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* fix resize tokens

* correct mobile_bert

* move embedding fix into modeling_utils.py

* refactor

* fix lm head resize

* refactor

* break lines to make sylvain happy

* add news tests

* fix typo

* improve test

* skip bart-like for now

* check if base_model = get(...) is necessary

* clean files

* improve test

* fix tests

* revert style templates

* Update templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py

* [trainer] improve code

This PR:
- removes redundant code 
```
self.model = model if model is not None else None
```
and
```
self.model = model
```
are the same.

* separate attribute assignment from code logic - which simplifies things further.

* whitespace

* Warning about too long input for fast tokenizers too

If truncation is not set in tokenizers, but the tokenization is too long
for the model (`model_max_length`), we used to trigger a warning that

The input would probably fail (which it most likely will).

This PR re-enables the warning for fast tokenizers too and uses common
code for the trigger to make sure it's consistent across.

* Checking for pair of inputs too.

* Making the function private and adding it's doc.

* Remove formatting ?? in odd place.

* Missed uppercase.

* Transfoxl sequence classification

* Transfoxl sequence classification

* Add a `distributed_env` property to TrainingArguments

* Change name

* Address comment

Prevent BatchEncoding from blindly passing casts down to the tensors it contains. Fixes #6582. (#8860)

Update src/transformers/tokenization_utils_base.py with review fix
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* 2 typos - from_question_encoder_generator_configs

fix 2 typos
from_encoder_generator_configs --> from_question_encoder_generator_configs

* apply make style

* add CTRLForSequenceClassification

* pass local test

* merge with master

* fix modeling test for sequence classification

* fix deco

* fix assert

* NerPipeline (TokenClassification) now outputs offsets of words

- It happens that the offsets are missing, it forces the user to pattern
match the "word" from his input, which is not always feasible.
For instance if a sentence contains the same word twice, then there
is no way to know which is which.
- This PR proposes to fix that by outputting 2 new keys for this
pipelines outputs, "start" and "end", which correspond to the string
offsets of the word. That means that we should always have the
invariant:

```python
input[entity["start"]: entity["end"]] == entity["entity_group"]
                                    # or entity["entity"] if not grouped
```

* Fixing doc style

* Remove deprecated `evalutate_during_training`

* Update src/transformers/training_args_tf.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Use model.from_pretrained for DataParallel also

When training on multiple GPUs, the code wraps a model with torch.nn.DataParallel. However if the model has custom from_pretrained logic, it does not get applied during load_best_model_at_end.

This commit uses the underlying model during load_best_model_at_end, and re-wraps the loaded model with DataParallel.

If you choose to reject this change, then could you please move the this logic to a function, e.g. def load_best_model_checkpoint(best_model_checkpoint) or something, so that it can be overridden?

* Fix silly bug

* Address review comments

Thanks for the feedback. I made the change that you proposed, but I also think we should update L811 to check if `self.mode` is an instance of `PreTrained`, otherwise we would still not get into that `if` section, right?

Related issue: https://github.com/huggingface/transformers/issues/8837

* Add T5 Encoder class for feature extraction

* fix T5 encoder add_start_docstrings indent

* update init with T5 encoder

* update init with TFT5ModelEncoder

* remove TFT5ModelEncoder

* change T5ModelEncoder order in init

* add T5ModelEncoder to transformers init

* clean T5ModelEncoder

* update init with TFT5ModelEncoder

* add TFModelEncoder for Tensorflow

* update init with TFT5ModelEncoder

* Update src/transformers/models/t5/modeling_t5.py

change output from Seq2SeqModelOutput to BaseModelOutput
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* remove encoder_outputs

1. remove encoder_outputs from the function call.
2. remove the encoder_outputs If statement.
3. remove isinstance from return_dict.

* Authorize missing decoder keys

* remove unnecessary input parameters

remove pask_key_values and use_cache

* remove use_cache

remove use_cache from the forward method

* add doctoring for T5 encoder

add doctoring for T5 encoder with T5_ENCODER_INPUTS_DOCSTRING

* change return_dict to dot access

* add T5_ENCODER_INPUTS_DOCSTRING for TF T5

* change TFT5Encoder output type to BaseModelOutput

* remove unnecessary parameters for TFT5Encoder

* remove unnecessary if statement

* add import BaseModelOutput

* fix BaseModelOutput typo to TFBaseModelOutput

* update T5 doc with T5ModelEncoder

* add T5ModelEncoder to tests

* finish pytorch

* finish docs and mt5

* add mtf to init

* fix init

* remove n_positions

* finish PR

* Update src/transformers/models/mt5/modeling_mt5.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/t5/modeling_t5.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/t5/modeling_tf_t5.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/mt5/modeling_tf_mt5.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Fix minor typos

* Additional typos

* Style fix
Co-authored-by: guyrosin <guyrosin@assist-561.cs.technion.ac.il>

* refactor

* further refactor

* fix the rest tomorrow

* save intermediate

* finish slow tokenizer

* make more tests pass

* finish refactor

* fix comment

* clean further

* fix name

* fix naming

* Update src/transformers/models/reformer/tokenization_reformer.py

* Apply suggestions from code review

* Apply suggestions from code review

* refactor

* fix init tokenizers

* refactor

* improve convert

* refactor

* correct convert slow tokenizer

* final fix for Pegasus Tok

* remove ipdb

* improve links

* Fix decoder not returning hidden states from the last layer

* Resolve conflict

* Change the way to gather hidden states

* Add decoder hidden states test

* Make pytest and black happy

* Remove redundant line

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

* Add init barthez

* Add barthez model, tokenizer and docs

BARThez is a pre-trained french seq2seq model that uses BART objective.

