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[i18n-KO] Translated video_classification.mdx to Korean #23026

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merged 18 commits into from May 30, 2023

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What does this PR do?

Translated the video_classification.mdx file of the documentation to Korean.
Thank you in advance for your review.

Before reviewing

  • Check for missing / redundant translations (번역 누락/중복 검사)
  • Grammar Check (맞춤법 검사)
  • Review or Add new terms to glossary (용어 확인 및 추가)
  • Check Inline TOC (e.g. [[lowercased-header]])
  • Check live-preview for gotchas (live-preview로 정상작동 확인)

Who can review(initial)?

Team PseudoLab, may you please review this PR? @0525hhgus, @HanNayeoniee, @sim-so, @gabrielwithappy, @HanNayeoniee, @wonhyeongseo, @jungnerd

Before submitting

  • This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
  • Did you read the contributor guideline,
    Pull Request section?
  • Was this discussed/approved via a Github issue or the forum? Please add a link
    to it if that's the case.
  • Did you make sure to update the documentation with your changes? Here are the
    documentation guidelines, and
    here are tips on formatting docstrings.
  • Did you write any new necessary tests?

Who can review(initial)?

@sgugger, @ArthurZucker, @eunseojo May you please review this PR?

Co-Authored-By: Hyeonseo Yun <0525_hhgus@naver.com>
Co-Authored-By: Gabriel Yang <gabrielwithhappy@gmail.com>
Co-Authored-By: Sohyun Sim <96299403+sim-so@users.noreply.github.com>
Co-Authored-By: Nayeon Han <nayeon2.han@gmail.com>
Co-Authored-By: Wonhyeong Seo <wonhseo@kakao.com>
Co-Authored-By: Jungnerd <46880056+jungnerd@users.noreply.github.com>
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HuggingFaceDocBuilderDev commented Apr 27, 2023

The documentation is not available anymore as the PR was closed or merged.

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lgtm

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```


**참고**: 위의 데이터셋 파이프라인은 [공식 파이토치 예제](https://pytorchvideo.org/docs/tutorial_classification#dataset)에서 가져온 것입니다. 우리는 UCF-101 데이터셋에 맞게 [`pytorchvideo.data.Ucf101()`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.Ucf101) 함수를 사용하고 있습니다. 내부적으로, 이 함수는 [`pytorchvideo.data.labeled_video_dataset.LabeledVideoDataset`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.LabeledVideoDataset) 객체를 반환합니다. `LabeledVideoDataset` 클래스는 PyTorchVideo 데이터셋에서 모든 영상 관련 작업의 기본 클래스입니다. 따라서 PyTorchVideo에서 미리 제공하지 않는 사용자 지정 데이터셋을 사용하려면, 이 클래스를 적절하게 확장하면 됩니다. 더 자세한 사항이 알고 싶다면 `data` API [documentation](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html) 를 참고하세요. 또한 위의 예시와 유사한 구조를 갖는 데이터셋을 사용하고 있다면, `pytorchvideo.data.Ucf101()` 함수를 사용하는 데 문제가 없을 것입니다.
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**참고**: 위의 데이터셋 파이프라인은 [공식 파이토치 예제](https://pytorchvideo.org/docs/tutorial_classification#dataset)에서 가져온 것입니다. 우리는 UCF-101 데이터셋에 맞게 [`pytorchvideo.data.Ucf101()`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.Ucf101) 함수를 사용하고 있습니다. 내부적으로, 이 함수는 [`pytorchvideo.data.labeled_video_dataset.LabeledVideoDataset`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.LabeledVideoDataset) 객체를 반환합니다. `LabeledVideoDataset` 클래스는 PyTorchVideo 데이터셋에서 모든 영상 관련 작업의 기본 클래스입니다. 따라서 PyTorchVideo에서 미리 제공하지 않는 사용자 지정 데이터셋을 사용하려면, 이 클래스를 적절하게 확장하면 됩니다. 더 자세한 사항이 알고 싶다면 `data` API [documentation](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html) 를 참고하세요. 또한 위의 예시와 유사한 구조를 갖는 데이터셋을 사용하고 있다면, `pytorchvideo.data.Ucf101()` 함수를 사용하는 데 문제가 없을 것입니다.
**참고**: 위의 데이터 세트 파이프라인은 [공식 파이토치 예제](https://pytorchvideo.org/docs/tutorial_classification#dataset)에서 가져온 것입니다. 우리는 UCF-101 데이터 세트에 맞게 [`pytorchvideo.data.Ucf101()`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.Ucf101) 함수를 사용하고 있습니다. 내부적으로, 이 함수는 [`pytorchvideo.data.labeled_video_dataset.LabeledVideoDataset`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.LabeledVideoDataset) 객체를 반환합니다. `LabeledVideoDataset` 클래스는 PyTorchVideo 데이터 세트에서 모든 영상 관련 작업의 기본 클래스입니다. 따라서 PyTorchVideo에서 미리 제공하지 않는 사용자 지정 데이터 세트를 사용하려면, 이 클래스를 적절하게 확장하면 됩니다. 더 자세한 사항이 알고 싶다면 `data` API [documentation](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html) 를 참고하세요. 또한 위의 예시와 유사한 구조를 갖는 데이터 세트를 사용하고 있다면, `pytorchvideo.data.Ucf101()` 함수를 사용하는 데 문제가 없을 것입니다.

