBackbone", "PvtV2Backbone", "ResNetBackbone", "SwinBackbone", "Swinv2Backbone", "TimmBackbone", "TimmBackboneConfig", "VitDetBackbone", ] def ignore_undocumented(name: str) -> bool: """Rules to determine if `name` should be undocumented (returns `True` if it should not be documented).""" # NOT DOCUMENTED ON PURPOSE. # Constants uppercase are not documented. if name.isupper(): return True # PreTrainedModels / Encoders / Decoders / Layers / Embeddings / Attention are not documented. if ( name.endswith("PreTrainedModel") or name.endswith("Decoder") or name.endswith("Encoder") or name.endswith("Layer") or name.endswith("Embeddings") or name.endswith("Attention") ): return True # Submodules are not documented. if os.path.isdir(os.path.join(PATH_TO_TRANSFORMERS, name)) or os.path.isfile( os.path.join(PATH_TO_TRANSFORMERS, f"{name}.py") ): return True # All load functions are not documented. if name.startswith("load_tf") or name.startswith("load_pytorch"): return True # is_xxx_available functions are not documented. if name.startswith("is_") and name.endswith("_available"): return True # Deprecated objects are not documented. if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS: return True # MMBT model does not really work. if name.startswith("MMBT"): return True if name in SHOULD_HAVE_THEIR_OWN_PAGE: return True return False def check_all_objects_are_documented(): """Check all models are properly documented.""" documented_objs = find_all_documented_objects() modules = transformers._modules objects = [c for c in dir(transformers) if c not in modules and not c.startswith("_")] undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)] if len(undocumented_objs) > 0: raise Exception( "The following objects are in the public init so should be documented:\n - " + "\n - ".join(undocumented_objs) ) check_docstrings_are_in_md() check_model_type_doc_match() def check_model_type_doc_match(): """Check all doc pages have a corresponding model type.""" model_doc_folder = Path(PATH_TO_DOC) / "model_doc" model_docs = [m.stem for m in model_doc_folder.glob("*.md")] model_types = list(transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys()) model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types] errors = [] for m in model_docs: if m not in model_types and m != "auto": close_matches = get_close_matches(m, model_types) error_message = f"{m} is not a proper model identifier." if len(close_matches) > 0: close_matches = "/".join(close_matches) error_message += f" Did you mean {close_matches}?" errors.append(error_message) if len(errors) > 0: raise ValueError( "Some model doc pages do not match any existing model type:\n" + "\n".join(errors) + "\nYou can add any missing model type to the `MODEL_NAMES_MAPPING` constant in " "models/auto/configuration_auto.py." ) # Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`. _re_rst_special_words = re.compile(r":(?:obj|func|class|meth):`([^`]+)`") # Re pattern to catch things between double backquotes. _re_double_backquotes = re.compile(r"(^|[^`])``([^`]+)``([^`]|$)") # Re pattern to catch example introduction. _re_rst_example = re.compile(r"^\s*Example.*::\s*$", flags=re.MULTILINE) def is_rst_docstring(docstring: str) -> True: """ Returns `True` if `docstring` is written in rst. """ if _re_rst_special_words.search(docstring) is not None: return True if _re_double_backquotes.search(docstring) is not None: return True if _re_rst_example.search(docstring) is not None: return True return False def check_docstrings_are_in_md(): """Check all docstrings are written in md and nor rst.""" files_with_rst = [] for file in Path(PATH_TO_TRANSFORMERS).glob("**/*.py"): with open(file, encoding="utf-8") as f: code = f.read() docstrings = code.split('"""') for idx, docstring in enumerate(docstrings): if idx % 2 == 0 or not is_rst_docstring(docstring): continue files_with_rst.append(file) break if len(files_with_rst) > 0: raise ValueError( "The following files have docstrings written in rst:\n" + "\n".join([f"- {f}" for f in files_with_rst]) + "\nTo fix this run `doc-builder convert path_to_py_file` after installing `doc-builder`\n" "(`pip install git+https://github.com/huggingface/doc-builder`)" ) def check_deprecated_constant_is_up_to_date(): """ Check if the constant `DEPRECATED_MODELS` in `models/auto/configuration_auto.py` is up to date. """ deprecated_folder = os.path.join(PATH_TO_TRANSFORMERS, "models", "deprecated") deprecated_models = [m for m in os.listdir(deprecated_folder) if not m.startswith("_")] constant_to_check = transformers.models.auto.configuration_auto.DEPRECATED_MODELS message = [] missing_models = sorted(set(deprecated_models) - set(constant_to_check)) if len(missing_models) != 0: missing_models = ", ".join(missing_models) message.append( "The following models are in the deprecated folder, make sure to add them to `DEPRECATED_MODELS` in " f"`models/auto/configuration_auto.py`: {missing_models}." ) extra_models = sorted(set(constant_to_check) - set(deprecated_models)) if len(extra_models) != 0: extra_models = ", ".join(extra_models) message.append( "The following models are in the `DEPRECATED_MODELS` constant but not in the deprecated folder. Either " f"remove them from the constant or move to the deprecated folder: {extra_models}." ) if len(message) > 0: raise Exception("\n".join(message)) def check_repo_quality(): """Check all models are properly tested and documented.""" print("Checking all models are included.") check_model_list() print("Checking all models are public.") check_models_are_in_init() print("Checking all models are properly tested.") check_all_decorator_order() check_all_models_are_tested() print("Checking all objects are properly documented.") check_all_objects_are_documented() print("Checking all models are in at least one auto class.") check_all_models_are_auto_configured() print("Checking all names in auto name mappings are defined.") check_all_auto_object_names_being_defined() print("Checking all keys in auto name mappings are defined in `CONFIG_MAPPING_NAMES`.") check_all_auto_mapping_names_in_config_mapping_names() print("Checking all auto mappings could be imported.") check_all_auto_mappings_importable() print("Checking all objects are equally (across frameworks) in the main __init__.") check_objects_being_equally_in_main_init() print("Checking the DEPRECATED_MODELS constant is up to date.") check_deprecated_constant_is_up_to_date() if __name__ == "__main__": check_repo_quality()

