* add tokenizer kwarg inputs
* Adding tokenizer_kwargs to _sanitize_parameters
* Add truncation=True example to tests
* Update test_pipelines_fill_mask.py
* Update test_pipelines_fill_mask.py
* make fix-copies and make style
* Update fill_mask.py
Replace single tick with double
* make fix-copies
* Style
---------
Co-authored-by: Lysandre <lysandre@huggingface.co>
* fix test for bart. Order is correct now let's skip BPEs
* ouf
* styling
* fix bert....
* slow refactoring
* current updates
* massive refactoring
* update
* NICE!
* update to see where I am at
* updates
* update
* update
* revert
* updates
* updates
* start supporting legacy_save
* styling
* big update
* revert some changes
* nits
* nniiiiiice
* small fixes
* kinda fix t5 with new behaviour
* major update
* fixup
* fix copies
* today's updates
* fix byt5
* upfate
* update
* update
* updates
* update vocab size test
* Barthez does not use not need the fairseq offset ids
* super calll must be after
* calll super
* move all super init
* move other super init
* fixup
* nits
* more fixes
* nits
* more fixes
* nits
* more fix
* remove useless files
* ouch all of them are affected
* and more!
* small imporvements
* no more sanitize token
* more changes around unique no split tokens
* partially fix more things
* keep legacy save but add warning
* so... more fixes
* updates
* guess deberta tokenizer could be nuked
* fixup
* fixup did some bad things
* nuke it if it breaks
* remove prints and pretrain fast from slow with new format.
* fixups
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* fiou
* nit
* by default specials should not be normalized?
* update
* remove brakpoint
* updates
* a lot of updates
* fixup
* fixes revert some changes to match fast
* small nits
* that makes it cleaner
* fix camembert accordingly
* update
* some lest breaking changes
* update
* fixup
* fix byt5 and whisper mostly
* some more fixes, canine's byte vocab
* fix gpt2
* fix most of the perceiver tests (4 left)
* fix layout lmv3
* fixup
* fix copies for gpt2 style
* make sure to only warn once
* fix perciever and gpt2 tests
* some more backward compatibility: also read special tokens map because some ppl use it........////.....
* fixup
* add else when reading
* nits
* fresh updates
* fix copies
* will this make everything faster?
* fixes
* more fixes
* update
* more fixes
* fixup
* is the source of truth right?
* sorry camembert for the troubles
* current updates
* fixup
* update led
* update
* fix regression
* fix single word
* more model specific fixes
* fix t5 tests
* fixup
* more comments
* update
* fix nllb
* rstrip removed
* small fixes
* better handle additional_special_tokens and vocab sizes
* fixing
* styling
* fix 4 / 21
* fixup
* fix nlbb's tests
* some fixes
* fix t5
* fixes
* style
* fix canine tests
* damn this is nice
* nits
* m2m100 nit
* fixups
* fixes!
* fixup
* stash
* fix merge
* revert bad change
* fixup
* correct order for code Llama
* fix speecht5 post merge
* styling
* revert source of 11 fails
* small nits
* all changes in one go
* fnet hack
* fix 2 more tests
* update based on main branch of tokenizers
* fixup
* fix VITS issues
* more fixes
* fix mgp test
* fix camembert issues
* oups camembert still has 2 failing tests
* mluke fixes
* decode fixes
* small nits
* nits
* fix llama and vits
* fix camembert
* smal nits
* more fixes when initialising a fast from a slow and etc
* fix one of the last test
* fix CPM tokenizer test
* fixups
* fix pop2piano
* fixup
* ⚠️ Change tokenizers required version ⚠️
* ⚠️ Change tokenizers required version ⚠️
* "tokenizers>=0.14,<0.15", don't forget smaller than
* fix musicgen tests and pretraiendtokenizerfast
* fix owlvit and all
* update t5
* fix 800 red
* fix tests
* fix the fix of the fix of t5
* styling
* documentation nits
* cache _added_tokens_encoder
* fixups
* Nit
* fix red tests
* one last nit!
