# 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. # When adding a new object to this init, remember to add it twice: once inside the `_import_structure` dictionary and # once inside the `if TYPE_CHECKING` branch. The `TYPE_CHECKING` should have import statements as usual, but they are # only there for type checking. The `_import_structure` is a dictionary submodule to list of object names, and is used # to defer the actual importing for when the objects are requested. This way `import transformers` provides the names # in the namespace without actually importing anything (and especially none of the backends). __version__ = "4.34.0" from typing import TYPE_CHECKING # Check the dependencies satisfy the minimal versions required. from . import dependency_versions_check from .utils import ( OptionalDependencyNotAvailable, _LazyModule, is_bitsandbytes_available, is_essentia_available, is_flax_available, is_keras_nlp_available, is_librosa_available, is_pretty_midi_available, is_scipy_available, is_sentencepiece_available, is_speech_available, is_tensorflow_text_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torchvision_available, is_vision_available, logging, ) logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Base objects, independent of any specific backend _import_structure = { "audio_utils": [], "benchmark": [], "commands": [], "configuration_utils": ["PretrainedConfig"], "convert_graph_to_onnx": [], "convert_slow_tokenizers_checkpoints_to_fast": [], "convert_tf_hub_seq_to_seq_bert_to_pytorch": [], "data": [ "DataProcessor", "InputExample", "InputFeatures", "SingleSentenceClassificationProcessor", "SquadExample", "SquadFeatures", "SquadV1Processor", "SquadV2Processor", "glue_compute_metrics", "glue_convert_examples_to_features", "glue_output_modes", "glue_processors", "glue_tasks_num_labels", "squad_convert_examples_to_features", "xnli_compute_metrics", "xnli_output_modes", "xnli_processors", "xnli_tasks_num_labels", ], "data.data_collator": [ "DataCollator", "DataCollatorForLanguageModeling", "DataCollatorForPermutationLanguageModeling", "DataCollatorForSeq2Seq", "DataCollatorForSOP", "DataCollatorForTokenClassification", "DataCollatorForWholeWordMask", "DataCollatorWithPadding", "DefaultDataCollator", "default_data_collator", ], "data.metrics": [], "data.processors": [], "debug_utils": [], "deepspeed": [], "dependency_versions_check": [], "dependency_versions_table": [], "dynamic_module_utils": [], "feature_extraction_sequence_utils": ["SequenceFeatureExtractor"], "feature_extraction_utils": ["BatchFeature", "FeatureExtractionMixin"], "file_utils": [], "generation": ["GenerationConfig", "TextIteratorStreamer", "TextStreamer"], "hf_argparser": ["HfArgumentParser"], "hyperparameter_search": [], "image_transforms": [], "integrations": [ "is_clearml_available", "is_comet_available", "is_neptune_available", "is_optuna_available", "is_ray_available", "is_ray_tune_available", "is_sigopt_available", "is_tensorboard_available", "is_wandb_available", ], "modelcard": ["ModelCard"], "modeling_tf_pytorch_utils": [ "convert_tf_weight_name_to_pt_weight_name", "load_pytorch_checkpoint_in_tf2_model", "load_pytorch_model_in_tf2_model", "load_pytorch_weights_in_tf2_model", "load_tf2_checkpoint_in_pytorch_model", "load_tf2_model_in_pytorch_model", "load_tf2_weights_in_pytorch_model", ], "models": [], # Models "models.albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig"], "models.align": [ "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlignConfig", "AlignProcessor", "AlignTextConfig", "AlignVisionConfig", ], "models.altclip": [ "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "AltCLIPConfig", "AltCLIPProcessor", "AltCLIPTextConfig", "AltCLIPVisionConfig", ], "models.audio_spectrogram_transformer": [ "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ASTConfig", ], "models.auto": [ "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "FEATURE_EXTRACTOR_MAPPING", "IMAGE_PROCESSOR_MAPPING", "MODEL_NAMES_MAPPING", "PROCESSOR_MAPPING", "TOKENIZER_MAPPING", "AutoConfig", "AutoFeatureExtractor", "AutoImageProcessor", "AutoProcessor", "AutoTokenizer", ], "models.autoformer": [ "AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "AutoformerConfig", ], "models.bark": [ "BarkCoarseConfig", "BarkConfig", "BarkFineConfig", "BarkProcessor", "BarkSemanticConfig", ], "models.bart": ["BartConfig", "BartTokenizer"], "models.barthez": [], "models.bartpho": [], "models.beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig"], "models.bert": [ "BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BasicTokenizer", "BertConfig", "BertTokenizer", "WordpieceTokenizer", ], "models.bert_generation": ["BertGenerationConfig"], "models.bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"], "models.bertweet": ["BertweetTokenizer"], "models.big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig"], "models.bigbird_pegasus": [ "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdPegasusConfig", ], "models.biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig", "BioGptTokenizer"], "models.bit": ["BIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BitConfig"], "models.blenderbot": ["BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotConfig", "BlenderbotTokenizer"], "models.blenderbot_small": [ "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotSmallConfig", "BlenderbotSmallTokenizer", ], "models.blip": [ "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlipConfig", "BlipProcessor", "BlipTextConfig", "BlipVisionConfig", ], "models.blip_2": [ "BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Blip2Config", "Blip2Processor", "Blip2QFormerConfig", "Blip2VisionConfig", ], "models.bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig"], "models.bridgetower": [ "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP", "BridgeTowerConfig", "BridgeTowerProcessor", "BridgeTowerTextConfig", "BridgeTowerVisionConfig", ], "models.bros": ["BROS_PRETRAINED_CONFIG_ARCHIVE_MAP", "BrosConfig", "BrosProcessor"], "models.byt5": ["ByT5Tokenizer"], "models.camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig"], "models.canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig", "CanineTokenizer"], "models.chinese_clip": [ "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "ChineseCLIPConfig", "ChineseCLIPProcessor", "ChineseCLIPTextConfig", "ChineseCLIPVisionConfig", ], "models.clap": [ "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST", "ClapAudioConfig", "ClapConfig", "ClapProcessor", "ClapTextConfig", ], "models.clip": [ "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "CLIPConfig", "CLIPProcessor", "CLIPTextConfig", "CLIPTokenizer", "CLIPVisionConfig", ], "models.clipseg": [ "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP", "CLIPSegConfig", "CLIPSegProcessor", "CLIPSegTextConfig", "CLIPSegVisionConfig", ], "models.code_llama": [], "models.codegen": ["CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP", "CodeGenConfig", "CodeGenTokenizer"], "models.conditional_detr": ["CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConditionalDetrConfig"], "models.convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertTokenizer"], "models.convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig"], "models.convnextv2": ["CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextV2Config"], "models.cpm": [], "models.cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig", "CpmAntTokenizer"], "models.ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig", "CTRLTokenizer"], "models.cvt": ["CVT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CvtConfig"], "models.data2vec": [ "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig", "Data2VecTextConfig", "Data2VecVisionConfig", ], "models.deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaTokenizer"], "models.deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config"], "models.decision_transformer": ["DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "DecisionTransformerConfig"], "models.deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"], "models.deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig"], "models.deprecated": [], "models.deprecated.bort": [], "models.deprecated.mctct": [ "MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig", "MCTCTFeatureExtractor", "MCTCTProcessor", ], "models.deprecated.mmbt": ["MMBTConfig"], "models.deprecated.open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"], "models.deprecated.retribert": [ "RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig", "RetriBertTokenizer", ], "models.deprecated.tapex": ["TapexTokenizer"], "models.deprecated.trajectory_transformer": [ "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrajectoryTransformerConfig", ], "models.deprecated.van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"], "models.deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"], "models.detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig"], "models.dialogpt": [], "models.dinat": ["DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DinatConfig"], "models.dinov2": ["DINOV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Dinov2Config"], "models.distilbert": ["DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DistilBertConfig", "DistilBertTokenizer"], "models.dit": [], "models.donut": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutProcessor", "DonutSwinConfig"], "models.dpr": [ "DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig", "DPRContextEncoderTokenizer", "DPRQuestionEncoderTokenizer", "DPRReaderOutput", "DPRReaderTokenizer", ], "models.dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"], "models.efficientformer": ["EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "EfficientFormerConfig"], "models.efficientnet": ["EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "EfficientNetConfig"], "models.electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraTokenizer"], "models.encodec": [ "ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP", "EncodecConfig", "EncodecFeatureExtractor", ], "models.encoder_decoder": ["EncoderDecoderConfig"], "models.ernie": [ "ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", ], "models.ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"], "models.esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig", "EsmTokenizer"], "models.falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"], "models.flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig", "FlaubertTokenizer"], "models.flava": [ "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlavaConfig", "FlavaImageCodebookConfig", "FlavaImageConfig", "FlavaMultimodalConfig", "FlavaTextConfig", ], "models.fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"], "models.focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"], "models.fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig", "FSMTTokenizer"], "models.funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig", "FunnelTokenizer"], "models.git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitProcessor", "GitVisionConfig"], "models.glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"], "models.gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2Tokenizer"], "models.gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"], "models.gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig"], "models.gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"], "models.gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"], "models.gpt_sw3": [], "models.gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig"], "models.gptsan_japanese": [ "GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTSanJapaneseConfig", "GPTSanJapaneseTokenizer", ], "models.graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"], "models.groupvit": [ "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GroupViTConfig", "GroupViTTextConfig", "GroupViTVisionConfig", ], "models.herbert": ["HerbertTokenizer"], "models.hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"], "models.ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig"], "models.idefics": [ "IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP", "IdeficsConfig", ], "models.imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig"], "models.informer": ["INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "InformerConfig"], "models.instructblip": [ "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "InstructBlipConfig", "InstructBlipProcessor", "InstructBlipQFormerConfig", "InstructBlipVisionConfig", ], "models.jukebox": [ "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "JukeboxConfig", "JukeboxPriorConfig", "JukeboxTokenizer", "JukeboxVQVAEConfig", ], "models.layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMTokenizer"], "models.layoutlmv2": [ "LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config", "LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor", "LayoutLMv2Processor", "LayoutLMv2Tokenizer", ], "models.layoutlmv3": [ "LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv3Config", "LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor", "LayoutLMv3Processor", "LayoutLMv3Tokenizer", ], "models.layoutxlm": ["LayoutXLMProcessor"], "models.led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig", "LEDTokenizer"], "models.levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig"], "models.lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"], "models.llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"], "models.longformer": ["LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongformerConfig", "LongformerTokenizer"], "models.longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config"], "models.luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig", "LukeTokenizer"], "models.lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig", "LxmertTokenizer"], "models.m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config"], "models.marian": ["MarianConfig"], "models.markuplm": [ "MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig", "MarkupLMFeatureExtractor", "MarkupLMProcessor", "MarkupLMTokenizer", ], "models.mask2former": [ "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "Mask2FormerConfig", ], "models.maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig", "MaskFormerSwinConfig"], "models.mbart": ["MBartConfig"], "models.mbart50": [], "models.mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig"], "models.megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"], "models.megatron_gpt2": [], "models.mgp_str": ["MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP", "MgpstrConfig", "MgpstrProcessor", "MgpstrTokenizer"], "models.mistral": ["MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP", "MistralConfig"], "models.mluke": [], "models.mobilebert": ["MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileBertConfig", "MobileBertTokenizer"], "models.mobilenet_v1": ["MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileNetV1Config"], "models.mobilenet_v2": ["MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileNetV2Config"], "models.mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig"], "models.mobilevitv2": ["MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTV2Config"], "models.mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig", "MPNetTokenizer"], "models.mpt": ["MPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MptConfig"], "models.mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"], "models.mt5": ["MT5Config"], "models.musicgen": [ "MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MusicgenConfig", "MusicgenDecoderConfig", ], "models.mvp": ["MvpConfig", "MvpTokenizer"], "models.nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"], "models.nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"], "models.nllb": [], "models.nllb_moe": ["NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP", "NllbMoeConfig"], "models.nougat": ["NougatProcessor"], "models.nystromformer": [ "NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig", ], "models.oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig", "OneFormerProcessor"], "models.openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig", "OpenAIGPTTokenizer"], "models.opt": ["OPTConfig"], "models.owlvit": [ "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OwlViTConfig", "OwlViTProcessor", "OwlViTTextConfig", "OwlViTVisionConfig", ], "models.pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig", "PegasusTokenizer"], "models.pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"], "models.perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverTokenizer"], "models.persimmon": ["PERSIMMON_PRETRAINED_CONFIG_ARCHIVE_MAP", "PersimmonConfig"], "models.phobert": ["PhobertTokenizer"], "models.pix2struct": [ "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Pix2StructConfig", "Pix2StructProcessor", "Pix2StructTextConfig", "Pix2StructVisionConfig", ], "models.plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"], "models.poolformer": ["POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PoolFormerConfig"], "models.pop2piano": [ "POP2PIANO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Pop2PianoConfig", ], "models.prophetnet": ["PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ProphetNetConfig", "ProphetNetTokenizer"], "models.pvt": ["PVT_PRETRAINED_CONFIG_ARCHIVE_MAP", "PvtConfig"], "models.qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"], "models.rag": ["RagConfig", "RagRetriever", "RagTokenizer"], "models.realm": ["REALM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RealmConfig", "RealmTokenizer"], "models.reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"], "models.regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"], "models.rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig"], "models.resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig"], "models.roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaTokenizer"], "models.roberta_prelayernorm": ["ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaPreLayerNormConfig"], "models.roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig", "RoCBertTokenizer"], "models.roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerTokenizer"], "models.rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig"], "models.sam": [ "SAM_PRETRAINED_CONFIG_ARCHIVE_MAP", "SamConfig", "SamMaskDecoderConfig", "SamProcessor", "SamPromptEncoderConfig", "SamVisionConfig", ], "models.segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig"], "models.sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"], "models.sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"], "models.speech_encoder_decoder": ["SpeechEncoderDecoderConfig"], "models.speech_to_text": [ "SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig", "Speech2TextProcessor", ], "models.speech_to_text_2": [ "SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config", "Speech2Text2Processor", "Speech2Text2Tokenizer", ], "models.speecht5": [ "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP", "SpeechT5Config", "SpeechT5FeatureExtractor", "SpeechT5HifiGanConfig", "SpeechT5Processor", ], "models.splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig", "SplinterTokenizer"], "models.squeezebert": ["SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SqueezeBertConfig", "SqueezeBertTokenizer"], "models.swiftformer": ["SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwiftFormerConfig"], "models.swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig"], "models.swin2sr": ["SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swin2SRConfig"], "models.swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"], "models.switch_transformers": ["SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwitchTransformersConfig"], "models.t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config"], "models.table_transformer": ["TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TableTransformerConfig"], "models.tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig", "TapasTokenizer"], "models.time_series_transformer": [ "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimeSeriesTransformerConfig", ], "models.timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"], "models.timm_backbone": ["TimmBackboneConfig"], "models.transfo_xl": [ "TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig", "TransfoXLCorpus", "TransfoXLTokenizer", ], "models.trocr": [ "TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig", "TrOCRProcessor", ], "models.tvlt": [ "TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP", "TvltConfig", "TvltFeatureExtractor", "TvltProcessor", ], "models.umt5": ["UMT5Config"], "models.unispeech": [ "UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig", ], "models.unispeech_sat": [ "UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig", ], "models.upernet": ["UperNetConfig"], "models.videomae": ["VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "VideoMAEConfig"], "models.vilt": [ "VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig", "ViltFeatureExtractor", "ViltImageProcessor", "ViltProcessor", ], "models.vision_encoder_decoder": ["VisionEncoderDecoderConfig"], "models.vision_text_dual_encoder": ["VisionTextDualEncoderConfig", "VisionTextDualEncoderProcessor"], "models.visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"], "models.vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"], "models.vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"], "models.vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"], "models.vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"], "models.vitdet": ["VITDET_PRETRAINED_CONFIG_ARCHIVE_MAP", "VitDetConfig"], "models.vitmatte": ["VITMATTE_PRETRAINED_CONFIG_ARCHIVE_MAP", "VitMatteConfig"], "models.vits": [ "VITS_PRETRAINED_CONFIG_ARCHIVE_MAP", "VitsConfig", "VitsTokenizer", ], "models.vivit": [ "VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig", ], "models.wav2vec2": [ "WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config", "Wav2Vec2CTCTokenizer", "Wav2Vec2FeatureExtractor", "Wav2Vec2Processor", "Wav2Vec2Tokenizer", ], "models.wav2vec2_conformer": [ "WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2ConformerConfig", ], "models.wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"], "models.wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"], "models.wavlm": [ "WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig", ], "models.whisper": [ "WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperFeatureExtractor", "WhisperProcessor", "WhisperTokenizer", ], "models.x_clip": [ "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "XCLIPConfig", "XCLIPProcessor", "XCLIPTextConfig", "XCLIPVisionConfig", ], "models.xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"], "models.xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMTokenizer"], "models.xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"], "models.xlm_roberta": ["XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaConfig"], "models.xlm_roberta_xl": ["XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaXLConfig"], "models.xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"], "models.xmod": ["XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP", "XmodConfig"], "models.yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig"], "models.yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"], "onnx": [], "pipelines": [ "AudioClassificationPipeline", "AutomaticSpeechRecognitionPipeline", "Conversation", "ConversationalPipeline", "CsvPipelineDataFormat", "DepthEstimationPipeline", "DocumentQuestionAnsweringPipeline", "FeatureExtractionPipeline", "FillMaskPipeline", "ImageClassificationPipeline", "ImageSegmentationPipeline", "ImageToImagePipeline", "ImageToTextPipeline", "JsonPipelineDataFormat", "NerPipeline", "ObjectDetectionPipeline", "PipedPipelineDataFormat", "Pipeline", "PipelineDataFormat", "QuestionAnsweringPipeline", "SummarizationPipeline", "TableQuestionAnsweringPipeline", "Text2TextGenerationPipeline", "TextClassificationPipeline", "TextGenerationPipeline", "TextToAudioPipeline", "TokenClassificationPipeline", "TranslationPipeline", "VideoClassificationPipeline", "VisualQuestionAnsweringPipeline", "ZeroShotAudioClassificationPipeline", "ZeroShotClassificationPipeline", "ZeroShotImageClassificationPipeline", "ZeroShotObjectDetectionPipeline", "pipeline", ], "processing_utils": ["ProcessorMixin"], "testing_utils": [], "tokenization_utils": ["PreTrainedTokenizer"], "tokenization_utils_base": [ "AddedToken", "BatchEncoding", "CharSpan", "PreTrainedTokenizerBase", "SpecialTokensMixin", "TokenSpan", ], "tools": [ "Agent", "AzureOpenAiAgent", "HfAgent", "LocalAgent", "OpenAiAgent", "PipelineTool", "RemoteTool", "Tool", "launch_gradio_demo", "load_tool", ], "trainer_callback": [ "DefaultFlowCallback", "EarlyStoppingCallback", "PrinterCallback", "ProgressCallback", "TrainerCallback", "TrainerControl", "TrainerState", ], "trainer_utils": ["EvalPrediction", "IntervalStrategy", "SchedulerType", "enable_full_determinism", "set_seed"], "training_args": ["TrainingArguments"], "training_args_seq2seq": ["Seq2SeqTrainingArguments"], "training_args_tf": ["TFTrainingArguments"], "utils": [ "CONFIG_NAME", "MODEL_CARD_NAME", "PYTORCH_PRETRAINED_BERT_CACHE", "PYTORCH_TRANSFORMERS_CACHE", "SPIECE_UNDERLINE", "TF2_WEIGHTS_NAME", "TF_WEIGHTS_NAME", "TRANSFORMERS_CACHE", "WEIGHTS_NAME", "TensorType", "add_end_docstrings", "add_start_docstrings", "is_apex_available", "is_bitsandbytes_available", "is_datasets_available", "is_decord_available", "is_faiss_available", "is_flax_available", "is_keras_nlp_available", "is_phonemizer_available", "is_psutil_available", "is_py3nvml_available", "is_pyctcdecode_available", "is_safetensors_available", "is_scipy_available", "is_sentencepiece_available", "is_sklearn_available", "is_speech_available", "is_tensorflow_text_available", "is_tf_available", "is_timm_available", "is_tokenizers_available", "is_torch_available", "is_torch_neuroncore_available", "is_torch_npu_available", "is_torch_tpu_available", "is_torchvision_available", "is_torch_xpu_available", "is_vision_available", "logging", ], "utils.quantization_config": ["BitsAndBytesConfig", "GPTQConfig"], } # 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.ernie_m"].append("ErnieMTokenizer") _import_structure["models.fnet"].append("FNetTokenizer") _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.speech_to_text"].append("Speech2TextTokenizer") _import_structure["models.speecht5"].append("SpeechT5Tokenizer") _import_structure["models.t5"].append("T5Tokenizer") _import_structure["models.xglm"].append("XGLMTokenizer") _import_structure["models.xlm_prophetnet"].append("XLMProphetNetTokenizer") _import_structure["models.xlm_roberta"].append("XLMRobertaTokenizer") _import_structure["models.xlnet"].append("XLNetTokenizer") # tokenizers-backed objects try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_tokenizers_objects _import_structure["utils.dummy_tokenizers_objects"] = [ name for name in dir(dummy_tokenizers_objects) if not name.startswith("_") ] 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.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.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.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.realm"].append("RealmTokenizerFast") _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.splinter"].append("SplinterTokenizerFast") _import_structure["models.squeezebert"].append("SqueezeBertTokenizerFast") _import_structure["models.t5"].append("T5TokenizerFast") _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"] try: if not (is_sentencepiece_available() and is_tokenizers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_sentencepiece_and_tokenizers_objects _import_structure["utils.dummy_sentencepiece_and_tokenizers_objects"] = [ name for name in dir(dummy_sentencepiece_and_tokenizers_objects) if not name.startswith("_") ] else: _import_structure["convert_slow_tokenizer"] = ["SLOW_TO_FAST_CONVERTERS", "convert_slow_tokenizer"] # Speech-specific objects try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_speech_objects _import_structure["utils.dummy_speech_objects"] = [ name for name in dir(dummy_speech_objects) if not name.startswith("_") ] else: _import_structure["models.audio_spectrogram_transformer"].append("ASTFeatureExtractor") _import_structure["models.speech_to_text"].append("Speech2TextFeatureExtractor") # 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(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_vision_objects _import_structure["utils.dummy_vision_objects"] = [ name for name in dir(dummy_vision_objects) if not name.startswith("_") ] else: _import_structure["image_processing_utils"] = ["ImageProcessingMixin"] _import_structure["image_utils"] = ["ImageFeatureExtractionMixin"] _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.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.deta"].append("DetaImageProcessor") _import_structure["models.detr"].extend(["DetrFeatureExtractor", "DetrImageProcessor"]) _import_structure["models.donut"].extend(["DonutFeatureExtractor", "DonutImageProcessor"]) _import_structure["models.dpt"].extend(["DPTFeatureExtractor", "DPTImageProcessor"]) _import_structure["models.efficientformer"].append("EfficientFormerImageProcessor") _import_structure["models.efficientnet"].append("EfficientNetImageProcessor") _import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaImageProcessor", "FlavaProcessor"]) _import_structure["models.glpn"].extend(["GLPNFeatureExtractor", "GLPNImageProcessor"]) _import_structure["models.idefics"].extend(["IdeficsImageProcessor"]) _import_structure["models.imagegpt"].extend(["ImageGPTFeatureExtractor", "ImageGPTImageProcessor"]) _import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"]) _import_structure["models.layoutlmv3"].extend(["LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor"]) _import_structure["models.levit"].extend(["LevitFeatureExtractor", "LevitImageProcessor"]) _import_structure["models.mask2former"].append("Mask2FormerImageProcessor") _import_structure["models.maskformer"].extend(["MaskFormerFeatureExtractor", "MaskFormerImageProcessor"]) _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.owlvit"].extend(["OwlViTFeatureExtractor", "OwlViTImageProcessor"]) _import_structure["models.perceiver"].extend(["PerceiverFeatureExtractor", "PerceiverImageProcessor"]) _import_structure["models.pix2struct"].extend(["Pix2StructImageProcessor"]) _import_structure["models.poolformer"].extend(["PoolFormerFeatureExtractor", "PoolFormerImageProcessor"]) _import_structure["models.pvt"].extend(["PvtImageProcessor"]) _import_structure["models.sam"].extend(["SamImageProcessor"]) _import_structure["models.segformer"].extend(["SegformerFeatureExtractor", "SegformerImageProcessor"]) _import_structure["models.swin2sr"].append("Swin2SRImageProcessor") _import_structure["models.tvlt"].append("TvltImageProcessor") _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.vit_hybrid"].extend(["ViTHybridImageProcessor"]) _import_structure["models.vitmatte"].append("VitMatteImageProcessor") _import_structure["models.vivit"].append("VivitImageProcessor") _import_structure["models.yolos"].extend(["YolosFeatureExtractor", "YolosImageProcessor"]) # PyTorch-backed objects try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_pt_objects _import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")] else: _import_structure["activations"] = [] _import_structure["benchmark.benchmark"] = ["PyTorchBenchmark"] _import_structure["benchmark.benchmark_args"] = ["PyTorchBenchmarkArguments"] _import_structure["data.datasets"] = [ "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "SquadDataset", "SquadDataTrainingArguments", "TextDataset", "TextDatasetForNextSentencePrediction", ] _import_structure["generation"].extend( [ "AlternatingCodebooksLogitsProcessor", "BeamScorer", "BeamSearchScorer", "ClassifierFreeGuidanceLogitsProcessor", "ConstrainedBeamSearchScorer", "Constraint", "ConstraintListState", "DisjunctiveConstraint", "EncoderNoRepeatNGramLogitsProcessor", "EncoderRepetitionPenaltyLogitsProcessor", "EpsilonLogitsWarper", "EtaLogitsWarper", "ExponentialDecayLengthPenalty", "ForcedBOSTokenLogitsProcessor", "ForcedEOSTokenLogitsProcessor", "ForceTokensLogitsProcessor", "GenerationMixin", "HammingDiversityLogitsProcessor", "InfNanRemoveLogitsProcessor", "LogitNormalization", "LogitsProcessor", "LogitsProcessorList", "LogitsWarper", "MaxLengthCriteria", "MaxTimeCriteria", "MinLengthLogitsProcessor", "MinNewTokensLengthLogitsProcessor", "NoBadWordsLogitsProcessor", "NoRepeatNGramLogitsProcessor", "PhrasalConstraint", "PrefixConstrainedLogitsProcessor", "RepetitionPenaltyLogitsProcessor", "SequenceBiasLogitsProcessor", "StoppingCriteria", "StoppingCriteriaList", "SuppressTokensAtBeginLogitsProcessor", "SuppressTokensLogitsProcessor", "TemperatureLogitsWarper", "TopKLogitsWarper", "TopPLogitsWarper", "TypicalLogitsWarper", "UnbatchedClassifierFreeGuidanceLogitsProcessor", "WhisperTimeStampLogitsProcessor", "top_k_top_p_filtering", ] ) _import_structure["generation_utils"] = [] _import_structure["modeling_outputs"] = [] _import_structure["modeling_utils"] = ["PreTrainedModel"] # PyTorch models structure _import_structure["models.albert"].extend( [ "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "AlbertForMaskedLM", "AlbertForMultipleChoice", "AlbertForPreTraining", "AlbertForQuestionAnswering", "AlbertForSequenceClassification", "AlbertForTokenClassification", "AlbertModel", "AlbertPreTrainedModel", "load_tf_weights_in_albert", ] ) _import_structure["models.align"].extend( [ "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST", "AlignModel", "AlignPreTrainedModel", "AlignTextModel", "AlignVisionModel", ] ) _import_structure["models.altclip"].extend( [ "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "AltCLIPModel", "AltCLIPPreTrainedModel", "AltCLIPTextModel", "AltCLIPVisionModel", ] ) _import_structure["models.audio_spectrogram_transformer"].extend( [ "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "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_SEGMENTATION_MAPPING", "MODEL_FOR_IMAGE_TO_IMAGE_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_SPECTROGRAM_MAPPING", "MODEL_FOR_TEXT_TO_WAVEFORM_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", "AutoModelForImageToImage", "AutoModelForInstanceSegmentation", "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( [ "AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "AutoformerForPrediction", "AutoformerModel", "AutoformerPreTrainedModel", ] ) _import_structure["models.bark"].extend( [ "BARK_PRETRAINED_MODEL_ARCHIVE_LIST", "BarkCausalModel", "BarkCoarseModel", "BarkFineModel", "BarkModel", "BarkPreTrainedModel", "BarkSemanticModel", ] ) _import_structure["models.bart"].extend( [ "BART_PRETRAINED_MODEL_ARCHIVE_LIST", "BartForCausalLM", "BartForConditionalGeneration", "BartForQuestionAnswering", "BartForSequenceClassification", "BartModel", "BartPretrainedModel", "BartPreTrainedModel", "PretrainedBartModel", ] ) _import_structure["models.beit"].extend( [ "BEIT_PRETRAINED_MODEL_ARCHIVE_LIST", "BeitForImageClassification", "BeitForMaskedImageModeling", "BeitForSemanticSegmentation", "BeitModel", "BeitPreTrainedModel", ] ) _import_structure["models.bert"].extend( [ "BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "BertForMaskedLM", "BertForMultipleChoice", "BertForNextSentencePrediction", "BertForPreTraining", "BertForQuestionAnswering", "BertForSequenceClassification", "BertForTokenClassification", "BertLayer", "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( [ "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST", "BigBirdForCausalLM", "BigBirdForMaskedLM", "BigBirdForMultipleChoice", "BigBirdForPreTraining", "BigBirdForQuestionAnswering", "BigBirdForSequenceClassification", "BigBirdForTokenClassification", "BigBirdLayer", "BigBirdModel", "BigBirdPreTrainedModel", "load_tf_weights_in_big_bird", ] ) _import_structure["models.bigbird_pegasus"].extend( [ "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST", "BigBirdPegasusForCausalLM", "BigBirdPegasusForConditionalGeneration", "BigBirdPegasusForQuestionAnswering", "BigBirdPegasusForSequenceClassification", "BigBirdPegasusModel", "BigBirdPegasusPreTrainedModel", ] ) _import_structure["models.biogpt"].extend( [ "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST", "BioGptForCausalLM", "BioGptForSequenceClassification", "BioGptForTokenClassification", "BioGptModel", "BioGptPreTrainedModel", ] ) _import_structure["models.bit"].extend( [ "BIT_PRETRAINED_MODEL_ARCHIVE_LIST", "BitBackbone", "BitForImageClassification", "BitModel", "BitPreTrainedModel", ] ) _import_structure["models.blenderbot"].extend( [ "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST", "BlenderbotForCausalLM", "BlenderbotForConditionalGeneration", "BlenderbotModel", "BlenderbotPreTrainedModel", ] ) _import_structure["models.blenderbot_small"].extend( [ "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST", "BlenderbotSmallForCausalLM", "BlenderbotSmallForConditionalGeneration", "BlenderbotSmallModel", "BlenderbotSmallPreTrainedModel", ] ) _import_structure["models.blip"].extend( [ "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "BlipForConditionalGeneration", "BlipForImageTextRetrieval", "BlipForQuestionAnswering", "BlipModel", "BlipPreTrainedModel", "BlipTextModel", "BlipVisionModel", ] ) _import_structure["models.blip_2"].extend( [ "BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST", "Blip2ForConditionalGeneration", "Blip2Model", "Blip2PreTrainedModel", "Blip2QFormerModel", "Blip2VisionModel", ] ) _import_structure["models.bloom"].extend( [ "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST", "BloomForCausalLM", "BloomForQuestionAnswering", "BloomForSequenceClassification", "BloomForTokenClassification", "BloomModel", "BloomPreTrainedModel", ] ) _import_structure["models.bridgetower"].extend( [ "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST", "BridgeTowerForContrastiveLearning", "BridgeTowerForImageAndTextRetrieval", "BridgeTowerForMaskedLM", "BridgeTowerModel", "BridgeTowerPreTrainedModel", ] ) _import_structure["models.bros"].extend( [ "BROS_PRETRAINED_MODEL_ARCHIVE_LIST", "BrosForTokenClassification", "BrosModel", "BrosPreTrainedModel", "BrosProcessor", "BrosSpadeEEForTokenClassification", "BrosSpadeELForTokenClassification", ] ) _import_structure["models.camembert"].extend( [ "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "CamembertForCausalLM", "CamembertForMaskedLM", "CamembertForMultipleChoice", "CamembertForQuestionAnswering", "CamembertForSequenceClassification", "CamembertForTokenClassification", "CamembertModel", "CamembertPreTrainedModel", ] ) _import_structure["models.canine"].extend( [ "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST", "CanineForMultipleChoice", "CanineForQuestionAnswering", "CanineForSequenceClassification", "CanineForTokenClassification", "CanineLayer", "CanineModel", "CaninePreTrainedModel", "load_tf_weights_in_canine", ] ) _import_structure["models.chinese_clip"].extend( [ "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "ChineseCLIPModel", "ChineseCLIPPreTrainedModel", "ChineseCLIPTextModel", "ChineseCLIPVisionModel", ] ) _import_structure["models.clap"].extend( [ "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST", "ClapAudioModel", "ClapAudioModelWithProjection", "ClapFeatureExtractor", "ClapModel", "ClapPreTrainedModel", "ClapTextModel", "ClapTextModelWithProjection", ] ) _import_structure["models.clip"].extend( [ "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "CLIPModel", "CLIPPreTrainedModel", "CLIPTextModel", "CLIPTextModelWithProjection", "CLIPVisionModel", "CLIPVisionModelWithProjection", ] ) _import_structure["models.clipseg"].extend( [ "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST", "CLIPSegForImageSegmentation", "CLIPSegModel", "CLIPSegPreTrainedModel", "CLIPSegTextModel", "CLIPSegVisionModel", ] ) _import_structure["models.codegen"].extend( [ "CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST", "CodeGenForCausalLM", "CodeGenModel", "CodeGenPreTrainedModel", ] ) _import_structure["models.conditional_detr"].extend( [ "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "ConditionalDetrForObjectDetection", "ConditionalDetrForSegmentation", "ConditionalDetrModel", "ConditionalDetrPreTrainedModel", ] ) _import_structure["models.convbert"].extend( [ "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "ConvBertForMaskedLM", "ConvBertForMultipleChoice", "ConvBertForQuestionAnswering", "ConvBertForSequenceClassification", "ConvBertForTokenClassification", "ConvBertLayer", "ConvBertModel", "ConvBertPreTrainedModel", "load_tf_weights_in_convbert", ] ) _import_structure["models.convnext"].extend( [ "CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "ConvNextBackbone", "ConvNextForImageClassification", "ConvNextModel", "ConvNextPreTrainedModel", ] ) _import_structure["models.convnextv2"].extend( [ "CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST", "ConvNextV2Backbone", "ConvNextV2ForImageClassification", "ConvNextV2Model", "ConvNextV2PreTrainedModel", ] ) _import_structure["models.cpmant"].extend( [ "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST", "CpmAntForCausalLM", "CpmAntModel", "CpmAntPreTrainedModel", ] ) _import_structure["models.ctrl"].extend( [ "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST", "CTRLForSequenceClassification", "CTRLLMHeadModel", "CTRLModel", "CTRLPreTrainedModel", ] ) _import_structure["models.cvt"].extend( [ "CVT_PRETRAINED_MODEL_ARCHIVE_LIST", "CvtForImageClassification", "CvtModel", "CvtPreTrainedModel", ] ) _import_structure["models.data2vec"].extend( [ "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST", "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST", "Data2VecAudioForAudioFrameClassification", "Data2VecAudioForCTC", "Data2VecAudioForSequenceClassification", "Data2VecAudioForXVector", "Data2VecAudioModel", "Data2VecAudioPreTrainedModel", "Data2VecTextForCausalLM", "Data2VecTextForMaskedLM", "Data2VecTextForMultipleChoice", "Data2VecTextForQuestionAnswering", "Data2VecTextForSequenceClassification", "Data2VecTextForTokenClassification", "Data2VecTextModel", "Data2VecTextPreTrainedModel", "Data2VecVisionForImageClassification", "Data2VecVisionForSemanticSegmentation", "Data2VecVisionModel", "Data2VecVisionPreTrainedModel", ] ) _import_structure["models.deberta"].extend( [ "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "DebertaForMaskedLM", "DebertaForQuestionAnswering", "DebertaForSequenceClassification", "DebertaForTokenClassification", "DebertaModel", "DebertaPreTrainedModel", ] ) _import_structure["models.deberta_v2"].extend( [ "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST", "DebertaV2ForMaskedLM", "DebertaV2ForMultipleChoice", "DebertaV2ForQuestionAnswering", "DebertaV2ForSequenceClassification", "DebertaV2ForTokenClassification", "DebertaV2Model", "DebertaV2PreTrainedModel", ] ) _import_structure["models.decision_transformer"].extend( [ "DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "DecisionTransformerGPT2Model", "DecisionTransformerGPT2PreTrainedModel", "DecisionTransformerModel", "DecisionTransformerPreTrainedModel", ] ) _import_structure["models.deformable_detr"].extend( [ "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DeformableDetrForObjectDetection", "DeformableDetrModel", "DeformableDetrPreTrainedModel", ] ) _import_structure["models.deit"].extend( [ "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST", "DeiTForImageClassification", "DeiTForImageClassificationWithTeacher", "DeiTForMaskedImageModeling", "DeiTModel", "DeiTPreTrainedModel", ] ) _import_structure["models.deprecated.mctct"].extend( [ "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST", "MCTCTForCTC", "MCTCTModel", "MCTCTPreTrainedModel", ] ) _import_structure["models.deprecated.mmbt"].extend(["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]) _import_structure["models.deprecated.open_llama"].extend( ["OpenLlamaForCausalLM", "OpenLlamaForSequenceClassification", "OpenLlamaModel", "OpenLlamaPreTrainedModel"] ) _import_structure["models.deprecated.retribert"].extend( ["RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RetriBertModel", "RetriBertPreTrainedModel"] ) _import_structure["models.deprecated.trajectory_transformer"].extend( [ "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TrajectoryTransformerModel", "TrajectoryTransformerPreTrainedModel", ] ) _import_structure["models.deprecated.van"].extend( [ "VAN_PRETRAINED_MODEL_ARCHIVE_LIST", "VanForImageClassification", "VanModel", "VanPreTrainedModel", ] ) _import_structure["models.deta"].extend( [ "DETA_PRETRAINED_MODEL_ARCHIVE_LIST", "DetaForObjectDetection", "DetaModel", "DetaPreTrainedModel", ] ) _import_structure["models.detr"].extend( [ "DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DetrForObjectDetection", "DetrForSegmentation", "DetrModel", "DetrPreTrainedModel", ] ) _import_structure["models.dinat"].extend( [ "DINAT_PRETRAINED_MODEL_ARCHIVE_LIST", "DinatBackbone", "DinatForImageClassification", "DinatModel", "DinatPreTrainedModel", ] ) _import_structure["models.dinov2"].extend( [ "DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST", "Dinov2Backbone", "Dinov2ForImageClassification", "Dinov2Model", "Dinov2PreTrainedModel", ] ) _import_structure["models.distilbert"].extend( [ "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "DistilBertForMaskedLM", "DistilBertForMultipleChoice", "DistilBertForQuestionAnswering", "DistilBertForSequenceClassification", "DistilBertForTokenClassification", "DistilBertModel", "DistilBertPreTrainedModel", ] ) _import_structure["models.donut"].extend( [ "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "DonutSwinModel", "DonutSwinPreTrainedModel", ] ) _import_structure["models.dpr"].extend( [ "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPRContextEncoder", "DPRPretrainedContextEncoder", "DPRPreTrainedModel", "DPRPretrainedQuestionEncoder", "DPRPretrainedReader", "DPRQuestionEncoder", "DPRReader", ] ) _import_structure["models.dpt"].extend( [ "DPT_PRETRAINED_MODEL_ARCHIVE_LIST", "DPTForDepthEstimation", "DPTForSemanticSegmentation", "DPTModel", "DPTPreTrainedModel", ] ) _import_structure["models.efficientformer"].extend( [ "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "EfficientFormerForImageClassification", "EfficientFormerForImageClassificationWithTeacher", "EfficientFormerModel", "EfficientFormerPreTrainedModel", ] ) _import_structure["models.efficientnet"].extend( [ "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST", "EfficientNetForImageClassification", "EfficientNetModel", "EfficientNetPreTrainedModel", ] ) _import_structure["models.electra"].extend( [ "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", "ElectraForCausalLM", "ElectraForMaskedLM", "ElectraForMultipleChoice", "ElectraForPreTraining", "ElectraForQuestionAnswering", "ElectraForSequenceClassification", "ElectraForTokenClassification", "ElectraModel", "ElectraPreTrainedModel", "load_tf_weights_in_electra", ] ) _import_structure["models.encodec"].extend( [ "ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST", "EncodecModel", "EncodecPreTrainedModel", ] ) _import_structure["models.encoder_decoder"].append("EncoderDecoderModel") _import_structure["models.ernie"].extend( [ "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST", "ErnieForCausalLM", "ErnieForMaskedLM", "ErnieForMultipleChoice", "ErnieForNextSentencePrediction", "ErnieForPreTraining", "ErnieForQuestionAnswering", "ErnieForSequenceClassification", "ErnieForTokenClassification", "ErnieModel", "ErniePreTrainedModel", ] ) _import_structure["models.ernie_m"].extend( [ "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST", "ErnieMForInformationExtraction", "ErnieMForMultipleChoice", "ErnieMForQuestionAnswering", "ErnieMForSequenceClassification", "ErnieMForTokenClassification", "ErnieMModel", "ErnieMPreTrainedModel", ] ) _import_structure["models.esm"].extend( [ "ESM_PRETRAINED_MODEL_ARCHIVE_LIST", "EsmFoldPreTrainedModel", "EsmForMaskedLM", "EsmForProteinFolding", "EsmForSequenceClassification", "EsmForTokenClassification", "EsmModel", "EsmPreTrainedModel", ] ) _import_structure["models.falcon"].extend( [ "FALCON_PRETRAINED_MODEL_ARCHIVE_LIST", "FalconForCausalLM", "FalconForQuestionAnswering", "FalconForSequenceClassification", "FalconForTokenClassification", "FalconModel", "FalconPreTrainedModel", ] ) _import_structure["models.flaubert"].extend( [ "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "FlaubertForMultipleChoice", "FlaubertForQuestionAnswering", "FlaubertForQuestionAnsweringSimple", "FlaubertForSequenceClassification", "FlaubertForTokenClassification", "FlaubertModel", "FlaubertPreTrainedModel", "FlaubertWithLMHeadModel", ] ) _import_structure["models.flava"].extend( [ "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST", "FlavaForPreTraining", "FlavaImageCodebook", "FlavaImageModel", "FlavaModel", "FlavaMultimodalModel", "FlavaPreTrainedModel", "FlavaTextModel", ] ) _import_structure["models.fnet"].extend( [ "FNET_PRETRAINED_MODEL_ARCHIVE_LIST", "FNetForMaskedLM", "FNetForMultipleChoice", "FNetForNextSentencePrediction", "FNetForPreTraining", "FNetForQuestionAnswering", "FNetForSequenceClassification", "FNetForTokenClassification", "FNetLayer", "FNetModel", "FNetPreTrainedModel", ] ) _import_structure["models.focalnet"].extend( [ "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST", "FocalNetBackbone", "FocalNetForImageClassification", "FocalNetForMaskedImageModeling", "FocalNetModel", "FocalNetPreTrainedModel", ] ) _import_structure["models.fsmt"].extend(["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]) _import_structure["models.funnel"].extend( [ "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST", "FunnelBaseModel", "FunnelForMaskedLM", "FunnelForMultipleChoice", "FunnelForPreTraining", "FunnelForQuestionAnswering", "FunnelForSequenceClassification", "FunnelForTokenClassification", "FunnelModel", "FunnelPreTrainedModel", "load_tf_weights_in_funnel", ] ) _import_structure["models.git"].extend( [ "GIT_PRETRAINED_MODEL_ARCHIVE_LIST", "GitForCausalLM", "GitModel", "GitPreTrainedModel", "GitVisionModel", ] ) _import_structure["models.glpn"].extend( [ "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST", "GLPNForDepthEstimation", "GLPNModel", "GLPNPreTrainedModel", ] ) _import_structure["models.gpt2"].extend( [ "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST", "GPT2DoubleHeadsModel", "GPT2ForQuestionAnswering", "GPT2ForSequenceClassification", "GPT2ForTokenClassification", "GPT2LMHeadModel", "GPT2Model", "GPT2PreTrainedModel", "load_tf_weights_in_gpt2", ] ) _import_structure["models.gpt_bigcode"].extend( [ "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTBigCodeForCausalLM", "GPTBigCodeForSequenceClassification", "GPTBigCodeForTokenClassification", "GPTBigCodeModel", "GPTBigCodePreTrainedModel", ] ) _import_structure["models.gpt_neo"].extend( [ "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoForCausalLM", "GPTNeoForQuestionAnswering", "GPTNeoForSequenceClassification", "GPTNeoForTokenClassification", "GPTNeoModel", "GPTNeoPreTrainedModel", "load_tf_weights_in_gpt_neo", ] ) _import_structure["models.gpt_neox"].extend( [ "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoXForCausalLM", "GPTNeoXForQuestionAnswering", "GPTNeoXForSequenceClassification", "GPTNeoXForTokenClassification", "GPTNeoXLayer", "GPTNeoXModel", "GPTNeoXPreTrainedModel", ] ) _import_structure["models.gpt_neox_japanese"].extend( [ "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoXJapaneseForCausalLM", "GPTNeoXJapaneseLayer", "GPTNeoXJapaneseModel", "GPTNeoXJapanesePreTrainedModel", ] ) _import_structure["models.gptj"].extend( [ "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTJForCausalLM", "GPTJForQuestionAnswering", "GPTJForSequenceClassification", "GPTJModel", "GPTJPreTrainedModel", ] ) _import_structure["models.gptsan_japanese"].extend( [ "GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTSanJapaneseForConditionalGeneration", "GPTSanJapaneseModel", "GPTSanJapanesePreTrainedModel", ] ) _import_structure["models.graphormer"].extend( [ "GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "GraphormerForGraphClassification", "GraphormerModel", "GraphormerPreTrainedModel", ] ) _import_structure["models.groupvit"].extend( [ "GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "GroupViTModel", "GroupViTPreTrainedModel", "GroupViTTextModel", "GroupViTVisionModel", ] ) _import_structure["models.hubert"].extend( [ "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "HubertForCTC", "HubertForSequenceClassification", "HubertModel", "HubertPreTrainedModel", ] ) _import_structure["models.ibert"].extend( [ "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "IBertForMaskedLM", "IBertForMultipleChoice", "IBertForQuestionAnswering", "IBertForSequenceClassification", "IBertForTokenClassification", "IBertModel", "IBertPreTrainedModel", ] ) _import_structure["models.idefics"].extend( [ "IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST", "IdeficsForVisionText2Text", "IdeficsModel", "IdeficsPreTrainedModel", "IdeficsProcessor", ] ) _import_structure["models.imagegpt"].extend( [ "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST", "ImageGPTForCausalImageModeling", "ImageGPTForImageClassification", "ImageGPTModel", "ImageGPTPreTrainedModel", "load_tf_weights_in_imagegpt", ] ) _import_structure["models.informer"].extend( [ "INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "InformerForPrediction", "InformerModel", "InformerPreTrainedModel", ] ) _import_structure["models.instructblip"].extend( [ "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "InstructBlipForConditionalGeneration", "InstructBlipPreTrainedModel", "InstructBlipQFormerModel", "InstructBlipVisionModel", ] ) _import_structure["models.jukebox"].extend( [ "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST", "JukeboxModel", "JukeboxPreTrainedModel", "JukeboxPrior", "JukeboxVQVAE", ] ) _import_structure["models.layoutlm"].extend( [ "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMForMaskedLM", "LayoutLMForQuestionAnswering", "LayoutLMForSequenceClassification", "LayoutLMForTokenClassification", "LayoutLMModel", "LayoutLMPreTrainedModel", ] ) _import_structure["models.layoutlmv2"].extend( [ "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMv2ForQuestionAnswering", "LayoutLMv2ForSequenceClassification", "LayoutLMv2ForTokenClassification", "LayoutLMv2Model", "LayoutLMv2PreTrainedModel", ] ) _import_structure["models.layoutlmv3"].extend( [ "LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMv3ForQuestionAnswering", "LayoutLMv3ForSequenceClassification", "LayoutLMv3ForTokenClassification", "LayoutLMv3Model", "LayoutLMv3PreTrainedModel", ] ) _import_structure["models.led"].extend( [ "LED_PRETRAINED_MODEL_ARCHIVE_LIST", "LEDForConditionalGeneration", "LEDForQuestionAnswering", "LEDForSequenceClassification", "LEDModel", "LEDPreTrainedModel", ] ) _import_structure["models.levit"].extend( [ "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "LevitForImageClassification", "LevitForImageClassificationWithTeacher", "LevitModel", "LevitPreTrainedModel", ] ) _import_structure["models.lilt"].extend( [ "LILT_PRETRAINED_MODEL_ARCHIVE_LIST", "LiltForQuestionAnswering", "LiltForSequenceClassification", "LiltForTokenClassification", "LiltModel", "LiltPreTrainedModel", ] ) _import_structure["models.llama"].extend( ["LlamaForCausalLM", "LlamaForSequenceClassification", "LlamaModel", "LlamaPreTrainedModel"] ) _import_structure["models.longformer"].extend( [ "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "LongformerForMaskedLM", "LongformerForMultipleChoice", "LongformerForQuestionAnswering", "LongformerForSequenceClassification", "LongformerForTokenClassification", "LongformerModel", "LongformerPreTrainedModel", "LongformerSelfAttention", ] ) _import_structure["models.longt5"].extend( [ "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST", "LongT5EncoderModel", "LongT5ForConditionalGeneration", "LongT5Model", "LongT5PreTrainedModel", ] ) _import_structure["models.luke"].extend( [ "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST", "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", "LukeForMaskedLM", "LukeForMultipleChoice", "LukeForQuestionAnswering", "LukeForSequenceClassification", "LukeForTokenClassification", "LukeModel", "LukePreTrainedModel", ] ) _import_structure["models.lxmert"].extend( [ "LxmertEncoder", "LxmertForPreTraining", "LxmertForQuestionAnswering", "LxmertModel", "LxmertPreTrainedModel", "LxmertVisualFeatureEncoder", "LxmertXLayer", ] ) _import_structure["models.m2m_100"].extend( [ "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST", "M2M100ForConditionalGeneration", "M2M100Model", "M2M100PreTrainedModel", ] ) _import_structure["models.marian"].extend(["MarianForCausalLM", "MarianModel", "MarianMTModel"]) _import_structure["models.markuplm"].extend( [ "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST", "MarkupLMForQuestionAnswering", "MarkupLMForSequenceClassification", "MarkupLMForTokenClassification", "MarkupLMModel", "MarkupLMPreTrainedModel", ] ) _import_structure["models.mask2former"].extend( [ "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "Mask2FormerForUniversalSegmentation", "Mask2FormerModel", "Mask2FormerPreTrainedModel", ] ) _import_structure["models.maskformer"].extend( [ "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "MaskFormerForInstanceSegmentation", "MaskFormerModel", "MaskFormerPreTrainedModel", "MaskFormerSwinBackbone", ] ) _import_structure["models.mbart"].extend( [ "MBartForCausalLM", "MBartForConditionalGeneration", "MBartForQuestionAnswering", "MBartForSequenceClassification", "MBartModel", "MBartPreTrainedModel", ] ) _import_structure["models.mega"].extend( [ "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST", "MegaForCausalLM", "MegaForMaskedLM", "MegaForMultipleChoice", "MegaForQuestionAnswering", "MegaForSequenceClassification", "MegaForTokenClassification", "MegaModel", "MegaPreTrainedModel", ] ) _import_structure["models.megatron_bert"].extend( [ "MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "MegatronBertForCausalLM", "MegatronBertForMaskedLM", "MegatronBertForMultipleChoice", "MegatronBertForNextSentencePrediction", "MegatronBertForPreTraining", "MegatronBertForQuestionAnswering", "MegatronBertForSequenceClassification", "MegatronBertForTokenClassification", "MegatronBertModel", "MegatronBertPreTrainedModel", ] ) _import_structure["models.mgp_str"].extend( [ "MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST", "MgpstrForSceneTextRecognition", "MgpstrModel", "MgpstrPreTrainedModel", ] ) _import_structure["models.mistral"].extend( ["MistralForCausalLM", "MistralForSequenceClassification", "MistralModel", "MistralPreTrainedModel"] ) _import_structure["models.mobilebert"].extend( [ "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileBertForMaskedLM", "MobileBertForMultipleChoice", "MobileBertForNextSentencePrediction", "MobileBertForPreTraining", "MobileBertForQuestionAnswering", "MobileBertForSequenceClassification", "MobileBertForTokenClassification", "MobileBertLayer", "MobileBertModel", "MobileBertPreTrainedModel", "load_tf_weights_in_mobilebert", ] ) _import_structure["models.mobilenet_v1"].extend( [ "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileNetV1ForImageClassification", "MobileNetV1Model", "MobileNetV1PreTrainedModel", "load_tf_weights_in_mobilenet_v1", ] ) _import_structure["models.mobilenet_v2"].extend( [ "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileNetV2ForImageClassification", "MobileNetV2ForSemanticSegmentation", "MobileNetV2Model", "MobileNetV2PreTrainedModel", "load_tf_weights_in_mobilenet_v2", ] ) _import_structure["models.mobilevit"].extend( [ "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileViTForImageClassification", "MobileViTForSemanticSegmentation", "MobileViTModel", "MobileViTPreTrainedModel", ] ) _import_structure["models.mobilevitv2"].extend( [ "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileViTV2ForImageClassification", "MobileViTV2ForSemanticSegmentation", "MobileViTV2Model", "MobileViTV2PreTrainedModel", ] ) _import_structure["models.mpnet"].extend( [ "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST", "MPNetForMaskedLM", "MPNetForMultipleChoice", "MPNetForQuestionAnswering", "MPNetForSequenceClassification", "MPNetForTokenClassification", "MPNetLayer", "MPNetModel", "MPNetPreTrainedModel", ] ) _import_structure["models.mpt"].extend( [ "MPT_PRETRAINED_MODEL_ARCHIVE_LIST", "MptForCausalLM", "MptForQuestionAnswering", "MptForSequenceClassification", "MptForTokenClassification", "MptModel", "MptPreTrainedModel", ] ) _import_structure["models.mra"].extend( [ "MRA_PRETRAINED_MODEL_ARCHIVE_LIST", "MraForMaskedLM", "MraForMultipleChoice", "MraForQuestionAnswering", "MraForSequenceClassification", "MraForTokenClassification", "MraModel", "MraPreTrainedModel", ] ) _import_structure["models.mt5"].extend( [ "MT5EncoderModel", "MT5ForConditionalGeneration", "MT5ForQuestionAnswering", "MT5ForSequenceClassification", "MT5Model", "MT5PreTrainedModel", ] ) _import_structure["models.musicgen"].extend( [ "MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST", "MusicgenForCausalLM", "MusicgenForConditionalGeneration", "MusicgenModel", "MusicgenPreTrainedModel", "MusicgenProcessor", ] ) _import_structure["models.mvp"].extend( [ "MVP_PRETRAINED_MODEL_ARCHIVE_LIST", "MvpForCausalLM", "MvpForConditionalGeneration", "MvpForQuestionAnswering", "MvpForSequenceClassification", "MvpModel", "MvpPreTrainedModel", ] ) _import_structure["models.nat"].extend( [ "NAT_PRETRAINED_MODEL_ARCHIVE_LIST", "NatBackbone", "NatForImageClassification", "NatModel", "NatPreTrainedModel", ] ) _import_structure["models.nezha"].extend( [ "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST", "NezhaForMaskedLM", "NezhaForMultipleChoice", "NezhaForNextSentencePrediction", "NezhaForPreTraining", "NezhaForQuestionAnswering", "NezhaForSequenceClassification", "NezhaForTokenClassification", "NezhaModel", "NezhaPreTrainedModel", ] ) _import_structure["models.nllb_moe"].extend( [ "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST", "NllbMoeForConditionalGeneration", "NllbMoeModel", "NllbMoePreTrainedModel", "NllbMoeSparseMLP", "NllbMoeTop2Router", ] ) _import_structure["models.nystromformer"].extend( [ "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "NystromformerForMaskedLM", "NystromformerForMultipleChoice", "NystromformerForQuestionAnswering", "NystromformerForSequenceClassification", "NystromformerForTokenClassification", "NystromformerLayer", "NystromformerModel", "NystromformerPreTrainedModel", ] ) _import_structure["models.oneformer"].extend( [ "ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "OneFormerForUniversalSegmentation", "OneFormerModel", "OneFormerPreTrainedModel", ] ) _import_structure["models.openai"].extend( [ "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST", "OpenAIGPTDoubleHeadsModel", "OpenAIGPTForSequenceClassification", "OpenAIGPTLMHeadModel", "OpenAIGPTModel", "OpenAIGPTPreTrainedModel", "load_tf_weights_in_openai_gpt", ] ) _import_structure["models.opt"].extend( [ "OPT_PRETRAINED_MODEL_ARCHIVE_LIST", "OPTForCausalLM", "OPTForQuestionAnswering", "OPTForSequenceClassification", "OPTModel", "OPTPreTrainedModel", ] ) _import_structure["models.owlvit"].extend( [ "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "OwlViTForObjectDetection", "OwlViTModel", "OwlViTPreTrainedModel", "OwlViTTextModel", "OwlViTVisionModel", ] ) _import_structure["models.pegasus"].extend( ["PegasusForCausalLM", "PegasusForConditionalGeneration", "PegasusModel", "PegasusPreTrainedModel"] ) _import_structure["models.pegasus_x"].extend( [ "PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST", "PegasusXForConditionalGeneration", "PegasusXModel", "PegasusXPreTrainedModel", ] ) _import_structure["models.perceiver"].extend( [ "PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST", "PerceiverForImageClassificationConvProcessing", "PerceiverForImageClassificationFourier", "PerceiverForImageClassificationLearned", "PerceiverForMaskedLM", "PerceiverForMultimodalAutoencoding", "PerceiverForOpticalFlow", "PerceiverForSequenceClassification", "PerceiverLayer", "PerceiverModel", "PerceiverPreTrainedModel", ] ) _import_structure["models.persimmon"].extend( ["PersimmonForCausalLM", "PersimmonForSequenceClassification", "PersimmonModel", "PersimmonPreTrainedModel"] ) _import_structure["models.pix2struct"].extend( [ "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST", "Pix2StructForConditionalGeneration", "Pix2StructPreTrainedModel", "Pix2StructTextModel", "Pix2StructVisionModel", ] ) _import_structure["models.plbart"].extend( [ "PLBART_PRETRAINED_MODEL_ARCHIVE_LIST", "PLBartForCausalLM", "PLBartForConditionalGeneration", "PLBartForSequenceClassification", "PLBartModel", "PLBartPreTrainedModel", ] ) _import_structure["models.poolformer"].extend( [ "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "PoolFormerForImageClassification", "PoolFormerModel", "PoolFormerPreTrainedModel", ] ) _import_structure["models.pop2piano"].extend( [ "POP2PIANO_PRETRAINED_MODEL_ARCHIVE_LIST", "Pop2PianoForConditionalGeneration", "Pop2PianoPreTrainedModel", ] ) _import_structure["models.prophetnet"].extend( [ "PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST", "ProphetNetDecoder", "ProphetNetEncoder", "ProphetNetForCausalLM", "ProphetNetForConditionalGeneration", "ProphetNetModel", "ProphetNetPreTrainedModel", ] ) _import_structure["models.pvt"].extend( [ "PVT_PRETRAINED_MODEL_ARCHIVE_LIST", "PvtForImageClassification", "PvtModel", "PvtPreTrainedModel", ] ) _import_structure["models.qdqbert"].extend( [ "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "QDQBertForMaskedLM", "QDQBertForMultipleChoice", "QDQBertForNextSentencePrediction", "QDQBertForQuestionAnswering", "QDQBertForSequenceClassification", "QDQBertForTokenClassification", "QDQBertLayer", "QDQBertLMHeadModel", "QDQBertModel", "QDQBertPreTrainedModel", "load_tf_weights_in_qdqbert", ] ) _import_structure["models.rag"].extend( ["RagModel", "RagPreTrainedModel", "RagSequenceForGeneration", "RagTokenForGeneration"] ) _import_structure["models.realm"].extend( [ "REALM_PRETRAINED_MODEL_ARCHIVE_LIST", "RealmEmbedder", "RealmForOpenQA", "RealmKnowledgeAugEncoder", "RealmPreTrainedModel", "RealmReader", "RealmRetriever", "RealmScorer", "load_tf_weights_in_realm", ] ) _import_structure["models.reformer"].extend( [ "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "ReformerAttention", "ReformerForMaskedLM", "ReformerForQuestionAnswering", "ReformerForSequenceClassification", "ReformerLayer", "ReformerModel", "ReformerModelWithLMHead", "ReformerPreTrainedModel", ] ) _import_structure["models.regnet"].extend( [ "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST", "RegNetForImageClassification", "RegNetModel", "RegNetPreTrainedModel", ] ) _import_structure["models.rembert"].extend( [ "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RemBertForCausalLM", "RemBertForMaskedLM", "RemBertForMultipleChoice", "RemBertForQuestionAnswering", "RemBertForSequenceClassification", "RemBertForTokenClassification", "RemBertLayer", "RemBertModel", "RemBertPreTrainedModel", "load_tf_weights_in_rembert", ] ) _import_structure["models.resnet"].extend( [ "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", "ResNetBackbone", "ResNetForImageClassification", "ResNetModel", "ResNetPreTrainedModel", ] ) _import_structure["models.roberta"].extend( [ "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "RobertaForCausalLM", "RobertaForMaskedLM", "RobertaForMultipleChoice", "RobertaForQuestionAnswering", "RobertaForSequenceClassification", "RobertaForTokenClassification", "RobertaModel", "RobertaPreTrainedModel", ] ) _import_structure["models.roberta_prelayernorm"].extend( [ "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "RobertaPreLayerNormForCausalLM", "RobertaPreLayerNormForMaskedLM", "RobertaPreLayerNormForMultipleChoice", "RobertaPreLayerNormForQuestionAnswering", "RobertaPreLayerNormForSequenceClassification", "RobertaPreLayerNormForTokenClassification", "RobertaPreLayerNormModel", "RobertaPreLayerNormPreTrainedModel", ] ) _import_structure["models.roc_bert"].extend( [ "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RoCBertForCausalLM", "RoCBertForMaskedLM", "RoCBertForMultipleChoice", "RoCBertForPreTraining", "RoCBertForQuestionAnswering", "RoCBertForSequenceClassification", "RoCBertForTokenClassification", "RoCBertLayer", "RoCBertModel", "RoCBertPreTrainedModel", "load_tf_weights_in_roc_bert", ] ) _import_structure["models.roformer"].extend( [ "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "RoFormerForCausalLM", "RoFormerForMaskedLM", "RoFormerForMultipleChoice", "RoFormerForQuestionAnswering", "RoFormerForSequenceClassification", "RoFormerForTokenClassification", "RoFormerLayer", "RoFormerModel", "RoFormerPreTrainedModel", "load_tf_weights_in_roformer", ] ) _import_structure["models.rwkv"].extend( [ "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST", "RwkvForCausalLM", "RwkvModel", "RwkvPreTrainedModel", ] ) _import_structure["models.sam"].extend( [ "SAM_PRETRAINED_MODEL_ARCHIVE_LIST", "SamModel", "SamPreTrainedModel", ] ) _import_structure["models.segformer"].extend( [ "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "SegformerDecodeHead", "SegformerForImageClassification", "SegformerForSemanticSegmentation", "SegformerLayer", "SegformerModel", "SegformerPreTrainedModel", ] ) _import_structure["models.sew"].extend( [ "SEW_PRETRAINED_MODEL_ARCHIVE_LIST", "SEWForCTC", "SEWForSequenceClassification", "SEWModel", "SEWPreTrainedModel", ] ) _import_structure["models.sew_d"].extend( [ "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST", "SEWDForCTC", "SEWDForSequenceClassification", "SEWDModel", "SEWDPreTrainedModel", ] ) _import_structure["models.speech_encoder_decoder"].extend(["SpeechEncoderDecoderModel"]) _import_structure["models.speech_to_text"].extend( [ "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "Speech2TextForConditionalGeneration", "Speech2TextModel", "Speech2TextPreTrainedModel", ] ) _import_structure["models.speech_to_text_2"].extend(["Speech2Text2ForCausalLM", "Speech2Text2PreTrainedModel"]) _import_structure["models.speecht5"].extend( [ "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST", "SpeechT5ForSpeechToSpeech", "SpeechT5ForSpeechToText", "SpeechT5ForTextToSpeech", "SpeechT5HifiGan", "SpeechT5Model", "SpeechT5PreTrainedModel", ] ) _import_structure["models.splinter"].extend( [ "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST", "SplinterForPreTraining", "SplinterForQuestionAnswering", "SplinterLayer", "SplinterModel", "SplinterPreTrainedModel", ] ) _import_structure["models.squeezebert"].extend( [ "SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "SqueezeBertForMaskedLM", "SqueezeBertForMultipleChoice", "SqueezeBertForQuestionAnswering", "SqueezeBertForSequenceClassification", "SqueezeBertForTokenClassification", "SqueezeBertModel", "SqueezeBertModule", "SqueezeBertPreTrainedModel", ] ) _import_structure["models.swiftformer"].extend( [ "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "SwiftFormerForImageClassification", "SwiftFormerModel", "SwiftFormerPreTrainedModel", ] ) _import_structure["models.swin"].extend( [ "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "SwinBackbone", "SwinForImageClassification", "SwinForMaskedImageModeling", "SwinModel", "SwinPreTrainedModel", ] ) _import_structure["models.swin2sr"].extend( [ "SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST", "Swin2SRForImageSuperResolution", "Swin2SRModel", "Swin2SRPreTrainedModel", ] ) _import_structure["models.swinv2"].extend( [ "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST", "Swinv2ForImageClassification", "Swinv2ForMaskedImageModeling", "Swinv2Model", "Swinv2PreTrainedModel", ] ) _import_structure["models.switch_transformers"].extend( [ "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST", "SwitchTransformersEncoderModel", "SwitchTransformersForConditionalGeneration", "SwitchTransformersModel", "SwitchTransformersPreTrainedModel", "SwitchTransformersSparseMLP", "SwitchTransformersTop1Router", ] ) _import_structure["models.t5"].extend( [ "T5_PRETRAINED_MODEL_ARCHIVE_LIST", "T5EncoderModel", "T5ForConditionalGeneration", "T5ForQuestionAnswering", "T5ForSequenceClassification", "T5Model", "T5PreTrainedModel", "load_tf_weights_in_t5", ] ) _import_structure["models.table_transformer"].extend( [ "TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TableTransformerForObjectDetection", "TableTransformerModel", "TableTransformerPreTrainedModel", ] ) _import_structure["models.tapas"].extend( [ "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", "TapasForMaskedLM", "TapasForQuestionAnswering", "TapasForSequenceClassification", "TapasModel", "TapasPreTrainedModel", "load_tf_weights_in_tapas", ] ) _import_structure["models.time_series_transformer"].extend( [ "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TimeSeriesTransformerForPrediction", "TimeSeriesTransformerModel", "TimeSeriesTransformerPreTrainedModel", ] ) _import_structure["models.timesformer"].extend( [ "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TimesformerForVideoClassification", "TimesformerModel", "TimesformerPreTrainedModel", ] ) _import_structure["models.timm_backbone"].extend(["TimmBackbone"]) _import_structure["models.transfo_xl"].extend( [ "TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "AdaptiveEmbedding", "TransfoXLForSequenceClassification", "TransfoXLLMHeadModel", "TransfoXLModel", "TransfoXLPreTrainedModel", "load_tf_weights_in_transfo_xl", ] ) _import_structure["models.trocr"].extend( ["TROCR_PRETRAINED_MODEL_ARCHIVE_LIST", "TrOCRForCausalLM", "TrOCRPreTrainedModel"] ) _import_structure["models.tvlt"].extend( [ "TVLT_PRETRAINED_MODEL_ARCHIVE_LIST", "TvltForAudioVisualClassification", "TvltForPreTraining", "TvltModel", "TvltPreTrainedModel", ] ) _import_structure["models.umt5"].extend( [ "UMT5EncoderModel", "UMT5ForConditionalGeneration", "UMT5ForQuestionAnswering", "UMT5ForSequenceClassification", "UMT5Model", "UMT5PreTrainedModel", ] ) _import_structure["models.unispeech"].extend( [ "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST", "UniSpeechForCTC", "UniSpeechForPreTraining", "UniSpeechForSequenceClassification", "UniSpeechModel", "UniSpeechPreTrainedModel", ] ) _import_structure["models.unispeech_sat"].extend( [ "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST", "UniSpeechSatForAudioFrameClassification", "UniSpeechSatForCTC", "UniSpeechSatForPreTraining", "UniSpeechSatForSequenceClassification", "UniSpeechSatForXVector", "UniSpeechSatModel", "UniSpeechSatPreTrainedModel", ] ) _import_structure["models.upernet"].extend( [ "UperNetForSemanticSegmentation", "UperNetPreTrainedModel", ] ) _import_structure["models.videomae"].extend( [ "VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST", "VideoMAEForPreTraining", "VideoMAEForVideoClassification", "VideoMAEModel", "VideoMAEPreTrainedModel", ] ) _import_structure["models.vilt"].extend( [ "VILT_PRETRAINED_MODEL_ARCHIVE_LIST", "ViltForImageAndTextRetrieval", "ViltForImagesAndTextClassification", "ViltForMaskedLM", "ViltForQuestionAnswering", "ViltForTokenClassification", "ViltLayer", "ViltModel", "ViltPreTrainedModel", ] ) _import_structure["models.vision_encoder_decoder"].extend(["VisionEncoderDecoderModel"]) _import_structure["models.vision_text_dual_encoder"].extend(["VisionTextDualEncoderModel"]) _import_structure["models.visual_bert"].extend( [ "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "VisualBertForMultipleChoice", "VisualBertForPreTraining", "VisualBertForQuestionAnswering", "VisualBertForRegionToPhraseAlignment", "VisualBertForVisualReasoning", "VisualBertLayer", "VisualBertModel", "VisualBertPreTrainedModel", ] ) _import_structure["models.vit"].extend( [ "VIT_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTForImageClassification", "ViTForMaskedImageModeling", "ViTModel", "ViTPreTrainedModel", ] ) _import_structure["models.vit_hybrid"].extend( [ "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTHybridForImageClassification", "ViTHybridModel", "ViTHybridPreTrainedModel", ] ) _import_structure["models.vit_mae"].extend( [ "VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTMAEForPreTraining", "ViTMAELayer", "ViTMAEModel", "ViTMAEPreTrainedModel", ] ) _import_structure["models.vit_msn"].extend( [ "VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTMSNForImageClassification", "ViTMSNModel", "ViTMSNPreTrainedModel", ] ) _import_structure["models.vitdet"].extend( [ "VITDET_PRETRAINED_MODEL_ARCHIVE_LIST", "VitDetBackbone", "VitDetModel", "VitDetPreTrainedModel", ] ) _import_structure["models.vitmatte"].extend( [ "VITMATTE_PRETRAINED_MODEL_ARCHIVE_LIST", "VitMatteForImageMatting", "VitMattePreTrainedModel", ] ) _import_structure["models.vits"].extend( [ "VITS_PRETRAINED_MODEL_ARCHIVE_LIST", "VitsModel", "VitsPreTrainedModel", ] ) _import_structure["models.vivit"].extend( [ "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "VivitForVideoClassification", "VivitModel", "VivitPreTrainedModel", ] ) _import_structure["models.wav2vec2"].extend( [ "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", "Wav2Vec2ForAudioFrameClassification", "Wav2Vec2ForCTC", "Wav2Vec2ForMaskedLM", "Wav2Vec2ForPreTraining", "Wav2Vec2ForSequenceClassification", "Wav2Vec2ForXVector", "Wav2Vec2Model", "Wav2Vec2PreTrainedModel", ] ) _import_structure["models.wav2vec2_conformer"].extend( [ "WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "Wav2Vec2ConformerForAudioFrameClassification", "Wav2Vec2ConformerForCTC", "Wav2Vec2ConformerForPreTraining", "Wav2Vec2ConformerForSequenceClassification", "Wav2Vec2ConformerForXVector", "Wav2Vec2ConformerModel", "Wav2Vec2ConformerPreTrainedModel", ] ) _import_structure["models.wavlm"].extend( [ "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST", "WavLMForAudioFrameClassification", "WavLMForCTC", "WavLMForSequenceClassification", "WavLMForXVector", "WavLMModel", "WavLMPreTrainedModel", ] ) _import_structure["models.whisper"].extend( [ "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", "WhisperForAudioClassification", "WhisperForConditionalGeneration", "WhisperModel", "WhisperPreTrainedModel", ] ) _import_structure["models.x_clip"].extend( [ "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "XCLIPModel", "XCLIPPreTrainedModel", "XCLIPTextModel", "XCLIPVisionModel", ] ) _import_structure["models.xglm"].extend( [ "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XGLMForCausalLM", "XGLMModel", "XGLMPreTrainedModel", ] ) _import_structure["models.xlm"].extend( [ "XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMForMultipleChoice", "XLMForQuestionAnswering", "XLMForQuestionAnsweringSimple", "XLMForSequenceClassification", "XLMForTokenClassification", "XLMModel", "XLMPreTrainedModel", "XLMWithLMHeadModel", ] ) _import_structure["models.xlm_prophetnet"].extend( [ "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMProphetNetDecoder", "XLMProphetNetEncoder", "XLMProphetNetForCausalLM", "XLMProphetNetForConditionalGeneration", "XLMProphetNetModel", "XLMProphetNetPreTrainedModel", ] ) _import_structure["models.xlm_roberta"].extend( [ "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMRobertaForCausalLM", "XLMRobertaForMaskedLM", "XLMRobertaForMultipleChoice", "XLMRobertaForQuestionAnswering", "XLMRobertaForSequenceClassification", "XLMRobertaForTokenClassification", "XLMRobertaModel", "XLMRobertaPreTrainedModel", ] ) _import_structure["models.xlm_roberta_xl"].extend( [ "XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMRobertaXLForCausalLM", "XLMRobertaXLForMaskedLM", "XLMRobertaXLForMultipleChoice", "XLMRobertaXLForQuestionAnswering", "XLMRobertaXLForSequenceClassification", "XLMRobertaXLForTokenClassification", "XLMRobertaXLModel", "XLMRobertaXLPreTrainedModel", ] ) _import_structure["models.xlnet"].extend( [ "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST", "XLNetForMultipleChoice", "XLNetForQuestionAnswering", "XLNetForQuestionAnsweringSimple", "XLNetForSequenceClassification", "XLNetForTokenClassification", "XLNetLMHeadModel", "XLNetModel", "XLNetPreTrainedModel", "load_tf_weights_in_xlnet", ] ) _import_structure["models.xmod"].extend( [ "XMOD_PRETRAINED_MODEL_ARCHIVE_LIST", "XmodForCausalLM", "XmodForMaskedLM", "XmodForMultipleChoice", "XmodForQuestionAnswering", "XmodForSequenceClassification", "XmodForTokenClassification", "XmodModel", "XmodPreTrainedModel", ] ) _import_structure["models.yolos"].extend( [ "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST", "YolosForObjectDetection", "YolosModel", "YolosPreTrainedModel", ] ) _import_structure["models.yoso"].extend( [ "YOSO_PRETRAINED_MODEL_ARCHIVE_LIST", "YosoForMaskedLM", "YosoForMultipleChoice", "YosoForQuestionAnswering", "YosoForSequenceClassification", "YosoForTokenClassification", "YosoLayer", "YosoModel", "YosoPreTrainedModel", ] ) _import_structure["optimization"] = [ "Adafactor", "AdamW", "get_constant_schedule", "get_constant_schedule_with_warmup", "get_cosine_schedule_with_warmup", "get_cosine_with_hard_restarts_schedule_with_warmup", "get_inverse_sqrt_schedule", "get_linear_schedule_with_warmup", "get_polynomial_decay_schedule_with_warmup", "get_scheduler", ] _import_structure["pytorch_utils"] = ["Conv1D", "apply_chunking_to_forward", "prune_layer"] _import_structure["sagemaker"] = [] _import_structure["time_series_utils"] = [] _import_structure["trainer"] = ["Trainer"] _import_structure["trainer_pt_utils"] = ["torch_distributed_zero_first"] _import_structure["trainer_seq2seq"] = ["Seq2SeqTrainer"] # TensorFlow-backed objects try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_tf_objects _import_structure["utils.dummy_tf_objects"] = [name for name in dir(dummy_tf_objects) if not name.startswith("_")] else: _import_structure["activations_tf"] = [] _import_structure["benchmark.benchmark_args_tf"] = ["TensorFlowBenchmarkArguments"] _import_structure["benchmark.benchmark_tf"] = ["TensorFlowBenchmark"] _import_structure["generation"].extend( [ "TFForcedBOSTokenLogitsProcessor", "TFForcedEOSTokenLogitsProcessor", "TFForceTokensLogitsProcessor", "TFGenerationMixin", "TFLogitsProcessor", "TFLogitsProcessorList", "TFLogitsWarper", "TFMinLengthLogitsProcessor", "TFNoBadWordsLogitsProcessor", "TFNoRepeatNGramLogitsProcessor", "TFRepetitionPenaltyLogitsProcessor", "TFSuppressTokensAtBeginLogitsProcessor", "TFSuppressTokensLogitsProcessor", "TFTemperatureLogitsWarper", "TFTopKLogitsWarper", "TFTopPLogitsWarper", "tf_top_k_top_p_filtering", ] ) _import_structure["generation_tf_utils"] = [] _import_structure["keras_callbacks"] = ["KerasMetricCallback", "PushToHubCallback"] _import_structure["modeling_tf_outputs"] = [] _import_structure["modeling_tf_utils"] = [ "TFPreTrainedModel", "TFSequenceSummary", "TFSharedEmbeddings", "shape_list", ] # TensorFlow models structure _import_structure["models.albert"].extend( [ "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "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( [ "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFBertEmbeddings", "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( [ "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "TFBlipForConditionalGeneration", "TFBlipForImageTextRetrieval", "TFBlipForQuestionAnswering", "TFBlipModel", "TFBlipPreTrainedModel", "TFBlipTextModel", "TFBlipVisionModel", ] ) _import_structure["models.camembert"].extend( [ "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCamembertForCausalLM", "TFCamembertForMaskedLM", "TFCamembertForMultipleChoice", "TFCamembertForQuestionAnswering", "TFCamembertForSequenceClassification", "TFCamembertForTokenClassification", "TFCamembertModel", "TFCamembertPreTrainedModel", ] ) _import_structure["models.clip"].extend( [ "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCLIPModel", "TFCLIPPreTrainedModel", "TFCLIPTextModel", "TFCLIPVisionModel", ] ) _import_structure["models.convbert"].extend( [ "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFConvBertForMaskedLM", "TFConvBertForMultipleChoice", "TFConvBertForQuestionAnswering", "TFConvBertForSequenceClassification", "TFConvBertForTokenClassification", "TFConvBertLayer", "TFConvBertModel", "TFConvBertPreTrainedModel", ] ) _import_structure["models.convnext"].extend( [ "TFConvNextForImageClassification", "TFConvNextModel", "TFConvNextPreTrainedModel", ] ) _import_structure["models.ctrl"].extend( [ "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCTRLForSequenceClassification", "TFCTRLLMHeadModel", "TFCTRLModel", "TFCTRLPreTrainedModel", ] ) _import_structure["models.cvt"].extend( [ "TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCvtForImageClassification", "TFCvtModel", "TFCvtPreTrainedModel", ] ) _import_structure["models.data2vec"].extend( [ "TFData2VecVisionForImageClassification", "TFData2VecVisionForSemanticSegmentation", "TFData2VecVisionModel", "TFData2VecVisionPreTrainedModel", ] ) _import_structure["models.deberta"].extend( [ "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDebertaForMaskedLM", "TFDebertaForQuestionAnswering", "TFDebertaForSequenceClassification", "TFDebertaForTokenClassification", "TFDebertaModel", "TFDebertaPreTrainedModel", ] ) _import_structure["models.deberta_v2"].extend( [ "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDebertaV2ForMaskedLM", "TFDebertaV2ForMultipleChoice", "TFDebertaV2ForQuestionAnswering", "TFDebertaV2ForSequenceClassification", "TFDebertaV2ForTokenClassification", "TFDebertaV2Model", "TFDebertaV2PreTrainedModel", ] ) _import_structure["models.deit"].extend( [ "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDeiTForImageClassification", "TFDeiTForImageClassificationWithTeacher", "TFDeiTForMaskedImageModeling", "TFDeiTModel", "TFDeiTPreTrainedModel", ] ) _import_structure["models.distilbert"].extend( [ "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDistilBertForMaskedLM", "TFDistilBertForMultipleChoice", "TFDistilBertForQuestionAnswering", "TFDistilBertForSequenceClassification", "TFDistilBertForTokenClassification", "TFDistilBertMainLayer", "TFDistilBertModel", "TFDistilBertPreTrainedModel", ] ) _import_structure["models.dpr"].extend( [ "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDPRContextEncoder", "TFDPRPretrainedContextEncoder", "TFDPRPretrainedQuestionEncoder", "TFDPRPretrainedReader", "TFDPRQuestionEncoder", "TFDPRReader", ] ) _import_structure["models.efficientformer"].extend( [ "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFEfficientFormerForImageClassification", "TFEfficientFormerForImageClassificationWithTeacher", "TFEfficientFormerModel", "TFEfficientFormerPreTrainedModel", ] ) _import_structure["models.electra"].extend( [ "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFElectraForMaskedLM", "TFElectraForMultipleChoice", "TFElectraForPreTraining", "TFElectraForQuestionAnswering", "TFElectraForSequenceClassification", "TFElectraForTokenClassification", "TFElectraModel", "TFElectraPreTrainedModel", ] ) _import_structure["models.encoder_decoder"].append("TFEncoderDecoderModel") _import_structure["models.esm"].extend( [ "ESM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFEsmForMaskedLM", "TFEsmForSequenceClassification", "TFEsmForTokenClassification", "TFEsmModel", "TFEsmPreTrainedModel", ] ) _import_structure["models.flaubert"].extend( [ "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFFlaubertForMultipleChoice", "TFFlaubertForQuestionAnsweringSimple", "TFFlaubertForSequenceClassification", "TFFlaubertForTokenClassification", "TFFlaubertModel", "TFFlaubertPreTrainedModel", "TFFlaubertWithLMHeadModel", ] ) _import_structure["models.funnel"].extend( [ "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFFunnelBaseModel", "TFFunnelForMaskedLM", "TFFunnelForMultipleChoice", "TFFunnelForPreTraining", "TFFunnelForQuestionAnswering", "TFFunnelForSequenceClassification", "TFFunnelForTokenClassification", "TFFunnelModel", "TFFunnelPreTrainedModel", ] ) _import_structure["models.gpt2"].extend( [ "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST", "TFGPT2DoubleHeadsModel", "TFGPT2ForSequenceClassification", "TFGPT2LMHeadModel", "TFGPT2MainLayer", "TFGPT2Model", "TFGPT2PreTrainedModel", ] ) _import_structure["models.gptj"].extend( [ "TFGPTJForCausalLM", "TFGPTJForQuestionAnswering", "TFGPTJForSequenceClassification", "TFGPTJModel", "TFGPTJPreTrainedModel", ] ) _import_structure["models.groupvit"].extend( [ "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFGroupViTModel", "TFGroupViTPreTrainedModel", "TFGroupViTTextModel", "TFGroupViTVisionModel", ] ) _import_structure["models.hubert"].extend( [ "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFHubertForCTC", "TFHubertModel", "TFHubertPreTrainedModel", ] ) _import_structure["models.layoutlm"].extend( [ "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLayoutLMForMaskedLM", "TFLayoutLMForQuestionAnswering", "TFLayoutLMForSequenceClassification", "TFLayoutLMForTokenClassification", "TFLayoutLMMainLayer", "TFLayoutLMModel", "TFLayoutLMPreTrainedModel", ] ) _import_structure["models.layoutlmv3"].extend( [ "TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLayoutLMv3ForQuestionAnswering", "TFLayoutLMv3ForSequenceClassification", "TFLayoutLMv3ForTokenClassification", "TFLayoutLMv3Model", "TFLayoutLMv3PreTrainedModel", ] ) _import_structure["models.led"].extend(["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]) _import_structure["models.longformer"].extend( [ "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLongformerForMaskedLM", "TFLongformerForMultipleChoice", "TFLongformerForQuestionAnswering", "TFLongformerForSequenceClassification", "TFLongformerForTokenClassification", "TFLongformerModel", "TFLongformerPreTrainedModel", "TFLongformerSelfAttention", ] ) _import_structure["models.lxmert"].extend( [ "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLxmertForPreTraining", "TFLxmertMainLayer", "TFLxmertModel", "TFLxmertPreTrainedModel", "TFLxmertVisualFeatureEncoder", ] ) _import_structure["models.marian"].extend(["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]) _import_structure["models.mbart"].extend( ["TFMBartForConditionalGeneration", "TFMBartModel", "TFMBartPreTrainedModel"] ) _import_structure["models.mobilebert"].extend( [ "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFMobileBertForMaskedLM", "TFMobileBertForMultipleChoice", "TFMobileBertForNextSentencePrediction", "TFMobileBertForPreTraining", "TFMobileBertForQuestionAnswering", "TFMobileBertForSequenceClassification", "TFMobileBertForTokenClassification", "TFMobileBertMainLayer", "TFMobileBertModel", "TFMobileBertPreTrainedModel", ] ) _import_structure["models.mobilevit"].extend( [ "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFMobileViTForImageClassification", "TFMobileViTForSemanticSegmentation", "TFMobileViTModel", "TFMobileViTPreTrainedModel", ] ) _import_structure["models.mpnet"].extend( [ "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFMPNetForMaskedLM", "TFMPNetForMultipleChoice", "TFMPNetForQuestionAnswering", "TFMPNetForSequenceClassification", "TFMPNetForTokenClassification", "TFMPNetMainLayer", "TFMPNetModel", "TFMPNetPreTrainedModel", ] ) _import_structure["models.mt5"].extend(["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]) _import_structure["models.openai"].extend( [ "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST", "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( [ "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRegNetForImageClassification", "TFRegNetModel", "TFRegNetPreTrainedModel", ] ) _import_structure["models.rembert"].extend( [ "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRemBertForCausalLM", "TFRemBertForMaskedLM", "TFRemBertForMultipleChoice", "TFRemBertForQuestionAnswering", "TFRemBertForSequenceClassification", "TFRemBertForTokenClassification", "TFRemBertLayer", "TFRemBertModel", "TFRemBertPreTrainedModel", ] ) _import_structure["models.resnet"].extend( [ "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFResNetForImageClassification", "TFResNetModel", "TFResNetPreTrainedModel", ] ) _import_structure["models.roberta"].extend( [ "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRobertaForCausalLM", "TFRobertaForMaskedLM", "TFRobertaForMultipleChoice", "TFRobertaForQuestionAnswering", "TFRobertaForSequenceClassification", "TFRobertaForTokenClassification", "TFRobertaMainLayer", "TFRobertaModel", "TFRobertaPreTrainedModel", ] ) _import_structure["models.roberta_prelayernorm"].extend( [ "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRobertaPreLayerNormForCausalLM", "TFRobertaPreLayerNormForMaskedLM", "TFRobertaPreLayerNormForMultipleChoice", "TFRobertaPreLayerNormForQuestionAnswering", "TFRobertaPreLayerNormForSequenceClassification", "TFRobertaPreLayerNormForTokenClassification", "TFRobertaPreLayerNormMainLayer", "TFRobertaPreLayerNormModel", "TFRobertaPreLayerNormPreTrainedModel", ] ) _import_structure["models.roformer"].extend( [ "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRoFormerForCausalLM", "TFRoFormerForMaskedLM", "TFRoFormerForMultipleChoice", "TFRoFormerForQuestionAnswering", "TFRoFormerForSequenceClassification", "TFRoFormerForTokenClassification", "TFRoFormerLayer", "TFRoFormerModel", "TFRoFormerPreTrainedModel", ] ) _import_structure["models.sam"].extend( [ "TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSamModel", "TFSamPreTrainedModel", ] ) _import_structure["models.segformer"].extend( [ "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSegformerDecodeHead", "TFSegformerForImageClassification", "TFSegformerForSemanticSegmentation", "TFSegformerModel", "TFSegformerPreTrainedModel", ] ) _import_structure["models.speech_to_text"].extend( [ "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSpeech2TextForConditionalGeneration", "TFSpeech2TextModel", "TFSpeech2TextPreTrainedModel", ] ) _import_structure["models.swin"].extend( [ "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSwinForImageClassification", "TFSwinForMaskedImageModeling", "TFSwinModel", "TFSwinPreTrainedModel", ] ) _import_structure["models.t5"].extend( [ "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST", "TFT5EncoderModel", "TFT5ForConditionalGeneration", "TFT5Model", "TFT5PreTrainedModel", ] ) _import_structure["models.tapas"].extend( [ "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", "TFTapasForMaskedLM", "TFTapasForQuestionAnswering", "TFTapasForSequenceClassification", "TFTapasModel", "TFTapasPreTrainedModel", ] ) _import_structure["models.transfo_xl"].extend( [ "TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFAdaptiveEmbedding", "TFTransfoXLForSequenceClassification", "TFTransfoXLLMHeadModel", "TFTransfoXLMainLayer", "TFTransfoXLModel", "TFTransfoXLPreTrainedModel", ] ) _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( [ "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", "TFWav2Vec2ForCTC", "TFWav2Vec2ForSequenceClassification", "TFWav2Vec2Model", "TFWav2Vec2PreTrainedModel", ] ) _import_structure["models.whisper"].extend( [ "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFWhisperForConditionalGeneration", "TFWhisperModel", "TFWhisperPreTrainedModel", ] ) _import_structure["models.xglm"].extend( [ "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXGLMForCausalLM", "TFXGLMModel", "TFXGLMPreTrainedModel", ] ) _import_structure["models.xlm"].extend( [ "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMForMultipleChoice", "TFXLMForQuestionAnsweringSimple", "TFXLMForSequenceClassification", "TFXLMForTokenClassification", "TFXLMMainLayer", "TFXLMModel", "TFXLMPreTrainedModel", "TFXLMWithLMHeadModel", ] ) _import_structure["models.xlm_roberta"].extend( [ "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMRobertaForCausalLM", "TFXLMRobertaForMaskedLM", "TFXLMRobertaForMultipleChoice", "TFXLMRobertaForQuestionAnswering", "TFXLMRobertaForSequenceClassification", "TFXLMRobertaForTokenClassification", "TFXLMRobertaModel", "TFXLMRobertaPreTrainedModel", ] ) _import_structure["models.xlnet"].extend( [ "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLNetForMultipleChoice", "TFXLNetForQuestionAnsweringSimple", "TFXLNetForSequenceClassification", "TFXLNetForTokenClassification", "TFXLNetLMHeadModel", "TFXLNetMainLayer", "TFXLNetModel", "TFXLNetPreTrainedModel", ] ) _import_structure["optimization_tf"] = ["AdamWeightDecay", "GradientAccumulator", "WarmUp", "create_optimizer"] _import_structure["tf_utils"] = [] _import_structure["trainer_tf"] = ["TFTrainer"] 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") # FLAX-backed objects try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_flax_objects _import_structure["utils.dummy_flax_objects"] = [ name for name in dir(dummy_flax_objects) if not name.startswith("_") ] else: _import_structure["generation"].extend( [ "FlaxForcedBOSTokenLogitsProcessor", "FlaxForcedEOSTokenLogitsProcessor", "FlaxForceTokensLogitsProcessor", "FlaxGenerationMixin", "FlaxLogitsProcessor", "FlaxLogitsProcessorList", "FlaxLogitsWarper", "FlaxMinLengthLogitsProcessor", "FlaxTemperatureLogitsWarper", "FlaxSuppressTokensAtBeginLogitsProcessor", "FlaxSuppressTokensLogitsProcessor", "FlaxTopKLogitsWarper", "FlaxTopPLogitsWarper", "FlaxWhisperTimeStampLogitsProcessor", ] ) _import_structure["generation_flax_utils"] = [] _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.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.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.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( [ "FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "FlaxXLMRobertaForMaskedLM", "FlaxXLMRobertaForMultipleChoice", "FlaxXLMRobertaForQuestionAnswering", "FlaxXLMRobertaForSequenceClassification", "FlaxXLMRobertaForTokenClassification", "FlaxXLMRobertaModel", "FlaxXLMRobertaForCausalLM", "FlaxXLMRobertaPreTrainedModel", ] ) # Direct imports for type-checking if TYPE_CHECKING: # Configuration from .configuration_utils import PretrainedConfig # Data from .data import ( DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor, SquadExample, SquadFeatures, SquadV1Processor, SquadV2Processor, glue_compute_metrics, glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels, squad_convert_examples_to_features, xnli_compute_metrics, xnli_output_modes, xnli_processors, xnli_tasks_num_labels, ) from .data.data_collator import ( DataCollator, DataCollatorForLanguageModeling, DataCollatorForPermutationLanguageModeling, DataCollatorForSeq2Seq, DataCollatorForSOP, DataCollatorForTokenClassification, DataCollatorForWholeWordMask, DataCollatorWithPadding, DefaultDataCollator, default_data_collator, ) from .feature_extraction_sequence_utils import SequenceFeatureExtractor # Feature Extractor from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin # Generation from .generation import GenerationConfig, TextIteratorStreamer, TextStreamer from .hf_argparser import HfArgumentParser # Integrations from .integrations import ( is_clearml_available, is_comet_available, is_neptune_available, is_optuna_available, is_ray_available, is_ray_tune_available, is_sigopt_available, is_tensorboard_available, is_wandb_available, ) # Model Cards from .modelcard import ModelCard # TF 2.0 <=> PyTorch conversion utilities from .modeling_tf_pytorch_utils import ( convert_tf_weight_name_to_pt_weight_name, load_pytorch_checkpoint_in_tf2_model, load_pytorch_model_in_tf2_model, load_pytorch_weights_in_tf2_model, load_tf2_checkpoint_in_pytorch_model, load_tf2_model_in_pytorch_model, load_tf2_weights_in_pytorch_model, ) from .models.albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig from .models.align import ( ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP, AlignConfig, AlignProcessor, AlignTextConfig, AlignVisionConfig, ) from .models.altclip import ( ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, AltCLIPConfig, AltCLIPProcessor, AltCLIPTextConfig, AltCLIPVisionConfig, ) from .models.audio_spectrogram_transformer import ( AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ASTConfig, ) from .models.auto import ( ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, FEATURE_EXTRACTOR_MAPPING, IMAGE_PROCESSOR_MAPPING, MODEL_NAMES_MAPPING, PROCESSOR_MAPPING, TOKENIZER_MAPPING, AutoConfig, AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer, ) from .models.autoformer import ( AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, AutoformerConfig, ) from .models.bark import ( BarkCoarseConfig, BarkConfig, BarkFineConfig, BarkProcessor, BarkSemanticConfig, ) from .models.bart import BartConfig, BartTokenizer from .models.beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig from .models.bert import ( BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, 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 BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig from .models.bigbird_pegasus import BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdPegasusConfig from .models.biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig, BioGptTokenizer from .models.bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig from .models.blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig, BlenderbotTokenizer from .models.blenderbot_small import ( BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotSmallConfig, BlenderbotSmallTokenizer, ) from .models.blip import ( BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipProcessor, BlipTextConfig, BlipVisionConfig, ) from .models.blip_2 import ( BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Blip2Config, Blip2Processor, Blip2QFormerConfig, Blip2VisionConfig, ) from .models.bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig from .models.bridgetower import ( BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP, BridgeTowerConfig, BridgeTowerProcessor, BridgeTowerTextConfig, BridgeTowerVisionConfig, ) from .models.bros import BROS_PRETRAINED_CONFIG_ARCHIVE_MAP, BrosConfig, BrosProcessor from .models.byt5 import ByT5Tokenizer from .models.camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig from .models.canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig, CanineTokenizer from .models.chinese_clip import ( CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, ChineseCLIPConfig, ChineseCLIPProcessor, ChineseCLIPTextConfig, ChineseCLIPVisionConfig, ) from .models.clap import ( CLAP_PRETRAINED_MODEL_ARCHIVE_LIST, ClapAudioConfig, ClapConfig, ClapProcessor, ClapTextConfig, ) from .models.clip import ( CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPConfig, CLIPProcessor, CLIPTextConfig, CLIPTokenizer, CLIPVisionConfig, ) from .models.clipseg import ( CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPSegConfig, CLIPSegProcessor, CLIPSegTextConfig, CLIPSegVisionConfig, ) from .models.codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenTokenizer from .models.conditional_detr import CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, ConditionalDetrConfig from .models.convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertTokenizer from .models.convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig from .models.convnextv2 import CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextV2Config from .models.cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig, CpmAntTokenizer from .models.ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig, CTRLTokenizer from .models.cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig from .models.data2vec import ( DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP, Data2VecAudioConfig, Data2VecTextConfig, Data2VecVisionConfig, ) from .models.deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaTokenizer from .models.deberta_v2 import DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaV2Config from .models.decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, DecisionTransformerConfig, ) from .models.deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig from .models.deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig from .models.deprecated.mctct import ( MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig, MCTCTFeatureExtractor, MCTCTProcessor, ) from .models.deprecated.mmbt import MMBTConfig from .models.deprecated.open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig from .models.deprecated.retribert import ( RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig, RetriBertTokenizer, ) from .models.deprecated.tapex import TapexTokenizer from .models.deprecated.trajectory_transformer import ( TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TrajectoryTransformerConfig, ) from .models.deprecated.van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig from .models.deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig from .models.detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig from .models.dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig from .models.dinov2 import DINOV2_PRETRAINED_CONFIG_ARCHIVE_MAP, Dinov2Config from .models.distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, DistilBertTokenizer from .models.donut import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutProcessor, DonutSwinConfig from .models.dpr import ( DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig, DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderOutput, DPRReaderTokenizer, ) from .models.dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig from .models.efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig from .models.efficientnet import EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientNetConfig from .models.electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraTokenizer from .models.encodec import ( ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP, EncodecConfig, EncodecFeatureExtractor, ) from .models.encoder_decoder import EncoderDecoderConfig from .models.ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig from .models.ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig from .models.esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig, EsmTokenizer from .models.falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig from .models.flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertTokenizer from .models.flava import ( FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP, FlavaConfig, FlavaImageCodebookConfig, FlavaImageConfig, FlavaMultimodalConfig, FlavaTextConfig, ) from .models.fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig from .models.focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig from .models.fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig, FSMTTokenizer from .models.funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig, FunnelTokenizer from .models.git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitProcessor, GitVisionConfig from .models.glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig from .models.gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2Tokenizer from .models.gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig from .models.gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig from .models.gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig from .models.gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig from .models.gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig from .models.gptsan_japanese import ( GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig, GPTSanJapaneseTokenizer, ) from .models.graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig from .models.groupvit import ( GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig, ) from .models.herbert import HerbertTokenizer from .models.hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig from .models.ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig from .models.idefics import ( IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP, IdeficsConfig, ) from .models.imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig from .models.informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig from .models.instructblip import ( INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, InstructBlipConfig, InstructBlipProcessor, InstructBlipQFormerConfig, InstructBlipVisionConfig, ) from .models.jukebox import ( JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP, JukeboxConfig, JukeboxPriorConfig, JukeboxTokenizer, JukeboxVQVAEConfig, ) from .models.layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMTokenizer from .models.layoutlmv2 import ( LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMv2Config, LayoutLMv2FeatureExtractor, LayoutLMv2ImageProcessor, LayoutLMv2Processor, LayoutLMv2Tokenizer, ) from .models.layoutlmv3 import ( LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMv3Config, LayoutLMv3FeatureExtractor, LayoutLMv3ImageProcessor, LayoutLMv3Processor, LayoutLMv3Tokenizer, ) from .models.layoutxlm import LayoutXLMProcessor from .models.led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig, LEDTokenizer from .models.levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig from .models.lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig from .models.llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig from .models.longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig, LongformerTokenizer from .models.longt5 import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongT5Config from .models.luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig, LukeTokenizer from .models.lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig, LxmertTokenizer from .models.m2m_100 import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, M2M100Config from .models.marian import MarianConfig from .models.markuplm import ( MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig, MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer, ) from .models.mask2former import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, Mask2FormerConfig from .models.maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig, MaskFormerSwinConfig from .models.mbart import MBartConfig from .models.mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig from .models.megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig from .models.mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig, MgpstrProcessor, MgpstrTokenizer from .models.mistral import MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP, MistralConfig from .models.mobilebert import MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileBertConfig, MobileBertTokenizer from .models.mobilenet_v1 import MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileNetV1Config from .models.mobilenet_v2 import MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileNetV2Config from .models.mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig from .models.mobilevitv2 import MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTV2Config from .models.mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig, MPNetTokenizer from .models.mpt import MPT_PRETRAINED_CONFIG_ARCHIVE_MAP, MptConfig from .models.mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig from .models.mt5 import MT5Config from .models.musicgen import ( MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, MusicgenConfig, MusicgenDecoderConfig, ) from .models.mvp import MvpConfig, MvpTokenizer from .models.nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig from .models.nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig from .models.nllb_moe import NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP, NllbMoeConfig from .models.nougat import NougatProcessor from .models.nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig from .models.oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig, OneFormerProcessor from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer from .models.opt import OPTConfig from .models.owlvit import ( OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, OwlViTConfig, OwlViTProcessor, OwlViTTextConfig, OwlViTVisionConfig, ) from .models.pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig, PegasusTokenizer from .models.pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig from .models.perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverTokenizer from .models.persimmon import PERSIMMON_PRETRAINED_CONFIG_ARCHIVE_MAP, PersimmonConfig from .models.phobert import PhobertTokenizer from .models.pix2struct import ( PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP, Pix2StructConfig, Pix2StructProcessor, Pix2StructTextConfig, Pix2StructVisionConfig, ) from .models.plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig from .models.poolformer import POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, PoolFormerConfig from .models.pop2piano import ( POP2PIANO_PRETRAINED_CONFIG_ARCHIVE_MAP, Pop2PianoConfig, ) from .models.prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig, ProphetNetTokenizer from .models.pvt import PVT_PRETRAINED_CONFIG_ARCHIVE_MAP, PvtConfig from .models.qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig from .models.rag import RagConfig, RagRetriever, RagTokenizer from .models.realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig, RealmTokenizer from .models.reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig from .models.regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig from .models.rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig from .models.resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig from .models.roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaTokenizer from .models.roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaPreLayerNormConfig, ) from .models.roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig, RoCBertTokenizer from .models.roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerTokenizer from .models.rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig from .models.sam import ( SAM_PRETRAINED_CONFIG_ARCHIVE_MAP, SamConfig, SamMaskDecoderConfig, SamProcessor, SamPromptEncoderConfig, SamVisionConfig, ) from .models.segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig from .models.sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig from .models.sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig from .models.speech_encoder_decoder import SpeechEncoderDecoderConfig from .models.speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig, Speech2TextProcessor, ) from .models.speech_to_text_2 import ( SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2Text2Config, Speech2Text2Processor, Speech2Text2Tokenizer, ) from .models.speecht5 import ( SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP, SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP, SpeechT5Config, SpeechT5FeatureExtractor, SpeechT5HifiGanConfig, SpeechT5Processor, ) from .models.splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig, SplinterTokenizer from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer from .models.swiftformer import SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SwiftFormerConfig from .models.swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig from .models.swin2sr import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP, Swin2SRConfig from .models.swinv2 import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, Swinv2Config from .models.switch_transformers import SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP, SwitchTransformersConfig from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config from .models.table_transformer import TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TableTransformerConfig from .models.tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig, TapasTokenizer from .models.time_series_transformer import ( TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimeSeriesTransformerConfig, ) from .models.timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig from .models.timm_backbone import TimmBackboneConfig from .models.transfo_xl import ( TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig, TransfoXLCorpus, TransfoXLTokenizer, ) from .models.trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig, TrOCRProcessor from .models.tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig, TvltFeatureExtractor, TvltProcessor from .models.umt5 import UMT5Config from .models.unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig from .models.unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig from .models.upernet import UperNetConfig from .models.videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig from .models.vilt import ( VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig, ViltFeatureExtractor, ViltImageProcessor, ViltProcessor, ) from .models.vision_encoder_decoder import VisionEncoderDecoderConfig from .models.vision_text_dual_encoder import VisionTextDualEncoderConfig, VisionTextDualEncoderProcessor from .models.visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig from .models.vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig from .models.vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig from .models.vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig from .models.vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig from .models.vitdet import VITDET_PRETRAINED_CONFIG_ARCHIVE_MAP, VitDetConfig from .models.vitmatte import VITMATTE_PRETRAINED_CONFIG_ARCHIVE_MAP, VitMatteConfig from .models.vits import ( VITS_PRETRAINED_CONFIG_ARCHIVE_MAP, VitsConfig, VitsTokenizer, ) from .models.vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig from .models.wav2vec2 import ( WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Wav2Vec2Config, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, Wav2Vec2Tokenizer, ) from .models.wav2vec2_conformer import WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, Wav2Vec2ConformerConfig from .models.wav2vec2_phoneme import Wav2Vec2PhonemeCTCTokenizer from .models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM from .models.wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig from .models.whisper import ( WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperFeatureExtractor, WhisperProcessor, WhisperTokenizer, ) from .models.x_clip import ( XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, XCLIPConfig, XCLIPProcessor, XCLIPTextConfig, XCLIPVisionConfig, ) from .models.xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig from .models.xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMTokenizer from .models.xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig from .models.xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig from .models.xlm_roberta_xl import XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaXLConfig from .models.xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig from .models.xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig from .models.yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig from .models.yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig # Pipelines from .pipelines import ( AudioClassificationPipeline, AutomaticSpeechRecognitionPipeline, Conversation, ConversationalPipeline, CsvPipelineDataFormat, DepthEstimationPipeline, DocumentQuestionAnsweringPipeline, FeatureExtractionPipeline, FillMaskPipeline, ImageClassificationPipeline, ImageSegmentationPipeline, ImageToImagePipeline, ImageToTextPipeline, JsonPipelineDataFormat, NerPipeline, ObjectDetectionPipeline, PipedPipelineDataFormat, Pipeline, PipelineDataFormat, QuestionAnsweringPipeline, SummarizationPipeline, TableQuestionAnsweringPipeline, Text2TextGenerationPipeline, TextClassificationPipeline, TextGenerationPipeline, TextToAudioPipeline, TokenClassificationPipeline, TranslationPipeline, VideoClassificationPipeline, VisualQuestionAnsweringPipeline, ZeroShotAudioClassificationPipeline, ZeroShotClassificationPipeline, ZeroShotImageClassificationPipeline, ZeroShotObjectDetectionPipeline, pipeline, ) from .processing_utils import ProcessorMixin # Tokenization from .tokenization_utils import PreTrainedTokenizer from .tokenization_utils_base import ( AddedToken, BatchEncoding, CharSpan, PreTrainedTokenizerBase, SpecialTokensMixin, TokenSpan, ) # Tools from .tools import ( Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent, PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool, ) # Trainer from .trainer_callback import ( DefaultFlowCallback, EarlyStoppingCallback, PrinterCallback, ProgressCallback, TrainerCallback, TrainerControl, TrainerState, ) from .trainer_utils import EvalPrediction, IntervalStrategy, SchedulerType, enable_full_determinism, set_seed from .training_args import TrainingArguments from .training_args_seq2seq import Seq2SeqTrainingArguments from .training_args_tf import TFTrainingArguments # Files and general utilities from .utils import ( CONFIG_NAME, MODEL_CARD_NAME, PYTORCH_PRETRAINED_BERT_CACHE, PYTORCH_TRANSFORMERS_CACHE, SPIECE_UNDERLINE, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, TRANSFORMERS_CACHE, WEIGHTS_NAME, TensorType, add_end_docstrings, add_start_docstrings, is_apex_available, is_bitsandbytes_available, is_datasets_available, is_decord_available, is_faiss_available, is_flax_available, is_keras_nlp_available, is_phonemizer_available, is_psutil_available, is_py3nvml_available, is_pyctcdecode_available, is_safetensors_available, is_scipy_available, is_sentencepiece_available, is_sklearn_available, is_speech_available, is_tensorflow_text_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torch_neuroncore_available, is_torch_npu_available, is_torch_tpu_available, is_torch_xpu_available, is_torchvision_available, is_vision_available, logging, ) # bitsandbytes config from .utils.quantization_config import BitsAndBytesConfig, GPTQConfig 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.ernie_m import ErnieMTokenizer from .models.fnet import FNetTokenizer 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 MBart50Tokenizer, MBartTokenizer 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.speech_to_text import Speech2TextTokenizer from .models.speecht5 import SpeechT5Tokenizer from .models.t5 import T5Tokenizer from .models.xglm import XGLMTokenizer from .models.xlm_prophetnet import XLMProphetNetTokenizer 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.convbert import ConvBertTokenizerFast from .models.cpm import CpmTokenizerFast from .models.deberta import DebertaTokenizerFast from .models.deberta_v2 import DebertaV2TokenizerFast 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.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.realm import RealmTokenizerFast from .models.reformer import ReformerTokenizerFast from .models.rembert import RemBertTokenizerFast from .models.roberta import RobertaTokenizerFast from .models.roformer import RoFormerTokenizerFast from .models.splinter import SplinterTokenizerFast from .models.squeezebert import SqueezeBertTokenizerFast from .models.t5 import T5TokenizerFast 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: if not (is_sentencepiece_available() and is_tokenizers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummies_sentencepiece_and_tokenizers_objects import * else: from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, convert_slow_tokenizer try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_speech_objects import * else: from .models.audio_spectrogram_transformer import ASTFeatureExtractor from .models.speech_to_text import Speech2TextFeatureExtractor 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() except OptionalDependencyNotAvailable: from .utils.dummy_vision_objects import * else: from .image_processing_utils import ImageProcessingMixin from .image_utils import ImageFeatureExtractionMixin from .models.beit import BeitFeatureExtractor, BeitImageProcessor from .models.bit import BitImageProcessor from .models.blip import BlipImageProcessor from .models.bridgetower import BridgeTowerImageProcessor 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.deta import DetaImageProcessor from .models.detr import DetrFeatureExtractor, DetrImageProcessor from .models.donut import DonutFeatureExtractor, DonutImageProcessor from .models.dpt import DPTFeatureExtractor, DPTImageProcessor from .models.efficientformer import EfficientFormerImageProcessor from .models.efficientnet import EfficientNetImageProcessor from .models.flava import FlavaFeatureExtractor, FlavaImageProcessor, FlavaProcessor from .models.glpn import GLPNFeatureExtractor, GLPNImageProcessor from .models.idefics import IdeficsImageProcessor from .models.imagegpt import ImageGPTFeatureExtractor, ImageGPTImageProcessor from .models.layoutlmv2 import LayoutLMv2FeatureExtractor, LayoutLMv2ImageProcessor from .models.layoutlmv3 import LayoutLMv3FeatureExtractor, LayoutLMv3ImageProcessor from .models.levit import LevitFeatureExtractor, LevitImageProcessor from .models.mask2former import Mask2FormerImageProcessor from .models.maskformer import MaskFormerFeatureExtractor, MaskFormerImageProcessor 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.owlvit import OwlViTFeatureExtractor, OwlViTImageProcessor from .models.perceiver import PerceiverFeatureExtractor, PerceiverImageProcessor from .models.pix2struct import Pix2StructImageProcessor from .models.poolformer import PoolFormerFeatureExtractor, PoolFormerImageProcessor from .models.pvt import PvtImageProcessor from .models.sam import SamImageProcessor from .models.segformer import SegformerFeatureExtractor, SegformerImageProcessor from .models.swin2sr import Swin2SRImageProcessor from .models.tvlt import TvltImageProcessor from .models.videomae import VideoMAEFeatureExtractor, VideoMAEImageProcessor from .models.vilt import ViltFeatureExtractor, ViltImageProcessor, ViltProcessor from .models.vit import ViTFeatureExtractor, ViTImageProcessor from .models.vit_hybrid import ViTHybridImageProcessor from .models.vitmatte import VitMatteImageProcessor from .models.vivit import VivitImageProcessor from .models.yolos import YolosFeatureExtractor, YolosImageProcessor # Modeling try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_pt_objects import * else: # Benchmarks from .benchmark.benchmark import PyTorchBenchmark from .benchmark.benchmark_args import PyTorchBenchmarkArguments from .data.datasets import ( GlueDataset, GlueDataTrainingArguments, LineByLineTextDataset, LineByLineWithRefDataset, LineByLineWithSOPTextDataset, SquadDataset, SquadDataTrainingArguments, TextDataset, TextDatasetForNextSentencePrediction, ) from .generation import ( AlternatingCodebooksLogitsProcessor, BeamScorer, BeamSearchScorer, ClassifierFreeGuidanceLogitsProcessor, ConstrainedBeamSearchScorer, Constraint, ConstraintListState, DisjunctiveConstraint, EncoderNoRepeatNGramLogitsProcessor, EncoderRepetitionPenaltyLogitsProcessor, EpsilonLogitsWarper, EtaLogitsWarper, ExponentialDecayLengthPenalty, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, ForceTokensLogitsProcessor, GenerationMixin, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitNormalization, LogitsProcessor, LogitsProcessorList, LogitsWarper, MaxLengthCriteria, MaxTimeCriteria, MinLengthLogitsProcessor, MinNewTokensLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PhrasalConstraint, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, SequenceBiasLogitsProcessor, StoppingCriteria, StoppingCriteriaList, SuppressTokensAtBeginLogitsProcessor, SuppressTokensLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper, UnbatchedClassifierFreeGuidanceLogitsProcessor, WhisperTimeStampLogitsProcessor, top_k_top_p_filtering, ) from .modeling_utils import PreTrainedModel # PyTorch model imports from .models.albert import ( ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, AlbertForMaskedLM, AlbertForMultipleChoice, AlbertForPreTraining, AlbertForQuestionAnswering, AlbertForSequenceClassification, AlbertForTokenClassification, AlbertModel, AlbertPreTrainedModel, load_tf_weights_in_albert, ) from .models.align import ( ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST, AlignModel, AlignPreTrainedModel, AlignTextModel, AlignVisionModel, ) from .models.altclip import ( ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST, AltCLIPModel, AltCLIPPreTrainedModel, AltCLIPTextModel, AltCLIPVisionModel, ) from .models.audio_spectrogram_transformer import ( AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, 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_SEGMENTATION_MAPPING, MODEL_FOR_IMAGE_TO_IMAGE_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_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_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_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, AutoModelForImageToImage, AutoModelForInstanceSegmentation, 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 ( AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, AutoformerForPrediction, AutoformerModel, AutoformerPreTrainedModel, ) from .models.bark import ( BARK_PRETRAINED_MODEL_ARCHIVE_LIST, BarkCausalModel, BarkCoarseModel, BarkFineModel, BarkModel, BarkPreTrainedModel, BarkSemanticModel, ) from .models.bart import ( BART_PRETRAINED_MODEL_ARCHIVE_LIST, BartForCausalLM, BartForConditionalGeneration, BartForQuestionAnswering, BartForSequenceClassification, BartModel, BartPreTrainedModel, BartPretrainedModel, PretrainedBartModel, ) from .models.beit import ( BEIT_PRETRAINED_MODEL_ARCHIVE_LIST, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation, BeitModel, BeitPreTrainedModel, ) from .models.bert import ( BERT_PRETRAINED_MODEL_ARCHIVE_LIST, BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertLayer, 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 ( BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST, BigBirdForCausalLM, BigBirdForMaskedLM, BigBirdForMultipleChoice, BigBirdForPreTraining, BigBirdForQuestionAnswering, BigBirdForSequenceClassification, BigBirdForTokenClassification, BigBirdLayer, BigBirdModel, BigBirdPreTrainedModel, load_tf_weights_in_big_bird, ) from .models.bigbird_pegasus import ( BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST, BigBirdPegasusForCausalLM, BigBirdPegasusForConditionalGeneration, BigBirdPegasusForQuestionAnswering, BigBirdPegasusForSequenceClassification, BigBirdPegasusModel, BigBirdPegasusPreTrainedModel, ) from .models.biogpt import ( BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification, BioGptModel, BioGptPreTrainedModel, ) from .models.bit import ( BIT_PRETRAINED_MODEL_ARCHIVE_LIST, BitBackbone, BitForImageClassification, BitModel, BitPreTrainedModel, ) from .models.blenderbot import ( BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST, BlenderbotForCausalLM, BlenderbotForConditionalGeneration, BlenderbotModel, BlenderbotPreTrainedModel, ) from .models.blenderbot_small import ( BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST, BlenderbotSmallForCausalLM, BlenderbotSmallForConditionalGeneration, BlenderbotSmallModel, BlenderbotSmallPreTrainedModel, ) from .models.blip import ( BLIP_PRETRAINED_MODEL_ARCHIVE_LIST, BlipForConditionalGeneration, BlipForImageTextRetrieval, BlipForQuestionAnswering, BlipModel, BlipPreTrainedModel, BlipTextModel, BlipVisionModel, ) from .models.blip_2 import ( BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST, Blip2ForConditionalGeneration, Blip2Model, Blip2PreTrainedModel, Blip2QFormerModel, Blip2VisionModel, ) from .models.bloom import ( BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST, BloomForCausalLM, BloomForQuestionAnswering, BloomForSequenceClassification, BloomForTokenClassification, BloomModel, BloomPreTrainedModel, ) from .models.bridgetower import ( BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST, BridgeTowerForContrastiveLearning, BridgeTowerForImageAndTextRetrieval, BridgeTowerForMaskedLM, BridgeTowerModel, BridgeTowerPreTrainedModel, ) from .models.bros import ( BROS_PRETRAINED_MODEL_ARCHIVE_LIST, BrosForTokenClassification, BrosModel, BrosPreTrainedModel, BrosProcessor, BrosSpadeEEForTokenClassification, BrosSpadeELForTokenClassification, ) from .models.camembert import ( CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, CamembertForCausalLM, CamembertForMaskedLM, CamembertForMultipleChoice, CamembertForQuestionAnswering, CamembertForSequenceClassification, CamembertForTokenClassification, CamembertModel, CamembertPreTrainedModel, ) from .models.canine import ( CANINE_PRETRAINED_MODEL_ARCHIVE_LIST, CanineForMultipleChoice, CanineForQuestionAnswering, CanineForSequenceClassification, CanineForTokenClassification, CanineLayer, CanineModel, CaninePreTrainedModel, load_tf_weights_in_canine, ) from .models.chinese_clip import ( CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST, ChineseCLIPModel, ChineseCLIPPreTrainedModel, ChineseCLIPTextModel, ChineseCLIPVisionModel, ) from .models.clap import ( CLAP_PRETRAINED_MODEL_ARCHIVE_LIST, ClapAudioModel, ClapAudioModelWithProjection, ClapFeatureExtractor, ClapModel, ClapPreTrainedModel, ClapTextModel, ClapTextModelWithProjection, ) from .models.clip import ( CLIP_PRETRAINED_MODEL_ARCHIVE_LIST, CLIPModel, CLIPPreTrainedModel, CLIPTextModel, CLIPTextModelWithProjection, CLIPVisionModel, CLIPVisionModelWithProjection, ) from .models.clipseg import ( CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST, CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegPreTrainedModel, CLIPSegTextModel, CLIPSegVisionModel, ) from .models.codegen import ( CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST, CodeGenForCausalLM, CodeGenModel, CodeGenPreTrainedModel, ) from .models.conditional_detr import ( CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, ConditionalDetrForObjectDetection, ConditionalDetrForSegmentation, ConditionalDetrModel, ConditionalDetrPreTrainedModel, ) from .models.convbert import ( CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST, ConvBertForMaskedLM, ConvBertForMultipleChoice, ConvBertForQuestionAnswering, ConvBertForSequenceClassification, ConvBertForTokenClassification, ConvBertLayer, ConvBertModel, ConvBertPreTrainedModel, load_tf_weights_in_convbert, ) from .models.convnext import ( CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST, ConvNextBackbone, ConvNextForImageClassification, ConvNextModel, ConvNextPreTrainedModel, ) from .models.convnextv2 import ( CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST, ConvNextV2Backbone, ConvNextV2ForImageClassification, ConvNextV2Model, ConvNextV2PreTrainedModel, ) from .models.cpmant import ( CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST, CpmAntForCausalLM, CpmAntModel, CpmAntPreTrainedModel, ) from .models.ctrl import ( CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, CTRLForSequenceClassification, CTRLLMHeadModel, CTRLModel, CTRLPreTrainedModel, ) from .models.cvt import ( CVT_PRETRAINED_MODEL_ARCHIVE_LIST, CvtForImageClassification, CvtModel, CvtPreTrainedModel, ) from .models.data2vec import ( DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST, DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST, Data2VecAudioForAudioFrameClassification, Data2VecAudioForCTC, Data2VecAudioForSequenceClassification, Data2VecAudioForXVector, Data2VecAudioModel, Data2VecAudioPreTrainedModel, Data2VecTextForCausalLM, Data2VecTextForMaskedLM, Data2VecTextForMultipleChoice, Data2VecTextForQuestionAnswering, Data2VecTextForSequenceClassification, Data2VecTextForTokenClassification, Data2VecTextModel, Data2VecTextPreTrainedModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation, Data2VecVisionModel, Data2VecVisionPreTrainedModel, ) from .models.deberta import ( DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, DebertaForMaskedLM, DebertaForQuestionAnswering, DebertaForSequenceClassification, DebertaForTokenClassification, DebertaModel, DebertaPreTrainedModel, ) from .models.deberta_v2 import ( DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST, DebertaV2ForMaskedLM, DebertaV2ForMultipleChoice, DebertaV2ForQuestionAnswering, DebertaV2ForSequenceClassification, DebertaV2ForTokenClassification, DebertaV2Model, DebertaV2PreTrainedModel, ) from .models.decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, DecisionTransformerGPT2Model, DecisionTransformerGPT2PreTrainedModel, DecisionTransformerModel, DecisionTransformerPreTrainedModel, ) from .models.deformable_detr import ( DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DeformableDetrForObjectDetection, DeformableDetrModel, DeformableDetrPreTrainedModel, ) from .models.deit import ( DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, DeiTForImageClassification, DeiTForImageClassificationWithTeacher, DeiTForMaskedImageModeling, DeiTModel, DeiTPreTrainedModel, ) from .models.deprecated.mctct import ( MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel, ) from .models.deprecated.mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings from .models.deprecated.open_llama import ( OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel, OpenLlamaPreTrainedModel, ) from .models.deprecated.retribert import ( RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST, RetriBertModel, RetriBertPreTrainedModel, ) from .models.deprecated.trajectory_transformer import ( TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TrajectoryTransformerModel, TrajectoryTransformerPreTrainedModel, ) from .models.deprecated.van import ( VAN_PRETRAINED_MODEL_ARCHIVE_LIST, VanForImageClassification, VanModel, VanPreTrainedModel, ) from .models.deta import ( DETA_PRETRAINED_MODEL_ARCHIVE_LIST, DetaForObjectDetection, DetaModel, DetaPreTrainedModel, ) from .models.detr import ( DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DetrForObjectDetection, DetrForSegmentation, DetrModel, DetrPreTrainedModel, ) from .models.dinat