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/tests_fetcher.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # 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. """ Welcome to tests_fetcher V2. This util is designed to fetch tests to run on a PR so that only the tests impacted by the modifications are run, and when too many models are being impacted, only run the tests of a subset of core models. It works like this. Stage 1: Identify the modified files. For jobs that run on the main branch, it's just the diff with the last commit. On a PR, this takes all the files from the branching point to the current commit (so all modifications in a PR, not just the last commit) but excludes modifications that are on docstrings or comments only. Stage 2: Extract the tests to run. This is done by looking at the imports in each module and test file: if module A imports module B, then changing module B impacts module A, so the tests using module A should be run. We thus get the dependencies of each model and then recursively builds the 'reverse' map of dependencies to get all modules and tests impacted by a given file. We then only keep the tests (and only the core models tests if there are too many modules). Caveats: - This module only filters tests by files (not individual tests) so it's better to have tests for different things in different files. - This module assumes inits are just importing things, not really building objects, so it's better to structure them this way and move objects building in separate submodules. Usage: Base use to fetch the tests in a pull request ```bash python utils/tests_fetcher.py ``` Base use to fetch the tests on a the main branch (with diff from the last commit): ```bash python utils/tests_fetcher.py --diff_with_last_commit ``` """ import argparse import collections import importlib.util import json import os import re import tempfile from contextlib import contextmanager from pathlib import Path from typing import Dict, List, Optional, Tuple, Union from git import Repo PATH_TO_REPO = Path(__file__).parent.parent.resolve() PATH_TO_EXAMPLES = PATH_TO_REPO / "examples" PATH_TO_TRANFORMERS = PATH_TO_REPO / "src/transformers" PATH_TO_TESTS = PATH_TO_REPO / "tests" # The value is just a heuristic to determine if we `guess` all models are impacted. # This variable has effect only if `filter_models=False`. NUM_MODELS_TO_TRIGGER_FULL_CI = 30 # List here the models to always test. IMPORTANT_MODELS = [ "auto", # Most downloaded models "bert", "clip", "t5", "xlm-roberta", "gpt2", "bart", "mpnet", "gpt-j", "wav2vec2", "deberta-v2", "layoutlm", "llama", "opt", "longformer", "vit", "whisper", # Pipeline-specific model (to be sure each pipeline has one model in this list) "tapas", "vilt", "clap", "detr", "owlvit", "dpt", "videomae", ] @contextmanager def checkout_commit(repo: Repo, commit_id: str): """ Context manager that checks out a given commit when entered, but gets back to the reference it was at on exit. Args: repo (`git.Repo`): A git repository (for instance the Transformers repo). commit_id (`str`): The commit reference to checkout inside the context manager. """ current_head = repo.head.commit if repo.head.is_detached else repo.head.ref try: repo.git.checkout(commit_id) yield finally: repo.git.checkout(current_head) def clean_code(content: str) -> str: """ Remove docstrings, empty line or comments from some code (used to detect if a diff is real or only concern comments or docstings). Args: content (`str`): The code to clean Returns: `str`: The cleaned code. """ # We need to deactivate autoformatting here to write escaped triple quotes (we cannot use real triple quotes or # this would mess up the result if this function applied to this particular file). # fmt: off # Remove docstrings by splitting on triple " then triple ': splits = content.split('\"\"\"') content = "".join(splits[::2]) splits = content.split("\'\'\'") # fmt: on content = "".join(splits[::2]) # Remove empty lines and comments lines_to_keep = [] for line in content.split("\n"): # remove anything that is after a # sign. line = re.sub("#.*$", "", line) # remove white lines if len(line) != 0 and not line.isspace(): lines_to_keep.append(line) return "\n".join(lines_to_keep) def keep_doc_examples_only(content: str) -> str: """ Remove everything from the code content except the doc examples (used to determined if a diff should trigger doc tests or not). Args: content (`str`): The code to clean Returns: `str`: The cleaned code. """ # Keep doc examples only by splitting on triple "`" splits = content.split("```") # Add leading and trailing "```" so the navigation is easier when compared to the original input `content` content = "```" + "```".join(splits[1::2]) + "```" # Remove empty lines and comments lines_to_keep = [] for line in content.split("\n"): # remove anything that is after a # sign. line = re.sub("#.*$", "", line) # remove white lines if len(line) != 0 and not line.isspace(): lines_to_keep.append(line) return "\n".join(lines_to_keep) def get_all_tests() -> List[str]: """ Walks the `tests` folder to return a list of files/subfolders. This is used to split the tests to run when using paralellism. The split is: - folders under `tests`: (`tokenization`, `pipelines`, etc) except the subfolder `models` is excluded. - folders under `tests/models`: `bert`, `gpt2`, etc. - test files under `tests`: `test_modeling_common.py`, `test_tokenization_common.py`, etc. """ # test folders/files directly under `tests` folder tests = os.listdir(PATH_TO_TESTS) tests = [f"tests/{f}" for f in tests if "__pycache__" not in f] tests = sorted([f for f in tests if (PATH_TO_REPO / f).is_dir() or f.startswith("tests/test_")]) # model specific test folders model_test_folders = os.listdir(PATH_TO_TESTS / "models") model_test_folders = [f"tests/models/{f}" for f in model_test_folders if "__pycache__" not in f] model_test_folders = sorted([f for f in model_test_folders if (PATH_TO_REPO / f).is_dir()]) tests.remove("tests/models") # Sagemaker tests are not meant to be run on the CI. if "tests/sagemaker" in tests: tests.remove("tests/sagemaker") tests = model_test_folders + tests return tests def diff_is_docstring_only(repo: Repo, branching_point: str, filename: str) -> bool: """ Check if the diff is only in docstrings (or comments and whitespace) in a filename. Args: repo (`git.Repo`): A git repository (for instance the Transformers repo). branching_point (`str`): The commit reference of where to compare for the diff. filename (`str`): The filename where we want to know if the diff isonly in docstrings/comments. Returns: `bool`: Whether the diff is docstring/comments only or not. """ folder = Path(repo.working_dir) with checkout_commit(repo, branching_point): with open(folder / filename, "r", encoding="utf-8") as f: old_content = f.read() with open(folder / filename, "r", encoding="utf-8") as f: new_content = f.read() old_content_clean = clean_code(old_content) new_content_clean = clean_code(new_content) return old_content_clean == new_content_clean def diff_contains_doc_examples(repo: Repo, branching_point: str, filename: str) -> bool: """ Check if the diff is only in code examples of the doc in a filename. Args: repo (`git.Repo`): A git repository (for instance the Transformers repo). branching_point (`str`): The commit reference of where to compare for the diff. filename (`str`): The filename where we want to know if the diff is only in codes examples. Returns: `bool`: Whether the diff is only in code examples of the doc or not. """ folder = Path(repo.working_dir) with checkout_commit(repo, branching_point): with open(folder / filename, "r", encoding="utf-8") as f: old_content = f.read() with open(folder / filename, "r", encoding="utf-8") as f: new_content = f.read() old_content_clean = keep_doc_examples_only(old_content) new_content_clean = keep_doc_examples_only(new_content) return old_content_clean != new_content_clean def get_impacted_files_from_tiny_model_summary(diff_with_last_commit: bool = False) -> List[str]: """ Return a list of python modeling files that are impacted by the changes of `tiny_model_summary.json` in between: - the current head and the main branch if `diff_with_last_commit=False` (default) - the current head and its parent commit otherwise. Returns: `List[str]`: The list of Python modeling files that are impacted by the changes of `tiny_model_summary.json`. """ repo = Repo(PATH_TO_REPO) folder = Path(repo.working_dir) if not diff_with_last_commit: print(f"main is at {repo.refs.main.commit}") print(f"Current head is at {repo.head.commit}") commits = repo.merge_base(repo.refs.main, repo.head) for commit in commits: print(f"Branching commit: {commit}") else: print(f"main is at {repo.head.commit}") commits = repo.head.commit.parents for commit in commits: print(f"Parent commit: {commit}") if not os.path.isfile(folder / "tests/utils/tiny_model_summary.json"): return [] files = set() for commit in commits: with checkout_commit(repo, commit): with open(folder / "tests/utils/tiny_model_summary.json", "r", encoding="utf-8") as f: old_content = f.read() with open(folder / "tests/utils/tiny_model_summary.json", "r", encoding="utf-8") as f: new_content = f.read() # get the content as json object old_content = json.loads(old_content) new_content = json.loads(new_content) old_keys = set(old_content.keys()) new_keys = set(new_content.keys()) # get the difference keys_with_diff = old_keys.symmetric_difference(new_keys) common_keys = old_keys.intersection(new_keys) # if both have the same key, check its content for key in common_keys: if old_content[key] != new_content[key]: keys_with_diff.add(key) # get the model classes impacted_model_classes = [] for key in keys_with_diff: if key in new_keys: impacted_model_classes.extend(new_content[key]["model_classes"]) # get the module where the model classes are defined. We want to use the main `__init__` file, but it requires # all the framework being installed, which is not ideal for a simple script like test fetcher. # So we create a temporary and modified main `__init__` and access its `_import_structure`. with open(folder / "src/transformers/__init__.py") as fp: lines = fp.readlines() new_lines = [] # Get all the code related to `_import_structure` for line in lines: if line == "_import_structure = {\n": new_lines.append(line) elif line == "# Direct imports for type-checking\n": break elif len(new_lines) > 0: # bypass the framework check so we can get all the information even if frameworks are not available line = re.sub(r"is_.+_available\(\)", "True", line) line = line.replace("OptionalDependencyNotAvailable", "Exception") line = line.replace("Exception()", "Exception") new_lines.append(line) # create and load the temporary module with tempfile.TemporaryDirectory() as tmpdirname: with open(os.path.join(tmpdirname, "temp_init.py"), "w") as fp: fp.write("".join(new_lines)) spec = importlib.util.spec_from_file_location("temp_init", os.path.join(tmpdirname, "temp_init.py")) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) # Finally, get `_import_structure` that we need import_structure = module._import_structure # map model classes to their defined module reversed_structure = {} for key, values in import_structure.items(): for value in values: reversed_structure[value] = key # Get the corresponding modeling file path for model_class in impacted_model_classes: module = reversed_structure[model_class] framework = "" if model_class.startswith("TF"): framework = "tf" elif model_class.startswith("Flax"): framework = "flax" fn = ( f"modeling_{module.split('.')[-1]}.py" if framework == "" else f"modeling_{framework}_{module.split('.')[-1]}.py" ) files.add( f"src.transformers.{module}.{fn}".replace(".", os.path.sep).replace(f"{os.path.sep}py", ".py") ) return sorted(files) def get_diff(repo: Repo, base_commit: str, commits: List[str]) -> List[str]: """ Get the diff between a base commit and one or several commits. Args: repo (`git.Repo`): A git repository (for instance the Transformers repo). base_commit (`str`): The commit reference of where to compare for the diff. This is the current commit, not the branching point! commits (`List[str]`): The list of commits with which to compare the repo at `base_commit` (so the branching point). Returns: `List[str]`: The list of Python files with a diff (files added, renamed or deleted are always returned, files modified are returned if the diff in the file is not only in docstrings or comments, see `diff_is_docstring_only`). """ print("\n### DIFF ###\n") code_diff = [] for commit in commits: for diff_obj in commit.diff(base_commit): # We always add new python files if diff_obj.change_type == "A" and diff_obj.b_path.endswith(".py"): code_diff.append(diff_obj.b_path) # We check that deleted python files won't break corresponding tests. elif diff_obj.change_type == "D" and diff_obj.a_path.endswith(".py"): code_diff.append(diff_obj.a_path) # Now for modified files elif diff_obj.change_type in ["M", "R"] and diff_obj.b_path.endswith(".py"): # In case of renames, we'll look at the tests using both the old and new name. if diff_obj.a_path != diff_obj.b_path: code_diff.extend([diff_obj.a_path, diff_obj.b_path]) else: # Otherwise, we check modifications are in code and not docstrings. if diff_is_docstring_only(repo, commit, diff_obj.b_path): print(f"Ignoring diff in {diff_obj.b_path} as it only concerns docstrings or comments.") else: code_diff.append(diff_obj.a_path) return code_diff def get_modified_python_files(diff_with_last_commit: bool = False) -> List[str]: """ Return a list of python files that have been modified between: - the current head and the main branch if `diff_with_last_commit=False` (default) - the current head and its parent commit otherwise. Returns: `List[str]`: The list of Python files with a diff (files added, renamed or deleted are always returned, files modified are returned if the diff in the file is not only in docstrings or comments, see `diff_is_docstring_only`). """ repo = Repo(PATH_TO_REPO) if not diff_with_last_commit: print(f"main is at {repo.refs.main.commit}") print(f"Current head is at {repo.head.commit}") branching_commits = repo.merge_base(repo.refs.main, repo.head) for commit in branching_commits: print(f"Branching commit: {commit}") return get_diff(repo, repo.head.commit, branching_commits) else: print(f"main is at {repo.head.commit}") parent_commits = repo.head.commit.parents for commit in parent_commits: print(f"Parent commit: {commit}") return get_diff(repo, repo.head.commit, parent_commits) def get_diff_for_doctesting(repo: Repo, base_commit: str, commits: List[str]) -> List[str]: """ Get the diff in doc examples between a base commit and one or several commits. Args: repo (`git.Repo`): A git repository (for instance the Transformers repo). base_commit (`str`): The commit