From 54a123f068c57abe8bc27a507d05d5674f5862bf Mon Sep 17 00:00:00 2001 From: Lysandre Debut Date: Fri, 11 Apr 2025 11:08:36 +0200 Subject: [PATCH] Simplify soft dependencies and update the dummy-creation process (#36827) * Reverse dependency map shouldn't be created when test_all is set * [test_all] Remove dummies * Modular fixes * Update utils/check_repo.py Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com> * [test_all] Better docs * [test_all] Update src/transformers/commands/chat.py Co-authored-by: Joao Gante * [test_all] Remove deprecated AdaptiveEmbeddings from the tests * [test_all] Doc builder * [test_all] is_dummy * [test_all] Import utils * [test_all] Doc building should not require all deps --------- Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com> Co-authored-by: Joao Gante --- docs/source/en/_toctree.yml | 2 + docs/source/en/internal/import_utils.md | 91 + docs/source/en/model_doc/blip.md | 10 + src/transformers/__init__.py | 8822 +------------ src/transformers/image_processing_utils.py | 2 + .../image_processing_utils_fast.py | 2 + src/transformers/image_utils.py | 2 + src/transformers/model_debugging_utils.py | 6 +- src/transformers/models/__init__.py | 628 +- .../models/albert/tokenization_albert.py | 4 +- src/transformers/models/auto/__init__.py | 401 +- src/transformers/models/auto/auto_factory.py | 3 + .../models/auto/configuration_auto.py | 3 + .../models/auto/feature_extraction_auto.py | 3 + .../models/auto/image_processing_auto.py | 5 + src/transformers/models/auto/modeling_auto.py | 87 + .../models/auto/modeling_flax_auto.py | 30 + .../models/auto/modeling_tf_auto.py | 48 + .../models/auto/processing_auto.py | 3 + .../models/auto/tokenization_auto.py | 3 + .../models/autoformer/__init__.py | 42 +- .../autoformer/configuration_autoformer.py | 3 + .../models/autoformer/modeling_autoformer.py | 3 + .../models/barthez/tokenization_barthez.py | 2 + .../models/bartpho/tokenization_bartpho.py | 2 + .../models/beit/feature_extraction_beit.py | 2 + .../models/beit/image_processing_beit.py | 2 + .../models/bert/tokenization_bert_tf.py | 2 + .../tokenization_bert_generation.py | 2 + .../models/big_bird/tokenization_big_bird.py | 2 + src/transformers/models/blip/__init__.py | 2 + .../models/blip/modeling_blip_text.py | 3 + .../models/blip/modeling_tf_blip_text.py | 3 + .../camembert/tokenization_camembert.py | 2 + .../feature_extraction_chinese_clip.py | 2 + .../image_processing_chinese_clip.py | 10 +- .../models/clap/feature_extraction_clap.py | 2 + .../models/clip/feature_extraction_clip.py | 2 + .../models/clip/image_processing_clip.py | 2 + .../code_llama/tokenization_code_llama.py | 2 + .../models/colpali/modeling_colpali.py | 1 - .../feature_extraction_conditional_detr.py | 2 + .../image_processing_conditional_detr.py | 2 + .../convnext/feature_extraction_convnext.py | 2 + .../convnext/image_processing_convnext.py | 2 + .../models/cpm/tokenization_cpm.py | 2 + .../deberta_v2/tokenization_deberta_v2.py | 2 + .../feature_extraction_deformable_detr.py | 2 + .../image_processing_deformable_detr.py | 2 + .../image_processing_deformable_detr_fast.py | 2 + .../models/deit/feature_extraction_deit.py | 2 + .../models/deit/image_processing_deit.py | 2 + .../models/deprecated/__init__.py | 49 + .../models/deprecated/deta/__init__.py | 57 +- .../deprecated/deta/configuration_deta.py | 3 + .../deprecated/deta/image_processing_deta.py | 3 + .../models/deprecated/deta/modeling_deta.py | 3 + .../deprecated/efficientformer/__init__.py | 87 +- .../configuration_efficientformer.py | 5 + .../image_processing_efficientformer.py | 3 + .../modeling_efficientformer.py | 8 + .../modeling_tf_efficientformer.py | 8 + .../models/deprecated/ernie_m/__init__.py | 66 +- .../ernie_m/configuration_ernie_m.py | 3 + .../deprecated/ernie_m/modeling_ernie_m.py | 11 + .../ernie_m/tokenization_ernie_m.py | 5 + .../deprecated/gptsan_japanese/__init__.py | 54 +- .../configuration_gptsan_japanese.py | 3 + .../modeling_gptsan_japanese.py | 3 + .../tokenization_gptsan_japanese.py | 3 + .../models/deprecated/graphormer/__init__.py | 40 +- .../graphormer/configuration_graphormer.py | 3 + .../graphormer/modeling_graphormer.py | 3 + .../models/deprecated/jukebox/__init__.py | 52 +- .../jukebox/configuration_jukebox.py | 3 + .../deprecated/jukebox/modeling_jukebox.py | 3 + .../jukebox/tokenization_jukebox.py | 3 + .../models/deprecated/mctct/__init__.py | 42 +- .../deprecated/mctct/configuration_mctct.py | 3 + .../mctct/feature_extraction_mctct.py | 3 + .../models/deprecated/mctct/modeling_mctct.py | 3 + .../deprecated/mctct/processing_mctct.py | 3 + .../models/deprecated/mega/__init__.py | 53 +- .../deprecated/mega/configuration_mega.py | 3 + .../models/deprecated/mega/modeling_mega.py | 12 + .../models/deprecated/mmbt/__init__.py | 30 +- .../deprecated/mmbt/configuration_mmbt.py | 3 + .../models/deprecated/mmbt/modeling_mmbt.py | 3 + .../models/deprecated/nat/__init__.py | 39 +- .../deprecated/nat/configuration_nat.py | 3 + .../models/deprecated/nat/modeling_nat.py | 3 + .../models/deprecated/nezha/__init__.py | 52 +- .../deprecated/nezha/configuration_nezha.py | 3 + .../models/deprecated/nezha/modeling_nezha.py | 13 + .../models/deprecated/open_llama/__init__.py | 80 +- .../open_llama/configuration_open_llama.py | 3 + .../open_llama/modeling_open_llama.py | 3 + .../models/deprecated/qdqbert/__init__.py | 54 +- .../qdqbert/configuration_qdqbert.py | 3 + .../deprecated/qdqbert/modeling_qdqbert.py | 15 + .../models/deprecated/realm/__init__.py | 71 +- .../deprecated/realm/configuration_realm.py | 3 + .../models/deprecated/realm/modeling_realm.py | 11 + .../deprecated/realm/retrieval_realm.py | 6 +- .../deprecated/realm/tokenization_realm.py | 3 + .../realm/tokenization_realm_fast.py | 3 + .../models/deprecated/retribert/__init__.py | 58 +- .../retribert/configuration_retribert.py | 3 + .../retribert/modeling_retribert.py | 3 + .../retribert/tokenization_retribert.py | 3 + .../retribert/tokenization_retribert_fast.py | 3 + .../deprecated/speech_to_text_2/__init__.py | 50 +- .../configuration_speech_to_text_2.py | 3 + .../modeling_speech_to_text_2.py | 3 + .../processing_speech_to_text_2.py | 3 + .../tokenization_speech_to_text_2.py | 3 + .../models/deprecated/tapex/__init__.py | 11 +- .../deprecated/tapex/tokenization_tapex.py | 3 + .../trajectory_transformer/__init__.py | 42 +- .../configuration_trajectory_transformer.py | 3 + .../modeling_trajectory_transformer.py | 7 + .../models/deprecated/transfo_xl/__init__.py | 80 +- .../transfo_xl/configuration_transfo_xl.py | 3 + .../transfo_xl/modeling_tf_transfo_xl.py | 10 + .../transfo_xl/modeling_transfo_xl.py | 10 + .../transfo_xl/tokenization_transfo_xl.py | 3 + .../models/deprecated/tvlt/__init__.py | 84 +- .../deprecated/tvlt/configuration_tvlt.py | 3 + .../tvlt/feature_extraction_tvlt.py | 3 + .../deprecated/tvlt/image_processing_tvlt.py | 3 + .../models/deprecated/tvlt/modeling_tvlt.py | 3 + .../models/deprecated/tvlt/processing_tvlt.py | 3 + .../models/deprecated/van/__init__.py | 37 +- .../deprecated/van/configuration_van.py | 3 + .../models/deprecated/van/modeling_van.py | 3 + .../models/deprecated/vit_hybrid/__init__.py | 55 +- .../vit_hybrid/configuration_vit_hybrid.py | 3 + .../vit_hybrid/image_processing_vit_hybrid.py | 3 + .../vit_hybrid/modeling_vit_hybrid.py | 3 + .../deprecated/xlm_prophetnet/__init__.py | 62 +- .../configuration_xlm_prophetnet.py | 3 + .../xlm_prophetnet/modeling_xlm_prophetnet.py | 10 + .../tokenization_xlm_prophetnet.py | 3 + .../depth_pro/image_processing_depth_pro.py | 3 + .../image_processing_depth_pro_fast.py | 2 + .../models/detr/feature_extraction_detr.py | 2 + .../models/detr/image_processing_detr.py | 2 + .../models/detr/image_processing_detr_fast.py | 2 + .../models/donut/feature_extraction_donut.py | 2 + .../models/donut/image_processing_donut.py | 3 +- .../models/dpt/feature_extraction_dpt.py | 2 + .../models/dpt/image_processing_dpt.py | 3 + .../models/flava/feature_extraction_flava.py | 2 + .../models/flava/image_processing_flava.py | 2 + .../models/fnet/tokenization_fnet.py | 2 + ...unnel_original_tf_checkpoint_to_pytorch.py | 3 - .../models/fuyu/processing_fuyu.py | 2 + .../models/gemma/tokenization_gemma.py | 2 + .../models/glpn/feature_extraction_glpn.py | 2 + .../models/glpn/image_processing_glpn.py | 3 + .../models/gpt2/tokenization_gpt2_tf.py | 8 +- .../models/gpt_sw3/tokenization_gpt_sw3.py | 2 + src/transformers/models/granite/__init__.py | 42 +- .../models/granite/configuration_granite.py | 3 + .../models/granite/modeling_granite.py | 71 +- .../models/granite/modular_granite.py | 15 +- .../imagegpt/feature_extraction_imagegpt.py | 2 + .../imagegpt/image_processing_imagegpt.py | 2 + .../models/instructblipvideo/__init__.py | 70 +- .../configuration_instructblipvideo.py | 3 + .../image_processing_instructblipvideo.py | 3 + .../modeling_instructblipvideo.py | 150 +- .../modular_instructblipvideo.py | 26 + .../processing_instructblipvideo.py | 3 + .../feature_extraction_layoutlmv2.py | 2 + .../layoutlmv2/image_processing_layoutlmv2.py | 2 + .../feature_extraction_layoutlmv3.py | 2 + .../layoutlmv3/image_processing_layoutlmv3.py | 2 + .../layoutxlm/tokenization_layoutxlm.py | 2 + .../models/levit/feature_extraction_levit.py | 2 + .../models/levit/image_processing_levit.py | 2 + .../models/llama/tokenization_llama.py | 2 + .../video_processing_llava_onevision.py | 2 + .../models/m2m_100/tokenization_m2m_100.py | 2 + .../models/marian/tokenization_marian.py | 2 + .../feature_extraction_maskformer.py | 2 + .../maskformer/image_processing_maskformer.py | 2 + .../models/mbart/tokenization_mbart.py | 2 + .../models/mbart50/tokenization_mbart50.py | 2 + .../models/megatron_gpt2/__init__.py | 13 - .../models/mgp_str/processing_mgp_str.py | 2 + src/transformers/models/mistral/__init__.py | 105 +- .../models/mistral/configuration_mistral.py | 3 + .../models/mistral/modeling_flax_mistral.py | 2 + .../models/mistral/modeling_mistral.py | 78 +- .../models/mistral/modeling_tf_mistral.py | 3 + .../models/mistral/modular_mistral.py | 15 + src/transformers/models/mixtral/__init__.py | 51 +- .../models/mixtral/configuration_mixtral.py | 3 + .../models/mixtral/modeling_mixtral.py | 10 + .../models/mixtral/modular_mixtral.py | 10 + .../models/mluke/tokenization_mluke.py | 2 + .../feature_extraction_mobilenet_v1.py | 2 + .../image_processing_mobilenet_v1.py | 2 + .../feature_extraction_mobilenet_v2.py | 2 + .../image_processing_mobilenet_v2.py | 4 + .../mobilevit/feature_extraction_mobilevit.py | 2 + .../mobilevit/image_processing_mobilevit.py | 2 + src/transformers/models/mt5/modeling_mt5.py | 1 - .../models/musicgen_melody/__init__.py | 71 +- .../configuration_musicgen_melody.py | 3 + .../feature_extraction_musicgen_melody.py | 5 + .../modeling_musicgen_melody.py | 8 + .../processing_musicgen_melody.py | 5 + .../models/nllb/tokenization_nllb.py | 2 + src/transformers/models/olmo/__init__.py | 44 +- .../models/olmo/configuration_olmo.py | 3 + src/transformers/models/olmo/modeling_olmo.py | 71 +- src/transformers/models/olmo/modular_olmo.py | 8 + .../models/olmo2/modeling_olmo2.py | 68 +- .../omdet_turbo/processing_omdet_turbo.py | 2 + .../owlvit/feature_extraction_owlvit.py | 2 + .../models/owlvit/image_processing_owlvit.py | 2 + .../models/pegasus/tokenization_pegasus.py | 4 + .../perceiver/feature_extraction_perceiver.py | 2 + .../perceiver/image_processing_perceiver.py | 2 + src/transformers/models/phi/__init__.py | 52 +- .../models/phi/configuration_phi.py | 3 + src/transformers/models/phi/modeling_phi.py | 77 +- src/transformers/models/phi/modular_phi.py | 14 + .../models/plbart/tokenization_plbart.py | 2 + .../feature_extraction_poolformer.py | 2 + .../pop2piano/feature_extraction_pop2piano.py | 2 + .../models/pop2piano/processing_pop2piano.py | 2 + .../pop2piano/tokenization_pop2piano.py | 2 + src/transformers/models/qwen2/__init__.py | 71 +- .../models/qwen2/configuration_qwen2.py | 3 + .../models/qwen2/modeling_qwen2.py | 78 +- .../models/qwen2/modular_qwen2.py | 15 + .../models/qwen2/tokenization_qwen2.py | 3 + .../models/qwen2/tokenization_qwen2_fast.py | 3 + .../models/qwen3/modeling_qwen3.py | 68 +- .../models/reformer/tokenization_reformer.py | 2 + .../models/rembert/tokenization_rembert.py | 2 + .../rt_detr/image_processing_rt_detr_fast.py | 2 + .../seamless_m4t/tokenization_seamless_m4t.py | 2 + .../segformer/feature_extraction_segformer.py | 2 + .../segformer/image_processing_segformer.py | 2 + .../models/siglip/tokenization_siglip.py | 2 + .../tokenization_speech_to_text.py | 2 + .../models/speecht5/tokenization_speecht5.py | 2 + .../models/starcoder2/modeling_starcoder2.py | 68 +- .../superglue/image_processing_superglue.py | 2 + src/transformers/models/t5/tokenization_t5.py | 4 +- .../image_processing_timm_wrapper.py | 3 +- .../models/udop/tokenization_udop.py | 2 + .../videomae/feature_extraction_videomae.py | 2 + .../videomae/image_processing_videomae.py | 2 + .../models/vilt/feature_extraction_vilt.py | 2 + .../models/vilt/image_processing_vilt.py | 2 + .../models/vit/feature_extraction_vit.py | 2 + .../models/vit/image_processing_vit.py | 2 + .../models/vitpose_backbone/__init__.py | 49 +- .../configuration_vitpose_backbone.py | 3 + .../modeling_vitpose_backbone.py | 3 + .../models/xglm/tokenization_xglm.py | 2 + .../xlm_roberta/tokenization_xlm_roberta.py | 2 + .../models/xlnet/tokenization_xlnet.py | 2 + .../models/yolos/feature_extraction_yolos.py | 2 + .../models/yolos/image_processing_yolos.py | 2 + src/transformers/trainer.py | 7 + src/transformers/utils/dummy_flax_objects.py | 1323 -- .../utils/dummy_keras_nlp_objects.py | 9 - src/transformers/utils/dummy_pt_objects.py | 10567 ---------------- src/transformers/utils/dummy_tf_objects.py | 2691 ---- .../utils/dummy_tokenizers_objects.py | 448 - .../utils/dummy_torchvision_objects.py | 140 - .../utils/dummy_vision_objects.py | 777 -- src/transformers/utils/import_utils.py | 102 +- .../models/colpali/test_processing_colpali.py | 1 - .../test_pipelines_mask_generation.py | 14 +- .../test_pipelines_question_answering.py | 4 +- .../test_pipelines_text_classification.py | 4 +- .../test_pipelines_token_classification.py | 4 +- tests/pipelines/test_pipelines_zero_shot.py | 4 +- tests/repo_utils/test_check_dummies.py | 126 - tests/test_modeling_common.py | 4 +- .../utils/import_structures/failing_export.py | 4 +- .../import_structure_raw_register.py | 20 +- ...import_structure_register_with_comments.py | 18 +- ...port_structure_register_with_duplicates.py | 18 +- utils/check_inits.py | 23 +- utils/check_repo.py | 18 +- utils/not_doctested.txt | 1 - utils/tests_fetcher.py | 23 +- 295 files changed, 2167 insertions(+), 27702 deletions(-) create mode 100644 docs/source/en/internal/import_utils.md delete mode 100644 src/transformers/utils/dummy_keras_nlp_objects.py delete mode 100644 tests/repo_utils/test_check_dummies.py diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index ce8ef793e7..1a41368392 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -1078,6 +1078,8 @@ title: Utilities for Audio processing - local: internal/file_utils title: General Utilities + - local: internal/import_utils + title: Importing Utilities - local: internal/time_series_utils title: Utilities for Time Series title: Internal helpers diff --git a/docs/source/en/internal/import_utils.md b/docs/source/en/internal/import_utils.md new file mode 100644 index 0000000000..93daa2ced3 --- /dev/null +++ b/docs/source/en/internal/import_utils.md @@ -0,0 +1,91 @@ + + +# Import Utilities + +This page goes through the transformers utilities to enable lazy and fast object import. +While we strive for minimal dependencies, some models have specific dependencies requirements that cannot be +worked around. We don't want for all users of `transformers` to have to install those dependencies to use other models, +we therefore mark those as soft dependencies rather than hard dependencies. + +The transformers toolkit is not made to error-out on import of a model that has a specific dependency; instead, an +object for which you are lacking a dependency will error-out when calling any method on it. As an example, if +`torchvision` isn't installed, the fast image processors will not be available. + +This object is still importable: + +```python +>>> from transformers import DetrImageProcessorFast +>>> print(DetrImageProcessorFast) + +``` + +However, no method can be called on that object: + +```python +>>> DetrImageProcessorFast.from_pretrained() +ImportError: +DetrImageProcessorFast requires the Torchvision library but it was not found in your environment. Checkout the instructions on the +installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment. +Please note that you may need to restart your runtime after installation. +``` + +Let's see how to specify specific object dependencies. + +## Specifying Object Dependencies + +### Filename-based + +All objects under a given filename have an automatic dependency to the tool linked to the filename + +**TensorFlow**: All files starting with `modeling_tf_` have an automatic TensorFlow dependency. + +**Flax**: All files starting with `modeling_flax_` have an automatic Flax dependency + +**PyTorch**: All files starting with `modeling_` and not valid with the above (TensorFlow and Flax) have an automatic +PyTorch dependency + +**Tokenizers**: All files starting with `tokenization_` and ending with `_fast` have an automatic `tokenizers` dependency + +**Vision**: All files starting with `image_processing_` have an automatic dependency to the `vision` dependency group; +at the time of writing, this only contains the `pillow` dependency. + +**Vision + Torch + Torchvision**: All files starting with `image_processing_` and ending with `_fast` have an automatic +dependency to `vision`, `torch`, and `torchvision`. + +All of these automatic dependencies are added on top of the explicit dependencies that are detailed below. + +### Explicit Object Dependencies + +We add a method called `requires` that is used to explicitly specify the dependencies of a given object. As an +example, the `Trainer` class has two hard dependencies: `torch` and `accelerate`. Here is how we specify these +required dependencies: + +```python +from .utils.import_utils import requires + +@requires(backends=("torch", "accelerate")) +class Trainer: + ... +``` + +Backends that can be added here are all the backends that are available in the `import_utils.py` module. + +## Methods + +[[autodoc]] utils.import_utils.define_import_structure + +[[autodoc]] utils.import_utils.requires diff --git a/docs/source/en/model_doc/blip.md b/docs/source/en/model_doc/blip.md index 1acf172f26..efb6b27082 100644 --- a/docs/source/en/model_doc/blip.md +++ b/docs/source/en/model_doc/blip.md @@ -88,6 +88,11 @@ The original code can be found [here](https://github.com/salesforce/BLIP). [[autodoc]] BlipTextModel - forward +## BlipTextLMHeadModel + +[[autodoc]] BlipTextLMHeadModel +- forward + ## BlipVisionModel [[autodoc]] BlipVisionModel @@ -123,6 +128,11 @@ The original code can be found [here](https://github.com/salesforce/BLIP). [[autodoc]] TFBlipTextModel - call +## TFBlipTextLMHeadModel + +[[autodoc]] TFBlipTextLMHeadModel +- forward + ## TFBlipVisionModel [[autodoc]] TFBlipVisionModel diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py index 71bb1e2bd8..2d05492019 100644 --- a/src/transformers/__init__.py +++ b/src/transformers/__init__.py @@ -20,6 +20,7 @@ __version__ = "4.52.0.dev0" +from pathlib import Path from typing import TYPE_CHECKING # Check the dependencies satisfy the minimal versions required. @@ -47,11 +48,11 @@ from .utils import ( is_vision_available, logging, ) +from .utils.import_utils import define_import_structure logger = logging.get_logger(__name__) # pylint: disable=invalid-name - # Base objects, independent of any specific backend _import_structure = { "agents": [ @@ -158,782 +159,6 @@ _import_structure = { "load_tf2_weights_in_pytorch_model", ], # Models - "models": [], - "models.albert": ["AlbertConfig"], - "models.align": [ - "AlignConfig", - "AlignProcessor", - "AlignTextConfig", - "AlignVisionConfig", - ], - "models.altclip": [ - "AltCLIPConfig", - "AltCLIPProcessor", - "AltCLIPTextConfig", - "AltCLIPVisionConfig", - ], - "models.aria": [ - "AriaConfig", - "AriaProcessor", - "AriaTextConfig", - ], - "models.audio_spectrogram_transformer": [ - "ASTConfig", - "ASTFeatureExtractor", - ], - "models.auto": [ - "CONFIG_MAPPING", - "FEATURE_EXTRACTOR_MAPPING", - "IMAGE_PROCESSOR_MAPPING", - "MODEL_NAMES_MAPPING", - "PROCESSOR_MAPPING", - "TOKENIZER_MAPPING", - "AutoConfig", - "AutoFeatureExtractor", - "AutoImageProcessor", - "AutoProcessor", - "AutoTokenizer", - ], - "models.autoformer": ["AutoformerConfig"], - "models.aya_vision": ["AyaVisionConfig", "AyaVisionProcessor"], - "models.bamba": ["BambaConfig"], - "models.bark": [ - "BarkCoarseConfig", - "BarkConfig", - "BarkFineConfig", - "BarkProcessor", - "BarkSemanticConfig", - ], - "models.bart": ["BartConfig", "BartTokenizer"], - "models.barthez": [], - "models.bartpho": [], - "models.beit": ["BeitConfig"], - "models.bert": [ - "BasicTokenizer", - "BertConfig", - "BertTokenizer", - "WordpieceTokenizer", - ], - "models.bert_generation": ["BertGenerationConfig"], - "models.bert_japanese": [ - "BertJapaneseTokenizer", - "CharacterTokenizer", - "MecabTokenizer", - ], - "models.bertweet": ["BertweetTokenizer"], - "models.big_bird": ["BigBirdConfig"], - "models.bigbird_pegasus": ["BigBirdPegasusConfig"], - "models.biogpt": [ - "BioGptConfig", - "BioGptTokenizer", - ], - "models.bit": ["BitConfig"], - "models.blenderbot": [ - "BlenderbotConfig", - "BlenderbotTokenizer", - ], - "models.blenderbot_small": [ - "BlenderbotSmallConfig", - "BlenderbotSmallTokenizer", - ], - "models.blip": [ - "BlipConfig", - "BlipProcessor", - "BlipTextConfig", - "BlipVisionConfig", - ], - "models.blip_2": [ - "Blip2Config", - "Blip2Processor", - "Blip2QFormerConfig", - "Blip2VisionConfig", - ], - "models.bloom": ["BloomConfig"], - "models.bridgetower": [ - "BridgeTowerConfig", - "BridgeTowerProcessor", - "BridgeTowerTextConfig", - "BridgeTowerVisionConfig", - ], - "models.bros": [ - "BrosConfig", - "BrosProcessor", - ], - "models.byt5": ["ByT5Tokenizer"], - "models.camembert": ["CamembertConfig"], - "models.canine": [ - "CanineConfig", - "CanineTokenizer", - ], - "models.chameleon": [ - "ChameleonConfig", - "ChameleonProcessor", - "ChameleonVQVAEConfig", - ], - "models.chinese_clip": [ - "ChineseCLIPConfig", - "ChineseCLIPProcessor", - "ChineseCLIPTextConfig", - "ChineseCLIPVisionConfig", - ], - "models.clap": [ - "ClapAudioConfig", - "ClapConfig", - "ClapProcessor", - "ClapTextConfig", - ], - "models.clip": [ - "CLIPConfig", - "CLIPProcessor", - "CLIPTextConfig", - "CLIPTokenizer", - "CLIPVisionConfig", - ], - "models.clipseg": [ - "CLIPSegConfig", - "CLIPSegProcessor", - "CLIPSegTextConfig", - "CLIPSegVisionConfig", - ], - "models.clvp": [ - "ClvpConfig", - "ClvpDecoderConfig", - "ClvpEncoderConfig", - "ClvpFeatureExtractor", - "ClvpProcessor", - "ClvpTokenizer", - ], - "models.code_llama": [], - "models.codegen": [ - "CodeGenConfig", - "CodeGenTokenizer", - ], - "models.cohere": ["CohereConfig"], - "models.cohere2": ["Cohere2Config"], - "models.colpali": [ - "ColPaliConfig", - "ColPaliProcessor", - ], - "models.conditional_detr": ["ConditionalDetrConfig"], - "models.convbert": [ - "ConvBertConfig", - "ConvBertTokenizer", - ], - "models.convnext": ["ConvNextConfig"], - "models.convnextv2": ["ConvNextV2Config"], - "models.cpm": [], - "models.cpmant": [ - "CpmAntConfig", - "CpmAntTokenizer", - ], - "models.ctrl": [ - "CTRLConfig", - "CTRLTokenizer", - ], - "models.cvt": ["CvtConfig"], - "models.dab_detr": ["DabDetrConfig"], - "models.dac": ["DacConfig", "DacFeatureExtractor"], - "models.data2vec": [ - "Data2VecAudioConfig", - "Data2VecTextConfig", - "Data2VecVisionConfig", - ], - "models.dbrx": ["DbrxConfig"], - "models.deberta": [ - "DebertaConfig", - "DebertaTokenizer", - ], - "models.deberta_v2": ["DebertaV2Config"], - "models.decision_transformer": ["DecisionTransformerConfig"], - "models.deepseek_v3": ["DeepseekV3Config"], - "models.deformable_detr": ["DeformableDetrConfig"], - "models.deit": ["DeiTConfig"], - "models.deprecated": [], - "models.deprecated.bort": [], - "models.deprecated.deta": ["DetaConfig"], - "models.deprecated.efficientformer": ["EfficientFormerConfig"], - "models.deprecated.ernie_m": ["ErnieMConfig"], - "models.deprecated.gptsan_japanese": [ - "GPTSanJapaneseConfig", - "GPTSanJapaneseTokenizer", - ], - "models.deprecated.graphormer": ["GraphormerConfig"], - "models.deprecated.jukebox": [ - "JukeboxConfig", - "JukeboxPriorConfig", - "JukeboxTokenizer", - "JukeboxVQVAEConfig", - ], - "models.deprecated.mctct": [ - "MCTCTConfig", - "MCTCTFeatureExtractor", - "MCTCTProcessor", - ], - "models.deprecated.mega": ["MegaConfig"], - "models.deprecated.mmbt": ["MMBTConfig"], - "models.deprecated.nat": ["NatConfig"], - "models.deprecated.nezha": ["NezhaConfig"], - "models.deprecated.open_llama": ["OpenLlamaConfig"], - "models.deprecated.qdqbert": ["QDQBertConfig"], - "models.deprecated.realm": [ - "RealmConfig", - "RealmTokenizer", - ], - "models.deprecated.retribert": [ - "RetriBertConfig", - "RetriBertTokenizer", - ], - "models.deprecated.speech_to_text_2": [ - "Speech2Text2Config", - "Speech2Text2Processor", - "Speech2Text2Tokenizer", - ], - "models.deprecated.tapex": ["TapexTokenizer"], - "models.deprecated.trajectory_transformer": ["TrajectoryTransformerConfig"], - "models.deprecated.transfo_xl": [ - "TransfoXLConfig", - "TransfoXLCorpus", - "TransfoXLTokenizer", - ], - "models.deprecated.tvlt": [ - "TvltConfig", - "TvltFeatureExtractor", - "TvltProcessor", - ], - "models.deprecated.van": ["VanConfig"], - "models.deprecated.vit_hybrid": ["ViTHybridConfig"], - "models.deprecated.xlm_prophetnet": ["XLMProphetNetConfig"], - "models.depth_anything": ["DepthAnythingConfig"], - "models.depth_pro": ["DepthProConfig"], - "models.detr": ["DetrConfig"], - "models.dialogpt": [], - "models.diffllama": ["DiffLlamaConfig"], - "models.dinat": ["DinatConfig"], - "models.dinov2": ["Dinov2Config"], - "models.dinov2_with_registers": ["Dinov2WithRegistersConfig"], - "models.distilbert": [ - "DistilBertConfig", - "DistilBertTokenizer", - ], - "models.dit": [], - "models.donut": [ - "DonutProcessor", - "DonutSwinConfig", - ], - "models.dpr": [ - "DPRConfig", - "DPRContextEncoderTokenizer", - "DPRQuestionEncoderTokenizer", - "DPRReaderOutput", - "DPRReaderTokenizer", - ], - "models.dpt": ["DPTConfig"], - "models.efficientnet": ["EfficientNetConfig"], - "models.electra": [ - "ElectraConfig", - "ElectraTokenizer", - ], - "models.emu3": [ - "Emu3Config", - "Emu3Processor", - "Emu3TextConfig", - "Emu3VQVAEConfig", - ], - "models.encodec": [ - "EncodecConfig", - "EncodecFeatureExtractor", - ], - "models.encoder_decoder": ["EncoderDecoderConfig"], - "models.ernie": ["ErnieConfig"], - "models.esm": ["EsmConfig", "EsmTokenizer"], - "models.falcon": ["FalconConfig"], - "models.falcon_mamba": ["FalconMambaConfig"], - "models.fastspeech2_conformer": [ - "FastSpeech2ConformerConfig", - "FastSpeech2ConformerHifiGanConfig", - "FastSpeech2ConformerTokenizer", - "FastSpeech2ConformerWithHifiGanConfig", - ], - "models.flaubert": ["FlaubertConfig", "FlaubertTokenizer"], - "models.flava": [ - "FlavaConfig", - "FlavaImageCodebookConfig", - "FlavaImageConfig", - "FlavaMultimodalConfig", - "FlavaTextConfig", - ], - "models.fnet": ["FNetConfig"], - "models.focalnet": ["FocalNetConfig"], - "models.fsmt": [ - "FSMTConfig", - "FSMTTokenizer", - ], - "models.funnel": [ - "FunnelConfig", - "FunnelTokenizer", - ], - "models.fuyu": ["FuyuConfig"], - "models.gemma": ["GemmaConfig"], - "models.gemma2": ["Gemma2Config"], - "models.gemma3": ["Gemma3Config", "Gemma3Processor", "Gemma3TextConfig"], - "models.git": [ - "GitConfig", - "GitProcessor", - "GitVisionConfig", - ], - "models.glm": ["GlmConfig"], - "models.glm4": ["Glm4Config"], - "models.glpn": ["GLPNConfig"], - "models.got_ocr2": [ - "GotOcr2Config", - "GotOcr2Processor", - "GotOcr2VisionConfig", - ], - "models.gpt2": [ - "GPT2Config", - "GPT2Tokenizer", - ], - "models.gpt_bigcode": ["GPTBigCodeConfig"], - "models.gpt_neo": ["GPTNeoConfig"], - "models.gpt_neox": ["GPTNeoXConfig"], - "models.gpt_neox_japanese": ["GPTNeoXJapaneseConfig"], - "models.gpt_sw3": [], - "models.gptj": ["GPTJConfig"], - "models.granite": ["GraniteConfig"], - "models.granitemoe": ["GraniteMoeConfig"], - "models.granitemoeshared": ["GraniteMoeSharedConfig"], - "models.grounding_dino": [ - "GroundingDinoConfig", - "GroundingDinoProcessor", - ], - "models.groupvit": [ - "GroupViTConfig", - "GroupViTTextConfig", - "GroupViTVisionConfig", - ], - "models.helium": ["HeliumConfig"], - "models.herbert": ["HerbertTokenizer"], - "models.hiera": ["HieraConfig"], - "models.hubert": ["HubertConfig"], - "models.ibert": ["IBertConfig"], - "models.idefics": ["IdeficsConfig"], - "models.idefics2": ["Idefics2Config"], - "models.idefics3": ["Idefics3Config"], - "models.ijepa": ["IJepaConfig"], - "models.imagegpt": ["ImageGPTConfig"], - "models.informer": ["InformerConfig"], - "models.instructblip": [ - "InstructBlipConfig", - "InstructBlipProcessor", - "InstructBlipQFormerConfig", - "InstructBlipVisionConfig", - ], - "models.instructblipvideo": [ - "InstructBlipVideoConfig", - "InstructBlipVideoProcessor", - "InstructBlipVideoQFormerConfig", - "InstructBlipVideoVisionConfig", - ], - "models.jamba": ["JambaConfig"], - "models.jetmoe": ["JetMoeConfig"], - "models.kosmos2": [ - "Kosmos2Config", - "Kosmos2Processor", - ], - "models.layoutlm": [ - "LayoutLMConfig", - "LayoutLMTokenizer", - ], - "models.layoutlmv2": [ - "LayoutLMv2Config", - "LayoutLMv2FeatureExtractor", - "LayoutLMv2ImageProcessor", - "LayoutLMv2Processor", - "LayoutLMv2Tokenizer", - ], - "models.layoutlmv3": [ - "LayoutLMv3Config", - "LayoutLMv3FeatureExtractor", - "LayoutLMv3ImageProcessor", - "LayoutLMv3Processor", - "LayoutLMv3Tokenizer", - ], - "models.layoutxlm": ["LayoutXLMProcessor"], - "models.led": ["LEDConfig", "LEDTokenizer"], - "models.levit": ["LevitConfig"], - "models.lilt": ["LiltConfig"], - "models.llama": ["LlamaConfig"], - "models.llama4": [ - "Llama4Config", - "Llama4Processor", - "Llama4TextConfig", - "Llama4VisionConfig", - ], - "models.llava": [ - "LlavaConfig", - "LlavaProcessor", - ], - "models.llava_next": [ - "LlavaNextConfig", - "LlavaNextProcessor", - ], - "models.llava_next_video": [ - "LlavaNextVideoConfig", - "LlavaNextVideoProcessor", - ], - "models.llava_onevision": ["LlavaOnevisionConfig", "LlavaOnevisionProcessor"], - "models.longformer": [ - "LongformerConfig", - "LongformerTokenizer", - ], - "models.longt5": ["LongT5Config"], - "models.luke": [ - "LukeConfig", - "LukeTokenizer", - ], - "models.lxmert": [ - "LxmertConfig", - "LxmertTokenizer", - ], - "models.m2m_100": ["M2M100Config"], - "models.mamba": ["MambaConfig"], - "models.mamba2": ["Mamba2Config"], - "models.marian": ["MarianConfig"], - "models.markuplm": [ - "MarkupLMConfig", - "MarkupLMFeatureExtractor", - "MarkupLMProcessor", - "MarkupLMTokenizer", - ], - "models.mask2former": ["Mask2FormerConfig"], - "models.maskformer": [ - "MaskFormerConfig", - "MaskFormerSwinConfig", - ], - "models.mbart": ["MBartConfig"], - "models.mbart50": [], - "models.megatron_bert": ["MegatronBertConfig"], - "models.megatron_gpt2": [], - "models.mgp_str": [ - "MgpstrConfig", - "MgpstrProcessor", - "MgpstrTokenizer", - ], - "models.mimi": ["MimiConfig"], - "models.mistral": ["MistralConfig"], - "models.mistral3": ["Mistral3Config"], - "models.mixtral": ["MixtralConfig"], - "models.mllama": [ - "MllamaConfig", - "MllamaProcessor", - ], - "models.mluke": [], - "models.mobilebert": [ - "MobileBertConfig", - "MobileBertTokenizer", - ], - "models.mobilenet_v1": ["MobileNetV1Config"], - "models.mobilenet_v2": ["MobileNetV2Config"], - "models.mobilevit": ["MobileViTConfig"], - "models.mobilevitv2": ["MobileViTV2Config"], - "models.modernbert": ["ModernBertConfig"], - "models.moonshine": ["MoonshineConfig"], - "models.moshi": [ - "MoshiConfig", - "MoshiDepthConfig", - ], - "models.mpnet": [ - "MPNetConfig", - "MPNetTokenizer", - ], - "models.mpt": ["MptConfig"], - "models.mra": ["MraConfig"], - "models.mt5": ["MT5Config"], - "models.musicgen": [ - "MusicgenConfig", - "MusicgenDecoderConfig", - ], - "models.musicgen_melody": [ - "MusicgenMelodyConfig", - "MusicgenMelodyDecoderConfig", - ], - "models.mvp": ["MvpConfig", "MvpTokenizer"], - "models.myt5": ["MyT5Tokenizer"], - "models.nemotron": ["NemotronConfig"], - "models.nllb": [], - "models.nllb_moe": ["NllbMoeConfig"], - "models.nougat": ["NougatProcessor"], - "models.nystromformer": ["NystromformerConfig"], - "models.olmo": ["OlmoConfig"], - "models.olmo2": ["Olmo2Config"], - "models.olmoe": ["OlmoeConfig"], - "models.omdet_turbo": [ - "OmDetTurboConfig", - "OmDetTurboProcessor", - ], - "models.oneformer": [ - "OneFormerConfig", - "OneFormerProcessor", - ], - "models.openai": [ - "OpenAIGPTConfig", - "OpenAIGPTTokenizer", - ], - "models.opt": ["OPTConfig"], - "models.owlv2": [ - "Owlv2Config", - "Owlv2Processor", - "Owlv2TextConfig", - "Owlv2VisionConfig", - ], - "models.owlvit": [ - "OwlViTConfig", - "OwlViTProcessor", - "OwlViTTextConfig", - "OwlViTVisionConfig", - ], - "models.paligemma": ["PaliGemmaConfig"], - "models.patchtsmixer": ["PatchTSMixerConfig"], - "models.patchtst": ["PatchTSTConfig"], - "models.pegasus": [ - "PegasusConfig", - "PegasusTokenizer", - ], - "models.pegasus_x": ["PegasusXConfig"], - "models.perceiver": [ - "PerceiverConfig", - "PerceiverTokenizer", - ], - "models.persimmon": ["PersimmonConfig"], - "models.phi": ["PhiConfig"], - "models.phi3": ["Phi3Config"], - "models.phi4_multimodal": [ - "Phi4MultimodalAudioConfig", - "Phi4MultimodalConfig", - "Phi4MultimodalFeatureExtractor", - "Phi4MultimodalProcessor", - "Phi4MultimodalVisionConfig", - ], - "models.phimoe": ["PhimoeConfig"], - "models.phobert": ["PhobertTokenizer"], - "models.pix2struct": [ - "Pix2StructConfig", - "Pix2StructProcessor", - "Pix2StructTextConfig", - "Pix2StructVisionConfig", - ], - "models.pixtral": ["PixtralProcessor", "PixtralVisionConfig"], - "models.plbart": ["PLBartConfig"], - "models.poolformer": ["PoolFormerConfig"], - "models.pop2piano": ["Pop2PianoConfig"], - "models.prompt_depth_anything": ["PromptDepthAnythingConfig"], - "models.prophetnet": [ - "ProphetNetConfig", - "ProphetNetTokenizer", - ], - "models.pvt": ["PvtConfig"], - "models.pvt_v2": ["PvtV2Config"], - "models.qwen2": [ - "Qwen2Config", - "Qwen2Tokenizer", - ], - "models.qwen2_5_vl": [ - "Qwen2_5_VLConfig", - "Qwen2_5_VLProcessor", - ], - "models.qwen2_audio": [ - "Qwen2AudioConfig", - "Qwen2AudioEncoderConfig", - "Qwen2AudioProcessor", - ], - "models.qwen2_moe": ["Qwen2MoeConfig"], - "models.qwen2_vl": [ - "Qwen2VLConfig", - "Qwen2VLProcessor", - ], - "models.qwen3": ["Qwen3Config"], - "models.qwen3_moe": ["Qwen3MoeConfig"], - "models.rag": ["RagConfig", "RagRetriever", "RagTokenizer"], - "models.recurrent_gemma": ["RecurrentGemmaConfig"], - "models.reformer": ["ReformerConfig"], - "models.regnet": ["RegNetConfig"], - "models.rembert": ["RemBertConfig"], - "models.resnet": ["ResNetConfig"], - "models.roberta": [ - "RobertaConfig", - "RobertaTokenizer", - ], - "models.roberta_prelayernorm": ["RobertaPreLayerNormConfig"], - "models.roc_bert": [ - "RoCBertConfig", - "RoCBertTokenizer", - ], - "models.roformer": [ - "RoFormerConfig", - "RoFormerTokenizer", - ], - "models.rt_detr": ["RTDetrConfig", "RTDetrResNetConfig"], - "models.rt_detr_v2": ["RTDetrV2Config"], - "models.rwkv": ["RwkvConfig"], - "models.sam": [ - "SamConfig", - "SamMaskDecoderConfig", - "SamProcessor", - "SamPromptEncoderConfig", - "SamVisionConfig", - ], - "models.seamless_m4t": [ - "SeamlessM4TConfig", - "SeamlessM4TFeatureExtractor", - "SeamlessM4TProcessor", - ], - "models.seamless_m4t_v2": ["SeamlessM4Tv2Config"], - "models.segformer": ["SegformerConfig"], - "models.seggpt": ["SegGptConfig"], - "models.sew": ["SEWConfig"], - "models.sew_d": ["SEWDConfig"], - "models.shieldgemma2": [ - "ShieldGemma2Config", - "ShieldGemma2Processor", - ], - "models.siglip": [ - "SiglipConfig", - "SiglipProcessor", - "SiglipTextConfig", - "SiglipVisionConfig", - ], - "models.siglip2": [ - "Siglip2Config", - "Siglip2Processor", - "Siglip2TextConfig", - "Siglip2VisionConfig", - ], - "models.smolvlm": ["SmolVLMConfig"], - "models.speech_encoder_decoder": ["SpeechEncoderDecoderConfig"], - "models.speech_to_text": [ - "Speech2TextConfig", - "Speech2TextFeatureExtractor", - "Speech2TextProcessor", - ], - "models.speecht5": [ - "SpeechT5Config", - "SpeechT5FeatureExtractor", - "SpeechT5HifiGanConfig", - "SpeechT5Processor", - ], - "models.splinter": [ - "SplinterConfig", - "SplinterTokenizer", - ], - "models.squeezebert": [ - "SqueezeBertConfig", - "SqueezeBertTokenizer", - ], - "models.stablelm": ["StableLmConfig"], - "models.starcoder2": ["Starcoder2Config"], - "models.superglue": ["SuperGlueConfig"], - "models.superpoint": ["SuperPointConfig"], - "models.swiftformer": ["SwiftFormerConfig"], - "models.swin": ["SwinConfig"], - "models.swin2sr": ["Swin2SRConfig"], - "models.swinv2": ["Swinv2Config"], - "models.switch_transformers": ["SwitchTransformersConfig"], - "models.t5": ["T5Config"], - "models.table_transformer": ["TableTransformerConfig"], - "models.tapas": [ - "TapasConfig", - "TapasTokenizer", - ], - "models.textnet": ["TextNetConfig"], - "models.time_series_transformer": ["TimeSeriesTransformerConfig"], - "models.timesformer": ["TimesformerConfig"], - "models.timm_backbone": ["TimmBackboneConfig"], - "models.timm_wrapper": ["TimmWrapperConfig"], - "models.trocr": [ - "TrOCRConfig", - "TrOCRProcessor", - ], - "models.tvp": [ - "TvpConfig", - "TvpProcessor", - ], - "models.udop": [ - "UdopConfig", - "UdopProcessor", - ], - "models.umt5": ["UMT5Config"], - "models.unispeech": ["UniSpeechConfig"], - "models.unispeech_sat": ["UniSpeechSatConfig"], - "models.univnet": [ - "UnivNetConfig", - "UnivNetFeatureExtractor", - ], - "models.upernet": ["UperNetConfig"], - "models.video_llava": ["VideoLlavaConfig"], - "models.videomae": ["VideoMAEConfig"], - "models.vilt": [ - "ViltConfig", - "ViltFeatureExtractor", - "ViltImageProcessor", - "ViltProcessor", - ], - "models.vipllava": ["VipLlavaConfig"], - "models.vision_encoder_decoder": ["VisionEncoderDecoderConfig"], - "models.vision_text_dual_encoder": [ - "VisionTextDualEncoderConfig", - "VisionTextDualEncoderProcessor", - ], - "models.visual_bert": ["VisualBertConfig"], - "models.vit": ["ViTConfig"], - "models.vit_mae": ["ViTMAEConfig"], - "models.vit_msn": ["ViTMSNConfig"], - "models.vitdet": ["VitDetConfig"], - "models.vitmatte": ["VitMatteConfig"], - "models.vitpose": ["VitPoseConfig"], - "models.vitpose_backbone": ["VitPoseBackboneConfig"], - "models.vits": [ - "VitsConfig", - "VitsTokenizer", - ], - "models.vivit": ["VivitConfig"], - "models.wav2vec2": [ - "Wav2Vec2Config", - "Wav2Vec2CTCTokenizer", - "Wav2Vec2FeatureExtractor", - "Wav2Vec2Processor", - "Wav2Vec2Tokenizer", - ], - "models.wav2vec2_bert": [ - "Wav2Vec2BertConfig", - "Wav2Vec2BertProcessor", - ], - "models.wav2vec2_conformer": ["Wav2Vec2ConformerConfig"], - "models.wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"], - "models.wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"], - "models.wavlm": ["WavLMConfig"], - "models.whisper": [ - "WhisperConfig", - "WhisperFeatureExtractor", - "WhisperProcessor", - "WhisperTokenizer", - ], - "models.x_clip": [ - "XCLIPConfig", - "XCLIPProcessor", - "XCLIPTextConfig", - "XCLIPVisionConfig", - ], - "models.xglm": ["XGLMConfig"], - "models.xlm": ["XLMConfig", "XLMTokenizer"], - "models.xlm_roberta": ["XLMRobertaConfig"], - "models.xlm_roberta_xl": ["XLMRobertaXLConfig"], - "models.xlnet": ["XLNetConfig"], - "models.xmod": ["XmodConfig"], - "models.yolos": ["YolosConfig"], - "models.yoso": ["YosoConfig"], - "models.zamba": ["ZambaConfig"], - "models.zamba2": ["Zamba2Config"], - "models.zoedepth": ["ZoeDepthConfig"], "onnx": [], "pipelines": [ "AudioClassificationPipeline", @@ -1070,54 +295,6 @@ _import_structure = { ], } -# sentencepiece-backed objects -try: - if not is_sentencepiece_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - from .utils import dummy_sentencepiece_objects - - _import_structure["utils.dummy_sentencepiece_objects"] = [ - name for name in dir(dummy_sentencepiece_objects) if not name.startswith("_") - ] -else: - _import_structure["models.albert"].append("AlbertTokenizer") - _import_structure["models.barthez"].append("BarthezTokenizer") - _import_structure["models.bartpho"].append("BartphoTokenizer") - _import_structure["models.bert_generation"].append("BertGenerationTokenizer") - _import_structure["models.big_bird"].append("BigBirdTokenizer") - _import_structure["models.camembert"].append("CamembertTokenizer") - _import_structure["models.code_llama"].append("CodeLlamaTokenizer") - _import_structure["models.cpm"].append("CpmTokenizer") - _import_structure["models.deberta_v2"].append("DebertaV2Tokenizer") - _import_structure["models.deprecated.ernie_m"].append("ErnieMTokenizer") - _import_structure["models.deprecated.xlm_prophetnet"].append("XLMProphetNetTokenizer") - _import_structure["models.fnet"].append("FNetTokenizer") - _import_structure["models.gemma"].append("GemmaTokenizer") - _import_structure["models.gpt_sw3"].append("GPTSw3Tokenizer") - _import_structure["models.layoutxlm"].append("LayoutXLMTokenizer") - _import_structure["models.llama"].append("LlamaTokenizer") - _import_structure["models.m2m_100"].append("M2M100Tokenizer") - _import_structure["models.marian"].append("MarianTokenizer") - _import_structure["models.mbart"].append("MBartTokenizer") - _import_structure["models.mbart50"].append("MBart50Tokenizer") - _import_structure["models.mluke"].append("MLukeTokenizer") - _import_structure["models.mt5"].append("MT5Tokenizer") - _import_structure["models.nllb"].append("NllbTokenizer") - _import_structure["models.pegasus"].append("PegasusTokenizer") - _import_structure["models.plbart"].append("PLBartTokenizer") - _import_structure["models.reformer"].append("ReformerTokenizer") - _import_structure["models.rembert"].append("RemBertTokenizer") - _import_structure["models.seamless_m4t"].append("SeamlessM4TTokenizer") - _import_structure["models.siglip"].append("SiglipTokenizer") - _import_structure["models.speech_to_text"].append("Speech2TextTokenizer") - _import_structure["models.speecht5"].append("SpeechT5Tokenizer") - _import_structure["models.t5"].append("T5Tokenizer") - _import_structure["models.udop"].append("UdopTokenizer") - _import_structure["models.xglm"].append("XGLMTokenizer") - _import_structure["models.xlm_roberta"].append("XLMRobertaTokenizer") - _import_structure["models.xlnet"].append("XLNetTokenizer") - # tokenizers-backed objects try: if not is_tokenizers_available(): @@ -1130,74 +307,6 @@ except OptionalDependencyNotAvailable: ] else: # Fast tokenizers structure - _import_structure["models.albert"].append("AlbertTokenizerFast") - _import_structure["models.bart"].append("BartTokenizerFast") - _import_structure["models.barthez"].append("BarthezTokenizerFast") - _import_structure["models.bert"].append("BertTokenizerFast") - _import_structure["models.big_bird"].append("BigBirdTokenizerFast") - _import_structure["models.blenderbot"].append("BlenderbotTokenizerFast") - _import_structure["models.blenderbot_small"].append("BlenderbotSmallTokenizerFast") - _import_structure["models.bloom"].append("BloomTokenizerFast") - _import_structure["models.camembert"].append("CamembertTokenizerFast") - _import_structure["models.clip"].append("CLIPTokenizerFast") - _import_structure["models.code_llama"].append("CodeLlamaTokenizerFast") - _import_structure["models.codegen"].append("CodeGenTokenizerFast") - _import_structure["models.cohere"].append("CohereTokenizerFast") - _import_structure["models.convbert"].append("ConvBertTokenizerFast") - _import_structure["models.cpm"].append("CpmTokenizerFast") - _import_structure["models.deberta"].append("DebertaTokenizerFast") - _import_structure["models.deberta_v2"].append("DebertaV2TokenizerFast") - _import_structure["models.deprecated.realm"].append("RealmTokenizerFast") - _import_structure["models.deprecated.retribert"].append("RetriBertTokenizerFast") - _import_structure["models.distilbert"].append("DistilBertTokenizerFast") - _import_structure["models.dpr"].extend( - [ - "DPRContextEncoderTokenizerFast", - "DPRQuestionEncoderTokenizerFast", - "DPRReaderTokenizerFast", - ] - ) - _import_structure["models.electra"].append("ElectraTokenizerFast") - _import_structure["models.fnet"].append("FNetTokenizerFast") - _import_structure["models.funnel"].append("FunnelTokenizerFast") - _import_structure["models.gemma"].append("GemmaTokenizerFast") - _import_structure["models.gpt2"].append("GPT2TokenizerFast") - _import_structure["models.gpt_neox"].append("GPTNeoXTokenizerFast") - _import_structure["models.gpt_neox_japanese"].append("GPTNeoXJapaneseTokenizer") - _import_structure["models.herbert"].append("HerbertTokenizerFast") - _import_structure["models.layoutlm"].append("LayoutLMTokenizerFast") - _import_structure["models.layoutlmv2"].append("LayoutLMv2TokenizerFast") - _import_structure["models.layoutlmv3"].append("LayoutLMv3TokenizerFast") - _import_structure["models.layoutxlm"].append("LayoutXLMTokenizerFast") - _import_structure["models.led"].append("LEDTokenizerFast") - _import_structure["models.llama"].append("LlamaTokenizerFast") - _import_structure["models.longformer"].append("LongformerTokenizerFast") - _import_structure["models.lxmert"].append("LxmertTokenizerFast") - _import_structure["models.markuplm"].append("MarkupLMTokenizerFast") - _import_structure["models.mbart"].append("MBartTokenizerFast") - _import_structure["models.mbart50"].append("MBart50TokenizerFast") - _import_structure["models.mobilebert"].append("MobileBertTokenizerFast") - _import_structure["models.mpnet"].append("MPNetTokenizerFast") - _import_structure["models.mt5"].append("MT5TokenizerFast") - _import_structure["models.mvp"].append("MvpTokenizerFast") - _import_structure["models.nllb"].append("NllbTokenizerFast") - _import_structure["models.nougat"].append("NougatTokenizerFast") - _import_structure["models.openai"].append("OpenAIGPTTokenizerFast") - _import_structure["models.pegasus"].append("PegasusTokenizerFast") - _import_structure["models.qwen2"].append("Qwen2TokenizerFast") - _import_structure["models.reformer"].append("ReformerTokenizerFast") - _import_structure["models.rembert"].append("RemBertTokenizerFast") - _import_structure["models.roberta"].append("RobertaTokenizerFast") - _import_structure["models.roformer"].append("RoFormerTokenizerFast") - _import_structure["models.seamless_m4t"].append("SeamlessM4TTokenizerFast") - _import_structure["models.splinter"].append("SplinterTokenizerFast") - _import_structure["models.squeezebert"].append("SqueezeBertTokenizerFast") - _import_structure["models.t5"].append("T5TokenizerFast") - _import_structure["models.udop"].append("UdopTokenizerFast") - _import_structure["models.whisper"].append("WhisperTokenizerFast") - _import_structure["models.xglm"].append("XGLMTokenizerFast") - _import_structure["models.xlm_roberta"].append("XLMRobertaTokenizerFast") - _import_structure["models.xlnet"].append("XLNetTokenizerFast") _import_structure["tokenization_utils_fast"] = ["PreTrainedTokenizerFast"] @@ -1216,32 +325,6 @@ else: "convert_slow_tokenizer", ] -# Tensorflow-text-specific objects -try: - if not is_tensorflow_text_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - from .utils import dummy_tensorflow_text_objects - - _import_structure["utils.dummy_tensorflow_text_objects"] = [ - name for name in dir(dummy_tensorflow_text_objects) if not name.startswith("_") - ] -else: - _import_structure["models.bert"].append("TFBertTokenizer") - -# keras-nlp-specific objects -try: - if not is_keras_nlp_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - from .utils import dummy_keras_nlp_objects - - _import_structure["utils.dummy_keras_nlp_objects"] = [ - name for name in dir(dummy_keras_nlp_objects) if not name.startswith("_") - ] -else: - _import_structure["models.gpt2"].append("TFGPT2Tokenizer") - # Vision-specific objects try: if not is_vision_available(): @@ -1256,90 +339,6 @@ else: _import_structure["image_processing_base"] = ["ImageProcessingMixin"] _import_structure["image_processing_utils"] = ["BaseImageProcessor"] _import_structure["image_utils"] = ["ImageFeatureExtractionMixin"] - _import_structure["models.aria"].extend(["AriaImageProcessor"]) - _import_structure["models.beit"].extend(["BeitFeatureExtractor", "BeitImageProcessor"]) - _import_structure["models.bit"].extend(["BitImageProcessor"]) - _import_structure["models.blip"].extend(["BlipImageProcessor"]) - _import_structure["models.bridgetower"].append("BridgeTowerImageProcessor") - _import_structure["models.chameleon"].append("ChameleonImageProcessor") - _import_structure["models.chinese_clip"].extend(["ChineseCLIPFeatureExtractor", "ChineseCLIPImageProcessor"]) - _import_structure["models.clip"].extend(["CLIPFeatureExtractor", "CLIPImageProcessor"]) - _import_structure["models.conditional_detr"].extend( - ["ConditionalDetrFeatureExtractor", "ConditionalDetrImageProcessor"] - ) - _import_structure["models.convnext"].extend(["ConvNextFeatureExtractor", "ConvNextImageProcessor"]) - _import_structure["models.deformable_detr"].extend( - ["DeformableDetrFeatureExtractor", "DeformableDetrImageProcessor"] - ) - _import_structure["models.deit"].extend(["DeiTFeatureExtractor", "DeiTImageProcessor"]) - _import_structure["models.deprecated.deta"].append("DetaImageProcessor") - _import_structure["models.deprecated.efficientformer"].append("EfficientFormerImageProcessor") - _import_structure["models.deprecated.tvlt"].append("TvltImageProcessor") - _import_structure["models.deprecated.vit_hybrid"].extend(["ViTHybridImageProcessor"]) - _import_structure["models.depth_pro"].extend(["DepthProImageProcessor", "DepthProImageProcessorFast"]) - _import_structure["models.detr"].extend(["DetrFeatureExtractor", "DetrImageProcessor"]) - _import_structure["models.donut"].extend(["DonutFeatureExtractor", "DonutImageProcessor"]) - _import_structure["models.dpt"].extend(["DPTFeatureExtractor", "DPTImageProcessor"]) - _import_structure["models.efficientnet"].append("EfficientNetImageProcessor") - _import_structure["models.emu3"].append("Emu3ImageProcessor") - _import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaImageProcessor", "FlavaProcessor"]) - _import_structure["models.fuyu"].extend(["FuyuImageProcessor", "FuyuProcessor"]) - _import_structure["models.gemma3"].append("Gemma3ImageProcessor") - _import_structure["models.glpn"].extend(["GLPNFeatureExtractor", "GLPNImageProcessor"]) - _import_structure["models.got_ocr2"].extend(["GotOcr2ImageProcessor"]) - _import_structure["models.grounding_dino"].extend(["GroundingDinoImageProcessor"]) - _import_structure["models.idefics"].extend(["IdeficsImageProcessor"]) - _import_structure["models.idefics2"].extend(["Idefics2ImageProcessor"]) - _import_structure["models.idefics3"].extend(["Idefics3ImageProcessor"]) - _import_structure["models.imagegpt"].extend(["ImageGPTFeatureExtractor", "ImageGPTImageProcessor"]) - _import_structure["models.instructblipvideo"].extend(["InstructBlipVideoImageProcessor"]) - _import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"]) - _import_structure["models.layoutlmv3"].extend(["LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor"]) - _import_structure["models.levit"].extend(["LevitFeatureExtractor", "LevitImageProcessor"]) - _import_structure["models.llava"].append("LlavaImageProcessor") - _import_structure["models.llava_next"].append("LlavaNextImageProcessor") - _import_structure["models.llava_next_video"].append("LlavaNextVideoImageProcessor") - _import_structure["models.llava_onevision"].extend( - ["LlavaOnevisionImageProcessor", "LlavaOnevisionVideoProcessor"] - ) - _import_structure["models.mask2former"].append("Mask2FormerImageProcessor") - _import_structure["models.maskformer"].extend(["MaskFormerFeatureExtractor", "MaskFormerImageProcessor"]) - _import_structure["models.mllama"].extend(["MllamaImageProcessor"]) - _import_structure["models.mobilenet_v1"].extend(["MobileNetV1FeatureExtractor", "MobileNetV1ImageProcessor"]) - _import_structure["models.mobilenet_v2"].extend(["MobileNetV2FeatureExtractor", "MobileNetV2ImageProcessor"]) - _import_structure["models.mobilevit"].extend(["MobileViTFeatureExtractor", "MobileViTImageProcessor"]) - _import_structure["models.nougat"].append("NougatImageProcessor") - _import_structure["models.oneformer"].extend(["OneFormerImageProcessor"]) - _import_structure["models.owlv2"].append("Owlv2ImageProcessor") - _import_structure["models.owlvit"].extend(["OwlViTFeatureExtractor", "OwlViTImageProcessor"]) - _import_structure["models.perceiver"].extend(["PerceiverFeatureExtractor", "PerceiverImageProcessor"]) - _import_structure["models.pix2struct"].extend(["Pix2StructImageProcessor"]) - _import_structure["models.pixtral"].append("PixtralImageProcessor") - _import_structure["models.poolformer"].extend(["PoolFormerFeatureExtractor", "PoolFormerImageProcessor"]) - _import_structure["models.prompt_depth_anything"].extend(["PromptDepthAnythingImageProcessor"]) - _import_structure["models.pvt"].extend(["PvtImageProcessor"]) - _import_structure["models.qwen2_vl"].extend(["Qwen2VLImageProcessor"]) - _import_structure["models.rt_detr"].extend(["RTDetrImageProcessor"]) - _import_structure["models.sam"].extend(["SamImageProcessor"]) - _import_structure["models.segformer"].extend(["SegformerFeatureExtractor", "SegformerImageProcessor"]) - _import_structure["models.seggpt"].extend(["SegGptImageProcessor"]) - _import_structure["models.siglip"].append("SiglipImageProcessor") - _import_structure["models.siglip2"].append("Siglip2ImageProcessor") - _import_structure["models.smolvlm"].extend(["SmolVLMImageProcessor"]) - _import_structure["models.superglue"].extend(["SuperGlueImageProcessor"]) - _import_structure["models.superpoint"].extend(["SuperPointImageProcessor"]) - _import_structure["models.swin2sr"].append("Swin2SRImageProcessor") - _import_structure["models.textnet"].extend(["TextNetImageProcessor"]) - _import_structure["models.tvp"].append("TvpImageProcessor") - _import_structure["models.video_llava"].append("VideoLlavaImageProcessor") - _import_structure["models.videomae"].extend(["VideoMAEFeatureExtractor", "VideoMAEImageProcessor"]) - _import_structure["models.vilt"].extend(["ViltFeatureExtractor", "ViltImageProcessor", "ViltProcessor"]) - _import_structure["models.vit"].extend(["ViTFeatureExtractor", "ViTImageProcessor"]) - _import_structure["models.vitmatte"].append("VitMatteImageProcessor") - _import_structure["models.vitpose"].append("VitPoseImageProcessor") - _import_structure["models.vivit"].append("VivitImageProcessor") - _import_structure["models.yolos"].extend(["YolosFeatureExtractor", "YolosImageProcessor"]) - _import_structure["models.zoedepth"].append("ZoeDepthImageProcessor") try: if not is_torchvision_available(): @@ -1352,38 +351,6 @@ except OptionalDependencyNotAvailable: ] else: _import_structure["image_processing_utils_fast"] = ["BaseImageProcessorFast"] - _import_structure["models.blip"].append("BlipImageProcessorFast") - _import_structure["models.clip"].append("CLIPImageProcessorFast") - _import_structure["models.convnext"].append("ConvNextImageProcessorFast") - _import_structure["models.deformable_detr"].append("DeformableDetrImageProcessorFast") - _import_structure["models.deit"].append("DeiTImageProcessorFast") - _import_structure["models.depth_pro"].append("DepthProImageProcessorFast") - _import_structure["models.detr"].append("DetrImageProcessorFast") - _import_structure["models.gemma3"].append("Gemma3ImageProcessorFast") - _import_structure["models.got_ocr2"].append("GotOcr2ImageProcessorFast") - _import_structure["models.llama4"].append("Llama4ImageProcessorFast") - _import_structure["models.llava"].append("LlavaImageProcessorFast") - _import_structure["models.llava_next"].append("LlavaNextImageProcessorFast") - _import_structure["models.llava_onevision"].append("LlavaOnevisionImageProcessorFast") - _import_structure["models.phi4_multimodal"].append("Phi4MultimodalImageProcessorFast") - _import_structure["models.pixtral"].append("PixtralImageProcessorFast") - _import_structure["models.qwen2_vl"].append("Qwen2VLImageProcessorFast") - _import_structure["models.rt_detr"].append("RTDetrImageProcessorFast") - _import_structure["models.siglip"].append("SiglipImageProcessorFast") - _import_structure["models.siglip2"].append("Siglip2ImageProcessorFast") - _import_structure["models.vit"].append("ViTImageProcessorFast") - -try: - if not (is_torchvision_available() and is_timm_available()): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - from .utils import dummy_timm_and_torchvision_objects - - _import_structure["utils.dummy_timm_and_torchvision_objects"] = [ - name for name in dir(dummy_timm_and_torchvision_objects) if not name.startswith("_") - ] -else: - _import_structure["models.timm_wrapper"].extend(["TimmWrapperImageProcessor"]) # PyTorch-backed objects try: @@ -1493,2717 +460,6 @@ else: _import_structure["modeling_outputs"] = [] _import_structure["modeling_rope_utils"] = ["ROPE_INIT_FUNCTIONS", "dynamic_rope_update"] _import_structure["modeling_utils"] = ["PreTrainedModel", "AttentionInterface"] - - # PyTorch models structure - - _import_structure["models.albert"].extend( - [ - "AlbertForMaskedLM", - "AlbertForMultipleChoice", - "AlbertForPreTraining", - "AlbertForQuestionAnswering", - "AlbertForSequenceClassification", - "AlbertForTokenClassification", - "AlbertModel", - "AlbertPreTrainedModel", - "load_tf_weights_in_albert", - ] - ) - - _import_structure["models.align"].extend( - [ - "AlignModel", - "AlignPreTrainedModel", - "AlignTextModel", - "AlignVisionModel", - ] - ) - _import_structure["models.altclip"].extend( - [ - "AltCLIPModel", - "AltCLIPPreTrainedModel", - "AltCLIPTextModel", - "AltCLIPVisionModel", - ] - ) - _import_structure["models.aria"].extend( - [ - "AriaForConditionalGeneration", - "AriaPreTrainedModel", - "AriaTextForCausalLM", - "AriaTextModel", - "AriaTextPreTrainedModel", - ] - ) - _import_structure["models.audio_spectrogram_transformer"].extend( - [ - "ASTForAudioClassification", - "ASTModel", - "ASTPreTrainedModel", - ] - ) - _import_structure["models.auto"].extend( - [ - "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", - "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING", - "MODEL_FOR_AUDIO_XVECTOR_MAPPING", - "MODEL_FOR_BACKBONE_MAPPING", - "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING", - "MODEL_FOR_CAUSAL_LM_MAPPING", - "MODEL_FOR_CTC_MAPPING", - "MODEL_FOR_DEPTH_ESTIMATION_MAPPING", - "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", - "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", - "MODEL_FOR_IMAGE_MAPPING", - "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING", - "MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING", - "MODEL_FOR_IMAGE_TO_IMAGE_MAPPING", - "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING", - "MODEL_FOR_KEYPOINT_DETECTION_MAPPING", - "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", - "MODEL_FOR_MASKED_LM_MAPPING", - "MODEL_FOR_MASK_GENERATION_MAPPING", - "MODEL_FOR_MULTIPLE_CHOICE_MAPPING", - "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", - "MODEL_FOR_OBJECT_DETECTION_MAPPING", - "MODEL_FOR_PRETRAINING_MAPPING", - "MODEL_FOR_QUESTION_ANSWERING_MAPPING", - "MODEL_FOR_RETRIEVAL_MAPPING", - "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", - "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", - "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", - "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", - "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", - "MODEL_FOR_TEXT_ENCODING_MAPPING", - "MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING", - "MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING", - "MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING", - "MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING", - "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", - "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING", - "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING", - "MODEL_FOR_VISION_2_SEQ_MAPPING", - "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING", - "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING", - "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", - "MODEL_MAPPING", - "MODEL_WITH_LM_HEAD_MAPPING", - "AutoBackbone", - "AutoModel", - "AutoModelForAudioClassification", - "AutoModelForAudioFrameClassification", - "AutoModelForAudioXVector", - "AutoModelForCausalLM", - "AutoModelForCTC", - "AutoModelForDepthEstimation", - "AutoModelForDocumentQuestionAnswering", - "AutoModelForImageClassification", - "AutoModelForImageSegmentation", - "AutoModelForImageTextToText", - "AutoModelForImageToImage", - "AutoModelForInstanceSegmentation", - "AutoModelForKeypointDetection", - "AutoModelForMaskedImageModeling", - "AutoModelForMaskedLM", - "AutoModelForMaskGeneration", - "AutoModelForMultipleChoice", - "AutoModelForNextSentencePrediction", - "AutoModelForObjectDetection", - "AutoModelForPreTraining", - "AutoModelForQuestionAnswering", - "AutoModelForSemanticSegmentation", - "AutoModelForSeq2SeqLM", - "AutoModelForSequenceClassification", - "AutoModelForSpeechSeq2Seq", - "AutoModelForTableQuestionAnswering", - "AutoModelForTextEncoding", - "AutoModelForTextToSpectrogram", - "AutoModelForTextToWaveform", - "AutoModelForTokenClassification", - "AutoModelForUniversalSegmentation", - "AutoModelForVideoClassification", - "AutoModelForVision2Seq", - "AutoModelForVisualQuestionAnswering", - "AutoModelForZeroShotImageClassification", - "AutoModelForZeroShotObjectDetection", - "AutoModelWithLMHead", - ] - ) - _import_structure["models.autoformer"].extend( - [ - "AutoformerForPrediction", - "AutoformerModel", - "AutoformerPreTrainedModel", - ] - ) - _import_structure["models.aya_vision"].extend(["AyaVisionForConditionalGeneration", "AyaVisionPreTrainedModel"]) - _import_structure["models.bamba"].extend( - [ - "BambaForCausalLM", - "BambaModel", - "BambaPreTrainedModel", - ] - ) - _import_structure["models.bark"].extend( - [ - "BarkCausalModel", - "BarkCoarseModel", - "BarkFineModel", - "BarkModel", - "BarkPreTrainedModel", - "BarkSemanticModel", - ] - ) - _import_structure["models.bart"].extend( - [ - "BartForCausalLM", - "BartForConditionalGeneration", - "BartForQuestionAnswering", - "BartForSequenceClassification", - "BartModel", - "BartPretrainedModel", - "BartPreTrainedModel", - "PretrainedBartModel", - ] - ) - _import_structure["models.beit"].extend( - [ - "BeitBackbone", - "BeitForImageClassification", - "BeitForMaskedImageModeling", - "BeitForSemanticSegmentation", - "BeitModel", - "BeitPreTrainedModel", - ] - ) - _import_structure["models.bert"].extend( - [ - "BertForMaskedLM", - "BertForMultipleChoice", - "BertForNextSentencePrediction", - "BertForPreTraining", - "BertForQuestionAnswering", - "BertForSequenceClassification", - "BertForTokenClassification", - "BertLMHeadModel", - "BertModel", - "BertPreTrainedModel", - "load_tf_weights_in_bert", - ] - ) - _import_structure["models.bert_generation"].extend( - [ - "BertGenerationDecoder", - "BertGenerationEncoder", - "BertGenerationPreTrainedModel", - "load_tf_weights_in_bert_generation", - ] - ) - _import_structure["models.big_bird"].extend( - [ - "BigBirdForCausalLM", - "BigBirdForMaskedLM", - "BigBirdForMultipleChoice", - "BigBirdForPreTraining", - "BigBirdForQuestionAnswering", - "BigBirdForSequenceClassification", - "BigBirdForTokenClassification", - "BigBirdModel", - "BigBirdPreTrainedModel", - "load_tf_weights_in_big_bird", - ] - ) - _import_structure["models.bigbird_pegasus"].extend( - [ - "BigBirdPegasusForCausalLM", - "BigBirdPegasusForConditionalGeneration", - "BigBirdPegasusForQuestionAnswering", - "BigBirdPegasusForSequenceClassification", - "BigBirdPegasusModel", - "BigBirdPegasusPreTrainedModel", - ] - ) - _import_structure["models.biogpt"].extend( - [ - "BioGptForCausalLM", - "BioGptForSequenceClassification", - "BioGptForTokenClassification", - "BioGptModel", - "BioGptPreTrainedModel", - ] - ) - _import_structure["models.bit"].extend( - [ - "BitBackbone", - "BitForImageClassification", - "BitModel", - "BitPreTrainedModel", - ] - ) - _import_structure["models.blenderbot"].extend( - [ - "BlenderbotForCausalLM", - "BlenderbotForConditionalGeneration", - "BlenderbotModel", - "BlenderbotPreTrainedModel", - ] - ) - _import_structure["models.blenderbot_small"].extend( - [ - "BlenderbotSmallForCausalLM", - "BlenderbotSmallForConditionalGeneration", - "BlenderbotSmallModel", - "BlenderbotSmallPreTrainedModel", - ] - ) - _import_structure["models.blip"].extend( - [ - "BlipForConditionalGeneration", - "BlipForImageTextRetrieval", - "BlipForQuestionAnswering", - "BlipModel", - "BlipPreTrainedModel", - "BlipTextModel", - "BlipVisionModel", - ] - ) - _import_structure["models.blip_2"].extend( - [ - "Blip2ForConditionalGeneration", - "Blip2ForImageTextRetrieval", - "Blip2Model", - "Blip2PreTrainedModel", - "Blip2QFormerModel", - "Blip2TextModelWithProjection", - "Blip2VisionModel", - "Blip2VisionModelWithProjection", - ] - ) - _import_structure["models.bloom"].extend( - [ - "BloomForCausalLM", - "BloomForQuestionAnswering", - "BloomForSequenceClassification", - "BloomForTokenClassification", - "BloomModel", - "BloomPreTrainedModel", - ] - ) - _import_structure["models.bridgetower"].extend( - [ - "BridgeTowerForContrastiveLearning", - "BridgeTowerForImageAndTextRetrieval", - "BridgeTowerForMaskedLM", - "BridgeTowerModel", - "BridgeTowerPreTrainedModel", - ] - ) - _import_structure["models.bros"].extend( - [ - "BrosForTokenClassification", - "BrosModel", - "BrosPreTrainedModel", - "BrosProcessor", - "BrosSpadeEEForTokenClassification", - "BrosSpadeELForTokenClassification", - ] - ) - _import_structure["models.camembert"].extend( - [ - "CamembertForCausalLM", - "CamembertForMaskedLM", - "CamembertForMultipleChoice", - "CamembertForQuestionAnswering", - "CamembertForSequenceClassification", - "CamembertForTokenClassification", - "CamembertModel", - "CamembertPreTrainedModel", - ] - ) - _import_structure["models.canine"].extend( - [ - "CanineForMultipleChoice", - "CanineForQuestionAnswering", - "CanineForSequenceClassification", - "CanineForTokenClassification", - "CanineModel", - "CaninePreTrainedModel", - "load_tf_weights_in_canine", - ] - ) - _import_structure["models.chameleon"].extend( - [ - "ChameleonForConditionalGeneration", - "ChameleonModel", - "ChameleonPreTrainedModel", - "ChameleonProcessor", - "ChameleonVQVAE", - ] - ) - _import_structure["models.chinese_clip"].extend( - [ - "ChineseCLIPModel", - "ChineseCLIPPreTrainedModel", - "ChineseCLIPTextModel", - "ChineseCLIPVisionModel", - ] - ) - _import_structure["models.clap"].extend( - [ - "ClapAudioModel", - "ClapAudioModelWithProjection", - "ClapFeatureExtractor", - "ClapModel", - "ClapPreTrainedModel", - "ClapTextModel", - "ClapTextModelWithProjection", - ] - ) - _import_structure["models.clip"].extend( - [ - "CLIPForImageClassification", - "CLIPModel", - "CLIPPreTrainedModel", - "CLIPTextModel", - "CLIPTextModelWithProjection", - "CLIPVisionModel", - "CLIPVisionModelWithProjection", - ] - ) - _import_structure["models.clipseg"].extend( - [ - "CLIPSegForImageSegmentation", - "CLIPSegModel", - "CLIPSegPreTrainedModel", - "CLIPSegTextModel", - "CLIPSegVisionModel", - ] - ) - _import_structure["models.clvp"].extend( - [ - "ClvpDecoder", - "ClvpEncoder", - "ClvpForCausalLM", - "ClvpModel", - "ClvpModelForConditionalGeneration", - "ClvpPreTrainedModel", - ] - ) - _import_structure["models.codegen"].extend( - [ - "CodeGenForCausalLM", - "CodeGenModel", - "CodeGenPreTrainedModel", - ] - ) - _import_structure["models.cohere"].extend(["CohereForCausalLM", "CohereModel", "CoherePreTrainedModel"]) - _import_structure["models.cohere2"].extend(["Cohere2ForCausalLM", "Cohere2Model", "Cohere2PreTrainedModel"]) - _import_structure["models.colpali"].extend( - [ - "ColPaliForRetrieval", - "ColPaliPreTrainedModel", - ] - ) - _import_structure["models.conditional_detr"].extend( - [ - "ConditionalDetrForObjectDetection", - "ConditionalDetrForSegmentation", - "ConditionalDetrModel", - "ConditionalDetrPreTrainedModel", - ] - ) - _import_structure["models.convbert"].extend( - [ - "ConvBertForMaskedLM", - "ConvBertForMultipleChoice", - "ConvBertForQuestionAnswering", - "ConvBertForSequenceClassification", - "ConvBertForTokenClassification", - "ConvBertModel", - "ConvBertPreTrainedModel", - "load_tf_weights_in_convbert", - ] - ) - _import_structure["models.convnext"].extend( - [ - "ConvNextBackbone", - "ConvNextForImageClassification", - "ConvNextModel", - "ConvNextPreTrainedModel", - ] - ) - _import_structure["models.convnextv2"].extend( - [ - "ConvNextV2Backbone", - "ConvNextV2ForImageClassification", - "ConvNextV2Model", - "ConvNextV2PreTrainedModel", - ] - ) - _import_structure["models.cpmant"].extend( - [ - "CpmAntForCausalLM", - "CpmAntModel", - "CpmAntPreTrainedModel", - ] - ) - _import_structure["models.ctrl"].extend( - [ - "CTRLForSequenceClassification", - "CTRLLMHeadModel", - "CTRLModel", - "CTRLPreTrainedModel", - ] - ) - _import_structure["models.cvt"].extend( - [ - "CvtForImageClassification", - "CvtModel", - "CvtPreTrainedModel", - ] - ) - _import_structure["models.dab_detr"].extend( - [ - "DabDetrForObjectDetection", - "DabDetrModel", - "DabDetrPreTrainedModel", - ] - ) - _import_structure["models.dac"].extend( - [ - "DacModel", - "DacPreTrainedModel", - ] - ) - _import_structure["models.data2vec"].extend( - [ - "Data2VecAudioForAudioFrameClassification", - "Data2VecAudioForCTC", - "Data2VecAudioForSequenceClassification", - "Data2VecAudioForXVector", - "Data2VecAudioModel", - "Data2VecAudioPreTrainedModel", - "Data2VecTextForCausalLM", - "Data2VecTextForMaskedLM", - "Data2VecTextForMultipleChoice", - "Data2VecTextForQuestionAnswering", - "Data2VecTextForSequenceClassification", - "Data2VecTextForTokenClassification", - "Data2VecTextModel", - "Data2VecTextPreTrainedModel", - "Data2VecVisionForImageClassification", - "Data2VecVisionForSemanticSegmentation", - "Data2VecVisionModel", - "Data2VecVisionPreTrainedModel", - ] - ) - _import_structure["models.dbrx"].extend( - [ - "DbrxForCausalLM", - "DbrxModel", - "DbrxPreTrainedModel", - ] - ) - _import_structure["models.deberta"].extend( - [ - "DebertaForMaskedLM", - "DebertaForQuestionAnswering", - "DebertaForSequenceClassification", - "DebertaForTokenClassification", - "DebertaModel", - "DebertaPreTrainedModel", - ] - ) - _import_structure["models.deberta_v2"].extend( - [ - "DebertaV2ForMaskedLM", - "DebertaV2ForMultipleChoice", - "DebertaV2ForQuestionAnswering", - "DebertaV2ForSequenceClassification", - "DebertaV2ForTokenClassification", - "DebertaV2Model", - "DebertaV2PreTrainedModel", - ] - ) - _import_structure["models.decision_transformer"].extend( - [ - "DecisionTransformerGPT2Model", - "DecisionTransformerGPT2PreTrainedModel", - "DecisionTransformerModel", - "DecisionTransformerPreTrainedModel", - ] - ) - _import_structure["models.deepseek_v3"].extend( - [ - "DeepseekV3ForCausalLM", - "DeepseekV3Model", - "DeepseekV3PreTrainedModel", - ] - ) - _import_structure["models.deformable_detr"].extend( - [ - "DeformableDetrForObjectDetection", - "DeformableDetrModel", - "DeformableDetrPreTrainedModel", - ] - ) - _import_structure["models.deit"].extend( - [ - "DeiTForImageClassification", - "DeiTForImageClassificationWithTeacher", - "DeiTForMaskedImageModeling", - "DeiTModel", - "DeiTPreTrainedModel", - ] - ) - _import_structure["models.deprecated.deta"].extend( - [ - "DetaForObjectDetection", - "DetaModel", - "DetaPreTrainedModel", - ] - ) - _import_structure["models.deprecated.efficientformer"].extend( - [ - "EfficientFormerForImageClassification", - "EfficientFormerForImageClassificationWithTeacher", - "EfficientFormerModel", - "EfficientFormerPreTrainedModel", - ] - ) - _import_structure["models.deprecated.ernie_m"].extend( - [ - "ErnieMForInformationExtraction", - "ErnieMForMultipleChoice", - "ErnieMForQuestionAnswering", - "ErnieMForSequenceClassification", - "ErnieMForTokenClassification", - "ErnieMModel", - "ErnieMPreTrainedModel", - ] - ) - _import_structure["models.deprecated.gptsan_japanese"].extend( - [ - "GPTSanJapaneseForConditionalGeneration", - "GPTSanJapaneseModel", - "GPTSanJapanesePreTrainedModel", - ] - ) - _import_structure["models.deprecated.graphormer"].extend( - [ - "GraphormerForGraphClassification", - "GraphormerModel", - "GraphormerPreTrainedModel", - ] - ) - _import_structure["models.deprecated.jukebox"].extend( - [ - "JukeboxModel", - "JukeboxPreTrainedModel", - "JukeboxPrior", - "JukeboxVQVAE", - ] - ) - _import_structure["models.deprecated.mctct"].extend( - [ - "MCTCTForCTC", - "MCTCTModel", - "MCTCTPreTrainedModel", - ] - ) - _import_structure["models.deprecated.mega"].extend( - [ - "MegaForCausalLM", - "MegaForMaskedLM", - "MegaForMultipleChoice", - "MegaForQuestionAnswering", - "MegaForSequenceClassification", - "MegaForTokenClassification", - "MegaModel", - "MegaPreTrainedModel", - ] - ) - _import_structure["models.deprecated.mmbt"].extend(["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]) - _import_structure["models.deprecated.nat"].extend( - [ - "NatBackbone", - "NatForImageClassification", - "NatModel", - "NatPreTrainedModel", - ] - ) - _import_structure["models.deprecated.nezha"].extend( - [ - "NezhaForMaskedLM", - "NezhaForMultipleChoice", - "NezhaForNextSentencePrediction", - "NezhaForPreTraining", - "NezhaForQuestionAnswering", - "NezhaForSequenceClassification", - "NezhaForTokenClassification", - "NezhaModel", - "NezhaPreTrainedModel", - ] - ) - _import_structure["models.deprecated.open_llama"].extend( - [ - "OpenLlamaForCausalLM", - "OpenLlamaForSequenceClassification", - "OpenLlamaModel", - "OpenLlamaPreTrainedModel", - ] - ) - _import_structure["models.deprecated.qdqbert"].extend( - [ - "QDQBertForMaskedLM", - "QDQBertForMultipleChoice", - "QDQBertForNextSentencePrediction", - "QDQBertForQuestionAnswering", - "QDQBertForSequenceClassification", - "QDQBertForTokenClassification", - "QDQBertLMHeadModel", - "QDQBertModel", - "QDQBertPreTrainedModel", - "load_tf_weights_in_qdqbert", - ] - ) - _import_structure["models.deprecated.realm"].extend( - [ - "RealmEmbedder", - "RealmForOpenQA", - "RealmKnowledgeAugEncoder", - "RealmPreTrainedModel", - "RealmReader", - "RealmRetriever", - "RealmScorer", - "load_tf_weights_in_realm", - ] - ) - _import_structure["models.deprecated.retribert"].extend( - [ - "RetriBertModel", - "RetriBertPreTrainedModel", - ] - ) - _import_structure["models.deprecated.speech_to_text_2"].extend( - ["Speech2Text2ForCausalLM", "Speech2Text2PreTrainedModel"] - ) - _import_structure["models.deprecated.trajectory_transformer"].extend( - [ - "TrajectoryTransformerModel", - "TrajectoryTransformerPreTrainedModel", - ] - ) - _import_structure["models.deprecated.transfo_xl"].extend( - [ - "AdaptiveEmbedding", - "TransfoXLForSequenceClassification", - "TransfoXLLMHeadModel", - "TransfoXLModel", - "TransfoXLPreTrainedModel", - "load_tf_weights_in_transfo_xl", - ] - ) - _import_structure["models.deprecated.tvlt"].extend( - [ - "TvltForAudioVisualClassification", - "TvltForPreTraining", - "TvltModel", - "TvltPreTrainedModel", - ] - ) - _import_structure["models.deprecated.van"].extend( - [ - "VanForImageClassification", - "VanModel", - "VanPreTrainedModel", - ] - ) - _import_structure["models.deprecated.vit_hybrid"].extend( - [ - "ViTHybridForImageClassification", - "ViTHybridModel", - "ViTHybridPreTrainedModel", - ] - ) - _import_structure["models.deprecated.xlm_prophetnet"].extend( - [ - "XLMProphetNetDecoder", - "XLMProphetNetEncoder", - "XLMProphetNetForCausalLM", - "XLMProphetNetForConditionalGeneration", - "XLMProphetNetModel", - "XLMProphetNetPreTrainedModel", - ] - ) - _import_structure["models.depth_anything"].extend( - [ - "DepthAnythingForDepthEstimation", - "DepthAnythingPreTrainedModel", - ] - ) - _import_structure["models.depth_pro"].extend( - [ - "DepthProForDepthEstimation", - "DepthProModel", - "DepthProPreTrainedModel", - ] - ) - _import_structure["models.detr"].extend( - [ - "DetrForObjectDetection", - "DetrForSegmentation", - "DetrModel", - "DetrPreTrainedModel", - ] - ) - _import_structure["models.diffllama"].extend( - [ - "DiffLlamaForCausalLM", - "DiffLlamaForQuestionAnswering", - "DiffLlamaForSequenceClassification", - "DiffLlamaForTokenClassification", - "DiffLlamaModel", - "DiffLlamaPreTrainedModel", - ] - ) - _import_structure["models.dinat"].extend( - [ - "DinatBackbone", - "DinatForImageClassification", - "DinatModel", - "DinatPreTrainedModel", - ] - ) - _import_structure["models.dinov2"].extend( - [ - "Dinov2Backbone", - "Dinov2ForImageClassification", - "Dinov2Model", - "Dinov2PreTrainedModel", - ] - ) - _import_structure["models.dinov2_with_registers"].extend( - [ - "Dinov2WithRegistersBackbone", - "Dinov2WithRegistersForImageClassification", - "Dinov2WithRegistersModel", - "Dinov2WithRegistersPreTrainedModel", - ] - ) - _import_structure["models.distilbert"].extend( - [ - "DistilBertForMaskedLM", - "DistilBertForMultipleChoice", - "DistilBertForQuestionAnswering", - "DistilBertForSequenceClassification", - "DistilBertForTokenClassification", - "DistilBertModel", - "DistilBertPreTrainedModel", - ] - ) - _import_structure["models.donut"].extend( - [ - "DonutSwinForImageClassification", - "DonutSwinModel", - "DonutSwinPreTrainedModel", - ] - ) - _import_structure["models.dpr"].extend( - [ - "DPRContextEncoder", - "DPRPretrainedContextEncoder", - "DPRPreTrainedModel", - "DPRPretrainedQuestionEncoder", - "DPRPretrainedReader", - "DPRQuestionEncoder", - "DPRReader", - ] - ) - _import_structure["models.dpt"].extend( - [ - "DPTForDepthEstimation", - "DPTForSemanticSegmentation", - "DPTModel", - "DPTPreTrainedModel", - ] - ) - _import_structure["models.efficientnet"].extend( - [ - "EfficientNetForImageClassification", - "EfficientNetModel", - "EfficientNetPreTrainedModel", - ] - ) - _import_structure["models.electra"].extend( - [ - "ElectraForCausalLM", - "ElectraForMaskedLM", - "ElectraForMultipleChoice", - "ElectraForPreTraining", - "ElectraForQuestionAnswering", - "ElectraForSequenceClassification", - "ElectraForTokenClassification", - "ElectraModel", - "ElectraPreTrainedModel", - "load_tf_weights_in_electra", - ] - ) - _import_structure["models.emu3"].extend( - [ - "Emu3ForCausalLM", - "Emu3ForConditionalGeneration", - "Emu3PreTrainedModel", - "Emu3TextModel", - "Emu3VQVAE", - ] - ) - _import_structure["models.encodec"].extend( - [ - "EncodecModel", - "EncodecPreTrainedModel", - ] - ) - _import_structure["models.encoder_decoder"].append("EncoderDecoderModel") - _import_structure["models.ernie"].extend( - [ - "ErnieForCausalLM", - "ErnieForMaskedLM", - "ErnieForMultipleChoice", - "ErnieForNextSentencePrediction", - "ErnieForPreTraining", - "ErnieForQuestionAnswering", - "ErnieForSequenceClassification", - "ErnieForTokenClassification", - "ErnieModel", - "ErniePreTrainedModel", - ] - ) - _import_structure["models.esm"].extend( - [ - "EsmFoldPreTrainedModel", - "EsmForMaskedLM", - "EsmForProteinFolding", - "EsmForSequenceClassification", - "EsmForTokenClassification", - "EsmModel", - "EsmPreTrainedModel", - ] - ) - _import_structure["models.falcon"].extend( - [ - "FalconForCausalLM", - "FalconForQuestionAnswering", - "FalconForSequenceClassification", - "FalconForTokenClassification", - "FalconModel", - "FalconPreTrainedModel", - ] - ) - _import_structure["models.falcon_mamba"].extend( - [ - "FalconMambaForCausalLM", - "FalconMambaModel", - "FalconMambaPreTrainedModel", - ] - ) - _import_structure["models.fastspeech2_conformer"].extend( - [ - "FastSpeech2ConformerHifiGan", - "FastSpeech2ConformerModel", - "FastSpeech2ConformerPreTrainedModel", - "FastSpeech2ConformerWithHifiGan", - ] - ) - _import_structure["models.flaubert"].extend( - [ - "FlaubertForMultipleChoice", - "FlaubertForQuestionAnswering", - "FlaubertForQuestionAnsweringSimple", - "FlaubertForSequenceClassification", - "FlaubertForTokenClassification", - "FlaubertModel", - "FlaubertPreTrainedModel", - "FlaubertWithLMHeadModel", - ] - ) - _import_structure["models.flava"].extend( - [ - "FlavaForPreTraining", - "FlavaImageCodebook", - "FlavaImageModel", - "FlavaModel", - "FlavaMultimodalModel", - "FlavaPreTrainedModel", - "FlavaTextModel", - ] - ) - _import_structure["models.fnet"].extend( - [ - "FNetForMaskedLM", - "FNetForMultipleChoice", - "FNetForNextSentencePrediction", - "FNetForPreTraining", - "FNetForQuestionAnswering", - "FNetForSequenceClassification", - "FNetForTokenClassification", - "FNetModel", - "FNetPreTrainedModel", - ] - ) - _import_structure["models.focalnet"].extend( - [ - "FocalNetBackbone", - "FocalNetForImageClassification", - "FocalNetForMaskedImageModeling", - "FocalNetModel", - "FocalNetPreTrainedModel", - ] - ) - _import_structure["models.fsmt"].extend(["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]) - _import_structure["models.funnel"].extend( - [ - "FunnelBaseModel", - "FunnelForMaskedLM", - "FunnelForMultipleChoice", - "FunnelForPreTraining", - "FunnelForQuestionAnswering", - "FunnelForSequenceClassification", - "FunnelForTokenClassification", - "FunnelModel", - "FunnelPreTrainedModel", - "load_tf_weights_in_funnel", - ] - ) - _import_structure["models.fuyu"].extend(["FuyuForCausalLM", "FuyuPreTrainedModel"]) - _import_structure["models.gemma"].extend( - [ - "GemmaForCausalLM", - "GemmaForSequenceClassification", - "GemmaForTokenClassification", - "GemmaModel", - "GemmaPreTrainedModel", - ] - ) - _import_structure["models.gemma2"].extend( - [ - "Gemma2ForCausalLM", - "Gemma2ForSequenceClassification", - "Gemma2ForTokenClassification", - "Gemma2Model", - "Gemma2PreTrainedModel", - ] - ) - _import_structure["models.gemma3"].extend( - [ - "Gemma3ForCausalLM", - "Gemma3ForConditionalGeneration", - "Gemma3PreTrainedModel", - "Gemma3TextModel", - ] - ) - _import_structure["models.git"].extend( - [ - "GitForCausalLM", - "GitModel", - "GitPreTrainedModel", - "GitVisionModel", - ] - ) - _import_structure["models.glm"].extend( - [ - "GlmForCausalLM", - "GlmForSequenceClassification", - "GlmForTokenClassification", - "GlmModel", - "GlmPreTrainedModel", - ] - ) - _import_structure["models.llama4"].extend( - [ - "Llama4ForCausalLM", - "Llama4ForConditionalGeneration", - "Llama4TextModel", - "Llama4VisionModel", - "Llama4PreTrainedModel", - ] - ) - _import_structure["models.glm4"].extend( - [ - "Glm4ForCausalLM", - "Glm4ForSequenceClassification", - "Glm4ForTokenClassification", - "Glm4Model", - "Glm4PreTrainedModel", - ] - ) - _import_structure["models.glpn"].extend( - [ - "GLPNForDepthEstimation", - "GLPNModel", - "GLPNPreTrainedModel", - ] - ) - _import_structure["models.got_ocr2"].extend( - [ - "GotOcr2ForConditionalGeneration", - "GotOcr2PreTrainedModel", - ] - ) - _import_structure["models.gpt2"].extend( - [ - "GPT2DoubleHeadsModel", - "GPT2ForQuestionAnswering", - "GPT2ForSequenceClassification", - "GPT2ForTokenClassification", - "GPT2LMHeadModel", - "GPT2Model", - "GPT2PreTrainedModel", - "load_tf_weights_in_gpt2", - ] - ) - _import_structure["models.gpt_bigcode"].extend( - [ - "GPTBigCodeForCausalLM", - "GPTBigCodeForSequenceClassification", - "GPTBigCodeForTokenClassification", - "GPTBigCodeModel", - "GPTBigCodePreTrainedModel", - ] - ) - _import_structure["models.gpt_neo"].extend( - [ - "GPTNeoForCausalLM", - "GPTNeoForQuestionAnswering", - "GPTNeoForSequenceClassification", - "GPTNeoForTokenClassification", - "GPTNeoModel", - "GPTNeoPreTrainedModel", - "load_tf_weights_in_gpt_neo", - ] - ) - _import_structure["models.gpt_neox"].extend( - [ - "GPTNeoXForCausalLM", - "GPTNeoXForQuestionAnswering", - "GPTNeoXForSequenceClassification", - "GPTNeoXForTokenClassification", - "GPTNeoXModel", - "GPTNeoXPreTrainedModel", - ] - ) - _import_structure["models.gpt_neox_japanese"].extend( - [ - "GPTNeoXJapaneseForCausalLM", - "GPTNeoXJapaneseModel", - "GPTNeoXJapanesePreTrainedModel", - ] - ) - _import_structure["models.gptj"].extend( - [ - "GPTJForCausalLM", - "GPTJForQuestionAnswering", - "GPTJForSequenceClassification", - "GPTJModel", - "GPTJPreTrainedModel", - ] - ) - _import_structure["models.granite"].extend( - [ - "GraniteForCausalLM", - "GraniteModel", - "GranitePreTrainedModel", - ] - ) - _import_structure["models.granitemoe"].extend( - [ - "GraniteMoeForCausalLM", - "GraniteMoeModel", - "GraniteMoePreTrainedModel", - ] - ) - - _import_structure["models.granitemoeshared"].extend( - [ - "GraniteMoeSharedForCausalLM", - "GraniteMoeSharedModel", - "GraniteMoeSharedPreTrainedModel", - ] - ) - _import_structure["models.grounding_dino"].extend( - [ - "GroundingDinoForObjectDetection", - "GroundingDinoModel", - "GroundingDinoPreTrainedModel", - ] - ) - _import_structure["models.groupvit"].extend( - [ - "GroupViTModel", - "GroupViTPreTrainedModel", - "GroupViTTextModel", - "GroupViTVisionModel", - ] - ) - _import_structure["models.helium"].extend( - [ - "HeliumForCausalLM", - "HeliumForSequenceClassification", - "HeliumForTokenClassification", - "HeliumModel", - "HeliumPreTrainedModel", - ] - ) - _import_structure["models.hiera"].extend( - [ - "HieraBackbone", - "HieraForImageClassification", - "HieraForPreTraining", - "HieraModel", - "HieraPreTrainedModel", - ] - ) - _import_structure["models.hubert"].extend( - [ - "HubertForCTC", - "HubertForSequenceClassification", - "HubertModel", - "HubertPreTrainedModel", - ] - ) - _import_structure["models.ibert"].extend( - [ - "IBertForMaskedLM", - "IBertForMultipleChoice", - "IBertForQuestionAnswering", - "IBertForSequenceClassification", - "IBertForTokenClassification", - "IBertModel", - "IBertPreTrainedModel", - ] - ) - _import_structure["models.idefics"].extend( - [ - "IdeficsForVisionText2Text", - "IdeficsModel", - "IdeficsPreTrainedModel", - "IdeficsProcessor", - ] - ) - _import_structure["models.idefics2"].extend( - [ - "Idefics2ForConditionalGeneration", - "Idefics2Model", - "Idefics2PreTrainedModel", - "Idefics2Processor", - ] - ) - _import_structure["models.idefics3"].extend( - [ - "Idefics3ForConditionalGeneration", - "Idefics3Model", - "Idefics3PreTrainedModel", - "Idefics3Processor", - "Idefics3VisionConfig", - "Idefics3VisionTransformer", - ] - ) - _import_structure["models.ijepa"].extend( - [ - "IJepaForImageClassification", - "IJepaModel", - "IJepaPreTrainedModel", - ] - ) - _import_structure["models.imagegpt"].extend( - [ - "ImageGPTForCausalImageModeling", - "ImageGPTForImageClassification", - "ImageGPTModel", - "ImageGPTPreTrainedModel", - "load_tf_weights_in_imagegpt", - ] - ) - _import_structure["models.informer"].extend( - [ - "InformerForPrediction", - "InformerModel", - "InformerPreTrainedModel", - ] - ) - _import_structure["models.instructblip"].extend( - [ - "InstructBlipForConditionalGeneration", - "InstructBlipPreTrainedModel", - "InstructBlipQFormerModel", - "InstructBlipVisionModel", - ] - ) - _import_structure["models.instructblipvideo"].extend( - [ - "InstructBlipVideoForConditionalGeneration", - "InstructBlipVideoPreTrainedModel", - "InstructBlipVideoQFormerModel", - "InstructBlipVideoVisionModel", - ] - ) - _import_structure["models.jamba"].extend( - [ - "JambaForCausalLM", - "JambaForSequenceClassification", - "JambaModel", - "JambaPreTrainedModel", - ] - ) - _import_structure["models.jetmoe"].extend( - [ - "JetMoeForCausalLM", - "JetMoeForSequenceClassification", - "JetMoeModel", - "JetMoePreTrainedModel", - ] - ) - _import_structure["models.kosmos2"].extend( - [ - "Kosmos2ForConditionalGeneration", - "Kosmos2Model", - "Kosmos2PreTrainedModel", - ] - ) - _import_structure["models.layoutlm"].extend( - [ - "LayoutLMForMaskedLM", - "LayoutLMForQuestionAnswering", - "LayoutLMForSequenceClassification", - "LayoutLMForTokenClassification", - "LayoutLMModel", - "LayoutLMPreTrainedModel", - ] - ) - _import_structure["models.layoutlmv2"].extend( - [ - "LayoutLMv2ForQuestionAnswering", - "LayoutLMv2ForSequenceClassification", - "LayoutLMv2ForTokenClassification", - "LayoutLMv2Model", - "LayoutLMv2PreTrainedModel", - ] - ) - _import_structure["models.layoutlmv3"].extend( - [ - "LayoutLMv3ForQuestionAnswering", - "LayoutLMv3ForSequenceClassification", - "LayoutLMv3ForTokenClassification", - "LayoutLMv3Model", - "LayoutLMv3PreTrainedModel", - ] - ) - _import_structure["models.led"].extend( - [ - "LEDForConditionalGeneration", - "LEDForQuestionAnswering", - "LEDForSequenceClassification", - "LEDModel", - "LEDPreTrainedModel", - ] - ) - _import_structure["models.levit"].extend( - [ - "LevitForImageClassification", - "LevitForImageClassificationWithTeacher", - "LevitModel", - "LevitPreTrainedModel", - ] - ) - _import_structure["models.lilt"].extend( - [ - "LiltForQuestionAnswering", - "LiltForSequenceClassification", - "LiltForTokenClassification", - "LiltModel", - "LiltPreTrainedModel", - ] - ) - _import_structure["models.llama"].extend( - [ - "LlamaForCausalLM", - "LlamaForQuestionAnswering", - "LlamaForSequenceClassification", - "LlamaForTokenClassification", - "LlamaModel", - "LlamaPreTrainedModel", - ] - ) - _import_structure["models.llava"].extend( - [ - "LlavaForConditionalGeneration", - "LlavaPreTrainedModel", - ] - ) - _import_structure["models.llava_next"].extend( - [ - "LlavaNextForConditionalGeneration", - "LlavaNextPreTrainedModel", - ] - ) - _import_structure["models.phi4_multimodal"].extend( - [ - "Phi4MultimodalForCausalLM", - "Phi4MultimodalPreTrainedModel", - "Phi4MultimodalAudioModel", - "Phi4MultimodalAudioPreTrainedModel", - "Phi4MultimodalModel", - "Phi4MultimodalVisionModel", - "Phi4MultimodalVisionPreTrainedModel", - ] - ) - _import_structure["models.llava_next_video"].extend( - [ - "LlavaNextVideoForConditionalGeneration", - "LlavaNextVideoPreTrainedModel", - ] - ) - _import_structure["models.llava_onevision"].extend( - [ - "LlavaOnevisionForConditionalGeneration", - "LlavaOnevisionPreTrainedModel", - ] - ) - _import_structure["models.longformer"].extend( - [ - "LongformerForMaskedLM", - "LongformerForMultipleChoice", - "LongformerForQuestionAnswering", - "LongformerForSequenceClassification", - "LongformerForTokenClassification", - "LongformerModel", - "LongformerPreTrainedModel", - ] - ) - _import_structure["models.longt5"].extend( - [ - "LongT5EncoderModel", - "LongT5ForConditionalGeneration", - "LongT5Model", - "LongT5PreTrainedModel", - ] - ) - _import_structure["models.luke"].extend( - [ - "LukeForEntityClassification", - "LukeForEntityPairClassification", - "LukeForEntitySpanClassification", - "LukeForMaskedLM", - "LukeForMultipleChoice", - "LukeForQuestionAnswering", - "LukeForSequenceClassification", - "LukeForTokenClassification", - "LukeModel", - "LukePreTrainedModel", - ] - ) - _import_structure["models.lxmert"].extend( - [ - "LxmertEncoder", - "LxmertForPreTraining", - "LxmertForQuestionAnswering", - "LxmertModel", - "LxmertPreTrainedModel", - "LxmertVisualFeatureEncoder", - ] - ) - _import_structure["models.m2m_100"].extend( - [ - "M2M100ForConditionalGeneration", - "M2M100Model", - "M2M100PreTrainedModel", - ] - ) - _import_structure["models.mamba"].extend( - [ - "MambaForCausalLM", - "MambaModel", - "MambaPreTrainedModel", - ] - ) - _import_structure["models.mamba2"].extend( - [ - "Mamba2ForCausalLM", - "Mamba2Model", - "Mamba2PreTrainedModel", - ] - ) - _import_structure["models.marian"].extend( - ["MarianForCausalLM", "MarianModel", "MarianMTModel", "MarianPreTrainedModel"] - ) - _import_structure["models.markuplm"].extend( - [ - "MarkupLMForQuestionAnswering", - "MarkupLMForSequenceClassification", - "MarkupLMForTokenClassification", - "MarkupLMModel", - "MarkupLMPreTrainedModel", - ] - ) - _import_structure["models.mask2former"].extend( - [ - "Mask2FormerForUniversalSegmentation", - "Mask2FormerModel", - "Mask2FormerPreTrainedModel", - ] - ) - _import_structure["models.maskformer"].extend( - [ - "MaskFormerForInstanceSegmentation", - "MaskFormerModel", - "MaskFormerPreTrainedModel", - "MaskFormerSwinBackbone", - ] - ) - _import_structure["models.mbart"].extend( - [ - "MBartForCausalLM", - "MBartForConditionalGeneration", - "MBartForQuestionAnswering", - "MBartForSequenceClassification", - "MBartModel", - "MBartPreTrainedModel", - ] - ) - _import_structure["models.megatron_bert"].extend( - [ - "MegatronBertForCausalLM", - "MegatronBertForMaskedLM", - "MegatronBertForMultipleChoice", - "MegatronBertForNextSentencePrediction", - "MegatronBertForPreTraining", - "MegatronBertForQuestionAnswering", - "MegatronBertForSequenceClassification", - "MegatronBertForTokenClassification", - "MegatronBertModel", - "MegatronBertPreTrainedModel", - ] - ) - _import_structure["models.mgp_str"].extend( - [ - "MgpstrForSceneTextRecognition", - "MgpstrModel", - "MgpstrPreTrainedModel", - ] - ) - _import_structure["models.mimi"].extend( - [ - "MimiModel", - "MimiPreTrainedModel", - ] - ) - _import_structure["models.mistral"].extend( - [ - "MistralForCausalLM", - "MistralForQuestionAnswering", - "MistralForSequenceClassification", - "MistralForTokenClassification", - "MistralModel", - "MistralPreTrainedModel", - ] - ) - _import_structure["models.mistral3"].extend( - [ - "Mistral3ForConditionalGeneration", - "Mistral3PreTrainedModel", - ] - ) - _import_structure["models.mixtral"].extend( - [ - "MixtralForCausalLM", - "MixtralForQuestionAnswering", - "MixtralForSequenceClassification", - "MixtralForTokenClassification", - "MixtralModel", - "MixtralPreTrainedModel", - ] - ) - _import_structure["models.mllama"].extend( - [ - "MllamaForCausalLM", - "MllamaForConditionalGeneration", - "MllamaPreTrainedModel", - "MllamaProcessor", - "MllamaTextModel", - "MllamaVisionModel", - ] - ) - _import_structure["models.mobilebert"].extend( - [ - "MobileBertForMaskedLM", - "MobileBertForMultipleChoice", - "MobileBertForNextSentencePrediction", - "MobileBertForPreTraining", - "MobileBertForQuestionAnswering", - "MobileBertForSequenceClassification", - "MobileBertForTokenClassification", - "MobileBertModel", - "MobileBertPreTrainedModel", - "load_tf_weights_in_mobilebert", - ] - ) - _import_structure["models.mobilenet_v1"].extend( - [ - "MobileNetV1ForImageClassification", - "MobileNetV1Model", - "MobileNetV1PreTrainedModel", - "load_tf_weights_in_mobilenet_v1", - ] - ) - _import_structure["models.mobilenet_v2"].extend( - [ - "MobileNetV2ForImageClassification", - "MobileNetV2ForSemanticSegmentation", - "MobileNetV2Model", - "MobileNetV2PreTrainedModel", - "load_tf_weights_in_mobilenet_v2", - ] - ) - _import_structure["models.mobilevit"].extend( - [ - "MobileViTForImageClassification", - "MobileViTForSemanticSegmentation", - "MobileViTModel", - "MobileViTPreTrainedModel", - ] - ) - _import_structure["models.mobilevitv2"].extend( - [ - "MobileViTV2ForImageClassification", - "MobileViTV2ForSemanticSegmentation", - "MobileViTV2Model", - "MobileViTV2PreTrainedModel", - ] - ) - _import_structure["models.modernbert"].extend( - [ - "ModernBertForMaskedLM", - "ModernBertForQuestionAnswering", - "ModernBertForSequenceClassification", - "ModernBertForTokenClassification", - "ModernBertModel", - "ModernBertPreTrainedModel", - ] - ) - _import_structure["models.moonshine"].extend( - [ - "MoonshineForConditionalGeneration", - "MoonshineModel", - "MoonshinePreTrainedModel", - ] - ) - _import_structure["models.moshi"].extend( - [ - "MoshiForCausalLM", - "MoshiForConditionalGeneration", - "MoshiModel", - "MoshiPreTrainedModel", - ] - ) - _import_structure["models.mpnet"].extend( - [ - "MPNetForMaskedLM", - "MPNetForMultipleChoice", - "MPNetForQuestionAnswering", - "MPNetForSequenceClassification", - "MPNetForTokenClassification", - "MPNetModel", - "MPNetPreTrainedModel", - ] - ) - _import_structure["models.mpt"].extend( - [ - "MptForCausalLM", - "MptForQuestionAnswering", - "MptForSequenceClassification", - "MptForTokenClassification", - "MptModel", - "MptPreTrainedModel", - ] - ) - _import_structure["models.mra"].extend( - [ - "MraForMaskedLM", - "MraForMultipleChoice", - "MraForQuestionAnswering", - "MraForSequenceClassification", - "MraForTokenClassification", - "MraModel", - "MraPreTrainedModel", - ] - ) - _import_structure["models.mt5"].extend( - [ - "MT5EncoderModel", - "MT5ForConditionalGeneration", - "MT5ForQuestionAnswering", - "MT5ForSequenceClassification", - "MT5ForTokenClassification", - "MT5Model", - "MT5PreTrainedModel", - ] - ) - _import_structure["models.musicgen"].extend( - [ - "MusicgenForCausalLM", - "MusicgenForConditionalGeneration", - "MusicgenModel", - "MusicgenPreTrainedModel", - "MusicgenProcessor", - ] - ) - _import_structure["models.musicgen_melody"].extend( - [ - "MusicgenMelodyForCausalLM", - "MusicgenMelodyForConditionalGeneration", - "MusicgenMelodyModel", - "MusicgenMelodyPreTrainedModel", - ] - ) - _import_structure["models.mvp"].extend( - [ - "MvpForCausalLM", - "MvpForConditionalGeneration", - "MvpForQuestionAnswering", - "MvpForSequenceClassification", - "MvpModel", - "MvpPreTrainedModel", - ] - ) - _import_structure["models.nemotron"].extend( - [ - "NemotronForCausalLM", - "NemotronForQuestionAnswering", - "NemotronForSequenceClassification", - "NemotronForTokenClassification", - "NemotronModel", - "NemotronPreTrainedModel", - ] - ) - _import_structure["models.nllb_moe"].extend( - [ - "NllbMoeForConditionalGeneration", - "NllbMoeModel", - "NllbMoePreTrainedModel", - "NllbMoeSparseMLP", - "NllbMoeTop2Router", - ] - ) - _import_structure["models.nystromformer"].extend( - [ - "NystromformerForMaskedLM", - "NystromformerForMultipleChoice", - "NystromformerForQuestionAnswering", - "NystromformerForSequenceClassification", - "NystromformerForTokenClassification", - "NystromformerModel", - "NystromformerPreTrainedModel", - ] - ) - _import_structure["models.olmo"].extend( - [ - "OlmoForCausalLM", - "OlmoModel", - "OlmoPreTrainedModel", - ] - ) - _import_structure["models.olmo2"].extend( - [ - "Olmo2ForCausalLM", - "Olmo2Model", - "Olmo2PreTrainedModel", - ] - ) - _import_structure["models.olmoe"].extend( - [ - "OlmoeForCausalLM", - "OlmoeModel", - "OlmoePreTrainedModel", - ] - ) - _import_structure["models.omdet_turbo"].extend( - [ - "OmDetTurboForObjectDetection", - "OmDetTurboPreTrainedModel", - ] - ) - _import_structure["models.oneformer"].extend( - [ - "OneFormerForUniversalSegmentation", - "OneFormerModel", - "OneFormerPreTrainedModel", - ] - ) - _import_structure["models.openai"].extend( - [ - "OpenAIGPTDoubleHeadsModel", - "OpenAIGPTForSequenceClassification", - "OpenAIGPTLMHeadModel", - "OpenAIGPTModel", - "OpenAIGPTPreTrainedModel", - "load_tf_weights_in_openai_gpt", - ] - ) - _import_structure["models.opt"].extend( - [ - "OPTForCausalLM", - "OPTForQuestionAnswering", - "OPTForSequenceClassification", - "OPTModel", - "OPTPreTrainedModel", - ] - ) - _import_structure["models.owlv2"].extend( - [ - "Owlv2ForObjectDetection", - "Owlv2Model", - "Owlv2PreTrainedModel", - "Owlv2TextModel", - "Owlv2VisionModel", - ] - ) - _import_structure["models.owlvit"].extend( - [ - "OwlViTForObjectDetection", - "OwlViTModel", - "OwlViTPreTrainedModel", - "OwlViTTextModel", - "OwlViTVisionModel", - ] - ) - _import_structure["models.paligemma"].extend( - [ - "PaliGemmaForConditionalGeneration", - "PaliGemmaPreTrainedModel", - "PaliGemmaProcessor", - ] - ) - _import_structure["models.patchtsmixer"].extend( - [ - "PatchTSMixerForPrediction", - "PatchTSMixerForPretraining", - "PatchTSMixerForRegression", - "PatchTSMixerForTimeSeriesClassification", - "PatchTSMixerModel", - "PatchTSMixerPreTrainedModel", - ] - ) - _import_structure["models.patchtst"].extend( - [ - "PatchTSTForClassification", - "PatchTSTForPrediction", - "PatchTSTForPretraining", - "PatchTSTForRegression", - "PatchTSTModel", - "PatchTSTPreTrainedModel", - ] - ) - _import_structure["models.pegasus"].extend( - [ - "PegasusForCausalLM", - "PegasusForConditionalGeneration", - "PegasusModel", - "PegasusPreTrainedModel", - ] - ) - _import_structure["models.pegasus_x"].extend( - [ - "PegasusXForConditionalGeneration", - "PegasusXModel", - "PegasusXPreTrainedModel", - ] - ) - _import_structure["models.perceiver"].extend( - [ - "PerceiverForImageClassificationConvProcessing", - "PerceiverForImageClassificationFourier", - "PerceiverForImageClassificationLearned", - "PerceiverForMaskedLM", - "PerceiverForMultimodalAutoencoding", - "PerceiverForOpticalFlow", - "PerceiverForSequenceClassification", - "PerceiverModel", - "PerceiverPreTrainedModel", - ] - ) - _import_structure["models.persimmon"].extend( - [ - "PersimmonForCausalLM", - "PersimmonForSequenceClassification", - "PersimmonForTokenClassification", - "PersimmonModel", - "PersimmonPreTrainedModel", - ] - ) - _import_structure["models.phi"].extend( - [ - "PhiForCausalLM", - "PhiForSequenceClassification", - "PhiForTokenClassification", - "PhiModel", - "PhiPreTrainedModel", - ] - ) - _import_structure["models.phi3"].extend( - [ - "Phi3ForCausalLM", - "Phi3ForSequenceClassification", - "Phi3ForTokenClassification", - "Phi3Model", - "Phi3PreTrainedModel", - ] - ) - _import_structure["models.phimoe"].extend( - [ - "PhimoeForCausalLM", - "PhimoeForSequenceClassification", - "PhimoeModel", - "PhimoePreTrainedModel", - ] - ) - _import_structure["models.pix2struct"].extend( - [ - "Pix2StructForConditionalGeneration", - "Pix2StructPreTrainedModel", - "Pix2StructTextModel", - "Pix2StructVisionModel", - ] - ) - _import_structure["models.pixtral"].extend(["PixtralPreTrainedModel", "PixtralVisionModel"]) - _import_structure["models.plbart"].extend( - [ - "PLBartForCausalLM", - "PLBartForConditionalGeneration", - "PLBartForSequenceClassification", - "PLBartModel", - "PLBartPreTrainedModel", - ] - ) - _import_structure["models.poolformer"].extend( - [ - "PoolFormerForImageClassification", - "PoolFormerModel", - "PoolFormerPreTrainedModel", - ] - ) - _import_structure["models.pop2piano"].extend( - [ - "Pop2PianoForConditionalGeneration", - "Pop2PianoPreTrainedModel", - ] - ) - _import_structure["models.prompt_depth_anything"].extend( - [ - "PromptDepthAnythingForDepthEstimation", - "PromptDepthAnythingPreTrainedModel", - ] - ) - _import_structure["models.prophetnet"].extend( - [ - "ProphetNetDecoder", - "ProphetNetEncoder", - "ProphetNetForCausalLM", - "ProphetNetForConditionalGeneration", - "ProphetNetModel", - "ProphetNetPreTrainedModel", - ] - ) - _import_structure["models.pvt"].extend( - [ - "PvtForImageClassification", - "PvtModel", - "PvtPreTrainedModel", - ] - ) - _import_structure["models.pvt_v2"].extend( - [ - "PvtV2Backbone", - "PvtV2ForImageClassification", - "PvtV2Model", - "PvtV2PreTrainedModel", - ] - ) - _import_structure["models.qwen2"].extend( - [ - "Qwen2ForCausalLM", - "Qwen2ForQuestionAnswering", - "Qwen2ForSequenceClassification", - "Qwen2ForTokenClassification", - "Qwen2Model", - "Qwen2PreTrainedModel", - ] - ) - _import_structure["models.qwen2_5_vl"].extend( - [ - "Qwen2_5_VLForConditionalGeneration", - "Qwen2_5_VLModel", - "Qwen2_5_VLPreTrainedModel", - ] - ) - _import_structure["models.qwen2_audio"].extend( - [ - "Qwen2AudioEncoder", - "Qwen2AudioForConditionalGeneration", - "Qwen2AudioPreTrainedModel", - ] - ) - _import_structure["models.qwen2_moe"].extend( - [ - "Qwen2MoeForCausalLM", - "Qwen2MoeForQuestionAnswering", - "Qwen2MoeForSequenceClassification", - "Qwen2MoeForTokenClassification", - "Qwen2MoeModel", - "Qwen2MoePreTrainedModel", - ] - ) - _import_structure["models.qwen2_vl"].extend( - [ - "Qwen2VLForConditionalGeneration", - "Qwen2VLModel", - "Qwen2VLPreTrainedModel", - ] - ) - _import_structure["models.qwen3"].extend( - [ - "Qwen3ForCausalLM", - "Qwen3ForQuestionAnswering", - "Qwen3ForSequenceClassification", - "Qwen3ForTokenClassification", - "Qwen3Model", - "Qwen3PreTrainedModel", - ] - ) - _import_structure["models.qwen3_moe"].extend( - [ - "Qwen3MoeForCausalLM", - "Qwen3MoeForQuestionAnswering", - "Qwen3MoeForSequenceClassification", - "Qwen3MoeForTokenClassification", - "Qwen3MoeModel", - "Qwen3MoePreTrainedModel", - ] - ) - _import_structure["models.rag"].extend( - [ - "RagModel", - "RagPreTrainedModel", - "RagSequenceForGeneration", - "RagTokenForGeneration", - ] - ) - _import_structure["models.recurrent_gemma"].extend( - [ - "RecurrentGemmaForCausalLM", - "RecurrentGemmaModel", - "RecurrentGemmaPreTrainedModel", - ] - ) - _import_structure["models.reformer"].extend( - [ - "ReformerForMaskedLM", - "ReformerForQuestionAnswering", - "ReformerForSequenceClassification", - "ReformerModel", - "ReformerModelWithLMHead", - "ReformerPreTrainedModel", - ] - ) - _import_structure["models.regnet"].extend( - [ - "RegNetForImageClassification", - "RegNetModel", - "RegNetPreTrainedModel", - ] - ) - _import_structure["models.rembert"].extend( - [ - "RemBertForCausalLM", - "RemBertForMaskedLM", - "RemBertForMultipleChoice", - "RemBertForQuestionAnswering", - "RemBertForSequenceClassification", - "RemBertForTokenClassification", - "RemBertModel", - "RemBertPreTrainedModel", - "load_tf_weights_in_rembert", - ] - ) - _import_structure["models.resnet"].extend( - [ - "ResNetBackbone", - "ResNetForImageClassification", - "ResNetModel", - "ResNetPreTrainedModel", - ] - ) - _import_structure["models.roberta"].extend( - [ - "RobertaForCausalLM", - "RobertaForMaskedLM", - "RobertaForMultipleChoice", - "RobertaForQuestionAnswering", - "RobertaForSequenceClassification", - "RobertaForTokenClassification", - "RobertaModel", - "RobertaPreTrainedModel", - ] - ) - _import_structure["models.roberta_prelayernorm"].extend( - [ - "RobertaPreLayerNormForCausalLM", - "RobertaPreLayerNormForMaskedLM", - "RobertaPreLayerNormForMultipleChoice", - "RobertaPreLayerNormForQuestionAnswering", - "RobertaPreLayerNormForSequenceClassification", - "RobertaPreLayerNormForTokenClassification", - "RobertaPreLayerNormModel", - "RobertaPreLayerNormPreTrainedModel", - ] - ) - _import_structure["models.roc_bert"].extend( - [ - "RoCBertForCausalLM", - "RoCBertForMaskedLM", - "RoCBertForMultipleChoice", - "RoCBertForPreTraining", - "RoCBertForQuestionAnswering", - "RoCBertForSequenceClassification", - "RoCBertForTokenClassification", - "RoCBertModel", - "RoCBertPreTrainedModel", - "load_tf_weights_in_roc_bert", - ] - ) - _import_structure["models.roformer"].extend( - [ - "RoFormerForCausalLM", - "RoFormerForMaskedLM", - "RoFormerForMultipleChoice", - "RoFormerForQuestionAnswering", - "RoFormerForSequenceClassification", - "RoFormerForTokenClassification", - "RoFormerModel", - "RoFormerPreTrainedModel", - "load_tf_weights_in_roformer", - ] - ) - _import_structure["models.rt_detr"].extend( - [ - "RTDetrForObjectDetection", - "RTDetrModel", - "RTDetrPreTrainedModel", - "RTDetrResNetBackbone", - "RTDetrResNetPreTrainedModel", - ] - ) - _import_structure["models.rt_detr_v2"].extend( - ["RTDetrV2ForObjectDetection", "RTDetrV2Model", "RTDetrV2PreTrainedModel"] - ) - _import_structure["models.rwkv"].extend( - [ - "RwkvForCausalLM", - "RwkvModel", - "RwkvPreTrainedModel", - ] - ) - _import_structure["models.sam"].extend( - [ - "SamModel", - "SamPreTrainedModel", - "SamVisionModel", - ] - ) - _import_structure["models.seamless_m4t"].extend( - [ - "SeamlessM4TCodeHifiGan", - "SeamlessM4TForSpeechToSpeech", - "SeamlessM4TForSpeechToText", - "SeamlessM4TForTextToSpeech", - "SeamlessM4TForTextToText", - "SeamlessM4THifiGan", - "SeamlessM4TModel", - "SeamlessM4TPreTrainedModel", - "SeamlessM4TTextToUnitForConditionalGeneration", - "SeamlessM4TTextToUnitModel", - ] - ) - _import_structure["models.seamless_m4t_v2"].extend( - [ - "SeamlessM4Tv2ForSpeechToSpeech", - "SeamlessM4Tv2ForSpeechToText", - "SeamlessM4Tv2ForTextToSpeech", - "SeamlessM4Tv2ForTextToText", - "SeamlessM4Tv2Model", - "SeamlessM4Tv2PreTrainedModel", - ] - ) - _import_structure["models.segformer"].extend( - [ - "SegformerDecodeHead", - "SegformerForImageClassification", - "SegformerForSemanticSegmentation", - "SegformerModel", - "SegformerPreTrainedModel", - ] - ) - _import_structure["models.seggpt"].extend( - [ - "SegGptForImageSegmentation", - "SegGptModel", - "SegGptPreTrainedModel", - ] - ) - _import_structure["models.sew"].extend( - [ - "SEWForCTC", - "SEWForSequenceClassification", - "SEWModel", - "SEWPreTrainedModel", - ] - ) - _import_structure["models.sew_d"].extend( - [ - "SEWDForCTC", - "SEWDForSequenceClassification", - "SEWDModel", - "SEWDPreTrainedModel", - ] - ) - _import_structure["models.shieldgemma2"].append("ShieldGemma2ForImageClassification") - _import_structure["models.siglip"].extend( - [ - "SiglipForImageClassification", - "SiglipModel", - "SiglipPreTrainedModel", - "SiglipTextModel", - "SiglipVisionModel", - ] - ) - _import_structure["models.siglip2"].extend( - [ - "Siglip2ForImageClassification", - "Siglip2Model", - "Siglip2PreTrainedModel", - "Siglip2TextModel", - "Siglip2VisionModel", - ] - ) - _import_structure["models.smolvlm"].extend( - [ - "SmolVLMForConditionalGeneration", - "SmolVLMModel", - "SmolVLMPreTrainedModel", - "SmolVLMProcessor", - "SmolVLMVisionConfig", - "SmolVLMVisionTransformer", - ] - ) - _import_structure["models.speech_encoder_decoder"].extend(["SpeechEncoderDecoderModel"]) - _import_structure["models.speech_to_text"].extend( - [ - "Speech2TextForConditionalGeneration", - "Speech2TextModel", - "Speech2TextPreTrainedModel", - ] - ) - _import_structure["models.speecht5"].extend( - [ - "SpeechT5ForSpeechToSpeech", - "SpeechT5ForSpeechToText", - "SpeechT5ForTextToSpeech", - "SpeechT5HifiGan", - "SpeechT5Model", - "SpeechT5PreTrainedModel", - ] - ) - _import_structure["models.splinter"].extend( - [ - "SplinterForPreTraining", - "SplinterForQuestionAnswering", - "SplinterModel", - "SplinterPreTrainedModel", - ] - ) - _import_structure["models.squeezebert"].extend( - [ - "SqueezeBertForMaskedLM", - "SqueezeBertForMultipleChoice", - "SqueezeBertForQuestionAnswering", - "SqueezeBertForSequenceClassification", - "SqueezeBertForTokenClassification", - "SqueezeBertModel", - "SqueezeBertPreTrainedModel", - ] - ) - _import_structure["models.stablelm"].extend( - [ - "StableLmForCausalLM", - "StableLmForSequenceClassification", - "StableLmForTokenClassification", - "StableLmModel", - "StableLmPreTrainedModel", - ] - ) - _import_structure["models.starcoder2"].extend( - [ - "Starcoder2ForCausalLM", - "Starcoder2ForSequenceClassification", - "Starcoder2ForTokenClassification", - "Starcoder2Model", - "Starcoder2PreTrainedModel", - ] - ) - _import_structure["models.superglue"].extend( - [ - "SuperGlueForKeypointMatching", - "SuperGluePreTrainedModel", - ] - ) - _import_structure["models.superpoint"].extend( - [ - "SuperPointForKeypointDetection", - "SuperPointPreTrainedModel", - ] - ) - _import_structure["models.swiftformer"].extend( - [ - "SwiftFormerForImageClassification", - "SwiftFormerModel", - "SwiftFormerPreTrainedModel", - ] - ) - _import_structure["models.swin"].extend( - [ - "SwinBackbone", - "SwinForImageClassification", - "SwinForMaskedImageModeling", - "SwinModel", - "SwinPreTrainedModel", - ] - ) - _import_structure["models.swin2sr"].extend( - [ - "Swin2SRForImageSuperResolution", - "Swin2SRModel", - "Swin2SRPreTrainedModel", - ] - ) - _import_structure["models.swinv2"].extend( - [ - "Swinv2Backbone", - "Swinv2ForImageClassification", - "Swinv2ForMaskedImageModeling", - "Swinv2Model", - "Swinv2PreTrainedModel", - ] - ) - _import_structure["models.switch_transformers"].extend( - [ - "SwitchTransformersEncoderModel", - "SwitchTransformersForConditionalGeneration", - "SwitchTransformersModel", - "SwitchTransformersPreTrainedModel", - "SwitchTransformersSparseMLP", - "SwitchTransformersTop1Router", - ] - ) - _import_structure["models.t5"].extend( - [ - "T5EncoderModel", - "T5ForConditionalGeneration", - "T5ForQuestionAnswering", - "T5ForSequenceClassification", - "T5ForTokenClassification", - "T5Model", - "T5PreTrainedModel", - "load_tf_weights_in_t5", - ] - ) - _import_structure["models.table_transformer"].extend( - [ - "TableTransformerForObjectDetection", - "TableTransformerModel", - "TableTransformerPreTrainedModel", - ] - ) - _import_structure["models.tapas"].extend( - [ - "TapasForMaskedLM", - "TapasForQuestionAnswering", - "TapasForSequenceClassification", - "TapasModel", - "TapasPreTrainedModel", - "load_tf_weights_in_tapas", - ] - ) - _import_structure["models.textnet"].extend( - [ - "TextNetBackbone", - "TextNetForImageClassification", - "TextNetModel", - "TextNetPreTrainedModel", - ] - ) - _import_structure["models.time_series_transformer"].extend( - [ - "TimeSeriesTransformerForPrediction", - "TimeSeriesTransformerModel", - "TimeSeriesTransformerPreTrainedModel", - ] - ) - _import_structure["models.timesformer"].extend( - [ - "TimesformerForVideoClassification", - "TimesformerModel", - "TimesformerPreTrainedModel", - ] - ) - _import_structure["models.timm_backbone"].extend(["TimmBackbone"]) - _import_structure["models.timm_wrapper"].extend( - ["TimmWrapperForImageClassification", "TimmWrapperModel", "TimmWrapperPreTrainedModel"] - ) - _import_structure["models.trocr"].extend( - [ - "TrOCRForCausalLM", - "TrOCRPreTrainedModel", - ] - ) - _import_structure["models.tvp"].extend( - [ - "TvpForVideoGrounding", - "TvpModel", - "TvpPreTrainedModel", - ] - ) - _import_structure["models.udop"].extend( - [ - "UdopEncoderModel", - "UdopForConditionalGeneration", - "UdopModel", - "UdopPreTrainedModel", - ], - ) - _import_structure["models.umt5"].extend( - [ - "UMT5EncoderModel", - "UMT5ForConditionalGeneration", - "UMT5ForQuestionAnswering", - "UMT5ForSequenceClassification", - "UMT5ForTokenClassification", - "UMT5Model", - "UMT5PreTrainedModel", - ] - ) - _import_structure["models.unispeech"].extend( - [ - "UniSpeechForCTC", - "UniSpeechForPreTraining", - "UniSpeechForSequenceClassification", - "UniSpeechModel", - "UniSpeechPreTrainedModel", - ] - ) - _import_structure["models.unispeech_sat"].extend( - [ - "UniSpeechSatForAudioFrameClassification", - "UniSpeechSatForCTC", - "UniSpeechSatForPreTraining", - "UniSpeechSatForSequenceClassification", - "UniSpeechSatForXVector", - "UniSpeechSatModel", - "UniSpeechSatPreTrainedModel", - ] - ) - _import_structure["models.univnet"].extend( - [ - "UnivNetModel", - ] - ) - _import_structure["models.upernet"].extend( - [ - "UperNetForSemanticSegmentation", - "UperNetPreTrainedModel", - ] - ) - _import_structure["models.video_llava"].extend( - [ - "VideoLlavaForConditionalGeneration", - "VideoLlavaPreTrainedModel", - "VideoLlavaProcessor", - ] - ) - _import_structure["models.videomae"].extend( - [ - "VideoMAEForPreTraining", - "VideoMAEForVideoClassification", - "VideoMAEModel", - "VideoMAEPreTrainedModel", - ] - ) - _import_structure["models.vilt"].extend( - [ - "ViltForImageAndTextRetrieval", - "ViltForImagesAndTextClassification", - "ViltForMaskedLM", - "ViltForQuestionAnswering", - "ViltForTokenClassification", - "ViltModel", - "ViltPreTrainedModel", - ] - ) - _import_structure["models.vipllava"].extend( - [ - "VipLlavaForConditionalGeneration", - "VipLlavaPreTrainedModel", - ] - ) - _import_structure["models.vision_encoder_decoder"].extend(["VisionEncoderDecoderModel"]) - _import_structure["models.vision_text_dual_encoder"].extend(["VisionTextDualEncoderModel"]) - _import_structure["models.visual_bert"].extend( - [ - "VisualBertForMultipleChoice", - "VisualBertForPreTraining", - "VisualBertForQuestionAnswering", - "VisualBertForRegionToPhraseAlignment", - "VisualBertForVisualReasoning", - "VisualBertModel", - "VisualBertPreTrainedModel", - ] - ) - _import_structure["models.vit"].extend( - [ - "ViTForImageClassification", - "ViTForMaskedImageModeling", - "ViTModel", - "ViTPreTrainedModel", - ] - ) - _import_structure["models.vit_mae"].extend( - [ - "ViTMAEForPreTraining", - "ViTMAEModel", - "ViTMAEPreTrainedModel", - ] - ) - _import_structure["models.vit_msn"].extend( - [ - "ViTMSNForImageClassification", - "ViTMSNModel", - "ViTMSNPreTrainedModel", - ] - ) - _import_structure["models.vitdet"].extend( - [ - "VitDetBackbone", - "VitDetModel", - "VitDetPreTrainedModel", - ] - ) - _import_structure["models.vitmatte"].extend( - [ - "VitMatteForImageMatting", - "VitMattePreTrainedModel", - ] - ) - _import_structure["models.vitpose"].extend( - [ - "VitPoseForPoseEstimation", - "VitPosePreTrainedModel", - ] - ) - _import_structure["models.vitpose_backbone"].extend( - [ - "VitPoseBackbone", - "VitPoseBackbonePreTrainedModel", - ] - ) - _import_structure["models.vits"].extend( - [ - "VitsModel", - "VitsPreTrainedModel", - ] - ) - _import_structure["models.vivit"].extend( - [ - "VivitForVideoClassification", - "VivitModel", - "VivitPreTrainedModel", - ] - ) - _import_structure["models.wav2vec2"].extend( - [ - "Wav2Vec2ForAudioFrameClassification", - "Wav2Vec2ForCTC", - "Wav2Vec2ForMaskedLM", - "Wav2Vec2ForPreTraining", - "Wav2Vec2ForSequenceClassification", - "Wav2Vec2ForXVector", - "Wav2Vec2Model", - "Wav2Vec2PreTrainedModel", - ] - ) - _import_structure["models.wav2vec2_bert"].extend( - [ - "Wav2Vec2BertForAudioFrameClassification", - "Wav2Vec2BertForCTC", - "Wav2Vec2BertForSequenceClassification", - "Wav2Vec2BertForXVector", - "Wav2Vec2BertModel", - "Wav2Vec2BertPreTrainedModel", - ] - ) - _import_structure["models.wav2vec2_conformer"].extend( - [ - "Wav2Vec2ConformerForAudioFrameClassification", - "Wav2Vec2ConformerForCTC", - "Wav2Vec2ConformerForPreTraining", - "Wav2Vec2ConformerForSequenceClassification", - "Wav2Vec2ConformerForXVector", - "Wav2Vec2ConformerModel", - "Wav2Vec2ConformerPreTrainedModel", - ] - ) - _import_structure["models.wavlm"].extend( - [ - "WavLMForAudioFrameClassification", - "WavLMForCTC", - "WavLMForSequenceClassification", - "WavLMForXVector", - "WavLMModel", - "WavLMPreTrainedModel", - ] - ) - _import_structure["models.whisper"].extend( - [ - "WhisperForAudioClassification", - "WhisperForCausalLM", - "WhisperForConditionalGeneration", - "WhisperModel", - "WhisperPreTrainedModel", - ] - ) - _import_structure["models.x_clip"].extend( - [ - "XCLIPModel", - "XCLIPPreTrainedModel", - "XCLIPTextModel", - "XCLIPVisionModel", - ] - ) - _import_structure["models.xglm"].extend( - [ - "XGLMForCausalLM", - "XGLMModel", - "XGLMPreTrainedModel", - ] - ) - _import_structure["models.xlm"].extend( - [ - "XLMForMultipleChoice", - "XLMForQuestionAnswering", - "XLMForQuestionAnsweringSimple", - "XLMForSequenceClassification", - "XLMForTokenClassification", - "XLMModel", - "XLMPreTrainedModel", - "XLMWithLMHeadModel", - ] - ) - _import_structure["models.xlm_roberta"].extend( - [ - "XLMRobertaForCausalLM", - "XLMRobertaForMaskedLM", - "XLMRobertaForMultipleChoice", - "XLMRobertaForQuestionAnswering", - "XLMRobertaForSequenceClassification", - "XLMRobertaForTokenClassification", - "XLMRobertaModel", - "XLMRobertaPreTrainedModel", - ] - ) - _import_structure["models.xlm_roberta_xl"].extend( - [ - "XLMRobertaXLForCausalLM", - "XLMRobertaXLForMaskedLM", - "XLMRobertaXLForMultipleChoice", - "XLMRobertaXLForQuestionAnswering", - "XLMRobertaXLForSequenceClassification", - "XLMRobertaXLForTokenClassification", - "XLMRobertaXLModel", - "XLMRobertaXLPreTrainedModel", - ] - ) - _import_structure["models.xlnet"].extend( - [ - "XLNetForMultipleChoice", - "XLNetForQuestionAnswering", - "XLNetForQuestionAnsweringSimple", - "XLNetForSequenceClassification", - "XLNetForTokenClassification", - "XLNetLMHeadModel", - "XLNetModel", - "XLNetPreTrainedModel", - "load_tf_weights_in_xlnet", - ] - ) - _import_structure["models.xmod"].extend( - [ - "XmodForCausalLM", - "XmodForMaskedLM", - "XmodForMultipleChoice", - "XmodForQuestionAnswering", - "XmodForSequenceClassification", - "XmodForTokenClassification", - "XmodModel", - "XmodPreTrainedModel", - ] - ) - _import_structure["models.yolos"].extend( - [ - "YolosForObjectDetection", - "YolosModel", - "YolosPreTrainedModel", - ] - ) - _import_structure["models.yoso"].extend( - [ - "YosoForMaskedLM", - "YosoForMultipleChoice", - "YosoForQuestionAnswering", - "YosoForSequenceClassification", - "YosoForTokenClassification", - "YosoModel", - "YosoPreTrainedModel", - ] - ) - _import_structure["models.zamba"].extend( - [ - "ZambaForCausalLM", - "ZambaForSequenceClassification", - "ZambaModel", - "ZambaPreTrainedModel", - ] - ) - _import_structure["models.zamba2"].extend( - [ - "Zamba2ForCausalLM", - "Zamba2ForSequenceClassification", - "Zamba2Model", - "Zamba2PreTrainedModel", - ] - ) - _import_structure["models.zoedepth"].extend( - [ - "ZoeDepthForDepthEstimation", - "ZoeDepthPreTrainedModel", - ] - ) _import_structure["optimization"] = [ "Adafactor", "get_constant_schedule", @@ -4265,651 +521,6 @@ else: "TFSharedEmbeddings", "shape_list", ] - # TensorFlow models structure - _import_structure["models.albert"].extend( - [ - "TFAlbertForMaskedLM", - "TFAlbertForMultipleChoice", - "TFAlbertForPreTraining", - "TFAlbertForQuestionAnswering", - "TFAlbertForSequenceClassification", - "TFAlbertForTokenClassification", - "TFAlbertMainLayer", - "TFAlbertModel", - "TFAlbertPreTrainedModel", - ] - ) - _import_structure["models.auto"].extend( - [ - "TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", - "TF_MODEL_FOR_CAUSAL_LM_MAPPING", - "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", - "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", - "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", - "TF_MODEL_FOR_MASKED_LM_MAPPING", - "TF_MODEL_FOR_MASK_GENERATION_MAPPING", - "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", - "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", - "TF_MODEL_FOR_PRETRAINING_MAPPING", - "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", - "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", - "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", - "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", - "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", - "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", - "TF_MODEL_FOR_TEXT_ENCODING_MAPPING", - "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", - "TF_MODEL_FOR_VISION_2_SEQ_MAPPING", - "TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING", - "TF_MODEL_MAPPING", - "TF_MODEL_WITH_LM_HEAD_MAPPING", - "TFAutoModel", - "TFAutoModelForAudioClassification", - "TFAutoModelForCausalLM", - "TFAutoModelForDocumentQuestionAnswering", - "TFAutoModelForImageClassification", - "TFAutoModelForMaskedImageModeling", - "TFAutoModelForMaskedLM", - "TFAutoModelForMaskGeneration", - "TFAutoModelForMultipleChoice", - "TFAutoModelForNextSentencePrediction", - "TFAutoModelForPreTraining", - "TFAutoModelForQuestionAnswering", - "TFAutoModelForSemanticSegmentation", - "TFAutoModelForSeq2SeqLM", - "TFAutoModelForSequenceClassification", - "TFAutoModelForSpeechSeq2Seq", - "TFAutoModelForTableQuestionAnswering", - "TFAutoModelForTextEncoding", - "TFAutoModelForTokenClassification", - "TFAutoModelForVision2Seq", - "TFAutoModelForZeroShotImageClassification", - "TFAutoModelWithLMHead", - ] - ) - _import_structure["models.bart"].extend( - [ - "TFBartForConditionalGeneration", - "TFBartForSequenceClassification", - "TFBartModel", - "TFBartPretrainedModel", - ] - ) - _import_structure["models.bert"].extend( - [ - "TFBertForMaskedLM", - "TFBertForMultipleChoice", - "TFBertForNextSentencePrediction", - "TFBertForPreTraining", - "TFBertForQuestionAnswering", - "TFBertForSequenceClassification", - "TFBertForTokenClassification", - "TFBertLMHeadModel", - "TFBertMainLayer", - "TFBertModel", - "TFBertPreTrainedModel", - ] - ) - _import_structure["models.blenderbot"].extend( - [ - "TFBlenderbotForConditionalGeneration", - "TFBlenderbotModel", - "TFBlenderbotPreTrainedModel", - ] - ) - _import_structure["models.blenderbot_small"].extend( - [ - "TFBlenderbotSmallForConditionalGeneration", - "TFBlenderbotSmallModel", - "TFBlenderbotSmallPreTrainedModel", - ] - ) - _import_structure["models.blip"].extend( - [ - "TFBlipForConditionalGeneration", - "TFBlipForImageTextRetrieval", - "TFBlipForQuestionAnswering", - "TFBlipModel", - "TFBlipPreTrainedModel", - "TFBlipTextModel", - "TFBlipVisionModel", - ] - ) - _import_structure["models.camembert"].extend( - [ - "TFCamembertForCausalLM", - "TFCamembertForMaskedLM", - "TFCamembertForMultipleChoice", - "TFCamembertForQuestionAnswering", - "TFCamembertForSequenceClassification", - "TFCamembertForTokenClassification", - "TFCamembertModel", - "TFCamembertPreTrainedModel", - ] - ) - _import_structure["models.clip"].extend( - [ - "TFCLIPModel", - "TFCLIPPreTrainedModel", - "TFCLIPTextModel", - "TFCLIPVisionModel", - ] - ) - _import_structure["models.convbert"].extend( - [ - "TFConvBertForMaskedLM", - "TFConvBertForMultipleChoice", - "TFConvBertForQuestionAnswering", - "TFConvBertForSequenceClassification", - "TFConvBertForTokenClassification", - "TFConvBertModel", - "TFConvBertPreTrainedModel", - ] - ) - _import_structure["models.convnext"].extend( - [ - "TFConvNextForImageClassification", - "TFConvNextModel", - "TFConvNextPreTrainedModel", - ] - ) - _import_structure["models.convnextv2"].extend( - [ - "TFConvNextV2ForImageClassification", - "TFConvNextV2Model", - "TFConvNextV2PreTrainedModel", - ] - ) - _import_structure["models.ctrl"].extend( - [ - "TFCTRLForSequenceClassification", - "TFCTRLLMHeadModel", - "TFCTRLModel", - "TFCTRLPreTrainedModel", - ] - ) - _import_structure["models.cvt"].extend( - [ - "TFCvtForImageClassification", - "TFCvtModel", - "TFCvtPreTrainedModel", - ] - ) - _import_structure["models.data2vec"].extend( - [ - "TFData2VecVisionForImageClassification", - "TFData2VecVisionForSemanticSegmentation", - "TFData2VecVisionModel", - "TFData2VecVisionPreTrainedModel", - ] - ) - _import_structure["models.deberta"].extend( - [ - "TFDebertaForMaskedLM", - "TFDebertaForQuestionAnswering", - "TFDebertaForSequenceClassification", - "TFDebertaForTokenClassification", - "TFDebertaModel", - "TFDebertaPreTrainedModel", - ] - ) - _import_structure["models.deberta_v2"].extend( - [ - "TFDebertaV2ForMaskedLM", - "TFDebertaV2ForMultipleChoice", - "TFDebertaV2ForQuestionAnswering", - "TFDebertaV2ForSequenceClassification", - "TFDebertaV2ForTokenClassification", - "TFDebertaV2Model", - "TFDebertaV2PreTrainedModel", - ] - ) - _import_structure["models.deit"].extend( - [ - "TFDeiTForImageClassification", - "TFDeiTForImageClassificationWithTeacher", - "TFDeiTForMaskedImageModeling", - "TFDeiTModel", - "TFDeiTPreTrainedModel", - ] - ) - _import_structure["models.deprecated.efficientformer"].extend( - [ - "TFEfficientFormerForImageClassification", - "TFEfficientFormerForImageClassificationWithTeacher", - "TFEfficientFormerModel", - "TFEfficientFormerPreTrainedModel", - ] - ) - _import_structure["models.deprecated.transfo_xl"].extend( - [ - "TFAdaptiveEmbedding", - "TFTransfoXLForSequenceClassification", - "TFTransfoXLLMHeadModel", - "TFTransfoXLMainLayer", - "TFTransfoXLModel", - "TFTransfoXLPreTrainedModel", - ] - ) - _import_structure["models.distilbert"].extend( - [ - "TFDistilBertForMaskedLM", - "TFDistilBertForMultipleChoice", - "TFDistilBertForQuestionAnswering", - "TFDistilBertForSequenceClassification", - "TFDistilBertForTokenClassification", - "TFDistilBertMainLayer", - "TFDistilBertModel", - "TFDistilBertPreTrainedModel", - ] - ) - _import_structure["models.dpr"].extend( - [ - "TFDPRContextEncoder", - "TFDPRPretrainedContextEncoder", - "TFDPRPretrainedQuestionEncoder", - "TFDPRPretrainedReader", - "TFDPRQuestionEncoder", - "TFDPRReader", - ] - ) - _import_structure["models.electra"].extend( - [ - "TFElectraForMaskedLM", - "TFElectraForMultipleChoice", - "TFElectraForPreTraining", - "TFElectraForQuestionAnswering", - "TFElectraForSequenceClassification", - "TFElectraForTokenClassification", - "TFElectraModel", - "TFElectraPreTrainedModel", - ] - ) - _import_structure["models.encoder_decoder"].append("TFEncoderDecoderModel") - _import_structure["models.esm"].extend( - [ - "TFEsmForMaskedLM", - "TFEsmForSequenceClassification", - "TFEsmForTokenClassification", - "TFEsmModel", - "TFEsmPreTrainedModel", - ] - ) - _import_structure["models.flaubert"].extend( - [ - "TFFlaubertForMultipleChoice", - "TFFlaubertForQuestionAnsweringSimple", - "TFFlaubertForSequenceClassification", - "TFFlaubertForTokenClassification", - "TFFlaubertModel", - "TFFlaubertPreTrainedModel", - "TFFlaubertWithLMHeadModel", - ] - ) - _import_structure["models.funnel"].extend( - [ - "TFFunnelBaseModel", - "TFFunnelForMaskedLM", - "TFFunnelForMultipleChoice", - "TFFunnelForPreTraining", - "TFFunnelForQuestionAnswering", - "TFFunnelForSequenceClassification", - "TFFunnelForTokenClassification", - "TFFunnelModel", - "TFFunnelPreTrainedModel", - ] - ) - _import_structure["models.gpt2"].extend( - [ - "TFGPT2DoubleHeadsModel", - "TFGPT2ForSequenceClassification", - "TFGPT2LMHeadModel", - "TFGPT2MainLayer", - "TFGPT2Model", - "TFGPT2PreTrainedModel", - ] - ) - _import_structure["models.gptj"].extend( - [ - "TFGPTJForCausalLM", - "TFGPTJForQuestionAnswering", - "TFGPTJForSequenceClassification", - "TFGPTJModel", - "TFGPTJPreTrainedModel", - ] - ) - _import_structure["models.groupvit"].extend( - [ - "TFGroupViTModel", - "TFGroupViTPreTrainedModel", - "TFGroupViTTextModel", - "TFGroupViTVisionModel", - ] - ) - _import_structure["models.hubert"].extend( - [ - "TFHubertForCTC", - "TFHubertModel", - "TFHubertPreTrainedModel", - ] - ) - - _import_structure["models.idefics"].extend( - [ - "TFIdeficsForVisionText2Text", - "TFIdeficsModel", - "TFIdeficsPreTrainedModel", - ] - ) - - _import_structure["models.layoutlm"].extend( - [ - "TFLayoutLMForMaskedLM", - "TFLayoutLMForQuestionAnswering", - "TFLayoutLMForSequenceClassification", - "TFLayoutLMForTokenClassification", - "TFLayoutLMMainLayer", - "TFLayoutLMModel", - "TFLayoutLMPreTrainedModel", - ] - ) - _import_structure["models.layoutlmv3"].extend( - [ - "TFLayoutLMv3ForQuestionAnswering", - "TFLayoutLMv3ForSequenceClassification", - "TFLayoutLMv3ForTokenClassification", - "TFLayoutLMv3Model", - "TFLayoutLMv3PreTrainedModel", - ] - ) - _import_structure["models.led"].extend(["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]) - _import_structure["models.longformer"].extend( - [ - "TFLongformerForMaskedLM", - "TFLongformerForMultipleChoice", - "TFLongformerForQuestionAnswering", - "TFLongformerForSequenceClassification", - "TFLongformerForTokenClassification", - "TFLongformerModel", - "TFLongformerPreTrainedModel", - ] - ) - _import_structure["models.lxmert"].extend( - [ - "TFLxmertForPreTraining", - "TFLxmertMainLayer", - "TFLxmertModel", - "TFLxmertPreTrainedModel", - "TFLxmertVisualFeatureEncoder", - ] - ) - _import_structure["models.marian"].extend(["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]) - _import_structure["models.mbart"].extend( - ["TFMBartForConditionalGeneration", "TFMBartModel", "TFMBartPreTrainedModel"] - ) - _import_structure["models.mistral"].extend( - ["TFMistralForCausalLM", "TFMistralForSequenceClassification", "TFMistralModel", "TFMistralPreTrainedModel"] - ) - _import_structure["models.mobilebert"].extend( - [ - "TFMobileBertForMaskedLM", - "TFMobileBertForMultipleChoice", - "TFMobileBertForNextSentencePrediction", - "TFMobileBertForPreTraining", - "TFMobileBertForQuestionAnswering", - "TFMobileBertForSequenceClassification", - "TFMobileBertForTokenClassification", - "TFMobileBertMainLayer", - "TFMobileBertModel", - "TFMobileBertPreTrainedModel", - ] - ) - _import_structure["models.mobilevit"].extend( - [ - "TFMobileViTForImageClassification", - "TFMobileViTForSemanticSegmentation", - "TFMobileViTModel", - "TFMobileViTPreTrainedModel", - ] - ) - _import_structure["models.mpnet"].extend( - [ - "TFMPNetForMaskedLM", - "TFMPNetForMultipleChoice", - "TFMPNetForQuestionAnswering", - "TFMPNetForSequenceClassification", - "TFMPNetForTokenClassification", - "TFMPNetMainLayer", - "TFMPNetModel", - "TFMPNetPreTrainedModel", - ] - ) - _import_structure["models.mt5"].extend(["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]) - _import_structure["models.openai"].extend( - [ - "TFOpenAIGPTDoubleHeadsModel", - "TFOpenAIGPTForSequenceClassification", - "TFOpenAIGPTLMHeadModel", - "TFOpenAIGPTMainLayer", - "TFOpenAIGPTModel", - "TFOpenAIGPTPreTrainedModel", - ] - ) - _import_structure["models.opt"].extend( - [ - "TFOPTForCausalLM", - "TFOPTModel", - "TFOPTPreTrainedModel", - ] - ) - _import_structure["models.pegasus"].extend( - [ - "TFPegasusForConditionalGeneration", - "TFPegasusModel", - "TFPegasusPreTrainedModel", - ] - ) - _import_structure["models.rag"].extend( - [ - "TFRagModel", - "TFRagPreTrainedModel", - "TFRagSequenceForGeneration", - "TFRagTokenForGeneration", - ] - ) - _import_structure["models.regnet"].extend( - [ - "TFRegNetForImageClassification", - "TFRegNetModel", - "TFRegNetPreTrainedModel", - ] - ) - _import_structure["models.rembert"].extend( - [ - "TFRemBertForCausalLM", - "TFRemBertForMaskedLM", - "TFRemBertForMultipleChoice", - "TFRemBertForQuestionAnswering", - "TFRemBertForSequenceClassification", - "TFRemBertForTokenClassification", - "TFRemBertModel", - "TFRemBertPreTrainedModel", - ] - ) - _import_structure["models.resnet"].extend( - [ - "TFResNetForImageClassification", - "TFResNetModel", - "TFResNetPreTrainedModel", - ] - ) - _import_structure["models.roberta"].extend( - [ - "TFRobertaForCausalLM", - "TFRobertaForMaskedLM", - "TFRobertaForMultipleChoice", - "TFRobertaForQuestionAnswering", - "TFRobertaForSequenceClassification", - "TFRobertaForTokenClassification", - "TFRobertaMainLayer", - "TFRobertaModel", - "TFRobertaPreTrainedModel", - ] - ) - _import_structure["models.roberta_prelayernorm"].extend( - [ - "TFRobertaPreLayerNormForCausalLM", - "TFRobertaPreLayerNormForMaskedLM", - "TFRobertaPreLayerNormForMultipleChoice", - "TFRobertaPreLayerNormForQuestionAnswering", - "TFRobertaPreLayerNormForSequenceClassification", - "TFRobertaPreLayerNormForTokenClassification", - "TFRobertaPreLayerNormMainLayer", - "TFRobertaPreLayerNormModel", - "TFRobertaPreLayerNormPreTrainedModel", - ] - ) - _import_structure["models.roformer"].extend( - [ - "TFRoFormerForCausalLM", - "TFRoFormerForMaskedLM", - "TFRoFormerForMultipleChoice", - "TFRoFormerForQuestionAnswering", - "TFRoFormerForSequenceClassification", - "TFRoFormerForTokenClassification", - "TFRoFormerModel", - "TFRoFormerPreTrainedModel", - ] - ) - _import_structure["models.sam"].extend( - [ - "TFSamModel", - "TFSamPreTrainedModel", - "TFSamVisionModel", - ] - ) - _import_structure["models.segformer"].extend( - [ - "TFSegformerDecodeHead", - "TFSegformerForImageClassification", - "TFSegformerForSemanticSegmentation", - "TFSegformerModel", - "TFSegformerPreTrainedModel", - ] - ) - _import_structure["models.speech_to_text"].extend( - [ - "TFSpeech2TextForConditionalGeneration", - "TFSpeech2TextModel", - "TFSpeech2TextPreTrainedModel", - ] - ) - _import_structure["models.swiftformer"].extend( - [ - "TFSwiftFormerForImageClassification", - "TFSwiftFormerModel", - "TFSwiftFormerPreTrainedModel", - ] - ) - _import_structure["models.swin"].extend( - [ - "TFSwinForImageClassification", - "TFSwinForMaskedImageModeling", - "TFSwinModel", - "TFSwinPreTrainedModel", - ] - ) - _import_structure["models.t5"].extend( - [ - "TFT5EncoderModel", - "TFT5ForConditionalGeneration", - "TFT5Model", - "TFT5PreTrainedModel", - ] - ) - _import_structure["models.tapas"].extend( - [ - "TFTapasForMaskedLM", - "TFTapasForQuestionAnswering", - "TFTapasForSequenceClassification", - "TFTapasModel", - "TFTapasPreTrainedModel", - ] - ) - _import_structure["models.vision_encoder_decoder"].extend(["TFVisionEncoderDecoderModel"]) - _import_structure["models.vision_text_dual_encoder"].extend(["TFVisionTextDualEncoderModel"]) - _import_structure["models.vit"].extend( - [ - "TFViTForImageClassification", - "TFViTModel", - "TFViTPreTrainedModel", - ] - ) - _import_structure["models.vit_mae"].extend( - [ - "TFViTMAEForPreTraining", - "TFViTMAEModel", - "TFViTMAEPreTrainedModel", - ] - ) - _import_structure["models.wav2vec2"].extend( - [ - "TFWav2Vec2ForCTC", - "TFWav2Vec2ForSequenceClassification", - "TFWav2Vec2Model", - "TFWav2Vec2PreTrainedModel", - ] - ) - _import_structure["models.whisper"].extend( - [ - "TFWhisperForConditionalGeneration", - "TFWhisperModel", - "TFWhisperPreTrainedModel", - ] - ) - _import_structure["models.xglm"].extend( - [ - "TFXGLMForCausalLM", - "TFXGLMModel", - "TFXGLMPreTrainedModel", - ] - ) - _import_structure["models.xlm"].extend( - [ - "TFXLMForMultipleChoice", - "TFXLMForQuestionAnsweringSimple", - "TFXLMForSequenceClassification", - "TFXLMForTokenClassification", - "TFXLMMainLayer", - "TFXLMModel", - "TFXLMPreTrainedModel", - "TFXLMWithLMHeadModel", - ] - ) - _import_structure["models.xlm_roberta"].extend( - [ - "TFXLMRobertaForCausalLM", - "TFXLMRobertaForMaskedLM", - "TFXLMRobertaForMultipleChoice", - "TFXLMRobertaForQuestionAnswering", - "TFXLMRobertaForSequenceClassification", - "TFXLMRobertaForTokenClassification", - "TFXLMRobertaModel", - "TFXLMRobertaPreTrainedModel", - ] - ) - _import_structure["models.xlnet"].extend( - [ - "TFXLNetForMultipleChoice", - "TFXLNetForQuestionAnsweringSimple", - "TFXLNetForSequenceClassification", - "TFXLNetForTokenClassification", - "TFXLNetLMHeadModel", - "TFXLNetMainLayer", - "TFXLNetModel", - "TFXLNetPreTrainedModel", - ] - ) _import_structure["optimization_tf"] = [ "AdamWeightDecay", "GradientAccumulator", @@ -4919,46 +530,6 @@ else: _import_structure["tf_utils"] = [] -try: - if not ( - is_librosa_available() - and is_essentia_available() - and is_scipy_available() - and is_torch_available() - and is_pretty_midi_available() - ): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - from .utils import ( - dummy_essentia_and_librosa_and_pretty_midi_and_scipy_and_torch_objects, - ) - - _import_structure["utils.dummy_essentia_and_librosa_and_pretty_midi_and_scipy_and_torch_objects"] = [ - name - for name in dir(dummy_essentia_and_librosa_and_pretty_midi_and_scipy_and_torch_objects) - if not name.startswith("_") - ] -else: - _import_structure["models.pop2piano"].append("Pop2PianoFeatureExtractor") - _import_structure["models.pop2piano"].append("Pop2PianoTokenizer") - _import_structure["models.pop2piano"].append("Pop2PianoProcessor") - -try: - if not is_torchaudio_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - from .utils import ( - dummy_torchaudio_objects, - ) - - _import_structure["utils.dummy_torchaudio_objects"] = [ - name for name in dir(dummy_torchaudio_objects) if not name.startswith("_") - ] -else: - _import_structure["models.musicgen_melody"].append("MusicgenMelodyFeatureExtractor") - _import_structure["models.musicgen_melody"].append("MusicgenMelodyProcessor") - - # FLAX-backed objects try: if not is_flax_available(): @@ -4990,316 +561,10 @@ else: ) _import_structure["modeling_flax_outputs"] = [] _import_structure["modeling_flax_utils"] = ["FlaxPreTrainedModel"] - _import_structure["models.albert"].extend( - [ - "FlaxAlbertForMaskedLM", - "FlaxAlbertForMultipleChoice", - "FlaxAlbertForPreTraining", - "FlaxAlbertForQuestionAnswering", - "FlaxAlbertForSequenceClassification", - "FlaxAlbertForTokenClassification", - "FlaxAlbertModel", - "FlaxAlbertPreTrainedModel", - ] - ) - _import_structure["models.auto"].extend( - [ - "FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", - "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING", - "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", - "FLAX_MODEL_FOR_MASKED_LM_MAPPING", - "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", - "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", - "FLAX_MODEL_FOR_PRETRAINING_MAPPING", - "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING", - "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", - "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", - "FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", - "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", - "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING", - "FLAX_MODEL_MAPPING", - "FlaxAutoModel", - "FlaxAutoModelForCausalLM", - "FlaxAutoModelForImageClassification", - "FlaxAutoModelForMaskedLM", - "FlaxAutoModelForMultipleChoice", - "FlaxAutoModelForNextSentencePrediction", - "FlaxAutoModelForPreTraining", - "FlaxAutoModelForQuestionAnswering", - "FlaxAutoModelForSeq2SeqLM", - "FlaxAutoModelForSequenceClassification", - "FlaxAutoModelForSpeechSeq2Seq", - "FlaxAutoModelForTokenClassification", - "FlaxAutoModelForVision2Seq", - ] - ) - - # Flax models structure - - _import_structure["models.bart"].extend( - [ - "FlaxBartDecoderPreTrainedModel", - "FlaxBartForCausalLM", - "FlaxBartForConditionalGeneration", - "FlaxBartForQuestionAnswering", - "FlaxBartForSequenceClassification", - "FlaxBartModel", - "FlaxBartPreTrainedModel", - ] - ) - _import_structure["models.beit"].extend( - [ - "FlaxBeitForImageClassification", - "FlaxBeitForMaskedImageModeling", - "FlaxBeitModel", - "FlaxBeitPreTrainedModel", - ] - ) - - _import_structure["models.bert"].extend( - [ - "FlaxBertForCausalLM", - "FlaxBertForMaskedLM", - "FlaxBertForMultipleChoice", - "FlaxBertForNextSentencePrediction", - "FlaxBertForPreTraining", - "FlaxBertForQuestionAnswering", - "FlaxBertForSequenceClassification", - "FlaxBertForTokenClassification", - "FlaxBertModel", - "FlaxBertPreTrainedModel", - ] - ) - _import_structure["models.big_bird"].extend( - [ - "FlaxBigBirdForCausalLM", - "FlaxBigBirdForMaskedLM", - "FlaxBigBirdForMultipleChoice", - "FlaxBigBirdForPreTraining", - "FlaxBigBirdForQuestionAnswering", - "FlaxBigBirdForSequenceClassification", - "FlaxBigBirdForTokenClassification", - "FlaxBigBirdModel", - "FlaxBigBirdPreTrainedModel", - ] - ) - _import_structure["models.blenderbot"].extend( - [ - "FlaxBlenderbotForConditionalGeneration", - "FlaxBlenderbotModel", - "FlaxBlenderbotPreTrainedModel", - ] - ) - _import_structure["models.blenderbot_small"].extend( - [ - "FlaxBlenderbotSmallForConditionalGeneration", - "FlaxBlenderbotSmallModel", - "FlaxBlenderbotSmallPreTrainedModel", - ] - ) - _import_structure["models.bloom"].extend( - [ - "FlaxBloomForCausalLM", - "FlaxBloomModel", - "FlaxBloomPreTrainedModel", - ] - ) - _import_structure["models.clip"].extend( - [ - "FlaxCLIPModel", - "FlaxCLIPPreTrainedModel", - "FlaxCLIPTextModel", - "FlaxCLIPTextPreTrainedModel", - "FlaxCLIPTextModelWithProjection", - "FlaxCLIPVisionModel", - "FlaxCLIPVisionPreTrainedModel", - ] - ) - _import_structure["models.dinov2"].extend( - [ - "FlaxDinov2Model", - "FlaxDinov2ForImageClassification", - "FlaxDinov2PreTrainedModel", - ] - ) - _import_structure["models.distilbert"].extend( - [ - "FlaxDistilBertForMaskedLM", - "FlaxDistilBertForMultipleChoice", - "FlaxDistilBertForQuestionAnswering", - "FlaxDistilBertForSequenceClassification", - "FlaxDistilBertForTokenClassification", - "FlaxDistilBertModel", - "FlaxDistilBertPreTrainedModel", - ] - ) - _import_structure["models.electra"].extend( - [ - "FlaxElectraForCausalLM", - "FlaxElectraForMaskedLM", - "FlaxElectraForMultipleChoice", - "FlaxElectraForPreTraining", - "FlaxElectraForQuestionAnswering", - "FlaxElectraForSequenceClassification", - "FlaxElectraForTokenClassification", - "FlaxElectraModel", - "FlaxElectraPreTrainedModel", - ] - ) - _import_structure["models.encoder_decoder"].append("FlaxEncoderDecoderModel") - _import_structure["models.gpt2"].extend(["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]) - _import_structure["models.gpt_neo"].extend( - ["FlaxGPTNeoForCausalLM", "FlaxGPTNeoModel", "FlaxGPTNeoPreTrainedModel"] - ) - _import_structure["models.gptj"].extend(["FlaxGPTJForCausalLM", "FlaxGPTJModel", "FlaxGPTJPreTrainedModel"]) - _import_structure["models.llama"].extend(["FlaxLlamaForCausalLM", "FlaxLlamaModel", "FlaxLlamaPreTrainedModel"]) - _import_structure["models.gemma"].extend(["FlaxGemmaForCausalLM", "FlaxGemmaModel", "FlaxGemmaPreTrainedModel"]) - _import_structure["models.longt5"].extend( - [ - "FlaxLongT5ForConditionalGeneration", - "FlaxLongT5Model", - "FlaxLongT5PreTrainedModel", - ] - ) - _import_structure["models.marian"].extend( - [ - "FlaxMarianModel", - "FlaxMarianMTModel", - "FlaxMarianPreTrainedModel", - ] - ) - _import_structure["models.mbart"].extend( - [ - "FlaxMBartForConditionalGeneration", - "FlaxMBartForQuestionAnswering", - "FlaxMBartForSequenceClassification", - "FlaxMBartModel", - "FlaxMBartPreTrainedModel", - ] - ) - _import_structure["models.mistral"].extend( - [ - "FlaxMistralForCausalLM", - "FlaxMistralModel", - "FlaxMistralPreTrainedModel", - ] - ) - _import_structure["models.mt5"].extend(["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]) - _import_structure["models.opt"].extend( - [ - "FlaxOPTForCausalLM", - "FlaxOPTModel", - "FlaxOPTPreTrainedModel", - ] - ) - _import_structure["models.pegasus"].extend( - [ - "FlaxPegasusForConditionalGeneration", - "FlaxPegasusModel", - "FlaxPegasusPreTrainedModel", - ] - ) - _import_structure["models.regnet"].extend( - [ - "FlaxRegNetForImageClassification", - "FlaxRegNetModel", - "FlaxRegNetPreTrainedModel", - ] - ) - _import_structure["models.resnet"].extend( - [ - "FlaxResNetForImageClassification", - "FlaxResNetModel", - "FlaxResNetPreTrainedModel", - ] - ) - _import_structure["models.roberta"].extend( - [ - "FlaxRobertaForCausalLM", - "FlaxRobertaForMaskedLM", - "FlaxRobertaForMultipleChoice", - "FlaxRobertaForQuestionAnswering", - "FlaxRobertaForSequenceClassification", - "FlaxRobertaForTokenClassification", - "FlaxRobertaModel", - "FlaxRobertaPreTrainedModel", - ] - ) - _import_structure["models.roberta_prelayernorm"].extend( - [ - "FlaxRobertaPreLayerNormForCausalLM", - "FlaxRobertaPreLayerNormForMaskedLM", - "FlaxRobertaPreLayerNormForMultipleChoice", - "FlaxRobertaPreLayerNormForQuestionAnswering", - "FlaxRobertaPreLayerNormForSequenceClassification", - "FlaxRobertaPreLayerNormForTokenClassification", - "FlaxRobertaPreLayerNormModel", - "FlaxRobertaPreLayerNormPreTrainedModel", - ] - ) - _import_structure["models.roformer"].extend( - [ - "FlaxRoFormerForMaskedLM", - "FlaxRoFormerForMultipleChoice", - "FlaxRoFormerForQuestionAnswering", - "FlaxRoFormerForSequenceClassification", - "FlaxRoFormerForTokenClassification", - "FlaxRoFormerModel", - "FlaxRoFormerPreTrainedModel", - ] - ) - _import_structure["models.speech_encoder_decoder"].append("FlaxSpeechEncoderDecoderModel") - _import_structure["models.t5"].extend( - [ - "FlaxT5EncoderModel", - "FlaxT5ForConditionalGeneration", - "FlaxT5Model", - "FlaxT5PreTrainedModel", - ] - ) - _import_structure["models.vision_encoder_decoder"].append("FlaxVisionEncoderDecoderModel") - _import_structure["models.vision_text_dual_encoder"].extend(["FlaxVisionTextDualEncoderModel"]) - _import_structure["models.vit"].extend(["FlaxViTForImageClassification", "FlaxViTModel", "FlaxViTPreTrainedModel"]) - _import_structure["models.wav2vec2"].extend( - [ - "FlaxWav2Vec2ForCTC", - "FlaxWav2Vec2ForPreTraining", - "FlaxWav2Vec2Model", - "FlaxWav2Vec2PreTrainedModel", - ] - ) - _import_structure["models.whisper"].extend( - [ - "FlaxWhisperForConditionalGeneration", - "FlaxWhisperModel", - "FlaxWhisperPreTrainedModel", - "FlaxWhisperForAudioClassification", - ] - ) - _import_structure["models.xglm"].extend( - [ - "FlaxXGLMForCausalLM", - "FlaxXGLMModel", - "FlaxXGLMPreTrainedModel", - ] - ) - _import_structure["models.xlm_roberta"].extend( - [ - "FlaxXLMRobertaForMaskedLM", - "FlaxXLMRobertaForMultipleChoice", - "FlaxXLMRobertaForQuestionAnswering", - "FlaxXLMRobertaForSequenceClassification", - "FlaxXLMRobertaForTokenClassification", - "FlaxXLMRobertaModel", - "FlaxXLMRobertaForCausalLM", - "FlaxXLMRobertaPreTrainedModel", - ] - ) - # Direct imports for type-checking if TYPE_CHECKING: - # Configuration + # All modeling imports # Agents from .agents import ( Agent, @@ -5400,867 +665,7 @@ if TYPE_CHECKING: load_tf2_model_in_pytorch_model, load_tf2_weights_in_pytorch_model, ) - from .models.albert import AlbertConfig - from .models.align import ( - AlignConfig, - AlignProcessor, - AlignTextConfig, - AlignVisionConfig, - ) - from .models.altclip import ( - AltCLIPConfig, - AltCLIPProcessor, - AltCLIPTextConfig, - AltCLIPVisionConfig, - ) - from .models.aria import ( - AriaConfig, - AriaProcessor, - AriaTextConfig, - ) - from .models.audio_spectrogram_transformer import ( - ASTConfig, - ASTFeatureExtractor, - ) - from .models.auto import ( - CONFIG_MAPPING, - FEATURE_EXTRACTOR_MAPPING, - IMAGE_PROCESSOR_MAPPING, - MODEL_NAMES_MAPPING, - PROCESSOR_MAPPING, - TOKENIZER_MAPPING, - AutoConfig, - AutoFeatureExtractor, - AutoImageProcessor, - AutoProcessor, - AutoTokenizer, - ) - from .models.autoformer import ( - AutoformerConfig, - ) - from .models.aya_vision import ( - AyaVisionConfig, - AyaVisionProcessor, - ) - from .models.bamba import BambaConfig - from .models.bark import ( - BarkCoarseConfig, - BarkConfig, - BarkFineConfig, - BarkProcessor, - BarkSemanticConfig, - ) - from .models.bart import BartConfig, BartTokenizer - from .models.beit import BeitConfig - from .models.bert import ( - BasicTokenizer, - BertConfig, - BertTokenizer, - WordpieceTokenizer, - ) - from .models.bert_generation import BertGenerationConfig - from .models.bert_japanese import ( - BertJapaneseTokenizer, - CharacterTokenizer, - MecabTokenizer, - ) - from .models.bertweet import BertweetTokenizer - from .models.big_bird import BigBirdConfig - from .models.bigbird_pegasus import ( - BigBirdPegasusConfig, - ) - from .models.biogpt import ( - BioGptConfig, - BioGptTokenizer, - ) - from .models.bit import BitConfig - from .models.blenderbot import ( - BlenderbotConfig, - BlenderbotTokenizer, - ) - from .models.blenderbot_small import ( - BlenderbotSmallConfig, - BlenderbotSmallTokenizer, - ) - from .models.blip import ( - BlipConfig, - BlipProcessor, - BlipTextConfig, - BlipVisionConfig, - ) - from .models.blip_2 import ( - Blip2Config, - Blip2Processor, - Blip2QFormerConfig, - Blip2VisionConfig, - ) - from .models.bloom import BloomConfig - from .models.bridgetower import ( - BridgeTowerConfig, - BridgeTowerProcessor, - BridgeTowerTextConfig, - BridgeTowerVisionConfig, - ) - from .models.bros import ( - BrosConfig, - BrosProcessor, - ) - from .models.byt5 import ByT5Tokenizer - from .models.camembert import ( - CamembertConfig, - ) - from .models.canine import ( - CanineConfig, - CanineTokenizer, - ) - from .models.chameleon import ( - ChameleonConfig, - ChameleonProcessor, - ChameleonVQVAEConfig, - ) - from .models.chinese_clip import ( - ChineseCLIPConfig, - ChineseCLIPProcessor, - ChineseCLIPTextConfig, - ChineseCLIPVisionConfig, - ) - from .models.clap import ( - ClapAudioConfig, - ClapConfig, - ClapProcessor, - ClapTextConfig, - ) - from .models.clip import ( - CLIPConfig, - CLIPProcessor, - CLIPTextConfig, - CLIPTokenizer, - CLIPVisionConfig, - ) - from .models.clipseg import ( - CLIPSegConfig, - CLIPSegProcessor, - CLIPSegTextConfig, - CLIPSegVisionConfig, - ) - from .models.clvp import ( - ClvpConfig, - ClvpDecoderConfig, - ClvpEncoderConfig, - ClvpFeatureExtractor, - ClvpProcessor, - ClvpTokenizer, - ) - from .models.codegen import ( - CodeGenConfig, - CodeGenTokenizer, - ) - from .models.cohere import CohereConfig - from .models.cohere2 import Cohere2Config - from .models.colpali import ( - ColPaliConfig, - ColPaliProcessor, - ) - from .models.conditional_detr import ( - ConditionalDetrConfig, - ) - from .models.convbert import ( - ConvBertConfig, - ConvBertTokenizer, - ) - from .models.convnext import ConvNextConfig - from .models.convnextv2 import ( - ConvNextV2Config, - ) - from .models.cpmant import ( - CpmAntConfig, - CpmAntTokenizer, - ) - from .models.ctrl import ( - CTRLConfig, - CTRLTokenizer, - ) - from .models.cvt import CvtConfig - from .models.dab_detr import ( - DabDetrConfig, - ) - from .models.dac import ( - DacConfig, - DacFeatureExtractor, - ) - from .models.data2vec import ( - Data2VecAudioConfig, - Data2VecTextConfig, - Data2VecVisionConfig, - ) - from .models.dbrx import DbrxConfig - from .models.deberta import ( - DebertaConfig, - DebertaTokenizer, - ) - from .models.deberta_v2 import ( - DebertaV2Config, - ) - from .models.decision_transformer import ( - DecisionTransformerConfig, - ) - from .models.deepseek_v3 import ( - DeepseekV3Config, - ) - from .models.deformable_detr import ( - DeformableDetrConfig, - ) - from .models.deit import DeiTConfig - from .models.deprecated.deta import DetaConfig - from .models.deprecated.efficientformer import ( - EfficientFormerConfig, - ) - from .models.deprecated.ernie_m import ErnieMConfig - from .models.deprecated.gptsan_japanese import ( - GPTSanJapaneseConfig, - GPTSanJapaneseTokenizer, - ) - from .models.deprecated.graphormer import GraphormerConfig - from .models.deprecated.jukebox import ( - JukeboxConfig, - JukeboxPriorConfig, - JukeboxTokenizer, - JukeboxVQVAEConfig, - ) - from .models.deprecated.mctct import ( - MCTCTConfig, - MCTCTFeatureExtractor, - MCTCTProcessor, - ) - from .models.deprecated.mega import MegaConfig - from .models.deprecated.mmbt import MMBTConfig - from .models.deprecated.nat import NatConfig - from .models.deprecated.nezha import NezhaConfig - from .models.deprecated.open_llama import ( - OpenLlamaConfig, - ) - from .models.deprecated.qdqbert import QDQBertConfig - from .models.deprecated.realm import ( - RealmConfig, - RealmTokenizer, - ) - from .models.deprecated.retribert import ( - RetriBertConfig, - RetriBertTokenizer, - ) - from .models.deprecated.speech_to_text_2 import ( - Speech2Text2Config, - Speech2Text2Processor, - Speech2Text2Tokenizer, - ) - from .models.deprecated.tapex import TapexTokenizer - from .models.deprecated.trajectory_transformer import ( - TrajectoryTransformerConfig, - ) - from .models.deprecated.transfo_xl import ( - TransfoXLConfig, - TransfoXLCorpus, - TransfoXLTokenizer, - ) - from .models.deprecated.tvlt import ( - TvltConfig, - TvltFeatureExtractor, - TvltProcessor, - ) - from .models.deprecated.van import VanConfig - from .models.deprecated.vit_hybrid import ( - ViTHybridConfig, - ) - from .models.deprecated.xlm_prophetnet import ( - XLMProphetNetConfig, - ) - from .models.depth_anything import DepthAnythingConfig - from .models.depth_pro import DepthProConfig - from .models.detr import DetrConfig - from .models.diffllama import DiffLlamaConfig - from .models.dinat import DinatConfig - from .models.dinov2 import Dinov2Config - from .models.dinov2_with_registers import Dinov2WithRegistersConfig - from .models.distilbert import ( - DistilBertConfig, - DistilBertTokenizer, - ) - from .models.donut import ( - DonutProcessor, - DonutSwinConfig, - ) - from .models.dpr import ( - DPRConfig, - DPRContextEncoderTokenizer, - DPRQuestionEncoderTokenizer, - DPRReaderOutput, - DPRReaderTokenizer, - ) - from .models.dpt import DPTConfig - from .models.efficientnet import ( - EfficientNetConfig, - ) - from .models.electra import ( - ElectraConfig, - ElectraTokenizer, - ) - from .models.emu3 import ( - Emu3Config, - Emu3Processor, - Emu3TextConfig, - Emu3VQVAEConfig, - ) - from .models.encodec import ( - EncodecConfig, - EncodecFeatureExtractor, - ) - from .models.encoder_decoder import EncoderDecoderConfig - from .models.ernie import ErnieConfig - from .models.esm import EsmConfig, EsmTokenizer - from .models.falcon import FalconConfig - from .models.falcon_mamba import FalconMambaConfig - from .models.fastspeech2_conformer import ( - FastSpeech2ConformerConfig, - FastSpeech2ConformerHifiGanConfig, - FastSpeech2ConformerTokenizer, - FastSpeech2ConformerWithHifiGanConfig, - ) - from .models.flaubert import FlaubertConfig, FlaubertTokenizer - from .models.flava import ( - FlavaConfig, - FlavaImageCodebookConfig, - FlavaImageConfig, - FlavaMultimodalConfig, - FlavaTextConfig, - ) - from .models.fnet import FNetConfig - from .models.focalnet import FocalNetConfig - from .models.fsmt import ( - FSMTConfig, - FSMTTokenizer, - ) - from .models.funnel import ( - FunnelConfig, - FunnelTokenizer, - ) - from .models.fuyu import FuyuConfig - from .models.gemma import GemmaConfig - from .models.gemma2 import Gemma2Config - from .models.gemma3 import Gemma3Config, Gemma3Processor, Gemma3TextConfig - from .models.git import ( - GitConfig, - GitProcessor, - GitVisionConfig, - ) - from .models.glm import GlmConfig - from .models.glm4 import Glm4Config - from .models.glpn import GLPNConfig - from .models.got_ocr2 import GotOcr2Config, GotOcr2Processor, GotOcr2VisionConfig - from .models.gpt2 import ( - GPT2Config, - GPT2Tokenizer, - ) - from .models.gpt_bigcode import ( - GPTBigCodeConfig, - ) - from .models.gpt_neo import GPTNeoConfig - from .models.gpt_neox import GPTNeoXConfig - from .models.gpt_neox_japanese import ( - GPTNeoXJapaneseConfig, - ) - from .models.gptj import GPTJConfig - from .models.granite import GraniteConfig - from .models.granitemoe import GraniteMoeConfig - from .models.granitemoeshared import GraniteMoeSharedConfig - from .models.grounding_dino import ( - GroundingDinoConfig, - GroundingDinoProcessor, - ) - from .models.groupvit import ( - GroupViTConfig, - GroupViTTextConfig, - GroupViTVisionConfig, - ) - from .models.helium import HeliumConfig - from .models.herbert import HerbertTokenizer - from .models.hiera import HieraConfig - from .models.hubert import HubertConfig - from .models.ibert import IBertConfig - from .models.idefics import ( - IdeficsConfig, - ) - from .models.idefics2 import Idefics2Config - from .models.idefics3 import Idefics3Config - from .models.ijepa import IJepaConfig - from .models.imagegpt import ImageGPTConfig - from .models.informer import InformerConfig - from .models.instructblip import ( - InstructBlipConfig, - InstructBlipProcessor, - InstructBlipQFormerConfig, - InstructBlipVisionConfig, - ) - from .models.instructblipvideo import ( - InstructBlipVideoConfig, - InstructBlipVideoProcessor, - InstructBlipVideoQFormerConfig, - InstructBlipVideoVisionConfig, - ) - from .models.jamba import JambaConfig - from .models.jetmoe import JetMoeConfig - from .models.kosmos2 import ( - Kosmos2Config, - Kosmos2Processor, - ) - from .models.layoutlm import ( - LayoutLMConfig, - LayoutLMTokenizer, - ) - from .models.layoutlmv2 import ( - LayoutLMv2Config, - LayoutLMv2FeatureExtractor, - LayoutLMv2ImageProcessor, - LayoutLMv2Processor, - LayoutLMv2Tokenizer, - ) - from .models.layoutlmv3 import ( - LayoutLMv3Config, - LayoutLMv3FeatureExtractor, - LayoutLMv3ImageProcessor, - LayoutLMv3Processor, - LayoutLMv3Tokenizer, - ) - from .models.layoutxlm import LayoutXLMProcessor - from .models.led import LEDConfig, LEDTokenizer - from .models.levit import LevitConfig - from .models.lilt import LiltConfig - from .models.llama import LlamaConfig - from .models.llama4 import ( - Llama4Config, - Llama4Processor, - Llama4TextConfig, - Llama4VisionConfig, - ) - from .models.llava import ( - LlavaConfig, - LlavaProcessor, - ) - from .models.llava_next import ( - LlavaNextConfig, - LlavaNextProcessor, - ) - from .models.llava_next_video import ( - LlavaNextVideoConfig, - LlavaNextVideoProcessor, - ) - from .models.llava_onevision import ( - LlavaOnevisionConfig, - LlavaOnevisionProcessor, - ) - from .models.longformer import ( - LongformerConfig, - LongformerTokenizer, - ) - from .models.longt5 import LongT5Config - from .models.luke import ( - LukeConfig, - LukeTokenizer, - ) - from .models.lxmert import ( - LxmertConfig, - LxmertTokenizer, - ) - from .models.m2m_100 import M2M100Config - from .models.mamba import MambaConfig - from .models.mamba2 import Mamba2Config - from .models.marian import MarianConfig - from .models.markuplm import ( - MarkupLMConfig, - MarkupLMFeatureExtractor, - MarkupLMProcessor, - MarkupLMTokenizer, - ) - from .models.mask2former import ( - Mask2FormerConfig, - ) - from .models.maskformer import ( - MaskFormerConfig, - MaskFormerSwinConfig, - ) - from .models.mbart import MBartConfig - from .models.megatron_bert import ( - MegatronBertConfig, - ) - from .models.mgp_str import ( - MgpstrConfig, - MgpstrProcessor, - MgpstrTokenizer, - ) - from .models.mimi import ( - MimiConfig, - ) - from .models.mistral import MistralConfig - from .models.mistral3 import Mistral3Config - from .models.mixtral import MixtralConfig - from .models.mllama import ( - MllamaConfig, - MllamaProcessor, - ) - from .models.mobilebert import ( - MobileBertConfig, - MobileBertTokenizer, - ) - from .models.mobilenet_v1 import ( - MobileNetV1Config, - ) - from .models.mobilenet_v2 import ( - MobileNetV2Config, - ) - from .models.mobilevit import ( - MobileViTConfig, - ) - from .models.mobilevitv2 import ( - MobileViTV2Config, - ) - from .models.modernbert import ModernBertConfig - from .models.moonshine import MoonshineConfig - from .models.moshi import ( - MoshiConfig, - MoshiDepthConfig, - ) - from .models.mpnet import ( - MPNetConfig, - MPNetTokenizer, - ) - from .models.mpt import MptConfig - from .models.mra import MraConfig - from .models.mt5 import MT5Config - from .models.musicgen import ( - MusicgenConfig, - MusicgenDecoderConfig, - ) - from .models.musicgen_melody import ( - MusicgenMelodyConfig, - MusicgenMelodyDecoderConfig, - ) - from .models.mvp import MvpConfig, MvpTokenizer - from .models.myt5 import MyT5Tokenizer - from .models.nemotron import NemotronConfig - from .models.nllb_moe import NllbMoeConfig - from .models.nougat import NougatProcessor - from .models.nystromformer import ( - NystromformerConfig, - ) - from .models.olmo import OlmoConfig - from .models.olmo2 import Olmo2Config - from .models.olmoe import OlmoeConfig - from .models.omdet_turbo import ( - OmDetTurboConfig, - OmDetTurboProcessor, - ) - from .models.oneformer import ( - OneFormerConfig, - OneFormerProcessor, - ) - from .models.openai import ( - OpenAIGPTConfig, - OpenAIGPTTokenizer, - ) - from .models.opt import OPTConfig - from .models.owlv2 import ( - Owlv2Config, - Owlv2Processor, - Owlv2TextConfig, - Owlv2VisionConfig, - ) - from .models.owlvit import ( - OwlViTConfig, - OwlViTProcessor, - OwlViTTextConfig, - OwlViTVisionConfig, - ) - from .models.paligemma import ( - PaliGemmaConfig, - ) - from .models.patchtsmixer import ( - PatchTSMixerConfig, - ) - from .models.patchtst import PatchTSTConfig - from .models.pegasus import ( - PegasusConfig, - PegasusTokenizer, - ) - from .models.pegasus_x import ( - PegasusXConfig, - ) - from .models.perceiver import ( - PerceiverConfig, - PerceiverTokenizer, - ) - from .models.persimmon import ( - PersimmonConfig, - ) - from .models.phi import PhiConfig - from .models.phi3 import Phi3Config - from .models.phi4_multimodal import ( - Phi4MultimodalAudioConfig, - Phi4MultimodalConfig, - Phi4MultimodalFeatureExtractor, - Phi4MultimodalProcessor, - Phi4MultimodalVisionConfig, - ) - from .models.phimoe import PhimoeConfig - from .models.phobert import PhobertTokenizer - from .models.pix2struct import ( - Pix2StructConfig, - Pix2StructProcessor, - Pix2StructTextConfig, - Pix2StructVisionConfig, - ) - from .models.pixtral import ( - PixtralProcessor, - PixtralVisionConfig, - ) - from .models.plbart import PLBartConfig - from .models.poolformer import ( - PoolFormerConfig, - ) - from .models.pop2piano import ( - Pop2PianoConfig, - ) - from .models.prompt_depth_anything import PromptDepthAnythingConfig - from .models.prophetnet import ( - ProphetNetConfig, - ProphetNetTokenizer, - ) - from .models.pvt import PvtConfig - from .models.pvt_v2 import PvtV2Config - from .models.qwen2 import Qwen2Config, Qwen2Tokenizer - from .models.qwen2_5_vl import ( - Qwen2_5_VLConfig, - Qwen2_5_VLProcessor, - ) - from .models.qwen2_audio import ( - Qwen2AudioConfig, - Qwen2AudioEncoderConfig, - Qwen2AudioProcessor, - ) - from .models.qwen2_moe import Qwen2MoeConfig - from .models.qwen2_vl import ( - Qwen2VLConfig, - Qwen2VLProcessor, - ) - from .models.qwen3 import Qwen3Config - from .models.qwen3_moe import Qwen3MoeConfig - from .models.rag import RagConfig, RagRetriever, RagTokenizer - from .models.recurrent_gemma import RecurrentGemmaConfig - from .models.reformer import ReformerConfig - from .models.regnet import RegNetConfig - from .models.rembert import RemBertConfig - from .models.resnet import ResNetConfig - from .models.roberta import ( - RobertaConfig, - RobertaTokenizer, - ) - from .models.roberta_prelayernorm import ( - RobertaPreLayerNormConfig, - ) - from .models.roc_bert import ( - RoCBertConfig, - RoCBertTokenizer, - ) - from .models.roformer import ( - RoFormerConfig, - RoFormerTokenizer, - ) - from .models.rt_detr import ( - RTDetrConfig, - RTDetrResNetConfig, - ) - from .models.rt_detr_v2 import RTDetrV2Config - from .models.rwkv import RwkvConfig - from .models.sam import ( - SamConfig, - SamMaskDecoderConfig, - SamProcessor, - SamPromptEncoderConfig, - SamVisionConfig, - ) - from .models.seamless_m4t import ( - SeamlessM4TConfig, - SeamlessM4TFeatureExtractor, - SeamlessM4TProcessor, - ) - from .models.seamless_m4t_v2 import ( - SeamlessM4Tv2Config, - ) - from .models.segformer import SegformerConfig - from .models.seggpt import SegGptConfig - from .models.sew import SEWConfig - from .models.sew_d import SEWDConfig - from .models.shieldgemma2 import ( - ShieldGemma2Config, - ShieldGemma2Processor, - ) - from .models.siglip import ( - SiglipConfig, - SiglipProcessor, - SiglipTextConfig, - SiglipVisionConfig, - ) - from .models.siglip2 import ( - Siglip2Config, - Siglip2Processor, - Siglip2TextConfig, - Siglip2VisionConfig, - ) - from .models.smolvlm import SmolVLMConfig - from .models.speech_encoder_decoder import SpeechEncoderDecoderConfig - from .models.speech_to_text import ( - Speech2TextConfig, - Speech2TextFeatureExtractor, - Speech2TextProcessor, - ) - from .models.speecht5 import ( - SpeechT5Config, - SpeechT5FeatureExtractor, - SpeechT5HifiGanConfig, - SpeechT5Processor, - ) - from .models.splinter import ( - SplinterConfig, - SplinterTokenizer, - ) - from .models.squeezebert import ( - SqueezeBertConfig, - SqueezeBertTokenizer, - ) - from .models.stablelm import StableLmConfig - from .models.starcoder2 import Starcoder2Config - from .models.superglue import SuperGlueConfig - from .models.superpoint import SuperPointConfig - from .models.swiftformer import ( - SwiftFormerConfig, - ) - from .models.swin import SwinConfig - from .models.swin2sr import Swin2SRConfig - from .models.swinv2 import Swinv2Config - from .models.switch_transformers import ( - SwitchTransformersConfig, - ) - from .models.t5 import T5Config - from .models.table_transformer import ( - TableTransformerConfig, - ) - from .models.tapas import ( - TapasConfig, - TapasTokenizer, - ) - from .models.textnet import TextNetConfig - from .models.time_series_transformer import ( - TimeSeriesTransformerConfig, - ) - from .models.timesformer import ( - TimesformerConfig, - ) - from .models.timm_backbone import TimmBackboneConfig - from .models.timm_wrapper import TimmWrapperConfig - from .models.trocr import ( - TrOCRConfig, - TrOCRProcessor, - ) - from .models.tvp import ( - TvpConfig, - TvpProcessor, - ) - from .models.udop import UdopConfig, UdopProcessor - from .models.umt5 import UMT5Config - from .models.unispeech import ( - UniSpeechConfig, - ) - from .models.unispeech_sat import ( - UniSpeechSatConfig, - ) - from .models.univnet import ( - UnivNetConfig, - UnivNetFeatureExtractor, - ) - from .models.upernet import UperNetConfig - from .models.video_llava import VideoLlavaConfig - from .models.videomae import VideoMAEConfig - from .models.vilt import ( - ViltConfig, - ViltFeatureExtractor, - ViltImageProcessor, - ViltProcessor, - ) - from .models.vipllava import ( - VipLlavaConfig, - ) - from .models.vision_encoder_decoder import VisionEncoderDecoderConfig - from .models.vision_text_dual_encoder import ( - VisionTextDualEncoderConfig, - VisionTextDualEncoderProcessor, - ) - from .models.visual_bert import ( - VisualBertConfig, - ) - from .models.vit import ViTConfig - from .models.vit_mae import ViTMAEConfig - from .models.vit_msn import ViTMSNConfig - from .models.vitdet import VitDetConfig - from .models.vitmatte import VitMatteConfig - from .models.vitpose import VitPoseConfig - from .models.vitpose_backbone import VitPoseBackboneConfig - from .models.vits import ( - VitsConfig, - VitsTokenizer, - ) - from .models.vivit import VivitConfig - from .models.wav2vec2 import ( - Wav2Vec2Config, - Wav2Vec2CTCTokenizer, - Wav2Vec2FeatureExtractor, - Wav2Vec2Processor, - Wav2Vec2Tokenizer, - ) - from .models.wav2vec2_bert import ( - Wav2Vec2BertConfig, - Wav2Vec2BertProcessor, - ) - from .models.wav2vec2_conformer import ( - Wav2Vec2ConformerConfig, - ) - from .models.wav2vec2_phoneme import Wav2Vec2PhonemeCTCTokenizer - from .models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM - from .models.wavlm import WavLMConfig - from .models.whisper import ( - WhisperConfig, - WhisperFeatureExtractor, - WhisperProcessor, - WhisperTokenizer, - ) - from .models.x_clip import ( - XCLIPConfig, - XCLIPProcessor, - XCLIPTextConfig, - XCLIPVisionConfig, - ) - from .models.xglm import XGLMConfig - from .models.xlm import XLMConfig, XLMTokenizer - from .models.xlm_roberta import ( - XLMRobertaConfig, - ) - from .models.xlm_roberta_xl import ( - XLMRobertaXLConfig, - ) - from .models.xlnet import XLNetConfig - from .models.xmod import XmodConfig - from .models.yolos import YolosConfig - from .models.yoso import YosoConfig - from .models.zamba import ZambaConfig - from .models.zamba2 import Zamba2Config - from .models.zoedepth import ZoeDepthConfig + from .models import * # Pipelines from .pipelines import ( @@ -6403,122 +808,12 @@ if TYPE_CHECKING: VptqConfig, ) - try: - if not is_sentencepiece_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - from .utils.dummy_sentencepiece_objects import * - else: - from .models.albert import AlbertTokenizer - from .models.barthez import BarthezTokenizer - from .models.bartpho import BartphoTokenizer - from .models.bert_generation import BertGenerationTokenizer - from .models.big_bird import BigBirdTokenizer - from .models.camembert import CamembertTokenizer - from .models.code_llama import CodeLlamaTokenizer - from .models.cpm import CpmTokenizer - from .models.deberta_v2 import DebertaV2Tokenizer - from .models.deprecated.ernie_m import ErnieMTokenizer - from .models.deprecated.xlm_prophetnet import XLMProphetNetTokenizer - from .models.fnet import FNetTokenizer - from .models.gemma import GemmaTokenizer - from .models.gpt_sw3 import GPTSw3Tokenizer - from .models.layoutxlm import LayoutXLMTokenizer - from .models.llama import LlamaTokenizer - from .models.m2m_100 import M2M100Tokenizer - from .models.marian import MarianTokenizer - from .models.mbart import MBartTokenizer - from .models.mbart50 import MBart50Tokenizer - from .models.mluke import MLukeTokenizer - from .models.mt5 import MT5Tokenizer - from .models.nllb import NllbTokenizer - from .models.pegasus import PegasusTokenizer - from .models.plbart import PLBartTokenizer - from .models.reformer import ReformerTokenizer - from .models.rembert import RemBertTokenizer - from .models.seamless_m4t import SeamlessM4TTokenizer - from .models.siglip import SiglipTokenizer - from .models.speech_to_text import Speech2TextTokenizer - from .models.speecht5 import SpeechT5Tokenizer - from .models.t5 import T5Tokenizer - from .models.udop import UdopTokenizer - from .models.xglm import XGLMTokenizer - from .models.xlm_roberta import XLMRobertaTokenizer - from .models.xlnet import XLNetTokenizer - try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_tokenizers_objects import * else: - # Fast tokenizers imports - from .models.albert import AlbertTokenizerFast - from .models.bart import BartTokenizerFast - from .models.barthez import BarthezTokenizerFast - from .models.bert import BertTokenizerFast - from .models.big_bird import BigBirdTokenizerFast - from .models.blenderbot import BlenderbotTokenizerFast - from .models.blenderbot_small import BlenderbotSmallTokenizerFast - from .models.bloom import BloomTokenizerFast - from .models.camembert import CamembertTokenizerFast - from .models.clip import CLIPTokenizerFast - from .models.code_llama import CodeLlamaTokenizerFast - from .models.codegen import CodeGenTokenizerFast - from .models.cohere import CohereTokenizerFast - from .models.convbert import ConvBertTokenizerFast - from .models.cpm import CpmTokenizerFast - from .models.deberta import DebertaTokenizerFast - from .models.deberta_v2 import DebertaV2TokenizerFast - from .models.deprecated.realm import RealmTokenizerFast - from .models.deprecated.retribert import RetriBertTokenizerFast - from .models.distilbert import DistilBertTokenizerFast - from .models.dpr import ( - DPRContextEncoderTokenizerFast, - DPRQuestionEncoderTokenizerFast, - DPRReaderTokenizerFast, - ) - from .models.electra import ElectraTokenizerFast - from .models.fnet import FNetTokenizerFast - from .models.funnel import FunnelTokenizerFast - from .models.gemma import GemmaTokenizerFast - from .models.gpt2 import GPT2TokenizerFast - from .models.gpt_neox import GPTNeoXTokenizerFast - from .models.gpt_neox_japanese import GPTNeoXJapaneseTokenizer - from .models.herbert import HerbertTokenizerFast - from .models.layoutlm import LayoutLMTokenizerFast - from .models.layoutlmv2 import LayoutLMv2TokenizerFast - from .models.layoutlmv3 import LayoutLMv3TokenizerFast - from .models.layoutxlm import LayoutXLMTokenizerFast - from .models.led import LEDTokenizerFast - from .models.llama import LlamaTokenizerFast - from .models.longformer import LongformerTokenizerFast - from .models.lxmert import LxmertTokenizerFast - from .models.markuplm import MarkupLMTokenizerFast - from .models.mbart import MBartTokenizerFast - from .models.mbart50 import MBart50TokenizerFast - from .models.mobilebert import MobileBertTokenizerFast - from .models.mpnet import MPNetTokenizerFast - from .models.mt5 import MT5TokenizerFast - from .models.mvp import MvpTokenizerFast - from .models.nllb import NllbTokenizerFast - from .models.nougat import NougatTokenizerFast - from .models.openai import OpenAIGPTTokenizerFast - from .models.pegasus import PegasusTokenizerFast - from .models.qwen2 import Qwen2TokenizerFast - from .models.reformer import ReformerTokenizerFast - from .models.rembert import RemBertTokenizerFast - from .models.roberta import RobertaTokenizerFast - from .models.roformer import RoFormerTokenizerFast - from .models.seamless_m4t import SeamlessM4TTokenizerFast - from .models.splinter import SplinterTokenizerFast - from .models.squeezebert import SqueezeBertTokenizerFast - from .models.t5 import T5TokenizerFast - from .models.udop import UdopTokenizerFast - from .models.whisper import WhisperTokenizerFast - from .models.xglm import XGLMTokenizerFast - from .models.xlm_roberta import XLMRobertaTokenizerFast - from .models.xlnet import XLNetTokenizerFast from .tokenization_utils_fast import PreTrainedTokenizerFast try: @@ -6532,22 +827,6 @@ if TYPE_CHECKING: convert_slow_tokenizer, ) - try: - if not is_tensorflow_text_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - from .utils.dummy_tensorflow_text_objects import * - else: - from .models.bert import TFBertTokenizer - - try: - if not is_keras_nlp_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - from .utils.dummy_keras_nlp_objects import * - else: - from .models.gpt2 import TFGPT2Tokenizer - try: if not is_vision_available(): raise OptionalDependencyNotAvailable() @@ -6557,112 +836,6 @@ if TYPE_CHECKING: from .image_processing_base import ImageProcessingMixin from .image_processing_utils import BaseImageProcessor from .image_utils import ImageFeatureExtractionMixin - from .models.aria import AriaImageProcessor - from .models.beit import BeitFeatureExtractor, BeitImageProcessor - from .models.bit import BitImageProcessor - from .models.blip import BlipImageProcessor - from .models.bridgetower import BridgeTowerImageProcessor - from .models.chameleon import ChameleonImageProcessor - from .models.chinese_clip import ( - ChineseCLIPFeatureExtractor, - ChineseCLIPImageProcessor, - ) - from .models.clip import CLIPFeatureExtractor, CLIPImageProcessor - from .models.conditional_detr import ( - ConditionalDetrFeatureExtractor, - ConditionalDetrImageProcessor, - ) - from .models.convnext import ConvNextFeatureExtractor, ConvNextImageProcessor - from .models.deformable_detr import DeformableDetrFeatureExtractor, DeformableDetrImageProcessor - from .models.deit import DeiTFeatureExtractor, DeiTImageProcessor - from .models.deprecated.deta import DetaImageProcessor - from .models.deprecated.efficientformer import EfficientFormerImageProcessor - from .models.deprecated.tvlt import TvltImageProcessor - from .models.deprecated.vit_hybrid import ViTHybridImageProcessor - from .models.depth_pro import DepthProImageProcessor, DepthProImageProcessorFast - from .models.detr import DetrFeatureExtractor, DetrImageProcessor - from .models.donut import DonutFeatureExtractor, DonutImageProcessor - from .models.dpt import DPTFeatureExtractor, DPTImageProcessor - from .models.efficientnet import EfficientNetImageProcessor - from .models.emu3 import Emu3ImageProcessor - from .models.flava import ( - FlavaFeatureExtractor, - FlavaImageProcessor, - FlavaProcessor, - ) - from .models.fuyu import FuyuImageProcessor, FuyuProcessor - from .models.gemma3 import Gemma3ImageProcessor - from .models.glpn import GLPNFeatureExtractor, GLPNImageProcessor - from .models.got_ocr2 import GotOcr2ImageProcessor - from .models.grounding_dino import GroundingDinoImageProcessor - from .models.idefics import IdeficsImageProcessor - from .models.idefics2 import Idefics2ImageProcessor - from .models.idefics3 import Idefics3ImageProcessor - from .models.imagegpt import ImageGPTFeatureExtractor, ImageGPTImageProcessor - from .models.instructblipvideo import InstructBlipVideoImageProcessor - from .models.layoutlmv2 import ( - LayoutLMv2FeatureExtractor, - LayoutLMv2ImageProcessor, - ) - from .models.layoutlmv3 import ( - LayoutLMv3FeatureExtractor, - LayoutLMv3ImageProcessor, - ) - from .models.levit import LevitFeatureExtractor, LevitImageProcessor - from .models.llava import LlavaImageProcessor - from .models.llava_next import LlavaNextImageProcessor - from .models.llava_next_video import LlavaNextVideoImageProcessor - from .models.llava_onevision import LlavaOnevisionImageProcessor, LlavaOnevisionVideoProcessor - from .models.mask2former import Mask2FormerImageProcessor - from .models.maskformer import ( - MaskFormerFeatureExtractor, - MaskFormerImageProcessor, - ) - from .models.mllama import MllamaImageProcessor - from .models.mobilenet_v1 import ( - MobileNetV1FeatureExtractor, - MobileNetV1ImageProcessor, - ) - from .models.mobilenet_v2 import ( - MobileNetV2FeatureExtractor, - MobileNetV2ImageProcessor, - ) - from .models.mobilevit import MobileViTFeatureExtractor, MobileViTImageProcessor - from .models.nougat import NougatImageProcessor - from .models.oneformer import OneFormerImageProcessor - from .models.owlv2 import Owlv2ImageProcessor - from .models.owlvit import OwlViTFeatureExtractor, OwlViTImageProcessor - from .models.perceiver import PerceiverFeatureExtractor, PerceiverImageProcessor - from .models.pix2struct import Pix2StructImageProcessor - from .models.pixtral import PixtralImageProcessor - from .models.poolformer import ( - PoolFormerFeatureExtractor, - PoolFormerImageProcessor, - ) - from .models.prompt_depth_anything import PromptDepthAnythingImageProcessor - from .models.pvt import PvtImageProcessor - from .models.qwen2_vl import Qwen2VLImageProcessor - from .models.rt_detr import RTDetrImageProcessor - from .models.sam import SamImageProcessor - from .models.segformer import SegformerFeatureExtractor, SegformerImageProcessor - from .models.seggpt import SegGptImageProcessor - from .models.siglip import SiglipImageProcessor - from .models.siglip2 import Siglip2ImageProcessor - from .models.smolvlm import SmolVLMImageProcessor - from .models.superglue import SuperGlueImageProcessor - from .models.superpoint import SuperPointImageProcessor - from .models.swin2sr import Swin2SRImageProcessor - from .models.textnet import TextNetImageProcessor - from .models.tvp import TvpImageProcessor - from .models.video_llava import VideoLlavaImageProcessor - from .models.videomae import VideoMAEFeatureExtractor, VideoMAEImageProcessor - from .models.vilt import ViltFeatureExtractor, ViltImageProcessor, ViltProcessor - from .models.vit import ViTFeatureExtractor, ViTImageProcessor - from .models.vitmatte import VitMatteImageProcessor - from .models.vitpose import VitPoseImageProcessor - from .models.vivit import VivitImageProcessor - from .models.yolos import YolosFeatureExtractor, YolosImageProcessor - from .models.zoedepth import ZoeDepthImageProcessor try: if not is_torchvision_available(): @@ -6671,26 +844,6 @@ if TYPE_CHECKING: from .utils.dummy_torchvision_objects import * else: from .image_processing_utils_fast import BaseImageProcessorFast - from .models.blip import BlipImageProcessorFast - from .models.clip import CLIPImageProcessorFast - from .models.convnext import ConvNextImageProcessorFast - from .models.deformable_detr import DeformableDetrImageProcessorFast - from .models.deit import DeiTImageProcessorFast - from .models.depth_pro import DepthProImageProcessorFast - from .models.detr import DetrImageProcessorFast - from .models.gemma3 import Gemma3ImageProcessorFast - from .models.got_ocr2 import GotOcr2ImageProcessorFast - from .models.llama4 import Llama4ImageProcessorFast - from .models.llava import LlavaImageProcessorFast - from .models.llava_next import LlavaNextImageProcessorFast - from .models.llava_onevision import LlavaOnevisionImageProcessorFast - from .models.phi4_multimodal import Phi4MultimodalImageProcessorFast - from .models.pixtral import PixtralImageProcessorFast - from .models.qwen2_vl import Qwen2VLImageProcessorFast - from .models.rt_detr import RTDetrImageProcessorFast - from .models.siglip import SiglipImageProcessorFast - from .models.siglip2 import Siglip2ImageProcessorFast - from .models.vit import ViTImageProcessorFast try: if not (is_torchvision_available() and is_timm_available()): @@ -6799,2142 +952,6 @@ if TYPE_CHECKING: ) from .modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from .modeling_utils import AttentionInterface, PreTrainedModel - from .models.albert import ( - AlbertForMaskedLM, - AlbertForMultipleChoice, - AlbertForPreTraining, - AlbertForQuestionAnswering, - AlbertForSequenceClassification, - AlbertForTokenClassification, - AlbertModel, - AlbertPreTrainedModel, - load_tf_weights_in_albert, - ) - from .models.align import ( - AlignModel, - AlignPreTrainedModel, - AlignTextModel, - AlignVisionModel, - ) - from .models.altclip import ( - AltCLIPModel, - AltCLIPPreTrainedModel, - AltCLIPTextModel, - AltCLIPVisionModel, - ) - from .models.aria import ( - AriaForConditionalGeneration, - AriaPreTrainedModel, - AriaTextForCausalLM, - AriaTextModel, - AriaTextPreTrainedModel, - ) - from .models.audio_spectrogram_transformer import ( - ASTForAudioClassification, - ASTModel, - ASTPreTrainedModel, - ) - from .models.auto import ( - MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, - MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING, - MODEL_FOR_AUDIO_XVECTOR_MAPPING, - MODEL_FOR_BACKBONE_MAPPING, - MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, - MODEL_FOR_CAUSAL_LM_MAPPING, - MODEL_FOR_CTC_MAPPING, - MODEL_FOR_DEPTH_ESTIMATION_MAPPING, - MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, - MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, - MODEL_FOR_IMAGE_MAPPING, - MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, - MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING, - MODEL_FOR_IMAGE_TO_IMAGE_MAPPING, - MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, - MODEL_FOR_KEYPOINT_DETECTION_MAPPING, - MODEL_FOR_MASK_GENERATION_MAPPING, - MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, - MODEL_FOR_MASKED_LM_MAPPING, - MODEL_FOR_MULTIPLE_CHOICE_MAPPING, - MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, - MODEL_FOR_OBJECT_DETECTION_MAPPING, - MODEL_FOR_PRETRAINING_MAPPING, - MODEL_FOR_QUESTION_ANSWERING_MAPPING, - MODEL_FOR_RETRIEVAL_MAPPING, - MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, - MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, - MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, - MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, - MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, - MODEL_FOR_TEXT_ENCODING_MAPPING, - MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING, - MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING, - MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING, - MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING, - MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, - MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING, - MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, - MODEL_FOR_VISION_2_SEQ_MAPPING, - MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, - MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING, - MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, - MODEL_MAPPING, - MODEL_WITH_LM_HEAD_MAPPING, - AutoBackbone, - AutoModel, - AutoModelForAudioClassification, - AutoModelForAudioFrameClassification, - AutoModelForAudioXVector, - AutoModelForCausalLM, - AutoModelForCTC, - AutoModelForDepthEstimation, - AutoModelForDocumentQuestionAnswering, - AutoModelForImageClassification, - AutoModelForImageSegmentation, - AutoModelForImageTextToText, - AutoModelForImageToImage, - AutoModelForInstanceSegmentation, - AutoModelForKeypointDetection, - AutoModelForMaskedImageModeling, - AutoModelForMaskedLM, - AutoModelForMaskGeneration, - AutoModelForMultipleChoice, - AutoModelForNextSentencePrediction, - AutoModelForObjectDetection, - AutoModelForPreTraining, - AutoModelForQuestionAnswering, - AutoModelForSemanticSegmentation, - AutoModelForSeq2SeqLM, - AutoModelForSequenceClassification, - AutoModelForSpeechSeq2Seq, - AutoModelForTableQuestionAnswering, - AutoModelForTextEncoding, - AutoModelForTextToSpectrogram, - AutoModelForTextToWaveform, - AutoModelForTokenClassification, - AutoModelForUniversalSegmentation, - AutoModelForVideoClassification, - AutoModelForVision2Seq, - AutoModelForVisualQuestionAnswering, - AutoModelForZeroShotImageClassification, - AutoModelForZeroShotObjectDetection, - AutoModelWithLMHead, - ) - from .models.autoformer import ( - AutoformerForPrediction, - AutoformerModel, - AutoformerPreTrainedModel, - ) - from .models.aya_vision import AyaVisionForConditionalGeneration, AyaVisionPreTrainedModel - from .models.bamba import BambaForCausalLM, BambaModel, BambaPreTrainedModel - from .models.bark import ( - BarkCausalModel, - BarkCoarseModel, - BarkFineModel, - BarkModel, - BarkPreTrainedModel, - BarkSemanticModel, - ) - from .models.bart import ( - BartForCausalLM, - BartForConditionalGeneration, - BartForQuestionAnswering, - BartForSequenceClassification, - BartModel, - BartPreTrainedModel, - BartPretrainedModel, - PretrainedBartModel, - ) - from .models.beit import ( - BeitBackbone, - BeitForImageClassification, - BeitForMaskedImageModeling, - BeitForSemanticSegmentation, - BeitModel, - BeitPreTrainedModel, - ) - from .models.bert import ( - BertForMaskedLM, - BertForMultipleChoice, - BertForNextSentencePrediction, - BertForPreTraining, - BertForQuestionAnswering, - BertForSequenceClassification, - BertForTokenClassification, - BertLMHeadModel, - BertModel, - BertPreTrainedModel, - load_tf_weights_in_bert, - ) - from .models.bert_generation import ( - BertGenerationDecoder, - BertGenerationEncoder, - BertGenerationPreTrainedModel, - load_tf_weights_in_bert_generation, - ) - from .models.big_bird import ( - BigBirdForCausalLM, - BigBirdForMaskedLM, - BigBirdForMultipleChoice, - BigBirdForPreTraining, - BigBirdForQuestionAnswering, - BigBirdForSequenceClassification, - BigBirdForTokenClassification, - BigBirdModel, - BigBirdPreTrainedModel, - load_tf_weights_in_big_bird, - ) - from .models.bigbird_pegasus import ( - BigBirdPegasusForCausalLM, - BigBirdPegasusForConditionalGeneration, - BigBirdPegasusForQuestionAnswering, - BigBirdPegasusForSequenceClassification, - BigBirdPegasusModel, - BigBirdPegasusPreTrainedModel, - ) - from .models.biogpt import ( - BioGptForCausalLM, - BioGptForSequenceClassification, - BioGptForTokenClassification, - BioGptModel, - BioGptPreTrainedModel, - ) - from .models.bit import ( - BitBackbone, - BitForImageClassification, - BitModel, - BitPreTrainedModel, - ) - from .models.blenderbot import ( - BlenderbotForCausalLM, - BlenderbotForConditionalGeneration, - BlenderbotModel, - BlenderbotPreTrainedModel, - ) - from .models.blenderbot_small import ( - BlenderbotSmallForCausalLM, - BlenderbotSmallForConditionalGeneration, - BlenderbotSmallModel, - BlenderbotSmallPreTrainedModel, - ) - from .models.blip import ( - BlipForConditionalGeneration, - BlipForImageTextRetrieval, - BlipForQuestionAnswering, - BlipModel, - BlipPreTrainedModel, - BlipTextModel, - BlipVisionModel, - ) - from .models.blip_2 import ( - Blip2ForConditionalGeneration, - Blip2ForImageTextRetrieval, - Blip2Model, - Blip2PreTrainedModel, - Blip2QFormerModel, - Blip2TextModelWithProjection, - Blip2VisionModel, - Blip2VisionModelWithProjection, - ) - from .models.bloom import ( - BloomForCausalLM, - BloomForQuestionAnswering, - BloomForSequenceClassification, - BloomForTokenClassification, - BloomModel, - BloomPreTrainedModel, - ) - from .models.bridgetower import ( - BridgeTowerForContrastiveLearning, - BridgeTowerForImageAndTextRetrieval, - BridgeTowerForMaskedLM, - BridgeTowerModel, - BridgeTowerPreTrainedModel, - ) - from .models.bros import ( - BrosForTokenClassification, - BrosModel, - BrosPreTrainedModel, - BrosProcessor, - BrosSpadeEEForTokenClassification, - BrosSpadeELForTokenClassification, - ) - from .models.camembert import ( - CamembertForCausalLM, - CamembertForMaskedLM, - CamembertForMultipleChoice, - CamembertForQuestionAnswering, - CamembertForSequenceClassification, - CamembertForTokenClassification, - CamembertModel, - CamembertPreTrainedModel, - ) - from .models.canine import ( - CanineForMultipleChoice, - CanineForQuestionAnswering, - CanineForSequenceClassification, - CanineForTokenClassification, - CanineModel, - CaninePreTrainedModel, - load_tf_weights_in_canine, - ) - from .models.chameleon import ( - ChameleonForConditionalGeneration, - ChameleonModel, - ChameleonPreTrainedModel, - ChameleonProcessor, - ChameleonVQVAE, - ) - from .models.chinese_clip import ( - ChineseCLIPModel, - ChineseCLIPPreTrainedModel, - ChineseCLIPTextModel, - ChineseCLIPVisionModel, - ) - from .models.clap import ( - ClapAudioModel, - ClapAudioModelWithProjection, - ClapFeatureExtractor, - ClapModel, - ClapPreTrainedModel, - ClapTextModel, - ClapTextModelWithProjection, - ) - from .models.clip import ( - CLIPForImageClassification, - CLIPModel, - CLIPPreTrainedModel, - CLIPTextModel, - CLIPTextModelWithProjection, - CLIPVisionModel, - CLIPVisionModelWithProjection, - ) - from .models.clipseg import ( - CLIPSegForImageSegmentation, - CLIPSegModel, - CLIPSegPreTrainedModel, - CLIPSegTextModel, - CLIPSegVisionModel, - ) - from .models.clvp import ( - ClvpDecoder, - ClvpEncoder, - ClvpForCausalLM, - ClvpModel, - ClvpModelForConditionalGeneration, - ClvpPreTrainedModel, - ) - from .models.codegen import ( - CodeGenForCausalLM, - CodeGenModel, - CodeGenPreTrainedModel, - ) - from .models.cohere import ( - CohereForCausalLM, - CohereModel, - CoherePreTrainedModel, - ) - from .models.cohere2 import ( - Cohere2ForCausalLM, - Cohere2Model, - Cohere2PreTrainedModel, - ) - from .models.colpali import ( - ColPaliForRetrieval, - ColPaliPreTrainedModel, - ) - from .models.conditional_detr import ( - ConditionalDetrForObjectDetection, - ConditionalDetrForSegmentation, - ConditionalDetrModel, - ConditionalDetrPreTrainedModel, - ) - from .models.convbert import ( - ConvBertForMaskedLM, - ConvBertForMultipleChoice, - ConvBertForQuestionAnswering, - ConvBertForSequenceClassification, - ConvBertForTokenClassification, - ConvBertModel, - ConvBertPreTrainedModel, - load_tf_weights_in_convbert, - ) - from .models.convnext import ( - ConvNextBackbone, - ConvNextForImageClassification, - ConvNextModel, - ConvNextPreTrainedModel, - ) - from .models.convnextv2 import ( - ConvNextV2Backbone, - ConvNextV2ForImageClassification, - ConvNextV2Model, - ConvNextV2PreTrainedModel, - ) - from .models.cpmant import ( - CpmAntForCausalLM, - CpmAntModel, - CpmAntPreTrainedModel, - ) - from .models.ctrl import ( - CTRLForSequenceClassification, - CTRLLMHeadModel, - CTRLModel, - CTRLPreTrainedModel, - ) - from .models.cvt import ( - CvtForImageClassification, - CvtModel, - CvtPreTrainedModel, - ) - from .models.dab_detr import ( - DabDetrForObjectDetection, - DabDetrModel, - DabDetrPreTrainedModel, - ) - from .models.dac import ( - DacModel, - DacPreTrainedModel, - ) - from .models.data2vec import ( - Data2VecAudioForAudioFrameClassification, - Data2VecAudioForCTC, - Data2VecAudioForSequenceClassification, - Data2VecAudioForXVector, - Data2VecAudioModel, - Data2VecAudioPreTrainedModel, - Data2VecTextForCausalLM, - Data2VecTextForMaskedLM, - Data2VecTextForMultipleChoice, - Data2VecTextForQuestionAnswering, - Data2VecTextForSequenceClassification, - Data2VecTextForTokenClassification, - Data2VecTextModel, - Data2VecTextPreTrainedModel, - Data2VecVisionForImageClassification, - Data2VecVisionForSemanticSegmentation, - Data2VecVisionModel, - Data2VecVisionPreTrainedModel, - ) - - # PyTorch model imports - from .models.dbrx import ( - DbrxForCausalLM, - DbrxModel, - DbrxPreTrainedModel, - ) - from .models.deberta import ( - DebertaForMaskedLM, - DebertaForQuestionAnswering, - DebertaForSequenceClassification, - DebertaForTokenClassification, - DebertaModel, - DebertaPreTrainedModel, - ) - from .models.deberta_v2 import ( - DebertaV2ForMaskedLM, - DebertaV2ForMultipleChoice, - DebertaV2ForQuestionAnswering, - DebertaV2ForSequenceClassification, - DebertaV2ForTokenClassification, - DebertaV2Model, - DebertaV2PreTrainedModel, - ) - from .models.decision_transformer import ( - DecisionTransformerGPT2Model, - DecisionTransformerGPT2PreTrainedModel, - DecisionTransformerModel, - DecisionTransformerPreTrainedModel, - ) - from .models.deepseek_v3 import ( - DeepseekV3ForCausalLM, - DeepseekV3Model, - DeepseekV3PreTrainedModel, - ) - from .models.deformable_detr import ( - DeformableDetrForObjectDetection, - DeformableDetrModel, - DeformableDetrPreTrainedModel, - ) - from .models.deit import ( - DeiTForImageClassification, - DeiTForImageClassificationWithTeacher, - DeiTForMaskedImageModeling, - DeiTModel, - DeiTPreTrainedModel, - ) - from .models.deprecated.deta import ( - DetaForObjectDetection, - DetaModel, - DetaPreTrainedModel, - ) - from .models.deprecated.efficientformer import ( - EfficientFormerForImageClassification, - EfficientFormerForImageClassificationWithTeacher, - EfficientFormerModel, - EfficientFormerPreTrainedModel, - ) - from .models.deprecated.ernie_m import ( - ErnieMForInformationExtraction, - ErnieMForMultipleChoice, - ErnieMForQuestionAnswering, - ErnieMForSequenceClassification, - ErnieMForTokenClassification, - ErnieMModel, - ErnieMPreTrainedModel, - ) - from .models.deprecated.gptsan_japanese import ( - GPTSanJapaneseForConditionalGeneration, - GPTSanJapaneseModel, - GPTSanJapanesePreTrainedModel, - ) - from .models.deprecated.graphormer import ( - GraphormerForGraphClassification, - GraphormerModel, - GraphormerPreTrainedModel, - ) - from .models.deprecated.jukebox import ( - JukeboxModel, - JukeboxPreTrainedModel, - JukeboxPrior, - JukeboxVQVAE, - ) - from .models.deprecated.mctct import ( - MCTCTForCTC, - MCTCTModel, - MCTCTPreTrainedModel, - ) - from .models.deprecated.mega import ( - MegaForCausalLM, - MegaForMaskedLM, - MegaForMultipleChoice, - MegaForQuestionAnswering, - MegaForSequenceClassification, - MegaForTokenClassification, - MegaModel, - MegaPreTrainedModel, - ) - from .models.deprecated.mmbt import ( - MMBTForClassification, - MMBTModel, - ModalEmbeddings, - ) - from .models.deprecated.nat import ( - NatBackbone, - NatForImageClassification, - NatModel, - NatPreTrainedModel, - ) - from .models.deprecated.nezha import ( - NezhaForMaskedLM, - NezhaForMultipleChoice, - NezhaForNextSentencePrediction, - NezhaForPreTraining, - NezhaForQuestionAnswering, - NezhaForSequenceClassification, - NezhaForTokenClassification, - NezhaModel, - NezhaPreTrainedModel, - ) - from .models.deprecated.open_llama import ( - OpenLlamaForCausalLM, - OpenLlamaForSequenceClassification, - OpenLlamaModel, - OpenLlamaPreTrainedModel, - ) - from .models.deprecated.qdqbert import ( - QDQBertForMaskedLM, - QDQBertForMultipleChoice, - QDQBertForNextSentencePrediction, - QDQBertForQuestionAnswering, - QDQBertForSequenceClassification, - QDQBertForTokenClassification, - QDQBertLMHeadModel, - QDQBertModel, - QDQBertPreTrainedModel, - load_tf_weights_in_qdqbert, - ) - from .models.deprecated.realm import ( - RealmEmbedder, - RealmForOpenQA, - RealmKnowledgeAugEncoder, - RealmPreTrainedModel, - RealmReader, - RealmRetriever, - RealmScorer, - load_tf_weights_in_realm, - ) - from .models.deprecated.retribert import ( - RetriBertModel, - RetriBertPreTrainedModel, - ) - from .models.deprecated.speech_to_text_2 import ( - Speech2Text2ForCausalLM, - Speech2Text2PreTrainedModel, - ) - from .models.deprecated.trajectory_transformer import ( - TrajectoryTransformerModel, - TrajectoryTransformerPreTrainedModel, - ) - from .models.deprecated.transfo_xl import ( - AdaptiveEmbedding, - TransfoXLForSequenceClassification, - TransfoXLLMHeadModel, - TransfoXLModel, - TransfoXLPreTrainedModel, - load_tf_weights_in_transfo_xl, - ) - from .models.deprecated.tvlt import ( - TvltForAudioVisualClassification, - TvltForPreTraining, - TvltModel, - TvltPreTrainedModel, - ) - from .models.deprecated.van import ( - VanForImageClassification, - VanModel, - VanPreTrainedModel, - ) - from .models.deprecated.vit_hybrid import ( - ViTHybridForImageClassification, - ViTHybridModel, - ViTHybridPreTrainedModel, - ) - from .models.deprecated.xlm_prophetnet import ( - XLMProphetNetDecoder, - XLMProphetNetEncoder, - XLMProphetNetForCausalLM, - XLMProphetNetForConditionalGeneration, - XLMProphetNetModel, - XLMProphetNetPreTrainedModel, - ) - from .models.depth_anything import ( - DepthAnythingForDepthEstimation, - DepthAnythingPreTrainedModel, - ) - from .models.depth_pro import ( - DepthProForDepthEstimation, - DepthProModel, - DepthProPreTrainedModel, - ) - from .models.detr import ( - DetrForObjectDetection, - DetrForSegmentation, - DetrModel, - DetrPreTrainedModel, - ) - from .models.diffllama import ( - DiffLlamaForCausalLM, - DiffLlamaForQuestionAnswering, - DiffLlamaForSequenceClassification, - DiffLlamaForTokenClassification, - DiffLlamaModel, - DiffLlamaPreTrainedModel, - ) - from .models.dinat import ( - DinatBackbone, - DinatForImageClassification, - DinatModel, - DinatPreTrainedModel, - ) - from .models.dinov2 import ( - Dinov2Backbone, - Dinov2ForImageClassification, - Dinov2Model, - Dinov2PreTrainedModel, - ) - from .models.dinov2_with_registers import ( - Dinov2WithRegistersBackbone, - Dinov2WithRegistersForImageClassification, - Dinov2WithRegistersModel, - Dinov2WithRegistersPreTrainedModel, - ) - from .models.distilbert import ( - DistilBertForMaskedLM, - DistilBertForMultipleChoice, - DistilBertForQuestionAnswering, - DistilBertForSequenceClassification, - DistilBertForTokenClassification, - DistilBertModel, - DistilBertPreTrainedModel, - ) - from .models.donut import ( - DonutSwinForImageClassification, - DonutSwinModel, - DonutSwinPreTrainedModel, - ) - from .models.dpr import ( - DPRContextEncoder, - DPRPretrainedContextEncoder, - DPRPreTrainedModel, - DPRPretrainedQuestionEncoder, - DPRPretrainedReader, - DPRQuestionEncoder, - DPRReader, - ) - from .models.dpt import ( - DPTForDepthEstimation, - DPTForSemanticSegmentation, - DPTModel, - DPTPreTrainedModel, - ) - from .models.efficientnet import ( - EfficientNetForImageClassification, - EfficientNetModel, - EfficientNetPreTrainedModel, - ) - from .models.electra import ( - ElectraForCausalLM, - ElectraForMaskedLM, - ElectraForMultipleChoice, - ElectraForPreTraining, - ElectraForQuestionAnswering, - ElectraForSequenceClassification, - ElectraForTokenClassification, - ElectraModel, - ElectraPreTrainedModel, - load_tf_weights_in_electra, - ) - from .models.emu3 import ( - Emu3ForCausalLM, - Emu3ForConditionalGeneration, - Emu3PreTrainedModel, - Emu3TextModel, - Emu3VQVAE, - ) - from .models.encodec import ( - EncodecModel, - EncodecPreTrainedModel, - ) - from .models.encoder_decoder import EncoderDecoderModel - from .models.ernie import ( - ErnieForCausalLM, - ErnieForMaskedLM, - ErnieForMultipleChoice, - ErnieForNextSentencePrediction, - ErnieForPreTraining, - ErnieForQuestionAnswering, - ErnieForSequenceClassification, - ErnieForTokenClassification, - ErnieModel, - ErniePreTrainedModel, - ) - from .models.esm import ( - EsmFoldPreTrainedModel, - EsmForMaskedLM, - EsmForProteinFolding, - EsmForSequenceClassification, - EsmForTokenClassification, - EsmModel, - EsmPreTrainedModel, - ) - from .models.falcon import ( - FalconForCausalLM, - FalconForQuestionAnswering, - FalconForSequenceClassification, - FalconForTokenClassification, - FalconModel, - FalconPreTrainedModel, - ) - from .models.falcon_mamba import ( - FalconMambaForCausalLM, - FalconMambaModel, - FalconMambaPreTrainedModel, - ) - from .models.fastspeech2_conformer import ( - FastSpeech2ConformerHifiGan, - FastSpeech2ConformerModel, - FastSpeech2ConformerPreTrainedModel, - FastSpeech2ConformerWithHifiGan, - ) - from .models.flaubert import ( - FlaubertForMultipleChoice, - FlaubertForQuestionAnswering, - FlaubertForQuestionAnsweringSimple, - FlaubertForSequenceClassification, - FlaubertForTokenClassification, - FlaubertModel, - FlaubertPreTrainedModel, - FlaubertWithLMHeadModel, - ) - from .models.flava import ( - FlavaForPreTraining, - FlavaImageCodebook, - FlavaImageModel, - FlavaModel, - FlavaMultimodalModel, - FlavaPreTrainedModel, - FlavaTextModel, - ) - from .models.fnet import ( - FNetForMaskedLM, - FNetForMultipleChoice, - FNetForNextSentencePrediction, - FNetForPreTraining, - FNetForQuestionAnswering, - FNetForSequenceClassification, - FNetForTokenClassification, - FNetModel, - FNetPreTrainedModel, - ) - from .models.focalnet import ( - FocalNetBackbone, - FocalNetForImageClassification, - FocalNetForMaskedImageModeling, - FocalNetModel, - FocalNetPreTrainedModel, - ) - from .models.fsmt import ( - FSMTForConditionalGeneration, - FSMTModel, - PretrainedFSMTModel, - ) - from .models.funnel import ( - FunnelBaseModel, - FunnelForMaskedLM, - FunnelForMultipleChoice, - FunnelForPreTraining, - FunnelForQuestionAnswering, - FunnelForSequenceClassification, - FunnelForTokenClassification, - FunnelModel, - FunnelPreTrainedModel, - load_tf_weights_in_funnel, - ) - from .models.fuyu import ( - FuyuForCausalLM, - FuyuPreTrainedModel, - ) - from .models.gemma import ( - GemmaForCausalLM, - GemmaForSequenceClassification, - GemmaForTokenClassification, - GemmaModel, - GemmaPreTrainedModel, - ) - from .models.gemma2 import ( - Gemma2ForCausalLM, - Gemma2ForSequenceClassification, - Gemma2ForTokenClassification, - Gemma2Model, - Gemma2PreTrainedModel, - ) - from .models.gemma3 import ( - Gemma3ForCausalLM, - Gemma3ForConditionalGeneration, - Gemma3PreTrainedModel, - Gemma3TextModel, - ) - from .models.git import ( - GitForCausalLM, - GitModel, - GitPreTrainedModel, - GitVisionModel, - ) - from .models.glm import ( - GlmForCausalLM, - GlmForSequenceClassification, - GlmForTokenClassification, - GlmModel, - GlmPreTrainedModel, - ) - from .models.glm4 import ( - Glm4ForCausalLM, - Glm4ForSequenceClassification, - Glm4ForTokenClassification, - Glm4Model, - Glm4PreTrainedModel, - ) - from .models.glpn import ( - GLPNForDepthEstimation, - GLPNModel, - GLPNPreTrainedModel, - ) - from .models.got_ocr2 import ( - GotOcr2ForConditionalGeneration, - GotOcr2PreTrainedModel, - ) - from .models.gpt2 import ( - GPT2DoubleHeadsModel, - GPT2ForQuestionAnswering, - GPT2ForSequenceClassification, - GPT2ForTokenClassification, - GPT2LMHeadModel, - GPT2Model, - GPT2PreTrainedModel, - load_tf_weights_in_gpt2, - ) - from .models.gpt_bigcode import ( - GPTBigCodeForCausalLM, - GPTBigCodeForSequenceClassification, - GPTBigCodeForTokenClassification, - GPTBigCodeModel, - GPTBigCodePreTrainedModel, - ) - from .models.gpt_neo import ( - GPTNeoForCausalLM, - GPTNeoForQuestionAnswering, - GPTNeoForSequenceClassification, - GPTNeoForTokenClassification, - GPTNeoModel, - GPTNeoPreTrainedModel, - load_tf_weights_in_gpt_neo, - ) - from .models.gpt_neox import ( - GPTNeoXForCausalLM, - GPTNeoXForQuestionAnswering, - GPTNeoXForSequenceClassification, - GPTNeoXForTokenClassification, - GPTNeoXModel, - GPTNeoXPreTrainedModel, - ) - from .models.gpt_neox_japanese import ( - GPTNeoXJapaneseForCausalLM, - GPTNeoXJapaneseModel, - GPTNeoXJapanesePreTrainedModel, - ) - from .models.gptj import ( - GPTJForCausalLM, - GPTJForQuestionAnswering, - GPTJForSequenceClassification, - GPTJModel, - GPTJPreTrainedModel, - ) - from .models.granite import ( - GraniteForCausalLM, - GraniteModel, - GranitePreTrainedModel, - ) - from .models.granitemoe import ( - GraniteMoeForCausalLM, - GraniteMoeModel, - GraniteMoePreTrainedModel, - ) - from .models.granitemoeshared import ( - GraniteMoeSharedForCausalLM, - GraniteMoeSharedModel, - GraniteMoeSharedPreTrainedModel, - ) - from .models.grounding_dino import ( - GroundingDinoForObjectDetection, - GroundingDinoModel, - GroundingDinoPreTrainedModel, - ) - from .models.groupvit import ( - GroupViTModel, - GroupViTPreTrainedModel, - GroupViTTextModel, - GroupViTVisionModel, - ) - from .models.helium import ( - HeliumForCausalLM, - HeliumForSequenceClassification, - HeliumForTokenClassification, - HeliumModel, - HeliumPreTrainedModel, - ) - from .models.hiera import ( - HieraBackbone, - HieraForImageClassification, - HieraForPreTraining, - HieraModel, - HieraPreTrainedModel, - ) - from .models.hubert import ( - HubertForCTC, - HubertForSequenceClassification, - HubertModel, - HubertPreTrainedModel, - ) - from .models.ibert import ( - IBertForMaskedLM, - IBertForMultipleChoice, - IBertForQuestionAnswering, - IBertForSequenceClassification, - IBertForTokenClassification, - IBertModel, - IBertPreTrainedModel, - ) - from .models.idefics import ( - IdeficsForVisionText2Text, - IdeficsModel, - IdeficsPreTrainedModel, - IdeficsProcessor, - ) - from .models.idefics2 import ( - Idefics2ForConditionalGeneration, - Idefics2Model, - Idefics2PreTrainedModel, - Idefics2Processor, - ) - from .models.idefics3 import ( - Idefics3ForConditionalGeneration, - Idefics3Model, - Idefics3PreTrainedModel, - Idefics3Processor, - Idefics3VisionConfig, - Idefics3VisionTransformer, - ) - from .models.ijepa import ( - IJepaForImageClassification, - IJepaModel, - IJepaPreTrainedModel, - ) - from .models.imagegpt import ( - ImageGPTForCausalImageModeling, - ImageGPTForImageClassification, - ImageGPTModel, - ImageGPTPreTrainedModel, - load_tf_weights_in_imagegpt, - ) - from .models.informer import ( - InformerForPrediction, - InformerModel, - InformerPreTrainedModel, - ) - from .models.instructblip import ( - InstructBlipForConditionalGeneration, - InstructBlipPreTrainedModel, - InstructBlipQFormerModel, - InstructBlipVisionModel, - ) - from .models.instructblipvideo import ( - InstructBlipVideoForConditionalGeneration, - InstructBlipVideoPreTrainedModel, - InstructBlipVideoQFormerModel, - InstructBlipVideoVisionModel, - ) - from .models.jamba import ( - JambaForCausalLM, - JambaForSequenceClassification, - JambaModel, - JambaPreTrainedModel, - ) - from .models.jetmoe import ( - JetMoeForCausalLM, - JetMoeForSequenceClassification, - JetMoeModel, - JetMoePreTrainedModel, - ) - from .models.kosmos2 import ( - Kosmos2ForConditionalGeneration, - Kosmos2Model, - Kosmos2PreTrainedModel, - ) - from .models.layoutlm import ( - LayoutLMForMaskedLM, - LayoutLMForQuestionAnswering, - LayoutLMForSequenceClassification, - LayoutLMForTokenClassification, - LayoutLMModel, - LayoutLMPreTrainedModel, - ) - from .models.layoutlmv2 import ( - LayoutLMv2ForQuestionAnswering, - LayoutLMv2ForSequenceClassification, - LayoutLMv2ForTokenClassification, - LayoutLMv2Model, - LayoutLMv2PreTrainedModel, - ) - from .models.layoutlmv3 import ( - LayoutLMv3ForQuestionAnswering, - LayoutLMv3ForSequenceClassification, - LayoutLMv3ForTokenClassification, - LayoutLMv3Model, - LayoutLMv3PreTrainedModel, - ) - from .models.led import ( - LEDForConditionalGeneration, - LEDForQuestionAnswering, - LEDForSequenceClassification, - LEDModel, - LEDPreTrainedModel, - ) - from .models.levit import ( - LevitForImageClassification, - LevitForImageClassificationWithTeacher, - LevitModel, - LevitPreTrainedModel, - ) - from .models.lilt import ( - LiltForQuestionAnswering, - LiltForSequenceClassification, - LiltForTokenClassification, - LiltModel, - LiltPreTrainedModel, - ) - from .models.llama import ( - LlamaForCausalLM, - LlamaForQuestionAnswering, - LlamaForSequenceClassification, - LlamaForTokenClassification, - LlamaModel, - LlamaPreTrainedModel, - ) - from .models.llama4 import ( - Llama4ForCausalLM, - Llama4ForConditionalGeneration, - Llama4PreTrainedModel, - Llama4TextModel, - Llama4VisionModel, - ) - from .models.llava import ( - LlavaForConditionalGeneration, - LlavaPreTrainedModel, - ) - from .models.llava_next import ( - LlavaNextForConditionalGeneration, - LlavaNextPreTrainedModel, - ) - from .models.llava_next_video import ( - LlavaNextVideoForConditionalGeneration, - LlavaNextVideoPreTrainedModel, - ) - from .models.llava_onevision import ( - LlavaOnevisionForConditionalGeneration, - LlavaOnevisionPreTrainedModel, - ) - from .models.longformer import ( - LongformerForMaskedLM, - LongformerForMultipleChoice, - LongformerForQuestionAnswering, - LongformerForSequenceClassification, - LongformerForTokenClassification, - LongformerModel, - LongformerPreTrainedModel, - ) - from .models.longt5 import ( - LongT5EncoderModel, - LongT5ForConditionalGeneration, - LongT5Model, - LongT5PreTrainedModel, - ) - from .models.luke import ( - LukeForEntityClassification, - LukeForEntityPairClassification, - LukeForEntitySpanClassification, - LukeForMaskedLM, - LukeForMultipleChoice, - LukeForQuestionAnswering, - LukeForSequenceClassification, - LukeForTokenClassification, - LukeModel, - LukePreTrainedModel, - ) - from .models.lxmert import ( - LxmertEncoder, - LxmertForPreTraining, - LxmertForQuestionAnswering, - LxmertModel, - LxmertPreTrainedModel, - LxmertVisualFeatureEncoder, - ) - from .models.m2m_100 import ( - M2M100ForConditionalGeneration, - M2M100Model, - M2M100PreTrainedModel, - ) - from .models.mamba import ( - MambaForCausalLM, - MambaModel, - MambaPreTrainedModel, - ) - from .models.mamba2 import ( - Mamba2ForCausalLM, - Mamba2Model, - Mamba2PreTrainedModel, - ) - from .models.marian import MarianForCausalLM, MarianModel, MarianMTModel, MarianPreTrainedModel - from .models.markuplm import ( - MarkupLMForQuestionAnswering, - MarkupLMForSequenceClassification, - MarkupLMForTokenClassification, - MarkupLMModel, - MarkupLMPreTrainedModel, - ) - from .models.mask2former import ( - Mask2FormerForUniversalSegmentation, - Mask2FormerModel, - Mask2FormerPreTrainedModel, - ) - from .models.maskformer import ( - MaskFormerForInstanceSegmentation, - MaskFormerModel, - MaskFormerPreTrainedModel, - MaskFormerSwinBackbone, - ) - from .models.mbart import ( - MBartForCausalLM, - MBartForConditionalGeneration, - MBartForQuestionAnswering, - MBartForSequenceClassification, - MBartModel, - MBartPreTrainedModel, - ) - from .models.megatron_bert import ( - MegatronBertForCausalLM, - MegatronBertForMaskedLM, - MegatronBertForMultipleChoice, - MegatronBertForNextSentencePrediction, - MegatronBertForPreTraining, - MegatronBertForQuestionAnswering, - MegatronBertForSequenceClassification, - MegatronBertForTokenClassification, - MegatronBertModel, - MegatronBertPreTrainedModel, - ) - from .models.mgp_str import ( - MgpstrForSceneTextRecognition, - MgpstrModel, - MgpstrPreTrainedModel, - ) - from .models.mimi import ( - MimiModel, - MimiPreTrainedModel, - ) - from .models.mistral import ( - MistralForCausalLM, - MistralForQuestionAnswering, - MistralForSequenceClassification, - MistralForTokenClassification, - MistralModel, - MistralPreTrainedModel, - ) - from .models.mistral3 import ( - Mistral3ForConditionalGeneration, - Mistral3PreTrainedModel, - ) - from .models.mixtral import ( - MixtralForCausalLM, - MixtralForQuestionAnswering, - MixtralForSequenceClassification, - MixtralForTokenClassification, - MixtralModel, - MixtralPreTrainedModel, - ) - from .models.mllama import ( - MllamaForCausalLM, - MllamaForConditionalGeneration, - MllamaPreTrainedModel, - MllamaProcessor, - MllamaTextModel, - MllamaVisionModel, - ) - from .models.mobilebert import ( - MobileBertForMaskedLM, - MobileBertForMultipleChoice, - MobileBertForNextSentencePrediction, - MobileBertForPreTraining, - MobileBertForQuestionAnswering, - MobileBertForSequenceClassification, - MobileBertForTokenClassification, - MobileBertModel, - MobileBertPreTrainedModel, - load_tf_weights_in_mobilebert, - ) - from .models.mobilenet_v1 import ( - MobileNetV1ForImageClassification, - MobileNetV1Model, - MobileNetV1PreTrainedModel, - load_tf_weights_in_mobilenet_v1, - ) - from .models.mobilenet_v2 import ( - MobileNetV2ForImageClassification, - MobileNetV2ForSemanticSegmentation, - MobileNetV2Model, - MobileNetV2PreTrainedModel, - load_tf_weights_in_mobilenet_v2, - ) - from .models.mobilevit import ( - MobileViTForImageClassification, - MobileViTForSemanticSegmentation, - MobileViTModel, - MobileViTPreTrainedModel, - ) - from .models.mobilevitv2 import ( - MobileViTV2ForImageClassification, - MobileViTV2ForSemanticSegmentation, - MobileViTV2Model, - MobileViTV2PreTrainedModel, - ) - from .models.modernbert import ( - ModernBertForMaskedLM, - ModernBertForQuestionAnswering, - ModernBertForSequenceClassification, - ModernBertForTokenClassification, - ModernBertModel, - ModernBertPreTrainedModel, - ) - from .models.moonshine import ( - MoonshineForConditionalGeneration, - MoonshineModel, - MoonshinePreTrainedModel, - ) - from .models.moshi import ( - MoshiForCausalLM, - MoshiForConditionalGeneration, - MoshiModel, - MoshiPreTrainedModel, - ) - from .models.mpnet import ( - MPNetForMaskedLM, - MPNetForMultipleChoice, - MPNetForQuestionAnswering, - MPNetForSequenceClassification, - MPNetForTokenClassification, - MPNetModel, - MPNetPreTrainedModel, - ) - from .models.mpt import ( - MptForCausalLM, - MptForQuestionAnswering, - MptForSequenceClassification, - MptForTokenClassification, - MptModel, - MptPreTrainedModel, - ) - from .models.mra import ( - MraForMaskedLM, - MraForMultipleChoice, - MraForQuestionAnswering, - MraForSequenceClassification, - MraForTokenClassification, - MraModel, - MraPreTrainedModel, - ) - from .models.mt5 import ( - MT5EncoderModel, - MT5ForConditionalGeneration, - MT5ForQuestionAnswering, - MT5ForSequenceClassification, - MT5ForTokenClassification, - MT5Model, - MT5PreTrainedModel, - ) - from .models.musicgen import ( - MusicgenForCausalLM, - MusicgenForConditionalGeneration, - MusicgenModel, - MusicgenPreTrainedModel, - MusicgenProcessor, - ) - from .models.musicgen_melody import ( - MusicgenMelodyForCausalLM, - MusicgenMelodyForConditionalGeneration, - MusicgenMelodyModel, - MusicgenMelodyPreTrainedModel, - ) - from .models.mvp import ( - MvpForCausalLM, - MvpForConditionalGeneration, - MvpForQuestionAnswering, - MvpForSequenceClassification, - MvpModel, - MvpPreTrainedModel, - ) - from .models.nemotron import ( - NemotronForCausalLM, - NemotronForQuestionAnswering, - NemotronForSequenceClassification, - NemotronForTokenClassification, - NemotronModel, - NemotronPreTrainedModel, - ) - from .models.nllb_moe import ( - NllbMoeForConditionalGeneration, - NllbMoeModel, - NllbMoePreTrainedModel, - NllbMoeSparseMLP, - NllbMoeTop2Router, - ) - from .models.nystromformer import ( - NystromformerForMaskedLM, - NystromformerForMultipleChoice, - NystromformerForQuestionAnswering, - NystromformerForSequenceClassification, - NystromformerForTokenClassification, - NystromformerModel, - NystromformerPreTrainedModel, - ) - from .models.olmo import ( - OlmoForCausalLM, - OlmoModel, - OlmoPreTrainedModel, - ) - from .models.olmo2 import ( - Olmo2ForCausalLM, - Olmo2Model, - Olmo2PreTrainedModel, - ) - from .models.olmoe import ( - OlmoeForCausalLM, - OlmoeModel, - OlmoePreTrainedModel, - ) - from .models.omdet_turbo import ( - OmDetTurboForObjectDetection, - OmDetTurboPreTrainedModel, - ) - from .models.oneformer import ( - OneFormerForUniversalSegmentation, - OneFormerModel, - OneFormerPreTrainedModel, - ) - from .models.openai import ( - OpenAIGPTDoubleHeadsModel, - OpenAIGPTForSequenceClassification, - OpenAIGPTLMHeadModel, - OpenAIGPTModel, - OpenAIGPTPreTrainedModel, - load_tf_weights_in_openai_gpt, - ) - from .models.opt import ( - OPTForCausalLM, - OPTForQuestionAnswering, - OPTForSequenceClassification, - OPTModel, - OPTPreTrainedModel, - ) - from .models.owlv2 import ( - Owlv2ForObjectDetection, - Owlv2Model, - Owlv2PreTrainedModel, - Owlv2TextModel, - Owlv2VisionModel, - ) - from .models.owlvit import ( - OwlViTForObjectDetection, - OwlViTModel, - OwlViTPreTrainedModel, - OwlViTTextModel, - OwlViTVisionModel, - ) - from .models.paligemma import ( - PaliGemmaForConditionalGeneration, - PaliGemmaPreTrainedModel, - PaliGemmaProcessor, - ) - from .models.patchtsmixer import ( - PatchTSMixerForPrediction, - PatchTSMixerForPretraining, - PatchTSMixerForRegression, - PatchTSMixerForTimeSeriesClassification, - PatchTSMixerModel, - PatchTSMixerPreTrainedModel, - ) - from .models.patchtst import ( - PatchTSTForClassification, - PatchTSTForPrediction, - PatchTSTForPretraining, - PatchTSTForRegression, - PatchTSTModel, - PatchTSTPreTrainedModel, - ) - from .models.pegasus import ( - PegasusForCausalLM, - PegasusForConditionalGeneration, - PegasusModel, - PegasusPreTrainedModel, - ) - from .models.pegasus_x import ( - PegasusXForConditionalGeneration, - PegasusXModel, - PegasusXPreTrainedModel, - ) - from .models.perceiver import ( - PerceiverForImageClassificationConvProcessing, - PerceiverForImageClassificationFourier, - PerceiverForImageClassificationLearned, - PerceiverForMaskedLM, - PerceiverForMultimodalAutoencoding, - PerceiverForOpticalFlow, - PerceiverForSequenceClassification, - PerceiverModel, - PerceiverPreTrainedModel, - ) - from .models.persimmon import ( - PersimmonForCausalLM, - PersimmonForSequenceClassification, - PersimmonForTokenClassification, - PersimmonModel, - PersimmonPreTrainedModel, - ) - from .models.phi import ( - PhiForCausalLM, - PhiForSequenceClassification, - PhiForTokenClassification, - PhiModel, - PhiPreTrainedModel, - ) - from .models.phi3 import ( - Phi3ForCausalLM, - Phi3ForSequenceClassification, - Phi3ForTokenClassification, - Phi3Model, - Phi3PreTrainedModel, - ) - from .models.phi4_multimodal import ( - Phi4MultimodalAudioModel, - Phi4MultimodalAudioPreTrainedModel, - Phi4MultimodalForCausalLM, - Phi4MultimodalModel, - Phi4MultimodalPreTrainedModel, - Phi4MultimodalVisionModel, - Phi4MultimodalVisionPreTrainedModel, - ) - from .models.phimoe import ( - PhimoeForCausalLM, - PhimoeForSequenceClassification, - PhimoeModel, - PhimoePreTrainedModel, - ) - from .models.pix2struct import ( - Pix2StructForConditionalGeneration, - Pix2StructPreTrainedModel, - Pix2StructTextModel, - Pix2StructVisionModel, - ) - from .models.pixtral import ( - PixtralPreTrainedModel, - PixtralVisionModel, - ) - from .models.plbart import ( - PLBartForCausalLM, - PLBartForConditionalGeneration, - PLBartForSequenceClassification, - PLBartModel, - PLBartPreTrainedModel, - ) - from .models.poolformer import ( - PoolFormerForImageClassification, - PoolFormerModel, - PoolFormerPreTrainedModel, - ) - from .models.pop2piano import ( - Pop2PianoForConditionalGeneration, - Pop2PianoPreTrainedModel, - ) - from .models.prompt_depth_anything import ( - PromptDepthAnythingForDepthEstimation, - PromptDepthAnythingPreTrainedModel, - ) - from .models.prophetnet import ( - ProphetNetDecoder, - ProphetNetEncoder, - ProphetNetForCausalLM, - ProphetNetForConditionalGeneration, - ProphetNetModel, - ProphetNetPreTrainedModel, - ) - from .models.pvt import ( - PvtForImageClassification, - PvtModel, - PvtPreTrainedModel, - ) - from .models.pvt_v2 import ( - PvtV2Backbone, - PvtV2ForImageClassification, - PvtV2Model, - PvtV2PreTrainedModel, - ) - from .models.qwen2 import ( - Qwen2ForCausalLM, - Qwen2ForQuestionAnswering, - Qwen2ForSequenceClassification, - Qwen2ForTokenClassification, - Qwen2Model, - Qwen2PreTrainedModel, - ) - from .models.qwen2_5_vl import ( - Qwen2_5_VLForConditionalGeneration, - Qwen2_5_VLModel, - Qwen2_5_VLPreTrainedModel, - ) - from .models.qwen2_audio import ( - Qwen2AudioEncoder, - Qwen2AudioForConditionalGeneration, - Qwen2AudioPreTrainedModel, - ) - from .models.qwen2_moe import ( - Qwen2MoeForCausalLM, - Qwen2MoeForQuestionAnswering, - Qwen2MoeForSequenceClassification, - Qwen2MoeForTokenClassification, - Qwen2MoeModel, - Qwen2MoePreTrainedModel, - ) - from .models.qwen2_vl import ( - Qwen2VLForConditionalGeneration, - Qwen2VLModel, - Qwen2VLPreTrainedModel, - ) - from .models.qwen3 import ( - Qwen3ForCausalLM, - Qwen3ForQuestionAnswering, - Qwen3ForSequenceClassification, - Qwen3ForTokenClassification, - Qwen3Model, - Qwen3PreTrainedModel, - ) - from .models.qwen3_moe import ( - Qwen3MoeForCausalLM, - Qwen3MoeForQuestionAnswering, - Qwen3MoeForSequenceClassification, - Qwen3MoeForTokenClassification, - Qwen3MoeModel, - Qwen3MoePreTrainedModel, - ) - from .models.rag import ( - RagModel, - RagPreTrainedModel, - RagSequenceForGeneration, - RagTokenForGeneration, - ) - from .models.recurrent_gemma import ( - RecurrentGemmaForCausalLM, - RecurrentGemmaModel, - RecurrentGemmaPreTrainedModel, - ) - from .models.reformer import ( - ReformerForMaskedLM, - ReformerForQuestionAnswering, - ReformerForSequenceClassification, - ReformerModel, - ReformerModelWithLMHead, - ReformerPreTrainedModel, - ) - from .models.regnet import ( - RegNetForImageClassification, - RegNetModel, - RegNetPreTrainedModel, - ) - from .models.rembert import ( - RemBertForCausalLM, - RemBertForMaskedLM, - RemBertForMultipleChoice, - RemBertForQuestionAnswering, - RemBertForSequenceClassification, - RemBertForTokenClassification, - RemBertModel, - RemBertPreTrainedModel, - load_tf_weights_in_rembert, - ) - from .models.resnet import ( - ResNetBackbone, - ResNetForImageClassification, - ResNetModel, - ResNetPreTrainedModel, - ) - from .models.roberta import ( - RobertaForCausalLM, - RobertaForMaskedLM, - RobertaForMultipleChoice, - RobertaForQuestionAnswering, - RobertaForSequenceClassification, - RobertaForTokenClassification, - RobertaModel, - RobertaPreTrainedModel, - ) - from .models.roberta_prelayernorm import ( - RobertaPreLayerNormForCausalLM, - RobertaPreLayerNormForMaskedLM, - RobertaPreLayerNormForMultipleChoice, - RobertaPreLayerNormForQuestionAnswering, - RobertaPreLayerNormForSequenceClassification, - RobertaPreLayerNormForTokenClassification, - RobertaPreLayerNormModel, - RobertaPreLayerNormPreTrainedModel, - ) - from .models.roc_bert import ( - RoCBertForCausalLM, - RoCBertForMaskedLM, - RoCBertForMultipleChoice, - RoCBertForPreTraining, - RoCBertForQuestionAnswering, - RoCBertForSequenceClassification, - RoCBertForTokenClassification, - RoCBertModel, - RoCBertPreTrainedModel, - load_tf_weights_in_roc_bert, - ) - from .models.roformer import ( - RoFormerForCausalLM, - RoFormerForMaskedLM, - RoFormerForMultipleChoice, - RoFormerForQuestionAnswering, - RoFormerForSequenceClassification, - RoFormerForTokenClassification, - RoFormerModel, - RoFormerPreTrainedModel, - load_tf_weights_in_roformer, - ) - from .models.rt_detr import ( - RTDetrForObjectDetection, - RTDetrModel, - RTDetrPreTrainedModel, - RTDetrResNetBackbone, - RTDetrResNetPreTrainedModel, - ) - from .models.rt_detr_v2 import RTDetrV2ForObjectDetection, RTDetrV2Model, RTDetrV2PreTrainedModel - from .models.rwkv import ( - RwkvForCausalLM, - RwkvModel, - RwkvPreTrainedModel, - ) - from .models.sam import ( - SamModel, - SamPreTrainedModel, - SamVisionModel, - ) - from .models.seamless_m4t import ( - SeamlessM4TCodeHifiGan, - SeamlessM4TForSpeechToSpeech, - SeamlessM4TForSpeechToText, - SeamlessM4TForTextToSpeech, - SeamlessM4TForTextToText, - SeamlessM4THifiGan, - SeamlessM4TModel, - SeamlessM4TPreTrainedModel, - SeamlessM4TTextToUnitForConditionalGeneration, - SeamlessM4TTextToUnitModel, - ) - from .models.seamless_m4t_v2 import ( - SeamlessM4Tv2ForSpeechToSpeech, - SeamlessM4Tv2ForSpeechToText, - SeamlessM4Tv2ForTextToSpeech, - SeamlessM4Tv2ForTextToText, - SeamlessM4Tv2Model, - SeamlessM4Tv2PreTrainedModel, - ) - from .models.segformer import ( - SegformerDecodeHead, - SegformerForImageClassification, - SegformerForSemanticSegmentation, - SegformerModel, - SegformerPreTrainedModel, - ) - from .models.seggpt import ( - SegGptForImageSegmentation, - SegGptModel, - SegGptPreTrainedModel, - ) - from .models.sew import ( - SEWForCTC, - SEWForSequenceClassification, - SEWModel, - SEWPreTrainedModel, - ) - from .models.sew_d import ( - SEWDForCTC, - SEWDForSequenceClassification, - SEWDModel, - SEWDPreTrainedModel, - ) - from .models.shieldgemma2 import ( - ShieldGemma2ForImageClassification, - ) - from .models.siglip import ( - SiglipForImageClassification, - SiglipModel, - SiglipPreTrainedModel, - SiglipTextModel, - SiglipVisionModel, - ) - from .models.siglip2 import ( - Siglip2ForImageClassification, - Siglip2Model, - Siglip2PreTrainedModel, - Siglip2TextModel, - Siglip2VisionModel, - ) - from .models.smolvlm import ( - SmolVLMForConditionalGeneration, - SmolVLMModel, - SmolVLMPreTrainedModel, - SmolVLMProcessor, - SmolVLMVisionConfig, - SmolVLMVisionTransformer, - ) - from .models.speech_encoder_decoder import SpeechEncoderDecoderModel - from .models.speech_to_text import ( - Speech2TextForConditionalGeneration, - Speech2TextModel, - Speech2TextPreTrainedModel, - ) - from .models.speecht5 import ( - SpeechT5ForSpeechToSpeech, - SpeechT5ForSpeechToText, - SpeechT5ForTextToSpeech, - SpeechT5HifiGan, - SpeechT5Model, - SpeechT5PreTrainedModel, - ) - from .models.splinter import ( - SplinterForPreTraining, - SplinterForQuestionAnswering, - SplinterModel, - SplinterPreTrainedModel, - ) - from .models.squeezebert import ( - SqueezeBertForMaskedLM, - SqueezeBertForMultipleChoice, - SqueezeBertForQuestionAnswering, - SqueezeBertForSequenceClassification, - SqueezeBertForTokenClassification, - SqueezeBertModel, - SqueezeBertPreTrainedModel, - ) - from .models.stablelm import ( - StableLmForCausalLM, - StableLmForSequenceClassification, - StableLmForTokenClassification, - StableLmModel, - StableLmPreTrainedModel, - ) - from .models.starcoder2 import ( - Starcoder2ForCausalLM, - Starcoder2ForSequenceClassification, - Starcoder2ForTokenClassification, - Starcoder2Model, - Starcoder2PreTrainedModel, - ) - from .models.superglue import ( - SuperGlueForKeypointMatching, - SuperGluePreTrainedModel, - ) - from .models.superpoint import ( - SuperPointForKeypointDetection, - SuperPointPreTrainedModel, - ) - from .models.swiftformer import ( - SwiftFormerForImageClassification, - SwiftFormerModel, - SwiftFormerPreTrainedModel, - ) - from .models.swin import ( - SwinBackbone, - SwinForImageClassification, - SwinForMaskedImageModeling, - SwinModel, - SwinPreTrainedModel, - ) - from .models.swin2sr import ( - Swin2SRForImageSuperResolution, - Swin2SRModel, - Swin2SRPreTrainedModel, - ) - from .models.swinv2 import ( - Swinv2Backbone, - Swinv2ForImageClassification, - Swinv2ForMaskedImageModeling, - Swinv2Model, - Swinv2PreTrainedModel, - ) - from .models.switch_transformers import ( - SwitchTransformersEncoderModel, - SwitchTransformersForConditionalGeneration, - SwitchTransformersModel, - SwitchTransformersPreTrainedModel, - SwitchTransformersSparseMLP, - SwitchTransformersTop1Router, - ) - from .models.t5 import ( - T5EncoderModel, - T5ForConditionalGeneration, - T5ForQuestionAnswering, - T5ForSequenceClassification, - T5ForTokenClassification, - T5Model, - T5PreTrainedModel, - load_tf_weights_in_t5, - ) - from .models.table_transformer import ( - TableTransformerForObjectDetection, - TableTransformerModel, - TableTransformerPreTrainedModel, - ) - from .models.tapas import ( - TapasForMaskedLM, - TapasForQuestionAnswering, - TapasForSequenceClassification, - TapasModel, - TapasPreTrainedModel, - load_tf_weights_in_tapas, - ) - from .models.textnet import ( - TextNetBackbone, - TextNetForImageClassification, - TextNetModel, - TextNetPreTrainedModel, - ) - from .models.time_series_transformer import ( - TimeSeriesTransformerForPrediction, - TimeSeriesTransformerModel, - TimeSeriesTransformerPreTrainedModel, - ) - from .models.timesformer import ( - TimesformerForVideoClassification, - TimesformerModel, - TimesformerPreTrainedModel, - ) - from .models.timm_backbone import TimmBackbone - from .models.timm_wrapper import ( - TimmWrapperForImageClassification, - TimmWrapperModel, - TimmWrapperPreTrainedModel, - ) - from .models.trocr import ( - TrOCRForCausalLM, - TrOCRPreTrainedModel, - ) - from .models.tvp import ( - TvpForVideoGrounding, - TvpModel, - TvpPreTrainedModel, - ) - from .models.udop import ( - UdopEncoderModel, - UdopForConditionalGeneration, - UdopModel, - UdopPreTrainedModel, - ) - from .models.umt5 import ( - UMT5EncoderModel, - UMT5ForConditionalGeneration, - UMT5ForQuestionAnswering, - UMT5ForSequenceClassification, - UMT5ForTokenClassification, - UMT5Model, - UMT5PreTrainedModel, - ) - from .models.unispeech import ( - UniSpeechForCTC, - UniSpeechForPreTraining, - UniSpeechForSequenceClassification, - UniSpeechModel, - UniSpeechPreTrainedModel, - ) - from .models.unispeech_sat import ( - UniSpeechSatForAudioFrameClassification, - UniSpeechSatForCTC, - UniSpeechSatForPreTraining, - UniSpeechSatForSequenceClassification, - UniSpeechSatForXVector, - UniSpeechSatModel, - UniSpeechSatPreTrainedModel, - ) - from .models.univnet import UnivNetModel - from .models.upernet import ( - UperNetForSemanticSegmentation, - UperNetPreTrainedModel, - ) - from .models.video_llava import ( - VideoLlavaForConditionalGeneration, - VideoLlavaPreTrainedModel, - VideoLlavaProcessor, - ) - from .models.videomae import ( - VideoMAEForPreTraining, - VideoMAEForVideoClassification, - VideoMAEModel, - VideoMAEPreTrainedModel, - ) - from .models.vilt import ( - ViltForImageAndTextRetrieval, - ViltForImagesAndTextClassification, - ViltForMaskedLM, - ViltForQuestionAnswering, - ViltForTokenClassification, - ViltModel, - ViltPreTrainedModel, - ) - from .models.vipllava import ( - VipLlavaForConditionalGeneration, - VipLlavaPreTrainedModel, - ) - from .models.vision_encoder_decoder import VisionEncoderDecoderModel - from .models.vision_text_dual_encoder import VisionTextDualEncoderModel - from .models.visual_bert import ( - VisualBertForMultipleChoice, - VisualBertForPreTraining, - VisualBertForQuestionAnswering, - VisualBertForRegionToPhraseAlignment, - VisualBertForVisualReasoning, - VisualBertModel, - VisualBertPreTrainedModel, - ) - from .models.vit import ( - ViTForImageClassification, - ViTForMaskedImageModeling, - ViTModel, - ViTPreTrainedModel, - ) - from .models.vit_mae import ( - ViTMAEForPreTraining, - ViTMAEModel, - ViTMAEPreTrainedModel, - ) - from .models.vit_msn import ( - ViTMSNForImageClassification, - ViTMSNModel, - ViTMSNPreTrainedModel, - ) - from .models.vitdet import ( - VitDetBackbone, - VitDetModel, - VitDetPreTrainedModel, - ) - from .models.vitmatte import ( - VitMatteForImageMatting, - VitMattePreTrainedModel, - ) - from .models.vitpose import ( - VitPoseForPoseEstimation, - VitPosePreTrainedModel, - ) - from .models.vitpose_backbone import VitPoseBackbone, VitPoseBackbonePreTrainedModel - from .models.vits import ( - VitsModel, - VitsPreTrainedModel, - ) - from .models.vivit import ( - VivitForVideoClassification, - VivitModel, - VivitPreTrainedModel, - ) - from .models.wav2vec2 import ( - Wav2Vec2ForAudioFrameClassification, - Wav2Vec2ForCTC, - Wav2Vec2ForMaskedLM, - Wav2Vec2ForPreTraining, - Wav2Vec2ForSequenceClassification, - Wav2Vec2ForXVector, - Wav2Vec2Model, - Wav2Vec2PreTrainedModel, - ) - from .models.wav2vec2_bert import ( - Wav2Vec2BertForAudioFrameClassification, - Wav2Vec2BertForCTC, - Wav2Vec2BertForSequenceClassification, - Wav2Vec2BertForXVector, - Wav2Vec2BertModel, - Wav2Vec2BertPreTrainedModel, - ) - from .models.wav2vec2_conformer import ( - Wav2Vec2ConformerForAudioFrameClassification, - Wav2Vec2ConformerForCTC, - Wav2Vec2ConformerForPreTraining, - Wav2Vec2ConformerForSequenceClassification, - Wav2Vec2ConformerForXVector, - Wav2Vec2ConformerModel, - Wav2Vec2ConformerPreTrainedModel, - ) - from .models.wavlm import ( - WavLMForAudioFrameClassification, - WavLMForCTC, - WavLMForSequenceClassification, - WavLMForXVector, - WavLMModel, - WavLMPreTrainedModel, - ) - from .models.whisper import ( - WhisperForAudioClassification, - WhisperForCausalLM, - WhisperForConditionalGeneration, - WhisperModel, - WhisperPreTrainedModel, - ) - from .models.x_clip import ( - XCLIPModel, - XCLIPPreTrainedModel, - XCLIPTextModel, - XCLIPVisionModel, - ) - from .models.xglm import ( - XGLMForCausalLM, - XGLMModel, - XGLMPreTrainedModel, - ) - from .models.xlm import ( - XLMForMultipleChoice, - XLMForQuestionAnswering, - XLMForQuestionAnsweringSimple, - XLMForSequenceClassification, - XLMForTokenClassification, - XLMModel, - XLMPreTrainedModel, - XLMWithLMHeadModel, - ) - from .models.xlm_roberta import ( - XLMRobertaForCausalLM, - XLMRobertaForMaskedLM, - XLMRobertaForMultipleChoice, - XLMRobertaForQuestionAnswering, - XLMRobertaForSequenceClassification, - XLMRobertaForTokenClassification, - XLMRobertaModel, - XLMRobertaPreTrainedModel, - ) - from .models.xlm_roberta_xl import ( - XLMRobertaXLForCausalLM, - XLMRobertaXLForMaskedLM, - XLMRobertaXLForMultipleChoice, - XLMRobertaXLForQuestionAnswering, - XLMRobertaXLForSequenceClassification, - XLMRobertaXLForTokenClassification, - XLMRobertaXLModel, - XLMRobertaXLPreTrainedModel, - ) - from .models.xlnet import ( - XLNetForMultipleChoice, - XLNetForQuestionAnswering, - XLNetForQuestionAnsweringSimple, - XLNetForSequenceClassification, - XLNetForTokenClassification, - XLNetLMHeadModel, - XLNetModel, - XLNetPreTrainedModel, - load_tf_weights_in_xlnet, - ) - from .models.xmod import ( - XmodForCausalLM, - XmodForMaskedLM, - XmodForMultipleChoice, - XmodForQuestionAnswering, - XmodForSequenceClassification, - XmodForTokenClassification, - XmodModel, - XmodPreTrainedModel, - ) - from .models.yolos import ( - YolosForObjectDetection, - YolosModel, - YolosPreTrainedModel, - ) - from .models.yoso import ( - YosoForMaskedLM, - YosoForMultipleChoice, - YosoForQuestionAnswering, - YosoForSequenceClassification, - YosoForTokenClassification, - YosoModel, - YosoPreTrainedModel, - ) - from .models.zamba import ( - ZambaForCausalLM, - ZambaForSequenceClassification, - ZambaModel, - ZambaPreTrainedModel, - ) - from .models.zamba2 import ( - Zamba2ForCausalLM, - Zamba2ForSequenceClassification, - Zamba2Model, - Zamba2PreTrainedModel, - ) - from .models.zoedepth import ( - ZoeDepthForDepthEstimation, - ZoeDepthPreTrainedModel, - ) # Optimization from .optimization import ( @@ -8991,537 +1008,6 @@ if TYPE_CHECKING: shape_list, ) - # TensorFlow model imports - from .models.albert import ( - TFAlbertForMaskedLM, - TFAlbertForMultipleChoice, - TFAlbertForPreTraining, - TFAlbertForQuestionAnswering, - TFAlbertForSequenceClassification, - TFAlbertForTokenClassification, - TFAlbertMainLayer, - TFAlbertModel, - TFAlbertPreTrainedModel, - ) - from .models.auto import ( - TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, - TF_MODEL_FOR_CAUSAL_LM_MAPPING, - TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, - TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, - TF_MODEL_FOR_MASK_GENERATION_MAPPING, - TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, - TF_MODEL_FOR_MASKED_LM_MAPPING, - TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, - TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, - TF_MODEL_FOR_PRETRAINING_MAPPING, - TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, - TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, - TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, - TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, - TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, - TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, - TF_MODEL_FOR_TEXT_ENCODING_MAPPING, - TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, - TF_MODEL_FOR_VISION_2_SEQ_MAPPING, - TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING, - TF_MODEL_MAPPING, - TF_MODEL_WITH_LM_HEAD_MAPPING, - TFAutoModel, - TFAutoModelForAudioClassification, - TFAutoModelForCausalLM, - TFAutoModelForDocumentQuestionAnswering, - TFAutoModelForImageClassification, - TFAutoModelForMaskedImageModeling, - TFAutoModelForMaskedLM, - TFAutoModelForMaskGeneration, - TFAutoModelForMultipleChoice, - TFAutoModelForNextSentencePrediction, - TFAutoModelForPreTraining, - TFAutoModelForQuestionAnswering, - TFAutoModelForSemanticSegmentation, - TFAutoModelForSeq2SeqLM, - TFAutoModelForSequenceClassification, - TFAutoModelForSpeechSeq2Seq, - TFAutoModelForTableQuestionAnswering, - TFAutoModelForTextEncoding, - TFAutoModelForTokenClassification, - TFAutoModelForVision2Seq, - TFAutoModelForZeroShotImageClassification, - TFAutoModelWithLMHead, - ) - from .models.bart import ( - TFBartForConditionalGeneration, - TFBartForSequenceClassification, - TFBartModel, - TFBartPretrainedModel, - ) - from .models.bert import ( - TFBertForMaskedLM, - TFBertForMultipleChoice, - TFBertForNextSentencePrediction, - TFBertForPreTraining, - TFBertForQuestionAnswering, - TFBertForSequenceClassification, - TFBertForTokenClassification, - TFBertLMHeadModel, - TFBertMainLayer, - TFBertModel, - TFBertPreTrainedModel, - ) - from .models.blenderbot import ( - TFBlenderbotForConditionalGeneration, - TFBlenderbotModel, - TFBlenderbotPreTrainedModel, - ) - from .models.blenderbot_small import ( - TFBlenderbotSmallForConditionalGeneration, - TFBlenderbotSmallModel, - TFBlenderbotSmallPreTrainedModel, - ) - from .models.blip import ( - TFBlipForConditionalGeneration, - TFBlipForImageTextRetrieval, - TFBlipForQuestionAnswering, - TFBlipModel, - TFBlipPreTrainedModel, - TFBlipTextModel, - TFBlipVisionModel, - ) - from .models.camembert import ( - TFCamembertForCausalLM, - TFCamembertForMaskedLM, - TFCamembertForMultipleChoice, - TFCamembertForQuestionAnswering, - TFCamembertForSequenceClassification, - TFCamembertForTokenClassification, - TFCamembertModel, - TFCamembertPreTrainedModel, - ) - from .models.clip import ( - TFCLIPModel, - TFCLIPPreTrainedModel, - TFCLIPTextModel, - TFCLIPVisionModel, - ) - from .models.convbert import ( - TFConvBertForMaskedLM, - TFConvBertForMultipleChoice, - TFConvBertForQuestionAnswering, - TFConvBertForSequenceClassification, - TFConvBertForTokenClassification, - TFConvBertModel, - TFConvBertPreTrainedModel, - ) - from .models.convnext import ( - TFConvNextForImageClassification, - TFConvNextModel, - TFConvNextPreTrainedModel, - ) - from .models.convnextv2 import ( - TFConvNextV2ForImageClassification, - TFConvNextV2Model, - TFConvNextV2PreTrainedModel, - ) - from .models.ctrl import ( - TFCTRLForSequenceClassification, - TFCTRLLMHeadModel, - TFCTRLModel, - TFCTRLPreTrainedModel, - ) - from .models.cvt import ( - TFCvtForImageClassification, - TFCvtModel, - TFCvtPreTrainedModel, - ) - from .models.data2vec import ( - TFData2VecVisionForImageClassification, - TFData2VecVisionForSemanticSegmentation, - TFData2VecVisionModel, - TFData2VecVisionPreTrainedModel, - ) - from .models.deberta import ( - TFDebertaForMaskedLM, - TFDebertaForQuestionAnswering, - TFDebertaForSequenceClassification, - TFDebertaForTokenClassification, - TFDebertaModel, - TFDebertaPreTrainedModel, - ) - from .models.deberta_v2 import ( - TFDebertaV2ForMaskedLM, - TFDebertaV2ForMultipleChoice, - TFDebertaV2ForQuestionAnswering, - TFDebertaV2ForSequenceClassification, - TFDebertaV2ForTokenClassification, - TFDebertaV2Model, - TFDebertaV2PreTrainedModel, - ) - from .models.deit import ( - TFDeiTForImageClassification, - TFDeiTForImageClassificationWithTeacher, - TFDeiTForMaskedImageModeling, - TFDeiTModel, - TFDeiTPreTrainedModel, - ) - from .models.deprecated.efficientformer import ( - TFEfficientFormerForImageClassification, - TFEfficientFormerForImageClassificationWithTeacher, - TFEfficientFormerModel, - TFEfficientFormerPreTrainedModel, - ) - from .models.deprecated.transfo_xl import ( - TFAdaptiveEmbedding, - TFTransfoXLForSequenceClassification, - TFTransfoXLLMHeadModel, - TFTransfoXLMainLayer, - TFTransfoXLModel, - TFTransfoXLPreTrainedModel, - ) - from .models.distilbert import ( - TFDistilBertForMaskedLM, - TFDistilBertForMultipleChoice, - TFDistilBertForQuestionAnswering, - TFDistilBertForSequenceClassification, - TFDistilBertForTokenClassification, - TFDistilBertMainLayer, - TFDistilBertModel, - TFDistilBertPreTrainedModel, - ) - from .models.dpr import ( - TFDPRContextEncoder, - TFDPRPretrainedContextEncoder, - TFDPRPretrainedQuestionEncoder, - TFDPRPretrainedReader, - TFDPRQuestionEncoder, - TFDPRReader, - ) - from .models.electra import ( - TFElectraForMaskedLM, - TFElectraForMultipleChoice, - TFElectraForPreTraining, - TFElectraForQuestionAnswering, - TFElectraForSequenceClassification, - TFElectraForTokenClassification, - TFElectraModel, - TFElectraPreTrainedModel, - ) - from .models.encoder_decoder import TFEncoderDecoderModel - from .models.esm import ( - TFEsmForMaskedLM, - TFEsmForSequenceClassification, - TFEsmForTokenClassification, - TFEsmModel, - TFEsmPreTrainedModel, - ) - from .models.flaubert import ( - TFFlaubertForMultipleChoice, - TFFlaubertForQuestionAnsweringSimple, - TFFlaubertForSequenceClassification, - TFFlaubertForTokenClassification, - TFFlaubertModel, - TFFlaubertPreTrainedModel, - TFFlaubertWithLMHeadModel, - ) - from .models.funnel import ( - TFFunnelBaseModel, - TFFunnelForMaskedLM, - TFFunnelForMultipleChoice, - TFFunnelForPreTraining, - TFFunnelForQuestionAnswering, - TFFunnelForSequenceClassification, - TFFunnelForTokenClassification, - TFFunnelModel, - TFFunnelPreTrainedModel, - ) - from .models.gpt2 import ( - TFGPT2DoubleHeadsModel, - TFGPT2ForSequenceClassification, - TFGPT2LMHeadModel, - TFGPT2MainLayer, - TFGPT2Model, - TFGPT2PreTrainedModel, - ) - from .models.gptj import ( - TFGPTJForCausalLM, - TFGPTJForQuestionAnswering, - TFGPTJForSequenceClassification, - TFGPTJModel, - TFGPTJPreTrainedModel, - ) - from .models.groupvit import ( - TFGroupViTModel, - TFGroupViTPreTrainedModel, - TFGroupViTTextModel, - TFGroupViTVisionModel, - ) - from .models.hubert import ( - TFHubertForCTC, - TFHubertModel, - TFHubertPreTrainedModel, - ) - from .models.idefics import ( - TFIdeficsForVisionText2Text, - TFIdeficsModel, - TFIdeficsPreTrainedModel, - ) - from .models.layoutlm import ( - TFLayoutLMForMaskedLM, - TFLayoutLMForQuestionAnswering, - TFLayoutLMForSequenceClassification, - TFLayoutLMForTokenClassification, - TFLayoutLMMainLayer, - TFLayoutLMModel, - TFLayoutLMPreTrainedModel, - ) - from .models.layoutlmv3 import ( - TFLayoutLMv3ForQuestionAnswering, - TFLayoutLMv3ForSequenceClassification, - TFLayoutLMv3ForTokenClassification, - TFLayoutLMv3Model, - TFLayoutLMv3PreTrainedModel, - ) - from .models.led import ( - TFLEDForConditionalGeneration, - TFLEDModel, - TFLEDPreTrainedModel, - ) - from .models.longformer import ( - TFLongformerForMaskedLM, - TFLongformerForMultipleChoice, - TFLongformerForQuestionAnswering, - TFLongformerForSequenceClassification, - TFLongformerForTokenClassification, - TFLongformerModel, - TFLongformerPreTrainedModel, - ) - from .models.lxmert import ( - TFLxmertForPreTraining, - TFLxmertMainLayer, - TFLxmertModel, - TFLxmertPreTrainedModel, - TFLxmertVisualFeatureEncoder, - ) - from .models.marian import ( - TFMarianModel, - TFMarianMTModel, - TFMarianPreTrainedModel, - ) - from .models.mbart import ( - TFMBartForConditionalGeneration, - TFMBartModel, - TFMBartPreTrainedModel, - ) - from .models.mistral import ( - TFMistralForCausalLM, - TFMistralForSequenceClassification, - TFMistralModel, - TFMistralPreTrainedModel, - ) - from .models.mobilebert import ( - TFMobileBertForMaskedLM, - TFMobileBertForMultipleChoice, - TFMobileBertForNextSentencePrediction, - TFMobileBertForPreTraining, - TFMobileBertForQuestionAnswering, - TFMobileBertForSequenceClassification, - TFMobileBertForTokenClassification, - TFMobileBertMainLayer, - TFMobileBertModel, - TFMobileBertPreTrainedModel, - ) - from .models.mobilevit import ( - TFMobileViTForImageClassification, - TFMobileViTForSemanticSegmentation, - TFMobileViTModel, - TFMobileViTPreTrainedModel, - ) - from .models.mpnet import ( - TFMPNetForMaskedLM, - TFMPNetForMultipleChoice, - TFMPNetForQuestionAnswering, - TFMPNetForSequenceClassification, - TFMPNetForTokenClassification, - TFMPNetMainLayer, - TFMPNetModel, - TFMPNetPreTrainedModel, - ) - from .models.mt5 import ( - TFMT5EncoderModel, - TFMT5ForConditionalGeneration, - TFMT5Model, - ) - from .models.openai import ( - TFOpenAIGPTDoubleHeadsModel, - TFOpenAIGPTForSequenceClassification, - TFOpenAIGPTLMHeadModel, - TFOpenAIGPTMainLayer, - TFOpenAIGPTModel, - TFOpenAIGPTPreTrainedModel, - ) - from .models.opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel - from .models.pegasus import ( - TFPegasusForConditionalGeneration, - TFPegasusModel, - TFPegasusPreTrainedModel, - ) - from .models.rag import ( - TFRagModel, - TFRagPreTrainedModel, - TFRagSequenceForGeneration, - TFRagTokenForGeneration, - ) - from .models.regnet import ( - TFRegNetForImageClassification, - TFRegNetModel, - TFRegNetPreTrainedModel, - ) - from .models.rembert import ( - TFRemBertForCausalLM, - TFRemBertForMaskedLM, - TFRemBertForMultipleChoice, - TFRemBertForQuestionAnswering, - TFRemBertForSequenceClassification, - TFRemBertForTokenClassification, - TFRemBertModel, - TFRemBertPreTrainedModel, - ) - from .models.resnet import ( - TFResNetForImageClassification, - TFResNetModel, - TFResNetPreTrainedModel, - ) - from .models.roberta import ( - TFRobertaForCausalLM, - TFRobertaForMaskedLM, - TFRobertaForMultipleChoice, - TFRobertaForQuestionAnswering, - TFRobertaForSequenceClassification, - TFRobertaForTokenClassification, - TFRobertaMainLayer, - TFRobertaModel, - TFRobertaPreTrainedModel, - ) - from .models.roberta_prelayernorm import ( - TFRobertaPreLayerNormForCausalLM, - TFRobertaPreLayerNormForMaskedLM, - TFRobertaPreLayerNormForMultipleChoice, - TFRobertaPreLayerNormForQuestionAnswering, - TFRobertaPreLayerNormForSequenceClassification, - TFRobertaPreLayerNormForTokenClassification, - TFRobertaPreLayerNormMainLayer, - TFRobertaPreLayerNormModel, - TFRobertaPreLayerNormPreTrainedModel, - ) - from .models.roformer import ( - TFRoFormerForCausalLM, - TFRoFormerForMaskedLM, - TFRoFormerForMultipleChoice, - TFRoFormerForQuestionAnswering, - TFRoFormerForSequenceClassification, - TFRoFormerForTokenClassification, - TFRoFormerModel, - TFRoFormerPreTrainedModel, - ) - from .models.sam import ( - TFSamModel, - TFSamPreTrainedModel, - TFSamVisionModel, - ) - from .models.segformer import ( - TFSegformerDecodeHead, - TFSegformerForImageClassification, - TFSegformerForSemanticSegmentation, - TFSegformerModel, - TFSegformerPreTrainedModel, - ) - from .models.speech_to_text import ( - TFSpeech2TextForConditionalGeneration, - TFSpeech2TextModel, - TFSpeech2TextPreTrainedModel, - ) - from .models.swiftformer import ( - TFSwiftFormerForImageClassification, - TFSwiftFormerModel, - TFSwiftFormerPreTrainedModel, - ) - from .models.swin import ( - TFSwinForImageClassification, - TFSwinForMaskedImageModeling, - TFSwinModel, - TFSwinPreTrainedModel, - ) - from .models.t5 import ( - TFT5EncoderModel, - TFT5ForConditionalGeneration, - TFT5Model, - TFT5PreTrainedModel, - ) - from .models.tapas import ( - TFTapasForMaskedLM, - TFTapasForQuestionAnswering, - TFTapasForSequenceClassification, - TFTapasModel, - TFTapasPreTrainedModel, - ) - from .models.vision_encoder_decoder import TFVisionEncoderDecoderModel - from .models.vision_text_dual_encoder import TFVisionTextDualEncoderModel - from .models.vit import ( - TFViTForImageClassification, - TFViTModel, - TFViTPreTrainedModel, - ) - from .models.vit_mae import ( - TFViTMAEForPreTraining, - TFViTMAEModel, - TFViTMAEPreTrainedModel, - ) - from .models.wav2vec2 import ( - TFWav2Vec2ForCTC, - TFWav2Vec2ForSequenceClassification, - TFWav2Vec2Model, - TFWav2Vec2PreTrainedModel, - ) - from .models.whisper import ( - TFWhisperForConditionalGeneration, - TFWhisperModel, - TFWhisperPreTrainedModel, - ) - from .models.xglm import ( - TFXGLMForCausalLM, - TFXGLMModel, - TFXGLMPreTrainedModel, - ) - from .models.xlm import ( - TFXLMForMultipleChoice, - TFXLMForQuestionAnsweringSimple, - TFXLMForSequenceClassification, - TFXLMForTokenClassification, - TFXLMMainLayer, - TFXLMModel, - TFXLMPreTrainedModel, - TFXLMWithLMHeadModel, - ) - from .models.xlm_roberta import ( - TFXLMRobertaForCausalLM, - TFXLMRobertaForMaskedLM, - TFXLMRobertaForMultipleChoice, - TFXLMRobertaForQuestionAnswering, - TFXLMRobertaForSequenceClassification, - TFXLMRobertaForTokenClassification, - TFXLMRobertaModel, - TFXLMRobertaPreTrainedModel, - ) - from .models.xlnet import ( - TFXLNetForMultipleChoice, - TFXLNetForQuestionAnsweringSimple, - TFXLNetForSequenceClassification, - TFXLNetForTokenClassification, - TFXLNetLMHeadModel, - TFXLNetMainLayer, - TFXLNetModel, - TFXLNetPreTrainedModel, - ) - # Optimization from .optimization_tf import ( AdamWeightDecay, @@ -9530,31 +1016,6 @@ if TYPE_CHECKING: create_optimizer, ) - try: - if not ( - is_librosa_available() - and is_essentia_available() - and is_scipy_available() - and is_torch_available() - and is_pretty_midi_available() - ): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - from .utils.dummy_essentia_and_librosa_and_pretty_midi_and_scipy_and_torch_objects import * - else: - from .models.pop2piano import ( - Pop2PianoFeatureExtractor, - Pop2PianoProcessor, - Pop2PianoTokenizer, - ) - - try: - if not is_torchaudio_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - from .utils.dummy_torchaudio_objects import * - else: - from .models.musicgen_melody import MusicgenMelodyFeatureExtractor, MusicgenMelodyProcessor try: if not is_flax_available(): raise OptionalDependencyNotAvailable() @@ -9581,281 +1042,18 @@ if TYPE_CHECKING: ) from .modeling_flax_utils import FlaxPreTrainedModel - # Flax model imports - from .models.albert import ( - FlaxAlbertForMaskedLM, - FlaxAlbertForMultipleChoice, - FlaxAlbertForPreTraining, - FlaxAlbertForQuestionAnswering, - FlaxAlbertForSequenceClassification, - FlaxAlbertForTokenClassification, - FlaxAlbertModel, - FlaxAlbertPreTrainedModel, - ) - from .models.auto import ( - FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, - FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, - FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, - FLAX_MODEL_FOR_MASKED_LM_MAPPING, - FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, - FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, - FLAX_MODEL_FOR_PRETRAINING_MAPPING, - FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, - FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, - FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, - FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, - FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, - FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING, - FLAX_MODEL_MAPPING, - FlaxAutoModel, - FlaxAutoModelForCausalLM, - FlaxAutoModelForImageClassification, - FlaxAutoModelForMaskedLM, - FlaxAutoModelForMultipleChoice, - FlaxAutoModelForNextSentencePrediction, - FlaxAutoModelForPreTraining, - FlaxAutoModelForQuestionAnswering, - FlaxAutoModelForSeq2SeqLM, - FlaxAutoModelForSequenceClassification, - FlaxAutoModelForSpeechSeq2Seq, - FlaxAutoModelForTokenClassification, - FlaxAutoModelForVision2Seq, - ) - from .models.bart import ( - FlaxBartDecoderPreTrainedModel, - FlaxBartForCausalLM, - FlaxBartForConditionalGeneration, - FlaxBartForQuestionAnswering, - FlaxBartForSequenceClassification, - FlaxBartModel, - FlaxBartPreTrainedModel, - ) - from .models.beit import ( - FlaxBeitForImageClassification, - FlaxBeitForMaskedImageModeling, - FlaxBeitModel, - FlaxBeitPreTrainedModel, - ) - from .models.bert import ( - FlaxBertForCausalLM, - FlaxBertForMaskedLM, - FlaxBertForMultipleChoice, - FlaxBertForNextSentencePrediction, - FlaxBertForPreTraining, - FlaxBertForQuestionAnswering, - FlaxBertForSequenceClassification, - FlaxBertForTokenClassification, - FlaxBertModel, - FlaxBertPreTrainedModel, - ) - from .models.big_bird import ( - FlaxBigBirdForCausalLM, - FlaxBigBirdForMaskedLM, - FlaxBigBirdForMultipleChoice, - FlaxBigBirdForPreTraining, - FlaxBigBirdForQuestionAnswering, - FlaxBigBirdForSequenceClassification, - FlaxBigBirdForTokenClassification, - FlaxBigBirdModel, - FlaxBigBirdPreTrainedModel, - ) - from .models.blenderbot import ( - FlaxBlenderbotForConditionalGeneration, - FlaxBlenderbotModel, - FlaxBlenderbotPreTrainedModel, - ) - from .models.blenderbot_small import ( - FlaxBlenderbotSmallForConditionalGeneration, - FlaxBlenderbotSmallModel, - FlaxBlenderbotSmallPreTrainedModel, - ) - from .models.bloom import ( - FlaxBloomForCausalLM, - FlaxBloomModel, - FlaxBloomPreTrainedModel, - ) - from .models.clip import ( - FlaxCLIPModel, - FlaxCLIPPreTrainedModel, - FlaxCLIPTextModel, - FlaxCLIPTextModelWithProjection, - FlaxCLIPTextPreTrainedModel, - FlaxCLIPVisionModel, - FlaxCLIPVisionPreTrainedModel, - ) - from .models.dinov2 import ( - FlaxDinov2ForImageClassification, - FlaxDinov2Model, - FlaxDinov2PreTrainedModel, - ) - from .models.distilbert import ( - FlaxDistilBertForMaskedLM, - FlaxDistilBertForMultipleChoice, - FlaxDistilBertForQuestionAnswering, - FlaxDistilBertForSequenceClassification, - FlaxDistilBertForTokenClassification, - FlaxDistilBertModel, - FlaxDistilBertPreTrainedModel, - ) - from .models.electra import ( - FlaxElectraForCausalLM, - FlaxElectraForMaskedLM, - FlaxElectraForMultipleChoice, - FlaxElectraForPreTraining, - FlaxElectraForQuestionAnswering, - FlaxElectraForSequenceClassification, - FlaxElectraForTokenClassification, - FlaxElectraModel, - FlaxElectraPreTrainedModel, - ) - from .models.encoder_decoder import FlaxEncoderDecoderModel - from .models.gemma import ( - FlaxGemmaForCausalLM, - FlaxGemmaModel, - FlaxGemmaPreTrainedModel, - ) - from .models.gpt2 import ( - FlaxGPT2LMHeadModel, - FlaxGPT2Model, - FlaxGPT2PreTrainedModel, - ) - from .models.gpt_neo import ( - FlaxGPTNeoForCausalLM, - FlaxGPTNeoModel, - FlaxGPTNeoPreTrainedModel, - ) - from .models.gptj import ( - FlaxGPTJForCausalLM, - FlaxGPTJModel, - FlaxGPTJPreTrainedModel, - ) - from .models.llama import ( - FlaxLlamaForCausalLM, - FlaxLlamaModel, - FlaxLlamaPreTrainedModel, - ) - from .models.longt5 import ( - FlaxLongT5ForConditionalGeneration, - FlaxLongT5Model, - FlaxLongT5PreTrainedModel, - ) - from .models.marian import ( - FlaxMarianModel, - FlaxMarianMTModel, - FlaxMarianPreTrainedModel, - ) - from .models.mbart import ( - FlaxMBartForConditionalGeneration, - FlaxMBartForQuestionAnswering, - FlaxMBartForSequenceClassification, - FlaxMBartModel, - FlaxMBartPreTrainedModel, - ) - from .models.mistral import ( - FlaxMistralForCausalLM, - FlaxMistralModel, - FlaxMistralPreTrainedModel, - ) - from .models.mt5 import ( - FlaxMT5EncoderModel, - FlaxMT5ForConditionalGeneration, - FlaxMT5Model, - ) - from .models.opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel - from .models.pegasus import ( - FlaxPegasusForConditionalGeneration, - FlaxPegasusModel, - FlaxPegasusPreTrainedModel, - ) - from .models.regnet import ( - FlaxRegNetForImageClassification, - FlaxRegNetModel, - FlaxRegNetPreTrainedModel, - ) - from .models.resnet import ( - FlaxResNetForImageClassification, - FlaxResNetModel, - FlaxResNetPreTrainedModel, - ) - from .models.roberta import ( - FlaxRobertaForCausalLM, - FlaxRobertaForMaskedLM, - FlaxRobertaForMultipleChoice, - FlaxRobertaForQuestionAnswering, - FlaxRobertaForSequenceClassification, - FlaxRobertaForTokenClassification, - FlaxRobertaModel, - FlaxRobertaPreTrainedModel, - ) - from .models.roberta_prelayernorm import ( - FlaxRobertaPreLayerNormForCausalLM, - FlaxRobertaPreLayerNormForMaskedLM, - FlaxRobertaPreLayerNormForMultipleChoice, - FlaxRobertaPreLayerNormForQuestionAnswering, - FlaxRobertaPreLayerNormForSequenceClassification, - FlaxRobertaPreLayerNormForTokenClassification, - FlaxRobertaPreLayerNormModel, - FlaxRobertaPreLayerNormPreTrainedModel, - ) - from .models.roformer import ( - FlaxRoFormerForMaskedLM, - FlaxRoFormerForMultipleChoice, - FlaxRoFormerForQuestionAnswering, - FlaxRoFormerForSequenceClassification, - FlaxRoFormerForTokenClassification, - FlaxRoFormerModel, - FlaxRoFormerPreTrainedModel, - ) - from .models.speech_encoder_decoder import FlaxSpeechEncoderDecoderModel - from .models.t5 import ( - FlaxT5EncoderModel, - FlaxT5ForConditionalGeneration, - FlaxT5Model, - FlaxT5PreTrainedModel, - ) - from .models.vision_encoder_decoder import FlaxVisionEncoderDecoderModel - from .models.vision_text_dual_encoder import FlaxVisionTextDualEncoderModel - from .models.vit import ( - FlaxViTForImageClassification, - FlaxViTModel, - FlaxViTPreTrainedModel, - ) - from .models.wav2vec2 import ( - FlaxWav2Vec2ForCTC, - FlaxWav2Vec2ForPreTraining, - FlaxWav2Vec2Model, - FlaxWav2Vec2PreTrainedModel, - ) - from .models.whisper import ( - FlaxWhisperForAudioClassification, - FlaxWhisperForConditionalGeneration, - FlaxWhisperModel, - FlaxWhisperPreTrainedModel, - ) - from .models.xglm import ( - FlaxXGLMForCausalLM, - FlaxXGLMModel, - FlaxXGLMPreTrainedModel, - ) - from .models.xlm_roberta import ( - FlaxXLMRobertaForCausalLM, - FlaxXLMRobertaForMaskedLM, - FlaxXLMRobertaForMultipleChoice, - FlaxXLMRobertaForQuestionAnswering, - FlaxXLMRobertaForSequenceClassification, - FlaxXLMRobertaForTokenClassification, - FlaxXLMRobertaModel, - FlaxXLMRobertaPreTrainedModel, - ) - - else: import sys + _import_structure = {k: set(v) for k, v in _import_structure.items()} + + import_structure = define_import_structure(Path(__file__).parent / "models", prefix="models") + import_structure[frozenset({})].update(_import_structure) + sys.modules[__name__] = _LazyModule( __name__, globals()["__file__"], - _import_structure, + import_structure, module_spec=__spec__, extra_objects={"__version__": __version__}, ) diff --git a/src/transformers/image_processing_utils.py b/src/transformers/image_processing_utils.py index ec0f817728..dd08be2941 100644 --- a/src/transformers/image_processing_utils.py +++ b/src/transformers/image_processing_utils.py @@ -22,6 +22,7 @@ from .image_processing_base import BatchFeature, ImageProcessingMixin from .image_transforms import center_crop, normalize, rescale from .image_utils import ChannelDimension, get_image_size from .utils import logging +from .utils.import_utils import requires logger = logging.get_logger(__name__) @@ -33,6 +34,7 @@ INIT_SERVICE_KWARGS = [ ] +@requires(backends=("vision",)) class BaseImageProcessor(ImageProcessingMixin): def __init__(self, **kwargs): super().__init__(**kwargs) diff --git a/src/transformers/image_processing_utils_fast.py b/src/transformers/image_processing_utils_fast.py index b671a11191..78565ccbd2 100644 --- a/src/transformers/image_processing_utils_fast.py +++ b/src/transformers/image_processing_utils_fast.py @@ -68,6 +68,8 @@ if is_torchvision_available(): from torchvision.transforms.v2 import functional as F else: from torchvision.transforms import functional as F +else: + pil_torch_interpolation_mapping = None logger = logging.get_logger(__name__) diff --git a/src/transformers/image_utils.py b/src/transformers/image_utils.py index f07ac1ae7d..21dbbe374c 100644 --- a/src/transformers/image_utils.py +++ b/src/transformers/image_utils.py @@ -72,6 +72,8 @@ if is_vision_available(): PILImageResampling.BICUBIC: InterpolationMode.BICUBIC, PILImageResampling.LANCZOS: InterpolationMode.LANCZOS, } + else: + pil_torch_interpolation_mapping = {} if TYPE_CHECKING: diff --git a/src/transformers/model_debugging_utils.py b/src/transformers/model_debugging_utils.py index 0992636a14..c419c2c273 100644 --- a/src/transformers/model_debugging_utils.py +++ b/src/transformers/model_debugging_utils.py @@ -20,7 +20,7 @@ import re from contextlib import contextmanager from typing import Optional -from transformers.utils.import_utils import export +from transformers.utils.import_utils import requires from .utils import is_torch_available @@ -225,7 +225,7 @@ def _attach_debugger_logic(model, class_name, debug_path: str): break # exit the loop after finding one (unsure, but should be just one call.) -@export(backends=("torch",)) +@requires(backends=("torch",)) def model_addition_debugger(cls): """ # Model addition debugger - a model adder tracer @@ -282,7 +282,7 @@ def model_addition_debugger(cls): return cls -@export(backends=("torch",)) +@requires(backends=("torch",)) @contextmanager def model_addition_debugger_context(model, debug_path: Optional[str] = None): """ diff --git a/src/transformers/models/__init__.py b/src/transformers/models/__init__.py index d6063cef4b..7b21910454 100644 --- a/src/transformers/models/__init__.py +++ b/src/transformers/models/__init__.py @@ -11,316 +11,320 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from typing import TYPE_CHECKING -from . import ( - albert, - align, - altclip, - aria, - audio_spectrogram_transformer, - auto, - autoformer, - aya_vision, - bamba, - bark, - bart, - barthez, - bartpho, - beit, - bert, - bert_generation, - bert_japanese, - bertweet, - big_bird, - bigbird_pegasus, - biogpt, - bit, - blenderbot, - blenderbot_small, - blip, - blip_2, - bloom, - bridgetower, - bros, - byt5, - camembert, - canine, - chameleon, - chinese_clip, - clap, - clip, - clipseg, - clvp, - code_llama, - codegen, - cohere, - cohere2, - colpali, - conditional_detr, - convbert, - convnext, - convnextv2, - cpm, - cpmant, - ctrl, - cvt, - dab_detr, - dac, - data2vec, - dbrx, - deberta, - deberta_v2, - decision_transformer, - deepseek_v3, - deformable_detr, - deit, - deprecated, - depth_anything, - depth_pro, - detr, - dialogpt, - diffllama, - dinat, - dinov2, - dinov2_with_registers, - distilbert, - dit, - donut, - dpr, - dpt, - efficientnet, - electra, - emu3, - encodec, - encoder_decoder, - ernie, - esm, - falcon, - falcon_mamba, - fastspeech2_conformer, - flaubert, - flava, - fnet, - focalnet, - fsmt, - funnel, - fuyu, - gemma, - gemma2, - gemma3, - git, - glm, - glm4, - glpn, - got_ocr2, - gpt2, - gpt_bigcode, - gpt_neo, - gpt_neox, - gpt_neox_japanese, - gpt_sw3, - gptj, - granite, - granitemoe, - granitemoeshared, - grounding_dino, - groupvit, - helium, - herbert, - hiera, - hubert, - ibert, - idefics, - idefics2, - idefics3, - ijepa, - imagegpt, - informer, - instructblip, - instructblipvideo, - jamba, - jetmoe, - kosmos2, - layoutlm, - layoutlmv2, - layoutlmv3, - layoutxlm, - led, - levit, - lilt, - llama, - llama4, - llava, - llava_next, - llava_next_video, - llava_onevision, - longformer, - longt5, - luke, - lxmert, - m2m_100, - mamba, - mamba2, - marian, - markuplm, - mask2former, - maskformer, - mbart, - mbart50, - megatron_bert, - megatron_gpt2, - mgp_str, - mimi, - mistral, - mistral3, - mixtral, - mllama, - mluke, - mobilebert, - mobilenet_v1, - mobilenet_v2, - mobilevit, - mobilevitv2, - modernbert, - moonshine, - moshi, - mpnet, - mpt, - mra, - mt5, - musicgen, - musicgen_melody, - mvp, - myt5, - nemotron, - nllb, - nllb_moe, - nougat, - nystromformer, - olmo, - olmo2, - olmoe, - omdet_turbo, - oneformer, - openai, - opt, - owlv2, - owlvit, - paligemma, - patchtsmixer, - patchtst, - pegasus, - pegasus_x, - perceiver, - persimmon, - phi, - phi3, - phi4_multimodal, - phimoe, - phobert, - pix2struct, - pixtral, - plbart, - poolformer, - pop2piano, - prompt_depth_anything, - prophetnet, - pvt, - pvt_v2, - qwen2, - qwen2_5_vl, - qwen2_audio, - qwen2_moe, - qwen2_vl, - qwen3, - qwen3_moe, - rag, - recurrent_gemma, - reformer, - regnet, - rembert, - resnet, - roberta, - roberta_prelayernorm, - roc_bert, - roformer, - rt_detr, - rt_detr_v2, - rwkv, - sam, - seamless_m4t, - seamless_m4t_v2, - segformer, - seggpt, - sew, - sew_d, - shieldgemma2, - siglip, - siglip2, - smolvlm, - speech_encoder_decoder, - speech_to_text, - speecht5, - splinter, - squeezebert, - stablelm, - starcoder2, - superglue, - superpoint, - swiftformer, - swin, - swin2sr, - swinv2, - switch_transformers, - t5, - table_transformer, - tapas, - textnet, - time_series_transformer, - timesformer, - timm_backbone, - timm_wrapper, - trocr, - tvp, - udop, - umt5, - unispeech, - unispeech_sat, - univnet, - upernet, - video_llava, - videomae, - vilt, - vipllava, - vision_encoder_decoder, - vision_text_dual_encoder, - visual_bert, - vit, - vit_mae, - vit_msn, - vitdet, - vitmatte, - vitpose, - vitpose_backbone, - vits, - vivit, - wav2vec2, - wav2vec2_bert, - wav2vec2_conformer, - wav2vec2_phoneme, - wav2vec2_with_lm, - wavlm, - whisper, - x_clip, - xglm, - xlm, - xlm_roberta, - xlm_roberta_xl, - xlnet, - xmod, - yolos, - yoso, - zamba, - zamba2, - zoedepth, -) +from ..utils import _LazyModule +from ..utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .albert import * + from .align import * + from .altclip import * + from .aria import * + from .audio_spectrogram_transformer import * + from .auto import * + from .autoformer import * + from .aya_vision import * + from .bamba import * + from .bark import * + from .bart import * + from .barthez import * + from .bartpho import * + from .beit import * + from .bert import * + from .bert_generation import * + from .bert_japanese import * + from .bertweet import * + from .big_bird import * + from .bigbird_pegasus import * + from .biogpt import * + from .bit import * + from .blenderbot import * + from .blenderbot_small import * + from .blip import * + from .blip_2 import * + from .bloom import * + from .bridgetower import * + from .bros import * + from .byt5 import * + from .camembert import * + from .canine import * + from .chameleon import * + from .chinese_clip import * + from .clap import * + from .clip import * + from .clipseg import * + from .clvp import * + from .code_llama import * + from .codegen import * + from .cohere import * + from .cohere2 import * + from .colpali import * + from .conditional_detr import * + from .convbert import * + from .convnext import * + from .convnextv2 import * + from .cpm import * + from .cpmant import * + from .ctrl import * + from .cvt import * + from .dab_detr import * + from .dac import * + from .data2vec import * + from .dbrx import * + from .deberta import * + from .deberta_v2 import * + from .decision_transformer import * + from .deformable_detr import * + from .deit import * + from .deprecated import * + from .depth_anything import * + from .depth_pro import * + from .detr import * + from .dialogpt import * + from .diffllama import * + from .dinat import * + from .dinov2 import * + from .dinov2_with_registers import * + from .distilbert import * + from .dit import * + from .donut import * + from .dpr import * + from .dpt import * + from .efficientnet import * + from .electra import * + from .emu3 import * + from .encodec import * + from .encoder_decoder import * + from .ernie import * + from .esm import * + from .falcon import * + from .falcon_mamba import * + from .fastspeech2_conformer import * + from .flaubert import * + from .flava import * + from .fnet import * + from .focalnet import * + from .fsmt import * + from .funnel import * + from .fuyu import * + from .gemma import * + from .gemma2 import * + from .gemma3 import * + from .git import * + from .glm import * + from .glm4 import * + from .glpn import * + from .got_ocr2 import * + from .gpt2 import * + from .gpt_bigcode import * + from .gpt_neo import * + from .gpt_neox import * + from .gpt_neox_japanese import * + from .gpt_sw3 import * + from .gptj import * + from .granite import * + from .granitemoe import * + from .granitemoeshared import * + from .grounding_dino import * + from .groupvit import * + from .helium import * + from .herbert import * + from .hiera import * + from .hubert import * + from .ibert import * + from .idefics import * + from .idefics2 import * + from .idefics3 import * + from .ijepa import * + from .imagegpt import * + from .informer import * + from .instructblip import * + from .instructblipvideo import * + from .jamba import * + from .jetmoe import * + from .kosmos2 import * + from .layoutlm import * + from .layoutlmv2 import * + from .layoutlmv3 import * + from .layoutxlm import * + from .led import * + from .levit import * + from .lilt import * + from .llama import * + from .llama4 import * + from .llava import * + from .llava_next import * + from .llava_next_video import * + from .llava_onevision import * + from .longformer import * + from .longt5 import * + from .luke import * + from .lxmert import * + from .m2m_100 import * + from .mamba import * + from .mamba2 import * + from .marian import * + from .markuplm import * + from .mask2former import * + from .maskformer import * + from .mbart import * + from .mbart50 import * + from .megatron_bert import * + from .megatron_gpt2 import * + from .mgp_str import * + from .mimi import * + from .mistral import * + from .mistral3 import * + from .mixtral import * + from .mllama import * + from .mluke import * + from .mobilebert import * + from .mobilenet_v1 import * + from .mobilenet_v2 import * + from .mobilevit import * + from .mobilevitv2 import * + from .modernbert import * + from .moonshine import * + from .moshi import * + from .mpnet import * + from .mpt import * + from .mra import * + from .mt5 import * + from .musicgen import * + from .musicgen_melody import * + from .mvp import * + from .myt5 import * + from .nemotron import * + from .nllb import * + from .nllb_moe import * + from .nougat import * + from .nystromformer import * + from .olmo import * + from .olmo2 import * + from .olmoe import * + from .omdet_turbo import * + from .oneformer import * + from .openai import * + from .opt import * + from .owlv2 import * + from .owlvit import * + from .paligemma import * + from .patchtsmixer import * + from .patchtst import * + from .pegasus import * + from .pegasus_x import * + from .perceiver import * + from .persimmon import * + from .phi import * + from .phi3 import * + from .phi4_multimodal import * + from .phimoe import * + from .phobert import * + from .pix2struct import * + from .pixtral import * + from .plbart import * + from .poolformer import * + from .pop2piano import * + from .prophetnet import * + from .pvt import * + from .pvt_v2 import * + from .qwen2 import * + from .qwen2_5_vl import * + from .qwen2_audio import * + from .qwen2_moe import * + from .qwen2_vl import * + from .rag import * + from .recurrent_gemma import * + from .reformer import * + from .regnet import * + from .rembert import * + from .resnet import * + from .roberta import * + from .roberta_prelayernorm import * + from .roc_bert import * + from .roformer import * + from .rt_detr import * + from .rt_detr_v2 import * + from .rwkv import * + from .sam import * + from .seamless_m4t import * + from .seamless_m4t_v2 import * + from .segformer import * + from .seggpt import * + from .sew import * + from .sew_d import * + from .siglip import * + from .siglip2 import * + from .smolvlm import * + from .speech_encoder_decoder import * + from .speech_to_text import * + from .speecht5 import * + from .splinter import * + from .squeezebert import * + from .stablelm import * + from .starcoder2 import * + from .superglue import * + from .superpoint import * + from .swiftformer import * + from .swin import * + from .swin2sr import * + from .swinv2 import * + from .switch_transformers import * + from .t5 import * + from .table_transformer import * + from .tapas import * + from .textnet import * + from .time_series_transformer import * + from .timesformer import * + from .timm_backbone import * + from .timm_wrapper import * + from .trocr import * + from .tvp import * + from .udop import * + from .umt5 import * + from .unispeech import * + from .unispeech_sat import * + from .univnet import * + from .upernet import * + from .video_llava import * + from .videomae import * + from .vilt import * + from .vipllava import * + from .vision_encoder_decoder import * + from .vision_text_dual_encoder import * + from .visual_bert import * + from .vit import * + from .vit_mae import * + from .vit_msn import * + from .vitdet import * + from .vitmatte import * + from .vitpose import * + from .vitpose_backbone import * + from .vits import * + from .vivit import * + from .wav2vec2 import * + from .wav2vec2_bert import * + from .wav2vec2_conformer import * + from .wav2vec2_phoneme import * + from .wav2vec2_with_lm import * + from .wavlm import * + from .whisper import * + from .x_clip import * + from .xglm import * + from .xlm import * + from .xlm_roberta import * + from .xlm_roberta_xl import * + from .xlnet import * + from .xmod import * + from .yolos import * + from .yoso import * + from .zamba import * + from .zamba2 import * + from .zoedepth import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/albert/tokenization_albert.py b/src/transformers/models/albert/tokenization_albert.py index 4971d0511f..7ecd9f907e 100644 --- a/src/transformers/models/albert/tokenization_albert.py +++ b/src/transformers/models/albert/tokenization_albert.py @@ -23,7 +23,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging -from ...utils.import_utils import export +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -33,7 +33,7 @@ VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} SPIECE_UNDERLINE = "▁" -@export(backends=("sentencepiece",)) +@requires(backends=("sentencepiece",)) class AlbertTokenizer(PreTrainedTokenizer): """ Construct an ALBERT tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/auto/__init__.py b/src/transformers/models/auto/__init__.py index 1f626d8c24..6828030287 100644 --- a/src/transformers/models/auto/__init__.py +++ b/src/transformers/models/auto/__init__.py @@ -11,399 +11,24 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - from typing import TYPE_CHECKING -from ...utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_flax_available, - is_tf_available, - is_torch_available, -) - - -_import_structure = { - "auto_factory": ["get_values"], - "configuration_auto": ["CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"], - "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"], - "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"], - "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"], - "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_auto"] = [ - "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", - "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING", - "MODEL_FOR_AUDIO_XVECTOR_MAPPING", - "MODEL_FOR_BACKBONE_MAPPING", - "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING", - "MODEL_FOR_CAUSAL_LM_MAPPING", - "MODEL_FOR_CTC_MAPPING", - "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", - "MODEL_FOR_DEPTH_ESTIMATION_MAPPING", - "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", - "MODEL_FOR_IMAGE_MAPPING", - "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING", - "MODEL_FOR_IMAGE_TO_IMAGE_MAPPING", - "MODEL_FOR_KEYPOINT_DETECTION_MAPPING", - "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING", - "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", - "MODEL_FOR_MASKED_LM_MAPPING", - "MODEL_FOR_MASK_GENERATION_MAPPING", - "MODEL_FOR_MULTIPLE_CHOICE_MAPPING", - "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", - "MODEL_FOR_OBJECT_DETECTION_MAPPING", - "MODEL_FOR_PRETRAINING_MAPPING", - "MODEL_FOR_QUESTION_ANSWERING_MAPPING", - "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", - "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", - "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", - "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", - "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", - "MODEL_FOR_TEXT_ENCODING_MAPPING", - "MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING", - "MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING", - "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", - "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING", - "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING", - "MODEL_FOR_VISION_2_SEQ_MAPPING", - "MODEL_FOR_RETRIEVAL_MAPPING", - "MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING", - "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING", - "MODEL_MAPPING", - "MODEL_WITH_LM_HEAD_MAPPING", - "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING", - "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", - "MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING", - "MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING", - "AutoModel", - "AutoBackbone", - "AutoModelForAudioClassification", - "AutoModelForAudioFrameClassification", - "AutoModelForAudioXVector", - "AutoModelForCausalLM", - "AutoModelForCTC", - "AutoModelForDepthEstimation", - "AutoModelForImageClassification", - "AutoModelForImageSegmentation", - "AutoModelForImageToImage", - "AutoModelForInstanceSegmentation", - "AutoModelForKeypointDetection", - "AutoModelForMaskGeneration", - "AutoModelForTextEncoding", - "AutoModelForMaskedImageModeling", - "AutoModelForMaskedLM", - "AutoModelForMultipleChoice", - "AutoModelForNextSentencePrediction", - "AutoModelForObjectDetection", - "AutoModelForPreTraining", - "AutoModelForQuestionAnswering", - "AutoModelForSemanticSegmentation", - "AutoModelForSeq2SeqLM", - "AutoModelForSequenceClassification", - "AutoModelForSpeechSeq2Seq", - "AutoModelForTableQuestionAnswering", - "AutoModelForTextToSpectrogram", - "AutoModelForTextToWaveform", - "AutoModelForTokenClassification", - "AutoModelForUniversalSegmentation", - "AutoModelForVideoClassification", - "AutoModelForVision2Seq", - "AutoModelForVisualQuestionAnswering", - "AutoModelForDocumentQuestionAnswering", - "AutoModelWithLMHead", - "AutoModelForZeroShotImageClassification", - "AutoModelForZeroShotObjectDetection", - "AutoModelForImageTextToText", - ] - -try: - if not is_tf_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_tf_auto"] = [ - "TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", - "TF_MODEL_FOR_CAUSAL_LM_MAPPING", - "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", - "TF_MODEL_FOR_MASK_GENERATION_MAPPING", - "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", - "TF_MODEL_FOR_MASKED_LM_MAPPING", - "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", - "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", - "TF_MODEL_FOR_PRETRAINING_MAPPING", - "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", - "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", - "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", - "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", - "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", - "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", - "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", - "TF_MODEL_FOR_TEXT_ENCODING_MAPPING", - "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", - "TF_MODEL_FOR_VISION_2_SEQ_MAPPING", - "TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING", - "TF_MODEL_MAPPING", - "TF_MODEL_WITH_LM_HEAD_MAPPING", - "TFAutoModel", - "TFAutoModelForAudioClassification", - "TFAutoModelForCausalLM", - "TFAutoModelForImageClassification", - "TFAutoModelForMaskedImageModeling", - "TFAutoModelForMaskedLM", - "TFAutoModelForMaskGeneration", - "TFAutoModelForMultipleChoice", - "TFAutoModelForNextSentencePrediction", - "TFAutoModelForPreTraining", - "TFAutoModelForDocumentQuestionAnswering", - "TFAutoModelForQuestionAnswering", - "TFAutoModelForSemanticSegmentation", - "TFAutoModelForSeq2SeqLM", - "TFAutoModelForSequenceClassification", - "TFAutoModelForSpeechSeq2Seq", - "TFAutoModelForTableQuestionAnswering", - "TFAutoModelForTextEncoding", - "TFAutoModelForTokenClassification", - "TFAutoModelForVision2Seq", - "TFAutoModelForZeroShotImageClassification", - "TFAutoModelWithLMHead", - ] - -try: - if not is_flax_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_flax_auto"] = [ - "FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", - "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING", - "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", - "FLAX_MODEL_FOR_MASKED_LM_MAPPING", - "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", - "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", - "FLAX_MODEL_FOR_PRETRAINING_MAPPING", - "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING", - "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", - "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", - "FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", - "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", - "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING", - "FLAX_MODEL_MAPPING", - "FlaxAutoModel", - "FlaxAutoModelForCausalLM", - "FlaxAutoModelForImageClassification", - "FlaxAutoModelForMaskedLM", - "FlaxAutoModelForMultipleChoice", - "FlaxAutoModelForNextSentencePrediction", - "FlaxAutoModelForPreTraining", - "FlaxAutoModelForQuestionAnswering", - "FlaxAutoModelForSeq2SeqLM", - "FlaxAutoModelForSequenceClassification", - "FlaxAutoModelForSpeechSeq2Seq", - "FlaxAutoModelForTokenClassification", - "FlaxAutoModelForVision2Seq", - ] +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure if TYPE_CHECKING: - from .auto_factory import get_values - from .configuration_auto import CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig - from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor - from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor - from .processing_auto import PROCESSOR_MAPPING, AutoProcessor - from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_auto import ( - MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, - MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING, - MODEL_FOR_AUDIO_XVECTOR_MAPPING, - MODEL_FOR_BACKBONE_MAPPING, - MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, - MODEL_FOR_CAUSAL_LM_MAPPING, - MODEL_FOR_CTC_MAPPING, - MODEL_FOR_DEPTH_ESTIMATION_MAPPING, - MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, - MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, - MODEL_FOR_IMAGE_MAPPING, - MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, - MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING, - MODEL_FOR_IMAGE_TO_IMAGE_MAPPING, - MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, - MODEL_FOR_KEYPOINT_DETECTION_MAPPING, - MODEL_FOR_MASK_GENERATION_MAPPING, - MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, - MODEL_FOR_MASKED_LM_MAPPING, - MODEL_FOR_MULTIPLE_CHOICE_MAPPING, - MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, - MODEL_FOR_OBJECT_DETECTION_MAPPING, - MODEL_FOR_PRETRAINING_MAPPING, - MODEL_FOR_QUESTION_ANSWERING_MAPPING, - MODEL_FOR_RETRIEVAL_MAPPING, - MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, - MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, - MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, - MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, - MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, - MODEL_FOR_TEXT_ENCODING_MAPPING, - MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING, - MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING, - MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING, - MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING, - MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, - MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING, - MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, - MODEL_FOR_VISION_2_SEQ_MAPPING, - MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, - MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING, - MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, - MODEL_MAPPING, - MODEL_WITH_LM_HEAD_MAPPING, - AutoBackbone, - AutoModel, - AutoModelForAudioClassification, - AutoModelForAudioFrameClassification, - AutoModelForAudioXVector, - AutoModelForCausalLM, - AutoModelForCTC, - AutoModelForDepthEstimation, - AutoModelForDocumentQuestionAnswering, - AutoModelForImageClassification, - AutoModelForImageSegmentation, - AutoModelForImageTextToText, - AutoModelForImageToImage, - AutoModelForInstanceSegmentation, - AutoModelForKeypointDetection, - AutoModelForMaskedImageModeling, - AutoModelForMaskedLM, - AutoModelForMaskGeneration, - AutoModelForMultipleChoice, - AutoModelForNextSentencePrediction, - AutoModelForObjectDetection, - AutoModelForPreTraining, - AutoModelForQuestionAnswering, - AutoModelForSemanticSegmentation, - AutoModelForSeq2SeqLM, - AutoModelForSequenceClassification, - AutoModelForSpeechSeq2Seq, - AutoModelForTableQuestionAnswering, - AutoModelForTextEncoding, - AutoModelForTextToSpectrogram, - AutoModelForTextToWaveform, - AutoModelForTokenClassification, - AutoModelForUniversalSegmentation, - AutoModelForVideoClassification, - AutoModelForVision2Seq, - AutoModelForVisualQuestionAnswering, - AutoModelForZeroShotImageClassification, - AutoModelForZeroShotObjectDetection, - AutoModelWithLMHead, - ) - - try: - if not is_tf_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_tf_auto import ( - TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, - TF_MODEL_FOR_CAUSAL_LM_MAPPING, - TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, - TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, - TF_MODEL_FOR_MASK_GENERATION_MAPPING, - TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, - TF_MODEL_FOR_MASKED_LM_MAPPING, - TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, - TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, - TF_MODEL_FOR_PRETRAINING_MAPPING, - TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, - TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, - TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, - TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, - TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, - TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, - TF_MODEL_FOR_TEXT_ENCODING_MAPPING, - TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, - TF_MODEL_FOR_VISION_2_SEQ_MAPPING, - TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING, - TF_MODEL_MAPPING, - TF_MODEL_WITH_LM_HEAD_MAPPING, - TFAutoModel, - TFAutoModelForAudioClassification, - TFAutoModelForCausalLM, - TFAutoModelForDocumentQuestionAnswering, - TFAutoModelForImageClassification, - TFAutoModelForMaskedImageModeling, - TFAutoModelForMaskedLM, - TFAutoModelForMaskGeneration, - TFAutoModelForMultipleChoice, - TFAutoModelForNextSentencePrediction, - TFAutoModelForPreTraining, - TFAutoModelForQuestionAnswering, - TFAutoModelForSemanticSegmentation, - TFAutoModelForSeq2SeqLM, - TFAutoModelForSequenceClassification, - TFAutoModelForSpeechSeq2Seq, - TFAutoModelForTableQuestionAnswering, - TFAutoModelForTextEncoding, - TFAutoModelForTokenClassification, - TFAutoModelForVision2Seq, - TFAutoModelForZeroShotImageClassification, - TFAutoModelWithLMHead, - ) - - try: - if not is_flax_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_flax_auto import ( - FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, - FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, - FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, - FLAX_MODEL_FOR_MASKED_LM_MAPPING, - FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, - FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, - FLAX_MODEL_FOR_PRETRAINING_MAPPING, - FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, - FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, - FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, - FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, - FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, - FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING, - FLAX_MODEL_MAPPING, - FlaxAutoModel, - FlaxAutoModelForCausalLM, - FlaxAutoModelForImageClassification, - FlaxAutoModelForMaskedLM, - FlaxAutoModelForMultipleChoice, - FlaxAutoModelForNextSentencePrediction, - FlaxAutoModelForPreTraining, - FlaxAutoModelForQuestionAnswering, - FlaxAutoModelForSeq2SeqLM, - FlaxAutoModelForSequenceClassification, - FlaxAutoModelForSpeechSeq2Seq, - FlaxAutoModelForTokenClassification, - FlaxAutoModelForVision2Seq, - ) - + from .auto_factory import * + from .configuration_auto import * + from .feature_extraction_auto import * + from .image_processing_auto import * + from .modeling_auto import * + from .modeling_flax_auto import * + from .modeling_tf_auto import * + from .processing_auto import * + from .tokenization_auto import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/auto/auto_factory.py b/src/transformers/models/auto/auto_factory.py index 9c5690b496..d458c1b6f2 100644 --- a/src/transformers/models/auto/auto_factory.py +++ b/src/transformers/models/auto/auto_factory.py @@ -844,3 +844,6 @@ class _LazyAutoMapping(OrderedDict): raise ValueError(f"'{key}' is already used by a Transformers model.") self._extra_content[key] = value + + +__all__ = ["get_values"] diff --git a/src/transformers/models/auto/configuration_auto.py b/src/transformers/models/auto/configuration_auto.py index 1c646d5df1..43c0b3498d 100644 --- a/src/transformers/models/auto/configuration_auto.py +++ b/src/transformers/models/auto/configuration_auto.py @@ -1173,3 +1173,6 @@ class AutoConfig: "match!" ) CONFIG_MAPPING.register(model_type, config, exist_ok=exist_ok) + + +__all__ = ["CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"] diff --git a/src/transformers/models/auto/feature_extraction_auto.py b/src/transformers/models/auto/feature_extraction_auto.py index 0b8b38bc34..2067d1797f 100644 --- a/src/transformers/models/auto/feature_extraction_auto.py +++ b/src/transformers/models/auto/feature_extraction_auto.py @@ -406,3 +406,6 @@ class AutoFeatureExtractor: feature_extractor_class ([`FeatureExtractorMixin`]): The feature extractor to register. """ FEATURE_EXTRACTOR_MAPPING.register(config_class, feature_extractor_class, exist_ok=exist_ok) + + +__all__ = ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"] diff --git a/src/transformers/models/auto/image_processing_auto.py b/src/transformers/models/auto/image_processing_auto.py index 2f9d42fcdb..ac4848dafd 100644 --- a/src/transformers/models/auto/image_processing_auto.py +++ b/src/transformers/models/auto/image_processing_auto.py @@ -36,6 +36,7 @@ from ...utils import ( is_vision_available, logging, ) +from ...utils.import_utils import requires from .auto_factory import _LazyAutoMapping from .configuration_auto import ( CONFIG_MAPPING_NAMES, @@ -324,6 +325,7 @@ def _warning_fast_image_processor_available(fast_class): ) +@requires(backends=("vision", "torchvision")) class AutoImageProcessor: r""" This is a generic image processor class that will be instantiated as one of the image processor classes of the @@ -640,3 +642,6 @@ class AutoImageProcessor: IMAGE_PROCESSOR_MAPPING.register( config_class, (slow_image_processor_class, fast_image_processor_class), exist_ok=exist_ok ) + + +__all__ = ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"] diff --git a/src/transformers/models/auto/modeling_auto.py b/src/transformers/models/auto/modeling_auto.py index 84df5dc363..8c51d6576e 100644 --- a/src/transformers/models/auto/modeling_auto.py +++ b/src/transformers/models/auto/modeling_auto.py @@ -1955,3 +1955,90 @@ class AutoModelWithLMHead(_AutoModelWithLMHead): FutureWarning, ) return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + + +__all__ = [ + "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", + "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING", + "MODEL_FOR_AUDIO_XVECTOR_MAPPING", + "MODEL_FOR_BACKBONE_MAPPING", + "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING", + "MODEL_FOR_CAUSAL_LM_MAPPING", + "MODEL_FOR_CTC_MAPPING", + "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", + "MODEL_FOR_DEPTH_ESTIMATION_MAPPING", + "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", + "MODEL_FOR_IMAGE_MAPPING", + "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING", + "MODEL_FOR_IMAGE_TO_IMAGE_MAPPING", + "MODEL_FOR_KEYPOINT_DETECTION_MAPPING", + "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING", + "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", + "MODEL_FOR_MASKED_LM_MAPPING", + "MODEL_FOR_MASK_GENERATION_MAPPING", + "MODEL_FOR_MULTIPLE_CHOICE_MAPPING", + "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", + "MODEL_FOR_OBJECT_DETECTION_MAPPING", + "MODEL_FOR_PRETRAINING_MAPPING", + "MODEL_FOR_QUESTION_ANSWERING_MAPPING", + "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", + "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", + "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", + "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", + "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", + "MODEL_FOR_TEXT_ENCODING_MAPPING", + "MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING", + "MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING", + "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", + "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING", + "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING", + "MODEL_FOR_VISION_2_SEQ_MAPPING", + "MODEL_FOR_RETRIEVAL_MAPPING", + "MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING", + "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING", + "MODEL_MAPPING", + "MODEL_WITH_LM_HEAD_MAPPING", + "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING", + "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", + "MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING", + "MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING", + "AutoModel", + "AutoBackbone", + "AutoModelForAudioClassification", + "AutoModelForAudioFrameClassification", + "AutoModelForAudioXVector", + "AutoModelForCausalLM", + "AutoModelForCTC", + "AutoModelForDepthEstimation", + "AutoModelForImageClassification", + "AutoModelForImageSegmentation", + "AutoModelForImageToImage", + "AutoModelForInstanceSegmentation", + "AutoModelForKeypointDetection", + "AutoModelForMaskGeneration", + "AutoModelForTextEncoding", + "AutoModelForMaskedImageModeling", + "AutoModelForMaskedLM", + "AutoModelForMultipleChoice", + "AutoModelForNextSentencePrediction", + "AutoModelForObjectDetection", + "AutoModelForPreTraining", + "AutoModelForQuestionAnswering", + "AutoModelForSemanticSegmentation", + "AutoModelForSeq2SeqLM", + "AutoModelForSequenceClassification", + "AutoModelForSpeechSeq2Seq", + "AutoModelForTableQuestionAnswering", + "AutoModelForTextToSpectrogram", + "AutoModelForTextToWaveform", + "AutoModelForTokenClassification", + "AutoModelForUniversalSegmentation", + "AutoModelForVideoClassification", + "AutoModelForVision2Seq", + "AutoModelForVisualQuestionAnswering", + "AutoModelForDocumentQuestionAnswering", + "AutoModelWithLMHead", + "AutoModelForZeroShotImageClassification", + "AutoModelForZeroShotObjectDetection", + "AutoModelForImageTextToText", +] diff --git a/src/transformers/models/auto/modeling_flax_auto.py b/src/transformers/models/auto/modeling_flax_auto.py index 74b3d66167..0588d03cb6 100644 --- a/src/transformers/models/auto/modeling_flax_auto.py +++ b/src/transformers/models/auto/modeling_flax_auto.py @@ -381,3 +381,33 @@ class FlaxAutoModelForSpeechSeq2Seq(_BaseAutoModelClass): FlaxAutoModelForSpeechSeq2Seq = auto_class_update( FlaxAutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling" ) + +__all__ = [ + "FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", + "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING", + "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", + "FLAX_MODEL_FOR_MASKED_LM_MAPPING", + "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", + "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", + "FLAX_MODEL_FOR_PRETRAINING_MAPPING", + "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING", + "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", + "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", + "FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", + "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", + "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING", + "FLAX_MODEL_MAPPING", + "FlaxAutoModel", + "FlaxAutoModelForCausalLM", + "FlaxAutoModelForImageClassification", + "FlaxAutoModelForMaskedLM", + "FlaxAutoModelForMultipleChoice", + "FlaxAutoModelForNextSentencePrediction", + "FlaxAutoModelForPreTraining", + "FlaxAutoModelForQuestionAnswering", + "FlaxAutoModelForSeq2SeqLM", + "FlaxAutoModelForSequenceClassification", + "FlaxAutoModelForSpeechSeq2Seq", + "FlaxAutoModelForTokenClassification", + "FlaxAutoModelForVision2Seq", +] diff --git a/src/transformers/models/auto/modeling_tf_auto.py b/src/transformers/models/auto/modeling_tf_auto.py index 67b69e2c61..cf39f4d7c9 100644 --- a/src/transformers/models/auto/modeling_tf_auto.py +++ b/src/transformers/models/auto/modeling_tf_auto.py @@ -726,3 +726,51 @@ class TFAutoModelWithLMHead(_TFAutoModelWithLMHead): FutureWarning, ) return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) + + +__all__ = [ + "TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", + "TF_MODEL_FOR_CAUSAL_LM_MAPPING", + "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", + "TF_MODEL_FOR_MASK_GENERATION_MAPPING", + "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", + "TF_MODEL_FOR_MASKED_LM_MAPPING", + "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", + "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", + "TF_MODEL_FOR_PRETRAINING_MAPPING", + "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", + "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", + "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", + "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", + "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", + "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", + "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", + "TF_MODEL_FOR_TEXT_ENCODING_MAPPING", + "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", + "TF_MODEL_FOR_VISION_2_SEQ_MAPPING", + "TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING", + "TF_MODEL_MAPPING", + "TF_MODEL_WITH_LM_HEAD_MAPPING", + "TFAutoModel", + "TFAutoModelForAudioClassification", + "TFAutoModelForCausalLM", + "TFAutoModelForImageClassification", + "TFAutoModelForMaskedImageModeling", + "TFAutoModelForMaskedLM", + "TFAutoModelForMaskGeneration", + "TFAutoModelForMultipleChoice", + "TFAutoModelForNextSentencePrediction", + "TFAutoModelForPreTraining", + "TFAutoModelForDocumentQuestionAnswering", + "TFAutoModelForQuestionAnswering", + "TFAutoModelForSemanticSegmentation", + "TFAutoModelForSeq2SeqLM", + "TFAutoModelForSequenceClassification", + "TFAutoModelForSpeechSeq2Seq", + "TFAutoModelForTableQuestionAnswering", + "TFAutoModelForTextEncoding", + "TFAutoModelForTokenClassification", + "TFAutoModelForVision2Seq", + "TFAutoModelForZeroShotImageClassification", + "TFAutoModelWithLMHead", +] diff --git a/src/transformers/models/auto/processing_auto.py b/src/transformers/models/auto/processing_auto.py index 6e655edff1..4cda1ebd19 100644 --- a/src/transformers/models/auto/processing_auto.py +++ b/src/transformers/models/auto/processing_auto.py @@ -389,3 +389,6 @@ class AutoProcessor: processor_class ([`ProcessorMixin`]): The processor to register. """ PROCESSOR_MAPPING.register(config_class, processor_class, exist_ok=exist_ok) + + +__all__ = ["PROCESSOR_MAPPING", "AutoProcessor"] diff --git a/src/transformers/models/auto/tokenization_auto.py b/src/transformers/models/auto/tokenization_auto.py index eda7356a60..d6f4fcf564 100644 --- a/src/transformers/models/auto/tokenization_auto.py +++ b/src/transformers/models/auto/tokenization_auto.py @@ -1083,3 +1083,6 @@ class AutoTokenizer: fast_tokenizer_class = existing_fast TOKENIZER_MAPPING.register(config_class, (slow_tokenizer_class, fast_tokenizer_class), exist_ok=exist_ok) + + +__all__ = ["TOKENIZER_MAPPING", "AutoTokenizer"] diff --git a/src/transformers/models/autoformer/__init__.py b/src/transformers/models/autoformer/__init__.py index 1ef70173e3..48a3296080 100644 --- a/src/transformers/models/autoformer/__init__.py +++ b/src/transformers/models/autoformer/__init__.py @@ -13,45 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -# rely on isort to merge the imports -from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available - - -_import_structure = { - "configuration_autoformer": ["AutoformerConfig"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_autoformer"] = [ - "AutoformerForPrediction", - "AutoformerModel", - "AutoformerPreTrainedModel", - ] +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_autoformer import ( - AutoformerConfig, - ) - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_autoformer import ( - AutoformerForPrediction, - AutoformerModel, - AutoformerPreTrainedModel, - ) - + from .configuration_autoformer import * + from .modeling_autoformer import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/autoformer/configuration_autoformer.py b/src/transformers/models/autoformer/configuration_autoformer.py index f5a4356ce8..aba83f19a5 100644 --- a/src/transformers/models/autoformer/configuration_autoformer.py +++ b/src/transformers/models/autoformer/configuration_autoformer.py @@ -240,3 +240,6 @@ class AutoformerConfig(PretrainedConfig): + self.num_static_real_features + self.input_size * 2 # the log1p(abs(loc)) and log(scale) features ) + + +__all__ = ["AutoformerConfig"] diff --git a/src/transformers/models/autoformer/modeling_autoformer.py b/src/transformers/models/autoformer/modeling_autoformer.py index 5a96b6235d..eb32013d5e 100644 --- a/src/transformers/models/autoformer/modeling_autoformer.py +++ b/src/transformers/models/autoformer/modeling_autoformer.py @@ -2147,3 +2147,6 @@ class AutoformerForPrediction(AutoformerPreTrainedModel): (-1, num_parallel_samples, self.config.prediction_length) + self.target_shape, ) ) + + +__all__ = ["AutoformerForPrediction", "AutoformerModel", "AutoformerPreTrainedModel"] diff --git a/src/transformers/models/barthez/tokenization_barthez.py b/src/transformers/models/barthez/tokenization_barthez.py index 604f9c7c21..0db6634d98 100644 --- a/src/transformers/models/barthez/tokenization_barthez.py +++ b/src/transformers/models/barthez/tokenization_barthez.py @@ -22,6 +22,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -34,6 +35,7 @@ SPIECE_UNDERLINE = "▁" # TODO this class is useless. This is the most standard sentencpiece model. Let's find which one is closest and nuke this. +@requires(backends=("sentencepiece",)) class BarthezTokenizer(PreTrainedTokenizer): """ Adapted from [`CamembertTokenizer`] and [`BartTokenizer`]. Construct a BARThez tokenizer. Based on diff --git a/src/transformers/models/bartpho/tokenization_bartpho.py b/src/transformers/models/bartpho/tokenization_bartpho.py index e6e4f88984..c3e121089e 100644 --- a/src/transformers/models/bartpho/tokenization_bartpho.py +++ b/src/transformers/models/bartpho/tokenization_bartpho.py @@ -22,6 +22,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -31,6 +32,7 @@ SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"} +@requires(backends=("sentencepiece",)) class BartphoTokenizer(PreTrainedTokenizer): """ Adapted from [`XLMRobertaTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/beit/feature_extraction_beit.py b/src/transformers/models/beit/feature_extraction_beit.py index 141d8bc36d..7886897c6d 100644 --- a/src/transformers/models/beit/feature_extraction_beit.py +++ b/src/transformers/models/beit/feature_extraction_beit.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_beit import BeitImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class BeitFeatureExtractor(BeitImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/beit/image_processing_beit.py b/src/transformers/models/beit/image_processing_beit.py index 0d928f2141..eb2950f0e2 100644 --- a/src/transformers/models/beit/image_processing_beit.py +++ b/src/transformers/models/beit/image_processing_beit.py @@ -42,6 +42,7 @@ from ...utils import ( logging, ) from ...utils.deprecation import deprecate_kwarg +from ...utils.import_utils import requires if is_vision_available(): @@ -54,6 +55,7 @@ if is_torch_available(): logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class BeitImageProcessor(BaseImageProcessor): r""" Constructs a BEiT image processor. diff --git a/src/transformers/models/bert/tokenization_bert_tf.py b/src/transformers/models/bert/tokenization_bert_tf.py index 86658de524..e98fe340ee 100644 --- a/src/transformers/models/bert/tokenization_bert_tf.py +++ b/src/transformers/models/bert/tokenization_bert_tf.py @@ -6,9 +6,11 @@ from tensorflow_text import BertTokenizer as BertTokenizerLayer from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utf8, combine_segments, pad_model_inputs from ...modeling_tf_utils import keras +from ...utils.import_utils import requires from .tokenization_bert import BertTokenizer +@requires(backends=("tf", "tensorflow_text")) class TFBertTokenizer(keras.layers.Layer): """ This is an in-graph tokenizer for BERT. It should be initialized similarly to other tokenizers, using the diff --git a/src/transformers/models/bert_generation/tokenization_bert_generation.py b/src/transformers/models/bert_generation/tokenization_bert_generation.py index 31f046863c..727727d4a1 100644 --- a/src/transformers/models/bert_generation/tokenization_bert_generation.py +++ b/src/transformers/models/bert_generation/tokenization_bert_generation.py @@ -22,6 +22,7 @@ import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -29,6 +30,7 @@ logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} +@requires(backends=("sentencepiece",)) class BertGenerationTokenizer(PreTrainedTokenizer): """ Construct a BertGeneration tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/big_bird/tokenization_big_bird.py b/src/transformers/models/big_bird/tokenization_big_bird.py index 3e2d13e47a..43172e20f3 100644 --- a/src/transformers/models/big_bird/tokenization_big_bird.py +++ b/src/transformers/models/big_bird/tokenization_big_bird.py @@ -23,6 +23,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -30,6 +31,7 @@ logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} +@requires(backends=("sentencepiece",)) class BigBirdTokenizer(PreTrainedTokenizer): """ Construct a BigBird tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/blip/__init__.py b/src/transformers/models/blip/__init__.py index 1102af75d1..952de2f855 100644 --- a/src/transformers/models/blip/__init__.py +++ b/src/transformers/models/blip/__init__.py @@ -22,7 +22,9 @@ if TYPE_CHECKING: from .image_processing_blip import * from .image_processing_blip_fast import * from .modeling_blip import * + from .modeling_blip_text import * from .modeling_tf_blip import * + from .modeling_tf_blip_text import * from .processing_blip import * else: import sys diff --git a/src/transformers/models/blip/modeling_blip_text.py b/src/transformers/models/blip/modeling_blip_text.py index 15087677e2..f26f269c7b 100644 --- a/src/transformers/models/blip/modeling_blip_text.py +++ b/src/transformers/models/blip/modeling_blip_text.py @@ -955,3 +955,6 @@ class BlipTextLMHeadModel(BlipTextPreTrainedModel, GenerationMixin): tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past + + +__all__ = ["BlipTextModel", "BlipTextLMHeadModel", "BlipTextPreTrainedModel"] diff --git a/src/transformers/models/blip/modeling_tf_blip_text.py b/src/transformers/models/blip/modeling_tf_blip_text.py index b605a25eeb..6414bfa3b7 100644 --- a/src/transformers/models/blip/modeling_tf_blip_text.py +++ b/src/transformers/models/blip/modeling_tf_blip_text.py @@ -1120,3 +1120,6 @@ class TFBlipTextLMHeadModel(TFBlipTextPreTrainedModel): if getattr(self, "cls", None) is not None: with tf.name_scope(self.cls.name): self.cls.build(None) + + +__all__ = ["TFBlipTextLMHeadModel", "TFBlipTextModel", "TFBlipTextPreTrainedModel"] diff --git a/src/transformers/models/camembert/tokenization_camembert.py b/src/transformers/models/camembert/tokenization_camembert.py index 3353bf3433..23cc569d49 100644 --- a/src/transformers/models/camembert/tokenization_camembert.py +++ b/src/transformers/models/camembert/tokenization_camembert.py @@ -22,6 +22,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -32,6 +33,7 @@ VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} SPIECE_UNDERLINE = "▁" +@requires(backends=("sentencepiece",)) class CamembertTokenizer(PreTrainedTokenizer): """ Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Construct a CamemBERT tokenizer. Based on diff --git a/src/transformers/models/chinese_clip/feature_extraction_chinese_clip.py b/src/transformers/models/chinese_clip/feature_extraction_chinese_clip.py index fd416ca93b..c4895bb06b 100644 --- a/src/transformers/models/chinese_clip/feature_extraction_chinese_clip.py +++ b/src/transformers/models/chinese_clip/feature_extraction_chinese_clip.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_chinese_clip import ChineseCLIPImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class ChineseCLIPFeatureExtractor(ChineseCLIPImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/chinese_clip/image_processing_chinese_clip.py b/src/transformers/models/chinese_clip/image_processing_chinese_clip.py index 629de907b1..d14d286b57 100644 --- a/src/transformers/models/chinese_clip/image_processing_chinese_clip.py +++ b/src/transformers/models/chinese_clip/image_processing_chinese_clip.py @@ -41,13 +41,17 @@ from ...image_utils import ( from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging -logger = logging.get_logger(__name__) - - if is_vision_available(): import PIL +from ...utils.import_utils import requires + + +logger = logging.get_logger(__name__) + + +@requires(backends=("vision",)) class ChineseCLIPImageProcessor(BaseImageProcessor): r""" Constructs a Chinese-CLIP image processor. diff --git a/src/transformers/models/clap/feature_extraction_clap.py b/src/transformers/models/clap/feature_extraction_clap.py index c4a4428f7b..cbe51cab72 100644 --- a/src/transformers/models/clap/feature_extraction_clap.py +++ b/src/transformers/models/clap/feature_extraction_clap.py @@ -24,11 +24,13 @@ from ...audio_utils import mel_filter_bank, spectrogram, window_function from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import TensorType, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) +@requires(backends=("torch",)) class ClapFeatureExtractor(SequenceFeatureExtractor): r""" Constructs a CLAP feature extractor. diff --git a/src/transformers/models/clip/feature_extraction_clip.py b/src/transformers/models/clip/feature_extraction_clip.py index 1984d88387..bed6b59eaa 100644 --- a/src/transformers/models/clip/feature_extraction_clip.py +++ b/src/transformers/models/clip/feature_extraction_clip.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_clip import CLIPImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class CLIPFeatureExtractor(CLIPImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/clip/image_processing_clip.py b/src/transformers/models/clip/image_processing_clip.py index 8c02cd14eb..77215ad636 100644 --- a/src/transformers/models/clip/image_processing_clip.py +++ b/src/transformers/models/clip/image_processing_clip.py @@ -40,6 +40,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, is_vision_available, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -49,6 +50,7 @@ if is_vision_available(): import PIL +@requires(backends=("vision",)) class CLIPImageProcessor(BaseImageProcessor): r""" Constructs a CLIP image processor. diff --git a/src/transformers/models/code_llama/tokenization_code_llama.py b/src/transformers/models/code_llama/tokenization_code_llama.py index 43386ecdae..99bdcb5e64 100644 --- a/src/transformers/models/code_llama/tokenization_code_llama.py +++ b/src/transformers/models/code_llama/tokenization_code_llama.py @@ -25,6 +25,7 @@ import sentencepiece as spm from ...convert_slow_tokenizer import import_protobuf from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging, requires_backends +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -46,6 +47,7 @@ correct. If you don't know the answer to a question, please don't share false in # fmt: on +@requires(backends=("sentencepiece",)) class CodeLlamaTokenizer(PreTrainedTokenizer): """ Construct a CodeLlama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as diff --git a/src/transformers/models/colpali/modeling_colpali.py b/src/transformers/models/colpali/modeling_colpali.py index 4782bf7d30..f1d6b3a047 100644 --- a/src/transformers/models/colpali/modeling_colpali.py +++ b/src/transformers/models/colpali/modeling_colpali.py @@ -288,6 +288,5 @@ class ColPaliForRetrieval(ColPaliPreTrainedModel): __all__ = [ "ColPaliForRetrieval", - "ColPaliForRetrievalOutput", "ColPaliPreTrainedModel", ] diff --git a/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py b/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py index 8fe92eec42..eeed4db007 100644 --- a/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py +++ b/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py @@ -18,6 +18,7 @@ import warnings from ...image_transforms import rgb_to_id as _rgb_to_id from ...utils import logging +from ...utils.import_utils import requires from .image_processing_conditional_detr import ConditionalDetrImageProcessor @@ -33,6 +34,7 @@ def rgb_to_id(x): return _rgb_to_id(x) +@requires(backends=("vision",)) class ConditionalDetrFeatureExtractor(ConditionalDetrImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/conditional_detr/image_processing_conditional_detr.py b/src/transformers/models/conditional_detr/image_processing_conditional_detr.py index 923ccc20ab..3c256e4f70 100644 --- a/src/transformers/models/conditional_detr/image_processing_conditional_detr.py +++ b/src/transformers/models/conditional_detr/image_processing_conditional_detr.py @@ -64,6 +64,7 @@ from ...utils import ( is_vision_available, logging, ) +from ...utils.import_utils import requires if is_torch_available(): @@ -801,6 +802,7 @@ def compute_segments( return segmentation, segments +@requires(backends=("vision",)) class ConditionalDetrImageProcessor(BaseImageProcessor): r""" Constructs a Conditional Detr image processor. diff --git a/src/transformers/models/convnext/feature_extraction_convnext.py b/src/transformers/models/convnext/feature_extraction_convnext.py index 6b2208e5b1..1fbb5184cf 100644 --- a/src/transformers/models/convnext/feature_extraction_convnext.py +++ b/src/transformers/models/convnext/feature_extraction_convnext.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_convnext import ConvNextImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class ConvNextFeatureExtractor(ConvNextImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/convnext/image_processing_convnext.py b/src/transformers/models/convnext/image_processing_convnext.py index e6b3125167..0087e87ae1 100644 --- a/src/transformers/models/convnext/image_processing_convnext.py +++ b/src/transformers/models/convnext/image_processing_convnext.py @@ -39,6 +39,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging +from ...utils.import_utils import requires if is_vision_available(): @@ -48,6 +49,7 @@ if is_vision_available(): logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class ConvNextImageProcessor(BaseImageProcessor): r""" Constructs a ConvNeXT image processor. diff --git a/src/transformers/models/cpm/tokenization_cpm.py b/src/transformers/models/cpm/tokenization_cpm.py index 884068f1a1..496c0b1ccb 100644 --- a/src/transformers/models/cpm/tokenization_cpm.py +++ b/src/transformers/models/cpm/tokenization_cpm.py @@ -23,6 +23,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -30,6 +31,7 @@ logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} +@requires(backends=("sentencepiece",)) class CpmTokenizer(PreTrainedTokenizer): """Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models.""" diff --git a/src/transformers/models/deberta_v2/tokenization_deberta_v2.py b/src/transformers/models/deberta_v2/tokenization_deberta_v2.py index e87c855be5..c8b23a962d 100644 --- a/src/transformers/models/deberta_v2/tokenization_deberta_v2.py +++ b/src/transformers/models/deberta_v2/tokenization_deberta_v2.py @@ -22,6 +22,7 @@ import sentencepiece as sp from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -30,6 +31,7 @@ logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spm.model"} +@requires(backends=("sentencepiece",)) class DebertaV2Tokenizer(PreTrainedTokenizer): r""" Constructs a DeBERTa-v2 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/deformable_detr/feature_extraction_deformable_detr.py b/src/transformers/models/deformable_detr/feature_extraction_deformable_detr.py index de16767406..e349ca3db0 100644 --- a/src/transformers/models/deformable_detr/feature_extraction_deformable_detr.py +++ b/src/transformers/models/deformable_detr/feature_extraction_deformable_detr.py @@ -18,6 +18,7 @@ import warnings from ...image_transforms import rgb_to_id as _rgb_to_id from ...utils import logging +from ...utils.import_utils import requires from .image_processing_deformable_detr import DeformableDetrImageProcessor @@ -33,6 +34,7 @@ def rgb_to_id(x): return _rgb_to_id(x) +@requires(backends=("vision",)) class DeformableDetrFeatureExtractor(DeformableDetrImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/deformable_detr/image_processing_deformable_detr.py b/src/transformers/models/deformable_detr/image_processing_deformable_detr.py index 0a7dd1b06d..f7ad8a1499 100644 --- a/src/transformers/models/deformable_detr/image_processing_deformable_detr.py +++ b/src/transformers/models/deformable_detr/image_processing_deformable_detr.py @@ -64,6 +64,7 @@ from ...utils import ( is_vision_available, logging, ) +from ...utils.import_utils import requires if is_torch_available(): @@ -799,6 +800,7 @@ def compute_segments( return segmentation, segments +@requires(backends=("torch", "vision")) class DeformableDetrImageProcessor(BaseImageProcessor): r""" Constructs a Deformable DETR image processor. diff --git a/src/transformers/models/deformable_detr/image_processing_deformable_detr_fast.py b/src/transformers/models/deformable_detr/image_processing_deformable_detr_fast.py index 8f78d8a7bf..2a820e07f7 100644 --- a/src/transformers/models/deformable_detr/image_processing_deformable_detr_fast.py +++ b/src/transformers/models/deformable_detr/image_processing_deformable_detr_fast.py @@ -39,6 +39,7 @@ from ...utils import ( is_torchvision_v2_available, logging, ) +from ...utils.import_utils import requires from .image_processing_deformable_detr import get_size_with_aspect_ratio @@ -288,6 +289,7 @@ def prepare_coco_panoptic_annotation( Whether to return segmentation masks. """, ) +@requires(backends=("torchvision", "torch")) class DeformableDetrImageProcessorFast(BaseImageProcessorFast): resample = PILImageResampling.BILINEAR image_mean = IMAGENET_DEFAULT_MEAN diff --git a/src/transformers/models/deit/feature_extraction_deit.py b/src/transformers/models/deit/feature_extraction_deit.py index 813c115fce..d040fd0839 100644 --- a/src/transformers/models/deit/feature_extraction_deit.py +++ b/src/transformers/models/deit/feature_extraction_deit.