GitLab

* Add DiNAT

* Adds DiNAT + tests

* Minor fixes

* Added HF model

* Add natten to dependencies.

* Cleanup

* Minor fixup

* Reformat

* Optional NATTEN import.

* Reformat & add doc to _toctree

* Reformat (finally)

* Dummy objects for DiNAT

* Add NAT + minor changes

Adds NAT as its own independent model + docs, tests
Adds NATTEN to ext deps to ensure ci picks it up.

* Remove natten from `all` and `dev-torch` deps, add manual pip install to ci tests

* Minor fixes.

* Fix READMEs.

* Requested changes to docs + minor fixes.

* Requested changes.

* Add NAT/DiNAT tests to layoutlm_job

* Correction to Dinat doc.

* Requested changes.

* future proof our tf code

* parse tf versions

* remove double brackets

* oops get other bracket

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

* [Proposal] Breaking change `zero-shot-object-detection` for improved
consistency.

This is a proposal to modify the output of `zero-shot-object-detection`
to provide better alignment with other pipelines.

The output is now strictly the same as `object-detection` whereas before
it would output lists of lists.

The name `candidate_labels` is used throughout for consistency with
other `zero-shot` pipelines.

The pipeline is changed to `ChunkPipeline` to support batching cleanly.

This removes all the lists and list of lists shenanigans, it's now a
matter of the base pipeline handling all this not this specific one.

**Breaking change**: It did remove complex calls potentials `pipe(images = [image1, image2],
text_queries=[candidates1, candidates2])` to support only
`pipe([{"image": image1, "candidate_labels": candidates1}, {"image": image2, "candidate_labels": candidates2}])`
when dealing with lists and/or datasets.
We could keep them, but it will add a lot of complexity to the code
base, since the pipeline is rather young, I'd rather break to keep the
code simpler, but we can revert this.

**Breaking change**: The name of the argument is now `image` instead of
`images` since it expects by default only 1 image. This is revertable
like the previous one.

**Breaking change**: The types is now simplified and flattened:

`pipe(inputs) == [{**object1}, {**object2}]`
instead of the previous
`pipe(inputs) == [[{**object1}, {**object1}], [{**object2}]]`
Where the different instances would be grouped by candidate labels
within lists.
IMHO this is not really desirable, since it would output empty lists and
is only adding superflous indirection compared to
`zero-shot-object-detection`.

It is relatively change free in terms of how the results, it does change
computation however since now the batching is handled by the pipeline
itself. It **did** change the results for the small models so there
seems to be a real difference in how the models handle this.

* Fixing the doctests.

* Behind is_torch_available.

* Add AnyPrecisionAdamW optimizer

* Add optim_args argument to TrainingArgs

* Add tests for AnyPrecisionOptimizer

* Change AnyPrecisionAdam default params to float32

* Move default_anyprecision_kwargs in trainer test

* Rename AnyPrecisionAdamW

* Add padding transformation

* Add in upstream changes

* Update tests & docs

* Code formatting tuples in docstring

* [ASR Examples] Update README for seq2seq

* add language info

* add training results

* re-word

* fix the doc to specify that add_prefix_space = False

* add correct expected output

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

- simplifies the devce checking test

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

* Add ResNetBackbone

* Define channels and strides as property

* Remove file

* Add test for backbone

* Update BackboneOutput class

* Remove strides property

* Fix docstring

* Add backbones to SHOULD_HAVE_THEIR_OWN_PAGE

* Fix auto mapping name

* Add sanity check for out_features

* Set stage names based on depths

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

* Add docstrings for canine model

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

set the default cache_enable to True, aligned with the default value in pytorch cpu/cuda amp autocast (#20289)
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* Fixing the doctests failures.

* Fixup.

* move contrastive search test to slow

* test hub tf callback

* create repo before cloning it

* Image transforms functionality used instead

* Import torch

* Import rather than copy

* Update src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py

* Adding doctest for `object-detection` pipeline.

* Removed nested_simplify.

* Adding `zero-shot-object-detection` pipeline doctest.

* Remove nested_simplify.

* add warning on 8-bit models

- added tests
- added wrapper

* move to a private attribute

- remove wrapper
- changed `save_pretrained` method

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

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

* update part of the doc

* add temp values, fix part of the doc

* add template outputs

* add correct models and outputss

* style

* fixup

* Update configuration_deformable_detr.py comment

* Add DeformableDetrConfig to documentation_tests.txt

* token cls data_collator pads labels column

* remove walrus operator for code quality

* remove redundat space

* remove comment that was fixed

* PR comments fix
Co-authored-by: Alexander Markov <amarkov.me@gmail.com>

* Update modeling_flava.py

* Update modeling_markuplm.py

* Update modeling_glpn.py

* Update modeling_roc_bert.py

* Update modeling_segformer.py

* Update modeling_tapas.py

* Update modeling_tapas.py

* Update modeling_tapas.py

* Update modeling_tapas.py

* Update modeling_trocr.py

* Update modeling_videomae.py

* Update modeling_videomae.py

* Update modeling_videomae.py

* Update modeling_yolos.py

* Update modeling_wav2vec2.py

* Update modeling_jukebox.py

* Update modeling_jukebox.py

* Update modeling_jukebox.py

* Update modeling_jukebox.py

* Adding doctest for `token-classification` pipeline.

* Adding doctest to `token-classification` pipeline.

* Remove nested_simplify.

* Adding `zero-shot-object-detection` pipeline doctest.

* Adding doctest for `image-to-text` pipeline.

* Remove nested_simplify.

* Add resources of OpenAI GPT

* Delete Deploy section and add .

* Add scripts

* Update docs/source/en/model_doc/openai-gpt.mdx
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Delete causal-language-modeling section

* Add TFOpenAIGPTLMHeadModel

* Add resources from community

* Delete a link
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Adding doctest for `question-answering` pipeline.

* Remove nested simplify.

* Adding doctest for `text-classification` pipeline.

* Remove nested_simplify.

* Adding doctest for `visual-question-answering` pipeline.

* Remove nested_simplify.

* Adding doctest for `zero-shot-classification` pipeline.

* Removing nested_simplify.

* Adding doctest for `zero-shot-image-classification` pipeline.

* Remove nested_simplify.