reference of where to compare for the diff. This is the current commit, not the branching point! commits (`List[str]`): The list of commits with which to compare the repo at `base_commit` (so the branching point). Returns: `List[str]`: The list of Python and Markdown files with a diff (files added or renamed are always returned, files modified are returned if the diff in the file is only in doctest examples). """ print("\n### DIFF ###\n") code_diff = [] for commit in commits: for diff_obj in commit.diff(base_commit): # We only consider Python files and doc files. if not diff_obj.b_path.endswith(".py") and not diff_obj.b_path.endswith(".md"): continue # We always add new python/md files if diff_obj.change_type in ["A"]: code_diff.append(diff_obj.b_path) # Now for modified files elif diff_obj.change_type in ["M", "R"]: # In case of renames, we'll look at the tests using both the old and new name. if diff_obj.a_path != diff_obj.b_path: code_diff.extend([diff_obj.a_path, diff_obj.b_path]) else: # Otherwise, we check modifications contain some doc example(s). if diff_contains_doc_examples(repo, commit, diff_obj.b_path): code_diff.append(diff_obj.a_path) else: print(f"Ignoring diff in {diff_obj.b_path} as it doesn't contain any doc example.") return code_diff def get_all_doctest_files() -> List[str]: """ Return the complete list of python and Markdown files on which we run doctest. At this moment, we restrict this to only take files from `src/` or `docs/source/en/` that are not in `utils/not_doctested.txt`. Returns: `List[str]`: The complete list of Python and Markdown files on which we run doctest. """ py_files = [str(x.relative_to(PATH_TO_REPO)) for x in PATH_TO_REPO.glob("**/*.py")] md_files = [str(x.relative_to(PATH_TO_REPO)) for x in PATH_TO_REPO.glob("**/*.md")] test_files_to_run = py_files + md_files # change to use "/" as path separator test_files_to_run = ["/".join(Path(x).parts) for x in test_files_to_run] # don't run doctest for files in `src/transformers/models/deprecated` test_files_to_run = [x for x in test_files_to_run if "models/deprecated" not in x] # only include files in `src` or `docs/source/en/` test_files_to_run = [x for x in test_files_to_run if x.startswith(("src/", "docs/source/en/"))] # not include init files test_files_to_run = [x for x in test_files_to_run if not x.endswith(("__init__.py",))] # These are files not doctested yet. with open("utils/not_doctested.txt") as fp: not_doctested = {x.split(" ")[0] for x in fp.read().strip().split("\n")} # So far we don't have 100% coverage for doctest. This line will be removed once we achieve 100%. test_files_to_run = [x for x in test_files_to_run if x not in not_doctested] return sorted(test_files_to_run) def get_new_doctest_files(repo, base_commit, branching_commit) -> List[str]: """ Get the list of files that were removed from "utils/not_doctested.txt", between `base_commit` and `branching_commit`. Returns: `List[str]`: List of files that were removed from "utils/not_doctested.txt". """ for diff_obj in branching_commit.diff(base_commit): # Ignores all but the "utils/not_doctested.txt" file. if diff_obj.a_path != "utils/not_doctested.txt": continue # Loads the two versions folder = Path(repo.working_dir) with checkout_commit(repo, branching_commit): with open(folder / "utils/not_doctested.txt", "r", encoding="utf-8") as f: old_content = f.read() with open(folder / "utils/not_doctested.txt", "r", encoding="utf-8") as f: new_content = f.read() # Compute the removed lines and return them removed_content = {x.split(" ")[0] for x in old_content.split("\n")} - { x.split(" ")[0] for x in new_content.split("\n") } return sorted(removed_content) return [] def get_doctest_files(diff_with_last_commit: bool = False) -> List[str]: """ Return a list of python and Markdown files where doc example have been modified between: - the current head and the main branch if `diff_with_last_commit=False` (default) - the current head and its parent commit otherwise. Returns: `List[str]`: The list of Python and Markdown files with a diff (files added or renamed are always returned, files modified are returned if the diff in the file is only in doctest examples). """ repo = Repo(PATH_TO_REPO) test_files_to_run = [] # noqa if not diff_with_last_commit: print(f"main is at {repo.refs.main.commit}") print(f"Current head is at {repo.head.commit}") branching_commits = repo.merge_base(repo.refs.main, repo.head) for commit in branching_commits: print(f"Branching commit: {commit}") test_files_to_run = get_diff_for_doctesting(repo, repo.head.commit, branching_commits) else: print(f"main is at {repo.head.commit}") parent_commits = repo.head.commit.parents for commit in parent_commits: print(f"Parent commit: {commit}") test_files_to_run = get_diff_for_doctesting(repo, repo.head.commit, parent_commits) all_test_files_to_run = get_all_doctest_files() # Add to the test files to run any removed entry from "utils/not_doctested.txt". new_test_files = get_new_doctest_files(repo, repo.head.commit, repo.refs.main.commit) test_files_to_run = list(set(test_files_to_run + new_test_files)) # Do not run slow doctest tests on CircleCI with open("utils/slow_documentation_tests.txt") as fp: slow_documentation_tests = set(fp.read().strip().split("\n")) test_files_to_run = [ x for x in test_files_to_run if x in all_test_files_to_run and x not in slow_documentation_tests ] # Make sure we did not end up with a test file that was removed test_files_to_run = [f for f in test_files_to_run if (PATH_TO_REPO / f).exists()] return sorted(test_files_to_run) # (:?^|\n) -> Non-catching group for the beginning of the doc or a new line. # \s*from\s+(\.+\S+)\s+import\s+([^\n]+) -> Line only contains from .xxx import yyy and we catch .xxx and yyy # (?=\n) -> Look-ahead to a new line. We can't just put \n here or using find_all on this re will only catch every # other import. _re_single_line_relative_imports = re.compile(r"(?:^|\n)\s*from\s+(\.+\S+)\s+import\s+([^\n]+)(?=\n)") # (:?^|\n) -> Non-catching group for the beginning of the doc or a new line. # \s*from\s+(\.+\S+)\s+import\s+\(([^\)]+)\) -> Line continues with from .xxx import (yyy) and we catch .xxx and yyy # yyy will take multiple lines otherwise there wouldn't be parenthesis. _re_multi_line_relative_imports = re.compile(r"(?:^|\n)\s*from\s+(\.+\S+)\s+import\s+\(([^\)]+)\)") # (:?^|\n) -> Non-catching group for the beginning of the doc or a new line. # \s*from\s+transformers(\S*)\s+import\s+([^\n]+) -> Line only contains from transformers.xxx import yyy and we catch # .xxx and yyy # (?=\n) -> Look-ahead to a new line. We can't just put \n here or using find_all on this re will only catch every # other import. _re_single_line_direct_imports = re.compile(r"(?:^|\n)\s*from\s+transformers(\S*)\s+import\s+([^\n]+)(?=\n)") # (:?^|\n) -> Non-catching group for the beginning of the doc or a new line. # \s*from\s+transformers(\S*)\s+import\s+\(([^\)]+)\) -> Line continues with from transformers.xxx import (yyy) and we # catch .xxx and yyy. yyy will take multiple lines otherwise there wouldn't be parenthesis. _re_multi_line_direct_imports = re.compile(r"(?:^|\n)\s*from\s+transformers(\S*)\s+import\s+\(([^\)]+)\)") def extract_imports(module_fname: str, cache: Dict[str, List[str]] = None) -> List[str]: """ Get the imports a given module makes. Args: module_fname (`str`): The name of the file of the module where we want to look at the imports (given relative to the root of the repo). cache (Dictionary `str` to `List[str]`, *optional*): To speed up this function if it was previously called on `module_fname`, the cache of all previously computed results. Returns: `List[str]`: The list of module filenames imported in the input `module_fname` (a submodule we import from that is a subfolder will give its init file). """ if cache is not None and module_fname in cache: return cache[module_fname] with open(PATH_TO_REPO / module_fname, "r", encoding="utf-8") as f: content = f.read() # Filter out all docstrings to not get imports in code examples. As before we need to deactivate formatting to # keep this as escaped quotes and avoid this function failing on this file. splits = content.split('\"\"\"') # fmt: skip content = "".join(splits[::2]) module_parts = str(module_fname).split(os.path.sep) imported_modules = [] # Let's start with relative imports relative_imports = _re_single_line_relative_imports.findall(content) relative_imports = [ (mod, imp) for mod, imp in relative_imports if "# tests_ignore" not in imp and imp.strip() != "(" ] multiline_relative_imports = _re_multi_line_relative_imports.findall(content) relative_imports += [(mod, imp) for mod, imp in multiline_relative_imports if "# tests_ignore" not in imp] # We need to remove parts of the module name depending on the depth of the relative imports. for module, imports in relative_imports: level = 0 while module.startswith("."): module = module[1:] level += 1 if len(module) > 0: dep_parts = module_parts[: len(module_parts) - level] + module.split(".") else: dep_parts = module_parts[: len(module_parts) - level] imported_module = os.path.sep.join(dep_parts) imported_modules.append((imported_module, [imp.strip() for imp in imports.split(",")])) # Let's continue with direct imports direct_imports = _re_single_line_direct_imports.findall(content) direct_imports = [(mod, imp) for mod, imp in direct_imports if "# tests_ignore" not in imp and imp.strip() != "("] multiline_direct_imports = _re_multi_line_direct_imports.findall(content) direct_imports += [(mod, imp) for mod, imp in multiline_direct_imports if "# tests_ignore" not in imp] # We need to find the relative path of those imports. for module, imports in direct_imports: import_parts = module.split(".")[1:] # ignore the name of the repo since we add it below. dep_parts = ["src", "transformers"] + import_parts imported_module = os.path.sep.join(dep_parts) imported_modules.append((imported_module, [imp.strip() for imp in imports.split(",")])) result = [] # Double check we get proper modules (either a python file or a folder with an init). for module_file, imports in imported_modules: if (PATH_TO_REPO / f"{module_file}.py").is_file(): module_file = f"{module_file}.py" elif (PATH_TO_REPO / module_file).is_dir() and (PATH_TO_REPO / module_file / "__init__.py").is_file(): module_file = os.path.sep.join([module_file, "__init__.py"]) imports = [imp for imp in imports if len(imp) > 0 and re.match("^[A-Za-z0-9_]*$", imp)] if len(imports) > 0: result.append((module_file, imports)) if cache is not None: cache[module_fname] = result return result def get_module_dependencies(module_fname: str, cache: Dict[str, List[str]] = None) -> List[str]: """ Refines the result of `extract_imports` to remove subfolders and get a proper list of module filenames: if a file as an import `from utils import Foo, Bar`, with `utils` being a subfolder containing many files, this will traverse the `utils` init file to check where those dependencies come from: for instance the files utils/foo.py and utils/bar.py. Warning: This presupposes that all intermediate inits are properly built (with imports from the respective submodules) and work better if objects are defined in submodules and not the intermediate init (otherwise the intermediate init is added, and inits usually have a lot of dependencies). Args: module_fname (`str`): The name of the file of the module where we want to look at the imports (given relative to the root of the repo). cache (Dictionary `str` to `List[str]`, *optional*): To speed up this function if it was previously called on `module_fname`, the cache of all previously computed results. Returns: `List[str]`: The list of module filenames imported in the input `module_fname` (with submodule imports refined). """ dependencies = [] imported_modules = extract_imports(module_fname, cache=cache) # The while loop is to recursively traverse all inits we may encounter: we will add things as we go. while len(imported_modules) > 0: new_modules = [] for module, imports in imported_modules: # If we end up in an __init__ we are often not actually importing from this init (except in the case where # the object is fully defined in the __init__) if module.endswith("__init__.py"): # So we get the imports from that init then try to find where our objects come from. new_imported_modules = extract_imports(module, cache=cache) for new_module, new_imports in new_imported_modules: if any(i in new_imports for i in imports): if new_module not in dependencies: new_modules.append((new_module, [i for i in new_imports if i in imports])) imports = [i for i in imports if i not in new_imports] if len(imports) > 0: # If there are any objects lefts, they may be a submodule path_to_module = PATH_TO_REPO / module.replace("__init__.py", "") dependencies.extend( [ os.path.join(module.replace("__init__.py", ""), f"{i}.py") for i in imports if (path_to_module / f"{i}.py").is_file() ] ) imports = [i for i in imports if not (path_to_module / f"{i}.py").is_file()] if len(imports) > 0: # Then if there are still objects left, they are fully defined in the init, so we keep it as a # dependency. dependencies.append(module) else: dependencies.append(module) imported_modules = new_modules return dependencies def create_reverse_dependency_tree() -> List[Tuple[str, str]]: """ Create a list of all edges (a, b) which mean that modifying a impacts b with a going over all module and test files. """ cache = {} all_modules = list(PATH_TO_TRANFORMERS.glob("**/*.py")) + list(PATH_TO_TESTS.glob("**/*.py")) all_modules = [str(mod.relative_to(PATH_TO_REPO)) for mod in all_modules] edges = [(dep, mod) for mod in all_modules for dep in get_module_dependencies(mod, cache=cache)] return list(set(edges)) def get_tree_starting_at(module: str, edges: List[Tuple[str, str]]) -> List[Union[str, List[str]]]: """ Returns the tree starting at a given module following all edges. Args: module (`str`): The module that will be the root of the subtree we want. eges (`List[Tuple[str, str]]`): The list of all edges of the tree. Returns: `List[Union[str, List[str]]]`: The tree to print in the following format: [module, [list of edges starting at module], [list of edges starting at the preceding level], ...] """ vertices_seen = [module] new_edges = [edge for edge in edges if edge[0] == module and edge[1] != module and "__init__.py" not in edge[1]] tree = [module] while len(new_edges) > 0: tree.append(new_edges) final_vertices = list({edge[1] for edge in new_edges}) vertices_seen.extend(final_vertices) new_edges = [ edge for edge in edges if edge[0] in final_vertices and edge[1] not in vertices_seen and "__init__.py" not in edge[1] ] return tree def print_tree_deps_of(module, all_edges=None): """ Prints the tree of modules depending on a given module. Args: module (`str`): The module that will be the root of the subtree we