Add UDOP (#22940)

* First draft

* More improvements

* More improvements

* More fixes

* Fix copies

* More improvements

* More fixes

* More improvements

* Convert checkpoint

* More improvements, set up tests

* Fix more tests

* Add UdopModel

* More improvements

* Fix equivalence test

* More fixes

* Redesign model

* Extend conversion script

* Use real inputs for conversion script

* Add image processor

* Improve conversion script

* Add UdopTokenizer

* Add fast tokenizer

* Add converter

* Update README's

* Add processor

* Add fully fledged tokenizer

* Add fast tokenizer

* Use processor in conversion script

* Add tokenizer tests

* Fix one more test

* Fix more tests

* Fix tokenizer tests

* Enable fast tokenizer tests

* Fix more tests

* Fix additional_special_tokens of fast tokenizer

* Fix tokenizer tests

* Fix more tests

* Fix equivalence test

* Rename image to pixel_values

* Rename seg_data to bbox

* More renamings

* Remove vis_special_token

* More improvements

* Add docs

* Fix copied from

* Update slow tokenizer

* Update fast tokenizer design

* Make text input optional

* Add first draft of processor tests

* Fix more processor tests

* Fix decoder_start_token_id

* Fix test_initialization

* Add integration test

* More improvements

* Improve processor, add test

* Add more copied from

* Add more copied from

* Add more copied from

* Add more copied from

* Remove print statement

* Update README and auto mapping

* Delete files

* Delete another file

* Remove code

* Fix test

* Fix docs

* Remove asserts

* Add doc tests

* Include UDOP in exotic model tests

* Add expected tesseract decodings

* Add sentencepiece

* Use same design as T5

* Add UdopEncoderModel

* Add UdopEncoderModel to tests

* More fixes

* Fix fast tokenizer

* Fix one more test

* Remove parallelisable attribute

* Fix copies

* Remove legacy file

* Copy from T5Tokenizer

* Fix rebase

* More fixes, copy from T5

* More fixes

* Fix init

* Use ArthurZ/udop for tests

* Make all model tests pass

* Remove UdopForConditionalGeneration from auto mapping

* Fix more tests

* fixups

* more fixups

* fix the tokenizers

* remove un-necessary changes

* nits

* nits

* replace truncate_sequences_boxes with truncate_sequences for fix-copies

* nit current path

* add a test for input ids

* ids that we should get taken from c9f7a32f57440d90ff79890270d376a1cc0acb68

* nits converting

* nits

* apply ruff

* nits

* nits

* style

* fix slow order of addition

* fix udop fast range as well

* fixup

* nits

* Add docstrings

* Fix gradient checkpointing

* Update code examples

* Skip tests

* Update integration test

* Address comment

* Make fixup

* Remove extra ids from tokenizer

* Skip test

* Apply suggestions from code review

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

* Update year

* Address comment

* Address more comments

* Address comments

* Add copied from

* Update CI

* Rename script

* Update model id

* Add AddedToken, skip tests

* Update CI

* Fix doc tests

* Do not use Tesseract for the doc tests

* Remove kwargs

* Add original inputs

* Update casting

* Fix doc test

* Update question

* Update question

* Use LayoutLMv3ImageProcessor

* Update organization

* Improve docs

* Update forward signature

* Make images optional

* Remove deprecated device argument

* Add comment, add add_prefix_space

* More improvements

* Remove kwargs

---------

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

README_ko.md

@@ -433,6 +433,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (Microsoft 에서) Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei 의 [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) 논문과 함께 발표했습니다.
 1. **[TVLT](https://huggingface.co/docs/transformers/model_doc/tvlt)** (from UNC Chapel Hill 에서) Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal 의 [TVLT: Textless Vision-Language Transformer](https://arxiv.org/abs/2209.14156) 논문과 함께 발표했습니다.
 1. **[TVP](https://huggingface.co/docs/transformers/model_doc/tvp)** (Intel 에서) Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding 의 [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://arxiv.org/abs/2303.04995) 논문과 함께 발표했습니다.
+1. **[UDOP](https://huggingface.co/docs/transformers/main/model_doc/udop)** (Microsoft Research 에서 제공)은 Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, Mohit Bansal.의 [Unifying Vision, Text, and Layout for Universal Document Processing](https://arxiv.org/abs/2212.02623)논문과 함께 발표했습니다.
 1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (Google Research 에서) Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzle 의 [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) 논문과 함께 발표했습니다.
 1. **[UMT5](https://huggingface.co/docs/transformers/model_doc/umt5)** (Google Research 에서 제공)은 Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.의 [UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi)논문과 함께 발표했습니다.
 1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (Microsoft Research 에서) Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang 의 [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) 논문과 함께 발표했습니다.