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

* Add license

* Change URLs scheme

* Remove barthez model keep tokenizer

* Fix style

* Fix quality

* Update tokenizer

* Add fast tokenizer

* Add fast tokenizer test
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* update configuration_utils.py typing to allow pathlike objects when sensible

* update modeling_utils.py typing to allow pathlike objects when sensible

* black

* update tokenization_utils_base.py typing to allow pathlike objects when sensible

* update tokenization_utils_fast.py typing to allow pathlike objects when sensible

* update configuration_auto.py typing to allow pathlike objects when sensible

* update configuration_auto.py docstring to allow pathlike objects when sensible

* update tokenization_auto.py docstring to allow pathlike objects when sensible

* black

* correct dpr test and bert pos fault

* fix dpr bert config problem

* fix layoutlm

* add config to dpr as well

* [FlaxBert] Fix non-broadcastable attention mask for batched forward-passes

* [FlaxRoberta] Fix non-broadcastable attention mask

* Use jax.numpy instead of ordinary numpy (otherwise not jit-able)

* Partially revert "Use jax.numpy ..."

* Add tests for batched forward passes

* Avoid unnecessary OOMs due to preallocation of GPU memory by XLA

* Auto-fix style

* Re-enable GPU memory preallocation but with mem fraction < 1/paralleism

* fix mems in xlnet

* fix use_mems

* fix use_mem_len

* fix use mems

* clean docs

* fix tf typo

* make xlnet tf for generation work

* fix tf test

* refactor use cache

* add use cache for missing models

* correct use_cache in generate

* correct use cache in tf generate

* fix tf

* correct getattr typo

* make sylvain happy

* change in docs as well

* do not apply to cookie cutter statements

* fix tf test

* make pytorch model fully backward compatible

* bart output hidden states upstream

* same w/ decoder

* add tests

* fix prophetnet

* fix gpt2 and ctrl

* fix fstm and skip test for reformer and longformer

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

* Fix QA argument handler

* Attempt to get a better fix for QA (#8768)
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

* First draft

* Styling

* With all changes staged

* Update docs/source/index.rst
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

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

* Apply on BERT and ALBERT

* Update TF Bart

* Add input processing to TF BART

* Add input processing for TF CTRL

* Add input processing to TF Distilbert

* Add input processing to TF DPR

* Add input processing to TF Electra

* Add input processing for TF Flaubert

* Add deprecated arguments

* Add input processing to TF XLM

* remove unused imports

* Add input processing to TF Funnel

* Add input processing to TF GPT2

* Add input processing to TF Longformer

* Add input processing to TF Lxmert

* Apply style

* Add input processing to TF Mobilebert

* Add input processing to TF GPT

* Add input processing to TF Roberta

* Add input processing to TF T5

* Add input processing to TF TransfoXL

* Apply style

* Rebase on master

* Bug fix

* Retry to bugfix

* Retry bug fix

* Fix wrong model name

* Try another fix

* Fix BART

* Fix input precessing

* Apply style

* Put the deprecated warnings in the input processing function

* Remove the unused imports

* Raise an error when len(kwargs)>0

* test ModelOutput instead of TFBaseModelOutput

* Bug fix

* Address Patrick's comments

* Address Patrick's comments

* Address Sylvain's comments

* Add the new inputs in new Longformer models

* Update the template with the new input processing

* Remove useless assert

* Apply style

* Trigger CI

* implement support for run-time dependency version checking

* try not escaping !

* use findall that works on py36

* small tweaks

* autoformatter worship

* simplify

* shorter names

* add support for non-versioned checks

* add deps

* revert

* tokenizers not required, check version only if installed

* make a proper distutils cmd and add make target

* tqdm must be checked before tokenizers

* workaround the DistributionNotFound peculiar setup

* handle the rest of packages in setup.py

* fully sync setup.py's install_requires - to check them all

* nit

* make install_requires more readable

* typo

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

* restyle

* add types

* simplify

* simplify2
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* MT5 should have an autotokenizer

* Different configurations should be able to point to same tokenizers

* Support BERT relative position embeddings

* Fix typo in README.md

* Address review comment

* Fix failing tests

* [tiny] Fix style_doc.py check by adding an empty line to configuration_bert.py

* make fix copies

* fix configs of electra and albert and fix longformer

* remove copy statement from longformer

* fix albert

* fix electra

* Add bert variants forward tests for various position embeddings

* [tiny] Fix style for test_modeling_bert.py

* improve docstring

* [tiny] improve docstring and remove unnecessary dependency

* [tiny] Remove unused import

* re-add to ALBERT

* make embeddings work for ALBERT

* add test for albert
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Add parallelize methods to the .rst files

* Correct format

* Add early stopping patience and minimum threshold metric must improve to prevent early stopping to pytorch trainer

* Add early stopping test

* Set patience counter to 0 if best metric not defined yet

* Make early stopping a callback. Add callback event for updating the best metric for early stopping callback to trigger on.

* Run make style

* make funciton name sensible

* Improve new argument docstring wording and hope that flakey CI test passes.

* Use on_evaluation callback instead of custom. Remove some debug printing

* Move early stopping arguments and state into early stopping callback

* Run make style

* Remove old code

* Fix docs formatting. make style went rogue on me.

* Remove copied attributes and fix variable

* Add assertions on training arguments instead of mutating them. Move comment out of public docs.

* Make separate test for early stopping callback. Add test of invalid arguments.

* Run make style... I remembered before CI this time!

* appease flake8

* Add EarlyStoppingCallback to callback docs

* Make docstring EarlyStoppingCallabck match other callbacks.

* Fix typo in docs

* consistent ignore keys + make private

* style

* - authorized_missing_keys    => _keys_to_ignore_on_load_missing
  - authorized_unexpected_keys => _keys_to_ignore_on_load_unexpected

* move public doc of private attributes to private comment