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* Add a `distributed_env` property to TrainingArguments

* Change name

* Address comment

* restore skip

* Revert "Remove deprecated `evalutate_during_training` (#8852)"

This reverts commit 55302990.

* check that pipeline.git.base_revision is defined before proceeding

* Revert "Revert "Remove deprecated `evalutate_during_training` (#8852)""

This reverts commit dfec84db3fdce1079f01f1bc8dfaf21db2ccaba1.

* check that pipeline.git.base_revision is defined before proceeding

* doc only

* doc + code

* restore

* restore

* typo

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

* fix DP case on multi-gpu

* make executable

* test all 3 modes

* use the correct check for distributed

* dp doesn't need a special case

* restore original name

* cleanup

* 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

* Slightly increase tolerance between pytorch and flax output
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* test_multiple_sentences doesn't require torch
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Simplify parameterization on "jit" to use boolean rather than str
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Use `require_torch` on `test_multiple_sentences` because we pull the weight from the hub.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Rename "jit" parameter to "use_jit" for (hopefully) making it self-documenting.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Remove pytest.mark.parametrize which seems to fail in some circumstances
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Fix unused imports.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Fix style.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Give default parameters values for traced model.
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

* Review comment: Change sentences to sequences
Signed-off-by: Morgan Funtowicz <funtowiczmo@gmail.com>

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

* 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>

* Migration guide from v3.x to v4.x

* Better wording

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

* Sylvain's comments

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

* implement job skipping for doc-only PRs

* silent grep is crucial

* wip

* wip

* wip

* wip

* wip

* wip

* wip

* wip

* let's add doc

* let's add code

* revert test commits

* restore

* Better name

* Better name

* Better name

* some more testing

* some more testing

* some more testing

* finish testing

* 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>

enforce unix newline encoding regardless of OS creating the file

* Create README.md

* Fix model path

* 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

* try flax fix

* same for roberta

The tokenizer called at the input_ids of example 2 is currently encoding text_1. I think this should be changed to text_2.

* [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