new model: IDEFICS via HuggingFaceM4 (#24796)

* rename

* restore

* mappings

* unedited tests+docs

* docs

* fixes

* fix auto-sync breakage

* cleanup

* wip

* wip

* add fetch_images

* remove einops dependency

* update

* fix

* fix

* fix

* fix

* fix

* re-add

* add batching

* rework

* fix

* improve

* add Leo as I am extending his work

* cleanup

* fix

* cleanup

* slow-test

* fix

* fix

* fixes

* deal with warning

* rename modified llama classes

* rework fetch_images

* alternative implementation

* cleanup

* strict version

* cleanup

* [`IDEFICS`] Fix idefics ci (#25056)

* Fix IDEFICS CI

* fix test file

* fixup

* some changes to make tests pass

* fix

* fixup

* Update src/transformers/models/idefics/configuration_idefics.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

---------
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* remove compat checks

* style

* explain that Idefics is not for training from scratch

* require pt>=2.0

* fix idefics vision config (#25092)

* fix idefics vision config

* fixup

* clean

* Update src/transformers/models/idefics/configuration_idefics.py

---------
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* cleanup

* style

* cleanup

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* upcase

* sequence of images

* handle the case with no images

* Update src/transformers/image_processing_utils.py
Co-authored-by: Victor SANH <victorsanh@gmail.com>

* support pure lm take 2

* support tokenizer options

* parameterize num_channels

* fix upcase

* s|IdeficsForCausalLM|IdeficsForVisionText2Text|g

* manual to one line

* addressing review

* unbreak

* remove clip dependency

* fix test

* consistency

* PIL import

* Idefics prefix

* Idefics prefix

* hack to make tests work

* style

* fix

* fix

* revert

* try/finally

* cleanup

* clean up

* move

* [`IDEFICS`] Fix idefics config refactor (#25149)

* refactor config

* nuke init weights

* more refactor

* oops

* remove visual question answering pipeline support

* Update src/transformers/models/idefics/clip.py
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Update src/transformers/models/idefics/modeling_idefics.py

* cleanup

* mv clip.py vision.py

* tidyup

---------
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
Co-authored-by: Stas Bekman <stas@stason.org>

* fix

* license

* condition on pt

* fix

* style

* fix

* rm torchvision dependency, allow custom transforms

* address review

* rework device arg

* add_eos_token

* s/transforms/transform/

* fix top level imports

* fix return value

* cleanup

* cleanup

* fix

* style

* license

* license

* Update src/transformers/models/idefics/image_processing_idefics.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* add a wrapper to freeze vision layears

* tidyup

* use the correct std/mean settings

* parameterize values from config

* add tests/models/idefics/test_image_processing_idefics.py

* add test_processor_idefics.py

* cleanup

* cleanups

* fix

* fix

* move to the right group

* style

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* add perceiver config

* reset

* missing arg docs

* Apply suggestions from code review
Co-authored-by: Leo Tronchon <leo.tronchon@gmail.com>

* address review comments

* inject automatic end of utterance tokens (#25218)

* inject automatic end of utterance tokens

* fix

* fix

* fix

* rework to not use the config

* not end_of_utterance_token at the end

* Update src/transformers/models/idefics/processing_idefics.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* address review

* Apply suggestions from code review
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/image_processing_utils.py
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

* [`Idefics`] add image_embeddings option in generate-related methods (#25442)

* add image_embeddings option in generate-related methods

* style

* rename image_embeddings and allow perceiver embeddings precomputation

* compute embeddings within generate

* make is_encoder_decoder= True the default in config

* nested if else fix

* better triple check

* switch if elif order for pixel values / img embeds

* update model_kwargs perceiver only at the end

* use _prepare_model_inputs instead of encoder_decoder logic

* fix comment typo

* fix config default for is_encoder_decoder

* style

* add typehints

* precompute in forward

* doc builder

* style

* pop instead of get image hidden states

* Trigger CI

* Update src/transformers/models/idefics/modeling_idefics.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/idefics/modeling_idefics.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* fix * + indentation + style

* simplify a bit the use_resampler logic using comments

* update diocstrings

* Trigger CI

---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* fix rebase changes

* unbreak #25237 - to be fixed in follow up PRs

* is_composition = False

* no longer needed

---------
Co-authored-by: leot13 <leo.tronchon@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Victor SANH <victorsanh@gmail.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

README.md

@@ -374,6 +374,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh.
 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.

