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* Fully rework training/prediction loops

* fix method name

* Fix variable name

* Fix property name

* Fix scope

* Fix method name

* Fix tuple index

* Fix tuple index

* Fix indentation

* Fix variable name

* fix eval before log

* Add drop remainder for test dataset

* Fix step number + fix logging datetime

* fix eval loss value

* use global step instead of step + fix logging at step 0

* Fix logging datetime

* Fix global_step usage

* Fix breaking loop + logging datetime

* Fix step in prediction loop

* Fix step breaking

* Fix train/test loops

* Force TF at least 2.2 for the trainer

* Use assert_cardinality to facilitate the dataset size computation

* Log steps per epoch

* Make tfds compliant with TPU

* Make tfds compliant with TPU

* Use TF dataset enumerate instead of the Python one

* revert previous commit

* Fix data_dir

* Apply style

* rebase on master

* Address Sylvain's comments

* Address Sylvain's and Lysandre comments

* Trigger CI

* Remove unused import

* add a link to README.md

* Update README.md

* MBART: support summarization tasks

* fix test

* Style

* add tokenizer test

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

The DataParallel training was fixed in https://github.com/huggingface/transformers/pull/5733, this commit also fixes the evaluation. It's more convenient when the user enables both `do_train` and `do_eval`.

Huge MT speedup!

* DataParallel fixes:

1. switched to a more precise check
-        if self.args.n_gpu > 1:
+        if isinstance(model, nn.DataParallel):

2. fix tests - require the same fixup under DataParallel as the training module

* another fix

Co-authored-by: Pradhy729 <49659913+Pradhy729@users.noreply.github.com>

Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* docs(wandb): explain how to use W&B integration

fix #5262

* Also mention TensorBoard
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

Co-Authored-By: Suraj Patil <surajp815@gmail.com>
Co-Authored-By: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Test XLA examples

* Style

* Using `require_torch_tpu`

* Style

* No need for pytest

* Add deebert code

* Add readme of deebert

* Add test for deebert

Update test for Deebert

* Update DeeBert (README, class names, function refactoring); remove requirements.txt

* Format update

* Update test

* Update readme and model init methods

improve unittests for finetuning, especially w.r.t testing frozen parameters
fix freeze_embeds for T5
add streamlit setup.cfg

[Almost all TF models] TF clean up: add missing CLM / MLM loss; fix T5 naming and keras compile (#5395)

* add first version of clm tf

* make style

* add more tests for bert

* update tf clm loss

* fix tests

* correct tf ner script

* add mlm loss

* delete bogus file

* clean tf auto model + add tests

* finish adding clm loss everywhere

* fix training in distilbert

* fix flake8

* save intermediate

* fix tf t5 naming

* remove prints

* finish up

* up

* fix tf gpt2

* fix new test utils import

* fix flake8

* keep backward compatibility

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

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

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

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

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

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

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

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

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

* add SquadDataset

* add DataCollatorForQuestionAnswering

* update __init__

* add run_squad with  trainer

* add DataCollatorForQuestionAnswering in __init__

* pass data_collator to trainer

* doc tweak

* Update run_squad_trainer.py

* Update __init__.py

* Update __init__.py
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Added data collator for XLNet language modeling and related calls

Added DataCollatorForXLNetLanguageModeling in data/data_collator.py
to generate necessary inputs for language modeling training with
XLNetLMHeadModel. Also added related arguments, logic and calls in
examples/language-modeling/run_language_modeling.py.

Resolves: #4739, #2008 (partially)

* Changed name to `DataCollatorForPermutationLanguageModeling`

Changed the name of `DataCollatorForXLNetLanguageModeling` to the more general `DataCollatorForPermutationLanguageModelling`.
Removed the `--mlm` flag requirement for the new collator and defined a separate `--plm_probability` flag for its use.
CTRL uses a CLM loss just like GPT and GPT-2, so should work out of the box with this script (provided `past` is taken care of
similar to `mems` for XLNet).
Changed calls and imports appropriately.

* Added detailed comments, changed variable names

Added more detailed comments to `DataCollatorForPermutationLanguageModeling` in `data/data_collator.py` to explain working. Also cleaned up variable names and made them more informative.

* Added tests for new data collator

Added tests in `tests/test_trainer.py` for DataCollatorForPermutationLanguageModeling based on those in DataCollatorForLanguageModeling. A specific test has been added to check for odd-length sequences.

* Fixed styling issues