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Fix device issue in `SwiftFormerModelIntegrationTest::test_inference_image_classification_head` (#23435)

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

* fix

* fix

* fix

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

Update error

Fix + link

* Awesome Transformers

* Update

* Update

* Keywords

* Keywords

* Complete document

* Add lm-evaluation-harness

* Edit txtai according to David's comments

* Update awesome-transformers.md

* Create test_tokenization_electra.py

* Update tests/models/electra/test_tokenization_electra.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

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

search model buffers for dtype as the last resort

Previously even after finding a stop token, other stop tokens were considered, which is unnecessary and slows down processing.

Currently, this unnecessary overhead is negligible since there are usually 2 stop tokens considered and they are fairly short, but in future it may become more expensive.

* fix sam slow test

* oops

* fix error message

* fix

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

It's more efficient to iterate over key, value dict pairs instead of iterating over keys and performing value lookups on each iteration. It's also more idiomatic.

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

* fix

* fix

* update

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

Logging an error and continuing is probably following the principle of least surprise.

It's more idiomatic and significantly more efficient because
1) it avoids repeated `append` call that Python has to resolve on each iteration
2) can preallocate the size of the final list avoiding resizing

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Add a test of the built release

* Polish everything

* Trigger CI

* [automodel] fix torch_dtype=auto in from_pretrained

* add test

* fix logic

* Update src/transformers/models/auto/auto_factory.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

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

* Fix translation

add conditional generation on docstring

* Few fixes in new Tools implementation

* code quality

* docs: ko: task/asr.mdx

* feat: manual draft

* fix: resolve suggestions
Co-authored-by: Hyeonseo Yun <0525yhs@gmail.com>

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Co-authored-by: Hyeonseo Yun <0525yhs@gmail.com>

fix chat prompts

* fix

* fix

* fix test + add more docs

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

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

* Use `mkstemp` to replace deprecated `mktemp`

The `tempfile.mktemp` function is [deprecated](https://docs.python.org/3/library/tempfile.html#tempfile.mktemp) due to [security issues](https://cwe.mitre.org/data/definitions/377.html

).

* Update src/transformers/utils/hub.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* 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