want. all_eges (`List[Tuple[str, str]]`, *optional*): The list of all edges of the tree. Will be set to `create_reverse_dependency_tree()` if not passed. """ if all_edges is None: all_edges = create_reverse_dependency_tree() tree = get_tree_starting_at(module, all_edges) # The list of lines is a list of tuples (line_to_be_printed, module) # Keeping the modules lets us know where to insert each new lines in the list. lines = [(tree[0], tree[0])] for index in range(1, len(tree)): edges = tree[index] start_edges = {edge[0] for edge in edges} for start in start_edges: end_edges = {edge[1] for edge in edges if edge[0] == start} # We will insert all those edges just after the line showing start. pos = 0 while lines[pos][1] != start: pos += 1 lines = lines[: pos + 1] + [(" " * (2 * index) + end, end) for end in end_edges] + lines[pos + 1 :] for line in lines: # We don't print the refs that where just here to help build lines. print(line[0]) def init_test_examples_dependencies() -> Tuple[Dict[str, List[str]], List[str]]: """ The test examples do not import from the examples (which are just scripts, not modules) so we need som extra care initializing the dependency map, which is the goal of this function. It initializes the dependency map for example files by linking each example to the example test file for the example framework. Returns: `Tuple[Dict[str, List[str]], List[str]]`: A tuple with two elements: the initialized dependency map which is a dict test example file to list of example files potentially tested by that test file, and the list of all example files (to avoid recomputing it later). """ test_example_deps = {} all_examples = [] for framework in ["flax", "pytorch", "tensorflow"]: test_files = list((PATH_TO_EXAMPLES / framework).glob("test_*.py")) all_examples.extend(test_files) # Remove the files at the root of examples/framework since they are not proper examples (they are eith utils # or example test files). examples = [ f for f in (PATH_TO_EXAMPLES / framework).glob("**/*.py") if f.parent != PATH_TO_EXAMPLES / framework ] all_examples.extend(examples) for test_file in test_files: with open(test_file, "r", encoding="utf-8") as f: content = f.read() # Map all examples to the test files found in examples/framework. test_example_deps[str(test_file.relative_to(PATH_TO_REPO))] = [ str(e.relative_to(PATH_TO_REPO)) for e in examples if e.name in content ] # Also map the test files to themselves. test_example_deps[str(test_file.relative_to(PATH_TO_REPO))].append( str(test_file.relative_to(PATH_TO_REPO)) ) return test_example_deps, all_examples def create_reverse_dependency_map() -> Dict[str, List[str]]: """ Create the dependency map from module/test filename to the list of modules/tests that depend on it recursively. Returns: `Dict[str, List[str]]`: The reverse dependency map as a dictionary mapping filenames to all the filenames depending on it recursively. This way the tests impacted by a change in file A are the test files in the list corresponding to key A in this result. """ cache = {} # Start from the example deps init. example_deps, examples = init_test_examples_dependencies() # Add all modules and all tests to all examples all_modules = list(PATH_TO_TRANFORMERS.glob("**/*.py")) + list(PATH_TO_TESTS.glob("**/*.py")) + examples all_modules = [str(mod.relative_to(PATH_TO_REPO)) for mod in all_modules] # Compute the direct dependencies of all modules. direct_deps = {m: get_module_dependencies(m, cache=cache) for m in all_modules} direct_deps.update(example_deps) # This recurses the dependencies something_changed = True while something_changed: something_changed = False for m in all_modules: for d in direct_deps[m]: # We stop recursing at an init (cause we always end up in the main init and we don't want to add all # files which the main init imports) if d.endswith("__init__.py"): continue if d not in direct_deps: raise ValueError(f"KeyError:{d}. From {m}") new_deps = set(direct_deps[d]) - set(direct_deps[m]) if len(new_deps) > 0: direct_deps[m].extend(list(new_deps)) something_changed = True # Finally we can build the reverse map. reverse_map = collections.defaultdict(list) for m in all_modules: for d in direct_deps[m]: reverse_map[d].append(m) # For inits, we don't do the reverse deps but the direct deps: if modifying an init, we want to make sure we test # all the modules impacted by that init. for m in [f for f in all_modules if f.endswith("__init__.py")]: direct_deps = get_module_dependencies(m, cache=cache) deps = sum([reverse_map[d] for d in direct_deps if not d.endswith("__init__.py")], direct_deps) reverse_map[m] = list(set(deps) - {m}) return reverse_map def create_module_to_test_map( reverse_map: Dict[str, List[str]] = None, filter_models: bool = False ) -> Dict[str, List[str]]: """ Extract the tests from the reverse_dependency_map and potentially filters the model tests. Args: reverse_map (`Dict[str, List[str]]`, *optional*): The reverse dependency map as created by `create_reverse_dependency_map`. Will default to the result of that function if not provided. filter_models (`bool`, *optional*, defaults to `False`): Whether or not to filter model tests to only include core models if a file impacts a lot of models. Returns: `Dict[str, List[str]]`: A dictionary that maps each file to the tests to execute if that file was modified. """ if reverse_map is None: reverse_map = create_reverse_dependency_map() # Utility that tells us if a given file is a test (taking test examples into account) def is_test(fname): if fname.startswith("tests"): return True if fname.startswith("examples") and fname.split(os.path.sep)[-1].startswith("test"): return True return False # Build the test map test_map = {module: [f for f in deps if is_test(f)] for module, deps in reverse_map.items()} if not filter_models: return test_map # Now we deal with the filtering if `filter_models` is True. num_model_tests = len(list(PATH_TO_TESTS.glob("models/*"))) def has_many_models(tests): # We filter to core models when a given file impacts more than half the model tests. model_tests = {Path(t).parts[2] for t in tests if t.startswith("tests/models/")} return len(model_tests) > num_model_tests // 2 # for each module (if specified in the argument `module`) of the form `models/my_model` (i.e. starting with it), # we always keep the tests (those are already in the argument `tests`) which are in `tests/models/my_model`. # This is to avoid them being excluded when a module has many impacted tests: the directly related test files should # always be included! def filter_tests(tests, module=""): return [ t for t in tests if not t.startswith("tests/models/") or Path(t).parts[2] in IMPORTANT_MODELS # at this point, `t` is of the form `tests/models/my_model`, and we check if `models/my_model` # (i.e. `parts[1:3]`) is in `module`. or "/".join(Path(t).parts[1:3]) in module ] return { module: (filter_tests(tests, module=module) if has_many_models(tests) else tests) for module, tests in test_map.items() } def check_imports_all_exist(): """ Isn't used per se by the test fetcher but might be used later as a quality check. Putting this here for now so the code is not lost. This checks all imports in a given file do exist. """ cache = {} all_modules = list(PATH_TO_TRANFORMERS.glob("**/*.py")) + list(PATH_TO_TESTS.glob("**/*.py")) all_modules = [str(mod.relative_to(PATH_TO_REPO)) for mod in all_modules] direct_deps = {m: get_module_dependencies(m, cache=cache) for m in all_modules} for module, deps in direct_deps.items(): for dep in deps: if not (PATH_TO_REPO / dep).is_file(): print(f"{module} has dependency on {dep} which does not exist.") def _print_list(l) -> str: """ Pretty print a list of elements with one line per element and a - starting each line. """ return "\n".join([f"- {f}" for f in l]) def create_json_map(test_files_to_run: List[str], json_output_file: str): """ Creates a map from a list of tests to run to easily split them by category, when running parallelism of slow tests. Args: test_files_to_run (`List[str]`): The list of tests to run. json_output_file (`str`): The path where to store the built json map. """ if json_output_file is None: return test_map = {} for test_file in test_files_to_run: # `test_file` is a path to a test folder/file, starting with `tests/`. For example, # - `tests/models/bert/test_modeling_bert.py` or `tests/models/bert` # - `tests/trainer/test_trainer.py` or `tests/trainer` # - `tests/test_modeling_common.py` names = test_file.split(os.path.sep) if names[1] == "models": # take the part like `models/bert` for modeling tests key = os.path.sep.join(names[1:3]) elif len(names) > 2 or not test_file.endswith(".py"): # test folders under `tests` or python files under them # take the part like tokenization, `pipeline`, etc. for other test categories key = os.path.sep.join(names[1:2]) else: # common test files directly under `tests/` key = "common" if key not in test_map: test_map[key] = [] test_map[key].append(test_file) # sort the keys & values keys = sorted(test_map.keys()) test_map = {k: " ".join(sorted(test_map[k])) for k in keys} with open(json_output_file, "w", encoding="UTF-8") as fp: json.dump(test_map, fp, ensure_ascii=False) def infer_tests_to_run( output_file: str, diff_with_last_commit: bool = False, filter_models: bool = True, json_output_file: Optional[str] = None, ): """ The main function called by the test fetcher. Determines the tests to run from the diff. Args: output_file (`str`): The path where to store the summary of the test fetcher analysis. Other files will be stored in the same folder: - examples_test_list.txt: The list of examples tests to run. - test_repo_utils.txt: Will indicate if the repo utils tests should be run or not. - doctest_list.txt: The list of doctests to run. diff_with_last_commit (`bool`, *optional*, defaults to `False`): Whether to analyze the diff with the last commit (for use on the main branch after a PR is merged) or with the branching point from main (for use on each PR). filter_models (`bool`, *optional*, defaults to `True`): Whether or not to filter the tests to core models only, when a file modified results in a lot of model tests. json_output_file (`str`, *optional*): The path where to store the json file mapping categories of tests to tests to run (used for parallelism or the slow tests). """ modified_files = get_modified_python_files(diff_with_last_commit=diff_with_last_commit) print(f"\n### MODIFIED FILES ###\n{_print_list(modified_files)}") # Create the map that will give us all impacted modules. reverse_map = create_reverse_dependency_map() impacted_files = modified_files.copy() for f in modified_files: if f in reverse_map: impacted_files.extend(reverse_map[f]) # Remove duplicates impacted_files = sorted(set(impacted_files)) print(f"\n### IMPACTED FILES ###\n{_print_list(impacted_files)}") model_impacted = {"/".join(x.split("/")[:3]) for x in impacted_files if x.startswith("tests/models/")} # Grab the corresponding test files: if any(x in modified_files for x in ["setup.py", ".circleci/create_circleci_config.py"]): test_files_to_run = ["tests", "examples"] repo_utils_launch = True elif not filter_models and len(model_impacted) >= NUM_MODELS_TO_TRIGGER_FULL_CI: print( f"More than {NUM_MODELS_TO_TRIGGER_FULL_CI - 1} models are impacted and `filter_models=False`. CI is configured to test everything." ) test_files_to_run = ["tests", "examples"] repo_utils_launch = True else: # All modified tests need to be run. test_files_to_run = [ f for f in modified_files if f.startswith("tests") and f.split(os.path.sep)[-1].startswith("test") ] impacted_files = get_impacted_files_from_tiny_model_summary(diff_with_last_commit=diff_with_last_commit) # Then we grab the corresponding test files. test_map = create_module_to_test_map(reverse_map=reverse_map, filter_models=filter_models) for f in modified_files + impacted_files: if f in test_map: test_files_to_run.extend(test_map[f]) test_files_to_run = sorted(set(test_files_to_run)) # Remove repo utils tests test_files_to_run = [f for f in test_files_to_run if not f.split(os.path.sep)[1] == "repo_utils"] # Remove SageMaker tests test_files_to_run = [f for f in test_files_to_run if not f.split(os.path.sep)[1] == "sagemaker"] # Make sure we did not end up with a test file that was removed test_files_to_run = [f for f in test_files_to_run if (PATH_TO_REPO / f).exists()] repo_utils_launch = any(f.split(os.path.sep)[0] == "utils" for f in modified_files) if repo_utils_launch: repo_util_file = Path(output_file).parent / "test_repo_utils.txt" with open(repo_util_file, "w", encoding="utf-8") as f: f.write("tests/repo_utils") examples_tests_to_run = [f for f in test_files_to_run if f.startswith("examples")] test_files_to_run = [f for f in test_files_to_run if not f.startswith("examples")] print(f"\n### TEST TO RUN ###\n{_print_list(test_files_to_run)}") if len(test_files_to_run) > 0: with open(output_file, "w", encoding="utf-8") as f: f.write(" ".join(test_files_to_run)) # Create a map that maps test categories to test files, i.e. `models/bert` -> [...test_modeling_bert.py, ...] # Get all test directories (and some common test files) under `tests` and `tests/models` if `test_files_to_run` # contains `tests` (i.e. when `setup.py` is changed). if "tests" in test_files_to_run: test_files_to_run = get_all_tests() create_json_map(test_files_to_run, json_output_file) print(f"\n### EXAMPLES TEST TO RUN ###\n{_print_list(examples_tests_to_run)}") if len(examples_tests_to_run) > 0: # We use `all` in the case `commit_flags["test_all"]` as well as in `create_circleci_config.py` for processing if examples_tests_to_run == ["examples"]: examples_tests_to_run = ["all"] example_file = Path(output_file).parent / "examples_test_list.txt" with open(example_file, "w", encoding="utf-8") as f: f.write(" ".join(examples_tests_to_run)) doctest_list = get_doctest_files() print(f"\n### DOCTEST TO RUN ###\n{_print_list(doctest_list)}") if len(doctest_list) > 0: doctest_file = Path(output_file).parent / "doctest_list.txt" with open(doctest_file, "w", encoding="utf-8") as f: f.write(" ".join(doctest_list)) def filter_tests(output_file: str, filters: List[str]): """ Reads the content of the output file and filters out all the tests in a list of given folders. Args: output_file (`str` or `os.PathLike`): The path to the output file of the tests fetcher. filters (`List[str]`): A list of folders to filter. """ if not os.path.isfile(output_file): print("No test file found.") return with open(output_file, "r", encoding="utf-8") as f: test_files = f.read().split(" ") if len(test_files) == 0 or test_files == [""]: print("No tests to filter.") return if test_files == ["tests"]: test_files = [os.path.join("tests", f) for f in os.listdir("tests") if f not in ["__init__.py"] + filters] else: test_files = [f for f in test_files if f.split(os.path.sep)[1] not