README_es.md

@@ -351,6 +351,7 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 
 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.

README_hd.md

@@ -323,6 +323,7 @@ conda install -c huggingface transformers
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (UCSD, NVIDIA से) साथ में कागज [GroupViT: टेक्स्ट सुपरविजन से सिमेंटिक सेगमेंटेशन इमर्जेस](https://arxiv .org/abs/2202.11094) जियारुई जू, शालिनी डी मेलो, सिफ़ी लियू, वोनमिन बायन, थॉमस ब्रेउएल, जान कौट्ज़, ज़ियाओलोंग वांग द्वारा।
 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (फेसबुक से) साथ में पेपर [ह्यूबर्ट: सेल्फ सुपरवाइज्ड स्पीच रिप्रेजेंटेशन लर्निंग बाय मास्क्ड प्रेडिक्शन ऑफ हिडन यूनिट्स](https ://arxiv.org/abs/2106.07447) वेई-निंग सू, बेंजामिन बोल्टे, याओ-हंग ह्यूबर्ट त्साई, कुशाल लखोटिया, रुस्लान सालाखुतदीनोव, अब्देलरहमान मोहम्मद द्वारा।
 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (बर्कले से) साथ में कागज [I-BERT: Integer-only BERT Quantization](https:// arxiv.org/abs/2101.01321) सेहून किम, अमीर घोलमी, ज़ेवेई याओ, माइकल डब्ल्यू महोनी, कर्ट केटज़र द्वारा।
+1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 
 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (Salesforce से) Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. द्वाराअनुसंधान पत्र [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) के साथ जारी किया गया

README_ja.md

@@ -385,6 +385,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (UCSD, NVIDIA から) Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang から公開された研究論文: [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094)
 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (Facebook から) Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed から公開された研究論文: [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447)
 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (Berkeley から) Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer から公開された研究論文: [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321)
+1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 
 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (OpenAI から) Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever から公開された研究論文: [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/)
 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (Salesforce から) Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. から公開された研究論文 [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500)

README_ko.md

@@ -300,6 +300,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (UCSD, NVIDIA 에서) Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang 의 [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) 논문과 함께 발표했습니다.
 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (Facebook 에서) Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed 의 [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) 논문과 함께 발표했습니다.
 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (Berkeley 에서) Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer 의 [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) 논문과 함께 발표했습니다.
+1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 
 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (OpenAI 에서) Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever 의 [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) 논문과 함께 발표했습니다.
 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (Salesforce 에서 제공)은 Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.의 [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500)논문과 함께 발표했습니다.

README_zh-hans.md

@@ -324,6 +324,7 @@ conda install -c huggingface transformers
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (来自 UCSD, NVIDIA) 伴随论文 [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) 由 Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang 发布。
 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (来自 Facebook) 伴随论文 [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) 由 Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed 发布。
 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (来自 Berkeley) 伴随论文 [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) 由 Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer 发布。
+1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 
 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (来自 OpenAI) 伴随论文 [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) 由 Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever 发布。
 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (来自 Salesforce) 伴随论文 [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) 由 Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi 发布。

README_zh-hant.md

@@ -336,6 +336,7 @@ conda install -c huggingface transformers
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 
 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.