import ( DINAT_PRETRAINED_MODEL_ARCHIVE_LIST, DinatBackbone, DinatForImageClassification, DinatModel, DinatPreTrainedModel, ) from .models.dinov2 import ( DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST, Dinov2Backbone, Dinov2ForImageClassification, Dinov2Model, Dinov2PreTrainedModel, ) from .models.distilbert import ( DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, DistilBertForMaskedLM, DistilBertForMultipleChoice, DistilBertForQuestionAnswering, DistilBertForSequenceClassification, DistilBertForTokenClassification, DistilBertModel, DistilBertPreTrainedModel, ) from .models.donut import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, DonutSwinModel, DonutSwinPreTrainedModel from .models.dpr import ( DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, DPRContextEncoder, DPRPretrainedContextEncoder, DPRPreTrainedModel, DPRPretrainedQuestionEncoder, DPRPretrainedReader, DPRQuestionEncoder, DPRReader, ) from .models.dpt import ( DPT_PRETRAINED_MODEL_ARCHIVE_LIST, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel, DPTPreTrainedModel, ) from .models.efficientformer import ( EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, EfficientFormerForImageClassification, EfficientFormerForImageClassificationWithTeacher, EfficientFormerModel, EfficientFormerPreTrainedModel, ) from .models.efficientnet import ( EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST, EfficientNetForImageClassification, EfficientNetModel, EfficientNetPreTrainedModel, ) from .models.electra import ( ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, ElectraForCausalLM, ElectraForMaskedLM, ElectraForMultipleChoice, ElectraForPreTraining, ElectraForQuestionAnswering, ElectraForSequenceClassification, ElectraForTokenClassification, ElectraModel, ElectraPreTrainedModel, load_tf_weights_in_electra, ) from .models.encodec import ( ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST, EncodecModel, EncodecPreTrainedModel, ) from .models.encoder_decoder import EncoderDecoderModel from .models.ernie import ( ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST, ErnieForCausalLM, ErnieForMaskedLM, ErnieForMultipleChoice, ErnieForNextSentencePrediction, ErnieForPreTraining, ErnieForQuestionAnswering, ErnieForSequenceClassification, ErnieForTokenClassification, ErnieModel, ErniePreTrainedModel, ) from .models.ernie_m import ( ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST, ErnieMForInformationExtraction, ErnieMForMultipleChoice, ErnieMForQuestionAnswering, ErnieMForSequenceClassification, ErnieMForTokenClassification, ErnieMModel, ErnieMPreTrainedModel, ) from .models.esm import ( ESM_PRETRAINED_MODEL_ARCHIVE_LIST, EsmFoldPreTrainedModel, EsmForMaskedLM, EsmForProteinFolding, EsmForSequenceClassification, EsmForTokenClassification, EsmModel, EsmPreTrainedModel, ) from .models.falcon import ( FALCON_PRETRAINED_MODEL_ARCHIVE_LIST, FalconForCausalLM, FalconForQuestionAnswering, FalconForSequenceClassification, FalconForTokenClassification, FalconModel, FalconPreTrainedModel, ) from .models.flaubert import ( FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, FlaubertForMultipleChoice, FlaubertForQuestionAnswering, FlaubertForQuestionAnsweringSimple, FlaubertForSequenceClassification, FlaubertForTokenClassification, FlaubertModel, FlaubertPreTrainedModel, FlaubertWithLMHeadModel, ) from .models.flava import ( FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST, FlavaForPreTraining, FlavaImageCodebook, FlavaImageModel, FlavaModel, FlavaMultimodalModel, FlavaPreTrainedModel, FlavaTextModel, ) from .models.fnet import ( FNET_PRETRAINED_MODEL_ARCHIVE_LIST, FNetForMaskedLM, FNetForMultipleChoice, FNetForNextSentencePrediction, FNetForPreTraining, FNetForQuestionAnswering, FNetForSequenceClassification, FNetForTokenClassification, FNetLayer, FNetModel, FNetPreTrainedModel, ) from .models.focalnet import ( FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST, FocalNetBackbone, FocalNetForImageClassification, FocalNetForMaskedImageModeling, FocalNetModel, FocalNetPreTrainedModel, ) from .models.fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel from .models.funnel import ( FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST, FunnelBaseModel, FunnelForMaskedLM, FunnelForMultipleChoice, FunnelForPreTraining, FunnelForQuestionAnswering, FunnelForSequenceClassification, FunnelForTokenClassification, FunnelModel, FunnelPreTrainedModel, load_tf_weights_in_funnel, ) from .models.git import ( GIT_PRETRAINED_MODEL_ARCHIVE_LIST, GitForCausalLM, GitModel, GitPreTrainedModel, GitVisionModel, ) from .models.glpn import ( GLPN_PRETRAINED_MODEL_ARCHIVE_LIST, GLPNForDepthEstimation, GLPNModel, GLPNPreTrainedModel, ) from .models.gpt2 import ( GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, GPT2DoubleHeadsModel, GPT2ForQuestionAnswering, GPT2ForSequenceClassification, GPT2ForTokenClassification, GPT2LMHeadModel, GPT2Model, GPT2PreTrainedModel, load_tf_weights_in_gpt2, ) from .models.gpt_bigcode import ( GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST, GPTBigCodeForCausalLM, GPTBigCodeForSequenceClassification, GPTBigCodeForTokenClassification, GPTBigCodeModel, GPTBigCodePreTrainedModel, ) from .models.gpt_neo import ( GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoForCausalLM, GPTNeoForQuestionAnswering, GPTNeoForSequenceClassification, GPTNeoForTokenClassification, GPTNeoModel, GPTNeoPreTrainedModel, load_tf_weights_in_gpt_neo, ) from .models.gpt_neox import ( GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoXForCausalLM, GPTNeoXForQuestionAnswering, GPTNeoXForSequenceClassification, GPTNeoXForTokenClassification, GPTNeoXLayer, GPTNeoXModel, GPTNeoXPreTrainedModel, ) from .models.gpt_neox_japanese import ( GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseLayer, GPTNeoXJapaneseModel, GPTNeoXJapanesePreTrainedModel, ) from .models.gptj import ( GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST, GPTJForCausalLM, GPTJForQuestionAnswering, GPTJForSequenceClassification, GPTJModel, GPTJPreTrainedModel, ) from .models.gptsan_japanese import ( GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST, GPTSanJapaneseForConditionalGeneration, GPTSanJapaneseModel, GPTSanJapanesePreTrainedModel, ) from .models.graphormer import ( GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST, GraphormerForGraphClassification, GraphormerModel, GraphormerPreTrainedModel, ) from .models.groupvit import ( GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, GroupViTModel, GroupViTPreTrainedModel, GroupViTTextModel, GroupViTVisionModel, ) from .models.hubert import ( HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, HubertForCTC, HubertForSequenceClassification, HubertModel, HubertPreTrainedModel, ) from .models.ibert import ( IBERT_PRETRAINED_MODEL_ARCHIVE_LIST, IBertForMaskedLM, IBertForMultipleChoice, IBertForQuestionAnswering, IBertForSequenceClassification, IBertForTokenClassification, IBertModel, IBertPreTrainedModel, ) from .models.idefics import ( IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST, IdeficsForVisionText2Text, IdeficsModel, IdeficsPreTrainedModel, IdeficsProcessor, ) from .models.imagegpt import ( IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST, ImageGPTForCausalImageModeling, ImageGPTForImageClassification, ImageGPTModel, ImageGPTPreTrainedModel, load_tf_weights_in_imagegpt, ) from .models.informer import ( INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, InformerForPrediction, InformerModel, InformerPreTrainedModel, ) from .models.instructblip import ( INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST, InstructBlipForConditionalGeneration, InstructBlipPreTrainedModel, InstructBlipQFormerModel, InstructBlipVisionModel, ) from .models.jukebox import ( JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST, JukeboxModel, JukeboxPreTrainedModel, JukeboxPrior, JukeboxVQVAE, ) from .models.layoutlm import ( LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMForMaskedLM, LayoutLMForQuestionAnswering, LayoutLMForSequenceClassification, LayoutLMForTokenClassification, LayoutLMModel, LayoutLMPreTrainedModel, ) from .models.layoutlmv2 import ( LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMv2ForQuestionAnswering, LayoutLMv2ForSequenceClassification, LayoutLMv2ForTokenClassification, LayoutLMv2Model, LayoutLMv2PreTrainedModel, ) from .models.layoutlmv3 import ( LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMv3ForQuestionAnswering, LayoutLMv3ForSequenceClassification, LayoutLMv3ForTokenClassification, LayoutLMv3Model, LayoutLMv3PreTrainedModel, ) from .models.led import ( LED_PRETRAINED_MODEL_ARCHIVE_LIST, LEDForConditionalGeneration, LEDForQuestionAnswering, LEDForSequenceClassification, LEDModel, LEDPreTrainedModel, ) from .models.levit import ( LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, LevitForImageClassification, LevitForImageClassificationWithTeacher, LevitModel, LevitPreTrainedModel, ) from .models.lilt import ( LILT_PRETRAINED_MODEL_ARCHIVE_LIST, LiltForQuestionAnswering, LiltForSequenceClassification, LiltForTokenClassification, LiltModel, LiltPreTrainedModel, ) from .models.llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel from .models.longformer import ( LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, LongformerForMaskedLM, LongformerForMultipleChoice, LongformerForQuestionAnswering, LongformerForSequenceClassification, LongformerForTokenClassification, LongformerModel, LongformerPreTrainedModel, LongformerSelfAttention, ) from .models.longt5 import ( LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST, LongT5EncoderModel, LongT5ForConditionalGeneration, LongT5Model, LongT5PreTrainedModel, ) from .models.luke import ( LUKE_PRETRAINED_MODEL_ARCHIVE_LIST, LukeForEntityClassification, LukeForEntityPairClassification, LukeForEntitySpanClassification, LukeForMaskedLM, LukeForMultipleChoice, LukeForQuestionAnswering, LukeForSequenceClassification, LukeForTokenClassification, LukeModel, LukePreTrainedModel, ) from .models.lxmert import ( LxmertEncoder, LxmertForPreTraining, LxmertForQuestionAnswering, LxmertModel, LxmertPreTrainedModel, LxmertVisualFeatureEncoder, LxmertXLayer, ) from .models.m2m_100 import ( M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST, M2M100ForConditionalGeneration, M2M100Model, M2M100PreTrainedModel, ) from .models.marian import MarianForCausalLM, MarianModel, MarianMTModel from .models.markuplm import ( MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST, MarkupLMForQuestionAnswering, MarkupLMForSequenceClassification, MarkupLMForTokenClassification, MarkupLMModel, MarkupLMPreTrainedModel, ) from .models.mask2former import ( MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST, Mask2FormerForUniversalSegmentation, Mask2FormerModel, Mask2FormerPreTrainedModel, ) from .models.maskformer import ( MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, MaskFormerForInstanceSegmentation, MaskFormerModel, MaskFormerPreTrainedModel, MaskFormerSwinBackbone, ) from .models.mbart import ( MBartForCausalLM, MBartForConditionalGeneration, MBartForQuestionAnswering, MBartForSequenceClassification, MBartModel, MBartPreTrainedModel, ) from .models.mega import ( MEGA_PRETRAINED_MODEL_ARCHIVE_LIST, MegaForCausalLM, MegaForMaskedLM, MegaForMultipleChoice, MegaForQuestionAnswering, MegaForSequenceClassification, MegaForTokenClassification, MegaModel, MegaPreTrainedModel, ) from .models.megatron_bert import ( MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, MegatronBertForCausalLM, MegatronBertForMaskedLM, MegatronBertForMultipleChoice, MegatronBertForNextSentencePrediction, MegatronBertForPreTraining, MegatronBertForQuestionAnswering, MegatronBertForSequenceClassification, MegatronBertForTokenClassification, MegatronBertModel, MegatronBertPreTrainedModel, ) from .models.mgp_str import ( MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST, MgpstrForSceneTextRecognition, MgpstrModel, MgpstrPreTrainedModel, ) from .models.mistral import ( MistralForCausalLM, MistralForSequenceClassification, MistralModel, MistralPreTrainedModel, ) from .models.mobilebert import ( MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, MobileBertForMaskedLM, MobileBertForMultipleChoice, MobileBertForNextSentencePrediction, MobileBertForPreTraining, MobileBertForQuestionAnswering, MobileBertForSequenceClassification, MobileBertForTokenClassification, MobileBertLayer, MobileBertModel, MobileBertPreTrainedModel, load_tf_weights_in_mobilebert, ) from .models.mobilenet_v1 import ( MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST, MobileNetV1ForImageClassification, MobileNetV1Model, MobileNetV1PreTrainedModel, load_tf_weights_in_mobilenet_v1, ) from .models.mobilenet_v2 import ( MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST, MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation, MobileNetV2Model, MobileNetV2PreTrainedModel, load_tf_weights_in_mobilenet_v2, ) from .models.mobilevit import ( MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel, MobileViTPreTrainedModel, ) from .models.mobilevitv2 import ( MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST, MobileViTV2ForImageClassification, MobileViTV2ForSemanticSegmentation, MobileViTV2Model, MobileViTV2PreTrainedModel, ) from .models.mpnet import ( MPNET_PRETRAINED_MODEL_ARCHIVE_LIST, MPNetForMaskedLM, MPNetForMultipleChoice, MPNetForQuestionAnswering, MPNetForSequenceClassification, MPNetForTokenClassification, MPNetLayer, MPNetModel, MPNetPreTrainedModel, ) from .models.mpt import ( MPT_PRETRAINED_MODEL_ARCHIVE_LIST, MptForCausalLM, MptForQuestionAnswering, MptForSequenceClassification, MptForTokenClassification, MptModel, MptPreTrainedModel, ) from .models.mra import ( MRA_PRETRAINED_MODEL_ARCHIVE_LIST, MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, MraModel, MraPreTrainedModel, ) from .models.mt5 import ( MT5EncoderModel, MT5ForConditionalGeneration, MT5ForQuestionAnswering, MT5ForSequenceClassification, MT5Model, MT5PreTrainedModel, ) from .models.musicgen import ( MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST, MusicgenForCausalLM, MusicgenForConditionalGeneration, MusicgenModel, MusicgenPreTrainedModel, MusicgenProcessor, ) from .models.mvp import ( MVP_PRETRAINED_MODEL_ARCHIVE_LIST, MvpForCausalLM, MvpForConditionalGeneration, MvpForQuestionAnswering, MvpForSequenceClassification, MvpModel, MvpPreTrainedModel, ) from .models.nat import ( NAT_PRETRAINED_MODEL_ARCHIVE_LIST, NatBackbone, NatForImageClassification, NatModel, NatPreTrainedModel, ) from .models.nezha import ( NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST, NezhaForMaskedLM, NezhaForMultipleChoice, NezhaForNextSentencePrediction, NezhaForPreTraining, NezhaForQuestionAnswering, NezhaForSequenceClassification, NezhaForTokenClassification, NezhaModel, NezhaPreTrainedModel, ) from .models.nllb_moe import ( NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST, NllbMoeForConditionalGeneration, NllbMoeModel, NllbMoePreTrainedModel, NllbMoeSparseMLP, NllbMoeTop2Router, ) from .models.nystromformer import ( NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, NystromformerForMaskedLM, NystromformerForMultipleChoice, NystromformerForQuestionAnswering, NystromformerForSequenceClassification, NystromformerForTokenClassification, NystromformerLayer, NystromformerModel, NystromformerPreTrainedModel, ) from .models.oneformer import ( ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, OneFormerForUniversalSegmentation, OneFormerModel, OneFormerPreTrainedModel, ) from .models.openai import ( OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification, OpenAIGPTLMHeadModel, OpenAIGPTModel, OpenAIGPTPreTrainedModel, load_tf_weights_in_openai_gpt, ) from .models.opt import ( OPT_PRETRAINED_MODEL_ARCHIVE_LIST, OPTForCausalLM, OPTForQuestionAnswering, OPTForSequenceClassification, OPTModel, OPTPreTrainedModel, ) from .models.owlvit import ( OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST, OwlViTForObjectDetection, OwlViTModel, OwlViTPreTrainedModel, OwlViTTextModel, OwlViTVisionModel, ) from .models.pegasus import ( PegasusForCausalLM, PegasusForConditionalGeneration, PegasusModel, PegasusPreTrainedModel, ) from .models.pegasus_x import ( PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST, PegasusXForConditionalGeneration, PegasusXModel, PegasusXPreTrainedModel, ) from .models.perceiver import ( PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST, PerceiverForImageClassificationConvProcessing, PerceiverForImageClassificationFourier, PerceiverForImageClassificationLearned, PerceiverForMaskedLM, PerceiverForMultimodalAutoencoding, PerceiverForOpticalFlow, PerceiverForSequenceClassification, PerceiverLayer, PerceiverModel, PerceiverPreTrainedModel, ) from .models.persimmon import ( PersimmonForCausalLM, PersimmonForSequenceClassification, PersimmonModel, PersimmonPreTrainedModel, ) from .models.pix2struct import ( PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST, Pix2StructForConditionalGeneration, Pix2StructPreTrainedModel, Pix2StructTextModel, Pix2StructVisionModel, ) from .models.plbart import ( PLBART_PRETRAINED_MODEL_ARCHIVE_LIST, PLBartForCausalLM, PLBartForConditionalGeneration, PLBartForSequenceClassification, PLBartModel, PLBartPreTrainedModel, ) from .models.poolformer import ( POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, PoolFormerForImageClassification, PoolFormerModel, PoolFormerPreTrainedModel, ) from .models.pop2piano import ( POP2PIANO_PRETRAINED_MODEL_ARCHIVE_LIST, Pop2PianoForConditionalGeneration, Pop2PianoPreTrainedModel, ) from .models.prophetnet import ( PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST, ProphetNetDecoder, ProphetNetEncoder, ProphetNetForCausalLM, ProphetNetForConditionalGeneration, ProphetNetModel, ProphetNetPreTrainedModel, ) from .models.pvt import ( PVT_PRETRAINED_MODEL_ARCHIVE_LIST, PvtForImageClassification, PvtModel, PvtPreTrainedModel, ) from .models.qdqbert import ( QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST, QDQBertForMaskedLM, QDQBertForMultipleChoice, QDQBertForNextSentencePrediction, QDQBertForQuestionAnswering, QDQBertForSequenceClassification, QDQBertForTokenClassification, QDQBertLayer, QDQBertLMHeadModel, QDQBertModel, QDQBertPreTrainedModel, load_tf_weights_in_qdqbert, ) from .models.rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration from .models.realm import ( REALM_PRETRAINED_MODEL_ARCHIVE_LIST, RealmEmbedder, RealmForOpenQA, RealmKnowledgeAugEncoder, RealmPreTrainedModel, RealmReader, RealmRetriever, RealmScorer, load_tf_weights_in_realm, ) from .models.reformer import ( REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ReformerAttention, ReformerForMaskedLM, ReformerForQuestionAnswering, ReformerForSequenceClassification, ReformerLayer, ReformerModel, ReformerModelWithLMHead, ReformerPreTrainedModel, ) from .models.regnet import ( REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, RegNetForImageClassification, RegNetModel, RegNetPreTrainedModel, ) from .models.rembert import ( REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, RemBertForCausalLM, RemBertForMaskedLM, RemBertForMultipleChoice, RemBertForQuestionAnswering, RemBertForSequenceClassification, RemBertForTokenClassification, RemBertLayer, RemBertModel, RemBertPreTrainedModel, load_tf_weights_in_rembert, ) from .models.resnet import ( RESNET_PRETRAINED_MODEL_ARCHIVE_LIST, ResNetBackbone, ResNetForImageClassification, ResNetModel, ResNetPreTrainedModel, ) from .models.roberta import ( ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, RobertaForCausalLM, RobertaForMaskedLM, RobertaForMultipleChoice, RobertaForQuestionAnswering, RobertaForSequenceClassification, RobertaForTokenClassification, RobertaModel, RobertaPreTrainedModel, ) from .models.roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, RobertaPreLayerNormForCausalLM, RobertaPreLayerNormForMaskedLM, RobertaPreLayerNormForMultipleChoice, RobertaPreLayerNormForQuestionAnswering, RobertaPreLayerNormForSequenceClassification, RobertaPreLayerNormForTokenClassification, RobertaPreLayerNormModel, RobertaPreLayerNormPreTrainedModel, ) from .models.roc_bert import ( ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, RoCBertForCausalLM, RoCBertForMaskedLM, RoCBertForMultipleChoice, RoCBertForPreTraining, RoCBertForQuestionAnswering, RoCBertForSequenceClassification, RoCBertForTokenClassification, RoCBertLayer, RoCBertModel, RoCBertPreTrainedModel, load_tf_weights_in_roc_bert, ) from .models.roformer import ( ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, RoFormerForCausalLM, RoFormerForMaskedLM, RoFormerForMultipleChoice, RoFormerForQuestionAnswering, RoFormerForSequenceClassification, RoFormerForTokenClassification, RoFormerLayer, RoFormerModel, RoFormerPreTrainedModel, load_tf_weights_in_roformer, ) from .models.rwkv import ( RWKV_PRETRAINED_MODEL_ARCHIVE_LIST, RwkvForCausalLM, RwkvModel, RwkvPreTrainedModel, ) from .models.sam import ( SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel, ) from .models.segformer import ( SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, SegformerDecodeHead, SegformerForImageClassification, SegformerForSemanticSegmentation, SegformerLayer, SegformerModel, SegformerPreTrainedModel, ) from .models.sew import ( SEW_PRETRAINED_MODEL_ARCHIVE_LIST, SEWForCTC, SEWForSequenceClassification, SEWModel, SEWPreTrainedModel, ) from .models.sew_d import ( SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST, SEWDForCTC, SEWDForSequenceClassification, SEWDModel, SEWDPreTrainedModel, ) from .models.speech_encoder_decoder import SpeechEncoderDecoderModel from .models.speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextPreTrainedModel, ) from .models.speech_to_text_2 import Speech2Text2ForCausalLM, Speech2Text2PreTrainedModel from .models.speecht5 import ( SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST, SpeechT5ForSpeechToSpeech, SpeechT5ForSpeechToText, SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Model, SpeechT5PreTrainedModel, ) from .models.splinter import ( SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST, SplinterForPreTraining, SplinterForQuestionAnswering, SplinterLayer, SplinterModel, SplinterPreTrainedModel, ) from .models.squeezebert import ( SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, SqueezeBertForMaskedLM, SqueezeBertForMultipleChoice, SqueezeBertForQuestionAnswering, SqueezeBertForSequenceClassification, SqueezeBertForTokenClassification, SqueezeBertModel, SqueezeBertModule, SqueezeBertPreTrainedModel, ) from .models.swiftformer import ( SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, SwiftFormerForImageClassification, SwiftFormerModel, SwiftFormerPreTrainedModel, ) from .models.swin import ( SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel, SwinPreTrainedModel, ) from .models.swin2sr import ( SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST, Swin2SRForImageSuperResolution, Swin2SRModel, Swin2SRPreTrainedModel, ) from .models.swinv2 import ( SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST, Swinv2ForImageClassification, Swinv2ForMaskedImageModeling, Swinv2Model, Swinv2PreTrainedModel, ) from .models.switch_transformers import ( SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST, SwitchTransformersEncoderModel, SwitchTransformersForConditionalGeneration, SwitchTransformersModel, SwitchTransformersPreTrainedModel, SwitchTransformersSparseMLP, SwitchTransformersTop1Router, ) from .models.t5 import ( T5_PRETRAINED_MODEL_ARCHIVE_LIST, T5EncoderModel, T5ForConditionalGeneration, T5ForQuestionAnswering, T5ForSequenceClassification, T5Model, T5PreTrainedModel, load_tf_weights_in_t5, ) from .models.table_transformer import ( TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TableTransformerForObjectDetection, TableTransformerModel, TableTransformerPreTrainedModel, ) from .models.tapas import ( TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, TapasModel, TapasPreTrainedModel, load_tf_weights_in_tapas, ) from .models.time_series_transformer import ( TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TimeSeriesTransformerForPrediction, TimeSeriesTransformerModel, TimeSeriesTransformerPreTrainedModel, ) from .models.timesformer import ( TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TimesformerForVideoClassification, TimesformerModel, TimesformerPreTrainedModel, ) from .models.timm_backbone import TimmBackbone from .models.transfo_xl import ( TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, AdaptiveEmbedding, TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel, TransfoXLPreTrainedModel, load_tf_weights_in_transfo_xl, ) from .models.trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel from .models.tvlt import ( TVLT_PRETRAINED_MODEL_ARCHIVE_LIST, TvltForAudioVisualClassification, TvltForPreTraining, TvltModel, TvltPreTrainedModel, ) from .models.umt5 import ( UMT5EncoderModel, UMT5ForConditionalGeneration, UMT5ForQuestionAnswering, UMT5ForSequenceClassification, UMT5Model, UMT5PreTrainedModel, ) from .models.unispeech import ( UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST, UniSpeechForCTC, UniSpeechForPreTraining, UniSpeechForSequenceClassification, UniSpeechModel, UniSpeechPreTrainedModel, ) from .models.unispeech_sat import ( UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST, UniSpeechSatForAudioFrameClassification, UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatForSequenceClassification, UniSpeechSatForXVector, UniSpeechSatModel, UniSpeechSatPreTrainedModel, ) from .models.upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel from .models.videomae import ( VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST, VideoMAEForPreTraining, VideoMAEForVideoClassification, VideoMAEModel, VideoMAEPreTrainedModel, ) from .models.vilt import ( VILT_PRETRAINED_MODEL_ARCHIVE_LIST, ViltForImageAndTextRetrieval, ViltForImagesAndTextClassification, ViltForMaskedLM, ViltForQuestionAnswering, ViltForTokenClassification, ViltLayer, ViltModel, ViltPreTrainedModel, ) from .models.vision_encoder_decoder import VisionEncoderDecoderModel from .models.vision_text_dual_encoder import VisionTextDualEncoderModel from .models.visual_bert import ( VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, VisualBertForMultipleChoice, VisualBertForPreTraining, VisualBertForQuestionAnswering, VisualBertForRegionToPhraseAlignment, VisualBertForVisualReasoning, VisualBertLayer, VisualBertModel, VisualBertPreTrainedModel, ) from .models.vit import ( VIT_PRETRAINED_MODEL_ARCHIVE_LIST, ViTForImageClassification, ViTForMaskedImageModeling, ViTModel, ViTPreTrainedModel, ) from .models.vit_hybrid import ( VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST, ViTHybridForImageClassification, ViTHybridModel, ViTHybridPreTrainedModel, ) from .models.vit_mae import ( VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST, ViTMAEForPreTraining, ViTMAELayer, ViTMAEModel, ViTMAEPreTrainedModel, ) from .models.vit_msn import ( VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST, ViTMSNForImageClassification, ViTMSNModel, ViTMSNPreTrainedModel, ) from .models.vitdet import ( VITDET_PRETRAINED_MODEL_ARCHIVE_LIST, VitDetBackbone, VitDetModel, VitDetPreTrainedModel, ) from .models.vitmatte import ( VITMATTE_PRETRAINED_MODEL_ARCHIVE_LIST, VitMatteForImageMatting, VitMattePreTrainedModel, ) from .models.vits import ( VITS_PRETRAINED_MODEL_ARCHIVE_LIST, VitsModel, VitsPreTrainedModel, ) from .models.vivit import ( VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST, VivitForVideoClassification, VivitModel, VivitPreTrainedModel, ) from .models.wav2vec2 import ( WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2ForAudioFrameClassification, Wav2Vec2ForCTC, Wav2Vec2ForMaskedLM, Wav2Vec2ForPreTraining, Wav2Vec2ForSequenceClassification, Wav2Vec2ForXVector, Wav2Vec2Model, Wav2Vec2PreTrainedModel, ) from .models.wav2vec2_conformer import ( WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForCTC, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForXVector, Wav2Vec2ConformerModel, Wav2Vec2ConformerPreTrainedModel, ) from .models.wavlm import ( WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST, WavLMForAudioFrameClassification, WavLMForCTC, WavLMForSequenceClassification, WavLMForXVector, WavLMModel, WavLMPreTrainedModel, ) from .models.whisper import ( WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST, WhisperForAudioClassification, WhisperForConditionalGeneration, WhisperModel, WhisperPreTrainedModel, ) from .models.x_clip import ( XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST, XCLIPModel, XCLIPPreTrainedModel, XCLIPTextModel, XCLIPVisionModel, ) from .models.xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel from .models.xlm import ( XLM_PRETRAINED_MODEL_ARCHIVE_LIST, XLMForMultipleChoice, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, XLMForSequenceClassification, XLMForTokenClassification, XLMModel, XLMPreTrainedModel, XLMWithLMHeadModel, ) from .models.xlm_prophetnet import ( XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST, XLMProphetNetDecoder, XLMProphetNetEncoder, XLMProphetNetForCausalLM, XLMProphetNetForConditionalGeneration, XLMProphetNetModel, XLMProphetNetPreTrainedModel, ) from .models.xlm_roberta import ( XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, XLMRobertaForCausalLM, XLMRobertaForMaskedLM, XLMRobertaForMultipleChoice, XLMRobertaForQuestionAnswering, XLMRobertaForSequenceClassification, XLMRobertaForTokenClassification, XLMRobertaModel, XLMRobertaPreTrainedModel, ) from .models.xlm_roberta_xl import ( XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST, XLMRobertaXLForCausalLM, XLMRobertaXLForMaskedLM, XLMRobertaXLForMultipleChoice, XLMRobertaXLForQuestionAnswering, XLMRobertaXLForSequenceClassification, XLMRobertaXLForTokenClassification, XLMRobertaXLModel, XLMRobertaXLPreTrainedModel, ) from .models.xlnet import ( XLNET_PRETRAINED_MODEL_ARCHIVE_LIST, XLNetForMultipleChoice, XLNetForQuestionAnswering, XLNetForQuestionAnsweringSimple, XLNetForSequenceClassification, XLNetForTokenClassification, XLNetLMHeadModel, XLNetModel, XLNetPreTrainedModel, load_tf_weights_in_xlnet, ) from .models.xmod import ( XMOD_PRETRAINED_MODEL_ARCHIVE_LIST, XmodForCausalLM, XmodForMaskedLM, XmodForMultipleChoice, XmodForQuestionAnswering, XmodForSequenceClassification, XmodForTokenClassification, XmodModel, XmodPreTrainedModel, ) from .models.yolos import ( YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST, YolosForObjectDetection, YolosModel, YolosPreTrainedModel, ) from .models.yoso import ( YOSO_PRETRAINED_MODEL_ARCHIVE_LIST, YosoForMaskedLM, YosoForMultipleChoice, YosoForQuestionAnswering, YosoForSequenceClassification, YosoForTokenClassification, YosoLayer, YosoModel, YosoPreTrainedModel, ) # Optimization from .optimization import ( Adafactor, AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_inverse_sqrt_schedule, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup, get_scheduler, ) from .pytorch_utils import Conv1D, apply_chunking_to_forward, prune_layer # Trainer from .trainer import Trainer from .trainer_pt_utils import torch_distributed_zero_first from .trainer_seq2seq import Seq2SeqTrainer # TensorFlow try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: # Import the same objects as dummies to get them in the namespace. # They will raise an import error if the user tries to instantiate / use them. from .utils.dummy_tf_objects import * else: from .benchmark.benchmark_args_tf import TensorFlowBenchmarkArguments # Benchmarks from .benchmark.benchmark_tf import TensorFlowBenchmark from .generation import ( TFForcedBOSTokenLogitsProcessor, TFForcedEOSTokenLogitsProcessor, TFForceTokensLogitsProcessor, TFGenerationMixin, TFLogitsProcessor, TFLogitsProcessorList, TFLogitsWarper, TFMinLengthLogitsProcessor, TFNoBadWordsLogitsProcessor, TFNoRepeatNGramLogitsProcessor, TFRepetitionPenaltyLogitsProcessor, TFSuppressTokensAtBeginLogitsProcessor, TFSuppressTokensLogitsProcessor, TFTemperatureLogitsWarper, TFTopKLogitsWarper, TFTopPLogitsWarper, tf_top_k_top_p_filtering, ) from .keras_callbacks import KerasMetricCallback, PushToHubCallback from .modeling_tf_utils import TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, shape_list # TensorFlow model imports from .models.albert import ( TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, 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 ( TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFBertEmbeddings, 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 ( TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST, TFBlipForConditionalGeneration, TFBlipForImageTextRetrieval, TFBlipForQuestionAnswering, TFBlipModel, TFBlipPreTrainedModel, TFBlipTextModel, TFBlipVisionModel, ) from .models.camembert import ( TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFCamembertForCausalLM, TFCamembertForMaskedLM, TFCamembertForMultipleChoice, TFCamembertForQuestionAnswering, TFCamembertForSequenceClassification, TFCamembertForTokenClassification, TFCamembertModel, TFCamembertPreTrainedModel, ) from .models.clip import ( TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST, TFCLIPModel, TFCLIPPreTrainedModel, TFCLIPTextModel, TFCLIPVisionModel, ) from .models.convbert import ( TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFConvBertForMaskedLM, TFConvBertForMultipleChoice, TFConvBertForQuestionAnswering, TFConvBertForSequenceClassification, TFConvBertForTokenClassification, TFConvBertLayer, TFConvBertModel, TFConvBertPreTrainedModel, ) from .models.convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel from .models.ctrl import ( TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, TFCTRLForSequenceClassification, TFCTRLLMHeadModel, TFCTRLModel, TFCTRLPreTrainedModel, ) from .models.cvt import ( TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST, TFCvtForImageClassification, TFCvtModel, TFCvtPreTrainedModel, ) from .models.data2vec import ( TFData2VecVisionForImageClassification, TFData2VecVisionForSemanticSegmentation, TFData2VecVisionModel, TFData2VecVisionPreTrainedModel, ) from .models.deberta import ( TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, TFDebertaForMaskedLM, TFDebertaForQuestionAnswering, TFDebertaForSequenceClassification, TFDebertaForTokenClassification, TFDebertaModel, TFDebertaPreTrainedModel, ) from .models.deberta_v2 import ( TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST, TFDebertaV2ForMaskedLM, TFDebertaV2ForMultipleChoice, TFDebertaV2ForQuestionAnswering, TFDebertaV2ForSequenceClassification, TFDebertaV2ForTokenClassification, TFDebertaV2Model, TFDebertaV2PreTrainedModel, ) from .models.deit import ( TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher, TFDeiTForMaskedImageModeling, TFDeiTModel, TFDeiTPreTrainedModel, ) from .models.distilbert import ( TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFDistilBertForMaskedLM, TFDistilBertForMultipleChoice, TFDistilBertForQuestionAnswering, TFDistilBertForSequenceClassification, TFDistilBertForTokenClassification, TFDistilBertMainLayer, TFDistilBertModel, TFDistilBertPreTrainedModel, ) from .models.dpr import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, TFDPRContextEncoder, TFDPRPretrainedContextEncoder, TFDPRPretrainedQuestionEncoder, TFDPRPretrainedReader, TFDPRQuestionEncoder, TFDPRReader, ) from .models.efficientformer import ( TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFEfficientFormerForImageClassification, TFEfficientFormerForImageClassificationWithTeacher, TFEfficientFormerModel, TFEfficientFormerPreTrainedModel, ) from .models.electra import ( TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, TFElectraForMaskedLM, TFElectraForMultipleChoice, TFElectraForPreTraining, TFElectraForQuestionAnswering, TFElectraForSequenceClassification, TFElectraForTokenClassification, TFElectraModel, TFElectraPreTrainedModel, ) from .models.encoder_decoder import TFEncoderDecoderModel from .models.esm import ( ESM_PRETRAINED_MODEL_ARCHIVE_LIST, TFEsmForMaskedLM, TFEsmForSequenceClassification, TFEsmForTokenClassification, TFEsmModel, TFEsmPreTrainedModel, ) from .models.flaubert import ( TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFFlaubertForMultipleChoice, TFFlaubertForQuestionAnsweringSimple, TFFlaubertForSequenceClassification, TFFlaubertForTokenClassification, TFFlaubertModel, TFFlaubertPreTrainedModel, TFFlaubertWithLMHeadModel, ) from .models.funnel import ( TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST, TFFunnelBaseModel, TFFunnelForMaskedLM, TFFunnelForMultipleChoice, TFFunnelForPreTraining, TFFunnelForQuestionAnswering, TFFunnelForSequenceClassification, TFFunnelForTokenClassification, TFFunnelModel, TFFunnelPreTrainedModel, ) from .models.gpt2 import ( TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, TFGPT2DoubleHeadsModel, TFGPT2ForSequenceClassification, TFGPT2LMHeadModel, TFGPT2MainLayer, TFGPT2Model, TFGPT2PreTrainedModel, ) from .models.gptj import ( TFGPTJForCausalLM, TFGPTJForQuestionAnswering, TFGPTJForSequenceClassification, TFGPTJModel, TFGPTJPreTrainedModel, ) from .models.groupvit import ( TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFGroupViTModel, TFGroupViTPreTrainedModel, TFGroupViTTextModel, TFGroupViTVisionModel, ) from .models.hubert import ( TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFHubertForCTC, TFHubertModel, TFHubertPreTrainedModel, ) from .models.layoutlm import ( TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFLayoutLMForMaskedLM, TFLayoutLMForQuestionAnswering, TFLayoutLMForSequenceClassification, TFLayoutLMForTokenClassification, TFLayoutLMMainLayer, TFLayoutLMModel, TFLayoutLMPreTrainedModel, ) from .models.layoutlmv3 import ( TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST, TFLayoutLMv3ForQuestionAnswering, TFLayoutLMv3ForSequenceClassification, TFLayoutLMv3ForTokenClassification, TFLayoutLMv3Model, TFLayoutLMv3PreTrainedModel, ) from .models.led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel from .models.longformer import ( TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFLongformerForMaskedLM, TFLongformerForMultipleChoice, TFLongformerForQuestionAnswering, TFLongformerForSequenceClassification, TFLongformerForTokenClassification, TFLongformerModel, TFLongformerPreTrainedModel, TFLongformerSelfAttention, ) from .models.lxmert import ( TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFLxmertForPreTraining, TFLxmertMainLayer, TFLxmertModel, TFLxmertPreTrainedModel, TFLxmertVisualFeatureEncoder, ) from .models.marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel from .models.mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel from .models.mobilebert import ( TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFMobileBertForMaskedLM, TFMobileBertForMultipleChoice, TFMobileBertForNextSentencePrediction, TFMobileBertForPreTraining, TFMobileBertForQuestionAnswering, TFMobileBertForSequenceClassification, TFMobileBertForTokenClassification, TFMobileBertMainLayer, TFMobileBertModel, TFMobileBertPreTrainedModel, ) from .models.mobilevit import ( TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel, TFMobileViTPreTrainedModel, ) from .models.mpnet import ( TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFMPNetForMaskedLM, TFMPNetForMultipleChoice, TFMPNetForQuestionAnswering, TFMPNetForSequenceClassification, TFMPNetForTokenClassification, TFMPNetMainLayer, TFMPNetModel, TFMPNetPreTrainedModel, ) from .models.mt5 import TFMT5EncoderModel, TFMT5ForConditionalGeneration, TFMT5Model from .models.openai import ( TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST, 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 ( TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel, TFRegNetPreTrainedModel, ) from .models.rembert import ( TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFRemBertForCausalLM, TFRemBertForMaskedLM, TFRemBertForMultipleChoice, TFRemBertForQuestionAnswering, TFRemBertForSequenceClassification, TFRemBertForTokenClassification, TFRemBertLayer, TFRemBertModel, TFRemBertPreTrainedModel, ) from .models.resnet import ( TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFResNetForImageClassification, TFResNetModel, TFResNetPreTrainedModel, ) from .models.roberta import ( TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, TFRobertaForCausalLM, TFRobertaForMaskedLM, TFRobertaForMultipleChoice, TFRobertaForQuestionAnswering, TFRobertaForSequenceClassification, TFRobertaForTokenClassification, TFRobertaMainLayer, TFRobertaModel, TFRobertaPreTrainedModel, ) from .models.roberta_prelayernorm import ( TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, TFRobertaPreLayerNormForCausalLM, TFRobertaPreLayerNormForMaskedLM, TFRobertaPreLayerNormForMultipleChoice, TFRobertaPreLayerNormForQuestionAnswering, TFRobertaPreLayerNormForSequenceClassification, TFRobertaPreLayerNormForTokenClassification, TFRobertaPreLayerNormMainLayer, TFRobertaPreLayerNormModel, TFRobertaPreLayerNormPreTrainedModel, ) from .models.roformer import ( TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFRoFormerForCausalLM, TFRoFormerForMaskedLM, TFRoFormerForMultipleChoice, TFRoFormerForQuestionAnswering, TFRoFormerForSequenceClassification, TFRoFormerForTokenClassification, TFRoFormerLayer, TFRoFormerModel, TFRoFormerPreTrainedModel, ) from .models.sam import ( TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel, ) from .models.segformer import ( TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFSegformerDecodeHead, TFSegformerForImageClassification, TFSegformerForSemanticSegmentation, TFSegformerModel, TFSegformerPreTrainedModel, ) from .models.speech_to_text import ( TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, TFSpeech2TextForConditionalGeneration, TFSpeech2TextModel, TFSpeech2TextPreTrainedModel, ) from .models.swin import ( TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, TFSwinForImageClassification, TFSwinForMaskedImageModeling, TFSwinModel, TFSwinPreTrainedModel, ) from .models.t5 import ( TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST, TFT5EncoderModel, TFT5ForConditionalGeneration, TFT5Model, TFT5PreTrainedModel, ) from .models.tapas import ( TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, TFTapasForMaskedLM, TFTapasForQuestionAnswering, TFTapasForSequenceClassification, TFTapasModel, TFTapasPreTrainedModel, ) from .models.transfo_xl import ( TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, TFAdaptiveEmbedding, TFTransfoXLForSequenceClassification, TFTransfoXLLMHeadModel, TFTransfoXLMainLayer, TFTransfoXLModel, TFTransfoXLPreTrainedModel, ) 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 ( TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, TFWav2Vec2ForCTC, TFWav2Vec2ForSequenceClassification, TFWav2Vec2Model, TFWav2Vec2PreTrainedModel, ) from .models.whisper import ( TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST, TFWhisperForConditionalGeneration, TFWhisperModel, TFWhisperPreTrainedModel, ) from .models.xglm import ( TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXGLMForCausalLM, TFXGLMModel, TFXGLMPreTrainedModel, ) from .models.xlm import ( TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMForMultipleChoice, TFXLMForQuestionAnsweringSimple, TFXLMForSequenceClassification, TFXLMForTokenClassification, TFXLMMainLayer, TFXLMModel, TFXLMPreTrainedModel, TFXLMWithLMHeadModel, ) from .models.xlm_roberta import ( TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMRobertaForCausalLM, TFXLMRobertaForMaskedLM, TFXLMRobertaForMultipleChoice, TFXLMRobertaForQuestionAnswering, TFXLMRobertaForSequenceClassification, TFXLMRobertaForTokenClassification, TFXLMRobertaModel, TFXLMRobertaPreTrainedModel, ) from .models.xlnet import ( TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLNetForMultipleChoice, TFXLNetForQuestionAnsweringSimple, TFXLNetForSequenceClassification, TFXLNetForTokenClassification, TFXLNetLMHeadModel, TFXLNetMainLayer, TFXLNetModel, TFXLNetPreTrainedModel, ) # Optimization from .optimization_tf import AdamWeightDecay, GradientAccumulator, WarmUp, create_optimizer # Trainer from .trainer_tf import TFTrainer 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_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: # Import the same objects as dummies to get them in the namespace. # They will raise an import error if the user tries to instantiate / use them. from .utils.dummy_flax_objects import * else: from .generation import ( FlaxForcedBOSTokenLogitsProcessor, FlaxForcedEOSTokenLogitsProcessor, FlaxForceTokensLogitsProcessor, FlaxGenerationMixin, FlaxLogitsProcessor, FlaxLogitsProcessorList, FlaxLogitsWarper, FlaxMinLengthLogitsProcessor, FlaxSuppressTokensAtBeginLogitsProcessor, FlaxSuppressTokensLogitsProcessor, FlaxTemperatureLogitsWarper, FlaxTopKLogitsWarper, FlaxTopPLogitsWarper, FlaxWhisperTimeStampLogitsProcessor, ) 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.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.gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel from .models.gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel from .models.gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel 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.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 ( FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, FlaxXLMRobertaForCausalLM, FlaxXLMRobertaForMaskedLM, FlaxXLMRobertaForMultipleChoice, FlaxXLMRobertaForQuestionAnswering, FlaxXLMRobertaForSequenceClassification, FlaxXLMRobertaForTokenClassification, FlaxXLMRobertaModel, FlaxXLMRobertaPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule( __name__, globals()["__file__"], _import_structure, module_spec=__spec__, extra_objects={"__version__": __version__}, ) if not is_tf_available() and not is_torch_available() and not is_flax_available(): logger.warning( "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. " "Models won't be available and only tokenizers, configuration " "and file/data utilities can be used." )