* make eveything a lot simpler
* Now it's over 😉
* few small nits
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* updates that work for now
* tests that should no be skipped / changed and fixed next
* fixup
* i am ashamed
* pushe the fix
* update
* fixups
* nits
* fix added_tokens_encoder
* fix canine test
* fix pegasus vocab
* fix transfoXL
* fixup
* whisper needs to be fixed for train new
* pegasus nits
* more pegasus fixes
* minor update
* better error message in failed test
* fix whisper failing test
* fix whisper failing test
* fix pegasus
* fixup
* fix **** pegasus
* reset things
* remove another file
* attempts to fix the strange custome encoder and offset
* nits here and there
* update
* fixup
* nit
* fix the whisper test
* nits nits
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* updates based on review
* some small update to potentially remove
* nits
* import rlu cache
* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Lysandre Debut <hi@lysand.re>
* move warning to `from_pretrained`
* update tests results now that the special tokens are always added
---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
* Fix word-level timestamps for audio < 30 seconds
* Fix code quality
* fix unit tests
* Fix unit tests
* Fix unit test
* temp: print out result
* temp: set max diff to None
* fix unit tests
* fix typo
* Fix typo
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Use generation config for `num_frames`
* fix docs
* Move `num_frames` to kwargs
* compute stride/attn_mask once
* mark test as slow
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: sanchit-gandhi <sanchit@huggingface.co>
* First commit while I figure this out
* make fixup
* Remove unused method
* Store prompt attrib
* Fix prompt argument for tests
* Make same changes in fast tokenizer
* Remove global prompts from fast tokenizer too
* stash commit
* stash commit
* Migrate PromptConfig to its True Final Location
* Replace Conversation entirely with the new class
* Import/dependency fixes
* Import/dependency fixes
* Change format for lots of default prompts
* More default prompt fixups
* Revert llama old methods so we can compare
* Fix some default configs
* Fix some default configs
* Fix misspelled kwarg
* Fixes for Blenderbot
* make fixup
* little rebase cleanup
* Add basic documentation
* Quick doc fix
* Truncate docstring for now
* Add handling for the case when messages is a single string
* Quick llama merges
* Update conversational pipeline and tests
* Add a couple of legacy properties for backward compatibility
* More legacy handling
* Add docstring for build_conversation_input_ids
* Restructure PromptConfig
* Let's start T E M P L A T I N G
* Refactor all default configs to use templates instead
* Revert changes to the special token properties since we don't need them anymore
* More class templates
* Make the sandbox even sandier
* Everything replaced with pure templating
* Remove docs for PromptConfig
* Add testing and optional requirement boilerplate
* Fix imports and make fixup
* Fix LLaMA tests and add Conversation docstring
* Finally get LLaMA working with the template system
* Finally get LLaMA working with the template system
* make fixup
* make fixup
* fmt-off for the long lists of test tokens
* Rename method to apply_chat_template for now
* Start on documentation
* Make chat_template a property that reads through to the default if it's not set
* Expand docs
* Expand chat templating doc some more
* trim/lstrip blocks by default and update doc
* Few doc tweaks
* rebase cleanup
* Clarify docstring
* rebase cleanup
* rebase cleanup
* make fixup
* Quick doc edit
* Reformat the standard template to match ChatML
* Re-add PEFT check
* Update docs/source/en/chat_templating.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add apply_chat_template to the tokenizer doc
* make fixup
* Add doc links
* Fix chat links
* Fix chat links
* Explain system messages in the doc
* Add chat template test
* Proper save-loading for chat template attribute
* Add test skips for layout models
* Remove _build_conversation_input_ids, add default_chat_template to code_llama
* Make sure all LLaMA models are using the latest template
* Remove default_system_prompt block in code_llama because it has no default prompt
* Update ConversationPipeline preprocess
* Add correct #Copied from links to the default_chat_templates
* Remove unneeded type checking line
* Add a dummy mark_processsed method
* Reorganize Conversation to have **deprecated_kwargs
* Update chat_templating.md
* Quick fix to LLAMA tests
* Small doc tweaks
* Add proper docstrings and "copied from" statements to all default chat templates
* Merge use_default_system_prompt support for code_llama too
* Improve clarity around self.chat_template
* Docstring fix
* Fix blenderbot default template
* More doctest fix
* Break out some tokenizer kwargs
* Update doc to explain default templates
* Quick tweaks to tokenizer args
* Cleanups for tokenizer args
* Add note about cacheing
* Quick tweak to the chat-templating doc
* Update the LLaMA template with error checking and correct system message embedding
* make fixup
* make fixup
* add requires_jinja
* Cleanup to expected output formatting
* Add cacheing
* Fix typo in llama default template
* Update LLaMA tests
* Update documentation
* Improved legacy handling in the Conversation class
* Update Jinja template with proper error handling
* Quick bugfix
* Proper exception raising
* Change cacheing behaviour so it doesn't try to pickle an entire Jinja env
* make fixup
* rebase cleanup
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add Blip2 model in VQA pipeline