docs/source/en/_toctree.yml

@@ -694,6 +694,8 @@
       sections:
       - local: model_doc/decision_transformer
         title: Decision Transformer
+      - local: model_doc/idefics
+        title: IDEFICS
       - local: model_doc/trajectory_transformer
         title: Trajectory Transformer
       title: Reinforcement learning models

docs/source/en/index.md

@@ -140,6 +140,7 @@ The documentation is organized into five sections:
 1. **[GroupViT](model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
 1. **[Hubert](model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
 1. **[I-BERT](model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[IDEFICS](model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh.
 1. **[ImageGPT](model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
 1. **[Informer](model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
 1. **[InstructBLIP](model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.
@@ -360,6 +361,7 @@ Flax), PyTorch, and/or TensorFlow.
 |           GroupViT            |       ✅        |         ✅         |      ❌      |
 |            Hubert             |       ✅        |         ✅         |      ❌      |
 |            I-BERT             |       ✅        |         ❌         |      ❌      |
+|            IDEFICS            |       ✅        |         ❌         |      ❌      |
 |           ImageGPT            |       ✅        |         ❌         |      ❌      |
 |           Informer            |       ✅        |         ❌         |      ❌      |
 |         InstructBLIP          |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/idefics.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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# IDEFICS
+
+## Overview
+
+The IDEFICS model was proposed in [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
+](https://huggingface.co/papers/2306.16527
+) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh
+
+The abstract from the paper is the following:
+
+*Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks that require reasoning over one or multiple images to generate a text. However, the datasets used to train these models have not been released, and the collection process has not been fully specified. We introduce the OBELICS dataset, an open web-scale filtered dataset of interleaved image-text documents comprising 141 million web pages extracted from Common Crawl, 353 million associated images, and 115 billion text tokens. We describe the dataset creation process, present comprehensive filtering rules, and provide an analysis of the dataset's content. To show the viability of OBELISC, we train an 80 billion parameters vision and language model on the dataset and obtain competitive performance on various multimodal benchmarks. We release the code to reproduce the dataset along with the dataset itself.*
+
+This model was contributed by [HuggingFaceM4](https://huggingface.co/HuggingFaceM4). The original code can be found [here](<INSERT LINK TO GITHUB REPO HERE>). (TODO: don't have a public link yet).
+
+
+<Tip warning={true}>
+
+Idefics modeling code in Transformers is for finetuning and inferencing the pre-trained Idefics models.
+
+To train a new Idefics model from scratch use the m4 codebase (a link will be provided once it's made public)
+
+</Tip>
+
+
+## IdeficsConfig
+
+[[autodoc]] IdeficsConfig
+
+## IdeficsModel
+
+[[autodoc]] IdeficsModel
+    - forward
+
+## IdeficsForVisionText2Text
+
+[[autodoc]] IdeficsForVisionText2Text
+    - forward
+
+## IdeficsImageProcessor
+
+[[autodoc]] IdeficsImageProcessor
+    - preprocess
+
+## IdeficsProcessor
+
+[[autodoc]] IdeficsProcessor
+    - __call__

