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Bump pyarrow in /examples/research_projects/visual_bert

Bumps [pyarrow](https://github.com/apache/arrow) from 1.0.1 to 15.0.0.
- [Commits](https://github.com/apache/arrow/compare/apache-arrow-1.0.1...go/v15.0.0

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Bump pyarrow in /examples/research_projects/decision_transformer

Bumps [pyarrow](https://github.com/apache/arrow) from 7.0.0 to 15.0.0.
- [Commits](https://github.com/apache/arrow/compare/go/v7.0.0...go/v15.0.0

)

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Bump gitpython in /examples/research_projects/distillation

Bumps [gitpython](https://github.com/gitpython-developers/GitPython) from 3.1.32 to 3.1.41.
- [Release notes](https://github.com/gitpython-developers/GitPython/releases)
- [Changelog](https://github.com/gitpython-developers/GitPython/blob/main/CHANGES)
- [Commits](https://github.com/gitpython-developers/GitPython/compare/3.1.32...3.1.41

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Bump grpcio in /examples/research_projects/decision_transformer

Bumps [grpcio](https://github.com/grpc/grpc) from 1.44.0 to 1.53.2.
- [Release notes](https://github.com/grpc/grpc/releases)
- [Changelog](https://github.com/grpc/grpc/blob/master/doc/grpc_release_schedule.md)
- [Commits](https://github.com/grpc/grpc/compare/v1.44.0...v1.53.2

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Bump gitpython in /examples/research_projects/decision_transformer

Bumps [gitpython](https://github.com/gitpython-developers/GitPython) from 3.1.32 to 3.1.41.
- [Release notes](https://github.com/gitpython-developers/GitPython/releases)
- [Changelog](https://github.com/gitpython-developers/GitPython/blob/main/CHANGES)
- [Commits](https://github.com/gitpython-developers/GitPython/compare/3.1.32...3.1.41

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* Gemma: only display act. warning when necessary

This is a nit PR, but I was confused. I got the warning even after I
had changed `hidden_act` to `gelu_pytorch_tanh`, telling me that I
was using the "legacy" `gelu_pytorch_tanh`.

Another option is to keep the warning but change the message to say
something like "`hidden_act` is ignored, please use `hidden_activation`
instead. Setting Gemma's activation function to `gelu_pytorch_tanh`".

* Change message, and set `config.hidden_activation`

Fix --model_type in examples

remove example

* Handle cases when CLS token is absent

* Use BOS token as a fallback

* Temporarily silence warnings in apply_chat_template until we can properly deprecate default chat templates

* make fixup

* Move the default chat template warning into apply_chat_template itself

* make fixup

* fix marian model coversion

* uncomment that line

* remove unnecessary code

* revert tie_weights, doesn't hurt

* move scaling to nn.Module

* let the test be here for now (need to fix)

* failing tests

* last failing models

* Revert commit 4c14817f38

* clean-up

* oops forgot

* codestyle

* raise NotImplemented when possible

* Update tests/test_modeling_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* skip tests in respective modeling files

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

use text config's vocab size

* fix

* add test

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

* More general PR slow CI

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

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

* fix doctest

* fix torch doctest

* make CI happy

* raise error

* make fixup

* added chat templating support for keydataset in generation pipeline

* fixed and improved test

* fix formatting test failures

* Fix tests

* Fix tests

* add_blip_get_multimodal_feautres

* Fix docstring error

* reimplement get_multimodal_features

* fix error

* recheck code quality

* add new necessary tests

* fix seq2seq data collator to respect the given padding strategy

further added tests for the seq2seq data collator in the style of the `data_collator_for_token_classification` (pt, tf, np)

* formatting and change bool equals "==" to "is"

* add missed return types in tests

* update numpy test as it can handle unequal shapes, not like pt or tf

* feat: support for dinov2

* feat: support for depth_anything

* feat: support for efficientformer

* feat: support for bert (is this right?)

