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correct the import statement

* Fix a broken link for deepspeed ZeRO inference

* fix link
Co-authored-by: Stas Bekman <stas@stason.org>

* Automate check for new pipelines and metadata update

* Add Datasets to quality extra

* add gpt-neox-japanese model and tokenizer as new model

* Correction to PR's comment for GPT NeoX Japanese
- Fix to be able to use gpu
- Add comment # Copied... at the top of RotaryEmbedding
- Implement nn.Linear instead of original linear class
- Add generation test under @slow

* fix bias treatment for gpt-neox-japanese

* Modidy gpt-neox-japanese following PR
- add doc for bias_dropout_add
- style change following a PR comment

* add document for gpt-neox-japanese

* remove unused import from gpt-neox-japanese

* fix README for gpt-neox-japanese

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

* Skip test_torchscript_output_attentions for PegasusXModelTest

* fix test_inference_no_head

* fix test_inference_head

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

* Clarify cookiecutter questions

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

* Make AutoProcessor a magic loading class for all modalities

* Quality

* First draft

* More improvements

* Improve model, add custom CUDA code

* Import torch before

* Add script that imports custom layer

* Add everything in new ops directory

* Import custom layer in modeling file

* Fix ARCHIVE_MAP typo

* Creating the custom kernel on the fly.

* Import custom layer in modeling file

* More improvements

* Fix CUDA loading

* More improvements

* Improve conversion script

* Improve conversion script

* Make it work until encoder_outputs

* Make forward pass work

* More improvements

* Make logits match original implementation

* Make implementation also support single_scale model

* Add support for single_scale and dilation checkpoint

* Add support for with_box_refine model

* Support also two stage model

* Improve tests

* Fix more tests

* Make more tests pass

* Upload all models to the hub

* Clean up some code

* Improve decoder outputs

* Rename intermediate hidden states and reference points

* Improve model outputs

* Move tests to dedicated folder

* Improve model outputs

* Fix retain_grad test

* Improve docs

* Clean up and make test_initialization pass

* Improve variable names

* Add copied from statements

* Improve docs

* Fix style

* Improve docs

* Improve docs, move tests to model folder

* Fix rebase

* Remove DetrForSegmentation from auto mapping

* Apply suggestions from code review

* Improve variable names and docstrings

* Apply some more suggestions from code review

* Apply suggestion from code review

* better docs and variables names

* hint to num_queries and two_stage confusion

* remove asserts and code refactor

* add exception if two_stage is True and with_box_refine is False

* use f-strings

* Improve docs and variable names

* Fix code quality

* Fix rebase

* Add require_torch_gpu decorator

* Add pip install ninja to CI jobs

* Apply suggestion of @sgugger

* Remove DeformableDetrForObjectDetection from auto mapping

* Remove DeformableDetrModel from auto mapping

* Add model to toctree

* Add model back to mappings, skip model in pipeline tests

* Apply @sgugger's suggestion

* Fix imports in the init

* Fix copies

* Add CPU implementation

* Comment out GPU function

* Undo previous change

* Apply more suggestions

* Remove require_torch_gpu annotator

* Fix quality

* Add logger.info

* Fix logger

* Fix variable names

* Fix initializaztion

* Add missing initialization

* Update checkpoint name

* Add model to doc tests

* Add CPU/GPU equivalence test

* Add Deformable DETR to pipeline tests

* Skip model for object detection pipeline
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Nouamane Tazi <nouamane98@gmail.com>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>

FlaxLongT5PreTrainedModel is missing "enable_gradient_checkpointing" function. This gives an error if someone tries to enable gradient checkpointing for longt5.
This pull request fixes it.

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

only main_process will have HPO, and pass argument to other process
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* fixed bug which caused overwrite_cache to always be True (#18967).

* reformatting changes

Co-authored-by: Ankur Goyal <ankur@impira.com>

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

* added type hints

* fixed typo

* fix preprocessing for instance segmentation maps

* add support for per-image instance2class_id mapping

* edit docstrings for clarity

Fix connected to [this issue](https://github.com/huggingface/transformers/issues/18944)

Fixed typo itmes --> items

* fix checkpoint name for wav2vec2 conformer
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Use int64 throughout TFLongFormer

* make style

* Do some more fixed casting in TFLongFormer

* Fix some wonky "is None" conditionals

* Cast all the dtypes, salt the earth

* Fix copies to TFLED as well and do some casting there

* dtype fix in TFLongformer test

* Make fixup

* Expand tolerances on the LED tests too (I think this is a TF32 thing)

* Expand test tolerances for LED a tiny bit (probably a Tensorfloat thing again)

* Align try_to_load_from_cache with huggingface_hub

* Fix tests