docs/source/ko/tasks/video_classification.mdx Outdated Show resolved Hide resolved
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... return {"pixel_values": pixel_values, "labels": labels}
```

그런 다음 이 모든 것을 데이터셋과 함께 `Trainer`에 전달하기만 하면 됩니다.
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그런 다음 이 모든 것을 데이터셋과 함께 `Trainer`에 전달하기만 하면 됩니다.
그런 다음 이 모든 것을 데이터 세트와 함께 `Trainer`에 전달하기만 하면 됩니다.

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. -> : 추가합니다!

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번역하는 데 고생이 정말 많으셨을 것 같아요!
저도 열심히 리뷰를 해보았습니다🔥

docs/source/ko/tasks/video_classification.mdx Outdated Show resolved Hide resolved
[[open-in-colab]]


영상 분류는 영상 전체에 레이블 또는 클래스를 지정하는 작업입니다. 각 영상에는 하나의 클래스가 있을 것으로 예상됩니다. 영상 분류 모델은 영상를 입력으로 받아 어느 클래스에 속하는지에 대한 예측을 반환합니다. 이러한 모델은 영상가 어떤 내용인지 분류하는 데 사용될 수 있습니다. 영상 분류의 실제 응용 예는 피트니스 앱에서 유용한 동작 / 운동 인식 서비스가 있습니다. 이는 또한 시각 장애인이 이동할 때 보조하는데 사용될 수 있습니다
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영상 분류는 영상 전체에 레이블 또는 클래스를 지정하는 작업입니다. 각 영상에는 하나의 클래스가 있을 것으로 예상됩니다. 영상 분류 모델은 영상를 입력으로 받아 어느 클래스에 속하는지에 대한 예측을 반환합니다. 이러한 모델은 영상가 어떤 내용인지 분류하는 데 사용될 수 있습니다. 영상 분류의 실제 응용 예는 피트니스 앱에서 유용한 동작 / 운동 인식 서비스가 있습니다. 이는 또한 시각 장애인이 이동할 때 보조하는데 사용될 수 있습니다
영상 분류는 영상 전체에 레이블 또는 클래스를 지정하는 작업입니다. 각 영상에는 하나의 클래스가 있을 것으로 간주합니다. 영상 분류 모델은 영상을 입력으로 받아 어느 클래스에 속하는지 예측하여 반환합니다. 이러한 모델은 영상이 어떤 내용인지 분류하는 데 사용될 수 있습니다. 영상 분류가 실생활에 적용된 사례로, 피트니스 앱에서 유용한 동작/운동 인식 서비스가 있습니다. 시각 장애인을 보조하는 데에도 사용되며, 특히 이동 시에 도움이 됩니다.

영상가영상을로 바꾸고, 문장을 수정해보았습니다.

docs/source/ko/tasks/video_classification.mdx Outdated Show resolved Hide resolved

이 가이드에서는 다음을 수행하는 방법을 보여줍니다.

<!--흠...-->
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이 부분은 원문에는 없는 것 같은데, 혹시 어떤 의미일까요?

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아 주석 부분 말씀이신가요??

Comment on lines 23 to 24
1. [UCF101](https://www.crcv.ucf.edu/data/UCF101.php) 데이터셋의 하위 집합을 통해 [VideoMAE](https://huggingface.co/docs/transformers/main/en/model_doc/videomae) 모델을 미세 조정하는 법.
2. 미세 조정한 모델을 추론에 사용하는 법.
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1. [UCF101](https://www.crcv.ucf.edu/data/UCF101.php) 데이터셋의 하위 집합을 통해 [VideoMAE](https://huggingface.co/docs/transformers/main/en/model_doc/videomae) 모델을 미세 조정하는 법.
2. 미세 조정한 모델을 추론에 사용하는 법.
1. [UCF101](https://www.crcv.ucf.edu/data/UCF101.php) 데이터 세트의 하위 집합을 통해 [VideoMAE](https://huggingface.co/docs/transformers/main/en/model_doc/videomae) 모델을 미세 조정하기
2. 미세 조정한 모델을 추론에 사용하기
  1. 현재 glossary에 따라 데이터 세트로 수정합니다.
  2. 위에 ~을 수행하는 방법이라고 이미 소개하고 있어서 항목에서는 을 빼보았는데 어떨까요?

docs/source/ko/tasks/video_classification.mdx Outdated Show resolved Hide resolved

## 추론하기 [[inference]]

좋습니다. 이제 미세 조정된 모델을 갖고 추론하는데 사용할 수 있습니다.
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좋습니다. 이제 미세 조정된 모델을 갖고 추론하는데 사용할 수 있습니다.
좋습니다. 이제 미세 조정된 모델을 갖고 추론하는 데 사용할 수 있습니다.

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docs/source/ko/tasks/video_classification.mdx Outdated Show resolved Hide resolved
>>> logits = run_inference(trained_model, sample_test_video["video"])
```