* update: embedding split

* remove: empty string

* feat: support for align

* fix: copies

* fix: QAQBertEmbeddings

* fix: more consistency issues

* revert: support for effientformer

* feat: support for altclip

* feat: support for blip_text

* support for ChineseCLIP

* feat: support for depth anything

* feat: support for dpt

* feat: support for dpt

* feat: support for git

* feat: support for groupvit

* update: format

* fix: support for clip

* fix: consistency

* feat: support for pvt

* feat: support for vit_msn

* fix: consistency

* fix: other copies

* remove: device transfer

* revert: in-place add

* update: support for align

* update: support for bert

* update: support for Chinese CLIP

* revert: changes to efficientformer

* update: support for dpt

* update: support for efficientformer

* revert: changes to git

* revert: changes to groupvit

* revert: changes to roc_bert

* update: support for vit_msn

* revert: changes to dpt

* remove: extra space

* style: extra space

pass use_cache in kwargs

* Include safetensors

* Cleanup

* Reenable SDPA's FA2 during training with torch.compile

* fix Olmo's SDPA FA2 dispatching too

* update formatting

* improved SDPA comment

* formatting and explanatory comment

* is_causal if statement to one-liner

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

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

Co-authored-by: Clint Adams <clint@debian.org>

* Pass attn_implementation when using AutoXXX.from_config

* Fix

* Allow boolean FSDP options in fsdp_config

* Use lower() to be safe

* Fixed SegGptImageProcessor to handle 2D and 3D prompt mask inputs

* Added new test to check prompt mask equivalence

* New proposal

* Better proposal

* Removed unnecessary method

* Updated seggpt docs

* Introduced do_convert_rgb

* nits

* Decode b64encode and encodebytes strings

* Remove conditional encode -- image is always a string

Fix GroundingDINO, DPR after BET SDPA update

* [examples] update whisper fine-tuning

* deprecate forced/suppress tokens

* item assignment

* update readme

* final fix

* Enable instantiating model with pretrained backbone weights

* Clarify pretrained import

* Use load_backbone instead

* Add backbone_kwargs to config

* Fix up

* Add tests

* Tidy up

* Enable instantiating model with pretrained backbone weights

* Update tests so backbone checkpoint isn't passed in

* Clarify pretrained import

* Update configs - docs and validation check

* Update src/transformers/utils/backbone_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Clarify exception message

* Update config init in tests

* Add test for when use_timm_backbone=True

* Use load_backbone instead

* Add use_timm_backbone to the model configs

* Add backbone_kwargs to config

* Pass kwargs to constructors

* Draft

* Fix tests

* Add back timm - weight naming

* More tidying up

* Whoops

* Tidy up

* Handle when kwargs are none

* Update tests

* Revert test changes

* Deformable detr test - don't use default

* Don't mutate; correct model attributes

* Add some clarifying comments

* nit - grammar is hard

---------
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* Remove skipping logic now that set_epoch exists

* Working version, clean

* Adding SDPA support for BERT

* Using the proper input name for testing model input in inference()

* Adding documentation for SDPA in BERT model page

* Use the stable link for the documentation

* Adding a gate to only call .contiguous() for torch < 2.2.0

* Additions and fixes to the documentation

* Minor updates to documentation

* Adding extra requirements needed for the contiguous() bug

* Adding "Adapted from" in plcae of the "Copied from"

* Add benchmark speedup tables to the documentation

* Minor fixes to the documentation

* Use ClapText as a replacemenet for Bert in the Copied-From

* Some more fixes for the fix-copies references

* Overriding the test_eager_matches_sdpa_generate in bert tests to not load with low_cpu_mem_usage

[test all]

* Undo changes to separate test

* Refactored SDPA self attention code for KV projections

* Change use_sdpa to attn_implementation

* Fix test_sdpa_can_dispatch_on_flash by preparing input (required for MultipleChoice models)

Use the Keras set_random_seed to ensure reproducible weight initialization