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* Fix failing test_xla_generate_slow tests

* Fix failing speech-to-text xla_generate tests

* Update pinned hhub version

* Make style

* Update no_trainer script for image-classification

* Update no_trainer scripts for language-modeling examples

* Remove unused variable

* Removing truncation from losses array for language modeling examples

* Add file in spanish docs to be translated

* Translate first two sections to Spanish

* Translate four additional sections to Spanish

* Finish translation to Spanish

* Improve writing style in Spanish

* Add suggested changes from reviewer

* support ONNX export of XDropout in deberta{,_v2}

* black

* copy to sew_d

* add test

* isort

* use pytest.mark.filterwarnings

* review comments

This PR moves GroupViT and LXMert to their correct sections. As pointed out by @NielsRogge and @LysandreJik, GroupViT and LXMert are both multimodal models.

The current wording makes it sound as if the programming languages are part of the 46 natural languages.

* Update pipeline word heuristic to work with whitespace in token offsets

This change checks for whitespace in the input string at either the
character preceding the token or in the first character of the token.
This works with tokenizers that return offsets excluding whitespace
between words or with offsets including whitespace.

fixes #18111

starting

* Use smaller model, ensure expected tokenization

* Re-run CI (please squash)

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* improve generate docstring

* Remove 'defaults to None' comment

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* update maskformer docs

* fix typo

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

`torch.Tensor` creates an unitialized tensor (as via `torch.empty`), this leads to undeterministic behavior, poor initialization, and nans if you have unlucky init. The paper does not specify the initialization for bias terms, so I guess zero seems like a good choice - no bias initially. `torch.Tensor` is usually populated with zeros, so this fix will be close to the intended behavior:

```
>>> torch.Tensor(100, 100).sum()
tensor(0.)
>>> torch.Tensor(100, 100).sum()
tensor(nan)
>>> torch.Tensor(100, 100).sum()
tensor(0.)
```

* Added option for users to modify config parameter used by pytesseract during feature extraction

- Added optional 'tess_config' kwarg when setting up LayoutLMV2 processor that is used by pytesseract during feature extraction
- Eg. Can be used to modify psm values by setting tess_config to '--psm 7'
- Different psm values significantly influences the output of layoutlmv2

* Update src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Updated variable names to be more explicit

* Fixed styles

* Added option for users to modify config parameter when calling pytesseract during feature extraction

- Added option to set "tesseract_config" parameter during LayoutLMV3 processor initialization
- Can be used to modify PSM values, eg. by setting tesseract_config="--psm 6"

* Removed  from function signature
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* 📝

 split up model list

* Adapt script to reorg

* apply niels feedback
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>

* Rewrite push_to_hub to use upload_files

* Adapt the doc a bit

* Address review comments and clean doc

* add bart pretraining flax script

* fixup

* add bart pretraining flax script

* add BART to README

* add BART to README

* add BART to README

* add BART to README

* add BART to README

* add bos eos document

* Update README.md

* Update README.md

* Update examples/flax/language-modeling/run_bart_dlm_flax.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* final

* final

* final

* remove use_auth_token ing from_config
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* Fix ROUGE add example check and update README

* Stay consistent in values

* add LUKE models for downstream tasks

* add new LUKE models to docs

* fix typos

* remove commented lines

* exclude None items from tuple return values

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

* Add balanced strategies for device_map in from_pretrained

* Add safeguards for Accelerate version

* Update src/transformers/modeling_utils.py
Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

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

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

Fix #18385
I don't know whether `use_auth_token`, `cache_dir` and `local_files_only` should be passed to `(cls.slow_tokenizer_class)._from_pretrained`, but I guess it should.

* Migrate metric to Evaluate in pytorch examples

* Remove unused imports

Bumps [mistune](https://github.com/lepture/mistune) from 0.8.4 to 2.0.3.
- [Release notes](https://github.com/lepture/mistune/releases)
- [Changelog](https://github.com/lepture/mistune/blob/master/docs/changes.rst)
- [Commits](https://github.com/lepture/mistune/compare/v0.8.4...v2.0.3

)

---
updated-dependencies:
- dependency-name: mistune
  dependency-type: direct:production
...
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Bumps [mistune](https://github.com/lepture/mistune) from 0.8.4 to 2.0.3.
- [Release notes](https://github.com/lepture/mistune/releases)
- [Changelog](https://github.com/lepture/mistune/blob/master/docs/changes.rst)
- [Commits](https://github.com/lepture/mistune/compare/v0.8.4...v2.0.3

)

---
updated-dependencies:
- dependency-name: mistune
  dependency-type: direct:production
...
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renaming it

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Bloom model can now be traced

* Bloom traced model can be torch scripted and serialized

* Bloom can be traced with variable keyword arguments

* Enable XLNet support

* Disable XLNet for now

* Fix some doctests
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>