__init__.py

# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.

# Copyright 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__ = "4.20.0.dev0"

from typing import TYPE_CHECKING

# Check the dependencies satisfy the minimal versions required.
from . import dependency_versions_check
from .utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_scatter_available,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
    logging,
)


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


# Base objects, independent of any specific backend
_import_structure = {
    "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": [],
    "dependency_versions_check": [],
    "dependency_versions_table": [],
    "dynamic_module_utils": [],
    "feature_extraction_sequence_utils": ["SequenceFeatureExtractor"],
    "feature_extraction_utils": ["BatchFeature", "FeatureExtractionMixin"],
    "file_utils": [],
    "hf_argparser": ["HfArgumentParser"],
    "integrations": [
        "is_comet_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.auto": [
        "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CONFIG_MAPPING",
        "FEATURE_EXTRACTOR_MAPPING",
        "MODEL_NAMES_MAPPING",
        "PROCESSOR_MAPPING",
        "TOKENIZER_MAPPING",
        "AutoConfig",
        "AutoFeatureExtractor",
        "AutoProcessor",
        "AutoTokenizer",
    ],
    "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.blenderbot": ["BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotConfig", "BlenderbotTokenizer"],
    "models.blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallTokenizer",
    ],
    "models.bort": [],
    "models.byt5": ["ByT5Tokenizer"],
    "models.camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig"],
    "models.canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig", "CanineTokenizer"],
    "models.clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPTextConfig",
        "CLIPTokenizer",
        "CLIPVisionConfig",
    ],
    "models.convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertTokenizer"],
    "models.convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig"],
    "models.cpm": [],
    "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.deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig"],
    "models.detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig"],
    "models.dialogpt": [],
    "models.distilbert": ["DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DistilBertConfig", "DistilBertTokenizer"],
    "models.dit": [],
    "models.dpr": [
        "DPR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DPRConfig",
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
    "models.dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"],
    "models.electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraTokenizer"],
    "models.encoder_decoder": ["EncoderDecoderConfig"],
    "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.fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig", "FSMTTokenizer"],
    "models.funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig", "FunnelTokenizer"],
    "models.glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"],
    "models.gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2Tokenizer"],
    "models.gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig"],
    "models.gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig"],
    "models.herbert": ["HerbertTokenizer"],
    "models.hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"],
    "models.ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig"],
    "models.imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig"],
    "models.layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMTokenizer"],
    "models.layoutlmv2": [
        "LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LayoutLMv2Config",
        "LayoutLMv2FeatureExtractor",
        "LayoutLMv2Processor",
        "LayoutLMv2Tokenizer",
    ],
    "models.layoutxlm": ["LayoutXLMProcessor"],
    "models.led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig", "LEDTokenizer"],
    "models.longformer": ["LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongformerConfig", "LongformerTokenizer"],
    "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.maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "models.mbart": ["MBartConfig"],
    "models.mbart50": [],
    "models.megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
    "models.megatron_gpt2": [],
    "models.mluke": [],
    "models.mmbt": ["MMBTConfig"],
    "models.mobilebert": ["MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileBertConfig", "MobileBertTokenizer"],
    "models.mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig", "MPNetTokenizer"],
    "models.mt5": ["MT5Config"],
    "models.nystromformer": [