`logits`을 디코딩하면, 우리는 다음을 얻을 수 있습니다.
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`logits`을 디코딩하면, 우리는 다음을 얻을 수 있습니다.
`logits`을 디코딩하면, 다음 결과를 얻을 수 있습니다.

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. -> :으로 수정이 필요하여 코멘트 남깁니다!

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좋은 번역 감사합니다! 긴 글인데 코드까지 고려해서 번역해주셨군요 👍
아래 리뷰 코멘트 남깁니다 😄
중복된 리뷰가 있어서 확인 후 삭제 중입니다 완료되었습니다!

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docs/source/ko/tasks/video_classification.mdx Outdated Show resolved Hide resolved
>>> clip_duration = num_frames_to_sample * sample_rate / fps
```

이제 데이터셋에 특화된 전처리(transform)과 데이터셋 자체를 정의합니다. 먼저 훈련 데이터셋으로 시작합니다.
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. -> :으로 번경되어야 하여 코멘트 남깁니다!

docs/source/ko/tasks/video_classification.mdx Outdated Show resolved Hide resolved
... return {"pixel_values": pixel_values, "labels": labels}
```

그런 다음 이 모든 것을 데이터셋과 함께 `Trainer`에 전달하기만 하면 됩니다.
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. -> : 추가합니다!

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kihoon71 and others added 10 commits May 2, 2023 21:45
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com>
Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com>
이 가이드에서는 다음을 수행하는 방법을 보여줍니다:

<!--흠...-->
1. [UCF101](https://www.crcv.ucf.edu/data/UCF101.php) 데이터 세트의 하위 집합을 통해 [VideoMAE](https://huggingface.co/docs/transformers/main/en/model_doc/videomae) 모델을 미세 조정하는 법.
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1. [UCF101](https://www.crcv.ucf.edu/data/UCF101.php) 데이터 세트의 하위 집합을 통해 [VideoMAE](https://huggingface.co/docs/transformers/main/en/model_doc/videomae) 모델을 미세 조정하는 법.
1. [UCF101](https://www.crcv.ucf.edu/data/UCF101.php) 데이터 세트의 하위 집합을 통해 [VideoMAE](https://huggingface.co/docs/transformers/main/en/model_doc/videomae) 모델을 미세 조정하기.

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~하기
방식도 생각해 봤습니다.

>>> file_path = hf_hub_download(repo_id=hf_dataset_identifier, filename=filename, repo_type="dataset")
```

데이터 세트의 하위 집합이 다운로드 되면, 압축된 아카이브를 해제해야 합니다.
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데이터 세트의 하위 집합이 다운로드 되면, 압축된 아카이브를 해제해야 합니다.
데이터 세트의 하위 집합이 다운로드 되면, 파일의 압축을 해제합니다:

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compressed archive는 압축 파일처럼 사용되는 것 같아 수정해봤습니다.

... )
```

학습 데이터셋 변환에는 '균일한 시간 샘플링(uniform temporal subsampling)', '픽셀 정규화(pixel normalization)', '무작위 잘라내기(random cropping)' 및 '무작위 수평 뒤집기(random horizontal flipping)'의 조합을 사용합니다. 검증 및 평가 데이터셋 변환에는 '무작위 잘라내기'와 '수평 뒤집기'를 제외한 동일한 변환 체인을 유지합니다. 이러한 변환의 자세한 내용을 알아보려면 [PyTorchVideo 공식 문서](https://pytorchvideo.org)를 확인하세요.
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무작위

랜덤
으로 쓰는게 더 이해가 쉬울 것 같습니다.