in filters] with open(output_file, "w", encoding="utf-8") as f: f.write(" ".join(test_files)) def parse_commit_message(commit_message: str) -> Dict[str, bool]: """ Parses the commit message to detect if a command is there to skip, force all or part of the CI. Args: commit_message (`str`): The commit message of the current commit. Returns: `Dict[str, bool]`: A dictionary of strings to bools with keys the following keys: `"skip"`, `"test_all_models"` and `"test_all"`. """ if commit_message is None: return {"skip": False, "no_filter": False, "test_all": False} command_search = re.search(r"\[([^\]]*)\]", commit_message) if command_search is not None: command = command_search.groups()[0] command = command.lower().replace("-", " ").replace("_", " ") skip = command in ["ci skip", "skip ci", "circleci skip", "skip circleci"] no_filter = set(command.split(" ")) == {"no", "filter"} test_all = set(command.split(" ")) == {"test", "all"} return {"skip": skip, "no_filter": no_filter, "test_all": test_all} else: return {"skip": False, "no_filter": False, "test_all": False} if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--output_file", type=str, default="test_list.txt", help="Where to store the list of tests to run" ) parser.add_argument( "--json_output_file", type=str, default="test_map.json", help="Where to store the tests to run in a dictionary format mapping test categories to test files", ) parser.add_argument( "--diff_with_last_commit", action="store_true", help="To fetch the tests between the current commit and the last commit", ) parser.add_argument( "--filter_tests", action="store_true", help="Will filter the pipeline/repo utils tests outside of the generated list of tests.", ) parser.add_argument( "--print_dependencies_of", type=str, help="Will only print the tree of modules depending on the file passed.", default=None, ) parser.add_argument( "--commit_message", type=str, help="The commit message (which could contain a command to force all tests or skip the CI).", default=None, ) args = parser.parse_args() if args.print_dependencies_of is not None: print_tree_deps_of(args.print_dependencies_of) elif args.filter_tests: filter_tests(args.output_file, ["pipelines", "repo_utils"]) else: repo = Repo(PATH_TO_REPO) commit_message = repo.head.commit.message commit_flags = parse_commit_message(commit_message) if commit_flags["skip"]: print("Force-skipping the CI") quit() if commit_flags["no_filter"]: print("Running all tests fetched without filtering.") if commit_flags["test_all"]: print("Force-launching all tests") is_main_branch = not repo.head.is_detached and repo.head.ref == repo.refs.main diff_with_last_commit = args.diff_with_last_commit if not diff_with_last_commit and is_main_branch: print("main branch detected, fetching tests against last commit.") diff_with_last_commit = True if not commit_flags["test_all"]: try: infer_tests_to_run( args.output_file, diff_with_last_commit=diff_with_last_commit, json_output_file=args.json_output_file, filter_models=(not (commit_flags["no_filter"] or is_main_branch)), ) filter_tests(args.output_file, ["repo_utils"]) except Exception as e: print(f"\nError when trying to grab the relevant tests: {e}\n\nRunning all tests.") commit_flags["test_all"] = True if commit_flags["test_all"]: with open(args.output_file, "w", encoding="utf-8") as f: f.write("tests") example_file = Path(args.output_file).parent / "examples_test_list.txt" with open(example_file, "w", encoding="utf-8") as f: f.write("all") test_files_to_run = get_all_tests() create_json_map(test_files_to_run, args.json_output_file)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/custom_init_isort.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # 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. """ Utility that sorts the imports in the custom inits of Transformers. Transformers uses init files that delay the import of an object to when it's actually needed. This is to avoid the main init importing all models, which would make the line `import transformers` very slow when the user has all optional dependencies installed. The inits with delayed imports have two halves: one definining a dictionary `_import_structure` which maps modules to the name of the objects in each module, and one in `TYPE_CHECKING` which looks like a normal init for type-checkers. `isort` or `ruff` properly sort the second half which looks like traditionl imports, the goal of this script is to sort the first half. Use from the root of the repo with: ```bash python utils/custom_init_isort.py ``` which will auto-sort the imports (used in `make style`). For a check only (as used in `make quality`) run: ```bash python utils/custom_init_isort.py --check_only ``` """ import argparse import os import re from typing import Any, Callable, List, Optional # Path is defined with the intent you should run this script from the root of the repo. PATH_TO_TRANSFORMERS = "src/transformers" # Pattern that looks at the indentation in a line. _re_indent = re.compile(r"^(\s*)\S") # Pattern that matches `"key":" and puts `key` in group 0. _re_direct_key = re.compile(r'^\s*"([^"]+)":') # Pattern that matches `_import_structure["key"]` and puts `key` in group 0. _re_indirect_key = re.compile(r'^\s*_import_structure\["([^"]+)"\]') # Pattern that matches `"key",` and puts `key` in group 0. _re_strip_line = re.compile(r'^\s*"([^"]+)",\s*$') # Pattern that matches any `[stuff]` and puts `stuff` in group 0. _re_bracket_content = re.compile(r"\[([^\]]+)\]") def get_indent(line: str) -> str: """Returns the indent in given line (as string).""" search = _re_indent.search(line) return "" if search is None else search.groups()[0] def split_code_in_indented_blocks( code: str, indent_level: str = "", start_prompt: Optional[str] = None, end_prompt: Optional[str] = None ) -> List[str]: """ Split some code into its indented blocks, starting at a given level. Args: code (`str`): The code to split. indent_level (`str`): The indent level (as string) to use for identifying the blocks to split. start_prompt (`str`, *optional*): If provided, only starts splitting at the line where this text is. end_prompt (`str`, *optional*): If provided, stops splitting at a line where this text is. Warning: The text before `start_prompt` or after `end_prompt` (if provided) is not ignored, just not split. The input `code` can thus be retrieved by joining the result. Returns: `List[str]`: The list of blocks. """ # Let's split the code into lines and move to start_index. index = 0 lines = code.split("\n") if start_prompt is not None: while not lines[index].startswith(start_prompt): index += 1 blocks = ["\n".join(lines[:index])] else: blocks = [] # This variable contains the block treated at a given time. current_block = [lines[index]] index += 1 # We split into blocks until we get to the `end_prompt` (or the end of the file). while index < len(lines) and (end_prompt is None or not lines[index].startswith(end_prompt)): # We have a non-empty line with the proper indent -> start of a new block if len(lines[index]) > 0 and get_indent(lines[index]) == indent_level: # Store the current block in the result and rest. There are two cases: the line is part of the block (like # a closing parenthesis) or not. if len(current_block) > 0 and get_indent(current_block[-1]).startswith(indent_level + " "): # Line is part of the current block current_block.append(lines[index]) blocks.append("\n".join(current_block)) if index < len(lines) - 1: current_block = [lines[index + 1]] index += 1 else: current_block = [] else: # Line is not part of the current block blocks.append("\n".join(current_block)) current_block = [lines[index]] else: # Just add the line to the current block current_block.append(lines[index]) index += 1 # Adds current block if it's nonempty. if len(current_block) > 0: blocks.append("\n".join(current_block)) # Add final block after end_prompt if provided. if end_prompt is not None and index < len(lines): blocks.append("\n".join(lines[index:])) return blocks def ignore_underscore_and_lowercase(key: Callable[[Any], str]) -> Callable[[Any], str]: """ Wraps a key function (as used in a sort) to lowercase and ignore underscores. """ def _inner(x): return key(x).lower().replace("_", "") return _inner def sort_objects(objects: List[Any], key: Optional[Callable[[Any], str]] = None) -> List[Any]: """ Sort a list of objects following the rules of isort (all uppercased first, camel-cased second and lower-cased last). Args: objects (`List[Any]`): The list of objects to sort. key (`Callable[[Any], str]`, *optional*): A function taking an object as input and returning a string, used to sort them by alphabetical order. If not provided, will default to noop (so a `key` must be provided if the `objects` are not of type string). Returns: `List[Any]`: The sorted list with the same elements as in the inputs """ # If no key is provided, we use a noop. def noop(x): return x if key is None: key = noop # Constants are all uppercase, they go first. constants = [obj for obj in objects if key(obj).isupper()] # Classes are not all uppercase but start with a capital, they go second. classes = [obj for obj in objects if key(obj)[0].isupper() and not key(obj).isupper()] # Functions begin with a lowercase, they go last. functions = [obj for obj in objects if not key(obj)[0].isupper()] # Then we sort each group. key1 = ignore_underscore_and_lowercase(key) return sorted(constants, key=key1) + sorted(classes, key=key1) + sorted(functions, key=key1) def sort_objects_in_import(import_statement: str) -> str: """ Sorts the imports in a single import statement. Args: import_statement (`str`): The import statement in which to sort the imports. Returns: `str`: The same as the input, but with objects properly sorted. """ # This inner function sort imports between [ ]. def _replace(match): imports = match.groups()[0] # If there is one import only, nothing to do. if "," not in imports: return f"[{imports}]" keys = [part.strip().replace('"', "") for part in imports.split(",")] # We will have a final empty element if the line finished with a comma. if len(keys[-1]) == 0: keys = keys[:-1] return "[" + ", ".join([f'"{k}"' for k in sort_objects(keys)]) + "]" lines = import_statement.split("\n") if len(lines) > 3: # Here we have to sort internal imports that are on several lines (one per name): # key: [ # "object1", # "object2", # ... # ] # We may have to ignore one or two lines on each side. idx = 2 if lines[1].strip() == "[" else 1 keys_to_sort = [(i, _re_strip_line.search(line).groups()[0]) for i, line in enumerate(lines[idx:-idx])] sorted_indices = sort_objects(keys_to_sort, key=lambda x: x[1]) sorted_lines = [lines[x[0] + idx] for x in sorted_indices] return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]) elif len(lines) == 3: # Here we have to sort internal imports that are on one separate line: # key: [ # "object1", "object2", ... # ] if _re_bracket_content.search(lines[1]) is not None: lines[1] = _re_bracket_content.sub(_replace, lines[1]) else: keys = [part.strip().replace('"', "") for part in lines[1].split(",")] # We will have a final empty element if the line finished with a comma. if len(keys[-1]) == 0: keys = keys[:-1] lines[1] = get_indent(lines[1]) + ", ".join([f'"{k}"' for k in sort_objects(keys)]) return "\n".join(lines) else: # Finally we have to deal with imports fitting on one line import_statement = _re_bracket_content.sub(_replace, import_statement) return import_statement def sort_imports(file: str, check_only: bool = True): """ Sort the imports defined in the `_import_structure` of a given init. Args: file (`str`): The path to the init to check/fix. check_only (`bool`, *optional*, defaults to `True`): Whether or not to just check (and not auto-fix) the init. """ with open(file, encoding="utf-8") as f: code = f.read() # If the file is not a custom init, there is nothing to do. if "_import_structure" not in code: return # Blocks of indent level 0 main_blocks = split_code_in_indented_blocks( code, start_prompt="_import_structure = {", end_prompt="if TYPE_CHECKING:" ) # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt). for block_idx in range(1, len(main_blocks) - 1): # Check if the block contains some `_import_structure`s thingy to sort. block = main_blocks[block_idx] block_lines = block.split("\n") # Get to the start of the imports. line_idx = 0 while line_idx < len(block_lines) and "_import_structure" not in block_lines[line_idx]: # Skip dummy import blocks if "import dummy" in block_lines[line_idx]: line_idx = len(block_lines) else: line_idx += 1 if line_idx >= len(block_lines): continue # Ignore beginning and last line: they don't contain anything. internal_block_code = "\n".join(block_lines[line_idx:-1]) indent = get_indent(block_lines[1]) # Slit the internal block into blocks of indent level 1. internal_blocks = split_code_in_indented_blocks(internal_block_code, indent_level=indent) # We have two categories of import key: list or _import_structure[key].append/extend pattern = _re_direct_key if "_import_structure = {" in block_lines[0] else _re_indirect_key # Grab the keys, but there is a trap: some lines are empty or just comments. keys = [(pattern.search(b).groups()[0] if pattern.search(b) is not None else None) for b in internal_blocks] # We only sort the lines with a key. keys_to_sort = [(i, key) for i, key in enumerate(keys) if key is not None] sorted_indices = [x[0] for x in sorted(keys_to_sort, key=lambda x: x[1])] # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest. count = 0 reorderded_blocks = [] for i in range(len(internal_blocks)): if keys[i] is None: reorderded_blocks.append(internal_blocks[i]) else: block = sort_objects_in_import(internal_blocks[sorted_indices[count]]) reorderded_blocks.append(block) count += 1 # And we put our main block back together with its first and last line. main_blocks[block_idx] = "\n".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]) if code != "\n".join(main_blocks): if check_only: return True else: print(f"Overwriting {file}.") with open(file, "w", encoding="utf-8") as f: f.write("\n".join(main_blocks)) def sort_imports_in_all_inits(check_only=True): """ Sort the imports defined in the `_import_structure` of all inits in the repo. Args: check_only (`bool`, *optional*, defaults to `True`): Whether or not to just check (and not auto-fix) the init. """ failures = [] for root, _, files in os.walk(PATH_TO_TRANSFORMERS): if "__init__.py" in files: result = sort_imports(os.path.join(root, "__init__.py"), check_only=check_only) if result: failures = [os.path.join(root, "__init__.py")] if len(failures) > 0: raise ValueError(f"Would overwrite {len(failures)} files, run `make style`.") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.") args = parser.parse_args() sort_imports_in_all_inits(check_only=args.check_only)