        "NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NystromformerConfig",
    ],
    "models.openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig", "OpenAIGPTTokenizer"],
    "models.opt": ["OPTConfig"],
    "models.pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig", "PegasusTokenizer"],
    "models.perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverTokenizer"],
    "models.phobert": ["PhobertTokenizer"],
    "models.plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"],
    "models.poolformer": ["POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PoolFormerConfig"],
    "models.prophetnet": ["PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ProphetNetConfig", "ProphetNetTokenizer"],
    "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.retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig", "RetriBertTokenizer"],
    "models.roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaTokenizer"],
    "models.roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerTokenizer"],
    "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",
    ],
    "models.speech_to_text_2": [
        "SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Speech2Text2Config",
        "Speech2Text2Processor",
        "Speech2Text2Tokenizer",
    ],
    "models.splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig", "SplinterTokenizer"],
    "models.squeezebert": ["SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SqueezeBertConfig", "SqueezeBertTokenizer"],
    "models.swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig"],
    "models.t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config"],
    "models.tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig", "TapasTokenizer"],
    "models.tapex": ["TapexTokenizer"],
    "models.trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
    "models.transfo_xl": [
        "TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TransfoXLConfig",
        "TransfoXLCorpus",
        "TransfoXLTokenizer",
    ],
    "models.trocr": [
        "TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrOCRConfig",
        "TrOCRProcessor",
    ],
    "models.unispeech": [
        "UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "UniSpeechConfig",
    ],
    "models.unispeech_sat": [
        "UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "UniSpeechSatConfig",
    ],
    "models.van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"],
    "models.vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig", "ViltFeatureExtractor", "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_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"],
    "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.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.yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig"],
    "models.yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"],
    "onnx": [],
    "pipelines": [
        "AudioClassificationPipeline",
        "AutomaticSpeechRecognitionPipeline",
        "Conversation",
        "ConversationalPipeline",
        "CsvPipelineDataFormat",
        "FeatureExtractionPipeline",
        "FillMaskPipeline",
        "ImageClassificationPipeline",
        "ImageSegmentationPipeline",
        "JsonPipelineDataFormat",
        "NerPipeline",
        "ObjectDetectionPipeline",
        "PipedPipelineDataFormat",
        "Pipeline",
        "PipelineDataFormat",
        "QuestionAnsweringPipeline",
        "SummarizationPipeline",
        "TableQuestionAnsweringPipeline",
        "Text2TextGenerationPipeline",
        "TextClassificationPipeline",
        "TextGenerationPipeline",
        "TokenClassificationPipeline",
        "TranslationPipeline",
        "ZeroShotClassificationPipeline",
        "ZeroShotImageClassificationPipeline",
        "pipeline",
    ],
    "processing_utils": ["ProcessorMixin"],
    "testing_utils": [],
    "tokenization_utils": ["PreTrainedTokenizer"],
    "tokenization_utils_base": [
        "AddedToken",
        "BatchEncoding",
        "CharSpan",
        "PreTrainedTokenizerBase",
        "SpecialTokensMixin",
        "TokenSpan",
    ],
    "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",
        "cached_path",
        "is_apex_available",
        "is_datasets_available",
        "is_faiss_available",
        "is_flax_available",
        "is_phonemizer_available",
        "is_psutil_available",
        "is_py3nvml_available",
        "is_pyctcdecode_available",
        "is_scipy_available",
        "is_sentencepiece_available",
        "is_sklearn_available",
        "is_speech_available",
        "is_tf_available",
        "is_timm_available",
        "is_tokenizers_available",
        "is_torch_available",
        "is_torch_tpu_available",
        "is_vision_available",
        "logging",
    ],
}

