GitLab

* Added is_fast property on BatchEncoding to indicate if the object comes from a Fast Tokenizer.

* Added __get_state__() & __set_state__() to be pickable.

* Correct tokens() return type from List[int] to List[str]

* Added unittest for BatchEncoding pickle/unpickle

* Added unittest for BatchEncoding is_fast

* More careful checking on BatchEncoding unpickle tests.

* Formatting.

* is_fast should assertTrue on Rust tokenizers.

* Ensure tensorflow has correct way of checking array_equal

* More formatting.

* Refactor tensor creation in tokenizers.

* Make sure to convert string to TensorType

* Refactor convert_to_tensors_

* Introduce numpy tensor creation

* Format

* Add unittest for TensorType creation from str

* sorting imports

* Added unittests for numpy tensor conversion.

* Do not use in-place version for squeeze as numpy doesn't provide such feature.

* Added extra parameter prepend_batch_axis: bool on prepare_for_model.

* Ensure test_np_encode_plus_sent_to_model is not executed if encoder/decoder model.

* style.

* numpy tests require_torch for now while flax not merged.

* Hopefully will make flake8 happy.

* One more time 🎶

This construct isn't used anymore these days.

Running python tests/test_foo.py puts the tests/ directory on
PYTHONPATH, which isn't representative of how we run tests.

Use python -m unittest tests/test_foo.py instead.

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.

* 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