GitLab

* Update tokenizer.rst

* Apply `make fixup`

     added save_directories for _psave_pretrained_pt and _tf, changed model to tf_model and pt_model, enable the notebook to run cleanly from top to bottom without error (#14529)

* added save_directories for _psave_pretrained_pt and _tf, changed model to tf_model and pt_model, enable the notebook to run cleanly from top to bottom without error

* Update quicktour.rst

* added >>>

* dependencies

* added space

* Improve tests

* Install vision for tf tests

When loading a pretrained tokenizer, a verification is done to ensure
that the actual tokenizer class matches the class it was called from.
If the tokenizer is absent, its config file is loaded from the repo.

However, the cache_dir for downloading is not provided, which leads to
ignoring of the user-specified cache_dir, storing files in several
places and and may result in incorrect warnings when the default
cache_dir is unreachsble.

This commit fixes that.

* [deepspeed] zero inference

* only z3 makes sense for inference

* fix and style

* docs

* rework

* fix test

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* responding to suggestions
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* remove sum for list flattening

* change to chain(*)

* make chain object a list

* delete empty lines

per sgugger's suggestions
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Nicholas Broad <nicholas@nmbroad.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

in case of left padding_side there was a copy/paste error
assigning the bbox data to the labels

* Add AutoProcessor class

* Init and tests

* Add doc

* Fix init

* Update src/transformers/models/auto/processing_auto.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Reverts to tokenizer or feature extractor when available

* Adapt test
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* add test for --config_overrides

* remove unneeded parts of the test

* Moving everything to `hf-internal-testing`.

* Fixing test values.

* Moving to other repo.

* Last touch?

* Fix dummy objects for quantization

* Add more models

* Update trainer_utils.py

* add Tuple type hints to all label_ids outputs

affects EvalLoopOutput and PredicctionOutput

* clean up branch for add-qdqbert-model

* README update for QAT example; update docstrings in modeling_qdqbert.py

* Update qdqbert.rst

* Update README.md

* Update README.md

* calibration data using traning set; QAT example runs in fp32

* re-use BERTtokenizer for qdqbert

* Update docs/source/model_doc/qdqbert.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update docs/source/model_doc/qdqbert.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update docs/source/model_doc/qdqbert.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* remove qdqbert tokenizer

* Update qdqbert.rst

* update evaluate-hf-trt-qa.py

* update configuration_qdqbert.py

* update modeling_qdqbert.py: add copied statement; replace assert with ValueError

* update copied from statement

* add is_quantization_available; run make fix-copies

* unittest add require_quantization

* add backend dependency to qdqbert model

* update README; update evaluate script; make style

* lint

* docs qdqbert update

* circleci build_doc add pytorch-quantization for qdqbert

* update README

* update example readme with instructions to upgrade TensorRT to 8.2

* Update src/transformers/models/qdqbert/configuration_qdqbert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/qdqbert/configuration_qdqbert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/qdqbert/configuration_qdqbert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/models/qdqbert/configuration_qdqbert.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* change quantization to pytorch_quantization for backend requirement

* feed_forward_chunking not supported in QDQBert

* make style

* update model docstrings and comments in testing scripts

* rename example to quantization-qdqbert; rename example scripts from qat to quant

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

* rm experimental functions in quant_trainer

* qa cleanup

* make fix-copies for docs index.rst

* fix doctree; use post_init() for qdqbert

* fix early device assignment for qdqbert

* fix CI:Model templates runner
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

support.

* up

* finalize

* finalize

* finish

* Update src/transformers/generation_utils.py

* apply feedback

* Add integration test

* Fix typo

* Update setup.py

* Update setup.py

* Update setup.py

* Remove GitPython install

Add more XLS-R training runs to the official examples

* fix early device assignment

* more models

* First draft

* More improvements

* Improve conversion script

* Fix init weights for layer norm

* Fix correct model for conversion script

* Don't tie input and output embeddings

* Add print statements for debugging

* Add print statements for debugging

* Fix vocab size of model

* Improve documentation, remove fast tokenizer

* Add ImageGPTForImageClassification, improve docs

* Fix docs issue

* Set verbosity level back to info

* Improve tests

* Fix tests and add figure

* Delete tokenizer file

* Remove ImageGPTTokenizer from init files

* Remove ImageGPTLayer from init files

* Remove ImageGPT tokenizer from docs

* First draft of ImageGPTFeatureExtractor

* Fix typo

* Fix bug

* More improvements

* Apply suggestions from code review, add tests for feature extractor

* Fix layernorm

* Update save_pretrained method

* Fix issue

* Make all tests of ImageGPTFeatureExtractor pass

* Update code examples

* Rename model inputs to pixel_values

* Improve code examples

* Update init_weights to post_init

* Fix post_init

* Add a post init method to all models

* Fix tests

* Fix last tests

* Fix templates

* Add comment

* Forgot to save

* test: make sure model configs are jsonifiable

* fix: return python dict instead of config object

* fix: accept pretrained config and use correct class

* Re-enabling slow tests and applying them to core models only

* Re-enabling slow tests and applying them to core models only

* Add new test file to fetcher

* Remove tooslow tests from test_modeling_tf_common.py

* make style

* Style fixes

* Style fixes

* Style fixes

* Style fixes

* Adding core tests to GPT2 and BART

* Removing unused imports
Co-authored-by: niklas.fruehauf <niklas.fruehauf@sovanta.com>
Co-authored-by: matt <rocketknight1@gmail.com>

Co-authored-by: Stas Bekman <stas@stason.org>

* Improve tests

* Improve documentation

* Add ignore_index attribute

* Add semantic_ignore_index to BEiT model

* Add segmentation maps argument to BEiTFeatureExtractor

* Simplify SegformerFeatureExtractor and corresponding tests

* Improve tests

* Apply suggestions from code review

* Minor docs improvements

* Streamline segmentation map tests of SegFormer and BEiT

* Improve reduce_labels docs and test

* Fix code quality

* Fix code quality again

* add new wav2vec2 translation

* correct

* up

* add tests

* correct end copy

* correct more

* up

* correct unispeech sat

* finish

* finalize

* finish

* up

* Create branch for tests

* Pin first upgrade

* Really pin

* Polish fix

* stop training when a finite IterableDataset is exhausted

when using an iterable dataset num_epochs is set to
sys.maxsize to make sure all data is consumed
likewise we want to set max_steps high enough
but still stop when all data is consumed

(cherry picked from commit 6f0e1d6363153da9051e93acffe1cbab3a3f3b12)

* fix typo flase -> false

* add test for stopping training on exhausted finite iterable dataset

* remove redundant gradient_accumulation_steps

* run make style

reformat training_args docstring