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* [model.from_pretrained] raise exception early on failed load

Currently if `load` pretrained weights fails in `from_pretrained`, we first print a whole bunch of successful messages and then fail - this PR puts the exception first to avoid all the misleading messages.

* style
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Fixing the pipeline optimization by rescaling the logits first.

* Add test for target equivalence
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

* Laying down building stone for more flexible ONNX export capabilities

* Ability to provide a map of config key to override before exporting.

* Makes it possible to export BART with/without past keys.

* Supports simple mathematical syntax for OnnxVariable.repeated

* Effectively apply value override from onnx config for model

* Supports export with additional features such as with-past for seq2seq

* Store the output path directly in the args for uniform usage across.

* Make BART_ONNX_CONFIG_* constants and fix imports.

* Support BERT model.

* Use tokenizer for more flexibility in defining the inputs of a model.

* Add TODO as remainder to provide the batch/sequence_length as CLI args

* Enable optimizations to be done on the model.

* Enable GPT2 + past

* Improve model validation with outputs containing nested structures

* Enable Roberta

* Enable Albert

* Albert requires opset >= 12

* BERT-like models requires opset >= 12

* Remove double printing.

* Enable XLM-Roberta

* Enable DistilBERT

* Disable optimization by default

* Fix missing setattr when applying optimizer_features

* Add value field to OnnxVariable to define constant input (not from tokenizers)

* Add T5 support.

* Simplify model type retrieval

* Example exporting token_classification pipeline for DistilBERT.

* Refactoring to package `transformers.onnx`

* Solve circular dependency & __main__

* Remove unnecessary imports in `__init__`

* Licences

* Use @Narsil's suggestion to forward the model's configuration to the ONNXConfig to avoid interpolation.

* Onnx export v2 fixes (#12388)

* Tiny fixes
Remove `convert_pytorch` from onnxruntime-less runtimes
Correct reference to model

* Style

* Fix Copied from

* LongFormer ONNX config.

* Removed optimizations

* Remvoe bad merge relicas.

* Remove unused constants.

* Remove some deleted constants from imports.

* Fix unittest to remove usage of PyTorch model for onnx.utils.

* Fix distilbert export

* Enable ONNX export test for supported model.

* Style.

* Fix lint.

* Enable all supported default models.

* GPT2 only has one output

* Fix bad property name when overriding config.

* Added unittests and docstrings.

* Disable with_past tests for now.

* Enable outputs validation for default export.

* Remove graph opt lvls.

* Last commit with on-going past commented.

* Style.

* Disabled `with_past` for now

* Remove unused imports.

* Remove framework argument

* Remove TFPreTrainedModel reference

* Add documentation

* Add onnxruntime tests to CircleCI

* Add test

* Rename `convert_pytorch` to `export`

* Use OrderedDict for dummy inputs

* WIP Wav2Vec2

* Revert "WIP Wav2Vec2"

This reverts commit f665efb04c92525c3530e589029f0ae7afdf603e.

* Style

* Use OrderedDict for I/O

* Style.

* Specify OrderedDict documentation.

* Style :)
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Try to pickle transformers

* Deal with special objs better

* Make picklable

* raise exception when arguments are incomplete

* change exception to runtime error

Successfully logging this requires Pytorch. For the purposes of this script we are not using Pytorch.

* Double check for attribute

* Use right name

It was used in shift_right.
After this change TF code is more similar to Pytorch implementations
Also, TF graphs are optimized (one node less)

Adding prepare_decoder_input_ids_from_labels methods to all ConditionalGeneration TF models (#12560)

* Adding support for `pipeline("automatic-speech-recognition")`.

- Ugly `"config"` choice for AutoModel. It would be great to have the
possibility to have something like `AutoModelFor` that would implement
the same logic (Load the config, check Architectures and load the first
one)

* Remove `model_id` was not needed in the end.

* Rebased !

* Remove old code.

* Rename `nlp`.

* fix_torch_device_generate_test

* remove @

* finish

* Validation split percentage to be used for custom data files also

Issue same as https://github.com/huggingface/transformers/issues/12406 fixed for pytorch branch run_mlm.py

* Validation split added in the right place

* Update run_clm.py

* validation split added for custom files

* Validation split added for custom files

* Update run_plm.py

* fixed validation split for custom files as input for pytorch examples in lm

* Update run_clm_no_trainer.py

* args modified

* Copy BART to MBart and rename some stuff

* Add copy statements pointing to FlaxBart

* Update/add some common files

* Update shift_tokens_rigth + fix imports

* Fix shift_tokens_right method according to MBart implementation

* Update shift_tokens_right in tests accordingly

* Fix the import issue and update docs file
* make style quality

* Do some minor changes according to patil-suraj suggestions

* Change the order of normalization layer and attention

* Add some copu statementes

* Update generate method and add integration test for mBart

* Make a few updates after a review

Besides, add `lang_code_to_id` to MBartTokenizeFast

* fix-copies; make style quality

* Apply suggestions from code review

* Apply suggestions from code review

* Apply suggestions from code review

* fix output type, style

* add copied from

* resolve conflicts
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* add adafactor

* Update examples/flax/language-modeling/run_mlm_flax.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix_torch_device_generate_test

* remove @

* up

* up

* correct

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

* implementing tflxmertmodel integration test

* move import

* revert and fix

* flax gpt neo

* fix query scaling

* update generation test

* use flax model for test

* add RoFormerTokenizerFast into AutoTokenizer

* fix typo in roformer docs

* make onnx export happy

* update RoFormerConfig embedding_size

* use jieba not rjieba

* fix 12244 and make test_alignement passed

* update ARCHIVE_MAP

* make style & quality & fixup

* update

* make style & quality & fixup

* make style quality fixup

* update

* suggestion from LysandreJik
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* make style

* use rjieba
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* fix saving and loading

* update readme

* fix_torch_device_generate_test

* remove @

* adapt flax pretrain script

* fix_torch_device_generate_test

* remove @

* up

* fix_torch_device_generate_test

* remove @

* push

* fix_torch_device_generate_test

* remove @

* add logging steps

* correct training scripts

* correct readme

* correct

* NER example for Tensorflow

* Style pass

* Style pass

* Added metric computation on the evaluation set

* Style pass

* Fixed label masking

* Style pass

* Style pass

* fix_torch_device_generate_test

* remove @

* upload

* finish dataset streaming

* adapt readme

* finish

* up

* up

* up

* up

* Apply suggestions from code review

* finish

* make style

* make style2

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

* begin script

* clean example, add readme

* update readme

* remove decay mask

* remove masking

* update readme & make flake happy