src/transformers/__init__.py

@@ -362,6 +362,10 @@ _import_structure = {
     "models.herbert": ["HerbertTokenizer"],
     "models.hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"],
     "models.ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig"],
+    "models.idefics": [
+        "IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "IdeficsConfig",
+    ],
     "models.imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig"],
     "models.informer": ["INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "InformerConfig"],
     "models.instructblip": [
@@ -939,6 +943,7 @@ else:
     _import_structure["models.efficientnet"].append("EfficientNetImageProcessor")
     _import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaImageProcessor", "FlavaProcessor"])
     _import_structure["models.glpn"].extend(["GLPNFeatureExtractor", "GLPNImageProcessor"])
+    _import_structure["models.idefics"].extend(["IdeficsImageProcessor"])
     _import_structure["models.imagegpt"].extend(["ImageGPTFeatureExtractor", "ImageGPTImageProcessor"])
     _import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"])
     _import_structure["models.layoutlmv3"].extend(["LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor"])
@@ -1929,6 +1934,15 @@ else:
             "IBertPreTrainedModel",
         ]
     )
+    _import_structure["models.idefics"].extend(
+        [
+            "IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "IdeficsForVisionText2Text",
+            "IdeficsModel",
+            "IdeficsPreTrainedModel",
+            "IdeficsProcessor",
+        ]
+    )
     _import_structure["models.imagegpt"].extend(
         [
             "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -4361,6 +4375,10 @@ if TYPE_CHECKING:
     from .models.herbert import HerbertTokenizer
     from .models.hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig
     from .models.ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig
+    from .models.idefics import (
+        IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        IdeficsConfig,
+    )
     from .models.imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig
     from .models.informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
     from .models.instructblip import (
@@ -4873,6 +4891,7 @@ if TYPE_CHECKING:
         from .models.efficientnet import EfficientNetImageProcessor
         from .models.flava import FlavaFeatureExtractor, FlavaImageProcessor, FlavaProcessor
         from .models.glpn import GLPNFeatureExtractor, GLPNImageProcessor
+        from .models.idefics import IdeficsImageProcessor
         from .models.imagegpt import ImageGPTFeatureExtractor, ImageGPTImageProcessor
         from .models.layoutlmv2 import LayoutLMv2FeatureExtractor, LayoutLMv2ImageProcessor
         from .models.layoutlmv3 import LayoutLMv3FeatureExtractor, LayoutLMv3ImageProcessor
@@ -5700,6 +5719,13 @@ if TYPE_CHECKING:
             IBertModel,
             IBertPreTrainedModel,
         )
+        from .models.idefics import (
+            IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST,
+            IdeficsForVisionText2Text,
+            IdeficsModel,
+            IdeficsPreTrainedModel,
+            IdeficsProcessor,
+        )
         from .models.imagegpt import (
             IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
             ImageGPTForCausalImageModeling,

src/transformers/image_processing_utils.py

@@ -17,9 +17,11 @@ import copy
 import json
 import os
 import warnings
-from typing import Any, Dict, Iterable, Optional, Tuple, Union
+from io import BytesIO
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
 import numpy as np
+import requests
 
 from .dynamic_module_utils import custom_object_save
 from .feature_extraction_utils import BatchFeature as BaseBatchFeature
@@ -34,10 +36,14 @@ from .utils import (
     download_url,
     is_offline_mode,
     is_remote_url,
+    is_vision_available,
     logging,
 )
 
 
+if is_vision_available():
+    from PIL import Image
+
 logger = logging.get_logger(__name__)
 
 
@@ -508,6 +514,28 @@ class ImageProcessingMixin(PushToHubMixin):
 
         cls._auto_class = auto_class
 
+    def fetch_images(self, image_url_or_urls: Union[str, List[str]]):
+        """
+        Convert a single or a list of urls into the corresponding `PIL.Image` objects.
+
+        If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
+        returned.
+        """
+        headers = {
+            "User-Agent": (
+                "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0"
+                " Safari/537.36"
+            )
+        }
+        if isinstance(image_url_or_urls, list):
+            return [self.fetch_images(x) for x in image_url_or_urls]
+        elif isinstance(image_url_or_urls, str):
+            response = requests.get(image_url_or_urls, stream=True, headers=headers)
+            response.raise_for_status()
+            return Image.open(BytesIO(response.content))
+        else:
+            raise ValueError(f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}")
+
 
 class BaseImageProcessor(ImageProcessingMixin):
     def __init__(self, **kwargs):

src/transformers/models/__init__.py

@@ -101,6 +101,7 @@ from . import (
     herbert,
     hubert,
     ibert,
+    idefics,
     imagegpt,
     informer,
     instructblip,

src/transformers/models/auto/configuration_auto.py

@@ -109,6 +109,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
         ("groupvit", "GroupViTConfig"),
         ("hubert", "HubertConfig"),
         ("ibert", "IBertConfig"),
+        ("idefics", "IdeficsConfig"),
         ("imagegpt", "ImageGPTConfig"),
         ("informer", "InformerConfig"),
         ("instructblip", "InstructBlipConfig"),
@@ -311,6 +312,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
         ("groupvit", "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("hubert", "HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("ibert", "IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
+        ("idefics", "IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("imagegpt", "IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("informer", "INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("instructblip", "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP"),
@@ -514,6 +516,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
         ("herbert", "HerBERT"),
         ("hubert", "Hubert"),
         ("ibert", "I-BERT"),
+        ("idefics", "IDEFICS"),
         ("imagegpt", "ImageGPT"),
         ("informer", "Informer"),
         ("instructblip", "InstructBLIP"),