* use require_torch_gpu for test_large_model_pt_blip2
* use can_generate in vqa pipeline
* test Blip2ForConditionalGeneration using float16
* remove custom can_generate from Blip2ForConditionalGeneration
* add AutoModelForTextToSpeech class
* add TTS pipeline and tessting
* add docstrings to text_to_speech pipeline
* fix torch dependency
* corrector 'processor is None' case in Pipeline
* correct repo id
* modify text-to-speech -> text-to-audio
* remove processor
* rename text_to_speech pipelines files to text_audio
* add textToWaveform and textToSpectrogram instead of textToAudio classes
* update TTS pipeline to the bare minimum
* update tests TTS pipeline
* make style and erase useless import torch in TTS pipeline tests
* modify how to check if generate or forward in TTS pipeline
* remove unnecessary extra new lines
* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* refactor input_texts -> text_inputs
* correct docstrings of TTS.__call__
* correct the shape of generated waveform
* take care of Bark tokenizer special case
* correct run_pipeline_test TTS
* make style
* update TTS docstrings
* address Sylvain nit refactors
* make style
* refactor into one liners
* correct squeeze
* correct way to test if forward or generate
* Update output audio waveform shape
* make style
* correct import
* modify how the TTS pipeline test if a model can generate
* align shape output of TTS pipeline with consistent shape
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* [ASR Pipeline] Fix init
* refactor test
* change default kwarg setting
* only perform checks if we have to
* override init
* move pre/forward/post checks to sanitize
* Fix rescaling bug
* Add tests
* Update integration tests
* Fix up
* Update src/transformers/image_transforms.py
* Update test - new possible order in list
* add llama
* add other readmes
* update padding id in readme
* add link to paper
* fix paths and tokenizer
* more nits
* styling
* fit operation in 2 lines when possible
* nits
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add form
* update reademe
* update readme, we don't have a default pad token
* update test and tokenization
* LLaMA instead of Llama
* nits
* add expected text
* add greeedy output
* styling
* Update src/transformers/models/llama/modeling_llama.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* sequential device map
* skip relevant changes
---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Allow dict input for audio classification pipeline
* make style
* Empty commit to trigger CI
* Empty commit to trigger CI
* check for torchaudio
* add pip instructions
Co-authored-by: Sylvain <sylvain.gugger@gmail.com>
* Update src/transformers/pipelines/audio_classification.py
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* asr -> audio class
* asr -> audio class
---------
Co-authored-by: Sylvain <sylvain.gugger@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* let's go!
* initial implementation of token-level timestamps
* only return a single timestamp per token
* remove token probabilities
* fix return type
* fix doc comment
* strip special tokens
* rename
* revert to not stripping special tokens
* only support models that have alignment_heads
* add integration test
* consistently name it token-level timestamps
* small DTW tweak
* initial support for ASR pipeline
* fix pipeline doc comments
* resolve token timestamps in pipeline with chunking
* change warning when no final timestamp is found
* return word-level timestamps
* fixup
* fix bug that skipped final word in each chunk
* fix failing unit tests
* merge punctuations into the words
* also return word tokens
* also return token indices
* add (failing) unit test for combine_tokens_into_words
* make combine_tokens_into_words private
* restore OpenAI's punctuation rules
* add pipeline tests
* make requested changes
* PR review changes
* fix failing pipeline test
* small stuff from PR
* only return words and their timestamps, not segments
* move alignment_heads into generation config
* forgot to set alignment_heads in pipeline tests
* tiny comment fix
* grr
* Chunkable classification pipeline
The TokenClassificationPipeline is now able to process sequences longer than 512. No matter the framework, the model, the tokenizer. We just have to pass process_all=True and a stride number (optional). The behavior remains the same if you don't pass these optional parameters. For overlapping parts when using stride above 0, we consider only the max scores for each overlapped token in all chunks where the token is.
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* update with latest black format
* update black format
* Update token_classification.py
* Update token_classification.py
* format correction
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update comments
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Update token_classification.py
Correct spaces, remove process_all and keep only stride. If stride is provided, the pipeline is applied to the whole text.
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update chunk aggregation
Update the chunk aggregation strategy based on entities aggregation.
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
Remove unnecessary pop from outputs dict
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add chunking tests
* correct formating
* correct formatting
* correct model id for test chunking
* update scores with nested simplify
* Update test_pipelines_token_classification.py
* Update test_pipelines_token_classification.py
* update model to a tiny one
* Update test_pipelines_token_classification.py
* Adding smaller test for chunking.