mavonic_private_repos/transformers

mavonic_private_repos/transformers/utils/models_to_deprecate.py

# Copyright 2024 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. """ Script to find a candidate list of models to deprecate based on the number of downloads and the date of the last commit. """ import argparse import glob import json import os from collections import defaultdict from datetime import datetime, timezone from pathlib import Path from git import Repo from huggingface_hub import HfApi api = HfApi() PATH_TO_REPO = Path(__file__).parent.parent.resolve() repo = Repo(PATH_TO_REPO) class HubModelLister: """ Utility for getting models from the hub based on tags. Handles errors without crashing the script. """ def __init__(self, tags): self.tags = tags self.model_list = api.list_models(tags=tags) def __iter__(self): try: yield from self.model_list except Exception as e: print(f"Error: {e}") return def _extract_commit_hash(commits): for commit in commits: if commit.startswith("commit "): return commit.split(" ")[1] return "" def get_list_of_repo_model_paths(models_dir): # Get list of all models in the library models = glob.glob(os.path.join(models_dir, "*/modeling_*.py")) # Remove flax and tf models models = [model for model in models if "_flax_" not in model] models = [model for model in models if "_tf_" not in model] # Get list of all deprecated models in the library deprecated_models = glob.glob(os.path.join(models_dir, "deprecated", "*")) # For each deprecated model, remove the deprecated models from the list of all models as well as the symlink path for deprecated_model in deprecated_models: deprecated_model_name = "/" + deprecated_model.split("/")[-1] + "/" models = [model for model in models if deprecated_model_name not in model] # Remove deprecated models models = [model for model in models if "/deprecated" not in model] # Remove auto models = [model for model in models if "/auto/" not in model] return models def get_list_of_models_to_deprecate( thresh_num_downloads=5_000, thresh_date=None, use_cache=False, save_model_info=False, max_num_models=-1, ): if thresh_date is None: thresh_date = datetime.now(timezone.utc).replace(year=datetime.now(timezone.utc).year - 1) else: thresh_date = datetime.strptime(thresh_date, "%Y-%m-%d").replace(tzinfo=timezone.utc) models_dir = PATH_TO_REPO / "src/transformers/models" model_paths = get_list_of_repo_model_paths(models_dir=models_dir) if use_cache and os.path.exists("models_info.json"): with open("models_info.json", "r") as f: models_info = json.load(f) # Convert datetimes back to datetime objects for model, info in models_info.items(): info["first_commit_datetime"] = datetime.fromisoformat(info["first_commit_datetime"]) else: # Build a dictionary of model info: first commit datetime, commit hash, model path models_info = defaultdict(dict) for model_path in model_paths: model = model_path.split("/")[-2] if model in models_info: continue commits = repo.git.log("--diff-filter=A", "--", model_path).split("\n") commit_hash = _extract_commit_hash(commits) commit_obj = repo.commit(commit_hash) committed_datetime = commit_obj.committed_datetime models_info[model]["commit_hash"] = commit_hash models_info[model]["first_commit_datetime"] = committed_datetime models_info[model]["model_path"] = model_path models_info[model]["downloads"] = 0 # Some tags on the hub are formatted differently than in the library tags = [model] if "_" in model: tags.append(model.replace("_", "-")) models_info[model]["tags"] = tags # Filter out models which were added less than a year ago models_info = { model: info for model, info in models_info.items() if info["first_commit_datetime"] < thresh_date } # We make successive calls to the hub, filtering based on the model tags n_seen = 0 for model, model_info in models_info.items(): for model_tag in model_info["tags"]: model_list = HubModelLister(tags=model_tag) for i, hub_model in enumerate(model_list): n_seen += 1 if i % 100 == 0: print(f"Processing model {i} for tag {model_tag}") if max_num_models != -1 and i > n_seen: break if hub_model.private: continue model_info["downloads"] += hub_model.downloads if save_model_info and not (use_cache and os.path.exists("models_info.json")): # Make datetimes serializable for model, info in models_info.items(): info["first_commit_datetime"] = info["first_commit_datetime"].isoformat() with open("models_info.json", "w") as f: json.dump(models_info, f, indent=4) print("\nModels to deprecate:") n_models_to_deprecate = 0 models_to_deprecate = {} for model, info in models_info.items(): n_downloads = info["downloads"] if n_downloads < thresh_num_downloads: n_models_to_deprecate += 1 models_to_deprecate[model] = info print(f"\nModel: {model}") print(f"Downloads: {n_downloads}") print(f"Date: {info['first_commit_datetime']}") print(f"\nNumber of models to deprecate: {n_models_to_deprecate}") print("Before deprecating make sure to verify the models, including if they're used as a module in other models.") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--save_model_info", action="store_true", help="Save the retrieved model info to a json file.") parser.add_argument( "--use_cache", action="store_true", help="Use the cached model info instead of calling the hub." ) parser.add_argument( "--thresh_num_downloads", type=int, default=5_000, help="Threshold number of downloads below which a model should be deprecated. Default is 5,000.", ) parser.add_argument( "--thresh_date", type=str, default=None, help="Date to consider the first commit from. Format: YYYY-MM-DD. If unset, defaults to one year ago from today.", ) parser.add_argument( "--max_num_models", type=int, default=-1, help="Maximum number of models to consider from the hub. -1 means all models. Useful for testing.", ) args = parser.parse_args() models_to_deprecate = get_list_of_models_to_deprecate( thresh_num_downloads=args.thresh_num_downloads, thresh_date=args.thresh_date, use_cache=args.use_cache, save_model_info=args.save_model_info, max_num_models=args.max_num_models, )

mavonic_private_repos/transformers/utils

mavonic_private_repos/transformers/utils/test_module/custom_pipeline.py

import numpy as np from transformers import Pipeline def softmax(outputs): maxes = np.max(outputs, axis=-1, keepdims=True) shifted_exp = np.exp(outputs - maxes) return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True) class PairClassificationPipeline(Pipeline): def _sanitize_parameters(self, **kwargs): preprocess_kwargs = {} if "second_text" in kwargs: preprocess_kwargs["second_text"] = kwargs["second_text"] return preprocess_kwargs, {}, {} def preprocess(self, text, second_text=None): return self.tokenizer(text, text_pair=second_text, return_tensors=self.framework) def _forward(self, model_inputs): return self.model(**model_inputs) def postprocess(self, model_outputs): logits = model_outputs.logits[0].numpy() probabilities = softmax(logits) best_class = np.argmax(probabilities) label = self.model.config.id2label[best_class] score = probabilities[best_class].item() logits = logits.tolist() return {"label": label, "score": score, "logits": logits}

mavonic_private_repos/transformers/utils

mavonic_private_repos/transformers/utils/test_module/custom_feature_extraction.py

from transformers import Wav2Vec2FeatureExtractor class CustomFeatureExtractor(Wav2Vec2FeatureExtractor): pass

mavonic_private_repos/transformers/utils

mavonic_private_repos/transformers/utils/test_module/custom_processing.py

from transformers import ProcessorMixin class CustomProcessor(ProcessorMixin): feature_extractor_class = "AutoFeatureExtractor" tokenizer_class = "AutoTokenizer"

mavonic_private_repos/transformers/utils

mavonic_private_repos/transformers/utils/test_module/custom_modeling.py

import torch from transformers import PreTrainedModel from .custom_configuration import CustomConfig, NoSuperInitConfig class CustomModel(PreTrainedModel): config_class = CustomConfig def __init__(self, config): super().__init__(config) self.linear = torch.nn.Linear(config.hidden_size, config.hidden_size) def forward(self, x): return self.linear(x) def _init_weights(self, module): pass class NoSuperInitModel(PreTrainedModel): config_class = NoSuperInitConfig def __init__(self, config): super().__init__(config) self.linear = torch.nn.Linear(config.attribute, config.attribute) def forward(self, x): return self.linear(x) def _init_weights(self, module): pass

mavonic_private_repos/transformers/utils

mavonic_private_repos/transformers/utils/test_module/custom_tokenization_fast.py

from transformers import BertTokenizerFast from .custom_tokenization import CustomTokenizer class CustomTokenizerFast(BertTokenizerFast): slow_tokenizer_class = CustomTokenizer pass

mavonic_private_repos/transformers/utils

mavonic_private_repos/transformers/utils/test_module/custom_image_processing.py

from transformers import CLIPImageProcessor class CustomImageProcessor(CLIPImageProcessor): pass

mavonic_private_repos/transformers/utils

mavonic_private_repos/transformers/utils/test_module/custom_tokenization.py

from transformers import BertTokenizer class CustomTokenizer(BertTokenizer): pass

mavonic_private_repos/transformers/utils

mavonic_private_repos/transformers/utils/test_module/custom_configuration.py

from transformers import PretrainedConfig class CustomConfig(PretrainedConfig): model_type = "custom" def __init__(self, attribute=1, **kwargs): self.attribute = attribute super().__init__(**kwargs) class NoSuperInitConfig(PretrainedConfig): model_type = "custom" def __init__(self, attribute=1, **kwargs): self.attribute = attribute

