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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

* Cleanup and unify Trainer/TFTrainer

* Forgot to adapt TFTrainingArgs

* In tf scripts n_gpu -> n_replicas

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

* Address review comments

* Formatting

* Fix typo
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

Otherwise, if label is not specified, the following error occurs:

	Traceback (most recent call last):
	  File "run_ner.py", line 303, in <module>
	    main()
	  File "run_ner.py", line 101, in main
	    model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
	  File "/home/user/anaconda3/envs/bert/lib/python3.7/site-packages/transformers/hf_argparser.py", line 159, in parse_json_file
	    obj = dtype(**inputs)
	TypeError: __init__() missing 1 required positional argument: 'labels'

* Fix the bug 'Attempted relative import with no known parent package' when using the bertabs example. Also change the used model from bertabs-finetuned-cnndm, since it seems not be accessible anymore

* Update run_summarization.py
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>