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We currently save the pretrained_weights of the encoder and decoder in
two separate directories `encoder` and `decoder`. However, for the
`from_pretrained` function to operate with automodels we need to
specify the type of model in the path to the weights.

The path to the encoder/decoder weights is handled by the
`PreTrainedEncoderDecoder` class in the `save_pretrained` function. Sice
there is no easy way to infer the type of model that was initialized for
the encoder and decoder we add a parameter `model_type` to the function.
This is not an ideal solution as it is error prone, and the model type
should be carried by the Model classes somehow.

This is a temporary fix that should be changed before merging.

Since I started my PR the `add_special_token_single_sequence` function
has been deprecated for another; I replaced it with the new function.

* Switch to plain unittest for skipping slow tests.

Add a RUN_SLOW environment variable for running them.

* Switch to plain unittest for PyTorch dependency.

* Switch to plain unittest for TensorFlow dependency.

* Avoid leaking open files in the test suite.

This prevents spurious warnings when running tests.

* Fix unicode warning on Python 2 when running tests.

The warning was:

    UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal

* Support running PyTorch tests on a GPU.

Reverts 27e015bd.

* Tests no longer require pytest.

* Make tests pass on cuda

* [cli] Uploads: add progress bar

see https://github.com/huggingface/transformers/pull/2044#discussion_r354057827 for context

* rename + documentation

* Add auto-referential comment

corrected documentation for past tensor shape for ctrl and gpt2 model

Add NER TF2 example.

Nicer error message when Bert's input is missing batch size

* license

* changes

* ok

* Update paper link and commands to run

* pointer to uber repo

Remove dead code in tests.

Fixing camembert tokenization

typo fix on the docs as per Pytorch v1.1+

fixed XLNet attention output for both attention streams whenever target_mapping is provided