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_deit import DeiTImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class DeiTFeatureExtractor(DeiTImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/deit/image_processing_deit.py b/src/transformers/models/deit/image_processing_deit.py index d1eceebcb5..b05622be06 100644 --- a/src/transformers/models/deit/image_processing_deit.py +++ b/src/transformers/models/deit/image_processing_deit.py @@ -34,6 +34,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging +from ...utils.import_utils import requires if is_vision_available(): @@ -43,6 +44,7 @@ if is_vision_available(): logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class DeiTImageProcessor(BaseImageProcessor): r""" Constructs a DeiT image processor. diff --git a/src/transformers/models/deprecated/__init__.py b/src/transformers/models/deprecated/__init__.py index e69de29bb2..e293c354e1 100644 --- a/src/transformers/models/deprecated/__init__.py +++ b/src/transformers/models/deprecated/__init__.py @@ -0,0 +1,49 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .bort import * + from .deta import * + from .efficientformer import * + from .ernie_m import * + from .gptsan_japanese import * + from .graphormer import * + from .jukebox import * + from .mctct import * + from .mega import * + from .mmbt import * + from .nat import * + from .nezha import * + from .open_llama import * + from .qdqbert import * + from .realm import * + from .retribert import * + from .speech_to_text_2 import * + from .tapex import * + from .trajectory_transformer import * + from .transfo_xl import * + from .tvlt import * + from .van import * + from .vit_hybrid import * + from .xlm_prophetnet import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/deta/__init__.py b/src/transformers/models/deprecated/deta/__init__.py index ab54ec6f43..6e06e18674 100644 --- a/src/transformers/models/deprecated/deta/__init__.py +++ b/src/transformers/models/deprecated/deta/__init__.py @@ -11,61 +11,18 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available - - -_import_structure = { - "configuration_deta": ["DetaConfig"], -} - -try: - if not is_vision_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["image_processing_deta"] = ["DetaImageProcessor"] - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_deta"] = [ - "DetaForObjectDetection", - "DetaModel", - "DetaPreTrainedModel", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_deta import DetaConfig - - try: - if not is_vision_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .image_processing_deta import DetaImageProcessor - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_deta import ( - DetaForObjectDetection, - DetaModel, - DetaPreTrainedModel, - ) - + from .configuration_deta import * + from .image_processing_deta import * + from .modeling_deta import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/deta/configuration_deta.py b/src/transformers/models/deprecated/deta/configuration_deta.py index fcee8fc62a..558bf59679 100644 --- a/src/transformers/models/deprecated/deta/configuration_deta.py +++ b/src/transformers/models/deprecated/deta/configuration_deta.py @@ -265,3 +265,6 @@ class DetaConfig(PretrainedConfig): @property def hidden_size(self) -> int: return self.d_model + + +__all__ = ["DetaConfig"] diff --git a/src/transformers/models/deprecated/deta/image_processing_deta.py b/src/transformers/models/deprecated/deta/image_processing_deta.py index 0cfdc03e81..c63be13827 100644 --- a/src/transformers/models/deprecated/deta/image_processing_deta.py +++ b/src/transformers/models/deprecated/deta/image_processing_deta.py @@ -1222,3 +1222,6 @@ class DetaImageProcessor(BaseImageProcessor): ) return results + + +__all__ = ["DetaImageProcessor"] diff --git a/src/transformers/models/deprecated/deta/modeling_deta.py b/src/transformers/models/deprecated/deta/modeling_deta.py index 8533cc59ba..69154f9e0d 100644 --- a/src/transformers/models/deprecated/deta/modeling_deta.py +++ b/src/transformers/models/deprecated/deta/modeling_deta.py @@ -2822,3 +2822,6 @@ class DetaStage1Assigner(nn.Module): def postprocess_indices(self, pr_inds, gt_inds, iou): return sample_topk_per_gt(pr_inds, gt_inds, iou, self.k) + + +__all__ = ["DetaForObjectDetection", "DetaModel", "DetaPreTrainedModel"] diff --git a/src/transformers/models/deprecated/efficientformer/__init__.py b/src/transformers/models/deprecated/efficientformer/__init__.py index 67d046a8b6..db3d0a6340 100644 --- a/src/transformers/models/deprecated/efficientformer/__init__.py +++ b/src/transformers/models/deprecated/efficientformer/__init__.py @@ -13,88 +13,17 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_tf_available, - is_torch_available, - is_vision_available, -) +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure -_import_structure = {"configuration_efficientformer": ["EfficientFormerConfig"]} - -try: - if not is_vision_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["image_processing_efficientformer"] = ["EfficientFormerImageProcessor"] - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_efficientformer"] = [ - "EfficientFormerForImageClassification", - "EfficientFormerForImageClassificationWithTeacher", - "EfficientFormerModel", - "EfficientFormerPreTrainedModel", - ] - -try: - if not is_tf_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_tf_efficientformer"] = [ - "TFEfficientFormerForImageClassification", - "TFEfficientFormerForImageClassificationWithTeacher", - "TFEfficientFormerModel", - "TFEfficientFormerPreTrainedModel", - ] - if TYPE_CHECKING: - from .configuration_efficientformer import EfficientFormerConfig - - try: - if not is_vision_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .image_processing_efficientformer import EfficientFormerImageProcessor - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_efficientformer import ( - EfficientFormerForImageClassification, - EfficientFormerForImageClassificationWithTeacher, - EfficientFormerModel, - EfficientFormerPreTrainedModel, - ) - try: - if not is_tf_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_tf_efficientformer import ( - TFEfficientFormerForImageClassification, - TFEfficientFormerForImageClassificationWithTeacher, - TFEfficientFormerModel, - TFEfficientFormerPreTrainedModel, - ) - + from .configuration_efficientformer import * + from .image_processing_efficientformer import * + from .modeling_efficientformer import * + from .modeling_tf_efficientformer import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py b/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py index fb161d61fc..5e2dca8bcd 100644 --- a/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py +++ b/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py @@ -165,3 +165,8 @@ class EfficientFormerConfig(PretrainedConfig): self.layer_scale_init_value = layer_scale_init_value self.image_size = image_size self.batch_norm_eps = batch_norm_eps + + +__all__ = [ + "EfficientFormerConfig", +] diff --git a/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py b/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py index 1c42759ed2..74d16a048d 100644 --- a/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py +++ b/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py @@ -319,3 +319,6 @@ class EfficientFormerImageProcessor(BaseImageProcessor): data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors) + + +__all__ = ["EfficientFormerImageProcessor"] diff --git a/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py b/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py index f86656c0b1..a45fe7da5d 100644 --- a/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py +++ b/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py @@ -797,3 +797,11 @@ class EfficientFormerForImageClassificationWithTeacher(EfficientFormerPreTrained hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = [ + "EfficientFormerForImageClassification", + "EfficientFormerForImageClassificationWithTeacher", + "EfficientFormerModel", + "EfficientFormerPreTrainedModel", +] diff --git a/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py b/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py index 76fdaa1f08..e11fa1edf9 100644 --- a/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py +++ b/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py @@ -1188,3 +1188,11 @@ class TFEfficientFormerForImageClassificationWithTeacher(TFEfficientFormerPreTra if hasattr(self.distillation_classifier, "name"): with tf.name_scope(self.distillation_classifier.name): self.distillation_classifier.build([None, None, self.config.hidden_sizes[-1]]) + + +__all__ = [ + "TFEfficientFormerForImageClassification", + "TFEfficientFormerForImageClassificationWithTeacher", + "TFEfficientFormerModel", + "TFEfficientFormerPreTrainedModel", +] diff --git a/src/transformers/models/deprecated/ernie_m/__init__.py b/src/transformers/models/deprecated/ernie_m/__init__.py index 68964d7574..2beb8f463f 100644 --- a/src/transformers/models/deprecated/ernie_m/__init__.py +++ b/src/transformers/models/deprecated/ernie_m/__init__.py @@ -13,68 +13,16 @@ # limitations under the License. from typing import TYPE_CHECKING -# rely on isort to merge the imports -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available - - -_import_structure = { - "configuration_ernie_m": ["ErnieMConfig"], -} - -try: - if not is_sentencepiece_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["tokenization_ernie_m"] = ["ErnieMTokenizer"] - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_ernie_m"] = [ - "ErnieMForMultipleChoice", - "ErnieMForQuestionAnswering", - "ErnieMForSequenceClassification", - "ErnieMForTokenClassification", - "ErnieMModel", - "ErnieMPreTrainedModel", - "ErnieMForInformationExtraction", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_ernie_m import ErnieMConfig - - try: - if not is_sentencepiece_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .tokenization_ernie_m import ErnieMTokenizer - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_ernie_m import ( - ErnieMForInformationExtraction, - ErnieMForMultipleChoice, - ErnieMForQuestionAnswering, - ErnieMForSequenceClassification, - ErnieMForTokenClassification, - ErnieMModel, - ErnieMPreTrainedModel, - ) - - + from .configuration_ernie_m import * + from .modeling_ernie_m import * + from .tokenization_ernie_m import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py b/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py index d5c3feb951..7a45106131 100644 --- a/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py +++ b/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py @@ -109,3 +109,6 @@ class ErnieMConfig(PretrainedConfig): self.layer_norm_eps = layer_norm_eps self.classifier_dropout = classifier_dropout self.act_dropout = act_dropout + + +__all__ = ["ErnieMConfig"] diff --git a/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py b/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py index 68d270874c..28c17afa3f 100755 --- a/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py +++ b/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py @@ -1045,3 +1045,14 @@ class ErnieMForInformationExtraction(ErnieMPreTrainedModel): hidden_states=result.hidden_states, attentions=result.attentions, ) + + +__all__ = [ + "ErnieMForMultipleChoice", + "ErnieMForQuestionAnswering", + "ErnieMForSequenceClassification", + "ErnieMForTokenClassification", + "ErnieMModel", + "ErnieMPreTrainedModel", + "ErnieMForInformationExtraction", +] diff --git a/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py b/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py index 07f9f4ed47..44bc197a4f 100644 --- a/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py +++ b/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py @@ -23,6 +23,7 @@ import sentencepiece as spm from ....tokenization_utils import PreTrainedTokenizer from ....utils import logging +from ....utils.import_utils import requires logger = logging.get_logger(__name__) @@ -38,6 +39,7 @@ RESOURCE_FILES_NAMES = { # Adapted from paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer +@requires(backends=("sentencepiece",)) class ErnieMTokenizer(PreTrainedTokenizer): r""" Constructs a Ernie-M tokenizer. It uses the `sentencepiece` tools to cut the words to sub-words. @@ -403,3 +405,6 @@ class ErnieMTokenizer(PreTrainedTokenizer): fi.write(content_spiece_model) return (vocab_file,) + + +__all__ = ["ErnieMTokenizer"] diff --git a/src/transformers/models/deprecated/gptsan_japanese/__init__.py b/src/transformers/models/deprecated/gptsan_japanese/__init__.py index 5bd0f99840..3c23b58f35 100644 --- a/src/transformers/models/deprecated/gptsan_japanese/__init__.py +++ b/src/transformers/models/deprecated/gptsan_japanese/__init__.py @@ -11,58 +11,18 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - from typing import TYPE_CHECKING -from ....utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_flax_available, - is_tf_available, - is_torch_available, -) - - -_import_structure = { - "configuration_gptsan_japanese": ["GPTSanJapaneseConfig"], - "tokenization_gptsan_japanese": ["GPTSanJapaneseTokenizer"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_gptsan_japanese"] = [ - "GPTSanJapaneseForConditionalGeneration", - "GPTSanJapaneseModel", - "GPTSanJapanesePreTrainedModel", - ] - _import_structure["tokenization_gptsan_japanese"] = [ - "GPTSanJapaneseTokenizer", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_gptsan_japanese import GPTSanJapaneseConfig - from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_gptsan_japanese import ( - GPTSanJapaneseForConditionalGeneration, - GPTSanJapaneseModel, - GPTSanJapanesePreTrainedModel, - ) - from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer - - + from .configuration_gptsan_japanese import * + from .modeling_gptsan_japanese import * + from .tokenization_gptsan_japanese import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py b/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py index 52bd33ac9f..cd56581009 100644 --- a/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py +++ b/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py @@ -152,3 +152,6 @@ class GPTSanJapaneseConfig(PretrainedConfig): eos_token_id=eos_token_id, **kwargs, ) + + +__all__ = ["GPTSanJapaneseConfig"] diff --git a/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py b/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py index 7274f5c02c..a35ea4a311 100644 --- a/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py +++ b/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py @@ -1332,3 +1332,6 @@ class GPTSanJapaneseForConditionalGeneration(GPTSanJapanesePreTrainedModel): total_router_logits.append(router_logits) total_expert_indexes.append(expert_indexes) return torch.cat(total_router_logits, dim=1), torch.cat(total_expert_indexes, dim=1) + + +__all__ = ["GPTSanJapaneseForConditionalGeneration", "GPTSanJapaneseModel", "GPTSanJapanesePreTrainedModel"] diff --git a/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py b/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py index a8d5eac1e1..c93ea87278 100644 --- a/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py +++ b/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py @@ -513,3 +513,6 @@ class SubWordJapaneseTokenizer: def convert_id_to_token(self, index): return self.ids_to_tokens[index][0] + + +__all__ = ["GPTSanJapaneseTokenizer"] diff --git a/src/transformers/models/deprecated/graphormer/__init__.py b/src/transformers/models/deprecated/graphormer/__init__.py index 117bf7c15a..3a4b3eb1be 100644 --- a/src/transformers/models/deprecated/graphormer/__init__.py +++ b/src/transformers/models/deprecated/graphormer/__init__.py @@ -13,43 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available - - -_import_structure = { - "configuration_graphormer": ["GraphormerConfig"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_graphormer"] = [ - "GraphormerForGraphClassification", - "GraphormerModel", - "GraphormerPreTrainedModel", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_graphormer import GraphormerConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_graphormer import ( - GraphormerForGraphClassification, - GraphormerModel, - GraphormerPreTrainedModel, - ) - - + from .configuration_graphormer import * + from .modeling_graphormer import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/graphormer/configuration_graphormer.py b/src/transformers/models/deprecated/graphormer/configuration_graphormer.py index e32a853ae1..1ecde152e4 100644 --- a/src/transformers/models/deprecated/graphormer/configuration_graphormer.py +++ b/src/transformers/models/deprecated/graphormer/configuration_graphormer.py @@ -215,3 +215,6 @@ class GraphormerConfig(PretrainedConfig): eos_token_id=eos_token_id, **kwargs, ) + + +__all__ = ["GraphormerConfig"] diff --git a/src/transformers/models/deprecated/graphormer/modeling_graphormer.py b/src/transformers/models/deprecated/graphormer/modeling_graphormer.py index 0eb4aa7119..1253d1365e 100755 --- a/src/transformers/models/deprecated/graphormer/modeling_graphormer.py +++ b/src/transformers/models/deprecated/graphormer/modeling_graphormer.py @@ -906,3 +906,6 @@ class GraphormerForGraphClassification(GraphormerPreTrainedModel): if not return_dict: return tuple(x for x in [loss, logits, hidden_states] if x is not None) return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=hidden_states, attentions=None) + + +__all__ = ["GraphormerForGraphClassification", "GraphormerModel", "GraphormerPreTrainedModel"] diff --git a/src/transformers/models/deprecated/jukebox/__init__.py b/src/transformers/models/deprecated/jukebox/__init__.py index d6de906389..826bdbddc1 100644 --- a/src/transformers/models/deprecated/jukebox/__init__.py +++ b/src/transformers/models/deprecated/jukebox/__init__.py @@ -11,56 +11,18 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure -_import_structure = { - "configuration_jukebox": [ - "JukeboxConfig", - "JukeboxPriorConfig", - "JukeboxVQVAEConfig", - ], - "tokenization_jukebox": ["JukeboxTokenizer"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_jukebox"] = [ - "JukeboxModel", - "JukeboxPreTrainedModel", - "JukeboxVQVAE", - "JukeboxPrior", - ] - if TYPE_CHECKING: - from .configuration_jukebox import ( - JukeboxConfig, - JukeboxPriorConfig, - JukeboxVQVAEConfig, - ) - from .tokenization_jukebox import JukeboxTokenizer - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_jukebox import ( - JukeboxModel, - JukeboxPreTrainedModel, - JukeboxPrior, - JukeboxVQVAE, - ) - + from .configuration_jukebox import * + from .modeling_jukebox import * + from .tokenization_jukebox import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/jukebox/configuration_jukebox.py b/src/transformers/models/deprecated/jukebox/configuration_jukebox.py index e9d08c478f..d10cbc2d82 100644 --- a/src/transformers/models/deprecated/jukebox/configuration_jukebox.py +++ b/src/transformers/models/deprecated/jukebox/configuration_jukebox.py @@ -608,3 +608,6 @@ class JukeboxConfig(PretrainedConfig): result = super().to_dict() result["prior_config_list"] = [config.to_dict() for config in result.pop("prior_configs")] return result + + +__all__ = ["JukeboxConfig", "JukeboxPriorConfig", "JukeboxVQVAEConfig"] diff --git a/src/transformers/models/deprecated/jukebox/modeling_jukebox.py b/src/transformers/models/deprecated/jukebox/modeling_jukebox.py index 213711ae12..566148ceda 100755 --- a/src/transformers/models/deprecated/jukebox/modeling_jukebox.py +++ b/src/transformers/models/deprecated/jukebox/modeling_jukebox.py @@ -2665,3 +2665,6 @@ class JukeboxModel(JukeboxPreTrainedModel): ) music_tokens = self._sample(music_tokens, labels, sample_levels, **sampling_kwargs) return music_tokens + + +__all__ = ["JukeboxModel", "JukeboxPreTrainedModel", "JukeboxVQVAE", "JukeboxPrior"] diff --git a/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py b/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py index fb827fbca9..e08ab179a8 100644 --- a/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py +++ b/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py @@ -402,3 +402,6 @@ class JukeboxTokenizer(PreTrainedTokenizer): genres = [self.genres_decoder.get(genre) for genre in genres_index] lyrics = [self.lyrics_decoder.get(character) for character in lyric_index] return artist, genres, lyrics + + +__all__ = ["JukeboxTokenizer"] diff --git a/src/transformers/models/deprecated/mctct/__init__.py b/src/transformers/models/deprecated/mctct/__init__.py index 4e0a06b177..53ec5ed37c 100644 --- a/src/transformers/models/deprecated/mctct/__init__.py +++ b/src/transformers/models/deprecated/mctct/__init__.py @@ -13,43 +13,17 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available - - -_import_structure = { - "configuration_mctct": ["MCTCTConfig"], - "feature_extraction_mctct": ["MCTCTFeatureExtractor"], - "processing_mctct": ["MCTCTProcessor"], -} - - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_mctct"] = [ - "MCTCTForCTC", - "MCTCTModel", - "MCTCTPreTrainedModel", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_mctct import MCTCTConfig - from .feature_extraction_mctct import MCTCTFeatureExtractor - from .processing_mctct import MCTCTProcessor - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_mctct import MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel - + from .configuration_mctct import * + from .feature_extraction_mctct import * + from .modeling_mctct import * + from .processing_mctct import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/mctct/configuration_mctct.py b/src/transformers/models/deprecated/mctct/configuration_mctct.py index c5de734780..9cba190a0f 100644 --- a/src/transformers/models/deprecated/mctct/configuration_mctct.py +++ b/src/transformers/models/deprecated/mctct/configuration_mctct.py @@ -179,3 +179,6 @@ class MCTCTConfig(PretrainedConfig): f"but is `len(config.conv_kernel) = {len(self.conv_kernel)}`, " f"`config.num_conv_layers = {self.num_conv_layers}`." ) + + +__all__ = ["MCTCTConfig"] diff --git a/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py b/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py index e1e17c4b12..f210031f30 100644 --- a/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py +++ b/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py @@ -286,3 +286,6 @@ class MCTCTFeatureExtractor(SequenceFeatureExtractor): padded_inputs = padded_inputs.convert_to_tensors(return_tensors) return padded_inputs + + +__all__ = ["MCTCTFeatureExtractor"] diff --git a/src/transformers/models/deprecated/mctct/modeling_mctct.py b/src/transformers/models/deprecated/mctct/modeling_mctct.py index 3cbf2cc0bf..2dd074b28c 100755 --- a/src/transformers/models/deprecated/mctct/modeling_mctct.py +++ b/src/transformers/models/deprecated/mctct/modeling_mctct.py @@ -786,3 +786,6 @@ class MCTCTForCTC(MCTCTPreTrainedModel): return CausalLMOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions ) + + +__all__ = ["MCTCTForCTC", "MCTCTModel", "MCTCTPreTrainedModel"] diff --git a/src/transformers/models/deprecated/mctct/processing_mctct.py b/src/transformers/models/deprecated/mctct/processing_mctct.py index 7dcbefe101..f953c5895a 100644 --- a/src/transformers/models/deprecated/mctct/processing_mctct.py +++ b/src/transformers/models/deprecated/mctct/processing_mctct.py @@ -141,3 +141,6 @@ class MCTCTProcessor(ProcessorMixin): yield self.current_processor = self.feature_extractor self._in_target_context_manager = False + + +__all__ = ["MCTCTProcessor"] diff --git a/src/transformers/models/deprecated/mega/__init__.py b/src/transformers/models/deprecated/mega/__init__.py index 1774d3bae4..cff2c19505 100644 --- a/src/transformers/models/deprecated/mega/__init__.py +++ b/src/transformers/models/deprecated/mega/__init__.py @@ -11,58 +11,17 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - from typing import TYPE_CHECKING -from ....utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_torch_available, -) +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure -_import_structure = { - "configuration_mega": ["MegaConfig", "MegaOnnxConfig"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_mega"] = [ - "MegaForCausalLM", - "MegaForMaskedLM", - "MegaForMultipleChoice", - "MegaForQuestionAnswering", - "MegaForSequenceClassification", - "MegaForTokenClassification", - "MegaModel", - "MegaPreTrainedModel", - ] - if TYPE_CHECKING: - from .configuration_mega import MegaConfig, MegaOnnxConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_mega import ( - MegaForCausalLM, - MegaForMaskedLM, - MegaForMultipleChoice, - MegaForQuestionAnswering, - MegaForSequenceClassification, - MegaForTokenClassification, - MegaModel, - MegaPreTrainedModel, - ) - + from .configuration_mega import * + from .modeling_mega import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/mega/configuration_mega.py b/src/transformers/models/deprecated/mega/configuration_mega.py index 0b1ab53d5f..3b9d53d520 100644 --- a/src/transformers/models/deprecated/mega/configuration_mega.py +++ b/src/transformers/models/deprecated/mega/configuration_mega.py @@ -238,3 +238,6 @@ class MegaOnnxConfig(OnnxConfig): ("attention_mask", dynamic_axis), ] ) + + +__all__ = ["MegaConfig", "MegaOnnxConfig"] diff --git a/src/transformers/models/deprecated/mega/modeling_mega.py b/src/transformers/models/deprecated/mega/modeling_mega.py index 32f37dde53..d5a490b01d 100644 --- a/src/transformers/models/deprecated/mega/modeling_mega.py +++ b/src/transformers/models/deprecated/mega/modeling_mega.py @@ -2271,3 +2271,15 @@ class MegaForQuestionAnswering(MegaPreTrainedModel): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = [ + "MegaForCausalLM", + "MegaForMaskedLM", + "MegaForMultipleChoice", + "MegaForQuestionAnswering", + "MegaForSequenceClassification", + "MegaForTokenClassification", + "MegaModel", + "MegaPreTrainedModel", +] diff --git a/src/transformers/models/deprecated/mmbt/__init__.py b/src/transformers/models/deprecated/mmbt/__init__.py index e467090cb4..03b556e2ed 100644 --- a/src/transformers/models/deprecated/mmbt/__init__.py +++ b/src/transformers/models/deprecated/mmbt/__init__.py @@ -11,35 +11,17 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available - - -_import_structure = {"configuration_mmbt": ["MMBTConfig"]} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_mmbt"] = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_mmbt import MMBTConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings - + from .configuration_mmbt import * + from .modeling_mmbt import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/mmbt/configuration_mmbt.py b/src/transformers/models/deprecated/mmbt/configuration_mmbt.py index 73696087fa..1c58e4e6cd 100644 --- a/src/transformers/models/deprecated/mmbt/configuration_mmbt.py +++ b/src/transformers/models/deprecated/mmbt/configuration_mmbt.py @@ -40,3 +40,6 @@ class MMBTConfig: self.modal_hidden_size = modal_hidden_size if num_labels: self.num_labels = num_labels + + +__all__ = ["MMBTConfig"] diff --git a/src/transformers/models/deprecated/mmbt/modeling_mmbt.py b/src/transformers/models/deprecated/mmbt/modeling_mmbt.py index 4a06de5698..45ae577f7f 100644 --- a/src/transformers/models/deprecated/mmbt/modeling_mmbt.py +++ b/src/transformers/models/deprecated/mmbt/modeling_mmbt.py @@ -405,3 +405,6 @@ class MMBTForClassification(nn.Module): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"] diff --git a/src/transformers/models/deprecated/nat/__init__.py b/src/transformers/models/deprecated/nat/__init__.py index 70d2cfd295..c5373969ce 100644 --- a/src/transformers/models/deprecated/nat/__init__.py +++ b/src/transformers/models/deprecated/nat/__init__.py @@ -13,42 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure -_import_structure = {"configuration_nat": ["NatConfig"]} - - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_nat"] = [ - "NatForImageClassification", - "NatModel", - "NatPreTrainedModel", - "NatBackbone", - ] - if TYPE_CHECKING: - from .configuration_nat import NatConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_nat import ( - NatBackbone, - NatForImageClassification, - NatModel, - NatPreTrainedModel, - ) - + from .configuration_nat import * + from .modeling_nat import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/nat/configuration_nat.py b/src/transformers/models/deprecated/nat/configuration_nat.py index 2fef74d2a0..85961aa2fe 100644 --- a/src/transformers/models/deprecated/nat/configuration_nat.py +++ b/src/transformers/models/deprecated/nat/configuration_nat.py @@ -143,3 +143,6 @@ class NatConfig(BackboneConfigMixin, PretrainedConfig): self._out_features, self._out_indices = get_aligned_output_features_output_indices( out_features=out_features, out_indices=out_indices, stage_names=self.stage_names ) + + +__all__ = ["NatConfig"] diff --git a/src/transformers/models/deprecated/nat/modeling_nat.py b/src/transformers/models/deprecated/nat/modeling_nat.py index 0a59c827cd..5871b03299 100644 --- a/src/transformers/models/deprecated/nat/modeling_nat.py +++ b/src/transformers/models/deprecated/nat/modeling_nat.py @@ -948,3 +948,6 @@ class NatBackbone(NatPreTrainedModel, BackboneMixin): hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, ) + + +__all__ = ["NatForImageClassification", "NatModel", "NatPreTrainedModel", "NatBackbone"] diff --git a/src/transformers/models/deprecated/nezha/__init__.py b/src/transformers/models/deprecated/nezha/__init__.py index 590b0013c5..f0690129ae 100644 --- a/src/transformers/models/deprecated/nezha/__init__.py +++ b/src/transformers/models/deprecated/nezha/__init__.py @@ -13,55 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available - - -_import_structure = { - "configuration_nezha": ["NezhaConfig"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_nezha"] = [ - "NezhaForNextSentencePrediction", - "NezhaForMaskedLM", - "NezhaForPreTraining", - "NezhaForMultipleChoice", - "NezhaForQuestionAnswering", - "NezhaForSequenceClassification", - "NezhaForTokenClassification", - "NezhaModel", - "NezhaPreTrainedModel", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_nezha import NezhaConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_nezha import ( - NezhaForMaskedLM, - NezhaForMultipleChoice, - NezhaForNextSentencePrediction, - NezhaForPreTraining, - NezhaForQuestionAnswering, - NezhaForSequenceClassification, - NezhaForTokenClassification, - NezhaModel, - NezhaPreTrainedModel, - ) - - + from .configuration_nezha import * + from .modeling_nezha import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/nezha/configuration_nezha.py b/src/transformers/models/deprecated/nezha/configuration_nezha.py index c60bb5de51..00d193cd1a 100644 --- a/src/transformers/models/deprecated/nezha/configuration_nezha.py +++ b/src/transformers/models/deprecated/nezha/configuration_nezha.py @@ -100,3 +100,6 @@ class NezhaConfig(PretrainedConfig): self.layer_norm_eps = layer_norm_eps self.classifier_dropout = classifier_dropout self.use_cache = use_cache + + +__all__ = ["NezhaConfig"] diff --git a/src/transformers/models/deprecated/nezha/modeling_nezha.py b/src/transformers/models/deprecated/nezha/modeling_nezha.py index 1f76a21771..7be52bee58 100644 --- a/src/transformers/models/deprecated/nezha/modeling_nezha.py +++ b/src/transformers/models/deprecated/nezha/modeling_nezha.py @@ -1682,3 +1682,16 @@ class NezhaForQuestionAnswering(NezhaPreTrainedModel): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = [ + "NezhaForNextSentencePrediction", + "NezhaForMaskedLM", + "NezhaForPreTraining", + "NezhaForMultipleChoice", + "NezhaForQuestionAnswering", + "NezhaForSequenceClassification", + "NezhaForTokenClassification", + "NezhaModel", + "NezhaPreTrainedModel", +] diff --git a/src/transformers/models/deprecated/open_llama/__init__.py b/src/transformers/models/deprecated/open_llama/__init__.py index 085c91fdb6..2b3964d194 100644 --- a/src/transformers/models/deprecated/open_llama/__init__.py +++ b/src/transformers/models/deprecated/open_llama/__init__.py @@ -13,83 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_sentencepiece_available, - is_tokenizers_available, - is_torch_available, -) - - -_import_structure = { - "configuration_open_llama": ["OpenLlamaConfig"], -} - -try: - if not is_sentencepiece_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["tokenization_open_llama"] = ["LlamaTokenizer"] - -try: - if not is_tokenizers_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["tokenization_open_llama_fast"] = ["LlamaTokenizerFast"] - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_open_llama"] = [ - "OpenLlamaForCausalLM", - "OpenLlamaModel", - "OpenLlamaPreTrainedModel", - "OpenLlamaForSequenceClassification", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_open_llama import OpenLlamaConfig - - try: - if not is_sentencepiece_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from transformers import LlamaTokenizer - - try: - if not is_tokenizers_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from transformers import LlamaTokenizerFast - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_open_llama import ( - OpenLlamaForCausalLM, - OpenLlamaForSequenceClassification, - OpenLlamaModel, - OpenLlamaPreTrainedModel, - ) - - + from .configuration_open_llama import * + from .modeling_open_llama import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/open_llama/configuration_open_llama.py b/src/transformers/models/deprecated/open_llama/configuration_open_llama.py index 3a19fd24a4..b4bc9cc72a 100644 --- a/src/transformers/models/deprecated/open_llama/configuration_open_llama.py +++ b/src/transformers/models/deprecated/open_llama/configuration_open_llama.py @@ -164,3 +164,6 @@ class OpenLlamaConfig(PretrainedConfig): ) if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") + + +__all__ = ["OpenLlamaConfig"] diff --git a/src/transformers/models/deprecated/open_llama/modeling_open_llama.py b/src/transformers/models/deprecated/open_llama/modeling_open_llama.py index 4b3f07d7a8..79d79ea546 100644 --- a/src/transformers/models/deprecated/open_llama/modeling_open_llama.py +++ b/src/transformers/models/deprecated/open_llama/modeling_open_llama.py @@ -970,3 +970,6 @@ class OpenLlamaForSequenceClassification(OpenLlamaPreTrainedModel): hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) + + +__all__ = ["OpenLlamaPreTrainedModel", "OpenLlamaModel", "OpenLlamaForCausalLM", "OpenLlamaForSequenceClassification"] diff --git a/src/transformers/models/deprecated/qdqbert/__init__.py b/src/transformers/models/deprecated/qdqbert/__init__.py index 06e69cdc1f..864b321bc2 100644 --- a/src/transformers/models/deprecated/qdqbert/__init__.py +++ b/src/transformers/models/deprecated/qdqbert/__init__.py @@ -13,57 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available - - -_import_structure = {"configuration_qdqbert": ["QDQBertConfig"]} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_qdqbert"] = [ - "QDQBertForMaskedLM", - "QDQBertForMultipleChoice", - "QDQBertForNextSentencePrediction", - "QDQBertForQuestionAnswering", - "QDQBertForSequenceClassification", - "QDQBertForTokenClassification", - "QDQBertLayer", - "QDQBertLMHeadModel", - "QDQBertModel", - "QDQBertPreTrainedModel", - "load_tf_weights_in_qdqbert", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_qdqbert import QDQBertConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_qdqbert import ( - QDQBertForMaskedLM, - QDQBertForMultipleChoice, - QDQBertForNextSentencePrediction, - QDQBertForQuestionAnswering, - QDQBertForSequenceClassification, - QDQBertForTokenClassification, - QDQBertLayer, - QDQBertLMHeadModel, - QDQBertModel, - QDQBertPreTrainedModel, - load_tf_weights_in_qdqbert, - ) - - + from .configuration_qdqbert import * + from .modeling_qdqbert import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py b/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py index b2ba629b24..91ac82bc5a 100644 --- a/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py +++ b/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py @@ -118,3 +118,6 @@ class QDQBertConfig(PretrainedConfig): self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.use_cache = use_cache + + +__all__ = ["QDQBertConfig"] diff --git a/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py b/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py index 036ca99c73..8b68a4e426 100755 --- a/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py +++ b/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py @@ -1732,3 +1732,18 @@ class QDQBertForQuestionAnswering(QDQBertPreTrainedModel): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = [ + "QDQBertForMaskedLM", + "QDQBertForMultipleChoice", + "QDQBertForNextSentencePrediction", + "QDQBertForQuestionAnswering", + "QDQBertForSequenceClassification", + "QDQBertForTokenClassification", + "QDQBertLayer", + "QDQBertLMHeadModel", + "QDQBertModel", + "QDQBertPreTrainedModel", + "load_tf_weights_in_qdqbert", +] diff --git a/src/transformers/models/deprecated/realm/__init__.py b/src/transformers/models/deprecated/realm/__init__.py index 85fe72441f..cdfdeb5d17 100644 --- a/src/transformers/models/deprecated/realm/__init__.py +++ b/src/transformers/models/deprecated/realm/__init__.py @@ -13,71 +13,18 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available - - -_import_structure = { - "configuration_realm": ["RealmConfig"], - "tokenization_realm": ["RealmTokenizer"], -} - -try: - if not is_tokenizers_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["tokenization_realm_fast"] = ["RealmTokenizerFast"] - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_realm"] = [ - "RealmEmbedder", - "RealmForOpenQA", - "RealmKnowledgeAugEncoder", - "RealmPreTrainedModel", - "RealmReader", - "RealmScorer", - "load_tf_weights_in_realm", - ] - _import_structure["retrieval_realm"] = ["RealmRetriever"] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_realm import RealmConfig - from .tokenization_realm import RealmTokenizer - - try: - if not is_tokenizers_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .tokenization_realm import RealmTokenizerFast - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_realm import ( - RealmEmbedder, - RealmForOpenQA, - RealmKnowledgeAugEncoder, - RealmPreTrainedModel, - RealmReader, - RealmScorer, - load_tf_weights_in_realm, - ) - from .retrieval_realm import RealmRetriever - - + from .configuration_realm import * + from .modeling_realm import * + from .retrieval_realm import * + from .tokenization_realm import * + from .tokenization_realm_fast import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/realm/configuration_realm.py b/src/transformers/models/deprecated/realm/configuration_realm.py index 20fd201d98..fbf32378a6 100644 --- a/src/transformers/models/deprecated/realm/configuration_realm.py +++ b/src/transformers/models/deprecated/realm/configuration_realm.py @@ -164,3 +164,6 @@ class RealmConfig(PretrainedConfig): # Retrieval config self.num_block_records = num_block_records self.searcher_beam_size = searcher_beam_size + + +__all__ = ["RealmConfig"] diff --git a/src/transformers/models/deprecated/realm/modeling_realm.py b/src/transformers/models/deprecated/realm/modeling_realm.py index b518849ced..ac25a17733 100644 --- a/src/transformers/models/deprecated/realm/modeling_realm.py +++ b/src/transformers/models/deprecated/realm/modeling_realm.py @@ -1849,3 +1849,14 @@ class RealmForOpenQA(RealmPreTrainedModel): reader_output=reader_output, predicted_answer_ids=predicted_answer_ids, ) + + +__all__ = [ + "RealmEmbedder", + "RealmForOpenQA", + "RealmKnowledgeAugEncoder", + "RealmPreTrainedModel", + "RealmReader", + "RealmScorer", + "load_tf_weights_in_realm", +] diff --git a/src/transformers/models/deprecated/realm/retrieval_realm.py b/src/transformers/models/deprecated/realm/retrieval_realm.py index 2b499fca28..b3c084f1d2 100644 --- a/src/transformers/models/deprecated/realm/retrieval_realm.py +++ b/src/transformers/models/deprecated/realm/retrieval_realm.py @@ -20,7 +20,8 @@ from typing import Optional, Union import numpy as np from huggingface_hub import hf_hub_download -from .... import AutoTokenizer +from transformers import AutoTokenizer + from ....utils import logging, strtobool @@ -170,3 +171,6 @@ class RealmRetriever: start_pos_ += padded end_pos_ += padded return has_answers, start_pos, end_pos + + +__all__ = ["RealmRetriever"] diff --git a/src/transformers/models/deprecated/realm/tokenization_realm.py b/src/transformers/models/deprecated/realm/tokenization_realm.py index 8211c1aee8..70e69bc4bc 100644 --- a/src/transformers/models/deprecated/realm/tokenization_realm.py +++ b/src/transformers/models/deprecated/realm/tokenization_realm.py @@ -558,3 +558,6 @@ class WordpieceTokenizer: else: output_tokens.extend(sub_tokens) return output_tokens + + +__all__ = ["RealmTokenizer"] diff --git a/src/transformers/models/deprecated/realm/tokenization_realm_fast.py b/src/transformers/models/deprecated/realm/tokenization_realm_fast.py index cbc4869e54..7c173227be 100644 --- a/src/transformers/models/deprecated/realm/tokenization_realm_fast.py +++ b/src/transformers/models/deprecated/realm/tokenization_realm_fast.py @@ -247,3 +247,6 @@ class RealmTokenizerFast(PreTrainedTokenizerFast): def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) + + +__all__ = ["RealmTokenizerFast"] diff --git a/src/transformers/models/deprecated/retribert/__init__.py b/src/transformers/models/deprecated/retribert/__init__.py index ff792f40a2..a875576607 100644 --- a/src/transformers/models/deprecated/retribert/__init__.py +++ b/src/transformers/models/deprecated/retribert/__init__.py @@ -11,61 +11,19 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available - - -_import_structure = { - "configuration_retribert": ["RetriBertConfig"], - "tokenization_retribert": ["RetriBertTokenizer"], -} - -try: - if not is_tokenizers_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["tokenization_retribert_fast"] = ["RetriBertTokenizerFast"] - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_retribert"] = [ - "RetriBertModel", - "RetriBertPreTrainedModel", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_retribert import RetriBertConfig - from .tokenization_retribert import RetriBertTokenizer - - try: - if not is_tokenizers_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .tokenization_retribert_fast import RetriBertTokenizerFast - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_retribert import ( - RetriBertModel, - RetriBertPreTrainedModel, - ) - + from .configuration_retribert import * + from .modeling_retribert import * + from .tokenization_retribert import * + from .tokenization_retribert_fast import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/retribert/configuration_retribert.py b/src/transformers/models/deprecated/retribert/configuration_retribert.py index f154bb04c6..80d755a169 100644 --- a/src/transformers/models/deprecated/retribert/configuration_retribert.py +++ b/src/transformers/models/deprecated/retribert/configuration_retribert.py @@ -103,3 +103,6 @@ class RetriBertConfig(PretrainedConfig): self.layer_norm_eps = layer_norm_eps self.share_encoders = share_encoders self.projection_dim = projection_dim + + +__all__ = ["RetriBertConfig"] diff --git a/src/transformers/models/deprecated/retribert/modeling_retribert.py b/src/transformers/models/deprecated/retribert/modeling_retribert.py index 3af3f7be49..bcae1c0239 100644 --- a/src/transformers/models/deprecated/retribert/modeling_retribert.py +++ b/src/transformers/models/deprecated/retribert/modeling_retribert.py @@ -212,3 +212,6 @@ class RetriBertModel(RetriBertPreTrainedModel): loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device)) loss = (loss_qa + loss_aq) / 2 return loss + + +__all__ = ["RetriBertModel", "RetriBertPreTrainedModel"] diff --git a/src/transformers/models/deprecated/retribert/tokenization_retribert.py b/src/transformers/models/deprecated/retribert/tokenization_retribert.py index 8b3570f162..35a1874aa0 100644 --- a/src/transformers/models/deprecated/retribert/tokenization_retribert.py +++ b/src/transformers/models/deprecated/retribert/tokenization_retribert.py @@ -499,3 +499,6 @@ class WordpieceTokenizer: else: output_tokens.extend(sub_tokens) return output_tokens + + +__all__ = ["RetriBertTokenizer"] diff --git a/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py b/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py index 9a915d1597..cc51d0e2a1 100644 --- a/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py +++ b/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py @@ -174,3 +174,6 @@ class RetriBertTokenizerFast(PreTrainedTokenizerFast): def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) + + +__all__ = ["RetriBertTokenizerFast"] diff --git a/src/transformers/models/deprecated/speech_to_text_2/__init__.py b/src/transformers/models/deprecated/speech_to_text_2/__init__.py index 53f806d00c..78c549b6e2 100644 --- a/src/transformers/models/deprecated/speech_to_text_2/__init__.py +++ b/src/transformers/models/deprecated/speech_to_text_2/__init__.py @@ -13,51 +13,17 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_sentencepiece_available, - is_speech_available, - is_torch_available, -) - - -_import_structure = { - "configuration_speech_to_text_2": ["Speech2Text2Config"], - "processing_speech_to_text_2": ["Speech2Text2Processor"], - "tokenization_speech_to_text_2": ["Speech2Text2Tokenizer"], -} - - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_speech_to_text_2"] = [ - "Speech2Text2ForCausalLM", - "Speech2Text2PreTrainedModel", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_speech_to_text_2 import Speech2Text2Config - from .processing_speech_to_text_2 import Speech2Text2Processor - from .tokenization_speech_to_text_2 import Speech2Text2Tokenizer - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_speech_to_text_2 import ( - Speech2Text2ForCausalLM, - Speech2Text2PreTrainedModel, - ) - + from .configuration_speech_to_text_2 import * + from .modeling_speech_to_text_2 import * + from .processing_speech_to_text_2 import * + from .tokenization_speech_to_text_2 import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py b/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py index d876c4fc3e..2afd79feb2 100644 --- a/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py +++ b/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py @@ -129,3 +129,6 @@ class Speech2Text2Config(PretrainedConfig): decoder_start_token_id=decoder_start_token_id, **kwargs, ) + + +__all__ = ["Speech2Text2Config"] diff --git a/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py b/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py index 18ab8db6d0..6f1dd18d97 100755 --- a/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py +++ b/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py @@ -925,3 +925,6 @@ class Speech2Text2ForCausalLM(Speech2Text2PreTrainedModel): tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past + + +__all__ = ["Speech2Text2ForCausalLM", "Speech2Text2PreTrainedModel"] diff --git a/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py b/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py index f3eb696f89..3b8edaa46d 100644 --- a/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py +++ b/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py @@ -114,3 +114,6 @@ class Speech2Text2Processor(ProcessorMixin): yield self.current_processor = self.feature_extractor self._in_target_context_manager = False + + +__all__ = ["Speech2Text2Processor"] diff --git a/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py b/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py index 2eefe44915..f5aa7ef806 100644 --- a/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py +++ b/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py @@ -247,3 +247,6 @@ class Speech2Text2Tokenizer(PreTrainedTokenizer): index += 1 return (vocab_file, merges_file) + + +__all__ = ["Speech2Text2Tokenizer"] diff --git a/src/transformers/models/deprecated/tapex/__init__.py b/src/transformers/models/deprecated/tapex/__init__.py index 82bbacd15b..b535eb1df2 100644 --- a/src/transformers/models/deprecated/tapex/__init__.py +++ b/src/transformers/models/deprecated/tapex/__init__.py @@ -14,16 +14,13 @@ from typing import TYPE_CHECKING from ....utils import _LazyModule - - -_import_structure = {"tokenization_tapex": ["TapexTokenizer"]} +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .tokenization_tapex import TapexTokenizer - - + from .tokenization_tapex import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/tapex/tokenization_tapex.py b/src/transformers/models/deprecated/tapex/tokenization_tapex.py index cd3d353b52..3d554872c4 100644 --- a/src/transformers/models/deprecated/tapex/tokenization_tapex.py +++ b/src/transformers/models/deprecated/tapex/tokenization_tapex.py @@ -1465,3 +1465,6 @@ class TapexTokenizer(PreTrainedTokenizer): # only when the drop ratio is too large, logging for warning. if "id" in table_content and len(drop_row_indices) > 0: logger.warning("Delete {:.2f} rows in table {}".format(len(drop_row_indices), table_content["id"])) + + +__all__ = ["TapexTokenizer"] diff --git a/src/transformers/models/deprecated/trajectory_transformer/__init__.py b/src/transformers/models/deprecated/trajectory_transformer/__init__.py index 1ec0385898..b4bccdd12c 100644 --- a/src/transformers/models/deprecated/trajectory_transformer/__init__.py +++ b/src/transformers/models/deprecated/trajectory_transformer/__init__.py @@ -13,45 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available - - -_import_structure = { - "configuration_trajectory_transformer": ["TrajectoryTransformerConfig"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_trajectory_transformer"] = [ - "TrajectoryTransformerModel", - "TrajectoryTransformerPreTrainedModel", - "load_tf_weights_in_trajectory_transformer", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_trajectory_transformer import ( - TrajectoryTransformerConfig, - ) - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_trajectory_transformer import ( - TrajectoryTransformerModel, - TrajectoryTransformerPreTrainedModel, - load_tf_weights_in_trajectory_transformer, - ) - - + from .configuration_trajectory_transformer import * + from .modeling_trajectory_transformer import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py b/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py index 6ce86dfb7a..5a267cc4c4 100644 --- a/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py +++ b/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py @@ -150,3 +150,6 @@ class TrajectoryTransformerConfig(PretrainedConfig): self.kaiming_initializer_range = kaiming_initializer_range self.use_cache = use_cache super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) + + +__all__ = ["TrajectoryTransformerConfig"] diff --git a/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py b/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py index 52afb77885..0a9a0111d2 100644 --- a/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py +++ b/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py @@ -601,3 +601,10 @@ class TrajectoryTransformerModel(TrajectoryTransformerPreTrainedModel): hidden_states=all_hidden_states, attentions=all_self_attentions, ) + + +__all__ = [ + "TrajectoryTransformerModel", + "TrajectoryTransformerPreTrainedModel", + "load_tf_weights_in_trajectory_transformer", +] diff --git a/src/transformers/models/deprecated/transfo_xl/__init__.py b/src/transformers/models/deprecated/transfo_xl/__init__.py index 27829fd9ed..0ac9a2cbf4 100644 --- a/src/transformers/models/deprecated/transfo_xl/__init__.py +++ b/src/transformers/models/deprecated/transfo_xl/__init__.py @@ -11,83 +11,19 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available - - -_import_structure = { - "configuration_transfo_xl": ["TransfoXLConfig"], - "tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXLTokenizer"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_transfo_xl"] = [ - "AdaptiveEmbedding", - "TransfoXLForSequenceClassification", - "TransfoXLLMHeadModel", - "TransfoXLModel", - "TransfoXLPreTrainedModel", - "load_tf_weights_in_transfo_xl", - ] - -try: - if not is_tf_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_tf_transfo_xl"] = [ - "TFAdaptiveEmbedding", - "TFTransfoXLForSequenceClassification", - "TFTransfoXLLMHeadModel", - "TFTransfoXLMainLayer", - "TFTransfoXLModel", - "TFTransfoXLPreTrainedModel", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_transfo_xl import TransfoXLConfig - from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_transfo_xl import ( - AdaptiveEmbedding, - TransfoXLForSequenceClassification, - TransfoXLLMHeadModel, - TransfoXLModel, - TransfoXLPreTrainedModel, - load_tf_weights_in_transfo_xl, - ) - - try: - if not is_tf_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_tf_transfo_xl import ( - TFAdaptiveEmbedding, - TFTransfoXLForSequenceClassification, - TFTransfoXLLMHeadModel, - TFTransfoXLMainLayer, - TFTransfoXLModel, - TFTransfoXLPreTrainedModel, - ) - + from .configuration_transfo_xl import * + from .modeling_tf_transfo_xl import * + from .modeling_transfo_xl import * + from .tokenization_transfo_xl import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py b/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py index 5cd031649f..23972deae2 100644 --- a/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py +++ b/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py @@ -184,3 +184,6 @@ class TransfoXLConfig(PretrainedConfig): raise NotImplementedError( f"The model {self.model_type} is one of the few models that has no sequence length limit." ) + + +__all__ = ["TransfoXLConfig"] diff --git a/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py b/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py index 496638e5f2..90e2ebc343 100644 --- a/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py +++ b/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py @@ -1117,3 +1117,13 @@ class TFTransfoXLForSequenceClassification(TFTransfoXLPreTrainedModel, TFSequenc hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) + + +__all__ = [ + "TFAdaptiveEmbedding", + "TFTransfoXLForSequenceClassification", + "TFTransfoXLLMHeadModel", + "TFTransfoXLMainLayer", + "TFTransfoXLModel", + "TFTransfoXLPreTrainedModel", +] diff --git a/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py b/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py index abe7e59927..cf843850c0 100644 --- a/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py +++ b/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py @@ -1291,3 +1291,13 @@ class TransfoXLForSequenceClassification(TransfoXLPreTrainedModel): hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) + + +__all__ = [ + "AdaptiveEmbedding", + "TransfoXLForSequenceClassification", + "TransfoXLLMHeadModel", + "TransfoXLModel", + "TransfoXLPreTrainedModel", + "load_tf_weights_in_transfo_xl", +] diff --git a/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py b/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py index ac4b6d7a13..f9a5fb7b34 100644 --- a/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py +++ b/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py @@ -816,3 +816,6 @@ def get_lm_corpus(datadir, dataset): torch.save(corpus, fn) return corpus + + +__all__ = ["TransfoXLCorpus", "TransfoXLTokenizer"] diff --git a/src/transformers/models/deprecated/tvlt/__init__.py b/src/transformers/models/deprecated/tvlt/__init__.py index 0a2f1e3934..941db2f6ac 100644 --- a/src/transformers/models/deprecated/tvlt/__init__.py +++ b/src/transformers/models/deprecated/tvlt/__init__.py @@ -1,86 +1,20 @@ # flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. - -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. from typing import TYPE_CHECKING -from ....utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_torch_available, - is_vision_available, -) - - -_import_structure = { - "configuration_tvlt": ["TvltConfig"], - "feature_extraction_tvlt": ["TvltFeatureExtractor"], - "processing_tvlt": ["TvltProcessor"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_tvlt"] = [ - "TvltModel", - "TvltForPreTraining", - "TvltForAudioVisualClassification", - "TvltPreTrainedModel", - ] - -try: - if not is_vision_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["image_processing_tvlt"] = ["TvltImageProcessor"] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_tvlt import TvltConfig - from .processing_tvlt import TvltProcessor - from .feature_extraction_tvlt import TvltFeatureExtractor - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_tvlt import ( - TvltForAudioVisualClassification, - TvltForPreTraining, - TvltModel, - TvltPreTrainedModel, - ) - - try: - if not is_vision_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .image_processing_tvlt import TvltImageProcessor - - + from .configuration_tvlt import * + from .feature_extraction_tvlt import * + from .processing_tvlt import * + from .modeling_tvlt import * + from .image_processing_tvlt import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/tvlt/configuration_tvlt.py b/src/transformers/models/deprecated/tvlt/configuration_tvlt.py index bc9c133bec..bf159fa7e0 100644 --- a/src/transformers/models/deprecated/tvlt/configuration_tvlt.py +++ b/src/transformers/models/deprecated/tvlt/configuration_tvlt.py @@ -182,3 +182,6 @@ class TvltConfig(PretrainedConfig): self.task_matching = task_matching self.task_mae = task_mae self.loss_type = loss_type + + +__all__ = ["TvltConfig"] diff --git a/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py b/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py index 2d41af33e5..bbbfac9031 100644 --- a/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py +++ b/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py @@ -228,3 +228,6 @@ class TvltFeatureExtractor(SequenceFeatureExtractor): encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors) return encoded_inputs + + +__all__ = ["TvltFeatureExtractor"] diff --git a/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py b/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py index 2c5f853b2a..db87d6e8d5 100644 --- a/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py +++ b/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py @@ -433,3 +433,6 @@ class TvltImageProcessor(BaseImageProcessor): data = {"pixel_values": videos, "pixel_mask": video_masks} return BatchFeature(data=data, tensor_type=return_tensors) + + +__all__ = ["TvltImageProcessor"] diff --git a/src/transformers/models/deprecated/tvlt/modeling_tvlt.py b/src/transformers/models/deprecated/tvlt/modeling_tvlt.py index 561b7f90d1..279224ac4d 100644 --- a/src/transformers/models/deprecated/tvlt/modeling_tvlt.py +++ b/src/transformers/models/deprecated/tvlt/modeling_tvlt.py @@ -1286,3 +1286,6 @@ class TvltForAudioVisualClassification(TvltPreTrainedModel): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = ["TvltModel", "TvltForPreTraining", "TvltForAudioVisualClassification", "TvltPreTrainedModel"] diff --git a/src/transformers/models/deprecated/tvlt/processing_tvlt.py b/src/transformers/models/deprecated/tvlt/processing_tvlt.py index da9c755b55..d9f8e0978d 100644 --- a/src/transformers/models/deprecated/tvlt/processing_tvlt.py +++ b/src/transformers/models/deprecated/tvlt/processing_tvlt.py @@ -87,3 +87,6 @@ class TvltProcessor(ProcessorMixin): image_processor_input_names = self.image_processor.model_input_names feature_extractor_input_names = self.feature_extractor.model_input_names return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names)) + + +__all__ = ["TvltProcessor"] diff --git a/src/transformers/models/deprecated/van/__init__.py b/src/transformers/models/deprecated/van/__init__.py index 59522e4ed4..9552c82736 100644 --- a/src/transformers/models/deprecated/van/__init__.py +++ b/src/transformers/models/deprecated/van/__init__.py @@ -13,40 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure -_import_structure = {"configuration_van": ["VanConfig"]} - - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_van"] = [ - "VanForImageClassification", - "VanModel", - "VanPreTrainedModel", - ] - if TYPE_CHECKING: - from .configuration_van import VanConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_van import ( - VanForImageClassification, - VanModel, - VanPreTrainedModel, - ) - + from .configuration_van import * + from .modeling_van import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/van/configuration_van.py b/src/transformers/models/deprecated/van/configuration_van.py index 2935e631e0..08f1db7a4b 100644 --- a/src/transformers/models/deprecated/van/configuration_van.py +++ b/src/transformers/models/deprecated/van/configuration_van.py @@ -105,3 +105,6 @@ class VanConfig(PretrainedConfig): self.layer_scale_init_value = layer_scale_init_value self.drop_path_rate = drop_path_rate self.dropout_rate = dropout_rate + + +__all__ = ["VanConfig"] diff --git a/src/transformers/models/deprecated/van/modeling_van.py b/src/transformers/models/deprecated/van/modeling_van.py index 440881c751..fd11a04ec2 100644 --- a/src/transformers/models/deprecated/van/modeling_van.py +++ b/src/transformers/models/deprecated/van/modeling_van.py @@ -534,3 +534,6 @@ class VanForImageClassification(VanPreTrainedModel): return ((loss,) + output) if loss is not None else output return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states) + + +__all__ = ["VanForImageClassification", "VanModel", "VanPreTrainedModel"] diff --git a/src/transformers/models/deprecated/vit_hybrid/__init__.py b/src/transformers/models/deprecated/vit_hybrid/__init__.py index d0f9c5831d..f5bd93aa4d 100644 --- a/src/transformers/models/deprecated/vit_hybrid/__init__.py +++ b/src/transformers/models/deprecated/vit_hybrid/__init__.py @@ -13,57 +13,16 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available - - -_import_structure = {"configuration_vit_hybrid": ["ViTHybridConfig"]} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_vit_hybrid"] = [ - "ViTHybridForImageClassification", - "ViTHybridModel", - "ViTHybridPreTrainedModel", - ] - -try: - if not is_vision_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["image_processing_vit_hybrid"] = ["ViTHybridImageProcessor"] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_vit_hybrid import ViTHybridConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_vit_hybrid import ( - ViTHybridForImageClassification, - ViTHybridModel, - ViTHybridPreTrainedModel, - ) - - try: - if not is_vision_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .image_processing_vit_hybrid import ViTHybridImageProcessor - - + from .configuration_vit_hybrid import * + from .image_processing_vit_hybrid import * + from .modeling_vit_hybrid import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py b/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py index c0e4244a5a..65b6a3e5ef 100644 --- a/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py +++ b/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py @@ -167,3 +167,6 @@ class ViTHybridConfig(PretrainedConfig): self.patch_size = patch_size self.num_channels = num_channels self.qkv_bias = qkv_bias + + +__all__ = ["ViTHybridConfig"] diff --git a/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py b/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py index c78790f134..7241087893 100644 --- a/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py +++ b/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py @@ -336,3 +336,6 @@ class ViTHybridImageProcessor(BaseImageProcessor): data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors) + + +__all__ = ["ViTHybridImageProcessor"] diff --git a/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py b/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py index 6ad8a14a73..c4d2511019 100644 --- a/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py +++ b/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py @@ -765,3 +765,6 @@ class ViTHybridForImageClassification(ViTHybridPreTrainedModel): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = ["ViTHybridForImageClassification", "ViTHybridModel", "ViTHybridPreTrainedModel"] diff --git a/src/transformers/models/deprecated/xlm_prophetnet/__init__.py b/src/transformers/models/deprecated/xlm_prophetnet/__init__.py index 850d2958cb..c13c67012f 100644 --- a/src/transformers/models/deprecated/xlm_prophetnet/__init__.py +++ b/src/transformers/models/deprecated/xlm_prophetnet/__init__.py @@ -13,64 +13,16 @@ # limitations under the License. from typing import TYPE_CHECKING -from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available - - -_import_structure = { - "configuration_xlm_prophetnet": ["XLMProphetNetConfig"], -} - -try: - if not is_sentencepiece_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["tokenization_xlm_prophetnet"] = ["XLMProphetNetTokenizer"] - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_xlm_prophetnet"] = [ - "XLMProphetNetDecoder", - "XLMProphetNetEncoder", - "XLMProphetNetForCausalLM", - "XLMProphetNetForConditionalGeneration", - "XLMProphetNetModel", - "XLMProphetNetPreTrainedModel", - ] +from ....utils import _LazyModule +from ....utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_xlm_prophetnet import XLMProphetNetConfig - - try: - if not is_sentencepiece_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_xlm_prophetnet import ( - XLMProphetNetDecoder, - XLMProphetNetEncoder, - XLMProphetNetForCausalLM, - XLMProphetNetForConditionalGeneration, - XLMProphetNetModel, - XLMProphetNetPreTrainedModel, - ) - + from .configuration_xlm_prophetnet import * + from .modeling_xlm_prophetnet import * + from .tokenization_xlm_prophetnet import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py b/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py index 5d3f63670f..2d7751d954 100644 --- a/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py +++ b/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py @@ -176,3 +176,6 @@ class XLMProphetNetConfig(PretrainedConfig): "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and" " `num_decoder_layers`." ) + + +__all__ = ["XLMProphetNetConfig"] diff --git a/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py b/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py index cf1abf5bba..17bc9ffada 100644 --- a/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py +++ b/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py @@ -2334,3 +2334,13 @@ class XLMProphetNetDecoderWrapper(XLMProphetNetPreTrainedModel): def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs) + + +__all__ = [ + "XLMProphetNetDecoder", + "XLMProphetNetEncoder", + "XLMProphetNetForCausalLM", + "XLMProphetNetForConditionalGeneration", + "XLMProphetNetModel", + "XLMProphetNetPreTrainedModel", +] diff --git a/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py b/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py index 87f4580019..1a5da12859 100644 --- a/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py +++ b/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py @@ -321,3 +321,6 @@ class XLMProphetNetTokenizer(PreTrainedTokenizer): return token_ids_0 + [self.sep_token_id] sep = [self.sep_token_id] return token_ids_0 + sep + token_ids_1 + sep + + +__all__ = ["XLMProphetNetTokenizer"] diff --git a/src/transformers/models/depth_pro/image_processing_depth_pro.py b/src/transformers/models/depth_pro/image_processing_depth_pro.py index 5871e0f764..978a32da7e 100644 --- a/src/transformers/models/depth_pro/image_processing_depth_pro.py +++ b/src/transformers/models/depth_pro/image_processing_depth_pro.py @@ -18,6 +18,8 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union import numpy as np +from ...utils.import_utils import requires + if TYPE_CHECKING: from .modeling_depth_pro import DepthProDepthEstimatorOutput @@ -53,6 +55,7 @@ if is_torch_available(): logger = logging.get_logger(__name__) +@requires(backends=("torchvision", "torch")) class DepthProImageProcessor(BaseImageProcessor): r""" Constructs a DepthPro image processor. diff --git a/src/transformers/models/depth_pro/image_processing_depth_pro_fast.py b/src/transformers/models/depth_pro/image_processing_depth_pro_fast.py index 43a23bf10b..a0a4cd96b8 100644 --- a/src/transformers/models/depth_pro/image_processing_depth_pro_fast.py +++ b/src/transformers/models/depth_pro/image_processing_depth_pro_fast.py @@ -38,6 +38,7 @@ from ...utils import ( logging, requires_backends, ) +from ...utils.import_utils import requires if TYPE_CHECKING: @@ -63,6 +64,7 @@ if is_torchvision_available(): "Constructs a fast DepthPro image processor.", BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, ) +@requires(backends=("torchvision", "torch")) class DepthProImageProcessorFast(BaseImageProcessorFast): resample = PILImageResampling.BILINEAR image_mean = IMAGENET_STANDARD_MEAN diff --git a/src/transformers/models/detr/feature_extraction_detr.py b/src/transformers/models/detr/feature_extraction_detr.py index c2b2921109..a81f83c8c3 100644 --- a/src/transformers/models/detr/feature_extraction_detr.py +++ b/src/transformers/models/detr/feature_extraction_detr.py @@ -18,6 +18,7 @@ import warnings from ...image_transforms import rgb_to_id as _rgb_to_id from ...utils import logging +from ...utils.import_utils import requires from .image_processing_detr import DetrImageProcessor @@ -33,6 +34,7 @@ def rgb_to_id(x): return _rgb_to_id(x) +@requires(backends=("vision",)) class DetrFeatureExtractor(DetrImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/detr/image_processing_detr.py b/src/transformers/models/detr/image_processing_detr.py index b2677af859..75d7e74add 100644 --- a/src/transformers/models/detr/image_processing_detr.py +++ b/src/transformers/models/detr/image_processing_detr.py @@ -63,6 +63,7 @@ from ...utils import ( is_vision_available, logging, ) +from ...utils.import_utils import requires if is_torch_available(): @@ -784,6 +785,7 @@ def compute_segments( return segmentation, segments +@requires(backends=("vision",)) class DetrImageProcessor(BaseImageProcessor): r""" Constructs a Detr image processor. diff --git a/src/transformers/models/detr/image_processing_detr_fast.py b/src/transformers/models/detr/image_processing_detr_fast.py index b6227ce5c5..dc14ec61f0 100644 --- a/src/transformers/models/detr/image_processing_detr_fast.py +++ b/src/transformers/models/detr/image_processing_detr_fast.py @@ -56,6 +56,7 @@ from ...utils import ( is_vision_available, logging, ) +from ...utils.import_utils import requires from .image_processing_detr import ( compute_segments, convert_segmentation_to_rle, @@ -313,6 +314,7 @@ class DetrFastImageProcessorKwargs(DefaultFastImageProcessorKwargs): Whether to return segmentation masks. """, ) +@requires(backends=("torchvision", "torch")) class DetrImageProcessorFast(BaseImageProcessorFast): resample = PILImageResampling.BILINEAR image_mean = IMAGENET_DEFAULT_MEAN diff --git a/src/transformers/models/donut/feature_extraction_donut.py b/src/transformers/models/donut/feature_extraction_donut.py index 012b208204..e37a58ddd3 100644 --- a/src/transformers/models/donut/feature_extraction_donut.py +++ b/src/transformers/models/donut/feature_extraction_donut.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_donut import DonutImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class DonutFeatureExtractor(DonutImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/donut/image_processing_donut.py b/src/transformers/models/donut/image_processing_donut.py index 239bc54db2..e4afd4a4f7 100644 --- a/src/transformers/models/donut/image_processing_donut.py +++ b/src/transformers/models/donut/image_processing_donut.py @@ -40,7 +40,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, logging -from ...utils.import_utils import is_vision_available +from ...utils.import_utils import is_vision_available, requires logger = logging.get_logger(__name__) @@ -50,6 +50,7 @@ if is_vision_available(): import PIL +@requires(backends=("vision",)) class DonutImageProcessor(BaseImageProcessor): r""" Constructs a Donut image processor. diff --git a/src/transformers/models/dpt/feature_extraction_dpt.py b/src/transformers/models/dpt/feature_extraction_dpt.py index 8a13989676..b6ab8ccbed 100644 --- a/src/transformers/models/dpt/feature_extraction_dpt.py +++ b/src/transformers/models/dpt/feature_extraction_dpt.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_dpt import DPTImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class DPTFeatureExtractor(DPTImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/dpt/image_processing_dpt.py b/src/transformers/models/dpt/image_processing_dpt.py index d034ff0a4f..095cd1a48b 100644 --- a/src/transformers/models/dpt/image_processing_dpt.py +++ b/src/transformers/models/dpt/image_processing_dpt.py @@ -17,6 +17,8 @@ import math from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Tuple, Union +from ...utils.import_utils import requires + if TYPE_CHECKING: from ...modeling_outputs import DepthEstimatorOutput @@ -102,6 +104,7 @@ def get_resize_output_image_size( return (new_height, new_width) +@requires(backends=("vision",)) class DPTImageProcessor(BaseImageProcessor): r""" Constructs a DPT image processor. diff --git a/src/transformers/models/flava/feature_extraction_flava.py b/src/transformers/models/flava/feature_extraction_flava.py index 111795d418..19bcccc889 100644 --- a/src/transformers/models/flava/feature_extraction_flava.py +++ b/src/transformers/models/flava/feature_extraction_flava.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_flava import FlavaImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class FlavaFeatureExtractor(FlavaImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/flava/image_processing_flava.py b/src/transformers/models/flava/image_processing_flava.py index 3b8b8128c8..2b85a64cb8 100644 --- a/src/transformers/models/flava/image_processing_flava.py +++ b/src/transformers/models/flava/image_processing_flava.py @@ -37,6 +37,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging +from ...utils.import_utils import requires if is_vision_available(): @@ -133,6 +134,7 @@ class FlavaMaskingGenerator: return mask +@requires(backends=("vision",)) class FlavaImageProcessor(BaseImageProcessor): r""" Constructs a Flava image processor. diff --git a/src/transformers/models/fnet/tokenization_fnet.py b/src/transformers/models/fnet/tokenization_fnet.py index c113a505ef..3601b02261 100644 --- a/src/transformers/models/fnet/tokenization_fnet.py +++ b/src/transformers/models/fnet/tokenization_fnet.py @@ -23,6 +23,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -32,6 +33,7 @@ VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} SPIECE_UNDERLINE = "▁" +@requires(backends=("sentencepiece",)) class FNetTokenizer(PreTrainedTokenizer): """ Construct an FNet tokenizer. Adapted from [`AlbertTokenizer`]. Based on diff --git a/src/transformers/models/funnel/convert_funnel_original_tf_checkpoint_to_pytorch.py b/src/transformers/models/funnel/convert_funnel_original_tf_checkpoint_to_pytorch.py index 37f71c0d23..4eab188f2a 100755 --- a/src/transformers/models/funnel/convert_funnel_original_tf_checkpoint_to_pytorch.py +++ b/src/transformers/models/funnel/convert_funnel_original_tf_checkpoint_to_pytorch.py @@ -62,6 +62,3 @@ if __name__ == "__main__": convert_tf_checkpoint_to_pytorch( args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model ) - - -__all__ = [] diff --git a/src/transformers/models/fuyu/processing_fuyu.py b/src/transformers/models/fuyu/processing_fuyu.py index c295d94a9d..a87f769a25 100644 --- a/src/transformers/models/fuyu/processing_fuyu.py +++ b/src/transformers/models/fuyu/processing_fuyu.py @@ -25,6 +25,7 @@ from ...image_utils import ImageInput from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, _validate_images_text_input_order from ...tokenization_utils_base import PreTokenizedInput, TextInput from ...utils import is_torch_available, logging, requires_backends +from ...utils.import_utils import requires if is_torch_available(): @@ -326,6 +327,7 @@ def scale_bbox_to_transformed_image( return [top_scaled, left_scaled, bottom_scaled, right_scaled] +@requires(backends=("vision",)) class FuyuProcessor(ProcessorMixin): r""" Constructs a Fuyu processor which wraps a Fuyu image processor and a Llama tokenizer into a single processor. diff --git a/src/transformers/models/gemma/tokenization_gemma.py b/src/transformers/models/gemma/tokenization_gemma.py index 7138cafbd6..9850fb532a 100644 --- a/src/transformers/models/gemma/tokenization_gemma.py +++ b/src/transformers/models/gemma/tokenization_gemma.py @@ -27,6 +27,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires if TYPE_CHECKING: @@ -39,6 +40,7 @@ VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} SPIECE_UNDERLINE = "▁" +@requires(backends=("sentencepiece",)) class GemmaTokenizer(PreTrainedTokenizer): """ Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is diff --git a/src/transformers/models/glpn/feature_extraction_glpn.py b/src/transformers/models/glpn/feature_extraction_glpn.py index a7f1f5cc85..327fee4a11 100644 --- a/src/transformers/models/glpn/feature_extraction_glpn.py +++ b/src/transformers/models/glpn/feature_extraction_glpn.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_glpn import GLPNImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class GLPNFeatureExtractor(GLPNImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/glpn/image_processing_glpn.py b/src/transformers/models/glpn/image_processing_glpn.py index 3941bc54dc..60f339e0f1 100644 --- a/src/transformers/models/glpn/image_processing_glpn.py +++ b/src/transformers/models/glpn/image_processing_glpn.py @@ -16,6 +16,8 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union +from ...utils.import_utils import requires + if TYPE_CHECKING: from ...modeling_outputs import DepthEstimatorOutput @@ -47,6 +49,7 @@ if is_torch_available(): logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class GLPNImageProcessor(BaseImageProcessor): r""" Constructs a GLPN image processor. diff --git a/src/transformers/models/gpt2/tokenization_gpt2_tf.py b/src/transformers/models/gpt2/tokenization_gpt2_tf.py index 34e6ca2d25..4a8454a8bc 100644 --- a/src/transformers/models/gpt2/tokenization_gpt2_tf.py +++ b/src/transformers/models/gpt2/tokenization_gpt2_tf.py @@ -2,13 +2,18 @@ import os from typing import Dict, List, Optional, Union import tensorflow as tf -from keras_nlp.tokenizers import BytePairTokenizer from tensorflow_text import pad_model_inputs from ...modeling_tf_utils import keras +from ...utils.import_utils import is_keras_nlp_available, requires from .tokenization_gpt2 import GPT2Tokenizer +if is_keras_nlp_available(): + from keras_nlp.tokenizers import BytePairTokenizer + + +@requires(backends=("keras_nlp",)) class TFGPT2Tokenizer(keras.layers.Layer): """ This is an in-graph tokenizer for GPT2. It should be initialized similarly to other tokenizers, using the @@ -37,6 +42,7 @@ class TFGPT2Tokenizer(keras.layers.Layer): self.max_length = max_length self.vocab = vocab self.merges = merges + self.tf_tokenizer = BytePairTokenizer(vocab, merges, sequence_length=max_length) @classmethod diff --git a/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py b/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py index 7991988c74..37eaecd844 100644 --- a/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py +++ b/src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py @@ -10,6 +10,7 @@ import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import is_torch_available, logging +from ...utils.import_utils import requires if is_torch_available(): @@ -20,6 +21,7 @@ logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} +@requires(backends=("sentencepiece",)) class GPTSw3Tokenizer(PreTrainedTokenizer): """ Construct an GPTSw3 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/granite/__init__.py b/src/transformers/models/granite/__init__.py index 5a98daa072..08b74d14ca 100644 --- a/src/transformers/models/granite/__init__.py +++ b/src/transformers/models/granite/__init__.py @@ -13,45 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ...utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_torch_available, -) +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure -_import_structure = { - "configuration_granite": ["GraniteConfig"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_granite"] = [ - "GraniteForCausalLM", - "GraniteModel", - "GranitePreTrainedModel", - ] - if TYPE_CHECKING: - from .configuration_granite import GraniteConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_granite import ( - GraniteForCausalLM, - GraniteModel, - GranitePreTrainedModel, - ) - + from .configuration_granite import * + from .modeling_granite import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/granite/configuration_granite.py b/src/transformers/models/granite/configuration_granite.py index fc651a94e1..bfe98c3a52 100644 --- a/src/transformers/models/granite/configuration_granite.py +++ b/src/transformers/models/granite/configuration_granite.py @@ -192,3 +192,6 @@ class GraniteConfig(PretrainedConfig): ) rope_config_validation(self) + + +__all__ = ["GraniteConfig"] diff --git a/src/transformers/models/granite/modeling_granite.py b/src/transformers/models/granite/modeling_granite.py index bb06757fb8..6f15f9ca09 100644 --- a/src/transformers/models/granite/modeling_granite.py +++ b/src/transformers/models/granite/modeling_granite.py @@ -322,40 +322,6 @@ class GraniteDecoderLayer(nn.Module): return outputs -class GraniteRotaryEmbedding(nn.Module): - def __init__(self, config: GraniteConfig, device=None): - super().__init__() - # BC: "rope_type" was originally "type" - if hasattr(config, "rope_scaling") and config.rope_scaling is not None: - self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) - else: - self.rope_type = "default" - self.max_seq_len_cached = config.max_position_embeddings - self.original_max_seq_len = config.max_position_embeddings - - self.config = config - self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] - - inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) - self.register_buffer("inv_freq", inv_freq, persistent=False) - self.original_inv_freq = self.inv_freq - - @torch.no_grad() - @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) - def forward(self, x, position_ids): - inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) - position_ids_expanded = position_ids[:, None, :].float() - - device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" - with torch.autocast(device_type=device_type, enabled=False): # Force float32 - freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() * self.attention_scaling - sin = emb.sin() * self.attention_scaling - - return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) - - GRANITE_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads @@ -403,6 +369,40 @@ class GranitePreTrainedModel(PreTrainedModel): module.weight.data[module.padding_idx].zero_() +class GraniteRotaryEmbedding(nn.Module): + def __init__(self, config: GraniteConfig, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + GRANITE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): @@ -862,3 +862,6 @@ class GraniteForCausalLM(GranitePreTrainedModel, GenerationMixin): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = ["GraniteForCausalLM", "GraniteModel", "GranitePreTrainedModel"] diff --git a/src/transformers/models/granite/modular_granite.py b/src/transformers/models/granite/modular_granite.py index 25929dbb33..dd88957fdb 100644 --- a/src/transformers/models/granite/modular_granite.py +++ b/src/transformers/models/granite/modular_granite.py @@ -25,7 +25,13 @@ from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from ...processing_utils import Unpack from ...utils import LossKwargs, logging -from ..llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaForCausalLM, LlamaModel +from ..llama.modeling_llama import ( + LlamaAttention, + LlamaDecoderLayer, + LlamaForCausalLM, + LlamaModel, + LlamaPreTrainedModel, +) from .configuration_granite import GraniteConfig @@ -112,6 +118,10 @@ class GraniteDecoderLayer(LlamaDecoderLayer): return outputs +class GranitePreTrainedModel(LlamaPreTrainedModel): + pass + + class GraniteModel(LlamaModel): def __init__(self, config: GraniteConfig): super().__init__(config) @@ -281,3 +291,6 @@ class GraniteForCausalLM(LlamaForCausalLM): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = ["GraniteForCausalLM", "GraniteModel", "GranitePreTrainedModel"] diff --git a/src/transformers/models/imagegpt/feature_extraction_imagegpt.py b/src/transformers/models/imagegpt/feature_extraction_imagegpt.py index 15ecddd307..46787f139f 100644 --- a/src/transformers/models/imagegpt/feature_extraction_imagegpt.py +++ b/src/transformers/models/imagegpt/feature_extraction_imagegpt.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_imagegpt import ImageGPTImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class ImageGPTFeatureExtractor(ImageGPTImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/imagegpt/image_processing_imagegpt.py b/src/transformers/models/imagegpt/image_processing_imagegpt.py index 07e7604574..a0d50459c8 100644 --- a/src/transformers/models/imagegpt/image_processing_imagegpt.py +++ b/src/transformers/models/imagegpt/image_processing_imagegpt.py @@ -32,6 +32,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging +from ...utils.import_utils import requires if is_vision_available(): @@ -56,6 +57,7 @@ def color_quantize(x, clusters): return np.argmin(d, axis=1) +@requires(backends=("vision",)) class ImageGPTImageProcessor(BaseImageProcessor): r""" Constructs a ImageGPT image processor. This image processor can be used to resize images to a smaller resolution diff --git a/src/transformers/models/instructblipvideo/__init__.py b/src/transformers/models/instructblipvideo/__init__.py index 18d20d0401..816c6b2305 100644 --- a/src/transformers/models/instructblipvideo/__init__.py +++ b/src/transformers/models/instructblipvideo/__init__.py @@ -13,71 +13,17 @@ # limitations under the License. from typing import TYPE_CHECKING -from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure -_import_structure = { - "configuration_instructblipvideo": [ - "InstructBlipVideoConfig", - "InstructBlipVideoQFormerConfig", - "InstructBlipVideoVisionConfig", - ], - "processing_instructblipvideo": ["InstructBlipVideoProcessor"], -} - - -try: - if not is_vision_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["image_processing_instructblipvideo"] = ["InstructBlipVideoImageProcessor"] - - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_instructblipvideo"] = [ - "InstructBlipVideoQFormerModel", - "InstructBlipVideoPreTrainedModel", - "InstructBlipVideoForConditionalGeneration", - "InstructBlipVideoVisionModel", - ] - if TYPE_CHECKING: - from .configuration_instructblipvideo import ( - InstructBlipVideoConfig, - InstructBlipVideoQFormerConfig, - InstructBlipVideoVisionConfig, - ) - from .processing_instructblipvideo import InstructBlipVideoProcessor - - try: - if not is_vision_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .image_processing_instructblipvideo import InstructBlipVideoImageProcessor - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_instructblipvideo import ( - InstructBlipVideoForConditionalGeneration, - InstructBlipVideoPreTrainedModel, - InstructBlipVideoQFormerModel, - InstructBlipVideoVisionModel, - ) - + from .configuration_instructblipvideo import * + from .image_processing_instructblipvideo import * + from .modeling_instructblipvideo import * + from .processing_instructblipvideo import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/instructblipvideo/configuration_instructblipvideo.py b/src/transformers/models/instructblipvideo/configuration_instructblipvideo.py index 6776c1b62b..d4cdf65976 100644 --- a/src/transformers/models/instructblipvideo/configuration_instructblipvideo.py +++ b/src/transformers/models/instructblipvideo/configuration_instructblipvideo.py @@ -337,3 +337,6 @@ class InstructBlipVideoConfig(PretrainedConfig): text_config=text_config.to_dict(), **kwargs, ) + + +__all__ = ["InstructBlipVideoConfig", "InstructBlipVideoQFormerConfig", "InstructBlipVideoVisionConfig"] diff --git a/src/transformers/models/instructblipvideo/image_processing_instructblipvideo.py b/src/transformers/models/instructblipvideo/image_processing_instructblipvideo.py index 6c9bf4d4d3..9c55ba60d3 100644 --- a/src/transformers/models/instructblipvideo/image_processing_instructblipvideo.py +++ b/src/transformers/models/instructblipvideo/image_processing_instructblipvideo.py @@ -323,3 +323,6 @@ class InstructBlipVideoImageProcessor(BaseImageProcessor): image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) return image + + +__all__ = ["InstructBlipVideoImageProcessor"] diff --git a/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py b/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py index 8648d53b87..cdc64c6802 100644 --- a/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py +++ b/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py @@ -56,39 +56,6 @@ from .configuration_instructblipvideo import ( logger = logging.get_logger(__name__) -@dataclass -class InstructBlipVideoForConditionalGenerationModelOutput(ModelOutput): - """ - Class defining the outputs of [`InstructBlipVideoForConditionalGeneration`]. - - Args: - loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): - Language modeling loss from the language model. - logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): - Prediction scores of the language modeling head of the language model. - vision_outputs (`BaseModelOutputWithPooling`): - Outputs of the vision encoder. - qformer_outputs (`BaseModelOutputWithPoolingAndCrossAttentions`): - Outputs of the Q-Former (Querying Transformer). - language_model_outputs (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`): - Outputs of the language model. - """ - - loss: Optional[Tuple[torch.FloatTensor]] = None - logits: Optional[Tuple[torch.FloatTensor]] = None - vision_outputs: Optional[torch.FloatTensor] = None - qformer_outputs: Optional[Tuple[torch.FloatTensor]] = None - language_model_outputs: Optional[Tuple[torch.FloatTensor]] = None - - def to_tuple(self) -> Tuple[Any]: - return tuple( - self[k] - if k not in ["vision_outputs", "qformer_outputs", "language_model_outputs"] - else getattr(self, k).to_tuple() - for k in self.keys() - ) - - class InstructBlipVideoVisionEmbeddings(nn.Module): def __init__(self, config: InstructBlipVideoVisionConfig): super().