* Remove mentions of flake8/isort

* Clean up inits

* Deall with all other inits

* Last special rule for dummy files

* Add first draft

* Make model importable

* Make SwinForMaskedImageModeling importable

* Fix imports

* Add missing inits

* Add support for Swin

* Fix bug

* Fix bug

* Fix another bug

* Fix Swin MIM implementation

* Fix default encoder stride

* Fix Swin

* Add print statements for debugging

* Add image_size data argument

* Fix Swin

* Fix image_size

* Add print statements for debugging

* Fix print statement

* Remove print statements

* Improve reshaping of bool_masked_pos

* Add support for DeiT, fix tests

* Improve docstrings

* Apply new black version

* Improve script

* Fix bug

* Improve README

* Apply suggestions from code review

* Remove DS_Store and add to gitignore

* Apply suggestions from code review + fix BEiT Flax

* Revert BEiT changes

* Improve README

* Fix code quality

* Improve README

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

* AutoFeatureExtractor

* Init and first tests

* Tests

* Damn you gitignore

* Quality

* Defensive test for when not all backends are here

* Use pattern for Speech2Text models

* Add new SQUAD example

* Same with a task-specific Trainer

* Address review comment.

* Small fixes

* Initial work for XLNet

* Apply suggestions from code review

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Final clean up and working XLNet script

* Test and debug

* Final working version

* Add new SQUAD example

* Same with a task-specific Trainer

* Address review comment.

* Small fixes

* Initial work for XLNet

* Apply suggestions from code review

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Final clean up and working XLNet script

* Test and debug

* Final working version

* Add tick

* Update README

* Address review comments

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Put models in subfolders

* Styling

* Fix imports in tests

* More fixes in test imports

* Sneaky hidden imports

* Fix imports in doc files

* More sneaky imports

* Finish fixing tests

* Fix examples

* Fix path for copies

* More fixes for examples

* Fix dummy files

* More fixes for example

* More model import fixes

* Is this why you're unhappy GitHub?

* Fix imports in conver command

* fix encoder decoder models

* add .gitignore

* splitting fast and slow tokenizers [WIP]

* [WIP] splitting sentencepiece and tokenizers dependencies

* update dummy objects

* add name_or_path to models and tokenizers

* prefix added to file names

* prefix

* styling + quality

* spliting all the tokenizer files - sorting sentencepiece based ones

* update tokenizer version up to 0.9.0

* remove hard dependency on sentencepiece 🎉

* and removed hard dependency on tokenizers 🎉



* update conversion script

* update missing models

* fixing tests

* move test_tokenization_fast to main tokenization tests - fix bugs

* bump up tokenizers

* fix bert_generation

* update ad fix several tokenizers

* keep sentencepiece in deps for now

* fix funnel and deberta tests

* fix fsmt

* fix marian tests

* fix layoutlm

* fix squeezebert and gpt2

* fix T5 tokenization

* fix xlnet tests

* style

* fix mbart

* bump up tokenizers to 0.9.2

* fix model tests

* fix tf models

* fix seq2seq examples

* fix tests without sentencepiece

* fix slow => fast  conversion without sentencepiece

* update auto and bert generation tests

* fix mbart tests

* fix auto and common test without tokenizers

* fix tests without tokenizers

* clean up tests lighten up when tokenizers + sentencepiece are both off

* style quality and tests fixing

* add sentencepiece to doc/examples reqs

* leave sentencepiece on for now

* style quality split hebert and fix pegasus

* WIP Herbert fast

* add sample_text_no_unicode and fix hebert tokenization

* skip FSMT example test for now

* fix style

* fix fsmt in example tests

* update following Lysandre and Sylvain's comments

* Update src/transformers/testing_utils.py

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

* Update src/transformers/testing_utils.py

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

* Update src/transformers/tokenization_utils_base.py

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

* Update src/transformers/tokenization_utils_base.py

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

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

* added rag WIP

* path fix

* Formatting / renaming prior to actual work

* added rag WIP

* path fix

* Formatting / renaming prior to actual work

* added rag WIP

* path fix

* Formatting / renaming prior to actual work

* added rag WIP

* Formatting / renaming prior to actual work

* First commit

* improve comments

* Retrieval evaluation scripts

* refactor to include modeling outputs + MPI retriever

* Fix rag-token model + refactor

* Various fixes + finetuning logic

* use_bos fix

* Retrieval refactor

* Finetuning refactoring and cleanup

* Add documentation and cleanup

* Remove set_up_rag_env.sh file

* Fix retrieval wit HF index

* Fix import errors

* Fix quality errors

* Refactor as per suggestions in https://github.com/huggingface/transformers/pull/6813#issuecomment-687208867



* fix quality

* Fix RAG Sequence generation

* minor cleanup plus initial tests

* fix test

* fix tests 2

* Comments fix

* post-merge fixes

* Improve readme + post-rebase refactor

* Extra dependencied for tests

* Fix tests

* Fix tests 2

* Refactor test requirements

* Fix tests 3

* Post-rebase refactor

* rename nlp->datasets

* RAG integration tests

* add tokenizer to slow integration test and allow retriever to run on cpu

* add tests; fix position ids warning

* change structure

* change structure

* add from encoder generator

* save working solution

* make all integration tests pass

* add RagTokenizer.save/from_pretrained and RagRetriever.save/from_pretrained

* don't save paths

* delete unnecessary imports

* pass config to AutoTokenizer.from_pretrained for Rag tokenizers

* init wiki_dpr only once

* hardcode legacy index and passages paths (todo: add the right urls)

