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[HUGE] Refactoring tokenizers backend - padding - truncation - pre-tokenized pipeline - fast tokenizers - tests (#4510)

* Use tokenizers pre-tokenized pipeline

* failing pretrokenized test

* Fix is_pretokenized in python

* add pretokenized tests

* style and quality

* better tests for batched pretokenized inputs

* tokenizers clean up - new padding_strategy - split the files

* [HUGE] refactoring tokenizers - padding - truncation - tests

* style and quality

* bump up requied tokenizers version to 0.8.0-rc1

* switched padding/truncation API - simpler better backward compat

* updating tests for custom tokenizers

* style and quality - tests on pad

* fix QA pipeline

* fix backward compatibility for max_length only

* style and quality

* Various cleans up - add verbose

* fix tests

* update docstrings

* Fix tests

* Docs reformatted

* __call__ method documented
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* 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 🎶

* Fix: unpin flake8 and fix cs errors

* Ok we still need to quote those

cc @julien-c

* Updating modeling tf files; adding tests

* Merge `encode_plus` and `batch_encode_plus`

* Renamed num_added_tokens to num_special_tokens_to_add
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Cherry-Pick: Partially fix space only input without special tokens added to the output #3091
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Added property is_fast on PretrainedTokenizer and PretrainedTokenizerFast
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Make fast tokenizers unittests work on Windows.

* Entirely refactored unittest for tokenizers fast.

* Remove ABC class for CommonFastTokenizerTest

* Added embeded_special_tokens tests from allenai @dirkgr

* Make embeded_special_tokens tests from allenai more generic

* Uniformize vocab_size as a property for both Fast and normal tokenizers

* Move special tokens handling out of PretrainedTokenizer (SpecialTokensMixin)

* Ensure providing None input raise the same ValueError than Python tokenizer + tests.

* Fix invalid input for assert_padding when testing batch_encode_plus

* Move add_special_tokens from constructor to tokenize/encode/[batch_]encode_plus methods parameter.

* Ensure tokenize() correctly forward add_special_tokens to rust.

* Adding None checking on top on encode / encode_batch for TransfoXLTokenizerFast.
Avoid stripping on None values.

* unittests ensure tokenize() also throws a ValueError if provided None

* Added add_special_tokens unittest for all supported models.

* Style

* Make sure TransfoXL test run only if PyTorch is provided.

* Split up tokenizers tests for each model type.

* Fix invalid unittest with new tokenizers API.

* Filter out Roberta openai detector models from unittests.

* Introduce BatchEncoding on fast tokenizers path.

This new structure exposes all the mappings retrieved from Rust.
It also keeps the current behavior with model forward.

* Introduce BatchEncoding on slow tokenizers path.

Backward compatibility.

* Improve error message on BatchEncoding for slow path

* Make add_prefix_space True by default on Roberta fast to match Python in majority of cases.

* Style and format.

* Added typing on all methods for PretrainedTokenizerFast

* Style and format

* Added path for feeding pretokenized (List[str]) input to PretrainedTokenizerFast.

* Style and format

* encode_plus now supports pretokenized inputs.

* Remove user warning about add_special_tokens when working on pretokenized inputs.

* Always go through the post processor.

* Added support for pretokenized input pairs on encode_plus

* Added is_pretokenized flag on encode_plus for clarity and improved error message on input TypeError.

* Added pretokenized inputs support on batch_encode_plus

* Update BatchEncoding methods name to match Encoding.

* Bump setup.py tokenizers dependency to 0.7.0rc1

* Remove unused parameters in BertTokenizerFast

* Make sure Roberta returns token_type_ids for unittests.

* Added missing typings

* Update add_tokens prototype to match tokenizers side and allow AddedToken

* Bumping tokenizers to 0.7.0rc2

* Added documentation for BatchEncoding

* Added (unused) is_pretokenized parameter on PreTrainedTokenizer encode_plus/batch_encode_plus methods.

* Added higher-level typing for tokenize / encode_plus / batch_encode_plus.

* Fix unittests failing because add_special_tokens was defined as a constructor parameter on Rust Tokenizers.

* Fix text-classification pipeline using the wrong tokenizer

* Make pipelines works with BatchEncoding

* Turn off add_special_tokens on tokenize by default.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Remove add_prefix_space from tokenize call in unittest.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Style and quality
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Correct message for batch_encode_plus none input exception.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Fix invalid list comprehension for offset_mapping overriding content every iteration.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* TransfoXL uses Strip normalizer.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Bump tokenizers dependency to 0.7.0rc3
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Support AddedTokens for special_tokens and use left stripping on mask for Roberta.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* SpecilaTokenMixin can use slots to faster access to underlying attributes.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Remove update_special_tokens from fast tokenizers.

* Ensure TransfoXL unittests are run only when torch is available.

* Style.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Style

* Style 🙏🙏



* Remove slots on SpecialTokensMixin, need deep dive into pickle protocol.

* Remove Roberta warning on __init__.

* Move documentation to Google style.
Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>

* Minimal example

* Proposal 2

* Proposal 2 for fast tokenizers

* Typings

* Docs

* Revert "Docs" for easier review

This reverts commit eaf0f97062e809887704a542144c537f769d5223.

* Remove unnecessary assignments

* Tests

* Fix faulty type

* Remove prints

* return_outputs -> model_input_names

* Revert "Revert "Docs" for easier review"

This reverts commit 6fdc69408102bf695797f2dfddbb6350c6b9e722.

* code quality

* force pad_token_id to be set before padding

* fix tests and forbid padding without having a padding_token_id set

* Testing that encode_plus and batch_encode_plus behave the same way

Spoiler alert: they don't

* Testing rest of arguments in batch_encode_plus

* Test tensor return in batch_encode_plus

* Addressing Sam's comments

* flake8

* Simplified with `num_added_tokens`

* Preserve spaces in GPT-2 tokenizers

Preserves spaces after special tokens in GPT-2 and inhereted (RoBERTa)
tokenizers, enabling correct BPE encoding. Automatically inserts a space
in front of first token in encode function when adding special tokens.

* Add tokenization preprocessing method

* Add framework argument to pipeline factory

Also fixes pipeline test issue. Each test input now treated as a
distinct sequence.

On Python 3, `open is io.open`.

This is the same change as for (TF)CommonTestCases for modeling.

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

As pointed out in #1545, when using an uncased model, and adding
a new uncased token, the tokenizer does not correctly identify this
in the case that the input text contains the token in a cased format.

For instance, if we load bert-base-uncased into BertTokenizer, and
then use .add_tokens() to add "cool-token", we get the expected
result for .tokenize('this is a cool-token'). However, we get a
possibly unexpected result for .tokenize('this is a cOOl-Token'),
which in fact mirrors the result for the former from before the new
token was added.

This commit adds
- functionality to PreTrainedTokenizer to handle this
situation in case a tokenizer (currently Bert, DistilBert,
and XLNet) has the do_lower_case=True kwarg by:
    1) lowercasing tokens added with .add_tokens()
    2) lowercasing text at the beginning of .tokenize()
- new common test case for tokenizers

https://github.com/huggingface/transformers/issues/1545