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kihoon71 and others added 7 commits May 4, 2023 17:20
Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com>
Co-authored-by: Hyeonseo Yun <0525yhs@gmail.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>
Co-authored-by: Gabriel Yang <gabrielwithhappy@gmail.com>
@kihoon71 kihoon71 marked this pull request as ready for review May 4, 2023 11:23
@ArthurZucker
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Hey! Sorry for the long delay. There seems to be 2 suggestions not adresses, should we wait for these? 🤗

@kihoon71
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@ArthurZucker the suggestions that i didn't accept were about same sentences or ealier version of our glossary so you don't need to wait for the other suggestions to be accepted!! Thank you!!

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Thanks for your contribution!

@sgugger sgugger merged commit 192aa04 into huggingface:main May 30, 2023
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mishig25 added a commit that referenced this pull request May 30, 2023
* Debug example code for MegaForCausalLM (#23382)

* Debug example code for MegaForCausalLM

set ignore_mismatched_sizes=True in model loading code

* Fix up

* Remove erroneous `img` closing tag (#23646)

See #23625

* Fix tensor device while attention_mask is not None (#23538)

* Fix tensor device while attention_mask is not None

* Fix tensor device while attention_mask is not None

* Fix accelerate logger bug (#23650)

* fix logger bug

* Update tests/mixed_int8/test_mixed_int8.py

Co-authored-by: Zachary Mueller <muellerzr@gmail.com>

* import `PartialState`

---------

Co-authored-by: Zachary Mueller <muellerzr@gmail.com>

* Muellerzr fix deepspeed (#23657)

* Fix deepspeed recursion

* Better fix

* Bugfix: LLaMA layer norm incorrectly changes input type and consumers lots of memory (#23535)

* Fixed bug where LLaMA layer norm would change input type.

* make fix-copies

---------

Co-authored-by: younesbelkada <younesbelkada@gmail.com>

* Fix wav2vec2 is_batched check to include 2-D numpy arrays (#23223)

* Fix wav2vec2 is_batched check to include 2-D numpy arrays

* address comment

* Add tests

* oops

* oops

* Switch to np array

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* Switch to np array

* condition merge

* Specify mono channel only in comment

* oops, add other comment too

* make style

* Switch list check from falsiness to empty

---------

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* changing the requirements to a cpu torch version that works (#23483)

* Fix SAM tests and use smaller checkpoints (#23656)

* Fix SAM tests and use smaller checkpoints

* Override test_model_from_pretrained to use sam-vit-base as well

* make fixup

* Update all no_trainer with skip_first_batches (#23664)

* Update workflow files (#23658)

* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* [image-to-text pipeline] Add conditional text support + GIT (#23362)

* First draft

* Remove print statements

* Add conditional generation

* Add more tests

* Remove scripts

* Remove BLIP specific linkes

* Add support for pix2struct

* Add fast test

* Address comment

* Fix style

* small fix to remove unused eos in processor when it's not used. (#23408)

* Bump requests from 2.27.1 to 2.31.0 in /examples/research_projects/decision_transformer (#23673)

Bump requests in /examples/research_projects/decision_transformer

Bumps [requests](https://github.com/psf/requests) from 2.27.1 to 2.31.0.
- [Release notes](https://github.com/psf/requests/releases)
- [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md)
- [Commits](psf/requests@v2.27.1...v2.31.0)

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  dependency-type: direct:production
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* Bump requests from 2.22.0 to 2.31.0 in /examples/research_projects/visual_bert (#23670)

Bump requests in /examples/research_projects/visual_bert

Bumps [requests](https://github.com/psf/requests) from 2.22.0 to 2.31.0.
- [Release notes](https://github.com/psf/requests/releases)
- [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md)
- [Commits](psf/requests@v2.22.0...v2.31.0)

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  dependency-type: direct:production
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* Bump requests from 2.22.0 to 2.31.0 in /examples/research_projects/lxmert (#23668)

Bump requests in /examples/research_projects/lxmert

Bumps [requests](https://github.com/psf/requests) from 2.22.0 to 2.31.0.
- [Release notes](https://github.com/psf/requests/releases)
- [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md)
- [Commits](psf/requests@v2.22.0...v2.31.0)

---
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  dependency-type: direct:production
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* Add PerSAM [bis] (#23659)

