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T5 with past ONNX export, and more explicit past_key_values inputs and outputs names for ONNX model
Authored-by: Michael Benayoun <michael@huggingface.co>

Changed the way dynamically inserted submodules are named and the method used to insert them
Authored-by: Michael Benayoun <michael@huggingface.co>

* Initial work

* All auto models

* All tf auto models

* All flax auto models

* Tokenizers

* Add feature extractors

* Fix typos

* Fix other typo

* Use the right config

* Remove old mapping names and update logic in AutoTokenizer

* Update check_table

* Fix copies and check_repo script

* Fix last test

* Add back name

* clean up

* Update template

* Update template

* Forgot a )

* Use alternative to fixup

* Fix TF model template

* Address review comments

* Address review comments

* Style

* fix_torch_device_generate_test

* remove @

* update

* up

* fix

* remove f-stings

* correct readme

* up
Co-authored-by: Patrick von Platen <patrick@huggingface.co>

* finish PR

* add tests

* correct tests

* finish

* correct other flax tests

* better naming

* correct naming

* finish

* apply sylvains suggestions

GPT-Neo ONNX export and task / feature refactoring
Authored-by: Michael Benayoun <michael@huggingface.co>

Updating the import for load_dataset

* First pass

* Make conversion script work

* Improve conversion script

* Fix bug, conversion script working

* Improve conversion script, implement BEiTFeatureExtractor

* Make conversion script work based on URL

* Improve conversion script

* Add tests, add documentation

* Fix bug in conversion script

* Fix another bug

* Add support for converting masked image modeling model

* Add support for converting masked image modeling

* Fix bug

* Add print statement for debugging

* Fix another bug

* Make conversion script finally work for masked image modeling models

* Move id2label for datasets to JSON files on the hub

* Make sure id's are read in as integers

* Add integration tests

* Make style & quality

* Fix test, add BEiT to README

* Apply suggestions from @sgugger's review

* Apply suggestions from code review

* Make quality

* Replace nielsr by microsoft in tests, add docs

* Rename BEiT to Beit

* Minor fix

* Fix docs of BeitForMaskedImageModeling
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix_torch_device_generate_test

* remove @

* fix flax docs

* correct more docs in flax

* another correction

* fix flax docs

* Apply suggestions from code review

* finish PR

* finish mt5

* push

* up

* Update tests/test_modeling_flax_mt5.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* [Flax] Align device name in docs

* make style

* fix import error

* pad_to_multiple_of added to DataCollatorForWholeWordMask

* pad_to_multiple_of added to DataCollatorForWholeWordMask
Co-authored-by: Цвигун Аким Олегович <AOTsvigun@sberbank.ru>

* Return raw outputs in TextClassificationPipeline

* Style

* Support for problem type

* Update src/transformers/pipelines/text_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Apply Nicolas' comments
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Fix from_pretrained with corrupted state_dict

* Adapt test

* Use better checkpoint

* Style

* Clean up

This change enables tf.keras.mixed_precision with bf16

* fix #12970

* Update tests/test_trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update tests/test_trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update tests/test_trainer.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* remove unnecessary issue link

* fix test formatting
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix typo in example/text-classification README

* add space to align the table

* Add multilingual documentation support

* Add multilingual documentation support

* make style

* make style

* revert

Co-authored-by: Lukas Wutschitz <lukas.wutschitz@microsoft.com>

* minor change to log azureml only for rank 0

* fix typo

help for `ModelArguments.gradient_checkpointing` should be
"If True, use gradient checkpointing to save memory
at the expense of slower backward pass."
not "Whether to freeze the feature extractor layers of the model."
(which is duplicated from `freeze_feature_extractor` arg)

* Add CpmTokenizerFast

* Fix isort

* Overwrite _batch_encode_plus

* Update feature extraction pipelilne.

* Leaving 1 small model for actual values check.

* Fixes tests

- Better support for tokenizer with no pad token
- Increasing PegasusModelTesterConfig for pipelines
- Test of feature extraction are more permissive + don't test Multimodel
models + encoder-decoder.

* Fixing model loading with incorrect shape (+ model with HEAD).

* Update tests/test_pipelines_common.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Revert modeling_utils modification.

* Some corrections.

* Update tests/test_pipelines_common.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update tests/test_pipelines_feature_extraction.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Syntax.

* Fixing text-classification tests.

* Don't modify this file.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Raise an issue if the pytorch version is < 1.8.0

* Attempt to add a test to ensure it correctly raises.

* Missing docstring.

* Second attempt, patch with string absolute import.

* Let's do the call before checking it was called ...

* use the correct function ... 🤦

* Raise ImportError and AssertionError respectively when unable to find torch and torch version is not sufficient.

* Correct path mock patching

* relax constraint for torch_onnx_dict_inputs to ge instead of eq.

* Style.

* Split each version requirements for torch.

* Let's compare version directly.

* Import torch_version after checking pytorch is installed.

* @require_torch

Change `PreTrainedConfig` -> `PretrainedConfig` in the docstring for `AutoTokenizer.from_pretrained(...)`.

Fix `config.decoder.__class` -> `config.decoder.__class__`

Change `torch.Tensor` -> `torch.FloatTensor` in `TemperatureLogitsWarper` to be consistent with the `LogitsWarper` ABC signature annotation.

While `Iterable[Iterable[int]]` is a nicer annotation (it's covariant!), the defensive statements parsing out `bad_words_ids` in `__init__(...)` force the caller to pass in `List[List[int]]`. I've changed the annotation to make that clear.

* fix distiller

* fix style

`_BaseAutoModelClass` was missing `classmethod` decorators on the `from_config(...)` and `from_pretrained(...)` methods.