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fix multi-gpu eval in torch examples

Fix special tokens addition in decoder #1807

sum() is replaced by itertools.chain.from_iterable()

replace LambdaLR scheduler wrappers by function

Token indices sequence length is longer than the specified maximum sequence length for this model

Custom schedulers are currently initiated by wrapping Pytorch's LambdaLR
class and passing a method of the wrapping class to the __init__
function of LambdaLR. This approach is not appropriate for several
reasons:

1. one does not need to define a class when it only defines a
__init__() method;
2. instantiating the parent class by passing a method of the child class
creates a cyclical reference which leads to memory leaks. See issues #1742 and #1134.

In this commit we replace the wrapper classes with functions that
instantiate `LambdaLR` with a custom learning rate function. We use a
closure to specify the parameter of the latter. We also do a bit of
renaming within the function to explicit the behaviour and removed
docstrings that were subsequently not necessary.

sum() is the leanest method to flatten a string list, so it's been replaced by itertools.chain.from_iterable()

Make imports and args.overwrite_cache match run_glue.py

converted from https://github.com/openai/gpt-2-output-dataset/tree/master/detector

Co-Authored-By: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
Co-Authored-By: Jong Wook Kim <jongwook@nyu.edu>
Co-Authored-By: Jeff Wu <wuthefwasthat@gmail.com>

cf. https://docs.pytest.org/en/latest/example/simple.html

model forwards can take an inputs_embeds param

add progress bar to convert_examples_to_features

Add speed log to examples/run_squad.py

Fixing mode in evaluate during training