* Add PerSAM args

* Make attn_sim optional

* Rename to attention_similarity

* Add docstrigns

* Improve docstrings

* Fix typo in a parameter name for open llama model (#23637)

* Update modeling_open_llama.py

Fix typo in `use_memorry_efficient_attention` parameter name

* Update configuration_open_llama.py

Fix typo in `use_memorry_efficient_attention` parameter name

* Update configuration_open_llama.py

Take care of backwards compatibility ensuring that the previous parameter name is taken into account if used

* Update configuration_open_llama.py

format to adjust the line length

* Update configuration_open_llama.py

proper code formatting using `make fixup`

* Update configuration_open_llama.py

pop the argument not to let it be set later down the line

* Fix PyTorch SAM tests (#23682)

fix

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Making `safetensors` a core dependency. (#23254)

* Making `safetensors` a core dependency.

To be merged later, I'm creating the PR so we can try it out.

* Update setup.py

* Remove duplicates.

* Even more redundant.

* 🌐 [i18n-KO] Translated `tasks/monocular_depth_estimation.mdx` to Korean (#23621)

docs: ko: `tasks/monocular_depth_estimation`

Co-authored-by: Hyeonseo Yun <0525yhs@gmail.com>
Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com>
Co-authored-by: Gabriel Yang <gabrielwithhappy@gmail.com>
Co-authored-by: Wonhyeong Seo <wonhseo@kakao.com>
Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com>

* Fix a `BridgeTower` test (#23694)

fix

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* [`SAM`] Fixes pipeline and adds a dummy pipeline test (#23684)

* add a dummy pipeline test

* change test name

* TF version compatibility fixes (#23663)

* New TF version compatibility fixes

* Remove dummy print statement, move expand_1d

* Make a proper framework inference function

* Make a proper framework inference function

* ValueError -> TypeError

* [`Blip`] Fix blip doctest (#23698)

fix blip doctest

* is_batched fix for remaining 2-D numpy arrays (#23309)

* Fix is_batched code to allow 2-D numpy arrays for audio

* Tests

* Fix typo

* Incorporate comments from PR #23223

* Skip `TFCvtModelTest::test_keras_fit_mixed_precision` for now (#23699)

fix

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* fix: load_best_model_at_end error when load_in_8bit is True (#23443)

Ref: huggingface/peft#394
    Loading a quantized checkpoint into non-quantized Linear8bitLt is not supported.
    call module.cuda() before module.load_state_dict()

* Fix some docs what layerdrop does (#23691)

* Fix some docs what layerdrop does

* Update src/transformers/models/data2vec/configuration_data2vec_audio.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Fix more docs

---------

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* add GPTJ/bloom/llama/opt into model list and enhance the jit support (#23291)

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* 4-bit QLoRA via bitsandbytes (4-bit base model + LoRA) (#23479)

* Added lion and paged optimizers and made original tests pass.

* Added tests for paged and lion optimizers.

* Added and fixed optimizer tests.

* Style and quality checks.

* Initial draft. Some tests fail.

* Fixed dtype bug.

* Fixed bug caused by torch_dtype='auto'.

* All test green for 8-bit and 4-bit layers.

* Added fix for fp32 layer norms and bf16 compute in LLaMA.

* Initial draft. Some tests fail.

* Fixed dtype bug.

* Fixed bug caused by torch_dtype='auto'.

* All test green for 8-bit and 4-bit layers.

* Added lion and paged optimizers and made original tests pass.

* Added tests for paged and lion optimizers.

* Added and fixed optimizer tests.

* Style and quality checks.

* Fixing issues for PR #23479.

* Added fix for fp32 layer norms and bf16 compute in LLaMA.

* Reverted variable name change.

* Initial draft. Some tests fail.

* Fixed dtype bug.

* Fixed bug caused by torch_dtype='auto'.

* All test green for 8-bit and 4-bit layers.

* Added lion and paged optimizers and made original tests pass.

* Added tests for paged and lion optimizers.

* Added and fixed optimizer tests.

* Style and quality checks.

* Added missing tests.

* Fixup changes.

* Added fixup changes.

* Missed some variables to rename.

* revert trainer tests

* revert test trainer

* another revert

* fix tests and safety checkers

* protect import

* simplify a bit

* Update src/transformers/trainer.py

* few fixes

* add warning

* replace with `load_in_kbit = load_in_4bit or load_in_8bit`

* fix test

* fix tests

* this time fix tests

* safety checker

* add docs

* revert torch_dtype

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* multiple fixes

* update docs

* version checks and multiple fixes

* replace `is_loaded_in_kbit`

* replace `load_in_kbit`

* change methods names

* better checks

* oops

* oops

* address final comments

---------

Co-authored-by: younesbelkada <younesbelkada@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Paged Optimizer + Lion Optimizer for Trainer (#23217)

* Added lion and paged optimizers and made original tests pass.

