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* Replace numpy operations with jax.numpy for JIT compatibility

Replaced numpy operations with their jax.numpy equivalents in the transformer library. This change was necessary to prevent errors during JIT compilation. Specifically, the modifications involve changing numpy's in-place assignments to jax.numpy's immutable update methods.

* rm numpy import

* rm numpy import and fix np->jnp

* fixed slices bug

* fixed decoder_start_tokens -> decoder_start_token_id

* fixed jnp in modleing mt5

* doc fix

* rm numpy import

* make

* Added type hints for `Graphormer` pytorch version

added type hints for graphormers pytorch , checked formating issues .

* made the code less bloated

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

[Bugfix] `OPTDecoderLayer` does not return attentions when `gradient_checkpointing` and `training` is enabled. (#23367)

Update modeling_opt.py

fix
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fix
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fix
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* fix

* fix

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Update graphormer.mdx

Typo suggestion

* fix

* fix

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Removing twice defined position_embeddings

The self.position_embeddings in LongFormerEmbeddings is defined twice.
Removing the first with padding_idx

* fix

* fix

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

replaced assert with raise ValueError for t5, switch_transformers, pix2struct, mt5, longt5, gptsan_japanese. (#23273)

* replaced assert with raise ValueError

* one liner

* reverse one liner and cache-decoder check

* Handle padding warning in generation when using `inputs_embeds`

* Simpler condition

* Black formatter

* Changed warning logic

or or and

fix kwargs

* min. version for pytest

* fix

* fix

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

When working on TorchInductor, I realised that there was a part from
`XLNetLMHeadModel` that was being compiled to CPU code.

This PR should allow to fuse this operation with other CUDA operations
in `torch.compile`. It also should be faster on eager mode, as it has a
this implementation has a lower foot-print.

If in-place operations are not allowed even in non-grad context, I still
believe that doing ones + tril rather than a ones + tril + zeros + cat
should be faster simply due to the number of memory reads/writes.

I tested that this code produces the same results for `0 <= qlen,mlen <
10` and `same_length in (True, False)`.

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

* Commit the automatically generated code

using add-new-model-like

* Update description at swiftformer.mdx file

* remove autogenerated code for MaskedImageModeling

* update weight conversion scripts

* Update modeling_swiftformer.py

* update configuration_swiftformer.py

* Update test_modeling_swiftformer.py

* update modeling code - remove einops dependency

* Update _toctree.yml

* update modeling code - remove copied from comments

* update docs

* Revert "update docs"

This reverts commit c2e05e2998fe2cd6eaee8b8cc31aca5222bac9fb.

* update docs

* remove unused reference SwiftFormerImageProcessor

* update dependency_versions_table.py

* update swiftformer.mdx

* update swiftformer.mdx

* change model output type - no attentions

* update model org name

* Fix typo

* fix copies

* Update tests/models/swiftformer/test_modeling_swiftformer.py
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* Update src/transformers/models/auto/image_processing_auto.py
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* Update src/transformers/models/auto/feature_extraction_auto.py
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* Update docs/source/en/model_doc/swiftformer.mdx
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* Update src/transformers/models/swiftformer/configuration_swiftformer.py
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* Apply suggestions from code review
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* Apply suggestions from code review
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* Apply suggestions from code review
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* Apply suggestions from code review
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* Update modeling_swiftformer.py

fix-copies

* make style, make quality, fix-copies

* Apply suggestions from code review
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* Apply suggestions from code review
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* make style
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* Add suggestions from code review
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* Add suggestions from code review
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* make fix-copies

* Update modeling_swiftformer.py

* Update modeling_swiftformer.py

* Add suggestions from code review
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remove LanguageIdentificationTool
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Revert "search buffers for dtype (#23159)"

This reverts commit ef42c2c4.

* unpin tf prob

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Fix typo

* Agents extras

* Add to docs

Add gradient_checkpointing parameter

* Better check for packages availability

* amend _optimumneuron_available

* amend torch_version

* amend PIL detection and lint

* lint

* amend _faiss_available

* remove overloaded signatures of _is_package_available

* fix sklearn and decord detection

* remove unused checks

* revert

skip
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* fix

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* convert numpy array to list before writing to json

per_category_iou and per_category_accuracy  are ndarray in the eval_metrics

* code reformatted with make style

Make `huggingface-tools` to [`huggingface-tools`](https://huggingface.co/huggingface-tools)

Wrong parantheses

* update min k_value of conditional detr post-processing

* feat: add top_k arg to post processing of deformable and conditional detr

* refactor: revert changes to deprecated methods

* refactor: move prob reshape to improve code clarity and reduce repetition