* Fixup
* Update token_classification.py
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [WIP] whisper refacto to support language output.
* Handling merges.
* A bit more cleanup and comments.
* Many improvements.
Lots of details everywhere.
* Cleanup old code and tests.
* Handle lone timestamp tokens (just recover when something bad happens).
* Adding return_language example.
* No ffmpeg.
* Hmm.
* Some corrections.
* Both fast and slow.
* New black.
* Update src/transformers/models/whisper/tokenization_whisper.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/whisper/tokenization_whisper.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove print.
* Undoing tests modifications.
* Smaller test modifications.
* Rename.
* Remove maxDiff.
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Mark pipeline tests to skip them easily
* Mark the mixin as pipeline test
* Update src/transformers/testing_utils.py
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
---------
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* add pipeline
* update init
* add zero shot to init
* update inits and correct checkpoints
* update base to support input features
* add tests
* Update src/transformers/pipelines/zero_shot_audio_classification.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Update src/transformers/pipelines/zero_shot_audio_classification.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* update pieline code
* use tiny checkpoint
* nits and expected value with tiny model
* style
* last nit on tests values
* fix styling
* fix collate fn that was casting t float
* update
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* fix: Change is_last chunk calc and add conditional break
* format fix
* account for 0 and full stride_rights, add comment
* add new test
* make style
* update slow whisper asr test timestamps
* use nested_simplify on output and round timestamp to hundreths place
* Result of black 23.1
* Update target to Python 3.7
* Switch flake8 to ruff
* Configure isort
* Configure isort
* Apply isort with line limit
* Put the right black version
* adapt black in check copies
* Fix copies
* update whisper logit processor
* add generate for whisper
* remove part of the whisper specific code from pipeline
* update logit processes
* major update
* enforce first timestamp
* update generate
* add more tests
* update new decoding strategy
* Apply suggestions from code review
* update docstring
* fixup
* default config will not have multilingual ar
* update expected tokenizer size, see pull on the hub for whisper-tiny
* Fixing the pipeline with image processor.
* Update the slow test.
* Using only the first image processor.
* Include exclusion mecanism for Image processor.
* Do not handle Gitconfig, deemed as a bug.
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove `conversational` changes. They are not supposed to be here.
* Address first row of comments.
* Remove OneFormer modifications.
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add draft logit processor
* add template functions
* update timesapmt processor parameters
* draft script
* simplify code
* cleanup
* fixup and clean
* update pipeline
* style
* clean up previous idea
* add tokenization utils
* update tokenizer and asr output
* fit whisper type
* style and update test
* clean test
* style test
* update tests
* update error test
* udpate code (not based on review yet)
* update tokenization
* update asr pipeline
* update code
* cleanup and update test
* fmt
* remove text verificatino
* cleanup
* cleanup
* add model test
* update tests
* update code add docstring
* update code and add docstring
* fix pipeline tests
* add draft logit processor
add template functions
update timesapmt processor parameters
draft script
simplify code
cleanup
fixup and clean
update pipeline
style
clean up previous idea
add tokenization utils
update tokenizer and asr output
fit whisper type
style and update test
clean test
style test
update tests
update error test
udpate code (not based on review yet)
update tokenization
update asr pipeline
update code
cleanup and update test
fmt
remove text verificatino
cleanup
cleanup
add model test
update tests
update code add docstring
update code and add docstring
fix pipeline tests
* Small update.
* Fixup.
* Tmp.
* More support.
* Making `forced_decoder_ids` non mandatory for users to set.
* update and fix first bug
* properly process sequence right after merge if last
* tofo
* allow list inputs + compute begin index better
* start adding tests
* add the 3 edge cases
* style
* format sequences
* fixup
* update
* update
* style
* test passes, edge cases should be good
* update last value
* remove Trie
* update tests and expec ted values
* handle bigger chunk_length
* clean tests a bit
* refactor chunk iter and clean pipeline
* update tests
* style
* refactor chunk iter and clean pipeline
* upade
* resolve comments
* Apply suggestions from code review
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* take stride right into account
* update test expected values
* Update code based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* major refactor
* add correct strides for tests
* Update src/transformers/pipelines/automatic_speech_recognition.py
* fix whisper timestamp test
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* add draft logit processor
* add template functions
* update timesapmt processor parameters
* draft script
* simplify code
* cleanup
* fixup and clean
* update pipeline
* style
* clean up previous idea
* add tokenization utils
* update tokenizer and asr output
* fit whisper type
* style and update test
* clean test
* style test
* update tests
* update error test
* udpate code (not based on review yet)
* update tokenization
* update asr pipeline
* update code
* cleanup and update test
* fmt
* remove text verificatino
* cleanup
* cleanup
* add model test
* update tests
* update code add docstring
* update code and add docstring
* fix pipeline tests
* add draft logit processor
add template functions
update timesapmt processor parameters
draft script
simplify code
cleanup
fixup and clean
update pipeline
style
clean up previous idea
add tokenization utils
update tokenizer and asr output
fit whisper type
style and update test
clean test
style test
update tests
update error test
udpate code (not based on review yet)
update tokenization
update asr pipeline
update code
cleanup and update test
fmt
remove text verificatino
cleanup
cleanup
add model test
update tests
update code add docstring
update code and add docstring
fix pipeline tests
* Small update.