* Added tests for paged and lion optimizers.

* Added and fixed optimizer tests.

* Style and quality checks.

---------

Co-authored-by: younesbelkada <younesbelkada@gmail.com>

* Export to ONNX doc refocused on using optimum, added tflite (#23434)

* doc refocused on using optimum, tflite

* minor updates to fix checks

* Apply suggestions from code review

Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>

* TFLite to separate page, added links

* Removed the onnx list builder

* make style

* Update docs/source/en/serialization.mdx

Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>

---------

Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>

* fix: use bool instead of uint8/byte in Deberta/DebertaV2/SEW-D to make it compatible with TensorRT (#23683)

* Use bool instead of uint8/byte in DebertaV2 to make it compatible with TensorRT

TensorRT cannot accept onnx graph with uint8/byte intermediate tensors. This PR uses bool tensors instead of unit8/byte tensors to make the exported onnx file can work with TensorRT.

* fix: use bool instead of uint8/byte in Deberta and SEW-D

---------

Co-authored-by: Yuxian Qiu <yuxianq@nvidia.com>

* fix gptj could not jit.trace in GPU (#23317)

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* Better TF docstring types (#23477)

* Rework TF type hints to use | None instead of Optional[] for tf.Tensor

* Rework TF type hints to use | None instead of Optional[] for tf.Tensor

* Don't forget the imports

* Add the imports to tests too

* make fixup

* Refactor tests that depended on get_type_hints

* Better test refactor

* Fix an old hidden bug in the test_keras_fit input creation code

* Fix for the Deit tests

* Minor awesome-transformers.md fixes (#23453)

Minor docs fixes

* TF SAM memory reduction (#23732)

* Extremely small change to TF SAM dummies to reduce memory usage on build

* remove debug breakpoint

* Debug print statement to track array sizes

* More debug shape printing

* More debug shape printing

* Now remove the debug shape printing

* make fixup

* make fixup

* fix: delete duplicate sentences in `document_question_answering.mdx` (#23735)

fix: delete duplicate sentence

* fix: Whisper generate, move text_prompt_ids trim up for max_new_tokens calculation (#23724)

move text_prompt_ids trimming to top

* Overhaul TF serving signatures + dummy inputs (#23234)

* Let's try autodetecting serving sigs

* Don't clobber existing sigs

* Change shapes for multiplechoice models

* Make default dummy inputs smarter too

* Fix missing f-string

* Let's YOLO a serving output too

* Read __class__.__name__ properly

* Don't just pass naked lists in there and expect it to be okay

* Code cleanup

* Update default serving sig

* Clearer error messages

* Further updates to the default serving output

* make fixup

* Update the serving output a bit more

* Cleanups and renames, raise errors appropriately when we can't infer inputs

* More renames

* we're building in a functional context again, yolo

* import DUMMY_INPUTS from the right place

* import DUMMY_INPUTS from the right place

* Support cross-attention in the dummies

* Support cross-attention in the dummies

* Complete removal of dummy/serving overrides in BERT

* Complete removal of dummy/serving overrides in RoBERTa

* Obliterate lots and lots of serving sig and dummy overrides

* merge type hint changes

* Fix for token_type_ids with vocab_size 1

* Add missing property decorator

* Fix T5 and hopefully some models that take conv inputs

* More signature pruning

* Fix T5's signature

* Fix Wav2Vec2 signature

* Fix LongformerForMultipleChoice input signature

* Fix BLIP and LED

* Better default serving output error handling

* Fix BART dummies

* Fix dummies for cross-attention, esp encoder-decoder models

* Fix visionencoderdecoder signature

* Fix BLIP serving output

* Small tweak to BART dummies

* Cleanup the ugly parameter inspection line that I used in a few places

* committed a breakpoint again

* Move the text_dims check

* Remove blip_text serving_output

* Add decoder_input_ids to the default input sig

* Remove all the manual overrides for encoder-decoder model signatures

* Tweak longformer/led input sigs

* Tweak default serving output

* output.keys() -> output

* make fixup

* [Whisper] Reduce batch size in tests (#23736)

* Fix the regex in `get_imports` to support multiline try blocks and excepts with specific exception types (#23725)

* fix and test get_imports for multiline try blocks, and excepts with specific errors

* fixup

* add some more tests

* add license

* Fix sagemaker DP/MP (#23681)

* Check for use_sagemaker_dp

* Add a check for is_sagemaker_mp when setting _n_gpu again. Should be last broken thing

* Try explicit check?

