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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

* Created using Colaboratory

* [examples] reorganize files

* remove run_tpu_glue.py as superseded by TPU support in Trainer

* Bugfix: int, not tuple

* move files around

* fix empty prompt

* fix length in generation pipeline

* fix n_gpu count when no_cuda flag is activated

* someone was left behind

Add preprocessing step for transfo-xl tokenization to avoid tokenizing words followed by punction to <unk> (#2987)

* add preprocessing to add space before punctuation for transfo_xl

* improve warning messages

* make style

* compile regex at instantination of tokenizer object

* improving generation

* finalized special token behaviour for no_beam_search generation

* solved modeling_utils merge conflict

* solve merge conflicts in modeling_utils.py

* add run_generation improvements from PR #2749

* adapted language generation to not use hardcoded -1 if no padding token is available

* remove the -1 removal as hard coded -1`s are not necessary anymore

* add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown

* add slow language generation tests for pretrained models using hardcoded output with pytorch seed

* delete ipdb

* check that all generated tokens are valid

* renaming

* renaming Generation -> Generate

* make style

* updated so that generate_beam_search has same token behavior than generate_no_beam_search

* consistent return format for run_generation.py

* deleted pretrain lm generate tests -> will be added in another PR

* cleaning of unused if statements and renaming

* run_generate will always return an iterable

* make style

* consistent renaming

* improve naming, make sure generate function always returns the same tensor, add docstring

* add slow tests for all lmhead models

* make style and improve example comments modeling_utils

* better naming and refactoring in modeling_utils

* improving generation

* finalized special token behaviour for no_beam_search generation

* solved modeling_utils merge conflict

* solve merge conflicts in modeling_utils.py

* add run_generation improvements from PR #2749

* adapted language generation to not use hardcoded -1 if no padding token is available

* remove the -1 removal as hard coded -1`s are not necessary anymore

* add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown

* add slow language generation tests for pretrained models using hardcoded output with pytorch seed

* delete ipdb

* check that all generated tokens are valid

* renaming

* renaming Generation -> Generate

* make style

* updated so that generate_beam_search has same token behavior than generate_no_beam_search

* consistent return format for run_generation.py

* deleted pretrain lm generate tests -> will be added in another PR

* cleaning of unused if statements and renaming

* run_generate will always return an iterable

* make style

* consistent renaming

* improve naming, make sure generate function always returns the same tensor, add docstring

* add slow tests for all lmhead models

* make style and improve example comments modeling_utils

* better naming and refactoring in modeling_utils

* changed fast random lm generation testing design to more general one

* delete in old testing design in gpt2

* correct old variable name

* temporary fix for encoder_decoder lm generation tests - has to be updated when t5 is fixed

* adapted all fast random generate tests to new design

* better warning description in modeling_utils

* better comment

* better comment and error message
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

Ignore warnings related to Python 2, because it's going away soon.

This is the result of:

    $ isort --recursive examples templates transformers utils hubconf.py setup.py

This is the result of:

    $ black --line-length 119 examples templates transformers utils hubconf.py setup.py

There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.

This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.

see also https://github.com/salesforce/ctrl/pull/50

adding conversion script

adding first draft of modeling & tokenization

adding placeholder for test files

bunch of changes

registering the tokenizer/model/etc

tests

change link; something is very VERY wrong here

weird end-of-word thingy going on

i think the tokenization works now ; wrote the unit tests

overall structure works;load w next

the monster is alive!

works after some cleanup as well

adding emacs autosave to gitignore

currently only supporting the 48 layer one; seems to infer fine on my macbook

cleanup

fixing some documentation

fixing some documentation

tests passing?

now works on CUDA also

adding greedy?

adding greedy sampling

works well

Add option to use a 'stop token' which will be used to truncate the output text to everything till right before the 'stop token'