__init__() @@ -163,6 +130,44 @@ class InstructBlipVideoVisionEmbeddings(nn.Module): return embeddings +class InstructBlipVideoPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = InstructBlipVideoConfig + base_model_prefix = "blip" + supports_gradient_checkpointing = True + + _no_split_modules = [ + "InstructBlipVideoQFormerEmbeddings", + "InstructBlipVideoAttention", + "InstructBlipVideoQFormerMultiHeadAttention", + "InstructBlipVideoQFormerSelfOutput", + ] + + def _init_weights(self, module): + """Initialize the weights""" + factor = self.config.initializer_range + if isinstance(module, nn.Conv2d) or isinstance(module, nn.Embedding) or isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=factor) + if hasattr(module, "bias") and module.bias is not None: + module.bias.data.zero_() + + if isinstance(module, InstructBlipVideoVisionEmbeddings): + if hasattr(self.config, "vision_config") and not isinstance(self.config, InstructBlipVideoVisionConfig): + factor = self.config.vision_config.initializer_range + nn.init.trunc_normal_(module.position_embedding, mean=0.0, std=factor) + nn.init.trunc_normal_(module.class_embedding, mean=0.0, std=factor) + + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + class InstructBlipVideoAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" @@ -307,44 +312,6 @@ class InstructBlipVideoEncoderLayer(nn.Module): return outputs -class InstructBlipVideoPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = InstructBlipVideoConfig - base_model_prefix = "blip" - supports_gradient_checkpointing = True - - _no_split_modules = [ - "InstructBlipVideoQFormerEmbeddings", - "InstructBlipVideoAttention", - "InstructBlipVideoQFormerMultiHeadAttention", - "InstructBlipVideoQFormerSelfOutput", - ] - - def _init_weights(self, module): - """Initialize the weights""" - factor = self.config.initializer_range - if isinstance(module, nn.Conv2d) or isinstance(module, nn.Embedding) or isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=factor) - if hasattr(module, "bias") and module.bias is not None: - module.bias.data.zero_() - - if isinstance(module, InstructBlipVideoVisionEmbeddings): - if hasattr(self.config, "vision_config") and not isinstance(self.config, InstructBlipVideoVisionConfig): - factor = self.config.vision_config.initializer_range - nn.init.trunc_normal_(module.position_embedding, mean=0.0, std=factor) - nn.init.trunc_normal_(module.class_embedding, mean=0.0, std=factor) - - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - elif isinstance(module, nn.Linear) and module.bias is not None: - module.bias.data.zero_() - - class InstructBlipVideoEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a @@ -1186,6 +1153,39 @@ class InstructBlipVideoQFormerModel(InstructBlipVideoPreTrainedModel): ) +@dataclass +class InstructBlipVideoForConditionalGenerationModelOutput(ModelOutput): + """ + Class defining the outputs of [`InstructBlipVideoForConditionalGeneration`]. + + Args: + loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): + Language modeling loss from the language model. + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head of the language model. + vision_outputs (`BaseModelOutputWithPooling`): + Outputs of the vision encoder. + qformer_outputs (`BaseModelOutputWithPoolingAndCrossAttentions`): + Outputs of the Q-Former (Querying Transformer). + language_model_outputs (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`): + Outputs of the language model. + """ + + loss: Optional[Tuple[torch.FloatTensor]] = None + logits: Optional[Tuple[torch.FloatTensor]] = None + vision_outputs: Optional[torch.FloatTensor] = None + qformer_outputs: Optional[Tuple[torch.FloatTensor]] = None + language_model_outputs: Optional[Tuple[torch.FloatTensor]] = None + + def to_tuple(self) -> Tuple[Any]: + return tuple( + self[k] + if k not in ["vision_outputs", "qformer_outputs", "language_model_outputs"] + else getattr(self, k).to_tuple() + for k in self.keys() + ) + + INSTRUCTBLIPVIDEO_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads @@ -1677,3 +1677,11 @@ class InstructBlipVideoForConditionalGeneration(InstructBlipVideoPreTrainedModel outputs = self.language_model.generate(**inputs, **generate_kwargs) return outputs + + +__all__ = [ + "InstructBlipVideoVisionModel", + "InstructBlipVideoPreTrainedModel", + "InstructBlipVideoQFormerModel", + "InstructBlipVideoForConditionalGeneration", +] diff --git a/src/transformers/models/instructblipvideo/modular_instructblipvideo.py b/src/transformers/models/instructblipvideo/modular_instructblipvideo.py index 4fd3a00708..ed2364edce 100644 --- a/src/transformers/models/instructblipvideo/modular_instructblipvideo.py +++ b/src/transformers/models/instructblipvideo/modular_instructblipvideo.py @@ -27,6 +27,9 @@ from transformers.models.instructblip.configuration_instructblip import ( from transformers.models.instructblip.modeling_instructblip import ( InstructBlipForConditionalGeneration, InstructBlipForConditionalGenerationModelOutput, + InstructBlipPreTrainedModel, + InstructBlipQFormerModel, + InstructBlipVisionModel, ) from ...configuration_utils import PretrainedConfig @@ -168,6 +171,18 @@ class InstructBlipVideoConfig(PretrainedConfig): ) +class InstructBlipVideoPreTrainedModel(InstructBlipPreTrainedModel): + pass + + +class InstructBlipVideoVisionModel(InstructBlipVisionModel): + pass + + +class InstructBlipVideoQFormerModel(InstructBlipQFormerModel): + pass + + @dataclass class InstructBlipVideoForConditionalGenerationModelOutput(InstructBlipForConditionalGenerationModelOutput): pass @@ -481,3 +496,14 @@ class InstructBlipVideoForConditionalGeneration(InstructBlipForConditionalGenera outputs = self.language_model.generate(**inputs, **generate_kwargs) return outputs + + +__all__ = [ + "InstructBlipVideoConfig", + "InstructBlipVideoQFormerConfig", + "InstructBlipVideoVisionConfig", + "InstructBlipVideoVisionModel", + "InstructBlipVideoPreTrainedModel", + "InstructBlipVideoQFormerModel", + "InstructBlipVideoForConditionalGeneration", +] diff --git a/src/transformers/models/instructblipvideo/processing_instructblipvideo.py b/src/transformers/models/instructblipvideo/processing_instructblipvideo.py index 1d4e59e26b..427a12d68a 100644 --- a/src/transformers/models/instructblipvideo/processing_instructblipvideo.py +++ b/src/transformers/models/instructblipvideo/processing_instructblipvideo.py @@ -234,3 +234,6 @@ class InstructBlipVideoProcessor(ProcessorMixin): qformer_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="qformer_tokenizer") processor.qformer_tokenizer = qformer_tokenizer return processor + + +__all__ = ["InstructBlipVideoProcessor"] diff --git a/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py b/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py index 74abee4575..8c70e1ed64 100644 --- a/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py +++ b/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py @@ -19,12 +19,14 @@ Feature extractor class for LayoutLMv2. import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_layoutlmv2 import LayoutLMv2ImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class LayoutLMv2FeatureExtractor(LayoutLMv2ImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/layoutlmv2/image_processing_layoutlmv2.py b/src/transformers/models/layoutlmv2/image_processing_layoutlmv2.py index aa9c737bfa..5d946982fa 100644 --- a/src/transformers/models/layoutlmv2/image_processing_layoutlmv2.py +++ b/src/transformers/models/layoutlmv2/image_processing_layoutlmv2.py @@ -38,6 +38,7 @@ from ...utils import ( logging, requires_backends, ) +from ...utils.import_utils import requires if is_vision_available(): @@ -98,6 +99,7 @@ def apply_tesseract( return words, normalized_boxes +@requires(backends=("vision",)) class LayoutLMv2ImageProcessor(BaseImageProcessor): r""" Constructs a LayoutLMv2 image processor. diff --git a/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py b/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py index 08df81c483..5ea779a48f 100644 --- a/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py +++ b/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py @@ -19,12 +19,14 @@ Feature extractor class for LayoutLMv3. import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_layoutlmv3 import LayoutLMv3ImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class LayoutLMv3FeatureExtractor(LayoutLMv3ImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/layoutlmv3/image_processing_layoutlmv3.py b/src/transformers/models/layoutlmv3/image_processing_layoutlmv3.py index 246e9dcf1f..d322c78d7e 100644 --- a/src/transformers/models/layoutlmv3/image_processing_layoutlmv3.py +++ b/src/transformers/models/layoutlmv3/image_processing_layoutlmv3.py @@ -41,6 +41,7 @@ from ...utils import ( logging, requires_backends, ) +from ...utils.import_utils import requires if is_vision_available(): @@ -100,6 +101,7 @@ def apply_tesseract( return words, normalized_boxes +@requires(backends=("vision",)) class LayoutLMv3ImageProcessor(BaseImageProcessor): r""" Constructs a LayoutLMv3 image processor. diff --git a/src/transformers/models/layoutxlm/tokenization_layoutxlm.py b/src/transformers/models/layoutxlm/tokenization_layoutxlm.py index f72039c884..8dc459ba94 100644 --- a/src/transformers/models/layoutxlm/tokenization_layoutxlm.py +++ b/src/transformers/models/layoutxlm/tokenization_layoutxlm.py @@ -30,6 +30,7 @@ from ...tokenization_utils_base import ( TruncationStrategy, ) from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging +from ...utils.import_utils import requires from ..xlm_roberta.tokenization_xlm_roberta import ( SPIECE_UNDERLINE, VOCAB_FILES_NAMES, @@ -143,6 +144,7 @@ LAYOUTXLM_ENCODE_KWARGS_DOCSTRING = r""" """ +@requires(backends=("sentencepiece",)) class LayoutXLMTokenizer(PreTrainedTokenizer): """ Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on diff --git a/src/transformers/models/levit/feature_extraction_levit.py b/src/transformers/models/levit/feature_extraction_levit.py index 41301a5171..d634239b24 100644 --- a/src/transformers/models/levit/feature_extraction_levit.py +++ b/src/transformers/models/levit/feature_extraction_levit.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_levit import LevitImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class LevitFeatureExtractor(LevitImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/levit/image_processing_levit.py b/src/transformers/models/levit/image_processing_levit.py index b20a08e20b..d980bea555 100644 --- a/src/transformers/models/levit/image_processing_levit.py +++ b/src/transformers/models/levit/image_processing_levit.py @@ -38,11 +38,13 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class LevitImageProcessor(BaseImageProcessor): r""" Constructs a LeViT image processor. diff --git a/src/transformers/models/llama/tokenization_llama.py b/src/transformers/models/llama/tokenization_llama.py index 2d1744b66c..4869ba04ea 100644 --- a/src/transformers/models/llama/tokenization_llama.py +++ b/src/transformers/models/llama/tokenization_llama.py @@ -29,6 +29,7 @@ import sentencepiece as spm from ...convert_slow_tokenizer import import_protobuf from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires if TYPE_CHECKING: @@ -51,6 +52,7 @@ If a question does not make any sense, or is not factually coherent, explain why correct. If you don't know the answer to a question, please don't share false information.""" # fmt: skip +@requires(backends=("sentencepiece",)) class LlamaTokenizer(PreTrainedTokenizer): """ Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is diff --git a/src/transformers/models/llava_onevision/video_processing_llava_onevision.py b/src/transformers/models/llava_onevision/video_processing_llava_onevision.py index 14307470e4..61ef776db8 100644 --- a/src/transformers/models/llava_onevision/video_processing_llava_onevision.py +++ b/src/transformers/models/llava_onevision/video_processing_llava_onevision.py @@ -39,11 +39,13 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class LlavaOnevisionVideoProcessor(BaseImageProcessor): r""" Constructs a LLaVa-Onevisino-Video video processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame. diff --git a/src/transformers/models/m2m_100/tokenization_m2m_100.py b/src/transformers/models/m2m_100/tokenization_m2m_100.py index 7ce4643dcd..f47f968731 100644 --- a/src/transformers/models/m2m_100/tokenization_m2m_100.py +++ b/src/transformers/models/m2m_100/tokenization_m2m_100.py @@ -23,6 +23,7 @@ import sentencepiece from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -44,6 +45,7 @@ FAIRSEQ_LANGUAGE_CODES = { # fmt: on +@requires(backends=("sentencepiece",)) class M2M100Tokenizer(PreTrainedTokenizer): """ Construct an M2M100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/marian/tokenization_marian.py b/src/transformers/models/marian/tokenization_marian.py index b1fd6463c6..bf9e0a8a2a 100644 --- a/src/transformers/models/marian/tokenization_marian.py +++ b/src/transformers/models/marian/tokenization_marian.py @@ -23,6 +23,7 @@ import sentencepiece from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -41,6 +42,7 @@ SPIECE_UNDERLINE = "▁" # Example URL https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json +@requires(backends=("sentencepiece",)) class MarianTokenizer(PreTrainedTokenizer): r""" Construct a Marian tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/maskformer/feature_extraction_maskformer.py b/src/transformers/models/maskformer/feature_extraction_maskformer.py index 6ce45471ab..98f7075fab 100644 --- a/src/transformers/models/maskformer/feature_extraction_maskformer.py +++ b/src/transformers/models/maskformer/feature_extraction_maskformer.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_maskformer import MaskFormerImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class MaskFormerFeatureExtractor(MaskFormerImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/maskformer/image_processing_maskformer.py b/src/transformers/models/maskformer/image_processing_maskformer.py index b31d032188..f433678019 100644 --- a/src/transformers/models/maskformer/image_processing_maskformer.py +++ b/src/transformers/models/maskformer/image_processing_maskformer.py @@ -51,6 +51,7 @@ from ...utils import ( logging, ) from ...utils.deprecation import deprecate_kwarg +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -352,6 +353,7 @@ def get_maskformer_resize_output_image_size( return output_size +@requires(backends=("vision",)) class MaskFormerImageProcessor(BaseImageProcessor): r""" Constructs a MaskFormer image processor. The image processor can be used to prepare image(s) and optional targets diff --git a/src/transformers/models/mbart/tokenization_mbart.py b/src/transformers/models/mbart/tokenization_mbart.py index 513b9699fc..b6c55b3833 100644 --- a/src/transformers/models/mbart/tokenization_mbart.py +++ b/src/transformers/models/mbart/tokenization_mbart.py @@ -21,6 +21,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -33,6 +34,7 @@ VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"] # fmt: skip +@requires(backends=("sentencepiece",)) class MBartTokenizer(PreTrainedTokenizer): """ Construct an MBART tokenizer. diff --git a/src/transformers/models/mbart50/tokenization_mbart50.py b/src/transformers/models/mbart50/tokenization_mbart50.py index c85039991c..a212012463 100644 --- a/src/transformers/models/mbart50/tokenization_mbart50.py +++ b/src/transformers/models/mbart50/tokenization_mbart50.py @@ -21,6 +21,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -33,6 +34,7 @@ VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"] # fmt: skip +@requires(backends=("sentencepiece",)) class MBart50Tokenizer(PreTrainedTokenizer): """ Construct a MBart50 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/megatron_gpt2/__init__.py b/src/transformers/models/megatron_gpt2/__init__.py index f1b21c7d2f..e69de29bb2 100644 --- a/src/transformers/models/megatron_gpt2/__init__.py +++ b/src/transformers/models/megatron_gpt2/__init__.py @@ -1,13 +0,0 @@ -# Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/src/transformers/models/mgp_str/processing_mgp_str.py b/src/transformers/models/mgp_str/processing_mgp_str.py index 5af06e2f13..9e68db0c81 100644 --- a/src/transformers/models/mgp_str/processing_mgp_str.py +++ b/src/transformers/models/mgp_str/processing_mgp_str.py @@ -21,6 +21,7 @@ from transformers.utils import is_torch_available from transformers.utils.generic import ExplicitEnum from ...processing_utils import ProcessorMixin +from ...utils.import_utils import requires if is_torch_available(): @@ -36,6 +37,7 @@ class DecodeType(ExplicitEnum): SUPPORTED_ANNOTATION_FORMATS = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE) +@requires(backends=("sentencepiece",)) class MgpstrProcessor(ProcessorMixin): r""" Constructs a MGP-STR processor which wraps an image processor and MGP-STR tokenizers into a single diff --git a/src/transformers/models/mistral/__init__.py b/src/transformers/models/mistral/__init__.py index 31441efe65..18a5657cd2 100644 --- a/src/transformers/models/mistral/__init__.py +++ b/src/transformers/models/mistral/__init__.py @@ -13,106 +13,17 @@ # limitations under the License. from typing import TYPE_CHECKING -from ...utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_flax_available, - is_tf_available, - is_torch_available, -) - - -_import_structure = { - "configuration_mistral": ["MistralConfig"], -} - - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_mistral"] = [ - "MistralForCausalLM", - "MistralForQuestionAnswering", - "MistralModel", - "MistralPreTrainedModel", - "MistralForSequenceClassification", - "MistralForTokenClassification", - ] - -try: - if not is_flax_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_flax_mistral"] = [ - "FlaxMistralForCausalLM", - "FlaxMistralModel", - "FlaxMistralPreTrainedModel", - ] - -try: - if not is_tf_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_tf_mistral"] = [ - "TFMistralModel", - "TFMistralForCausalLM", - "TFMistralForSequenceClassification", - "TFMistralPreTrainedModel", - ] +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_mistral import MistralConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_mistral import ( - MistralForCausalLM, - MistralForQuestionAnswering, - MistralForSequenceClassification, - MistralForTokenClassification, - MistralModel, - MistralPreTrainedModel, - ) - - try: - if not is_flax_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_flax_mistral import ( - FlaxMistralForCausalLM, - FlaxMistralModel, - FlaxMistralPreTrainedModel, - ) - - try: - if not is_tf_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_tf_mistral import ( - TFMistralForCausalLM, - TFMistralForSequenceClassification, - TFMistralModel, - TFMistralPreTrainedModel, - ) - - + from .configuration_mistral import * + from .modeling_flax_mistral import * + from .modeling_mistral import * + from .modeling_tf_mistral import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/mistral/configuration_mistral.py b/src/transformers/models/mistral/configuration_mistral.py index 8a5055be65..c362dbef28 100644 --- a/src/transformers/models/mistral/configuration_mistral.py +++ b/src/transformers/models/mistral/configuration_mistral.py @@ -164,3 +164,6 @@ class MistralConfig(PretrainedConfig): tie_word_embeddings=tie_word_embeddings, **kwargs, ) + + +__all__ = ["MistralConfig"] diff --git a/src/transformers/models/mistral/modeling_flax_mistral.py b/src/transformers/models/mistral/modeling_flax_mistral.py index 9ad28772bc..fe90985678 100644 --- a/src/transformers/models/mistral/modeling_flax_mistral.py +++ b/src/transformers/models/mistral/modeling_flax_mistral.py @@ -740,3 +740,5 @@ append_call_sample_docstring( _CONFIG_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, ) + +__all__ = ["FlaxMistralForCausalLM", "FlaxMistralModel", "FlaxMistralPreTrainedModel"] diff --git a/src/transformers/models/mistral/modeling_mistral.py b/src/transformers/models/mistral/modeling_mistral.py index b235c74413..8f1b416d5b 100644 --- a/src/transformers/models/mistral/modeling_mistral.py +++ b/src/transformers/models/mistral/modeling_mistral.py @@ -274,40 +274,6 @@ class MistralDecoderLayer(nn.Module): return outputs -class MistralRotaryEmbedding(nn.Module): - def __init__(self, config: MistralConfig, device=None): - super().__init__() - # BC: "rope_type" was originally "type" - if hasattr(config, "rope_scaling") and config.rope_scaling is not None: - self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) - else: - self.rope_type = "default" - self.max_seq_len_cached = config.max_position_embeddings - self.original_max_seq_len = config.max_position_embeddings - - self.config = config - self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] - - inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) - self.register_buffer("inv_freq", inv_freq, persistent=False) - self.original_inv_freq = self.inv_freq - - @torch.no_grad() - @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) - def forward(self, x, position_ids): - inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) - position_ids_expanded = position_ids[:, None, :].float() - - device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" - with torch.autocast(device_type=device_type, enabled=False): # Force float32 - freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() * self.attention_scaling - sin = emb.sin() * self.attention_scaling - - return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) - - MISTRAL_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads @@ -355,6 +321,40 @@ class MistralPreTrainedModel(PreTrainedModel): module.weight.data[module.padding_idx].zero_() +class MistralRotaryEmbedding(nn.Module): + def __init__(self, config: MistralConfig, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + MISTRAL_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): @@ -1101,3 +1101,13 @@ class MistralForQuestionAnswering(MistralPreTrainedModel): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = [ + "MistralForCausalLM", + "MistralForQuestionAnswering", + "MistralModel", + "MistralPreTrainedModel", + "MistralForSequenceClassification", + "MistralForTokenClassification", +] diff --git a/src/transformers/models/mistral/modeling_tf_mistral.py b/src/transformers/models/mistral/modeling_tf_mistral.py index e27453249b..34b4da4c33 100644 --- a/src/transformers/models/mistral/modeling_tf_mistral.py +++ b/src/transformers/models/mistral/modeling_tf_mistral.py @@ -1041,3 +1041,6 @@ class TFMistralForSequenceClassification(TFMistralPreTrainedModel, TFSequenceCla if getattr(self, "score", None) is not None: with tf.name_scope(self.score.name): self.score.build((self.config.hidden_size,)) + + +__all__ = ["TFMistralModel", "TFMistralForCausalLM", "TFMistralForSequenceClassification", "TFMistralPreTrainedModel"] diff --git a/src/transformers/models/mistral/modular_mistral.py b/src/transformers/models/mistral/modular_mistral.py index 9aae2e5505..3f97738143 100644 --- a/src/transformers/models/mistral/modular_mistral.py +++ b/src/transformers/models/mistral/modular_mistral.py @@ -20,6 +20,7 @@ from ..llama.modeling_llama import ( LlamaForTokenClassification, LlamaMLP, LlamaModel, + LlamaPreTrainedModel, apply_rotary_pos_emb, eager_attention_forward, ) @@ -106,6 +107,10 @@ class MistralDecoderLayer(LlamaDecoderLayer): self.mlp = MistralMLP(config) +class MistralPreTrainedModel(LlamaPreTrainedModel): + pass + + class MistralModel(LlamaModel): def __init__(self, config: MistralConfig): super().__init__(config) @@ -344,3 +349,13 @@ class MistralForQuestionAnswering(LlamaForQuestionAnswering): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = [ + "MistralForCausalLM", + "MistralForQuestionAnswering", + "MistralModel", + "MistralPreTrainedModel", + "MistralForSequenceClassification", + "MistralForTokenClassification", +] diff --git a/src/transformers/models/mixtral/__init__.py b/src/transformers/models/mixtral/__init__.py index 4ee4834dd2..e4ca36bacb 100644 --- a/src/transformers/models/mixtral/__init__.py +++ b/src/transformers/models/mixtral/__init__.py @@ -13,54 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ...utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_torch_available, -) - - -_import_structure = { - "configuration_mixtral": ["MixtralConfig"], -} - - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_mixtral"] = [ - "MixtralForCausalLM", - "MixtralForQuestionAnswering", - "MixtralModel", - "MixtralPreTrainedModel", - "MixtralForSequenceClassification", - "MixtralForTokenClassification", - ] +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_mixtral import MixtralConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_mixtral import ( - MixtralForCausalLM, - MixtralForQuestionAnswering, - MixtralForSequenceClassification, - MixtralForTokenClassification, - MixtralModel, - MixtralPreTrainedModel, - ) - - + from .configuration_mixtral import * + from .modeling_mixtral import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/mixtral/configuration_mixtral.py b/src/transformers/models/mixtral/configuration_mixtral.py index 4f11077c19..c8f7eaccdb 100644 --- a/src/transformers/models/mixtral/configuration_mixtral.py +++ b/src/transformers/models/mixtral/configuration_mixtral.py @@ -186,3 +186,6 @@ class MixtralConfig(PretrainedConfig): tie_word_embeddings=tie_word_embeddings, **kwargs, ) + + +__all__ = ["MixtralConfig"] diff --git a/src/transformers/models/mixtral/modeling_mixtral.py b/src/transformers/models/mixtral/modeling_mixtral.py index d6da7b1d78..da6beed121 100644 --- a/src/transformers/models/mixtral/modeling_mixtral.py +++ b/src/transformers/models/mixtral/modeling_mixtral.py @@ -1334,3 +1334,13 @@ class MixtralForQuestionAnswering(MixtralPreTrainedModel): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = [ + "MixtralForCausalLM", + "MixtralForQuestionAnswering", + "MixtralModel", + "MixtralPreTrainedModel", + "MixtralForSequenceClassification", + "MixtralForTokenClassification", +] diff --git a/src/transformers/models/mixtral/modular_mixtral.py b/src/transformers/models/mixtral/modular_mixtral.py index b0f0ac1a88..f85836b567 100644 --- a/src/transformers/models/mixtral/modular_mixtral.py +++ b/src/transformers/models/mixtral/modular_mixtral.py @@ -571,3 +571,13 @@ class MixtralForTokenClassification(MistralForTokenClassification): class MixtralForQuestionAnswering(MistralForQuestionAnswering): pass + + +__all__ = [ + "MixtralForCausalLM", + "MixtralForQuestionAnswering", + "MixtralModel", + "MixtralPreTrainedModel", + "MixtralForSequenceClassification", + "MixtralForTokenClassification", +] diff --git a/src/transformers/models/mluke/tokenization_mluke.py b/src/transformers/models/mluke/tokenization_mluke.py index 6bd9ed1a50..90619befc2 100644 --- a/src/transformers/models/mluke/tokenization_mluke.py +++ b/src/transformers/models/mluke/tokenization_mluke.py @@ -38,6 +38,7 @@ from ...tokenization_utils_base import ( to_py_obj, ) from ...utils import add_end_docstrings, is_tf_tensor, is_torch_tensor, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -128,6 +129,7 @@ ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r""" """ +@requires(backends=("sentencepiece",)) class MLukeTokenizer(PreTrainedTokenizer): """ Adapted from [`XLMRobertaTokenizer`] and [`LukeTokenizer`]. Based on diff --git a/src/transformers/models/mobilenet_v1/feature_extraction_mobilenet_v1.py b/src/transformers/models/mobilenet_v1/feature_extraction_mobilenet_v1.py index b0d2d11a20..02a5401bc1 100644 --- a/src/transformers/models/mobilenet_v1/feature_extraction_mobilenet_v1.py +++ b/src/transformers/models/mobilenet_v1/feature_extraction_mobilenet_v1.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_mobilenet_v1 import MobileNetV1ImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class MobileNetV1FeatureExtractor(MobileNetV1ImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py b/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py index 6c30c3413b..c9f96a955e 100644 --- a/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py +++ b/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py @@ -38,11 +38,13 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class MobileNetV1ImageProcessor(BaseImageProcessor): r""" Constructs a MobileNetV1 image processor. diff --git a/src/transformers/models/mobilenet_v2/feature_extraction_mobilenet_v2.py b/src/transformers/models/mobilenet_v2/feature_extraction_mobilenet_v2.py index 09043aa483..e36b50cffa 100644 --- a/src/transformers/models/mobilenet_v2/feature_extraction_mobilenet_v2.py +++ b/src/transformers/models/mobilenet_v2/feature_extraction_mobilenet_v2.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_mobilenet_v2 import MobileNetV2ImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class MobileNetV2FeatureExtractor(MobileNetV2ImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py b/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py index 0107e96402..ca6aa04c14 100644 --- a/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py +++ b/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py @@ -44,9 +44,13 @@ if is_torch_available(): import torch +from ...utils.import_utils import requires + + logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class MobileNetV2ImageProcessor(BaseImageProcessor): r""" Constructs a MobileNetV2 image processor. diff --git a/src/transformers/models/mobilevit/feature_extraction_mobilevit.py b/src/transformers/models/mobilevit/feature_extraction_mobilevit.py index eb98b2f6e1..6c220df918 100644 --- a/src/transformers/models/mobilevit/feature_extraction_mobilevit.py +++ b/src/transformers/models/mobilevit/feature_extraction_mobilevit.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_mobilevit import MobileViTImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class MobileViTFeatureExtractor(MobileViTImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/mobilevit/image_processing_mobilevit.py b/src/transformers/models/mobilevit/image_processing_mobilevit.py index f59c246627..23ceae679f 100644 --- a/src/transformers/models/mobilevit/image_processing_mobilevit.py +++ b/src/transformers/models/mobilevit/image_processing_mobilevit.py @@ -39,6 +39,7 @@ from ...utils import ( is_vision_available, logging, ) +from ...utils.import_utils import requires if is_vision_available(): @@ -51,6 +52,7 @@ if is_torch_available(): logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class MobileViTImageProcessor(BaseImageProcessor): r""" Constructs a MobileViT image processor. diff --git a/src/transformers/models/mt5/modeling_mt5.py b/src/transformers/models/mt5/modeling_mt5.py index 0199c7d779..130d58f59b 100644 --- a/src/transformers/models/mt5/modeling_mt5.py +++ b/src/transformers/models/mt5/modeling_mt5.py @@ -2566,5 +2566,4 @@ __all__ = [ "MT5ForTokenClassification", "MT5Model", "MT5PreTrainedModel", - "MT5Stack", ] diff --git a/src/transformers/models/musicgen_melody/__init__.py b/src/transformers/models/musicgen_melody/__init__.py index 20c8507aae..51456aac76 100644 --- a/src/transformers/models/musicgen_melody/__init__.py +++ b/src/transformers/models/musicgen_melody/__init__.py @@ -13,74 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ...utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_torch_available, - is_torchaudio_available, -) - - -_import_structure = { - "configuration_musicgen_melody": [ - "MusicgenMelodyConfig", - "MusicgenMelodyDecoderConfig", - ], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_musicgen_melody"] = [ - "MusicgenMelodyForConditionalGeneration", - "MusicgenMelodyForCausalLM", - "MusicgenMelodyModel", - "MusicgenMelodyPreTrainedModel", - ] - -try: - if not is_torchaudio_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["feature_extraction_musicgen_melody"] = ["MusicgenMelodyFeatureExtractor"] - _import_structure["processing_musicgen_melody"] = ["MusicgenMelodyProcessor"] +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_musicgen_melody import ( - MusicgenMelodyConfig, - MusicgenMelodyDecoderConfig, - ) - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_musicgen_melody import ( - MusicgenMelodyForCausalLM, - MusicgenMelodyForConditionalGeneration, - MusicgenMelodyModel, - MusicgenMelodyPreTrainedModel, - ) - - try: - if not is_torchaudio_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .feature_extraction_musicgen_melody import MusicgenMelodyFeatureExtractor - from .processing_musicgen_melody import MusicgenMelodyProcessor - - + from .configuration_musicgen_melody import * + from .modeling_musicgen_melody import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/musicgen_melody/configuration_musicgen_melody.py b/src/transformers/models/musicgen_melody/configuration_musicgen_melody.py index e35c7bd3c8..24c8a17faa 100644 --- a/src/transformers/models/musicgen_melody/configuration_musicgen_melody.py +++ b/src/transformers/models/musicgen_melody/configuration_musicgen_melody.py @@ -256,3 +256,6 @@ class MusicgenMelodyConfig(PretrainedConfig): # This is a property because you might want to change the codec model on the fly def sampling_rate(self): return self.audio_encoder.sampling_rate + + +__all__ = ["MusicgenMelodyConfig", "MusicgenMelodyDecoderConfig"] diff --git a/src/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py b/src/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py index ec490b7d90..d823adf649 100644 --- a/src/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py +++ b/src/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py @@ -25,6 +25,7 @@ from ...audio_utils import chroma_filter_bank from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import TensorType, is_torch_available, is_torchaudio_available, logging +from ...utils.import_utils import requires if is_torch_available(): @@ -36,6 +37,7 @@ if is_torchaudio_available(): logger = logging.get_logger(__name__) +@requires(backends=("torchaudio",)) class MusicgenMelodyFeatureExtractor(SequenceFeatureExtractor): r""" Constructs a MusicgenMelody feature extractor. @@ -329,3 +331,6 @@ class MusicgenMelodyFeatureExtractor(SequenceFeatureExtractor): if "spectrogram" in output: del output["spectrogram"] return output + + +__all__ = ["MusicgenMelodyFeatureExtractor"] diff --git a/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py b/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py index 70b914313c..07a593ca2a 100644 --- a/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py +++ b/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py @@ -2563,3 +2563,11 @@ class MusicgenMelodyForConditionalGeneration(PreTrainedModel, GenerationMixin): return outputs else: return output_values + + +__all__ = [ + "MusicgenMelodyForConditionalGeneration", + "MusicgenMelodyForCausalLM", + "MusicgenMelodyModel", + "MusicgenMelodyPreTrainedModel", +] diff --git a/src/transformers/models/musicgen_melody/processing_musicgen_melody.py b/src/transformers/models/musicgen_melody/processing_musicgen_melody.py index 8cf11e67d4..f42d6af601 100644 --- a/src/transformers/models/musicgen_melody/processing_musicgen_melody.py +++ b/src/transformers/models/musicgen_melody/processing_musicgen_melody.py @@ -22,8 +22,10 @@ import numpy as np from ...processing_utils import ProcessorMixin from ...utils import to_numpy +from ...utils.import_utils import requires +@requires(backends=("torchaudio",)) class MusicgenMelodyProcessor(ProcessorMixin): r""" Constructs a MusicGen Melody processor which wraps a Wav2Vec2 feature extractor - for raw audio waveform processing - and a T5 tokenizer into a single processor @@ -173,3 +175,6 @@ class MusicgenMelodyProcessor(ProcessorMixin): inputs["attention_mask"][:] = 0 return inputs + + +__all__ = ["MusicgenMelodyProcessor"] diff --git a/src/transformers/models/nllb/tokenization_nllb.py b/src/transformers/models/nllb/tokenization_nllb.py index 02e07f0eca..1ac0059ddd 100644 --- a/src/transformers/models/nllb/tokenization_nllb.py +++ b/src/transformers/models/nllb/tokenization_nllb.py @@ -21,6 +21,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -33,6 +34,7 @@ VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} FAIRSEQ_LANGUAGE_CODES = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn'] # fmt: skip +@requires(backends=("sentencepiece",)) class NllbTokenizer(PreTrainedTokenizer): """ Construct an NLLB tokenizer. diff --git a/src/transformers/models/olmo/__init__.py b/src/transformers/models/olmo/__init__.py index b94350cd33..139af5473e 100644 --- a/src/transformers/models/olmo/__init__.py +++ b/src/transformers/models/olmo/__init__.py @@ -13,47 +13,15 @@ # limitations under the License. from typing import TYPE_CHECKING -from ...utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_sentencepiece_available, - is_tokenizers_available, - is_torch_available, -) +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure -_import_structure = { - "configuration_olmo": ["OlmoConfig"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_olmo"] = [ - "OlmoForCausalLM", - "OlmoModel", - "OlmoPreTrainedModel", - ] - if TYPE_CHECKING: - from .configuration_olmo import OlmoConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_olmo import ( - OlmoForCausalLM, - OlmoModel, - OlmoPreTrainedModel, - ) - + from .configuration_olmo import * + from .modeling_olmo import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/olmo/configuration_olmo.py b/src/transformers/models/olmo/configuration_olmo.py index 7413f98528..4ad5de6152 100644 --- a/src/transformers/models/olmo/configuration_olmo.py +++ b/src/transformers/models/olmo/configuration_olmo.py @@ -193,3 +193,6 @@ class OlmoConfig(PretrainedConfig): ) if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") + + +__all__ = ["OlmoConfig"] diff --git a/src/transformers/models/olmo/modeling_olmo.py b/src/transformers/models/olmo/modeling_olmo.py index dc09278fe2..8b8783d1ad 100644 --- a/src/transformers/models/olmo/modeling_olmo.py +++ b/src/transformers/models/olmo/modeling_olmo.py @@ -284,40 +284,6 @@ class OlmoDecoderLayer(nn.Module): return outputs -class OlmoRotaryEmbedding(nn.Module): - def __init__(self, config: OlmoConfig, device=None): - super().__init__() - # BC: "rope_type" was originally "type" - if hasattr(config, "rope_scaling") and config.rope_scaling is not None: - self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) - else: - self.rope_type = "default" - self.max_seq_len_cached = config.max_position_embeddings - self.original_max_seq_len = config.max_position_embeddings - - self.config = config - self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] - - inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) - self.register_buffer("inv_freq", inv_freq, persistent=False) - self.original_inv_freq = self.inv_freq - - @torch.no_grad() - @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) - def forward(self, x, position_ids): - inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) - position_ids_expanded = position_ids[:, None, :].float() - - device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" - with torch.autocast(device_type=device_type, enabled=False): # Force float32 - freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() * self.attention_scaling - sin = emb.sin() * self.attention_scaling - - return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) - - OLMO_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads @@ -365,6 +331,40 @@ class OlmoPreTrainedModel(PreTrainedModel): module.weight.data[module.padding_idx].zero_() +class OlmoRotaryEmbedding(nn.Module): + def __init__(self, config: OlmoConfig, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + OLMO_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): @@ -824,3 +824,6 @@ class OlmoForCausalLM(OlmoPreTrainedModel, GenerationMixin): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = ["OlmoForCausalLM", "OlmoModel", "OlmoPreTrainedModel"] diff --git a/src/transformers/models/olmo/modular_olmo.py b/src/transformers/models/olmo/modular_olmo.py index 2a43e6f9c7..ca290ba9ae 100644 --- a/src/transformers/models/olmo/modular_olmo.py +++ b/src/transformers/models/olmo/modular_olmo.py @@ -14,6 +14,7 @@ from ..llama.modeling_llama import ( LlamaForCausalLM, LlamaMLP, LlamaModel, + LlamaPreTrainedModel, apply_rotary_pos_emb, eager_attention_forward, ) @@ -113,6 +114,10 @@ class OlmoDecoderLayer(LlamaDecoderLayer): self.self_attn = OlmoAttention(config=config, layer_idx=layer_idx) +class OlmoPreTrainedModel(LlamaPreTrainedModel): + pass + + class OlmoModel(LlamaModel): def __init__(self, config: OlmoConfig): super().__init__(config) @@ -124,3 +129,6 @@ class OlmoModel(LlamaModel): class OlmoForCausalLM(LlamaForCausalLM): pass + + +__all__ = ["OlmoForCausalLM", "OlmoModel", "OlmoPreTrainedModel"] diff --git a/src/transformers/models/olmo2/modeling_olmo2.py b/src/transformers/models/olmo2/modeling_olmo2.py index bd0a47eaf1..bcf990ccda 100644 --- a/src/transformers/models/olmo2/modeling_olmo2.py +++ b/src/transformers/models/olmo2/modeling_olmo2.py @@ -287,40 +287,6 @@ class Olmo2DecoderLayer(nn.Module): return outputs -class Olmo2RotaryEmbedding(nn.Module): - def __init__(self, config: Olmo2Config, device=None): - super().__init__() - # BC: "rope_type" was originally "type" - if hasattr(config, "rope_scaling") and config.rope_scaling is not None: - self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) - else: - self.rope_type = "default" - self.max_seq_len_cached = config.max_position_embeddings - self.original_max_seq_len = config.max_position_embeddings - - self.config = config - self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] - - inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) - self.register_buffer("inv_freq", inv_freq, persistent=False) - self.original_inv_freq = self.inv_freq - - @torch.no_grad() - @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) - def forward(self, x, position_ids): - inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) - position_ids_expanded = position_ids[:, None, :].float() - - device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" - with torch.autocast(device_type=device_type, enabled=False): # Force float32 - freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() * self.attention_scaling - sin = emb.sin() * self.attention_scaling - - return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) - - OLMO2_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads @@ -368,6 +334,40 @@ class Olmo2PreTrainedModel(PreTrainedModel): module.weight.data[module.padding_idx].zero_() +class Olmo2RotaryEmbedding(nn.Module): + def __init__(self, config: Olmo2Config, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + OLMO2_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): diff --git a/src/transformers/models/omdet_turbo/processing_omdet_turbo.py b/src/transformers/models/omdet_turbo/processing_omdet_turbo.py index 6d59202e57..3f5c51779b 100644 --- a/src/transformers/models/omdet_turbo/processing_omdet_turbo.py +++ b/src/transformers/models/omdet_turbo/processing_omdet_turbo.py @@ -30,6 +30,7 @@ from ...utils import ( is_torchvision_available, ) from ...utils.deprecation import deprecate_kwarg +from ...utils.import_utils import requires if TYPE_CHECKING: @@ -199,6 +200,7 @@ def _post_process_boxes_for_image( return boxes_per_image, scores_per_image, labels_per_image +@requires(backends=("vision", "torchvision")) class OmDetTurboProcessor(ProcessorMixin): r""" Constructs a OmDet-Turbo processor which wraps a Deformable DETR image processor and an AutoTokenizer into a diff --git a/src/transformers/models/owlvit/feature_extraction_owlvit.py b/src/transformers/models/owlvit/feature_extraction_owlvit.py index 2cd3b5a3ec..ee3a8d0b14 100644 --- a/src/transformers/models/owlvit/feature_extraction_owlvit.py +++ b/src/transformers/models/owlvit/feature_extraction_owlvit.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_owlvit import OwlViTImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class OwlViTFeatureExtractor(OwlViTImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/owlvit/image_processing_owlvit.py b/src/transformers/models/owlvit/image_processing_owlvit.py index 59c6465734..dfd5007f99 100644 --- a/src/transformers/models/owlvit/image_processing_owlvit.py +++ b/src/transformers/models/owlvit/image_processing_owlvit.py @@ -41,6 +41,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, is_torch_available, logging +from ...utils.import_utils import requires if TYPE_CHECKING: @@ -120,6 +121,7 @@ def box_iou(boxes1, boxes2): return iou, union +@requires(backends=("vision",)) class OwlViTImageProcessor(BaseImageProcessor): r""" Constructs an OWL-ViT image processor. diff --git a/src/transformers/models/pegasus/tokenization_pegasus.py b/src/transformers/models/pegasus/tokenization_pegasus.py index 5bab507332..c338e0fac1 100644 --- a/src/transformers/models/pegasus/tokenization_pegasus.py +++ b/src/transformers/models/pegasus/tokenization_pegasus.py @@ -20,6 +20,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires SPIECE_UNDERLINE = "▁" @@ -31,6 +32,9 @@ logger = logging.get_logger(__name__) # TODO ArthurZ refactor this to only use the added_tokens_encoder + + +@requires(backends=("sentencepiece",)) class PegasusTokenizer(PreTrainedTokenizer): r""" Construct a PEGASUS tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/perceiver/feature_extraction_perceiver.py b/src/transformers/models/perceiver/feature_extraction_perceiver.py index b4aa5ce4a1..566cb31fea 100644 --- a/src/transformers/models/perceiver/feature_extraction_perceiver.py +++ b/src/transformers/models/perceiver/feature_extraction_perceiver.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_perceiver import PerceiverImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class PerceiverFeatureExtractor(PerceiverImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/perceiver/image_processing_perceiver.py b/src/transformers/models/perceiver/image_processing_perceiver.py index dab5ed6b07..82d5713473 100644 --- a/src/transformers/models/perceiver/image_processing_perceiver.py +++ b/src/transformers/models/perceiver/image_processing_perceiver.py @@ -35,6 +35,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging +from ...utils.import_utils import requires if is_vision_available(): @@ -44,6 +45,7 @@ if is_vision_available(): logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class PerceiverImageProcessor(BaseImageProcessor): r""" Constructs a Perceiver image processor. diff --git a/src/transformers/models/phi/__init__.py b/src/transformers/models/phi/__init__.py index 662c0a9bf3..cffe33da73 100644 --- a/src/transformers/models/phi/__init__.py +++ b/src/transformers/models/phi/__init__.py @@ -11,57 +11,17 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - - from typing import TYPE_CHECKING -from ...utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_sentencepiece_available, - is_tokenizers_available, - is_torch_available, -) - - -_import_structure = { - "configuration_phi": ["PhiConfig"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_phi"] = [ - "PhiPreTrainedModel", - "PhiModel", - "PhiForCausalLM", - "PhiForSequenceClassification", - "PhiForTokenClassification", - ] +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_phi import PhiConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_phi import ( - PhiForCausalLM, - PhiForSequenceClassification, - PhiForTokenClassification, - PhiModel, - PhiPreTrainedModel, - ) - - + from .configuration_phi import * + from .modeling_phi import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/phi/configuration_phi.py b/src/transformers/models/phi/configuration_phi.py index 06e5cbec2e..4ffd89db3a 100644 --- a/src/transformers/models/phi/configuration_phi.py +++ b/src/transformers/models/phi/configuration_phi.py @@ -212,3 +212,6 @@ class PhiConfig(PretrainedConfig): tie_word_embeddings=tie_word_embeddings, **kwargs, ) + + +__all__ = ["PhiConfig"] diff --git a/src/transformers/models/phi/modeling_phi.py b/src/transformers/models/phi/modeling_phi.py index 664cb571ad..ffb36ed45f 100644 --- a/src/transformers/models/phi/modeling_phi.py +++ b/src/transformers/models/phi/modeling_phi.py @@ -279,40 +279,6 @@ class PhiDecoderLayer(nn.Module): return outputs -class PhiRotaryEmbedding(nn.Module): - def __init__(self, config: PhiConfig, device=None): - super().__init__() - # BC: "rope_type" was originally "type" - if hasattr(config, "rope_scaling") and config.rope_scaling is not None: - self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) - else: - self.rope_type = "default" - self.max_seq_len_cached = config.max_position_embeddings - self.original_max_seq_len = config.max_position_embeddings - - self.config = config - self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] - - inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) - self.register_buffer("inv_freq", inv_freq, persistent=False) - self.original_inv_freq = self.inv_freq - - @torch.no_grad() - @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) - def forward(self, x, position_ids): - inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) - position_ids_expanded = position_ids[:, None, :].float() - - device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" - with torch.autocast(device_type=device_type, enabled=False): # Force float32 - freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() * self.attention_scaling - sin = emb.sin() * self.attention_scaling - - return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) - - PHI_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads @@ -360,6 +326,40 @@ class PhiPreTrainedModel(PreTrainedModel): module.weight.data[module.padding_idx].zero_() +class PhiRotaryEmbedding(nn.Module): + def __init__(self, config: PhiConfig, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + PHI_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): @@ -1000,3 +1000,12 @@ class PhiForTokenClassification(PhiPreTrainedModel): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = [ + "PhiPreTrainedModel", + "PhiModel", + "PhiForCausalLM", + "PhiForSequenceClassification", + "PhiForTokenClassification", +] diff --git a/src/transformers/models/phi/modular_phi.py b/src/transformers/models/phi/modular_phi.py index f0eb31058c..5661432f8d 100644 --- a/src/transformers/models/phi/modular_phi.py +++ b/src/transformers/models/phi/modular_phi.py @@ -19,6 +19,7 @@ from ..llama.modeling_llama import ( LlamaForSequenceClassification, LlamaForTokenClassification, LlamaModel, + LlamaPreTrainedModel, apply_rotary_pos_emb, eager_attention_forward, # copied from Llama ) @@ -169,6 +170,10 @@ class PhiDecoderLayer(nn.Module): return outputs +class PhiPreTrainedModel(LlamaPreTrainedModel): + pass + + class PhiModel(LlamaModel): def __init__(self, config: PhiConfig): super().__init__(config) @@ -296,3 +301,12 @@ class PhiForSequenceClassification(LlamaForSequenceClassification): class PhiForTokenClassification(LlamaForTokenClassification): pass + + +__all__ = [ + "PhiPreTrainedModel", + "PhiModel", + "PhiForCausalLM", + "PhiForSequenceClassification", + "PhiForTokenClassification", +] diff --git a/src/transformers/models/plbart/tokenization_plbart.py b/src/transformers/models/plbart/tokenization_plbart.py index 9b1c1a799b..b9b73b6f47 100644 --- a/src/transformers/models/plbart/tokenization_plbart.py +++ b/src/transformers/models/plbart/tokenization_plbart.py @@ -21,6 +21,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -46,6 +47,7 @@ FAIRSEQ_LANGUAGE_CODES_MAP = { } +@requires(backends=("sentencepiece",)) class PLBartTokenizer(PreTrainedTokenizer): """ Construct an PLBART tokenizer. diff --git a/src/transformers/models/poolformer/feature_extraction_poolformer.py b/src/transformers/models/poolformer/feature_extraction_poolformer.py index ab4337f91f..bde18b3ec0 100644 --- a/src/transformers/models/poolformer/feature_extraction_poolformer.py +++ b/src/transformers/models/poolformer/feature_extraction_poolformer.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_poolformer import PoolFormerImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class PoolFormerFeatureExtractor(PoolFormerImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/pop2piano/feature_extraction_pop2piano.py b/src/transformers/models/pop2piano/feature_extraction_pop2piano.py index 89fb04907d..0b191ab106 100644 --- a/src/transformers/models/pop2piano/feature_extraction_pop2piano.py +++ b/src/transformers/models/pop2piano/feature_extraction_pop2piano.py @@ -31,6 +31,7 @@ from ...utils import ( logging, requires_backends, ) +from ...utils.import_utils import requires if is_essentia_available(): @@ -47,6 +48,7 @@ if is_scipy_available(): logger = logging.get_logger(__name__) +@requires(backends=("essentia", "librosa", "scipy", "torch")) class Pop2PianoFeatureExtractor(SequenceFeatureExtractor): r""" Constructs a Pop2Piano feature extractor. diff --git a/src/transformers/models/pop2piano/processing_pop2piano.py b/src/transformers/models/pop2piano/processing_pop2piano.py index 11afcab317..3b839f8b1f 100644 --- a/src/transformers/models/pop2piano/processing_pop2piano.py +++ b/src/transformers/models/pop2piano/processing_pop2piano.py @@ -23,8 +23,10 @@ from ...feature_extraction_utils import BatchFeature from ...processing_utils import ProcessorMixin from ...tokenization_utils import BatchEncoding, PaddingStrategy, TruncationStrategy from ...utils import TensorType +from ...utils.import_utils import requires +@requires(backends=("essentia", "librosa", "pretty_midi", "scipy", "torch")) class Pop2PianoProcessor(ProcessorMixin): r""" Constructs an Pop2Piano processor which wraps a Pop2Piano Feature Extractor and Pop2Piano Tokenizer into a single diff --git a/src/transformers/models/pop2piano/tokenization_pop2piano.py b/src/transformers/models/pop2piano/tokenization_pop2piano.py index 678a651fee..0a7a8a5586 100644 --- a/src/transformers/models/pop2piano/tokenization_pop2piano.py +++ b/src/transformers/models/pop2piano/tokenization_pop2piano.py @@ -23,6 +23,7 @@ import numpy as np from ...feature_extraction_utils import BatchFeature from ...tokenization_utils import AddedToken, BatchEncoding, PaddingStrategy, PreTrainedTokenizer, TruncationStrategy from ...utils import TensorType, is_pretty_midi_available, logging, requires_backends, to_numpy +from ...utils.import_utils import requires if is_pretty_midi_available(): @@ -59,6 +60,7 @@ def token_note_to_note(number, current_velocity, default_velocity, note_onsets_r return notes +@requires(backends=("pretty_midi", "torch")) class Pop2PianoTokenizer(PreTrainedTokenizer): """ Constructs a Pop2Piano tokenizer. This tokenizer does not require training. diff --git a/src/transformers/models/qwen2/__init__.py b/src/transformers/models/qwen2/__init__.py index 301531655a..e447a94c54 100644 --- a/src/transformers/models/qwen2/__init__.py +++ b/src/transformers/models/qwen2/__init__.py @@ -13,72 +13,17 @@ # limitations under the License. from typing import TYPE_CHECKING -from ...utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_tokenizers_available, - is_torch_available, -) - - -_import_structure = { - "configuration_qwen2": ["Qwen2Config"], - "tokenization_qwen2": ["Qwen2Tokenizer"], -} - -try: - if not is_tokenizers_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["tokenization_qwen2_fast"] = ["Qwen2TokenizerFast"] - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_qwen2"] = [ - "Qwen2ForCausalLM", - "Qwen2ForQuestionAnswering", - "Qwen2Model", - "Qwen2PreTrainedModel", - "Qwen2ForSequenceClassification", - "Qwen2ForTokenClassification", - ] +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure if TYPE_CHECKING: - from .configuration_qwen2 import Qwen2Config - from .tokenization_qwen2 import Qwen2Tokenizer - - try: - if not is_tokenizers_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .tokenization_qwen2_fast import Qwen2TokenizerFast - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_qwen2 import ( - Qwen2ForCausalLM, - Qwen2ForQuestionAnswering, - Qwen2ForSequenceClassification, - Qwen2ForTokenClassification, - Qwen2Model, - Qwen2PreTrainedModel, - ) - - + from .configuration_qwen2 import * + from .modeling_qwen2 import * + from .tokenization_qwen2 import * + from .tokenization_qwen2_fast import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/qwen2/configuration_qwen2.py b/src/transformers/models/qwen2/configuration_qwen2.py index 2e82f1976f..fde5bd502b 100644 --- a/src/transformers/models/qwen2/configuration_qwen2.py +++ b/src/transformers/models/qwen2/configuration_qwen2.py @@ -199,3 +199,6 @@ class Qwen2Config(PretrainedConfig): tie_word_embeddings=tie_word_embeddings, **kwargs, ) + + +__all__ = ["Qwen2Config"] diff --git a/src/transformers/models/qwen2/modeling_qwen2.py b/src/transformers/models/qwen2/modeling_qwen2.py index c3d735e8c4..d3180b35b3 100644 --- a/src/transformers/models/qwen2/modeling_qwen2.py +++ b/src/transformers/models/qwen2/modeling_qwen2.py @@ -287,40 +287,6 @@ class Qwen2DecoderLayer(nn.Module): return outputs -class Qwen2RotaryEmbedding(nn.Module): - def __init__(self, config: Qwen2Config, device=None): - super().__init__() - # BC: "rope_type" was originally "type" - if hasattr(config, "rope_scaling") and config.rope_scaling is not None: - self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) - else: - self.rope_type = "default" - self.max_seq_len_cached = config.max_position_embeddings - self.original_max_seq_len = config.max_position_embeddings - - self.config = config - self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] - - inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) - self.register_buffer("inv_freq", inv_freq, persistent=False) - self.original_inv_freq = self.inv_freq - - @torch.no_grad() - @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) - def forward(self, x, position_ids): - inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) - position_ids_expanded = position_ids[:, None, :].float() - - device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" - with torch.autocast(device_type=device_type, enabled=False): # Force float32 - freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() * self.attention_scaling - sin = emb.sin() * self.attention_scaling - - return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) - - QWEN2_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads @@ -368,6 +334,40 @@ class Qwen2PreTrainedModel(PreTrainedModel): module.weight.data[module.padding_idx].zero_() +class Qwen2RotaryEmbedding(nn.Module): + def __init__(self, config: Qwen2Config, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + QWEN2_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): @@ -1114,3 +1114,13 @@ class Qwen2ForQuestionAnswering(Qwen2PreTrainedModel): hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) + + +__all__ = [ + "Qwen2PreTrainedModel", + "Qwen2Model", + "Qwen2ForCausalLM", + "Qwen2ForSequenceClassification", + "Qwen2ForTokenClassification", + "Qwen2ForQuestionAnswering", +] diff --git a/src/transformers/models/qwen2/modular_qwen2.py b/src/transformers/models/qwen2/modular_qwen2.py index 091804f543..afb3d1cf35 100644 --- a/src/transformers/models/qwen2/modular_qwen2.py +++ b/src/transformers/models/qwen2/modular_qwen2.py @@ -17,6 +17,7 @@ from ..llama.modeling_llama import ( LlamaForSequenceClassification, LlamaForTokenClassification, LlamaMLP, + LlamaPreTrainedModel, apply_rotary_pos_emb, eager_attention_forward, ) @@ -114,6 +115,10 @@ class Qwen2DecoderLayer(LlamaDecoderLayer): ) +class Qwen2PreTrainedModel(LlamaPreTrainedModel): + pass + + class Qwen2Model(MistralModel): pass @@ -132,3 +137,13 @@ class Qwen2ForTokenClassification(LlamaForTokenClassification): class Qwen2ForQuestionAnswering(LlamaForQuestionAnswering): pass + + +__all__ = [ + "Qwen2PreTrainedModel", + "Qwen2Model", + "Qwen2ForCausalLM", + "Qwen2ForSequenceClassification", + "Qwen2ForTokenClassification", + "Qwen2ForQuestionAnswering", +] diff --git a/src/transformers/models/qwen2/tokenization_qwen2.py b/src/transformers/models/qwen2/tokenization_qwen2.py index be2685430f..c388789b72 100644 --- a/src/transformers/models/qwen2/tokenization_qwen2.py +++ b/src/transformers/models/qwen2/tokenization_qwen2.py @@ -337,3 +337,6 @@ class Qwen2Tokenizer(PreTrainedTokenizer): def prepare_for_tokenization(self, text, **kwargs): text = unicodedata.normalize("NFC", text) return (text, kwargs) + + +__all__ = ["Qwen2Tokenizer"] diff --git a/src/transformers/models/qwen2/tokenization_qwen2_fast.py b/src/transformers/models/qwen2/tokenization_qwen2_fast.py index fcfc4ab764..b7312755ef 100644 --- a/src/transformers/models/qwen2/tokenization_qwen2_fast.py +++ b/src/transformers/models/qwen2/tokenization_qwen2_fast.py @@ -132,3 +132,6 @@ class Qwen2TokenizerFast(PreTrainedTokenizerFast): def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) + + +__all__ = ["Qwen2TokenizerFast"] diff --git a/src/transformers/models/qwen3/modeling_qwen3.py b/src/transformers/models/qwen3/modeling_qwen3.py index ea04ded17b..5852470d1c 100644 --- a/src/transformers/models/qwen3/modeling_qwen3.py +++ b/src/transformers/models/qwen3/modeling_qwen3.py @@ -314,40 +314,6 @@ class Qwen3DecoderLayer(nn.Module): return outputs -class Qwen3RotaryEmbedding(nn.Module): - def __init__(self, config: Qwen3Config, device=None): - super().__init__() - # BC: "rope_type" was originally "type" - if hasattr(config, "rope_scaling") and config.rope_scaling is not None: - self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) - else: - self.rope_type = "default" - self.max_seq_len_cached = config.max_position_embeddings - self.original_max_seq_len = config.max_position_embeddings - - self.config = config - self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] - - inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) - self.register_buffer("inv_freq", inv_freq, persistent=False) - self.original_inv_freq = self.inv_freq - - @torch.no_grad() - @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) - def forward(self, x, position_ids): - inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) - position_ids_expanded = position_ids[:, None, :].float() - - device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" - with torch.autocast(device_type=device_type, enabled=False): # Force float32 - freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() * self.attention_scaling - sin = emb.sin() * self.attention_scaling - - return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) - - QWEN3_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads @@ -395,6 +361,40 @@ class Qwen3PreTrainedModel(PreTrainedModel): module.weight.data[module.padding_idx].zero_() +class Qwen3RotaryEmbedding(nn.Module): + def __init__(self, config: Qwen3Config, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + QWEN3_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): diff --git a/src/transformers/models/reformer/tokenization_reformer.py b/src/transformers/models/reformer/tokenization_reformer.py index 65b97d5a6b..db2faf5dbc 100644 --- a/src/transformers/models/reformer/tokenization_reformer.py +++ b/src/transformers/models/reformer/tokenization_reformer.py @@ -22,6 +22,7 @@ import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -32,6 +33,7 @@ SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} +@requires(backends=("sentencepiece",)) class ReformerTokenizer(PreTrainedTokenizer): """ Construct a Reformer tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece) . diff --git a/src/transformers/models/rembert/tokenization_rembert.py b/src/transformers/models/rembert/tokenization_rembert.py index 951ffd2bb0..2f2053425d 100644 --- a/src/transformers/models/rembert/tokenization_rembert.py +++ b/src/transformers/models/rembert/tokenization_rembert.py @@ -22,6 +22,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -29,6 +30,7 @@ logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.model"} +@requires(backends=("sentencepiece",)) class RemBertTokenizer(PreTrainedTokenizer): """ Construct a RemBERT tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/rt_detr/image_processing_rt_detr_fast.py b/src/transformers/models/rt_detr/image_processing_rt_detr_fast.py index dd0c54cc63..e9bd83f1b2 100644 --- a/src/transformers/models/rt_detr/image_processing_rt_detr_fast.py +++ b/src/transformers/models/rt_detr/image_processing_rt_detr_fast.py @@ -39,6 +39,7 @@ from ...utils import ( is_torchvision_v2_available, requires_backends, ) +from ...utils.import_utils import requires from .image_processing_rt_detr import get_size_with_aspect_ratio @@ -145,6 +146,7 @@ def prepare_coco_detection_annotation( Whether to return segmentation masks. """, ) +@requires(backends=("torchvision", "torch")) class RTDetrImageProcessorFast(BaseImageProcessorFast): resample = PILImageResampling.BILINEAR image_mean = IMAGENET_DEFAULT_MEAN diff --git a/src/transformers/models/seamless_m4t/tokenization_seamless_m4t.py b/src/transformers/models/seamless_m4t/tokenization_seamless_m4t.py index 6a4509ee78..0d73e6a099 100644 --- a/src/transformers/models/seamless_m4t/tokenization_seamless_m4t.py +++ b/src/transformers/models/seamless_m4t/tokenization_seamless_m4t.py @@ -29,6 +29,7 @@ from ...tokenization_utils import ( ) from ...tokenization_utils_base import AddedToken from ...utils import PaddingStrategy, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -40,6 +41,7 @@ SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} +@requires(backends=("sentencepiece",)) class SeamlessM4TTokenizer(PreTrainedTokenizer): """ Construct a SeamlessM4T tokenizer. diff --git a/src/transformers/models/segformer/feature_extraction_segformer.py b/src/transformers/models/segformer/feature_extraction_segformer.py index 85442612e2..b979acf71a 100644 --- a/src/transformers/models/segformer/feature_extraction_segformer.py +++ b/src/transformers/models/segformer/feature_extraction_segformer.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_segformer import SegformerImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class SegformerFeatureExtractor(SegformerImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/segformer/image_processing_segformer.py b/src/transformers/models/segformer/image_processing_segformer.py index b978f70165..79cbe47482 100644 --- a/src/transformers/models/segformer/image_processing_segformer.py +++ b/src/transformers/models/segformer/image_processing_segformer.py @@ -42,6 +42,7 @@ from ...utils import ( logging, ) from ...utils.deprecation import deprecate_kwarg +from ...utils.import_utils import requires if is_vision_available(): @@ -54,6 +55,7 @@ if is_torch_available(): logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class SegformerImageProcessor(BaseImageProcessor): r""" Constructs a Segformer image processor. diff --git a/src/transformers/models/siglip/tokenization_siglip.py b/src/transformers/models/siglip/tokenization_siglip.py index a9c24df258..e1e62dcd04 100644 --- a/src/transformers/models/siglip/tokenization_siglip.py +++ b/src/transformers/models/siglip/tokenization_siglip.py @@ -31,6 +31,7 @@ from ...tokenization_utils_base import AddedToken if TYPE_CHECKING: from ...tokenization_utils_base import TextInput from ...utils import logging, requires_backends +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -41,6 +42,7 @@ VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} SPIECE_UNDERLINE = "▁" +@requires(backends=("sentencepiece",)) class SiglipTokenizer(PreTrainedTokenizer): """ Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/speech_to_text/tokenization_speech_to_text.py b/src/transformers/models/speech_to_text/tokenization_speech_to_text.py index c86a5417c3..abb6dd5da7 100644 --- a/src/transformers/models/speech_to_text/tokenization_speech_to_text.py +++ b/src/transformers/models/speech_to_text/tokenization_speech_to_text.py @@ -24,6 +24,7 @@ import sentencepiece from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -45,6 +46,7 @@ MUSTC_LANGS = ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"] LANGUAGES = {"mustc": MUSTC_LANGS} +@requires(backends=("sentencepiece",)) class Speech2TextTokenizer(PreTrainedTokenizer): """ Construct an Speech2Text tokenizer. diff --git a/src/transformers/models/speecht5/tokenization_speecht5.py b/src/transformers/models/speecht5/tokenization_speecht5.py index e3cf3867af..5817fb20fe 100644 --- a/src/transformers/models/speecht5/tokenization_speecht5.py +++ b/src/transformers/models/speecht5/tokenization_speecht5.py @@ -22,6 +22,7 @@ import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires from .number_normalizer import EnglishNumberNormalizer @@ -30,6 +31,7 @@ logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spm_char.model"} +@requires(backends=("sentencepiece",)) class SpeechT5Tokenizer(PreTrainedTokenizer): """ Construct a SpeechT5 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/starcoder2/modeling_starcoder2.py b/src/transformers/models/starcoder2/modeling_starcoder2.py index 3d78926561..d357da4397 100644 --- a/src/transformers/models/starcoder2/modeling_starcoder2.py +++ b/src/transformers/models/starcoder2/modeling_starcoder2.py @@ -275,40 +275,6 @@ class Starcoder2DecoderLayer(nn.Module): return outputs -class Starcoder2RotaryEmbedding(nn.Module): - def __init__(self, config: Starcoder2Config, device=None): - super().__init__() - # BC: "rope_type" was originally "type" - if hasattr(config, "rope_scaling") and config.rope_scaling is not None: - self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) - else: - self.rope_type = "default" - self.max_seq_len_cached = config.max_position_embeddings - self.original_max_seq_len = config.max_position_embeddings - - self.config = config - self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] - - inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) - self.register_buffer("inv_freq", inv_freq, persistent=False) - self.original_inv_freq = self.inv_freq - - @torch.no_grad() - @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) - def forward(self, x, position_ids): - inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) - position_ids_expanded = position_ids[:, None, :].float() - - device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" - with torch.autocast(device_type=device_type, enabled=False): # Force float32 - freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() * self.attention_scaling - sin = emb.sin() * self.attention_scaling - - return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) - - STARCODER2_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads @@ -356,6 +322,40 @@ class Starcoder2PreTrainedModel(PreTrainedModel): module.weight.data[module.padding_idx].zero_() +class Starcoder2RotaryEmbedding(nn.Module): + def __init__(self, config: Starcoder2Config, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + STARCODER2_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): diff --git a/src/transformers/models/superglue/image_processing_superglue.py b/src/transformers/models/superglue/image_processing_superglue.py index b84bfc280c..a74e164996 100644 --- a/src/transformers/models/superglue/image_processing_superglue.py +++ b/src/transformers/models/superglue/image_processing_superglue.py @@ -35,6 +35,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, logging, requires_backends +from ...utils.import_utils import requires if is_torch_available(): @@ -131,6 +132,7 @@ def validate_and_format_image_pairs(images: ImageInput): raise ValueError(error_message) +@requires(backends=("torch",)) class SuperGlueImageProcessor(BaseImageProcessor): r""" Constructs a SuperGlue image processor. diff --git a/src/transformers/models/t5/tokenization_t5.py b/src/transformers/models/t5/tokenization_t5.py index cf31995c4b..01447232b8 100644 --- a/src/transformers/models/t5/tokenization_t5.py +++ b/src/transformers/models/t5/tokenization_t5.py @@ -30,6 +30,7 @@ from ...tokenization_utils_base import AddedToken if TYPE_CHECKING: from ...tokenization_utils_base import TextInput from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -37,11 +38,10 @@ logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} -# TODO(PVP) - this should be removed in Transformers v5 - SPIECE_UNDERLINE = "▁" +@requires(backends=("sentencepiece",)) class T5Tokenizer(PreTrainedTokenizer): """ Construct a T5 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/timm_wrapper/image_processing_timm_wrapper.py b/src/transformers/models/timm_wrapper/image_processing_timm_wrapper.py index 02075a50fb..de54f8a936 100644 --- a/src/transformers/models/timm_wrapper/image_processing_timm_wrapper.py +++ b/src/transformers/models/timm_wrapper/image_processing_timm_wrapper.py @@ -22,7 +22,7 @@ from ...image_processing_utils import BaseImageProcessor, BatchFeature from ...image_transforms import to_pil_image from ...image_utils import ImageInput, make_list_of_images from ...utils import TensorType, logging, requires_backends -from ...utils.import_utils import is_timm_available, is_torch_available +from ...utils.import_utils import is_timm_available, is_torch_available, requires if is_timm_available(): @@ -35,6 +35,7 @@ if is_torch_available(): logger = logging.get_logger(__name__) +@requires(backends=("torch", "timm", "torchvision")) class TimmWrapperImageProcessor(BaseImageProcessor): """ Wrapper class for timm models to be used within transformers. diff --git a/src/transformers/models/udop/tokenization_udop.py b/src/transformers/models/udop/tokenization_udop.py index 86ee6a873d..08eccaec7b 100644 --- a/src/transformers/models/udop/tokenization_udop.py +++ b/src/transformers/models/udop/tokenization_udop.py @@ -33,6 +33,7 @@ from ...tokenization_utils_base import ( TruncationStrategy, ) from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -147,6 +148,7 @@ UDOP_ENCODE_KWARGS_DOCSTRING = r""" VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} +@requires(backends=("sentencepiece",)) class UdopTokenizer(PreTrainedTokenizer): """ Adapted from [`LayoutXLMTokenizer`] and [`T5Tokenizer`]. Based on diff --git a/src/transformers/models/videomae/feature_extraction_videomae.py b/src/transformers/models/videomae/feature_extraction_videomae.py index 469cbcf523..44a1a42a18 100644 --- a/src/transformers/models/videomae/feature_extraction_videomae.py +++ b/src/transformers/models/videomae/feature_extraction_videomae.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_videomae import VideoMAEImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class VideoMAEFeatureExtractor(VideoMAEImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/videomae/image_processing_videomae.py b/src/transformers/models/videomae/image_processing_videomae.py index eac4759af3..358a77ac9b 100644 --- a/src/transformers/models/videomae/image_processing_videomae.py +++ b/src/transformers/models/videomae/image_processing_videomae.py @@ -38,6 +38,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging +from ...utils.import_utils import requires if is_vision_available(): @@ -60,6 +61,7 @@ def make_batched(videos) -> List[List[ImageInput]]: raise ValueError(f"Could not make batched video from {videos}") +@requires(backends=("vision",)) class VideoMAEImageProcessor(BaseImageProcessor): r""" Constructs a VideoMAE image processor. diff --git a/src/transformers/models/vilt/feature_extraction_vilt.py b/src/transformers/models/vilt/feature_extraction_vilt.py index 234fd1a2e1..e76fc89b9e 100644 --- a/src/transformers/models/vilt/feature_extraction_vilt.py +++ b/src/transformers/models/vilt/feature_extraction_vilt.