* Quality

* Enable prompts on the Hub (#23662)

* Enable prompts on the Hub

* Update src/transformers/tools/prompts.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Address review comments

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Remove the last few TF serving sigs (#23738)

Remove some more serving methods that (I think?) turned up while this PR was open

* Fix `pip install --upgrade accelerate` command in modeling_utils.py (#23747)

Fix command in modeling_utils.py

* Add LlamaIndex to awesome-transformers.md (#23484)

* Fix psuh_to_hub in Trainer when nothing needs pushing (#23751)

* Revamp test selection for the example tests (#23737)

* Revamp test selection for the example tests

* Rename old XLA test and fake modif in run_glue

* Fixes

* Fake Trainer modif

* Remove fake modifs

* [LongFormer] code nits, removed unused parameters  (#23749)

* remove unused parameters

* remove unused parameters in config

* Fix is_ninja_available() (#23752)

* Fix is_ninja_available()

search ninja using subprocess instead of importlib.

* Fix style

* Fix doc

* Fix style

* Bump tornado from 6.0.4 to 6.3.2 in /examples/research_projects/lxmert (#23766)

Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.0.4 to 6.3.2.
- [Changelog](https://github.com/tornadoweb/tornado/blob/master/docs/releases.rst)
- [Commits](tornadoweb/tornado@v6.0.4...v6.3.2)

---
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* Bump tornado from 6.0.4 to 6.3.2 in /examples/research_projects/visual_bert (#23767)

Bump tornado in /examples/research_projects/visual_bert

Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.0.4 to 6.3.2.
- [Changelog](https://github.com/tornadoweb/tornado/blob/master/docs/releases.rst)
- [Commits](tornadoweb/tornado@v6.0.4...v6.3.2)

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* [`Nllb-Moe`] Fix nllb moe accelerate issue (#23758)

fix nllb moe accelerate issue

* [OPT] Doc nit, using fast is fine (#23789)

small doc nit

* Fix RWKV backward on GPU (#23774)

* Update trainer.mdx class_weights example (#23787)

class_weights tensor should follow model's device

* no_cuda does not take effect in non distributed environment (#23795)

Signed-off-by: Wang, Yi <yi.a.wang@intel.com>

* Fix no such file or directory error (#23783)

* Fix no such file or directory error

* Address comment

* Fix formatting issue

* Log the right train_batch_size if using auto_find_batch_size and also log the adjusted value seperately. (#23800)

* Log right bs

* Log

* Diff message

* Enable code-specific revision for code on the Hub (#23799)

* Enable code-specific revision for code on the Hub

* invalidate old revision

* [Time-Series] Autoformer model (#21891)

* ran `transformers-cli add-new-model-like`

* added `AutoformerLayernorm` and `AutoformerSeriesDecomposition`

* added `decomposition_layer` in `init` and `moving_avg` to config

* added `AutoformerAutoCorrelation` to encoder & decoder

* removed caninical self attention `AutoformerAttention`

* added arguments in config and model tester. Init works! 😁

* WIP autoformer attention with autocorrlation

* fixed `attn_weights` size

* wip time_delay_agg_training

* fixing sizes and debug time_delay_agg_training

* aggregation in training works! 😁

* `top_k_delays` -> `top_k_delays_index` and added `contiguous()`

* wip time_delay_agg_inference

* finish time_delay_agg_inference 😎

* added resize to autocorrelation

* bug fix: added the length of the output signal to `irfft`

* `attention_mask = None` in the decoder

* fixed test: changed attention expected size, `test_attention_outputs` works!

* removed unnecessary code

* apply AutoformerLayernorm in final norm in enc & dec

* added series decomposition to the encoder

* added series decomp to decoder, with inputs

* added trend todos

* added autoformer to README

* added to index

* added autoformer.mdx

* remove scaling and init attention_mask in the decoder

* make style

* fix copies

* make fix-copies

* inital fix-copies

* fix from #22076

* make style

* fix class names

* added trend

* added d_model and projection layers

* added `trend_projection` source, and decomp layer init

* added trend & seasonal init for decoder input

* AutoformerModel cannot be copied as it has the decomp layer too

* encoder can be copied from time series transformer

* fixed generation and made distrb. out more robust

* use context window to calculate decomposition

* use the context_window for decomposition

* use output_params helper

* clean up AutoformerAttention

* subsequences_length off by 1

* make fix copies

* fix test

* added init for nn.Conv1d

* fix IGNORE_NON_TESTED

* added model_doc

* fix ruff

* ignore tests

* remove dup

* fix SPECIAL_CASES_TO_ALLOW

* do not copy due to conv1d weight init

* remove unused imports

* added short summary

* added label_length and made the model non-autoregressive

* added params docs

* better doc for `factor`

* fix tests

* renamed `moving_avg` to `moving_average`

* renamed `factor` to `autocorrelation_factor`

* make style

* Update src/transformers/models/autoformer/configuration_autoformer.py

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* Update src/transformers/models/autoformer/configuration_autoformer.py

