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* Allow tests in examples to use cuda or fp16,if they are available

The tests in examples didn't use the cuda or fp16 even if they where available.
- The text classification example (`run_glue.py`) didn't use the fp16 even if it was available but
  the device was take based on the availablity(cuda/cpu).
- The language-modeling example (`run_language_modeling.py`) was having `--no_cuda` argument
  which made the test to work without cuda. This example is having issue when running with fp16
  thus it not enabled (got an assertion error for perplexity due to it higher value).
- The cuda and fp16 is not enabled for question-answering example (`run_squad.py`) as it is having a
  difference in the f1 score.
- The text-generation example (`run_generation.py`) will take the cuda or fp16 whenever it is available.

Resolves some of: #5057

* Unwanted import of is_apex_available was removed

* Made changes to test examples file to have the pass --fp16 only if cuda and apex is avaliable
- run_glue.py: Removed the check for cuda and fp16.
- run_generation.py: Removed the check for cuda and fp16 also removed unwanted flag creation.

* Incorrectly sorted imports fixed

* The model needs to be converted to half precision

* Formatted single line if condition statement to multiline

* The torch_device also needed to be checked before running the test on examples
- The tests in examples which uses cuda should also depend from the USE_CUDA flag,
  similarly to the rest of the test suite. Even if we decide to set USE_CUDA to
  True by default, setting USE_CUDA to False should result in the examples not using CUDA

* Format some of the code in test_examples file

* The improper import of is_apex_available was sorted

* Formatted the code to keep the style standards

* The comma at the end of list giving a flake8 issue was fixed

* Import sort was fixed

* Removed the clean_test_dir function as its not used right now

* add overload for type checker

* black

* add dpr to models summary

* minor

* minor

* Update docs/source/model_summary.rst

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

* Update docs/source/model_summary.rst

qa -> question ansering (cont'd)
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add model card for singbert.

Adding a model card for singbert- bert for singlish and manglish.

* Update README.md

Add additional tags and model name.

* Update README.md

Fix tag for malay.

* Update model_cards/zanelim/singbert/README.md

Fix language
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>

* Add examples and custom widget input.

Add examples and custom widget input.
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>

As suggested here: https://github.com/huggingface/transformers/issues/6651#issuecomment-678594233
this removes generic `generate` doc with examples not-relevant to bart.

* Create PULL_REQUEST_TEMPLATE.md

Proposing to copy this neat feature from pytorch. This is a small template that let's a PR submitter tell which issue that PR closes.

* Update .github/PULL_REQUEST_TEMPLATE.md
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>
Co-authored-by: Kevin Canwen Xu <canwenxu@126.com>

* Add optuna hyperparameter search to Trainer

* @julien-c suggestions
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

* Make compute_objective an arg function

* Formatting

* Rework to make it easier to add ray

* Formatting

* Initial support for Ray

* Formatting

* Polish and finalize

* Add trial id to checkpoint with Ray

* Smaller default

* Use GPU in ray if available

* Formatting

* Fix test

* Update install instruction
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>

* Address review comments

* Formatting post-merge
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>

* Run new isort

* More changes

* Update CI, CONTRIBUTING and benchmarks

* Fixed DataCollatorForLanguageModeling + PermutationLanguageModeling not accepting lists of lists

* Update data_collator.py

* black was grumpy

* Don't reset the type of the dataset

* Formatting

* Update trainer.py
Co-authored-by: Teven <teven.lescao@gmail.com>

* Create README.md

* Update README.md

* Create README.md

* Update README.md

* added multiple codeswitch model

* Create README.md

* Update README.md

* Create README.md

* Update README.md

Model was trained with the wrong tokenizer. Retrained with correct tokenizer - thanks for spotting @lhoestq !

I works like a charm!
Look at the output of the example code!

@julien-c

* add pegasus to docs

* Update docs/source/model_summary.rst

* added CamembertForCausalLM

* add in __init__ and auto model

* style

* doc

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

* Add a classmethod to easily build a Trainer from nlp dataset and metric

* Fix docstrings

* Split train/eval

* Formatting

* Log dropped columns + docs

* Authorize callable activations

* Poc for auto activation

* Be framework-agnostic

* Formatting

* Remove class method

* Remove unnecessary code

* Move threshold up for flaky test with Electra

* Update above as well

* xlnet fp16 bug fix

* comment cast added

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

* fix distilbert

* fix typo