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* Pin TF until tests are fixed

* Also pin TF CPU

* Removing Keras version pinning

* make fixup

Fixes #14272

* Fiixng slow pipeline tests

* Remove the image-segmentaiton override.

* Fixing clamping only in training.

* Wav2vec2.

* Remove last mention of `no_grad`.

* Fixing copies.

* Rename.

* Add more instructions to the release guide

* Apply suggestions from code review
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Address review comment
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Start PR doc

* Cleanup the quality checks and document them

* Add reference in the contributing guide

* Apply suggestions from code review
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Rename file as per review suggestion
Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>

* Pin Keras cause they messed their release

* Put != instead of <

* Try this way

* Back to the beginning but more agressive

* Fix of issue #13327: Wrong weight initialization for TF t5 model

* run black formatter

* fix typo

* remove my name tag from comments
Co-authored-by: Shirron <dan.shirron@intel.com>

* Adding support for `truncation` parameter on `feature-extraction`
pipeline.

Fixes #14183

* Fixing tests on ibert, longformer, and roberta.

* Rebase fix.

minimal fixes to run DataCollatorForWholeWordMask with return_tensors="np" and return_tensors="tf" (#13891)

* minimal fixes to run DataCollatorForWholeWordMask with return_tensors="np" and return_tensors="tf"

* more consinstent implementation for numpy_mask_tokens

* Fix img load rotation

* Add `load_image` to `image_utils.py`

* Implement LoadImageTester

* Use hf-internal-testing dataset

* Add img utils comments

* Refactor LoadImageTester

* Import load_image under is_vision_available

* Add cross attentions to TFGPT2Model

* change to is_pt_tf_cross_test

* A minor correction to a comment

* Remove n_ctx when creating self.crossattention
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Add LayoutXLMTokenizer and LayoutXLMTokenizerFast

* Fix styling issues

* Fix more styling issues

* Fix more styling issues

* Fix docstring

* Fix unit tests

* Fix docs

* Fix unit tests

* Fix typos and styling issues

* Fix styling issues

* Fix docstring

* Make all tests of test_tokenization_layoutxlm pass

* Add LayoutXLMProcessor

* Make fixup

* Make all LayoutXLMProcessor tests pass

* Minor fixes

* Leave LayoutLMv2Processor tests unchanged

* Fix code quality

* Move LayoutXLM tokenizers and processor to separate folder

* Fix code quality

* Apply suggestions from code review

* Replace assertions by value errors

* Remove methods from fast tokenizer
Co-authored-by: King Yiu Suen <kingyiusuen@gmail.com>

* Update Transformers to huggingface_hub >= 0.1.0

* Forgot to save...

* Style

* Fix test

* add Beit model ouput class

* inherting from BaseModelOuputWithPooling

* updated docs if use_mean_pooling is False

* added beit specific outputs in model docs

* changed the import path

* Fix docs
Co-authored-by: Niels Rogge <niels.rogge1@gmail.com>

* Add audio-classification benchmarking results

* fix distilhubert path

* check test_configuration_tie

* Fix test_configuration_tie

* make test slow again

* Remove property and use model.module.bind

* revert to slow test
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Fix generation docstring

* Style

* Add first draft

* Make forward pass work

* Improve conversion script

* Add notebook that checks if it works

* Add BeitForSemanticSegmentation to the tests

* More improvements

* Make BeitForSemanticSegmentation consistent with Segformer

* Small bug fix

* Add BeitForSemanticSegmentation to docs

* Make sure model doesn't output hidden states when the user doesn't want to

* Make it possible to convert the large model

* Fix issue

* Fix conversion script for large model

* Add auxiliary_head option to semantic segmentation model

* Apply suggestions from @sgugger's review

* Apply suggestions from code review

* Fix failing test
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

Signed-off-by: Walter Martin <wamartin@microsoft.com>

* enable common tests, small fixes

* don't tie word embeds

* don't ignore lm_head

* raising exceptions instead of using assertions for few models

* fixed formatting issues

* fixing copy inconsistencies

in `base.py` not in subclasses.

* Add missing models to models/__init__.py

* Fix issues previously undetected

* Add UniSpeechSatForPreTraining to all_model_classes

* fix unispeech sat

* fix

* Add check_model_list() to check_repo.py

* Remove _ignore_models = ["bort"]
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>

* Torch 1.10

* torch scatter for 1.10

* style

* Skip tests
ok

* Fixing image segmentation for inference mode.

* Update src/transformers/pipelines/base.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Generalize problem_type to all classification models

* Missing import

* Deberta BC and fix tests

* Fix template

* Missing imports

* Revert change to reformer test

* Fix style