mavonic_private_repos/transformers/utils

mavonic_private_repos/transformers/utils/tf_ops/onnx.json

{ "opsets": { "1": [ "Abs", "Add", "AddV2", "ArgMax", "ArgMin", "AvgPool", "AvgPool3D", "BatchMatMul", "BatchMatMulV2", "BatchToSpaceND", "BiasAdd", "BiasAddV1", "Cast", "Ceil", "CheckNumerics", "ComplexAbs", "Concat", "ConcatV2", "Const", "ConstV2", "Conv1D", "Conv2D", "Conv2DBackpropInput", "Conv3D", "Conv3DBackpropInputV2", "DepthToSpace", "DepthwiseConv2d", "DepthwiseConv2dNative", "Div", "Dropout", "Elu", "Equal", "Erf", "Exp", "ExpandDims", "Flatten", "Floor", "Gather", "GatherNd", "GatherV2", "Greater", "Identity", "IdentityN", "If", "LRN", "LSTMBlockCell", "LeakyRelu", "Less", "Log", "LogSoftmax", "LogicalAnd", "LogicalNot", "LogicalOr", "LookupTableSizeV2", "MatMul", "Max", "MaxPool", "MaxPool3D", "MaxPoolV2", "Maximum", "Mean", "Min", "Minimum", "MirrorPad", "Mul", "Neg", "NoOp", "NotEqual", "OneHot", "Pack", "Pad", "PadV2", "Placeholder", "PlaceholderV2", "PlaceholderWithDefault", "Pow", "Prod", "RFFT", "RandomNormal", "RandomNormalLike", "RandomUniform", "RandomUniformLike", "RealDiv", "Reciprocal", "Relu", "Relu6", "Reshape", "Rsqrt", "Selu", "Shape", "Sigmoid", "Sign", "Size", "Slice", "Softmax", "Softplus", "Softsign", "SpaceToBatchND", "SpaceToDepth", "Split", "SplitV", "Sqrt", "Square", "SquaredDifference", "Squeeze", "StatelessIf", "StopGradient", "StridedSlice", "StringJoin", "Sub", "Sum", "Tanh", "Tile", "TopKV2", "Transpose", "TruncateDiv", "Unpack", "ZerosLike" ], "2": [], "3": [], "4": [], "5": [], "6": [ "AddN", "All", "Any", "FloorDiv", "FusedBatchNorm", "FusedBatchNormV2", "FusedBatchNormV3" ], "7": [ "Acos", "Asin", "Atan", "Cos", "Fill", "FloorMod", "GreaterEqual", "LessEqual", "Loop", "MatrixBandPart", "Multinomial", "Range", "ResizeBilinear", "ResizeNearestNeighbor", "Scan", "Select", "SelectV2", "Sin", "SoftmaxCrossEntropyWithLogits", "SparseSoftmaxCrossEntropyWithLogits", "StatelessWhile", "Tan", "TensorListFromTensor", "TensorListGetItem", "TensorListLength", "TensorListReserve", "TensorListResize", "TensorListSetItem", "TensorListStack", "While" ], "8": [ "BroadcastTo", "ClipByValue", "FIFOQueueV2", "HashTableV2", "IteratorGetNext", "IteratorV2", "LookupTableFindV2", "MaxPoolWithArgmax", "QueueDequeueManyV2", "QueueDequeueUpToV2", "QueueDequeueV2", "ReverseSequence" ], "9": [ "SegmentMax", "SegmentMean", "SegmentMin", "SegmentProd", "SegmentSum", "Sinh", "SparseSegmentMean", "SparseSegmentMeanWithNumSegments", "SparseSegmentSqrtN", "SparseSegmentSqrtNWithNumSegments", "SparseSegmentSum", "SparseSegmentSumWithNumSegments", "UnsortedSegmentMax", "UnsortedSegmentMin", "UnsortedSegmentProd", "UnsortedSegmentSum", "Where" ], "10": [ "CropAndResize", "CudnnRNN", "DynamicStitch", "FakeQuantWithMinMaxArgs", "IsFinite", "IsInf", "NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV4", "NonMaxSuppressionV5", "ParallelDynamicStitch", "ReverseV2", "Roll" ], "11": [ "Bincount", "Cumsum", "InvertPermutation", "LeftShift", "MatrixDeterminant", "MatrixDiagPart", "MatrixDiagPartV2", "MatrixDiagPartV3", "RaggedRange", "RightShift", "Round", "ScatterNd", "SparseFillEmptyRows", "SparseReshape", "SparseToDense", "TensorScatterUpdate", "Unique" ], "12": [ "Einsum", "MatrixDiag", "MatrixDiagV2", "MatrixDiagV3", "MatrixSetDiagV3", "SquaredDistance" ], "13": [] } }

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/quality.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y time git ENV VIRTUAL_ENV=/usr/local RUN pip install uv && uv venv RUN uv pip install --no-cache-dir -U pip setuptools GitPython transformers "ruff==0.1.5" urllib3 RUN apt-get install -y jq curl && apt-get clean && rm -rf /var/lib/apt/lists/*

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/pipeline-tf.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake g++ ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools RUN pip install --no-cache-dir "transformers[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]" RUN uv pip install --no-cache-dir "protobuf==3.20.3" tensorflow_probability RUN apt-get clean && rm -rf /var/lib/apt/lists/*

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/examples-tf.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git RUN apt-get install -y g++ cmake ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv RUN uv pip install --no-cache-dir -U pip setuptools albumentations seqeval RUN pip install --upgrade --no-cache-dir "transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]" RUN uv pip install --no-cache-dir "protobuf==3.20.3" RUN pip uninstall -y transformers RUN apt-get clean && rm -rf /var/lib/apt/lists/*

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/exotic-models.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 ARG REF=main USER root RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git libgl1-mesa-glx libgl1 g++ tesseract-ocr ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu RUN uv pip install --no-cache-dir --no-deps timm accelerate RUN pip install -U --upgrade-strategy eager --no-cache-dir pytesseract python-Levenshtein opencv-python nltk # RUN uv pip install --no-cache-dir natten==0.15.1+torch210cpu -f https://shi-labs.com/natten/wheels RUN pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[testing, vision]" 'scikit-learn' 'torch-stft' 'nose' 'dataset' # RUN git clone https://github.com/facebookresearch/detectron2.git # RUN python3 -m pip install --no-cache-dir -e detectron2 RUN pip install 'git+https://github.com/facebookresearch/detectron2.git@92ae9f0b92aba5867824b4f12aa06a22a60a45d3' RUN pip uninstall -y transformers RUN apt-get clean && rm -rf /var/lib/apt/lists/*

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/torch-light.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu RUN uv pip install --no-cache-dir librosa "transformers[sklearn,sentencepiece,vision,testing]" RUN pip uninstall -y transformers

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/consistency.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y time git pkg-config make git-lfs ENV VIRTUAL_ENV=/usr/local RUN pip install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools GitPython RUN uv pip install --no-cache-dir --upgrade 'torch' --index-url https://download.pytorch.org/whl/cpu RUN uv pip install --no-cache-dir tensorflow-cpu tf-keras RUN uv pip install --no-cache-dir "transformers[flax,quality,vision,testing]" RUN git lfs install RUN pip uninstall -y transformers RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/jax-light.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools RUN pip install --no-cache-dir "scipy<1.13" "transformers[flax,testing,sentencepiece,flax-speech,vision]" RUN pip uninstall -y transformers RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/pipeline-torch.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu RUN uv pip install --no-cache-dir librosa "transformers[sklearn,sentencepiece,vision,testing]" RUN pip uninstall -y transformers

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/torch-tf-light.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 ARG REF=main RUN echo ${REF} USER root RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools RUN uv pip install --no-cache-dir --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu RUN git lfs install RUN uv pip install --no-cache-dir pypi-kenlm RUN pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,sentencepiece,vision,testing]" RUN uv pip install --no-cache-dir "protobuf==3.20.3" librosa RUN pip uninstall -y transformers RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/torch-jax-light.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools RUN uv pip install --no-deps accelerate RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu RUN pip install --no-cache-dir "scipy<1.13" "transformers[flax, audio, sklearn,sentencepiece,vision,testing]" # RUN pip install --no-cache-dir "scipy<1.13" "transformers[flax,testing,sentencepiece,flax-speech,vision]" RUN pip uninstall -y transformers RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/custom-tokenizers.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake wget xz-utils build-essential g++5 libprotobuf-dev protobuf-compiler ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools RUN wget https://github.com/ku-nlp/jumanpp/releases/download/v2.0.0-rc3/jumanpp-2.0.0-rc3.tar.xz RUN tar xvf jumanpp-2.0.0-rc3.tar.xz RUN mkdir jumanpp-2.0.0-rc3/bld WORKDIR ./jumanpp-2.0.0-rc3/bld RUN wget -LO catch.hpp https://github.com/catchorg/Catch2/releases/download/v2.13.8/catch.hpp RUN mv catch.hpp ../libs/ RUN cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local RUN make install -j 10 RUN uv pip install --no-cache --upgrade 'torch' --index-url https://download.pytorch.org/whl/cpu RUN uv pip install --no-cache-dir --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu RUN uv pip install --no-cache-dir "transformers[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]" unidic unidic-lite # spacy is not used so not tested. Causes to failures. TODO fix later RUN python3 -m unidic download RUN pip uninstall -y transformers RUN apt-get clean && rm -rf /var/lib/apt/lists/* RUN apt remove -y g++ cmake xz-utils libprotobuf-dev protobuf-compiler

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/examples-torch.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu RUN uv pip install --no-cache-dir librosa "transformers[sklearn,sentencepiece,vision,testing]" seqeval albumentations jiwer RUN pip uninstall -y transformers RUN apt-get clean && rm -rf /var/lib/apt/lists/*

mavonic_private_repos/transformers

mavonic_private_repos/transformers/docker/tf-light.dockerfile

FROM python:3.10-slim ENV PYTHONDONTWRITEBYTECODE=1 USER root RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ pkg-config openssh-client git RUN apt-get install -y cmake ENV VIRTUAL_ENV=/usr/local RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools RUN pip install --upgrade --no-cache-dir "transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]" RUN uv pip install --no-cache-dir "protobuf==3.20.3" RUN pip uninstall -y transformers RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

mavonic_private_repos/transformers/docker

mavonic_private_repos/transformers/docker/transformers-pytorch-tpu/docker-entrypoint.sh

