Add BROS (#23190)

* add Bros boilerplate

* copy and pasted modeling_bros.py from official Bros repo

* update copyright of bros files

* copy tokenization_bros.py from official repo and update import path

* copy tokenization_bros_fast.py from official repo and update import path

* copy configuration_bros.py from official repo and update import path

* remove trailing period in copyright line

* copy and paste bros/__init__.py from official repo

* save formatting

* remove unused unnecessary pe_type argument - using only crel type

* resolve import issue

* remove unused model classes

* remove unnecessary tests

* remove unused classes

* fix original code's bug - layer_module's argument order

* clean up modeling auto

* add bbox to prepare_config_and_inputs

* set temporary value to hidden_size (32 is too low because of the of the
Bros' positional embedding)

* remove decoder test, update create_and_check* input arguemnts

* add missing variable to model tests

* do make fixup

* update bros.mdx

* add boilerate plate for no_head inference test

* update BROS_PRETRAINED_MODEL_ARCHIVE_LIST (add naver-clova-ocr prefix)

* add prepare_bros_batch_inputs function

* update modeling_common to add bbox inputs in Bros Model Test

* remove unnecessary model inference

* add test case

* add model_doc

* add test case for token_classification

* apply fixup

* update modeling code

* update BrosForTokenClassification loss calculation logic

* revert logits preprocessing logic to make sure logits have original shape

* - update class name

* - add BrosSpadeOutput
- update BrosConfig arguments

* add boilerate plate for no_head inference test

* add prepare_bros_batch_inputs function

* add test case

* add test case for token_classification

* update modeling code

* update BrosForTokenClassification loss calculation logic

* revert logits preprocessing logic to make sure logits have original shape

* apply masking on the fly

* add BrosSpadeForTokenLinking

* update class name
put docstring to the beginning of the file

* separate the logits calculation logic and loss calculation logic

* update logic for loss calculation so that logits shape doesn't change
when return

* update typo

* update prepare_config_and_inputs

* update dummy node initialization

* update last_hidden_states getting logic to consider when return_dict is False

* update box first token mask param

* bugfix: remove random attention mask generation

* update keys to ignore on load missing

* run make style and quality

* apply make style and quality of other codes

* update box_first_token_mask to bool type

* update index.md

* apply make style and quality

* apply make fix-copies

* pass check_repo

* update bros model doc

* docstring bugfix fix

* add checkpoint for doc, tokenizer for doc

* Update README.md

* Update docs/source/en/model_doc/bros.md

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

* Update bros.md

* Update src/transformers/__init__.py

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

* Update docs/source/en/model_doc/bros.md

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

* Apply suggestions from code review

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

* apply suggestions from code review

* apply suggestions from code review

* revert test_processor_markuplm.py

* Update test_processor_markuplm.py

* apply suggestions from code review

* apply suggestions from code review

* apply suggestions from code review

* update BrosSpadeELForTokenClassification head name to entity linker

* add doc string for config params

* update class, var names to more explicit and apply suggestions from code review

* remove unnecessary keys to ignore

* update relation extractor to be initialized with config

* add bros processor

* apply make style and quality

* update bros.md

* remove bros tokenizer, add bros processor that wraps bert tokenizer

* revert change

* apply make fix-copies

* update processor code, update itc -> initial token, stc -> subsequent token

* add type hint

* remove unnecessary condition branches in embedding forward

* fix auto tokenizer fail

* update docstring for each classes

* update bbox input dimension as standard 2 points and convert them to 4
points in forward pass

* update bros docs

* apply suggestions from code review : update Bros -> BROS in bros.md

* 1. box prefix var -> bbox
2. update variable names to be more explicit

* replace einsum with torch matmul

* apply style and quality

* remove unused argument

* remove unused arguments

* update docstrings

* apply suggestions from code review: add BrosBboxEmbeddings, replace
einsum with classical matrix operations

* revert einsum update

* update bros processor

* apply suggestions from code review

* add conversion script for bros

* Apply suggestions from code review

* fix readme

* apply fix-copies

---------

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

README_zh-hans.md

@@ -260,6 +260,7 @@ conda install -c huggingface transformers
 1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (来自 Alexa) 伴随论文 [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) 由 Adrian de Wynter and Daniel J. Perry 发布。
 1. **[BridgeTower](https://huggingface.co/docs/transformers/model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
+1. **[BROS](https://huggingface.co/docs/transformers/model_doc/bros)** (来自 NAVER CLOVA) 伴随论文 [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://arxiv.org/abs/2108.04539) 由 Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park 发布。
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (来自 Google Research) 伴随论文 [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) 由 Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel 发布。
 1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (来自 Inria/Facebook/Sorbonne) 伴随论文 [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) 由 Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot 发布。
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (来自 Google Research) 伴随论文 [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) 由 Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting 发布。
@@ -382,7 +383,7 @@ conda install -c huggingface transformers
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (来自 Google) 伴随论文 [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) 由 Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu 发布。
 1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (来自 Google) 伴随论文 [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) 由 Jason Phang, Yao Zhao, Peter J. Liu 发布。
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (来自 Deepmind) 伴随论文 [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) 由 Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira 发布。
-1. **[Persimmon](https://huggingface.co/docs/transformers/model_doc/persimmon)** (来自 ADEPT) 伴随论文 [blog post](https://www.adept.ai/blog/persimmon-8b) 由 Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani 发布。
+1. **[Persimmon](https://huggingface.co/docs/transformers/main/model_doc/persimmon)** (来自 ADEPT) 伴随论文 [blog post](https://www.adept.ai/blog/persimmon-8b) 由 Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani 发布。
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (来自 VinAI Research) 伴随论文 [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) 由 Dat Quoc Nguyen and Anh Tuan Nguyen 发布。
 1. **[Pix2Struct](https://huggingface.co/docs/transformers/model_doc/pix2struct)** (来自 Google) 伴随论文 [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://arxiv.org/abs/2210.03347) 由 Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova 发布。
 1. **[PLBart](https://huggingface.co/docs/transformers/model_doc/plbart)** (来自 UCLA NLP) 伴随论文 [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) 由 Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang 发布。