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* fix configurations

* fix integration tests

* Update src/transformers/models/autoformer/configuration_autoformer.py

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* fixing `lags_sequence` doc

* Revert "fixing `lags_sequence` doc"

This reverts commit 21e3491.

* Update src/transformers/models/autoformer/modeling_autoformer.py

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* Update src/transformers/models/autoformer/modeling_autoformer.py

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* Update src/transformers/models/autoformer/modeling_autoformer.py

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* Apply suggestions from code review

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* Update src/transformers/models/autoformer/configuration_autoformer.py

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* model layers now take the config

* added `layer_norm_eps` to the config

* Update src/transformers/models/autoformer/modeling_autoformer.py

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* added `config.layer_norm_eps` to AutoformerLayernorm

* added `config.layer_norm_eps` to all layernorm layers

* Update src/transformers/models/autoformer/configuration_autoformer.py

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* Update src/transformers/models/autoformer/configuration_autoformer.py

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* Update src/transformers/models/autoformer/configuration_autoformer.py

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* Update src/transformers/models/autoformer/configuration_autoformer.py

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* fix variable names

* added inital pretrained model

* added use_cache docstring

* doc strings for trend and use_cache

* fix order of args

* imports on one line

* fixed get_lagged_subsequences docs

* add docstring for create_network_inputs

* get rid of layer_norm_eps config

* add back layernorm

* update fixture location

* fix signature

* use AutoformerModelOutput dataclass

* fix pretrain config

* no need as default exists

* subclass ModelOutput

* remove layer_norm_eps config

* fix test_model_outputs_equivalence test

* test hidden_states_output

* make fix-copies

* Update src/transformers/models/autoformer/configuration_autoformer.py

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* removed unused attr

* Update tests/models/autoformer/test_modeling_autoformer.py

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* Update src/transformers/models/autoformer/modeling_autoformer.py

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* Update src/transformers/models/autoformer/modeling_autoformer.py

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* Update src/transformers/models/autoformer/modeling_autoformer.py

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* Update src/transformers/models/autoformer/modeling_autoformer.py

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* Update src/transformers/models/autoformer/modeling_autoformer.py

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---------

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* add type hint in pipeline model argument (#23740)

* add type hint in pipeline model argument

* add pretrainedmodel and tfpretainedmodel type hint

* make type hints string

* TF SAM shape flexibility fixes (#23842)

SAM shape flexibility fixes for compilation

* fix Whisper tests on GPU (#23753)

* move input features to GPU

* skip these tests because undefined behavior

* unskip tests

* 🌐 [i18n-KO] Translated `fast_tokenizers.mdx` to Korean (#22956)

* docs: ko: fast_tokenizer.mdx

content - translated

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* [i18n-KO] Translated video_classification.mdx to Korean (#23026)

* task/video_classification translated

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* 🌐 [i18n-KO] Translated `troubleshooting.mdx` to Korean (#23166)

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---------

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* Adds a FlyteCallback (#23759)

* initial flyte callback

* lint

* logs should still be saved to Flyte even if pandas isn't install (unlikely)

* cr - flyte team

* add docs for Flytecallback

* fix doc string - cr sgugger

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---------

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* Update collating_graphormer.py (#23862)

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* #23388 Issue: Update RoBERTa configuration (#23863)

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* Better warning

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sheonhan pushed a commit to sheonhan/transformers that referenced this pull request Jun 1, 2023
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* task/video_classification translated

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* Update docs/source/ko/tasks/video_classification.mdx

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---------

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gojiteji pushed a commit to gojiteji/transformers that referenced this pull request Jun 5, 2023
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* Update docs/source/ko/tasks/video_classification.mdx

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* Apply suggestions from code review

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---------

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novice03 pushed a commit to novice03/transformers that referenced this pull request Jun 23, 2023
…23026)

* task/video_classification translated

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* Update docs/source/ko/tasks/video_classification.mdx

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* Update video_classification.mdx

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---------

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