#!/bin/bash source ~/.bashrc echo "running docker-entrypoint.sh" conda activate container echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS echo "printed TPU info" export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}" exec "$@"#!/bin/bash

mavonic_private_repos/transformers/docker

mavonic_private_repos/transformers/docker/transformers-pytorch-tpu/dataset.yaml

apiVersion: v1 kind: PersistentVolume metadata: name: huggingface-cluster-disk spec: storageClassName: "" capacity: storage: 500Gi accessModes: - ReadOnlyMany claimRef: namespace: default name: huggingface-cluster-disk-claim gcePersistentDisk: pdName: huggingface-cluster-disk fsType: ext4 readOnly: true --- apiVersion: v1 kind: PersistentVolumeClaim metadata: name: huggingface-cluster-disk-claim spec: # Specify "" as the storageClassName so it matches the PersistentVolume's StorageClass. # A nil storageClassName value uses the default StorageClass. For details, see # https://kubernetes.io/docs/concepts/storage/persistent-volumes/#class-1 storageClassName: "" accessModes: - ReadOnlyMany resources: requests: storage: 1Ki

mavonic_private_repos/transformers/docker

mavonic_private_repos/transformers/docker/transformers-pytorch-tpu/bert-base-cased.jsonnet

local base = import 'templates/base.libsonnet'; local tpus = import 'templates/tpus.libsonnet'; local utils = import "templates/utils.libsonnet"; local volumes = import "templates/volumes.libsonnet"; local bertBaseCased = base.BaseTest { frameworkPrefix: "hf", modelName: "bert-base-cased", mode: "example", configMaps: [], timeout: 3600, # 1 hour, in seconds image: std.extVar('image'), imageTag: std.extVar('image-tag'), tpuSettings+: { softwareVersion: "pytorch-nightly", }, accelerator: tpus.v3_8, volumeMap+: { datasets: volumes.PersistentVolumeSpec { name: "huggingface-cluster-disk", mountPath: "/datasets", }, }, command: utils.scriptCommand( ||| python -m pytest -s transformers/examples/pytorch/test_xla_examples.py -v test_exit_code=$? echo "\nFinished running commands.\n" test $test_exit_code -eq 0 ||| ), }; bertBaseCased.oneshotJob

mavonic_private_repos/transformers/docker

mavonic_private_repos/transformers/docker/transformers-pytorch-tpu/Dockerfile

FROM google/cloud-sdk:slim # Build args. ARG GITHUB_REF=refs/heads/main # TODO: This Dockerfile installs pytorch/xla 3.6 wheels. There are also 3.7 # wheels available; see below. ENV PYTHON_VERSION=3.6 RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential \ cmake \ git \ curl \ ca-certificates # Install conda and python. # NOTE new Conda does not forward the exit status... https://github.com/conda/conda/issues/8385 RUN curl -o ~/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-4.7.12-Linux-x86_64.sh && \ chmod +x ~/miniconda.sh && \ ~/miniconda.sh -b && \ rm ~/miniconda.sh ENV PATH=/root/miniconda3/bin:$PATH RUN conda create -y --name container python=$PYTHON_VERSION # Run the rest of commands within the new conda env. # Use absolute path to appease Codefactor. SHELL ["/root/miniconda3/bin/conda", "run", "-n", "container", "/bin/bash", "-c"] RUN conda install -y python=$PYTHON_VERSION mkl RUN pip uninstall -y torch && \ # Python 3.7 wheels are available. Replace cp36-cp36m with cp37-cp37m gsutil cp 'gs://tpu-pytorch/wheels/torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \ gsutil cp 'gs://tpu-pytorch/wheels/torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \ gsutil cp 'gs://tpu-pytorch/wheels/torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \ pip install 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ pip install 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ pip install 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ rm 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ rm 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ rm 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ apt-get install -y libomp5 ENV LD_LIBRARY_PATH=root/miniconda3/envs/container/lib # Install huggingface/transformers at the current PR, plus dependencies. RUN git clone https://github.com/huggingface/transformers.git && \ cd transformers && \ git fetch origin $GITHUB_REF:CI && \ git checkout CI && \ cd .. && \ pip install ./transformers && \ pip install -r ./transformers/examples/pytorch/_test_requirements.txt && \ pip install pytest RUN python -c "import torch_xla; print(torch_xla.__version__)" RUN python -c "import transformers as trf; print(trf.__version__)" RUN conda init bash COPY docker-entrypoint.sh /usr/local/bin/ RUN chmod +x /usr/local/bin/docker-entrypoint.sh ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"] CMD ["bash"]

mavonic_private_repos/transformers/docker

mavonic_private_repos/transformers/docker/transformers-tensorflow-gpu/Dockerfile

FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04 LABEL maintainer="Hugging Face" ARG DEBIAN_FRONTEND=noninteractive RUN apt update RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg RUN python3 -m pip install --no-cache-dir --upgrade pip ARG REF=main RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-tensorflow,testing] # If set to nothing, will install the latest version ARG TENSORFLOW='2.13' RUN [ ${#TENSORFLOW} -gt 0 ] && VERSION='tensorflow=='$TENSORFLOW'.*' || VERSION='tensorflow'; python3 -m pip install --no-cache-dir -U $VERSION RUN python3 -m pip uninstall -y torch flax RUN python3 -m pip install -U "itsdangerous<2.1.0" RUN python3 -m pip install --no-cache-dir -U tensorflow_probability # When installing in editable mode, `transformers` is not recognized as a package. # this line must be added in order for python to be aware of transformers. RUN cd transformers && python3 setup.py develop

mavonic_private_repos/transformers/docker

mavonic_private_repos/transformers/docker/transformers-pytorch-gpu/Dockerfile

FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu20.04 LABEL maintainer="Hugging Face" ARG DEBIAN_FRONTEND=noninteractive RUN apt update RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg RUN python3 -m pip install --no-cache-dir --upgrade pip ARG REF=main RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF # If set to nothing, will install the latest version ARG PYTORCH='2.1.1' ARG TORCH_VISION='' ARG TORCH_AUDIO='' # Example: `cu102`, `cu113`, etc. ARG CUDA='cu121' RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' || VERSION='torch'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA RUN [ ${#TORCH_VISION} -gt 0 ] && VERSION='torchvision=='TORCH_VISION'.*' || VERSION='torchvision'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA RUN [ ${#TORCH_AUDIO} -gt 0 ] && VERSION='torchaudio=='TORCH_AUDIO'.*' || VERSION='torchaudio'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video] RUN python3 -m pip uninstall -y tensorflow flax RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract RUN python3 -m pip install -U "itsdangerous<2.1.0" # When installing in editable mode, `transformers` is not recognized as a package. # this line must be added in order for python to be aware of transformers. RUN cd transformers && python3 setup.py develop

mavonic_private_repos/transformers/docker

mavonic_private_repos/transformers/docker/transformers-doc-builder/Dockerfile

FROM python:3.10 LABEL maintainer="Hugging Face" RUN apt update RUN git clone https://github.com/huggingface/transformers RUN python3 -m pip install --no-cache-dir --upgrade pip && python3 -m pip install --no-cache-dir git+https://github.com/huggingface/doc-builder ./transformers[dev] RUN apt-get -y update && apt-get install -y libsndfile1-dev && apt install -y tesseract-ocr # Torch needs to be installed before deepspeed RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed] RUN python3 -m pip install --no-cache-dir torchvision git+https://github.com/facebookresearch/detectron2.git pytesseract RUN python3 -m pip install -U "itsdangerous<2.1.0" # Test if the image could successfully build the doc. before publishing the image RUN doc-builder build transformers transformers/docs/source/en --build_dir doc-build-dev --notebook_dir notebooks/transformers_doc --clean RUN rm -rf doc-build-dev

mavonic_private_repos/transformers/docker

mavonic_private_repos/transformers/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile

FROM rocm/dev-ubuntu-22.04:5.6 LABEL maintainer="Hugging Face" ARG DEBIAN_FRONTEND=noninteractive ARG PYTORCH='2.1.1' ARG TORCH_VISION='0.16.1' ARG TORCH_AUDIO='2.1.1' ARG ROCM='5.6' RUN apt update && \ apt install -y --no-install-recommends \ libaio-dev \ git \ # These are required to build deepspeed. python3-dev \ python-is-python3 \ rocrand-dev \ rocthrust-dev \ hipsparse-dev \ hipblas-dev \ rocblas-dev && \ apt clean && \ rm -rf /var/lib/apt/lists/* RUN python3 -m pip install --no-cache-dir --upgrade pip ninja "pydantic<2" RUN python3 -m pip uninstall -y apex torch torchvision torchaudio RUN python3 -m pip install torch==$PYTORCH torchvision==$TORCH_VISION torchaudio==$TORCH_AUDIO --index-url https://download.pytorch.org/whl/rocm$ROCM --no-cache-dir # Pre-build DeepSpeed, so it's be ready for testing (to avoid timeout) RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache-dir -v --disable-pip-version-check 2>&1 ARG REF=main WORKDIR / # Invalidate docker cache from here if new commit is available. ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN python3 -m pip install --no-cache-dir ./transformers[accelerate,testing,sentencepiece,sklearn] # When installing in editable mode, `transformers` is not recognized as a package. # this line must be added in order for python to be aware of transformers. RUN cd transformers && python3 setup.py develop RUN python3 -c "from deepspeed.launcher.runner import main" # Remove nvml as it is not compatible with ROCm RUN python3 -m pip uninstall py3nvml pynvml -y

mavonic_private_repos/transformers/docker

mavonic_private_repos/transformers/docker/transformers-pytorch-amd-gpu/Dockerfile

FROM rocm/dev-ubuntu-20.04:5.6 # rocm/pytorch has no version with 2.1.0 LABEL maintainer="Hugging Face" ARG DEBIAN_FRONTEND=noninteractive ARG PYTORCH='2.1.0' ARG TORCH_VISION='0.16.0' ARG TORCH_AUDIO='2.1.0' ARG ROCM='5.6' RUN apt update && \ apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip ffmpeg && \ apt clean && \ rm -rf /var/lib/apt/lists/* RUN python3 -m pip install --no-cache-dir --upgrade pip RUN python3 -m pip install torch==$PYTORCH torchvision==$TORCH_VISION torchaudio==$TORCH_AUDIO --index-url https://download.pytorch.org/whl/rocm$ROCM RUN python3 -m pip install --no-cache-dir --upgrade pip setuptools ninja git+https://github.com/facebookresearch/detectron2.git pytesseract "itsdangerous<2.1.0" ARG REF=main WORKDIR / # Invalidate docker cache from here if new commit is available. ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video] RUN python3 -m pip uninstall -y tensorflow flax # When installing in editable mode, `transformers` is not recognized as a package. # this line must be added in order for python to be aware of transformers. RUN cd transformers && python3 setup.py develop # Remove nvml as it is not compatible with ROCm RUN python3 -m pip uninstall py3nvml pynvml -y

mavonic_private_repos/transformers/docker

mavonic_private_repos/transformers/docker/transformers-quantization-latest-gpu/Dockerfile

FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04 LABEL maintainer="Hugging Face" ARG DEBIAN_FRONTEND=noninteractive # Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands) SHELL ["sh", "-lc"] # The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant # to be used as arguments for docker build (so far). ARG PYTORCH='2.2.1' # Example: `cu102`, `cu113`, etc. ARG CUDA='cu118' RUN apt update RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python python3-pip ffmpeg RUN python3 -m pip install --no-cache-dir --upgrade pip ARG REF=main RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' || VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile RUN echo torch=$VERSION # `torchvision` and `torchaudio` should be installed along with `torch`, especially for nightly build. # Currently, let's just use their latest releases (when `torch` is installed with a release version) RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch] RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate # needed in bnb and awq RUN python3 -m pip install --no-cache-dir einops # Add bitsandbytes for mixed int8 testing RUN python3 -m pip install --no-cache-dir bitsandbytes # Add auto-gptq for gtpq quantization testing RUN python3 -m pip install --no-cache-dir auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Add optimum for gptq quantization testing RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum # Add aqlm for quantization testing RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2 # Add hqq for quantization testing RUN python3 -m pip install --no-cache-dir hqq # Add autoawq for quantization testing # >=v0.2.3 needed for compatibility with torch 2.2.1 RUN python3 -m pip install --no-cache-dir https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.3/autoawq-0.2.3+cu118-cp38-cp38-linux_x86_64.whl # Add quanto for quantization testing RUN python3 -m pip install --no-cache-dir quanto # Add eetq for quantization testing RUN python3 -m pip install git+https://github.com/NetEase-FuXi/EETQ.git # When installing in editable mode, `transformers` is not recognized as a package. # this line must be added in order for python to be aware of transformers. RUN cd transformers && python3 setup.py develop