GitLab

* Fix generation docstring

* Style

* Add first draft

* Make forward pass work

* Improve conversion script

* Add notebook that checks if it works

* Add BeitForSemanticSegmentation to the tests

* More improvements

* Make BeitForSemanticSegmentation consistent with Segformer

* Small bug fix

* Add BeitForSemanticSegmentation to docs

* Make sure model doesn't output hidden states when the user doesn't want to

* Make it possible to convert the large model

* Fix issue

* Fix conversion script for large model

* Add auxiliary_head option to semantic segmentation model

* Apply suggestions from @sgugger's review

* Apply suggestions from code review

* Fix failing test
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

Signed-off-by: Walter Martin <wamartin@microsoft.com>

* enable common tests, small fixes

* don't tie word embeds

* don't ignore lm_head

* raising exceptions instead of using assertions for few models

* fixed formatting issues

* fixing copy inconsistencies

in `base.py` not in subclasses.

* Add missing models to models/__init__.py

* Fix issues previously undetected

* Add UniSpeechSatForPreTraining to all_model_classes

* fix unispeech sat

* fix

* Add check_model_list() to check_repo.py

* Remove _ignore_models = ["bort"]
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>

* Torch 1.10

* torch scatter for 1.10

* style

* Skip tests
ok

* Fixing image segmentation for inference mode.

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

* Generalize problem_type to all classification models

* Missing import

* Deberta BC and fix tests

* Fix template

* Missing imports

* Revert change to reformer test

* Fix style

* Fix pipeline tests env and fetch

* Fix quality

* Adding `handle_long_generation` paramters for `text-generation` pipeline.

* More error handling

* Fixing tests by dropping tf support on this functionality, it needs

`max_new_tokens` to make it possible to understand user's intent.
Otherwise, `max_length` == `tokenizer.model_max_length` <
input_ids.shape[0].

* Fixing doc ?

* Doc ?

* Remove link from doc.

* Catched an issue on roberta.

* Damn doc.

* Non BC proposal ?

* Cleaning the fix ?

* Finally using only a test override.

* Don't need to modify this.

* Bad print.

* Add the support for the fast (rust) implementation of BlenbderbotTokenizer

* Fix a converter and a typo in a doc

* Apply the patil-suraj's suggestion

* (Nitpick) Fast tokenization -> Fast Tokenization in doc

* Apply the SaulLu's suggestion

* Apply Narsil's suggestion to fix test pipelines

* Add encoder_no_repeat_ngram_size according to the Narsil's suggestion

* Revert the last (unnecessary) commit

* Override pipeline config for Blenderbot to allow for larger pos. emb.

* make fix-copies

* Remove n_ctx from configs

* Fix GPTJ and OpenAIGPT, both are acceptable breaking changes as there are no configs such that it breaks

* Remove unecessary n_positions from TFOpenAIGPT

* Tentative enabling of `batch_size` for pipelines.

* Add systematic test for pipeline batching.

* Enabling batch_size on almost all pipelines

- Not `zero-shot` (it's already passing stuff as batched so trickier)
- Not `QA` (preprocess uses squad features, we need to switch to real
tensors at this boundary.

* Adding `min_length_for_response` for conversational.

* Making CTC, speech mappings avaiable regardless of framework.

* Attempt at fixing automatic tests (ffmpeg not enabled for fast tests)

* Removing ffmpeg dependency in tests.

* Small fixes.

* Slight cleanup.

* Adding docs

and adressing comments.

* Quality.

* Update docs/source/main_classes/pipelines.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

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

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

* Improving docs.

* Update docs/source/main_classes/pipelines.rst
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>

* N -> oberved_batch_size

softmax trick.

* Follow `padding_side`.

* Supporting image pipeline batching (and padding).

* Rename `unbatch` -> `loader_batch`.

* unbatch_size forgot.

* Custom padding for offset mappings.

* Attempt to remove librosa.

* Adding require_audio.

* torchaudio.

* Back to using datasets librosa.

* Adding help to set a pad_token on the tokenizer.

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

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

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

* Quality.
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>

* Fix docs

* Apply suggestions from review + fix bug

* First draft

* Make tuple output more readable

* Replace assertions by value errors

* Make it possible to predict_with_generate for vision and speech models

* Adapt Seq2SeqTrainer to work with VisionEncoderDecoder/SpeechEncoderDecoder

* Add deprecation warning

* Add copied from statements to vision and speech encoder decoders

* Fix failing test

* Apply @patrickvonplaten's suggestion

* Use reshape instead of view for consistency

* Fix SEW-D

* Update tests

* isort

* First draft

* Make style & quality

* Improve conversion script

* Add print statement to see actual slice

* Make absolute tolerance smaller

* Fix image classification models

* Add post_process_semantic method

* Disable padding

* Improve conversion script

* Rename to ForSemanticSegmentation, add integration test, remove post_process methods

* Improve docs

* Fix code quality

* Fix feature extractor tests

* Fix tests for image classification model

* Delete file

* Add is_torch_available to feature extractor

* Improve documentation of feature extractor methods

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

* Apply some more suggestions of code review

* Rebase with master

* Fix rebase issues

* Make sure model only outputs hidden states when the user wants to

* Apply suggestions from code review

* Add pad method

* Support padding of 2d images

* Add print statement

* Add print statement

* Move padding method to SegformerFeatureExtractor

* Fix issue

* Add casting of segmentation maps

* Add test for padding

* Add small note about padding
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* respect dtype in _get_resized_lm_head

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

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

* up

* up

* fix

* up

* Update examples/pytorch/test_xla_examples.py

* correct labels

* up

* up

* up

* up

* up

* up

* Add conversion

* Rename

* Add an integration test and remove layer_norm

* Remove layer_norm from the converter

* wording

* Fix imports

* Replace assert of data_collator.py by ValueError

* Replace assert of data_collator.py by ValueError

* up

* up

* finish

* up

* final changes

* Add SEW CTC models

* Update paths

* Update paths