src/transformers/models/auto/image_processing_auto.py

@@ -67,6 +67,7 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
         ("git", "CLIPImageProcessor"),
         ("glpn", "GLPNImageProcessor"),
         ("groupvit", "CLIPImageProcessor"),
+        ("idefics", "IdeficsImageProcessor"),
         ("imagegpt", "ImageGPTImageProcessor"),
         ("instructblip", "BlipImageProcessor"),
         ("layoutlmv2", "LayoutLMv2ImageProcessor"),

src/transformers/models/auto/modeling_auto.py

@@ -106,6 +106,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
         ("groupvit", "GroupViTModel"),
         ("hubert", "HubertModel"),
         ("ibert", "IBertModel"),
+        ("idefics", "IdeficsModel"),
         ("imagegpt", "ImageGPTModel"),
         ("informer", "InformerModel"),
         ("jukebox", "JukeboxModel"),
@@ -246,6 +247,7 @@ MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict(
         ("gpt_bigcode", "GPTBigCodeForCausalLM"),
         ("gptsan-japanese", "GPTSanJapaneseForConditionalGeneration"),
         ("ibert", "IBertForMaskedLM"),
+        ("idefics", "IdeficsForVisionText2Text"),
         ("layoutlm", "LayoutLMForMaskedLM"),
         ("longformer", "LongformerForMaskedLM"),
         ("luke", "LukeForMaskedLM"),

src/transformers/models/auto/processing_auto.py

@@ -57,6 +57,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict(
         ("git", "GitProcessor"),
         ("groupvit", "CLIPProcessor"),
         ("hubert", "Wav2Vec2Processor"),
+        ("idefics", "IdeficsProcessor"),
         ("instructblip", "InstructBlipProcessor"),
         ("layoutlmv2", "LayoutLMv2Processor"),
         ("layoutlmv3", "LayoutLMv3Processor"),

src/transformers/models/auto/tokenization_auto.py

@@ -169,6 +169,7 @@ else:
             ("herbert", ("HerbertTokenizer", "HerbertTokenizerFast" if is_tokenizers_available() else None)),
             ("hubert", ("Wav2Vec2CTCTokenizer", None)),
             ("ibert", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)),
+            ("idefics", (None, "LlamaTokenizerFast" if is_tokenizers_available() else None)),
             ("instructblip", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
             ("jukebox", ("JukeboxTokenizer", None)),
             ("layoutlm", ("LayoutLMTokenizer", "LayoutLMTokenizerFast" if is_tokenizers_available() else None)),

src/transformers/models/idefics/__init__.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.
+from typing import TYPE_CHECKING
+
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
+
+
+_import_structure = {"configuration_idefics": ["IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP", "IdeficsConfig"]}
+
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["image_processing_idefics"] = ["IdeficsImageProcessor"]
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_idefics"] = [
+        "IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "IdeficsForVisionText2Text",
+        "IdeficsGatedCrossAttentionLayer",
+        "IdeficsModel",
+        "IdeficsPreTrainedModel",
+    ]
+    _import_structure["processing_idefics"] = ["IdeficsProcessor"]
+
+
+if TYPE_CHECKING:
+    from .configuration_idefics import IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP, IdeficsConfig
+
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .image_processing_idefics import IdeficsImageProcessor
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_idefics import (
+            IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST,
+            IdeficsForVisionText2Text,
+            IdeficsGatedCrossAttentionLayer,
+            IdeficsModel,
+            IdeficsPreTrainedModel,
+        )
+        from .processing_idefics import IdeficsProcessor
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)