GitLab

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

Speedup training by using numpy instead of jnp for batch shuffling
Co-authored-by: Yeb Havinga <y.t.havinga@mgrid.net>

* Returning outputs only when asked for for MaskFormer.

* Adding `output_auxiliary_logits` to the config.

* Fix test across both cpu and gpu

* Fix typo

* Fix LayoutLMv2 test

* Update black

* set scale_embedding to False to avoid large (> 1e-5) output differences between PT/TF
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* 📝 first draft

* 🖍 apply feedback

* 🖍 remove examples from toctree

* 🗑 remove examples from docs/source

Make is_thing_map in Feature Extractor post_process_panoptic_segmentation defaults to all instances (#15954)

* is_thing_map defaults to all instances

* better naming

* control flow

* resolving conversations

* Backprop Test for Freeze FlaxWav2Vec2 Feature Encoder

* remove jnp.ndarray type suggestion

* assert frozen grads are precisely zero

* Support modern list type hint in HfArgumentParser

* Fix formatting with black

[Bug Fix] Beam search example in docs fails & a fix (integrating `max_length` in `BeamScorer.finalize()`) (#15555)

* added the test and fix

* had left out a comment

* added classes to get started with constrained beam search

* in progress, think i can directly force tokens now but not yet with the round robin

* think now i have total control, now need to code the bank selection

* technically works as desired, need to optimize and fix design choices leading to undersirable outputs

* complete PR #1 without disjunctive decoding

* removed incorrect tests

* Delete k.txt

* Delete test.py

* Delete test.sh

* revert changes to test scripts

* genutils

* full implementation with testing, no disjunctive yet

* shifted docs

* passing all tests realistically ran locally

* removing accidentally included print statements

* fixed source of error in initial PR test

* fixing the get_device() vs device trap

* fixed documentation docstrings about constrained_beam_search

* fixed tests having failing for Speech2TextModel's floating point inputs

* fix cuda long tensor

* added examples and testing for them and founx & fixed a bug in beam_search and constrained_beam_search

* deleted accidentally added test halting code with assert False

* code reformat

* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/test_generation_utils.py

* fixing based on comments on PR

* took out the testing code that should but work fails without the beam search moditification ; style changes

* fixing comments issues

* docstrings for ConstraintListState

* typo in PhrsalConstraint docstring

* docstrings improvements

* finished adding what is sort of an opinionated implementation of disjunctive generation, but it revealed errors in inner beam search logic during testing.

* fixed bug found in constrained beam search that used beam_idx that were not global across all the batches

* disjunctive constraint working 100% correctly

* passing all tests

* Accidentally included mlruns

* Update src/transformers/generation_beam_constraints.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/transformers/generation_beam_constraints.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* complete overhaul of type complexities and other nits

* strict type checks in generate()

* fixing second round of feedback by narsil

* fixed failing generation test because of type check overhaul

* generation test fail fix

* fixing test fails
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* proper tests for post_process*** methods in feature extractor

* mask th == 0

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

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

* Do not change the output from tuple to list - to match PT's version

* Fix the same issues for 5 other models and the template
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

* Adding Flax XLM-RoBERTa

* Add Flax to __init__

* Adding doc and dummy objects

* Add tests

* Add Flax XLM-R models autodoc

* Fix tests

* Add Flask XLM-RoBERTa to TEST_FILES_WITH_NO_COMMON_TESTS

* Update src/transformers/models/xlm_roberta/modeling_flax_xlm_roberta.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update tests/xlm_roberta/test_modeling_flax_xlm_roberta.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update tests/xlm_roberta/test_modeling_flax_xlm_roberta.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Remove test on large Flask XLM-RoBERTa

* Add tokenizer to the test
Co-authored-by: Suraj Patil <surajp815@gmail.com>

Small adjustments.

Adding in type hint.

Last fix ?

Only include the default dict thing, not the pipelines.

Linked to https://github.com/huggingface/transformers/pull/15826

* Fix to support fast tokenizer with `CLIPProcessor`

* Update CLIPProcessor test for fast tokenizer

* Fix Docstring Style

* Rename into meaningful Variable name in test code

* Add vision models to doc tests

* Apply suggestions from code review

* Add more models
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

* Enabling MaskFormer in ppipelines

No AutoModel though :(

* Ooops local file.

* finish speech doc tests

* finish

* boom

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

in the scoring (which is more correct)

* Freeze FlaxWav2Vec2 Feature Encoder

* add to all module apply

* add backprop test

* Draft

* Add test

* Update src/transformers/models/realm/modeling_realm.py

* Apply suggestion

* Add block_mask

* Update

* Update

* Add block_embedding_to

* Remove no_grad

* Use AutoTokenizer

* Remove model.to overridding

* send PyTorch inputs to the correct device

* Fix: TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>