* finalize config

* finalize retriver api and config api

* LegacyIndex index download refactor

* add dpr to autotokenizer

* make from pretrained more flexible

* fix ragfortokengeneration

* small name changes in tokenizer

* add labels to models

* change default index name

* add retrieval tests

* finish token generate

* align test with previous version and make all tests pass

* add tests

* finalize tests

* implement thoms suggestions

* add first version of test

* make first tests work

* make retriever platform agnostic

* naming

* style

* add legacy index URL

* docstrings + simple retrieval test for distributed

* clean model api

* add doc_ids to retriever's outputs

* fix retrieval tests

* finish model outputs

* finalize model api

* fix generate problem for rag

* fix generate for other modles

* fix some tests

* save intermediate

* set generate to default

* big refactor generate

* delete rag_api

* correct pip faiss install

* fix auto tokenization test

* fix faiss install

* fix test

* move the distributed logic to examples

* model page

* docs

* finish tests

* fix dependencies

* fix import in __init__

* Refactor eval_rag and finetune scripts

* start docstring

* add psutil to test

* fix tf test

* move require torch to top

* fix retrieval test

* align naming

* finish automodel

* fix repo consistency

* test ragtokenizer save/load

* add rag model output docs

* fix ragtokenizer save/load from pretrained

* fix tokenizer dir

* remove torch in retrieval

* fix docs

* fixe finetune scripts

* finish model docs

* finish docs

* remove auto model for now

* add require torch

* remove solved todos

* integrate sylvains suggestions

* sams comments

* correct mistake on purpose

* improve README

* Add generation test cases

* fix rag token

* clean token generate

* fix test

* add note to test

* fix attention mask

* add t5 test for rag

* Fix handling prefix in finetune.py

* don't overwrite index_name

Co-authored-by: Patrick Lewis <plewis@fb.com>
Co-authored-by: Aleksandra Piktus <piktus@devfair0141.h2.fair>
Co-authored-by: Aleksandra Piktus <piktus@learnfair5102.h2.fair>
Co-authored-by: Aleksandra Piktus <piktus@learnfair5067.h2.fair>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com>

* Better None gradients handling

* Apply Style

* Apply Style

* Create a loss class per task to compute its respective loss

* Add loss classes to the ALBERT TF models

* Add loss classes to the BERT TF models

* Add question answering and multiple choice to TF Camembert

* Remove prints

* Add multiple choice model to TF DistilBERT + loss computation

* Add question answering model to TF Electra + loss computation

* Add token classification, question answering and multiple choice models to TF Flaubert

* Add multiple choice model to TF Roberta + loss computation

* Add multiple choice model to TF XLM + loss computation

* Add multiple choice and question answering models to TF XLM-Roberta

* Add multiple choice model to TF XLNet + loss computation

* Remove unused parameters

* Add task loss classes

* Reorder TF imports + add new model classes

* Add new model classes

* Bugfix in TF T5 model

* Bugfix for TF T5 tests

* Bugfix in TF T5 model

* Fix TF T5 model tests

* Fix T5 tests + some renaming

* Fix inheritance issue in the AutoX tests

* Add tests for TF Flaubert and TF XLM Roberta

* Add tests for TF Flaubert and TF XLM Roberta

* Remove unused piece of code in the TF trainer

* bugfix and remove unused code

* Bugfix for TF 2.2

* Apply Style

* Divide TFSequenceClassificationAndMultipleChoiceLoss into their two respective name

* Apply style

* Mirror the PT Trainer in the TF one: fp16, optimizers and tb_writer as class parameter and better dataset handling

* Fix TF optimizations tests and apply style

* Remove useless parameter

* Bugfix and apply style

* Fix TF Trainer prediction

* Now the TF models return the loss such as their PyTorch couterparts

* Apply Style

* Ignore some tests output

* Take into account the SQuAD cls_index, p_mask and is_impossible parameters for the QuestionAnswering task models.

* Fix names for SQuAD data

* Apply Style

* Fix conflicts with 2.11 release

* Fix conflicts with 2.11

* Fix wrongname

* Add better documentation on the new create_optimizer function

* Fix isort

* logging_dir: use same default as PyTorch

Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Created using Colaboratory

* [examples] reorganize files

* remove run_tpu_glue.py as superseded by TPU support in Trainer

* Bugfix: int, not tuple

* move files around

* feat: add logging through Weights & Biases

* feat(wandb): make logging compatible with all scripts

* style(trainer.py): fix formatting

* [Trainer] Tweak wandb integration

Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* doc

* [tests] Add sample files for a regression task

* [HUGE] Trainer

* Feedback from @sshleifer

* Feedback from @thomwolf + logging tweak

* [file_utils] when downloading concurrently, get_from_cache will use the cached file for subsequent processes

* [glue] Use default max_seq_length of 128 like before

* [glue] move DataTrainingArguments around

* [ner] Change interface of InputExample, and align run_{tf,pl}

* Re-align the pl scripts a little bit

* ner

* [ner] Add integration test

* Fix language_modeling with API tweak

* [ci] Tweak loss target

* Don't break console output

* amp.initialize: model must be on right device before

* [multiple-choice] update for Trainer

* Re-align to 827d6d6e

adding conversion script

adding first draft of modeling & tokenization

adding placeholder for test files

bunch of changes

registering the tokenizer/model/etc

tests

change link; something is very VERY wrong here

weird end-of-word thingy going on

i think the tokenization works now ; wrote the unit tests

overall structure works;load w next

the monster is alive!

works after some cleanup as well

adding emacs autosave to gitignore

currently only supporting the 48 layer one; seems to infer fine on my macbook

cleanup

fixing some documentation

fixing some documentation

tests passing?

now works on CUDA also

adding greedy?

adding greedy sampling

works well