* Fixup.
* Tmp.
* More support.
* Making `forced_decoder_ids` non mandatory for users to set.
* update and fix first bug
* properly process sequence right after merge if last
* tofo
* allow list inputs + compute begin index better
* start adding tests
* add the 3 edge cases
* style
* format sequences
* fixup
* update
* update
* style
* test passes, edge cases should be good
* update last value
* remove Trie
* update tests and expec ted values
* handle bigger chunk_length
* clean tests a bit
* refactor chunk iter and clean pipeline
* update tests
* style
* refactor chunk iter and clean pipeline
* upade
* resolve comments
* Apply suggestions from code review
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* take stride right into account
* update test expected values
* Update code based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* Fixing #20783
* Update src/transformers/pipelines/base.py
* Fixing some tests.
* Fixup.
* Remove ffmpeg dep + a bit more relaxed for bigbird QA precision.
* Better dataset.
* Prevent failing on TF.
* Better condition. We can't use `can_use_iterator` since we cannot use it
directly.
* add torch_dtype attribute to Pipeline
* Use torch_dtype to cast input tensor type in AutomaticSpeechRecognitionPipeline
* Fix code quality
* Add TextGenerationPipeline fp16 test
* Fix code quality
* Remove useless require in tests
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Supporting `fp16` for asr pipeline
* Adding test.
* Style.
* Oops.
* Flake8 update ?
* Fixing flake8 ?
* Revert "Flake8 update ?"
This reverts commit 0b917fcb52.
* Style (acctidentally deleted flake8 F401.)
* Move to a bigger test (no small whisper model, and s2t doesn't seem to
accept torch_dtype=fp16).
Also we need to use a GPU to actually compute on fp16.
* Using BatchFeature capability.
* Copy RoBERTa
* formatting
* implement RoBERTa with prelayer normalization
* update test expectations
* add documentation
* add convertion script for DinkyTrain weights
* update checkpoint repo
Unfortunately the original checkpoints assumes a hacked roberta model
* add to RoBERTa-PreLayerNorm docs to toc
* run utils/check_copies.py
* lint files
* remove unused import
* fix check_repo reporting wrongly a test is missing
* fix import error, caused by rebase
* run make fix-copies
* add RobertaPreLayerNormConfig to ROBERTA_EMBEDDING_ADJUSMENT_CONFIGS
* Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup: Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add missing Flax header
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* expected_slice -> EXPECTED_SLICE
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update copies after rebase
* add missing copied from statements
* make fix-copies
* make prelayernorm explicit in code
* fix checkpoint path for the original implementation
* add flax integration tests
* improve docs
* update utils/documentation_tests.txt
* lint files
* Remove Copyright notice
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make fix-copies
* Remove EXPECTED_SLICE calculation comments
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* First draft
* Make conversion script work
* Add id2label mapping, run code quality
* Fix copies
* Add first draft of feature extractor
* Update conversion script to use feature extractor
* Make more tests pass
* Add docs
* update input_features to input_values + pad by default to max length
* Fix doc tests
* Add feature extractor tests
* Add proper padding/truncation to feature extractor
* Add support for conversion of all audioset checkpoints
* Improve docs and extend conversion script
* Fix README
* Rename spectogram to spectrogram
* Fix copies
* Add integration test
* Remove dummy conv
* Update to ast
* Update organization
* Fix init
* Rename model to AST
* Add require_torchaudio annotator
* Move import of ASTFeatureExtractor under a is_speech_available
* Fix rebase
* Add pipeline config
* Update name of classifier head
* Rename time_dimension and frequency_dimension for clarity
* Remove print statement
* Fix pipeline test
* Fix pipeline test
* Fix index table
* Fix init
* Fix conversion script
* Rename to ForAudioClassification
* Fix index table
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* [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.