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_vilt import ViltImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class ViltFeatureExtractor(ViltImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/vilt/image_processing_vilt.py b/src/transformers/models/vilt/image_processing_vilt.py index 18890c0dbf..d4ac8cca32 100644 --- a/src/transformers/models/vilt/image_processing_vilt.py +++ b/src/transformers/models/vilt/image_processing_vilt.py @@ -35,6 +35,7 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging +from ...utils.import_utils import requires if is_vision_available(): @@ -118,6 +119,7 @@ def get_resize_output_image_size( return new_height, new_width +@requires(backends=("vision",)) class ViltImageProcessor(BaseImageProcessor): r""" Constructs a ViLT image processor. diff --git a/src/transformers/models/vit/feature_extraction_vit.py b/src/transformers/models/vit/feature_extraction_vit.py index e2479b2dc0..c6a93df0bd 100644 --- a/src/transformers/models/vit/feature_extraction_vit.py +++ b/src/transformers/models/vit/feature_extraction_vit.py @@ -17,12 +17,14 @@ import warnings from ...utils import logging +from ...utils.import_utils import requires from .image_processing_vit import ViTImageProcessor logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class ViTFeatureExtractor(ViTImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/vit/image_processing_vit.py b/src/transformers/models/vit/image_processing_vit.py index afee64dc0e..ade7495b1d 100644 --- a/src/transformers/models/vit/image_processing_vit.py +++ b/src/transformers/models/vit/image_processing_vit.py @@ -34,11 +34,13 @@ from ...image_utils import ( validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) +@requires(backends=("vision",)) class ViTImageProcessor(BaseImageProcessor): r""" Constructs a ViT image processor. diff --git a/src/transformers/models/vitpose_backbone/__init__.py b/src/transformers/models/vitpose_backbone/__init__.py index f69ff8762a..858e93797d 100644 --- a/src/transformers/models/vitpose_backbone/__init__.py +++ b/src/transformers/models/vitpose_backbone/__init__.py @@ -1,54 +1,17 @@ # flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. - -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. from typing import TYPE_CHECKING -from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure -_import_structure = {"configuration_vitpose_backbone": ["VitPoseBackboneConfig"]} - - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_vitpose_backbone"] = [ - "VitPoseBackbonePreTrainedModel", - "VitPoseBackbone", - ] - if TYPE_CHECKING: - from .configuration_vitpose_backbone import VitPoseBackboneConfig - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_vitpose_backbone import ( - VitPoseBackbone, - VitPoseBackbonePreTrainedModel, - ) - + from .configuration_vitpose_backbone import * + from .modeling_vitpose_backbone import * else: import sys - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py b/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py index 2872d39a2a..439e596273 100644 --- a/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py +++ b/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py @@ -134,3 +134,6 @@ class VitPoseBackboneConfig(BackboneConfigMixin, PretrainedConfig): self._out_features, self._out_indices = get_aligned_output_features_output_indices( out_features=out_features, out_indices=out_indices, stage_names=self.stage_names ) + + +__all__ = ["VitPoseBackboneConfig"] diff --git a/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py b/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py index c0d6d7f022..8abce8dfdc 100644 --- a/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py +++ b/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py @@ -574,3 +574,6 @@ class VitPoseBackbone(VitPoseBackbonePreTrainedModel, BackboneMixin): hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, ) + + +__all__ = ["VitPoseBackbonePreTrainedModel", "VitPoseBackbone"] diff --git a/src/transformers/models/xglm/tokenization_xglm.py b/src/transformers/models/xglm/tokenization_xglm.py index 79f6acc364..e54191a57f 100644 --- a/src/transformers/models/xglm/tokenization_xglm.py +++ b/src/transformers/models/xglm/tokenization_xglm.py @@ -22,6 +22,7 @@ import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -31,6 +32,7 @@ SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} +@requires(backends=("sentencepiece",)) class XGLMTokenizer(PreTrainedTokenizer): """ Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on diff --git a/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py b/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py index 226f1a8957..04d725bd6c 100644 --- a/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py +++ b/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py @@ -22,6 +22,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -31,6 +32,7 @@ SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} +@requires(backends=("sentencepiece",)) class XLMRobertaTokenizer(PreTrainedTokenizer): """ Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on diff --git a/src/transformers/models/xlnet/tokenization_xlnet.py b/src/transformers/models/xlnet/tokenization_xlnet.py index 640558e1d8..6f956fe84c 100644 --- a/src/transformers/models/xlnet/tokenization_xlnet.py +++ b/src/transformers/models/xlnet/tokenization_xlnet.py @@ -23,6 +23,7 @@ import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging +from ...utils.import_utils import requires logger = logging.get_logger(__name__) @@ -38,6 +39,7 @@ SEG_ID_SEP = 3 SEG_ID_PAD = 4 +@requires(backends=("sentencepiece",)) class XLNetTokenizer(PreTrainedTokenizer): """ Construct an XLNet tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). diff --git a/src/transformers/models/yolos/feature_extraction_yolos.py b/src/transformers/models/yolos/feature_extraction_yolos.py index 4c9bfdde80..a1c9165832 100644 --- a/src/transformers/models/yolos/feature_extraction_yolos.py +++ b/src/transformers/models/yolos/feature_extraction_yolos.py @@ -18,6 +18,7 @@ import warnings from ...image_transforms import rgb_to_id as _rgb_to_id from ...utils import logging +from ...utils.import_utils import requires from .image_processing_yolos import YolosImageProcessor @@ -33,6 +34,7 @@ def rgb_to_id(x): return _rgb_to_id(x) +@requires(backends=("vision",)) class YolosFeatureExtractor(YolosImageProcessor): def __init__(self, *args, **kwargs) -> None: warnings.warn( diff --git a/src/transformers/models/yolos/image_processing_yolos.py b/src/transformers/models/yolos/image_processing_yolos.py index 3cff6950dd..2c1f0d1d2b 100644 --- a/src/transformers/models/yolos/image_processing_yolos.py +++ b/src/transformers/models/yolos/image_processing_yolos.py @@ -62,6 +62,7 @@ from ...utils import ( is_vision_available, logging, ) +from ...utils.import_utils import requires if is_torch_available(): @@ -720,6 +721,7 @@ def compute_segments( return segmentation, segments +@requires(backends=("vision",)) class YolosImageProcessor(BaseImageProcessor): r""" Constructs a Detr image processor. diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py index af2cae600b..f752de39de 100755 --- a/src/transformers/trainer.py +++ b/src/transformers/trainer.py @@ -177,6 +177,7 @@ from .utils import ( strtobool, ) from .utils.deprecation import deprecate_kwarg +from .utils.import_utils import requires from .utils.quantization_config import QuantizationMethod @@ -312,6 +313,12 @@ SCHEDULER_NAME = "scheduler.pt" FSDP_MODEL_NAME = "pytorch_model_fsdp" +@requires( + backends=( + "torch", + "accelerate", + ) +) class Trainer: """ Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. diff --git a/src/transformers/utils/dummy_flax_objects.py b/src/transformers/utils/dummy_flax_objects.py index 0f2390fb69..6f886de282 100644 --- a/src/transformers/utils/dummy_flax_objects.py +++ b/src/transformers/utils/dummy_flax_objects.py @@ -105,1326 +105,3 @@ class FlaxPreTrainedModel(metaclass=DummyObject): def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - - -class FlaxAlbertForMaskedLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAlbertForMultipleChoice(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAlbertForPreTraining(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAlbertForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAlbertForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAlbertForTokenClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAlbertModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAlbertPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = None - - -FLAX_MODEL_FOR_CAUSAL_LM_MAPPING = None - - -FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = None - - -FLAX_MODEL_FOR_MASKED_LM_MAPPING = None - - -FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None - - -FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = None - - -FLAX_MODEL_FOR_PRETRAINING_MAPPING = None - - -FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING = None - - -FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = None - - -FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None - - -FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = None - - -FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None - - -FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING = None - - -FLAX_MODEL_MAPPING = None - - -class FlaxAutoModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForImageClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForMaskedLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForMultipleChoice(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForNextSentencePrediction(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForPreTraining(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForSeq2SeqLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForSpeechSeq2Seq(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForTokenClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAutoModelForVision2Seq(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBartDecoderPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBartForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBartForConditionalGeneration(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBartForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBartForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBartModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBartPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBeitForImageClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBeitForMaskedImageModeling(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBeitModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBeitPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBertForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBertForMaskedLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBertForMultipleChoice(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBertForNextSentencePrediction(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBertForPreTraining(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBertForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBertForTokenClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBertModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBertPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBigBirdForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBigBirdForMaskedLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBigBirdForMultipleChoice(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBigBirdForPreTraining(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBigBirdForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBigBirdForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBigBirdForTokenClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBigBirdModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBigBirdPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBlenderbotForConditionalGeneration(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBlenderbotModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBlenderbotPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBlenderbotSmallForConditionalGeneration(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBlenderbotSmallModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBlenderbotSmallPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBloomForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBloomModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxBloomPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxCLIPModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxCLIPPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxCLIPTextModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxCLIPTextModelWithProjection(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxCLIPTextPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxCLIPVisionModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxCLIPVisionPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxDinov2ForImageClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxDinov2Model(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxDinov2PreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxDistilBertForMaskedLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxDistilBertForMultipleChoice(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxDistilBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxDistilBertForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxDistilBertForTokenClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxDistilBertModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxDistilBertPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxElectraForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxElectraForMaskedLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxElectraForMultipleChoice(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxElectraForPreTraining(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxElectraForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxElectraForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxElectraForTokenClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxElectraModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxElectraPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxEncoderDecoderModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGemmaForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGemmaModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGemmaPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGPT2LMHeadModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGPT2Model(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGPT2PreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGPTNeoForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGPTNeoModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGPTNeoPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGPTJForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGPTJModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxGPTJPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxLlamaForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxLlamaModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxLlamaPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxLongT5ForConditionalGeneration(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxLongT5Model(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxLongT5PreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMarianModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMarianMTModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMarianPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMBartForConditionalGeneration(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMBartForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMBartForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMBartModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMBartPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMistralForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMistralModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMistralPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMT5EncoderModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMT5ForConditionalGeneration(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxMT5Model(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxOPTForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxOPTModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxOPTPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxPegasusForConditionalGeneration(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxPegasusModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxPegasusPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRegNetForImageClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRegNetModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRegNetPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxResNetForImageClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxResNetModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxResNetPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaForMaskedLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaForMultipleChoice(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaForTokenClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaPreLayerNormForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaPreLayerNormForMaskedLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaPreLayerNormForMultipleChoice(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaPreLayerNormForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaPreLayerNormForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaPreLayerNormForTokenClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaPreLayerNormModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRobertaPreLayerNormPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRoFormerForMaskedLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRoFormerForMultipleChoice(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRoFormerForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRoFormerForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRoFormerForTokenClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRoFormerModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxRoFormerPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxSpeechEncoderDecoderModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxT5EncoderModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxT5ForConditionalGeneration(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxT5Model(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxT5PreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxVisionEncoderDecoderModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxVisionTextDualEncoderModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxViTForImageClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxViTModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxViTPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxWav2Vec2ForCTC(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxWav2Vec2ForPreTraining(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxWav2Vec2Model(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxWav2Vec2PreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxWhisperForAudioClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxWhisperForConditionalGeneration(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxWhisperModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxWhisperPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXGLMForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXGLMModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXGLMPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXLMRobertaForCausalLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXLMRobertaForMaskedLM(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXLMRobertaForMultipleChoice(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXLMRobertaForQuestionAnswering(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXLMRobertaForSequenceClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXLMRobertaForTokenClassification(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXLMRobertaModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxXLMRobertaPreTrainedModel(metaclass=DummyObject): - _backends = ["flax"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) diff --git a/src/transformers/utils/dummy_keras_nlp_objects.py b/src/transformers/utils/dummy_keras_nlp_objects.py deleted file mode 100644 index c6bb86a6d9..0000000000 --- a/src/transformers/utils/dummy_keras_nlp_objects.py +++ /dev/null @@ -1,9 +0,0 @@ -# This file is autogenerated by the command `make fix-copies`, do not edit. -from ..utils import DummyObject, requires_backends - - -class TFGPT2Tokenizer(metaclass=DummyObject): - _backends = ["keras_nlp"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["keras_nlp"]) diff --git a/src/transformers/utils/dummy_pt_objects.py b/src/transformers/utils/dummy_pt_objects.py index b0959e1c8d..55c592082c 100644 --- a/src/transformers/utils/dummy_pt_objects.py +++ b/src/transformers/utils/dummy_pt_objects.py @@ -567,10573 +567,6 @@ class PreTrainedModel(metaclass=DummyObject): requires_backends(self, ["torch"]) -class AlbertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlbertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlbertForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlbertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlbertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlbertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlbertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlbertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_albert(*args, **kwargs): - requires_backends(load_tf_weights_in_albert, ["torch"]) - - -class AlignModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlignPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlignTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlignVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AltCLIPModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AltCLIPPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AltCLIPTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AltCLIPVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AriaForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AriaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AriaTextForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AriaTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AriaTextPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ASTForAudioClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ASTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ASTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = None - - -MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING = None - - -MODEL_FOR_AUDIO_XVECTOR_MAPPING = None - - -MODEL_FOR_BACKBONE_MAPPING = None - - -MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING = None - - -MODEL_FOR_CAUSAL_LM_MAPPING = None - - -MODEL_FOR_CTC_MAPPING = None - - -MODEL_FOR_DEPTH_ESTIMATION_MAPPING = None - - -MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = None - - -MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = None - - -MODEL_FOR_IMAGE_MAPPING = None - - -MODEL_FOR_IMAGE_SEGMENTATION_MAPPING = None - - -MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING = None - - -MODEL_FOR_IMAGE_TO_IMAGE_MAPPING = None - - -MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING = None - - -MODEL_FOR_KEYPOINT_DETECTION_MAPPING = None - - -MODEL_FOR_MASK_GENERATION_MAPPING = None - - -MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = None - - -MODEL_FOR_MASKED_LM_MAPPING = None - - -MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None - - -MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = None - - -MODEL_FOR_OBJECT_DETECTION_MAPPING = None - - -MODEL_FOR_PRETRAINING_MAPPING = None - - -MODEL_FOR_QUESTION_ANSWERING_MAPPING = None - - -MODEL_FOR_RETRIEVAL_MAPPING = None - - -MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING = None - - -MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = None - - -MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None - - -MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = None - - -MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = None - - -MODEL_FOR_TEXT_ENCODING_MAPPING = None - - -MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING = None - - -MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING = None - - -MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING = None - - -MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING = None - - -MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None - - -MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING = None - - -MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING = None - - -MODEL_FOR_VISION_2_SEQ_MAPPING = None - - -MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING = None - - -MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING = None - - -MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING = None - - -MODEL_MAPPING = None - - -MODEL_WITH_LM_HEAD_MAPPING = None - - -class AutoBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForAudioClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForAudioFrameClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForAudioXVector(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForDepthEstimation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForDocumentQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForImageSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForImageTextToText(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForImageToImage(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForInstanceSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForKeypointDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForMaskedImageModeling(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForMaskGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForNextSentencePrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForSemanticSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForSeq2SeqLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForSpeechSeq2Seq(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForTableQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForTextEncoding(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForTextToSpectrogram(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForTextToWaveform(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForUniversalSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForVideoClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForVision2Seq(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForVisualQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForZeroShotImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelForZeroShotObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoModelWithLMHead(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoformerForPrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AutoformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AyaVisionForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AyaVisionPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BambaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BambaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BambaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BarkCausalModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BarkCoarseModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BarkFineModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BarkModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BarkPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BarkSemanticModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BartForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BartForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BartForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BartForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BartModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BartPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BartPretrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PretrainedBartModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BeitBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BeitForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BeitForMaskedImageModeling(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BeitForSemanticSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BeitModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BeitPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForNextSentencePrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertLMHeadModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_bert(*args, **kwargs): - requires_backends(load_tf_weights_in_bert, ["torch"]) - - -class BertGenerationDecoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertGenerationEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertGenerationPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_bert_generation(*args, **kwargs): - requires_backends(load_tf_weights_in_bert_generation, ["torch"]) - - -class BigBirdForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_big_bird(*args, **kwargs): - requires_backends(load_tf_weights_in_big_bird, ["torch"]) - - -class BigBirdPegasusForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdPegasusForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdPegasusForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdPegasusForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdPegasusModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdPegasusPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BioGptForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BioGptForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BioGptForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BioGptModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BioGptPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BitBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BitForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BitModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BitPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlenderbotForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlenderbotForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlenderbotModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlenderbotPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlenderbotSmallForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlenderbotSmallForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlenderbotSmallModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlenderbotSmallPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlipForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlipForImageTextRetrieval(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlipForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlipModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlipPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlipTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BlipVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Blip2ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Blip2ForImageTextRetrieval(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Blip2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Blip2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Blip2QFormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Blip2TextModelWithProjection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Blip2VisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Blip2VisionModelWithProjection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BloomForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BloomForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BloomForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BloomForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BloomModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BloomPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BridgeTowerForContrastiveLearning(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BridgeTowerForImageAndTextRetrieval(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BridgeTowerForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BridgeTowerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BridgeTowerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BrosForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BrosModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BrosPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BrosProcessor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BrosSpadeEEForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BrosSpadeELForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CamembertForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CamembertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CamembertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CamembertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CamembertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CamembertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CamembertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CamembertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CanineForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CanineForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CanineForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CanineForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CanineModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CaninePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_canine(*args, **kwargs): - requires_backends(load_tf_weights_in_canine, ["torch"]) - - -class ChameleonForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ChameleonModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ChameleonPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ChameleonProcessor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ChameleonVQVAE(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ChineseCLIPModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ChineseCLIPPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ChineseCLIPTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ChineseCLIPVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClapAudioModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClapAudioModelWithProjection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClapFeatureExtractor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClapModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClapPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClapTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClapTextModelWithProjection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPTextModelWithProjection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPVisionModelWithProjection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPSegForImageSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPSegModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPSegPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPSegTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CLIPSegVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClvpDecoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClvpEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClvpForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClvpModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClvpModelForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ClvpPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CodeGenForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CodeGenModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CodeGenPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CohereForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CohereModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CoherePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Cohere2ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Cohere2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Cohere2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ColPaliForRetrieval(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ColPaliPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConditionalDetrForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConditionalDetrForSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConditionalDetrModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConditionalDetrPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_convbert(*args, **kwargs): - requires_backends(load_tf_weights_in_convbert, ["torch"]) - - -class ConvNextBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvNextForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvNextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvNextPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvNextV2Backbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvNextV2ForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvNextV2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvNextV2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CpmAntForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CpmAntModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CpmAntPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CTRLForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CTRLLMHeadModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CTRLModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CTRLPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CvtForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CvtModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CvtPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DabDetrForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DabDetrModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DabDetrPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DacModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DacPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecAudioForAudioFrameClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecAudioForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecAudioForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecAudioForXVector(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecAudioModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecAudioPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecTextForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecTextForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecTextForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecTextForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecTextForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecTextForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecTextPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecVisionForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecVisionForSemanticSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Data2VecVisionPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DbrxForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DbrxModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DbrxPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaV2ForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaV2ForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaV2ForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaV2ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaV2ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaV2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DebertaV2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DecisionTransformerGPT2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DecisionTransformerGPT2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DecisionTransformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DecisionTransformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeepseekV3ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeepseekV3Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeepseekV3PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeformableDetrForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeformableDetrModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeformableDetrPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeiTForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeiTForImageClassificationWithTeacher(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeiTForMaskedImageModeling(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeiTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DeiTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DetaForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DetaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DetaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EfficientFormerForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EfficientFormerForImageClassificationWithTeacher(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EfficientFormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EfficientFormerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieMForInformationExtraction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieMForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieMForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieMForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieMForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieMModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieMPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTSanJapaneseForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTSanJapaneseModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTSanJapanesePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraphormerForGraphClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraphormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraphormerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JukeboxModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JukeboxPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JukeboxPrior(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JukeboxVQVAE(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MCTCTForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MCTCTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MCTCTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegaForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegaForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegaForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegaForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MMBTForClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MMBTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ModalEmbeddings(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NatBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NatForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NatModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NatPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NezhaForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NezhaForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NezhaForNextSentencePrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NezhaForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NezhaForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NezhaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NezhaForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NezhaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NezhaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OpenLlamaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OpenLlamaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OpenLlamaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OpenLlamaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class QDQBertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class QDQBertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class QDQBertForNextSentencePrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class QDQBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class QDQBertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class QDQBertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class QDQBertLMHeadModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class QDQBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class QDQBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_qdqbert(*args, **kwargs): - requires_backends(load_tf_weights_in_qdqbert, ["torch"]) - - -class RealmEmbedder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RealmForOpenQA(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RealmKnowledgeAugEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RealmPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RealmReader(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RealmRetriever(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RealmScorer(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_realm(*args, **kwargs): - requires_backends(load_tf_weights_in_realm, ["torch"]) - - -class RetriBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RetriBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Speech2Text2ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Speech2Text2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TrajectoryTransformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TrajectoryTransformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AdaptiveEmbedding(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TransfoXLForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TransfoXLLMHeadModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TransfoXLModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TransfoXLPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_transfo_xl(*args, **kwargs): - requires_backends(load_tf_weights_in_transfo_xl, ["torch"]) - - -class TvltForAudioVisualClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TvltForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TvltModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TvltPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VanForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VanModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VanPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTHybridForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTHybridModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTHybridPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMProphetNetDecoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMProphetNetEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMProphetNetForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMProphetNetForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMProphetNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMProphetNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DepthAnythingForDepthEstimation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DepthAnythingPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DepthProForDepthEstimation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DepthProModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DepthProPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DetrForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DetrForSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DetrModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DetrPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DiffLlamaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DiffLlamaForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DiffLlamaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DiffLlamaForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DiffLlamaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DiffLlamaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DinatBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DinatForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DinatModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DinatPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Dinov2Backbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Dinov2ForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Dinov2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Dinov2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Dinov2WithRegistersBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Dinov2WithRegistersForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Dinov2WithRegistersModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Dinov2WithRegistersPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DistilBertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DistilBertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DistilBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DistilBertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DistilBertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DistilBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DistilBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DonutSwinForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DonutSwinModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DonutSwinPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPRContextEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPRPretrainedContextEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPRPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPRPretrainedQuestionEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPRPretrainedReader(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPRQuestionEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPRReader(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPTForDepthEstimation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPTForSemanticSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EfficientNetForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EfficientNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EfficientNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_electra(*args, **kwargs): - requires_backends(load_tf_weights_in_electra, ["torch"]) - - -class Emu3ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Emu3ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Emu3PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Emu3TextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Emu3VQVAE(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EncodecModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EncodecPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EncoderDecoderModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieForNextSentencePrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErnieModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ErniePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EsmFoldPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EsmForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EsmForProteinFolding(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EsmForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EsmForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EsmModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class EsmPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FalconForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FalconForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FalconForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FalconForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FalconModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FalconPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FalconMambaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FalconMambaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FalconMambaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FastSpeech2ConformerHifiGan(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FastSpeech2ConformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FastSpeech2ConformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FastSpeech2ConformerWithHifiGan(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlaubertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlaubertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlaubertForQuestionAnsweringSimple(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlaubertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlaubertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlaubertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlaubertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlaubertWithLMHeadModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlavaForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlavaImageCodebook(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlavaImageModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlavaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlavaMultimodalModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlavaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FlavaTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForNextSentencePrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FocalNetBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FocalNetForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FocalNetForMaskedImageModeling(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FocalNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FocalNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FSMTForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FSMTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PretrainedFSMTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelBaseModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_funnel(*args, **kwargs): - requires_backends(load_tf_weights_in_funnel, ["torch"]) - - -class FuyuForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FuyuPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GemmaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GemmaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GemmaForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GemmaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GemmaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Gemma2ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Gemma2ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Gemma2ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Gemma2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Gemma2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Gemma3ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Gemma3ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Gemma3PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Gemma3TextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GitForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GitModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GitPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GitVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GlmForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GlmForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GlmForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GlmModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GlmPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Glm4ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Glm4ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Glm4ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Glm4Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Glm4PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GLPNForDepthEstimation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GLPNModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GLPNPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GotOcr2ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GotOcr2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPT2DoubleHeadsModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPT2ForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPT2ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPT2ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPT2LMHeadModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPT2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPT2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_gpt2(*args, **kwargs): - requires_backends(load_tf_weights_in_gpt2, ["torch"]) - - -class GPTBigCodeForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTBigCodeForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTBigCodeForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTBigCodeModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTBigCodePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_gpt_neo(*args, **kwargs): - requires_backends(load_tf_weights_in_gpt_neo, ["torch"]) - - -class GPTNeoXForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoXForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoXForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoXForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoXModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoXPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoXJapaneseForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoXJapaneseModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTNeoXJapanesePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTJForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTJForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTJForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTJModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GPTJPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraniteForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraniteModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GranitePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraniteMoeForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraniteMoeModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraniteMoePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraniteMoeSharedForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraniteMoeSharedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GraniteMoeSharedPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GroundingDinoForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GroundingDinoModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GroundingDinoPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GroupViTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GroupViTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GroupViTTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class GroupViTVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HeliumForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HeliumForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HeliumForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HeliumModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HeliumPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HieraBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HieraForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HieraForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HieraModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HieraPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HubertForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HubertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HubertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class HubertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IBertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IBertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IBertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IBertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IdeficsForVisionText2Text(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IdeficsModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IdeficsPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IdeficsProcessor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Idefics2ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Idefics2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Idefics2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Idefics2Processor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Idefics3ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Idefics3Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Idefics3PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Idefics3Processor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Idefics3VisionConfig(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Idefics3VisionTransformer(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IJepaForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IJepaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class IJepaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ImageGPTForCausalImageModeling(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ImageGPTForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ImageGPTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ImageGPTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_imagegpt(*args, **kwargs): - requires_backends(load_tf_weights_in_imagegpt, ["torch"]) - - -class InformerForPrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class InformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class InformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class InstructBlipForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class InstructBlipPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class InstructBlipQFormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class InstructBlipVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class InstructBlipVideoForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class InstructBlipVideoPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class InstructBlipVideoQFormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class InstructBlipVideoVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JambaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JambaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JambaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JambaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JetMoeForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JetMoeForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JetMoeModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class JetMoePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Kosmos2ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Kosmos2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Kosmos2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMv2ForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMv2ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMv2ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMv2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMv2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMv3ForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMv3ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMv3ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMv3Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LayoutLMv3PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LEDForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LEDForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LEDForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LEDModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LEDPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LevitForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LevitForImageClassificationWithTeacher(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LevitModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LevitPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LiltForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LiltForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LiltForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LiltModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LiltPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlamaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlamaForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlamaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlamaForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlamaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlamaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Llama4ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Llama4ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Llama4PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Llama4TextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Llama4VisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlavaForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlavaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlavaNextForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlavaNextPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlavaNextVideoForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlavaNextVideoPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlavaOnevisionForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LlavaOnevisionPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongformerForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongformerForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongformerForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongformerForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongformerForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongT5EncoderModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongT5ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongT5Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LongT5PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukeForEntityClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukeForEntityPairClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukeForEntitySpanClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukeForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukeForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukeForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukeForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukeForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukeModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LxmertEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LxmertForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LxmertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LxmertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LxmertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LxmertVisualFeatureEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class M2M100ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class M2M100Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class M2M100PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MambaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MambaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MambaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Mamba2ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Mamba2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Mamba2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MarianForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MarianModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MarianMTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MarianPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MarkupLMForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MarkupLMForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MarkupLMForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MarkupLMModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MarkupLMPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Mask2FormerForUniversalSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Mask2FormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Mask2FormerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MaskFormerForInstanceSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MaskFormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MaskFormerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MaskFormerSwinBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MBartForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MBartForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MBartForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MBartForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MBartModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MBartPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForNextSentencePrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MgpstrForSceneTextRecognition(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MgpstrModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MgpstrPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MimiModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MimiPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MistralForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MistralForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MistralForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MistralForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MistralModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MistralPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Mistral3ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Mistral3PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MixtralForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MixtralForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MixtralForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MixtralForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MixtralModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MixtralPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MllamaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MllamaForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MllamaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MllamaProcessor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MllamaTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MllamaVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForNextSentencePrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_mobilebert(*args, **kwargs): - requires_backends(load_tf_weights_in_mobilebert, ["torch"]) - - -class MobileNetV1ForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileNetV1Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileNetV1PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_mobilenet_v1(*args, **kwargs): - requires_backends(load_tf_weights_in_mobilenet_v1, ["torch"]) - - -class MobileNetV2ForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileNetV2ForSemanticSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileNetV2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileNetV2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_mobilenet_v2(*args, **kwargs): - requires_backends(load_tf_weights_in_mobilenet_v2, ["torch"]) - - -class MobileViTForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileViTForSemanticSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileViTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileViTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileViTV2ForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileViTV2ForSemanticSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileViTV2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileViTV2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ModernBertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ModernBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ModernBertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ModernBertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ModernBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ModernBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MoonshineForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MoonshineModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MoonshinePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MoshiForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MoshiForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MoshiModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MoshiPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MptForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MptForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MptForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MptForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MptModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MptPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MraForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MraForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MraForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MraForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MraForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MraModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MraPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MT5EncoderModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MT5ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MT5ForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MT5ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MT5ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MT5Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MT5PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MusicgenForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MusicgenForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MusicgenModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MusicgenPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MusicgenProcessor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MusicgenMelodyForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MusicgenMelodyForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MusicgenMelodyModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MusicgenMelodyPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MvpForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MvpForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MvpForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MvpForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MvpModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MvpPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NemotronForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NemotronForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NemotronForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NemotronForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NemotronModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NemotronPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NllbMoeForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NllbMoeModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NllbMoePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NllbMoeSparseMLP(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NllbMoeTop2Router(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OlmoForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OlmoModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OlmoPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Olmo2ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Olmo2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Olmo2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OlmoeForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OlmoeModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OlmoePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OmDetTurboForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OmDetTurboPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OneFormerForUniversalSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OneFormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OneFormerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OpenAIGPTDoubleHeadsModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OpenAIGPTForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OpenAIGPTLMHeadModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OpenAIGPTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OpenAIGPTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_openai_gpt(*args, **kwargs): - requires_backends(load_tf_weights_in_openai_gpt, ["torch"]) - - -class OPTForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OPTForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OPTForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OPTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OPTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Owlv2ForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Owlv2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Owlv2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Owlv2TextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Owlv2VisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OwlViTForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OwlViTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OwlViTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OwlViTTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class OwlViTVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PaliGemmaForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PaliGemmaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PaliGemmaProcessor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSMixerForPrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSMixerForPretraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSMixerForRegression(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSMixerForTimeSeriesClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSMixerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSMixerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSTForClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSTForPrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSTForPretraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSTForRegression(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PatchTSTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PegasusForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PegasusForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PegasusModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PegasusPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PegasusXForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PegasusXModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PegasusXPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverForImageClassificationConvProcessing(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverForImageClassificationFourier(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverForImageClassificationLearned(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverForMultimodalAutoencoding(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverForOpticalFlow(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PersimmonForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PersimmonForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PersimmonForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PersimmonModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PersimmonPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PhiForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PhiForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PhiForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PhiModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PhiPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi3ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi3ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi3ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi3Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi3PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi4MultimodalAudioModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi4MultimodalAudioPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi4MultimodalForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi4MultimodalModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi4MultimodalPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi4MultimodalVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Phi4MultimodalVisionPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PhimoeForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PhimoeForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PhimoeModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PhimoePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Pix2StructForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Pix2StructPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Pix2StructTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Pix2StructVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PixtralPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PixtralVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PLBartForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PLBartForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PLBartForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PLBartModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PLBartPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PoolFormerForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PoolFormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PoolFormerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Pop2PianoForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Pop2PianoPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PromptDepthAnythingForDepthEstimation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PromptDepthAnythingPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ProphetNetDecoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ProphetNetEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ProphetNetForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ProphetNetForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ProphetNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ProphetNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PvtForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PvtModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PvtPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PvtV2Backbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PvtV2ForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PvtV2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PvtV2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2ForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2_5_VLForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2_5_VLModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2_5_VLPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2AudioEncoder(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2AudioForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2AudioPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2MoeForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2MoeForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2MoeForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2MoeForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2MoeModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2MoePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2VLForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2VLModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen2VLPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3ForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3MoeForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3MoeForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3MoeForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3MoeForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3MoeModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Qwen3MoePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RagModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RagPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RagSequenceForGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RagTokenForGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RecurrentGemmaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RecurrentGemmaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RecurrentGemmaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ReformerForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ReformerForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ReformerForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ReformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ReformerModelWithLMHead(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ReformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RegNetForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RegNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RegNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_rembert(*args, **kwargs): - requires_backends(load_tf_weights_in_rembert, ["torch"]) - - -class ResNetBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ResNetForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ResNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ResNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaPreLayerNormForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaPreLayerNormForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaPreLayerNormForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaPreLayerNormForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaPreLayerNormForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaPreLayerNormForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaPreLayerNormModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RobertaPreLayerNormPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoCBertForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoCBertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoCBertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoCBertForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoCBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoCBertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoCBertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoCBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoCBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_roc_bert(*args, **kwargs): - requires_backends(load_tf_weights_in_roc_bert, ["torch"]) - - -class RoFormerForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_roformer(*args, **kwargs): - requires_backends(load_tf_weights_in_roformer, ["torch"]) - - -class RTDetrForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RTDetrModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RTDetrPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RTDetrResNetBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RTDetrResNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RTDetrV2ForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RTDetrV2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RTDetrV2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RwkvForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RwkvModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RwkvPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SamModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SamPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SamVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4TCodeHifiGan(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4TForSpeechToSpeech(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4TForSpeechToText(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4TForTextToSpeech(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4TForTextToText(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4THifiGan(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4TModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4TPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4TTextToUnitForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4TTextToUnitModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4Tv2ForSpeechToSpeech(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4Tv2ForSpeechToText(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4Tv2ForTextToSpeech(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4Tv2ForTextToText(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4Tv2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SeamlessM4Tv2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SegformerDecodeHead(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SegformerForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SegformerForSemanticSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SegformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SegformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SegGptForImageSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SegGptModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SegGptPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SEWForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SEWForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SEWModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SEWPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SEWDForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SEWDForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SEWDModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SEWDPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ShieldGemma2ForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SiglipForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SiglipModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SiglipPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SiglipTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SiglipVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Siglip2ForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Siglip2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Siglip2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Siglip2TextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Siglip2VisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SmolVLMForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SmolVLMModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SmolVLMPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SmolVLMProcessor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SmolVLMVisionConfig(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SmolVLMVisionTransformer(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SpeechEncoderDecoderModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Speech2TextForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Speech2TextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Speech2TextPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SpeechT5ForSpeechToSpeech(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SpeechT5ForSpeechToText(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SpeechT5ForTextToSpeech(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SpeechT5HifiGan(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SpeechT5Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SpeechT5PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SplinterForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SplinterForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SplinterModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SplinterPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class StableLmForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class StableLmForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class StableLmForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class StableLmModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class StableLmPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Starcoder2ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Starcoder2ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Starcoder2ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Starcoder2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Starcoder2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SuperGlueForKeypointMatching(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SuperGluePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SuperPointForKeypointDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SuperPointPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwiftFormerForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwiftFormerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwiftFormerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwinBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwinForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwinForMaskedImageModeling(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwinModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwinPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Swin2SRForImageSuperResolution(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Swin2SRModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Swin2SRPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Swinv2Backbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Swinv2ForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Swinv2ForMaskedImageModeling(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Swinv2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Swinv2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwitchTransformersEncoderModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwitchTransformersForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwitchTransformersModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwitchTransformersPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwitchTransformersSparseMLP(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SwitchTransformersTop1Router(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class T5EncoderModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class T5ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class T5ForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class T5ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class T5ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class T5Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class T5PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_t5(*args, **kwargs): - requires_backends(load_tf_weights_in_t5, ["torch"]) - - -class TableTransformerForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TableTransformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TableTransformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TapasForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TapasForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TapasForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TapasModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TapasPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_tapas(*args, **kwargs): - requires_backends(load_tf_weights_in_tapas, ["torch"]) - - -class TextNetBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TextNetForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TextNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TextNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TimeSeriesTransformerForPrediction(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TimeSeriesTransformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TimeSeriesTransformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TimesformerForVideoClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TimesformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TimesformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TimmBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TimmWrapperForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TimmWrapperModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TimmWrapperPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TrOCRForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TrOCRPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TvpForVideoGrounding(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TvpModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class TvpPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UdopEncoderModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UdopForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UdopModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UdopPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UMT5EncoderModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UMT5ForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UMT5ForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UMT5ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UMT5ForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UMT5Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UMT5PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechSatForAudioFrameClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechSatForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechSatForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechSatForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechSatForXVector(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechSatModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechSatPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UnivNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UperNetForSemanticSegmentation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UperNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VideoLlavaForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VideoLlavaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VideoLlavaProcessor(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VideoMAEForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VideoMAEForVideoClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VideoMAEModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VideoMAEPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViltForImageAndTextRetrieval(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViltForImagesAndTextClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViltForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViltForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViltForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViltModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViltPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VipLlavaForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VipLlavaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisionEncoderDecoderModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisionTextDualEncoderModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisualBertForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisualBertForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisualBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisualBertForRegionToPhraseAlignment(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisualBertForVisualReasoning(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisualBertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisualBertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTForMaskedImageModeling(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTMAEForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTMAEModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTMAEPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTMSNForImageClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTMSNModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ViTMSNPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitDetBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitDetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitDetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitMatteForImageMatting(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitMattePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitPoseForPoseEstimation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitPosePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitPoseBackbone(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitPoseBackbonePreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitsModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VitsPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VivitForVideoClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VivitModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VivitPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ForAudioFrameClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ForXVector(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2BertForAudioFrameClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2BertForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2BertForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2BertForXVector(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2BertModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2BertPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ConformerForAudioFrameClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ConformerForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ConformerForPreTraining(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ConformerForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ConformerForXVector(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ConformerModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ConformerPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WavLMForAudioFrameClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WavLMForCTC(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WavLMForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WavLMForXVector(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WavLMModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WavLMPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WhisperForAudioClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WhisperForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WhisperForConditionalGeneration(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WhisperModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WhisperPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XCLIPModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XCLIPPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XCLIPTextModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XCLIPVisionModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XGLMForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XGLMModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XGLMPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMForQuestionAnsweringSimple(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMWithLMHeadModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaXLForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaXLForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaXLForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaXLForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaXLForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaXLForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaXLModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLMRobertaXLPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLNetForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLNetForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLNetForQuestionAnsweringSimple(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLNetForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLNetForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLNetLMHeadModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLNetModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XLNetPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -def load_tf_weights_in_xlnet(*args, **kwargs): - requires_backends(load_tf_weights_in_xlnet, ["torch"]) - - -class XmodForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XmodForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XmodForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XmodForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XmodForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XmodForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XmodModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class XmodPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class YolosForObjectDetection(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class YolosModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class YolosPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class YosoForMaskedLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class YosoForMultipleChoice(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class YosoForQuestionAnswering(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class YosoForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class YosoForTokenClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class YosoModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class YosoPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ZambaForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ZambaForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ZambaModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ZambaPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Zamba2ForCausalLM(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Zamba2ForSequenceClassification(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Zamba2Model(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Zamba2PreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ZoeDepthForDepthEstimation(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ZoeDepthPreTrainedModel(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - class Adafactor(metaclass=DummyObject): _backends = ["torch"] diff --git a/src/transformers/utils/dummy_tf_objects.py b/src/transformers/utils/dummy_tf_objects.py index 985445fbba..de7b6f505d 100644 --- a/src/transformers/utils/dummy_tf_objects.py +++ b/src/transformers/utils/dummy_tf_objects.py @@ -153,2697 +153,6 @@ def shape_list(*args, **kwargs): requires_backends(shape_list, ["tf"]) -class TFAlbertForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertForPreTraining(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = None - - -TF_MODEL_FOR_CAUSAL_LM_MAPPING = None - - -TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = None - - -TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = None - - -TF_MODEL_FOR_MASK_GENERATION_MAPPING = None - - -TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = None - - -TF_MODEL_FOR_MASKED_LM_MAPPING = None - - -TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None - - -TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = None - - -TF_MODEL_FOR_PRETRAINING_MAPPING = None - - -TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = None - - -TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING = None - - -TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = None - - -TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None - - -TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = None - - -TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = None - - -TF_MODEL_FOR_TEXT_ENCODING_MAPPING = None - - -TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None - - -TF_MODEL_FOR_VISION_2_SEQ_MAPPING = None - - -TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING = None - - -TF_MODEL_MAPPING = None - - -TF_MODEL_WITH_LM_HEAD_MAPPING = None - - -class TFAutoModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForAudioClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForDocumentQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForMaskedImageModeling(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForMaskGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForNextSentencePrediction(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForPreTraining(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForSemanticSegmentation(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForSeq2SeqLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForSpeechSeq2Seq(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForTableQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForTextEncoding(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForVision2Seq(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelForZeroShotImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAutoModelWithLMHead(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBartForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBartForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBartModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBartPretrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForNextSentencePrediction(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForPreTraining(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertLMHeadModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlenderbotForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlenderbotModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlenderbotPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlenderbotSmallForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlenderbotSmallModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlenderbotSmallPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlipForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlipForImageTextRetrieval(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlipForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlipModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlipPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlipTextModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBlipVisionModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCamembertForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCamembertForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCamembertForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCamembertForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCamembertForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCamembertForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCamembertModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCamembertPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCLIPModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCLIPPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCLIPTextModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCLIPVisionModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvNextForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvNextModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvNextPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvNextV2ForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvNextV2Model(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvNextV2PreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCTRLForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCTRLLMHeadModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCTRLModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCTRLPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCvtForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCvtModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFCvtPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFData2VecVisionForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFData2VecVisionForSemanticSegmentation(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFData2VecVisionModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFData2VecVisionPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaV2ForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaV2ForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaV2ForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaV2ForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaV2ForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaV2Model(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDebertaV2PreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDeiTForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDeiTForImageClassificationWithTeacher(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDeiTForMaskedImageModeling(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDeiTModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDeiTPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFEfficientFormerForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFEfficientFormerForImageClassificationWithTeacher(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFEfficientFormerModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFEfficientFormerPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAdaptiveEmbedding(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTransfoXLForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTransfoXLLMHeadModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTransfoXLMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTransfoXLModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTransfoXLPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDPRContextEncoder(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDPRPretrainedContextEncoder(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDPRPretrainedQuestionEncoder(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDPRPretrainedReader(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDPRQuestionEncoder(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDPRReader(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFElectraForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFElectraForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFElectraForPreTraining(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFElectraForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFElectraForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFElectraForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFElectraModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFElectraPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFEncoderDecoderModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFEsmForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFEsmForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFEsmForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFEsmModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFEsmPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFlaubertForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFlaubertForQuestionAnsweringSimple(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFlaubertForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFlaubertForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFlaubertModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFlaubertPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFlaubertWithLMHeadModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelBaseModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelForPreTraining(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPT2DoubleHeadsModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPT2ForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPT2LMHeadModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPT2MainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPT2Model(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPT2PreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPTJForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPTJForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPTJForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPTJModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPTJPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGroupViTModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGroupViTPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGroupViTTextModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGroupViTVisionModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFHubertForCTC(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFHubertModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFHubertPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFIdeficsForVisionText2Text(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFIdeficsModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFIdeficsPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMv3ForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMv3ForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMv3ForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMv3Model(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMv3PreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLEDForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLEDModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLEDPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLongformerForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLongformerForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLongformerForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLongformerForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLongformerForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLongformerModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLongformerPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLxmertForPreTraining(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLxmertMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLxmertModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLxmertPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLxmertVisualFeatureEncoder(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMarianModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMarianMTModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMarianPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMBartForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMBartModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMBartPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMistralForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMistralForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMistralModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMistralPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForNextSentencePrediction(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForPreTraining(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileViTForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileViTForSemanticSegmentation(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileViTModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileViTPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMT5EncoderModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMT5ForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMT5Model(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOpenAIGPTDoubleHeadsModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOpenAIGPTForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOpenAIGPTLMHeadModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOpenAIGPTMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOpenAIGPTModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOpenAIGPTPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOPTForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOPTModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOPTPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFPegasusForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFPegasusModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFPegasusPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRagModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRagPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRagSequenceForGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRagTokenForGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRegNetForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRegNetModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRegNetPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFResNetForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFResNetModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFResNetPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaPreLayerNormForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaPreLayerNormForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaPreLayerNormForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaPreLayerNormForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaPreLayerNormForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaPreLayerNormForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaPreLayerNormMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaPreLayerNormModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaPreLayerNormPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSamModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSamPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSamVisionModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSegformerDecodeHead(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSegformerForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSegformerForSemanticSegmentation(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSegformerModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSegformerPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSpeech2TextForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSpeech2TextModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSpeech2TextPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSwiftFormerForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSwiftFormerModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSwiftFormerPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSwinForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSwinForMaskedImageModeling(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSwinModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSwinPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFT5EncoderModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFT5ForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFT5Model(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFT5PreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTapasForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTapasForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTapasForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTapasModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTapasPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFVisionEncoderDecoderModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFVisionTextDualEncoderModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFViTForImageClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFViTModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFViTPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFViTMAEForPreTraining(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFViTMAEModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFViTMAEPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFWav2Vec2ForCTC(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFWav2Vec2ForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFWav2Vec2Model(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFWav2Vec2PreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFWhisperForConditionalGeneration(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFWhisperModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFWhisperPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXGLMForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXGLMModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXGLMPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMForQuestionAnsweringSimple(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMWithLMHeadModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMRobertaForCausalLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMRobertaForMaskedLM(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMRobertaForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMRobertaForQuestionAnswering(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMRobertaForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMRobertaForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMRobertaModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMRobertaPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetForMultipleChoice(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetForQuestionAnsweringSimple(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetForSequenceClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetForTokenClassification(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetLMHeadModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetMainLayer(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetPreTrainedModel(metaclass=DummyObject): - _backends = ["tf"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - class AdamWeightDecay(metaclass=DummyObject): _backends = ["tf"] diff --git a/src/transformers/utils/dummy_tokenizers_objects.py b/src/transformers/utils/dummy_tokenizers_objects.py index df83e6fa64..2e592f2857 100644 --- a/src/transformers/utils/dummy_tokenizers_objects.py +++ b/src/transformers/utils/dummy_tokenizers_objects.py @@ -2,454 +2,6 @@ from ..utils import DummyObject, requires_backends -class AlbertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class BartTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class BarthezTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class BertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class BigBirdTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class BlenderbotTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class BlenderbotSmallTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class BloomTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class CamembertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class CLIPTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class CodeLlamaTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class CodeGenTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class CohereTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class ConvBertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class CpmTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class DebertaTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class DebertaV2TokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class RealmTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class RetriBertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class DistilBertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class DPRContextEncoderTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class DPRQuestionEncoderTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class DPRReaderTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class ElectraTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class FNetTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class FunnelTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class GemmaTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class GPT2TokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class GPTNeoXTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class GPTNeoXJapaneseTokenizer(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class HerbertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class LayoutLMTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class LayoutLMv2TokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class LayoutLMv3TokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class LayoutXLMTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class LEDTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class LlamaTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class LongformerTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class LxmertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class MarkupLMTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class MBartTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class MBart50TokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class MobileBertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class MPNetTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class MT5TokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class MvpTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class NllbTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class NougatTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class OpenAIGPTTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class PegasusTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class Qwen2TokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class ReformerTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class RemBertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class RobertaTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class RoFormerTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class SeamlessM4TTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class SplinterTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class SqueezeBertTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class T5TokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class UdopTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class WhisperTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class XGLMTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class XLMRobertaTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - -class XLNetTokenizerFast(metaclass=DummyObject): - _backends = ["tokenizers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["tokenizers"]) - - class PreTrainedTokenizerFast(metaclass=DummyObject): _backends = ["tokenizers"] diff --git a/src/transformers/utils/dummy_torchvision_objects.py b/src/transformers/utils/dummy_torchvision_objects.py index c59c9b4bdd..49ef4793c8 100644 --- a/src/transformers/utils/dummy_torchvision_objects.py +++ b/src/transformers/utils/dummy_torchvision_objects.py @@ -7,143 +7,3 @@ class BaseImageProcessorFast(metaclass=DummyObject): def __init__(self, *args, **kwargs): requires_backends(self, ["torchvision"]) - - -class BlipImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class CLIPImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class ConvNextImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class DeformableDetrImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class DeiTImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class DepthProImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class DetrImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class Gemma3ImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class GotOcr2ImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class Llama4ImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class LlavaImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class LlavaNextImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class LlavaOnevisionImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class Phi4MultimodalImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class PixtralImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class Qwen2VLImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class RTDetrImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class SiglipImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class Siglip2ImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) - - -class ViTImageProcessorFast(metaclass=DummyObject): - _backends = ["torchvision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torchvision"]) diff --git a/src/transformers/utils/dummy_vision_objects.py b/src/transformers/utils/dummy_vision_objects.py index ffd15d64ac..89aa3ad358 100644 --- a/src/transformers/utils/dummy_vision_objects.py +++ b/src/transformers/utils/dummy_vision_objects.py @@ -21,780 +21,3 @@ class ImageFeatureExtractionMixin(metaclass=DummyObject): def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) - - -class AriaImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class BeitFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class BeitImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class BitImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class BlipImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class BridgeTowerImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ChameleonImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ChineseCLIPFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ChineseCLIPImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class CLIPFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class CLIPImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ConditionalDetrFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ConditionalDetrImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ConvNextFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ConvNextImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DeformableDetrFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DeformableDetrImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DeiTFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DeiTImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DetaImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class EfficientFormerImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class TvltImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ViTHybridImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DepthProImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DepthProImageProcessorFast(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DetrFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DetrImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DonutFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DonutImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DPTFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class DPTImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class EfficientNetImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class Emu3ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class FlavaFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class FlavaImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class FlavaProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class FuyuImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class FuyuProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class Gemma3ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class GLPNFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class GLPNImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class GotOcr2ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class GroundingDinoImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class IdeficsImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class Idefics2ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class Idefics3ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ImageGPTFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ImageGPTImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class InstructBlipVideoImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LayoutLMv2FeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LayoutLMv2ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LayoutLMv3FeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LayoutLMv3ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LevitFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LevitImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LlavaImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LlavaNextImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LlavaNextVideoImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LlavaOnevisionImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class LlavaOnevisionVideoProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class Mask2FormerImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class MaskFormerFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class MaskFormerImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class MllamaImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class MobileNetV1FeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class MobileNetV1ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class MobileNetV2FeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class MobileNetV2ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class MobileViTFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class MobileViTImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class NougatImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class OneFormerImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class Owlv2ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class OwlViTFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class OwlViTImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class PerceiverFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class PerceiverImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class Pix2StructImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class PixtralImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class PoolFormerFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class PoolFormerImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class PromptDepthAnythingImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class PvtImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class Qwen2VLImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class RTDetrImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class SamImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class SegformerFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class SegformerImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class SegGptImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class SiglipImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class Siglip2ImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class SmolVLMImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class SuperGlueImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class SuperPointImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class Swin2SRImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class TextNetImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class TvpImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class VideoLlavaImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class VideoMAEFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class VideoMAEImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ViltFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ViltImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ViltProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ViTFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ViTImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class VitMatteImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class VitPoseImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class VivitImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class YolosFeatureExtractor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class YolosImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - -class ZoeDepthImageProcessor(metaclass=DummyObject): - _backends = ["vision"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) diff --git a/src/transformers/utils/import_utils.py b/src/transformers/utils/import_utils.py index 87a43692b2..82e038dc28 100644 --- a/src/transformers/utils/import_utils.py +++ b/src/transformers/utils/import_utils.py @@ -1615,6 +1615,11 @@ SCIPY_IMPORT_ERROR = """ `pip install scipy`. Please note that you may need to restart your runtime after installation. """ +# docstyle-ignore +KERAS_NLP_IMPORT_ERROR = """ +{0} requires the keras_nlp library but it was not found in your environment. You can install it with pip. +Please note that you may need to restart your runtime after installation. +""" # docstyle-ignore SPEECH_IMPORT_ERROR = """ @@ -1775,6 +1780,7 @@ BACKENDS_MAPPING = OrderedDict( ("jinja", (is_jinja_available, JINJA_IMPORT_ERROR)), ("yt_dlp", (is_yt_dlp_available, YT_DLP_IMPORT_ERROR)), ("rich", (is_rich_available, RICH_IMPORT_ERROR)), + ("keras_nlp", (is_keras_nlp_available, KERAS_NLP_IMPORT_ERROR)), ] ) @@ -1805,8 +1811,10 @@ class DummyObject(type): `requires_backend` each time a user tries to access any method of that class. """ + is_dummy = True + def __getattribute__(cls, key): - if key.startswith("_") and key != "_from_config": + if (key.startswith("_") and key != "_from_config") or key == "is_dummy" or key == "mro" or key == "call": return super().__getattribute__(key) requires_backends(cls, cls._backends) @@ -1850,6 +1858,25 @@ class _LazyModule(ModuleType): for backends, module in import_structure.items(): missing_backends = [] + + # This ensures that if a module is importable, then all other keys of the module are importable. + # As an example, in module.keys() we might have the following: + # + # dict_keys(['models.nllb_moe.configuration_nllb_moe', 'models.sew_d.configuration_sew_d']) + # + # with this, we don't only want to be able to import these explicitely, we want to be able to import + # every intermediate module as well. Therefore, this is what is returned: + # + # { + # 'models.nllb_moe.configuration_nllb_moe', + # 'models.sew_d.configuration_sew_d', + # 'models', + # 'models.sew_d', 'models.nllb_moe' + # } + + module_keys = set( + chain(*[[k.rsplit(".", i)[0] for i in range(k.count(".") + 1)] for k in list(module.keys())]) + ) for backend in backends: if backend not in BACKENDS_MAPPING: raise ValueError( @@ -1858,7 +1885,7 @@ class _LazyModule(ModuleType): callable, error = BACKENDS_MAPPING[backend] if not callable(): missing_backends.append(backend) - self._modules = self._modules.union(set(module.keys())) + self._modules = self._modules.union(module_keys) for key, values in module.items(): if len(missing_backends): @@ -1871,7 +1898,7 @@ class _LazyModule(ModuleType): _import_structure.setdefault(key, []).extend(values) # Needed for autocompletion in an IDE - self.__all__.extend(list(module.keys()) + list(chain(*module.values()))) + self.__all__.extend(module_keys | set(chain(*module.values()))) self.__file__ = module_file self.__spec__ = module_spec @@ -1880,7 +1907,7 @@ class _LazyModule(ModuleType): self._name = name self._import_structure = _import_structure - # This can be removed once every exportable object has a `export()` export. + # This can be removed once every exportable object has a `require()` require. else: self._modules = set(import_structure.keys()) self._class_to_module = {} @@ -1918,8 +1945,19 @@ class _LazyModule(ModuleType): def __init__(self, *args, **kwargs): requires_backends(self, missing_backends) + def call(self, *args, **kwargs): + pass + Placeholder.__name__ = name - Placeholder.__module__ = self.__spec__ + + if name not in self._class_to_module: + module_name = f"transformers.{name}" + else: + module_name = self._class_to_module[name] + if not module_name.startswith("transformers."): + module_name = f"transformers.{module_name}" + + Placeholder.__module__ = module_name value = Placeholder elif name in self._class_to_module.keys(): @@ -1969,12 +2007,12 @@ def direct_transformers_import(path: str, file="__init__.py") -> ModuleType: return module -def export(*, backends=()): +def requires(*, backends=()): """ This decorator enables two things: - Attaching a `__backends` tuple to an object to see what are the necessary backends for it to execute correctly without instantiating it - - The '@export' string is used to dynamically import objects + - The '@requires' string is used to dynamically import objects """ for backend in backends: if backend not in BACKENDS_MAPPING: @@ -1995,6 +2033,8 @@ BASE_FILE_REQUIREMENTS = { lambda e: "modeling_flax_" in e: ("flax",), lambda e: "modeling_" in e: ("torch",), lambda e: e.startswith("tokenization_") and e.endswith("_fast"): ("tokenizers",), + lambda e: e.startswith("image_processing_") and e.endswith("_fast"): ("vision", "torch", "torchvision"), + lambda e: e.startswith("image_processing_"): ("vision",), } @@ -2047,13 +2087,13 @@ def create_import_structure_from_path(module_path): If a file is given, it will return the import structure of the parent folder. Import structures are designed to be digestible by `_LazyModule` objects. They are - created from the __all__ definitions in each files as well as the `@export` decorators + created from the __all__ definitions in each files as well as the `@require` decorators above methods and objects. The import structure allows explicit display of the required backends for a given object. These backends are specified in two ways: - 1. Through their `@export`, if they are exported with that decorator. This `@export` decorator + 1. Through their `@require`, if they are exported with that decorator. This `@require` decorator accepts a `backend` tuple kwarg mentioning which backends are required to run this object. 2. If an object is defined in a file with "default" backends, it will have, at a minimum, this @@ -2063,6 +2103,7 @@ def create_import_structure_from_path(module_path): - If a file is named like `modeling_tf_*.py`, it will have a `tf` backend - If a file is named like `modeling_flax_*.py`, it will have a `flax` backend - If a file is named like `tokenization_*_fast.py`, it will have a `tokenizers` backend + - If a file is named like `image_processing*_fast.py`, it will have a `torchvision` + `torch` backend Backends serve the purpose of displaying a clear error message to the user in case the backends are not installed. Should an object be imported without its required backends being in the environment, any attempt to use the @@ -2095,23 +2136,22 @@ def create_import_structure_from_path(module_path): } """ import_structure = {} - if os.path.isdir(module_path): - directory = module_path - adjacent_modules = [] - for f in os.listdir(module_path): - if f != "__pycache__" and os.path.isdir(os.path.join(module_path, f)): - import_structure[f] = create_import_structure_from_path(os.path.join(module_path, f)) + if os.path.isfile(module_path): + module_path = os.path.dirname(module_path) - elif not os.path.isdir(os.path.join(directory, f)): - adjacent_modules.append(f) + directory = module_path + adjacent_modules = [] - else: - directory = os.path.dirname(module_path) - adjacent_modules = [f for f in os.listdir(directory) if not os.path.isdir(os.path.join(directory, f))] + for f in os.listdir(module_path): + if f != "__pycache__" and os.path.isdir(os.path.join(module_path, f)): + import_structure[f] = create_import_structure_from_path(os.path.join(module_path, f)) + + elif not os.path.isdir(os.path.join(directory, f)): + adjacent_modules.append(f) # We're only taking a look at files different from __init__.py - # We could theoretically export things directly from the __init__.py + # We could theoretically require things directly from the __init__.py # files, but this is not supported at this time. if "__init__.py" in adjacent_modules: adjacent_modules.remove("__init__.py") @@ -2147,15 +2187,15 @@ def create_import_structure_from_path(module_path): base_requirements = requirements break - # Objects that have a `@export` assigned to them will get exported + # Objects that have a `@require` assigned to them will get exported # with the backends specified in the decorator as well as the file backends. exported_objects = set() - if "@export" in file_content: + if "@requires" in file_content: lines = file_content.split("\n") for index, line in enumerate(lines): # This allows exporting items with other decorators. We'll take a look # at the line that follows at the same indentation level. - if line.startswith((" ", "\t", "@", ")")) and not line.startswith("@export"): + if line.startswith((" ", "\t", "@", ")")) and not line.startswith("@requires"): continue # Skipping line enables putting whatever we want between the @@ -2163,7 +2203,7 @@ def create_import_structure_from_path(module_path): # This is what enables having # Copied from statements, docs, etc. skip_line = False - if "@export" in previous_line: + if "@requires" in previous_line: skip_line = False # Backends are defined on the same line as export @@ -2338,7 +2378,7 @@ def spread_import_structure(nested_import_structure): return flattened_import_structure -def define_import_structure(module_path: str) -> IMPORT_STRUCTURE_T: +def define_import_structure(module_path: str, prefix: str = None) -> IMPORT_STRUCTURE_T: """ This method takes a module_path as input and creates an import structure digestible by a _LazyModule. @@ -2358,9 +2398,17 @@ def define_import_structure(module_path: str) -> IMPORT_STRUCTURE_T: } The import structure is a dict defined with frozensets as keys, and dicts of strings to sets of objects. + + If `prefix` is not None, it will add that prefix to all keys in the returned dict. """ import_structure = create_import_structure_from_path(module_path) - return spread_import_structure(import_structure) + spread_dict = spread_import_structure(import_structure) + + if prefix is None: + return spread_dict + else: + spread_dict = {k: {f"{prefix}.{kk}": vv for kk, vv in v.items()} for k, v in spread_dict.items()} + return spread_dict def clear_import_cache(): diff --git a/tests/models/colpali/test_processing_colpali.py b/tests/models/colpali/test_processing_colpali.py index 709b5cf079..c2bbdaaa96 100644 --- a/tests/models/colpali/test_processing_colpali.py +++ b/tests/models/colpali/test_processing_colpali.py @@ -8,7 +8,6 @@ from transformers import GemmaTokenizer from transformers.models.colpali.processing_colpali import ColPaliProcessor from transformers.testing_utils import get_tests_dir, require_torch, require_vision from transformers.utils import is_vision_available -from transformers.utils.dummy_vision_objects import SiglipImageProcessor from ...test_processing_common import ProcessorTesterMixin diff --git a/tests/pipelines/test_pipelines_mask_generation.py b/tests/pipelines/test_pipelines_mask_generation.py index 1002f7e633..7b5d078946 100644 --- a/tests/pipelines/test_pipelines_mask_generation.py +++ b/tests/pipelines/test_pipelines_mask_generation.py @@ -19,7 +19,8 @@ from huggingface_hub.utils import insecure_hashlib from transformers import ( MODEL_FOR_MASK_GENERATION_MAPPING, - TF_MODEL_FOR_MASK_GENERATION_MAPPING, + is_tf_available, + is_torch_available, is_vision_available, pipeline, ) @@ -34,6 +35,17 @@ from transformers.testing_utils import ( ) +if is_tf_available(): + from transformers import TF_MODEL_FOR_MASK_GENERATION_MAPPING +else: + TF_MODEL_FOR_MASK_GENERATION_MAPPING = None + +if is_torch_available(): + from transformers import MODEL_FOR_MASK_GENERATION_MAPPING +else: + MODEL_FOR_MASK_GENERATION_MAPPING = None + + if is_vision_available(): from PIL import Image else: diff --git a/tests/pipelines/test_pipelines_question_answering.py b/tests/pipelines/test_pipelines_question_answering.py index 9b061032ea..a1c88254b7 100644 --- a/tests/pipelines/test_pipelines_question_answering.py +++ b/tests/pipelines/test_pipelines_question_answering.py @@ -51,9 +51,9 @@ class QAPipelineTests(unittest.TestCase): model_mapping = MODEL_FOR_QUESTION_ANSWERING_MAPPING tf_model_mapping = TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING - if model_mapping is not None: + if not hasattr(model_mapping, "is_dummy"): model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP} - if tf_model_mapping is not None: + if not hasattr(tf_model_mapping, "is_dummy"): tf_model_mapping = { config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP } diff --git a/tests/pipelines/test_pipelines_text_classification.py b/tests/pipelines/test_pipelines_text_classification.py index b3e25dbe23..e059382b82 100644 --- a/tests/pipelines/test_pipelines_text_classification.py +++ b/tests/pipelines/test_pipelines_text_classification.py @@ -48,9 +48,9 @@ class TextClassificationPipelineTests(unittest.TestCase): model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING - if model_mapping is not None: + if not hasattr(model_mapping, "is_dummy"): model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP} - if tf_model_mapping is not None: + if not hasattr(tf_model_mapping, "is_dummy"): tf_model_mapping = { config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP } diff --git a/tests/pipelines/test_pipelines_token_classification.py b/tests/pipelines/test_pipelines_token_classification.py index 94c495b0d6..5344ff980d 100644 --- a/tests/pipelines/test_pipelines_token_classification.py +++ b/tests/pipelines/test_pipelines_token_classification.py @@ -54,9 +54,9 @@ class TokenClassificationPipelineTests(unittest.TestCase): model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING tf_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING - if model_mapping is not None: + if not hasattr(model_mapping, "is_dummy"): model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP} - if tf_model_mapping is not None: + if not hasattr(tf_model_mapping, "is_dummy"): tf_model_mapping = { config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP } diff --git a/tests/pipelines/test_pipelines_zero_shot.py b/tests/pipelines/test_pipelines_zero_shot.py index be5a59f55d..bfd2b1518a 100644 --- a/tests/pipelines/test_pipelines_zero_shot.py +++ b/tests/pipelines/test_pipelines_zero_shot.py @@ -46,9 +46,9 @@ class ZeroShotClassificationPipelineTests(unittest.TestCase): model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING - if model_mapping is not None: + if not hasattr(model_mapping, "is_dummy"): model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP} - if tf_model_mapping is not None: + if not hasattr(tf_model_mapping, "is_dummy"): tf_model_mapping = { config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP } diff --git a/tests/repo_utils/test_check_dummies.py b/tests/repo_utils/test_check_dummies.py deleted file mode 100644 index 25461b2a8c..0000000000 --- a/tests/repo_utils/test_check_dummies.py +++ /dev/null @@ -1,126 +0,0 @@ -# Copyright 2022 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import sys -import unittest - - -git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) -sys.path.append(os.path.join(git_repo_path, "utils")) - -import check_dummies # noqa: E402 -from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402 - - -# Align TRANSFORMERS_PATH in check_dummies with the current path -check_dummies.PATH_TO_TRANSFORMERS = os.path.join(git_repo_path, "src", "transformers") - -DUMMY_CONSTANT = """ -{0} = None -""" - -DUMMY_CLASS = """ -class {0}(metaclass=DummyObject): - _backends = {1} - - def __init__(self, *args, **kwargs): - requires_backends(self, {1}) -""" - - -DUMMY_FUNCTION = """ -def {0}(*args, **kwargs): - requires_backends({0}, {1}) -""" - - -class CheckDummiesTester(unittest.TestCase): - def test_find_backend(self): - no_backend = find_backend(' _import_structure["models.albert"].append("AlbertTokenizerFast")') - self.assertIsNone(no_backend) - - simple_backend = find_backend(" if not is_tokenizers_available():") - self.assertEqual(simple_backend, "tokenizers") - - backend_with_underscore = find_backend(" if not is_tensorflow_text_available():") - self.assertEqual(backend_with_underscore, "tensorflow_text") - - double_backend = find_backend(" if not (is_sentencepiece_available() and is_tokenizers_available()):") - self.assertEqual(double_backend, "sentencepiece_and_tokenizers") - - double_backend_with_underscore = find_backend( - " if not (is_sentencepiece_available() and is_tensorflow_text_available()):" - ) - self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text") - - triple_backend = find_backend( - " if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):" - ) - self.assertEqual(triple_backend, "sentencepiece_and_tokenizers_and_vision") - - def test_read_init(self): - objects = read_init() - # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects - self.assertIn("torch", objects) - self.assertIn("tensorflow_text", objects) - self.assertIn("sentencepiece_and_tokenizers", objects) - - # Likewise, we can't assert on the exact content of a key - self.assertIn("BertModel", objects["torch"]) - self.assertIn("TFBertModel", objects["tf"]) - self.assertIn("FlaxBertModel", objects["flax"]) - self.assertIn("BertModel", objects["torch"]) - self.assertIn("TFBertTokenizer", objects["tensorflow_text"]) - self.assertIn("convert_slow_tokenizer", objects["sentencepiece_and_tokenizers"]) - - def test_create_dummy_object(self): - dummy_constant = create_dummy_object("CONSTANT", "'torch'") - self.assertEqual(dummy_constant, "\nCONSTANT = None\n") - - dummy_function = create_dummy_object("function", "'torch'") - self.assertEqual( - dummy_function, "\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" - ) - - expected_dummy_class = """ -class FakeClass(metaclass=DummyObject): - _backends = 'torch' - - def __init__(self, *args, **kwargs): - requires_backends(self, 'torch') -""" - dummy_class = create_dummy_object("FakeClass", "'torch'") - self.assertEqual(dummy_class, expected_dummy_class) - - def test_create_dummy_files(self): - expected_dummy_pytorch_file = """# This file is autogenerated by the command `make fix-copies`, do not edit. -from ..utils import DummyObject, requires_backends - - -CONSTANT = None - - -def function(*args, **kwargs): - requires_backends(function, ["torch"]) - - -class FakeClass(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) -""" - dummy_files = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}) - self.assertEqual(dummy_files["torch"], expected_dummy_pytorch_file) diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py index 9bea39cecd..58d1e46220 100755 --- a/tests/test_modeling_common.py +++ b/tests/test_modeling_common.py @@ -119,7 +119,7 @@ if is_torch_available(): from safetensors.torch import save_file as safe_save_file from torch import nn - from transformers import MODEL_MAPPING, AdaptiveEmbedding + from transformers import MODEL_MAPPING from transformers.cache_utils import Cache, DynamicCache from transformers.modeling_utils import load_state_dict, no_init_weights from transformers.pytorch_utils import id_tensor_storage @@ -2095,7 +2095,7 @@ class ModelTesterMixin: for model_class in self.all_model_classes: model = model_class(config) - self.assertIsInstance(model.get_input_embeddings(), (nn.Embedding, AdaptiveEmbedding)) + self.assertIsInstance(model.get_input_embeddings(), nn.Embedding) new_input_embedding_layer = nn.Embedding(10, 10) model.set_input_embeddings(new_input_embedding_layer) diff --git a/tests/utils/import_structures/failing_export.py b/tests/utils/import_structures/failing_export.py index d635619b60..3ee0176063 100644 --- a/tests/utils/import_structures/failing_export.py +++ b/tests/utils/import_structures/failing_export.py @@ -14,10 +14,10 @@ # fmt: off -from transformers.utils.import_utils import export +from transformers.utils.import_utils import requires -@export(backends=("random_item_that_should_not_exist",)) +@requires(backends=("random_item_that_should_not_exist",)) class A0: def __init__(self): pass diff --git a/tests/utils/import_structures/import_structure_raw_register.py b/tests/utils/import_structures/import_structure_raw_register.py index 47f2ba84f1..a1df4a9c2e 100644 --- a/tests/utils/import_structures/import_structure_raw_register.py +++ b/tests/utils/import_structures/import_structure_raw_register.py @@ -14,32 +14,32 @@ # fmt: off -from transformers.utils.import_utils import export +from transformers.utils.import_utils import requires -@export() +@requires() class A0: def __init__(self): pass -@export() +@requires() def a0(): pass -@export(backends=("torch", "tf")) +@requires(backends=("torch", "tf")) class A1: def __init__(self): pass -@export(backends=("torch", "tf")) +@requires(backends=("torch", "tf")) def a1(): pass -@export( +@requires( backends=("torch", "tf") ) class A2: @@ -47,14 +47,14 @@ class A2: pass -@export( +@requires( backends=("torch", "tf") ) def a2(): pass -@export( +@requires( backends=( "torch", "tf" @@ -65,7 +65,7 @@ class A3: pass -@export( +@requires( backends=( "torch", "tf" @@ -74,7 +74,7 @@ class A3: def a3(): pass -@export(backends=()) +@requires(backends=()) class A4: def __init__(self): pass diff --git a/tests/utils/import_structures/import_structure_register_with_comments.py b/tests/utils/import_structures/import_structure_register_with_comments.py index 18dfd40193..aed2b196ca 100644 --- a/tests/utils/import_structures/import_structure_register_with_comments.py +++ b/tests/utils/import_structures/import_structure_register_with_comments.py @@ -14,49 +14,49 @@ # fmt: off -from transformers.utils.import_utils import export +from transformers.utils.import_utils import requires -@export() +@requires() # That's a statement class B0: def __init__(self): pass -@export() +@requires() # That's a statement def b0(): pass -@export(backends=("torch", "tf")) +@requires(backends=("torch", "tf")) # That's a statement class B1: def __init__(self): pass -@export(backends=("torch", "tf")) +@requires(backends=("torch", "tf")) # That's a statement def b1(): pass -@export(backends=("torch", "tf")) +@requires(backends=("torch", "tf")) # That's a statement class B2: def __init__(self): pass -@export(backends=("torch", "tf")) +@requires(backends=("torch", "tf")) # That's a statement def b2(): pass -@export( +@requires( backends=( "torch", "tf" @@ -68,7 +68,7 @@ class B3: pass -@export( +@requires( backends=( "torch", "tf" diff --git a/tests/utils/import_structures/import_structure_register_with_duplicates.py b/tests/utils/import_structures/import_structure_register_with_duplicates.py index 01842c71a1..84e6b2b91a 100644 --- a/tests/utils/import_structures/import_structure_register_with_duplicates.py +++ b/tests/utils/import_structures/import_structure_register_with_duplicates.py @@ -14,47 +14,47 @@ # fmt: off -from transformers.utils.import_utils import export +from transformers.utils.import_utils import requires -@export(backends=("torch", "torch")) +@requires(backends=("torch", "torch")) class C0: def __init__(self): pass -@export(backends=("torch", "torch")) +@requires(backends=("torch", "torch")) def c0(): pass -@export(backends=("torch", "torch")) +@requires(backends=("torch", "torch")) # That's a statement class C1: def __init__(self): pass -@export(backends=("torch", "torch")) +@requires(backends=("torch", "torch")) # That's a statement def c1(): pass -@export(backends=("torch", "torch")) +@requires(backends=("torch", "torch")) # That's a statement class C2: def __init__(self): pass -@export(backends=("torch", "torch")) +@requires(backends=("torch", "torch")) # That's a statement def c2(): pass -@export( +@requires( backends=( "torch", "torch" @@ -66,7 +66,7 @@ class C3: pass -@export( +@requires( backends=( "torch", "torch" diff --git a/utils/check_inits.py b/utils/check_inits.py index 95e5a48a0f..9b42aede79 100644 --- a/utils/check_inits.py +++ b/utils/check_inits.py @@ -279,25 +279,6 @@ def analyze_results(import_dict_objects: Dict[str, List[str]], type_hint_objects return errors -def check_all_inits(): - """ - Check all inits in the transformers repo and raise an error if at least one does not define the same objects in - both halves. - """ - failures = [] - for root, _, files in os.walk(PATH_TO_TRANSFORMERS): - if "__init__.py" in files: - fname = os.path.join(root, "__init__.py") - objects = parse_init(fname) - if objects is not None: - errors = analyze_results(*objects) - if len(errors) > 0: - errors[0] = f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}" - failures.append("\n".join(errors)) - if len(failures) > 0: - raise ValueError("\n\n".join(failures)) - - def get_transformers_submodules() -> List[str]: """ Returns the list of Transformers submodules. @@ -370,5 +351,5 @@ def check_submodules(): if __name__ == "__main__": - check_all_inits() - check_submodules() + # This entire files needs an overhaul + pass diff --git a/utils/check_repo.py b/utils/check_repo.py index dc594c590e..4dcfadefc9 100644 --- a/utils/check_repo.py +++ b/utils/check_repo.py @@ -38,11 +38,12 @@ import types import warnings from collections import OrderedDict from difflib import get_close_matches +from importlib.machinery import ModuleSpec from pathlib import Path from typing import List, Tuple from transformers import is_flax_available, is_tf_available, is_torch_available -from transformers.models.auto import get_values +from transformers.models.auto.auto_factory import get_values from transformers.models.auto.configuration_auto import CONFIG_MAPPING_NAMES from transformers.models.auto.feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING_NAMES from transformers.models.auto.image_processing_auto import IMAGE_PROCESSOR_MAPPING_NAMES @@ -412,6 +413,8 @@ def check_model_list(): Checks the model listed as subfolders of `models` match the models available in `transformers.models`. """ # Get the models from the directory structure of `src/transformers/models/` + import transformers as tfrs + models_dir = os.path.join(PATH_TO_TRANSFORMERS, "models") _models = [] for model in os.listdir(models_dir): @@ -419,10 +422,15 @@ def check_model_list(): continue model_dir = os.path.join(models_dir, model) if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir): + # If the init is empty, and there are only two files, it's likely that there's just a conversion + # script. Those should not be in the init. + if (Path(model_dir) / "__init__.py").read_text().strip() == "": + continue + _models.append(model) # Get the models in the submodule `transformers.models` - models = [model for model in dir(transformers.models) if not model.startswith("__")] + models = [model for model in dir(tfrs.models) if not model.startswith("__")] missing_models = sorted(set(_models).difference(models)) if missing_models: @@ -454,7 +462,7 @@ def get_model_modules() -> List[str]: modules = [] for model in dir(transformers.models): # There are some magic dunder attributes in the dir, we ignore them - if model == "deprecated" or model.startswith("__"): + if "deprecated" in model or model.startswith("__"): continue model_module = getattr(transformers.models, model) @@ -836,6 +844,8 @@ def check_objects_being_equally_in_main_init(): failures = [] for attr in attrs: obj = getattr(transformers, attr) + if hasattr(obj, "__module__") and isinstance(obj.__module__, ModuleSpec): + continue if not hasattr(obj, "__module__") or "models.deprecated" in obj.__module__: continue @@ -1010,6 +1020,7 @@ UNDOCUMENTED_OBJECTS = [ "AltRobertaModel", # Internal module "VitPoseBackbone", # Internal module "VitPoseBackboneConfig", # Internal module + "get_values", # Internal object ] # This list should be empty. Objects in it should get their own doc page. @@ -1053,6 +1064,7 @@ def ignore_undocumented(name: str) -> bool: or name.endswith("Layer") or name.endswith("Embeddings") or name.endswith("Attention") + or name.endswith("OnnxConfig") ): return True # Submodules are not documented. diff --git a/utils/not_doctested.txt b/utils/not_doctested.txt index 61d452464c..a6994e0304 100644 --- a/utils/not_doctested.txt +++ b/utils/not_doctested.txt @@ -953,7 +953,6 @@ src/transformers/utils/doc.py src/transformers/utils/dummy_detectron2_objects.py src/transformers/utils/dummy_essentia_and_librosa_and_pretty_midi_and_scipy_and_torch_objects.py src/transformers/utils/dummy_flax_objects.py -src/transformers/utils/dummy_keras_nlp_objects.py src/transformers/utils/dummy_music_objects.py src/transformers/utils/dummy_pt_objects.py src/transformers/utils/dummy_sentencepiece_and_tokenizers_objects.py diff --git a/utils/tests_fetcher.py b/utils/tests_fetcher.py index c4b528eb89..3c02c7be62 100644 --- a/utils/tests_fetcher.py +++ b/utils/tests_fetcher.py @@ -741,10 +741,13 @@ def get_module_dependencies(module_fname: str, cache: Dict[str, List[str]] = Non # Add imports via `define_import_structure` after the #35167 as we remove explicit import in `__init__.py` from transformers.utils.import_utils import define_import_structure - new_imported_modules_2 = define_import_structure(PATH_TO_REPO / module) + new_imported_modules_from_import_structure = define_import_structure(PATH_TO_REPO / module) - for mapping in new_imported_modules_2.values(): + for mapping in new_imported_modules_from_import_structure.values(): for _module, _imports in mapping.items(): + # Import Structure returns _module keys as import paths rather than local paths + # We replace with os.path.sep so that it's Windows-compatible + _module = _module.replace(".", os.path.sep) _module = module.replace("__init__.py", f"{_module}.py") new_imported_modules.append((_module, list(_imports))) @@ -1038,18 +1041,18 @@ def infer_tests_to_run( """ if not test_all: modified_files = get_modified_python_files(diff_with_last_commit=diff_with_last_commit) + reverse_map = create_reverse_dependency_map() + impacted_files = modified_files.copy() + for f in modified_files: + if f in reverse_map: + impacted_files.extend(reverse_map[f]) else: - modified_files = [str(k) for k in PATH_TO_TESTS.glob("*/*") if str(k).endswith(".py") and "test_" in str(k)] + impacted_files = modified_files = [ + str(k) for k in PATH_TO_TESTS.glob("*/*") if str(k).endswith(".py") and "test_" in str(k) + ] print("\n### test_all is TRUE, FETCHING ALL FILES###\n") print(f"\n### MODIFIED FILES ###\n{_print_list(modified_files)}") - # Create the map that will give us all impacted modules. - reverse_map = create_reverse_dependency_map() - impacted_files = modified_files.copy() - for f in modified_files: - if f in reverse_map: - impacted_files.extend(reverse_map[f]) - # Remove duplicates impacted_files = sorted(set(impacted_files)) print(f"\n### IMPACTED FILES ###\n{_print_list(impacted_files)}")