# 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.cpm"].append("CpmTokenizer")
    _import_structure["models.deberta_v2"].append("DebertaV2Tokenizer")
    _import_structure["models.fnet"].append("FNetTokenizer")
    _import_structure["models.layoutxlm"].append("LayoutXLMTokenizer")
    _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.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.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.camembert"].append("CamembertTokenizerFast")
    _import_structure["models.clip"].append("CLIPTokenizerFast")
    _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.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.herbert"].append("HerbertTokenizerFast")
    _import_structure["models.layoutlm"].append("LayoutLMTokenizerFast")
    _import_structure["models.layoutlmv2"].append("LayoutLMv2TokenizerFast")
    _import_structure["models.layoutxlm"].append("LayoutXLMTokenizerFast")
    _import_structure["models.led"].append("LEDTokenizerFast")
    _import_structure["models.longformer"].append("LongformerTokenizerFast")
    _import_structure["models.lxmert"].append("LxmertTokenizerFast")
    _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.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.retribert"].append("RetriBertTokenizerFast")
    _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.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.speech_to_text"].append("Speech2TextFeatureExtractor")

try:
    if not (is_sentencepiece_available() and is_speech_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils import dummy_sentencepiece_and_speech_objects

    _import_structure["utils.dummy_sentencepiece_and_speech_objects"] = [
        name for name in dir(dummy_sentencepiece_and_speech_objects) if not name.startswith("_")
    ]
else:
    _import_structure["models.speech_to_text"].append("Speech2TextProcessor")

# 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_utils"] = ["ImageFeatureExtractionMixin"]
    _import_structure["models.beit"].append("BeitFeatureExtractor")
    _import_structure["models.clip"].append("CLIPFeatureExtractor")
    _import_structure["models.clip"].append("CLIPProcessor")
    _import_structure["models.convnext"].append("ConvNextFeatureExtractor")
    _import_structure["models.deit"].append("DeiTFeatureExtractor")
    _import_structure["models.detr"].append("DetrFeatureExtractor")
    _import_structure["models.dpt"].append("DPTFeatureExtractor")
    _import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaProcessor"])
    _import_structure["models.glpn"].append("GLPNFeatureExtractor")
    _import_structure["models.imagegpt"].append("ImageGPTFeatureExtractor")
    _import_structure["models.layoutlmv2"].append("LayoutLMv2FeatureExtractor")
    _import_structure["models.layoutlmv2"].append("LayoutLMv2Processor")
    _import_structure["models.layoutxlm"].append("LayoutXLMProcessor")
    _import_structure["models.maskformer"].append("MaskFormerFeatureExtractor")
    _import_structure["models.perceiver"].append("PerceiverFeatureExtractor")
    _import_structure["models.poolformer"].append("PoolFormerFeatureExtractor")
    _import_structure["models.segformer"].append("SegformerFeatureExtractor")
    _import_structure["models.vilt"].append("ViltFeatureExtractor")
    _import_structure["models.vilt"].append("ViltProcessor")
    _import_structure["models.vit"].append("ViTFeatureExtractor")
    _import_structure["models.yolos"].append("YolosFeatureExtractor")

# Timm-backed objects
try:
    if not (is_timm_available() and is_vision_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils import dummy_timm_objects

    _import_structure["utils.dummy_timm_objects"] = [
        name for name in dir(dummy_timm_objects) if not name.startswith("_")
    ]
else:
    _import_structure["models.detr"].extend(
        [
            "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
            "DetrForObjectDetection",
            "DetrForSegmentation",
            "DetrModel",
            "DetrPreTrainedModel",
        ]
    )

try:
    if not is_scatter_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from .utils import dummy_scatter_objects

    _import_structure["utils.dummy_scatter_objects"] = [
        name for name in dir(dummy_scatter_objects) if not name.startswith("_")
    ]
else:
    _import_structure["models.tapas"].extend(
        [
            "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
            "TapasForMaskedLM",
            "TapasForQuestionAnswering",
            "TapasForSequenceClassification",
            "TapasModel",
            "TapasPreTrainedModel",
            "load_tf_weights_in_tapas",
        ]
    )


# 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["deepspeed"] = []
    _import_structure["generation_beam_constraints"] = [
        "Constraint",
        "ConstraintListState",
        "DisjunctiveConstraint",
        "PhrasalConstraint",
    ]
    _import_structure["generation_beam_search"] = ["BeamScorer", "BeamSearchScorer", "ConstrainedBeamSearchScorer"]
    _import_structure["generation_logits_process"] = [
        "ForcedBOSTokenLogitsProcessor",
        "ForcedEOSTokenLogitsProcessor",
        "HammingDiversityLogitsProcessor",
        "InfNanRemoveLogitsProcessor",
        "LogitsProcessor",
        "LogitsProcessorList",
        "LogitsWarper",
        "MinLengthLogitsProcessor",
        "NoBadWordsLogitsProcessor",
        "NoRepeatNGramLogitsProcessor",
        "PrefixConstrainedLogitsProcessor",
        "RepetitionPenaltyLogitsProcessor",
        "TemperatureLogitsWarper",
        "TopKLogitsWarper",
        "TopPLogitsWarper",
    ]
    _import_structure["generation_stopping_criteria"] = [
        "MaxLengthCriteria",
        "MaxTimeCriteria",
        "StoppingCriteria",
        "StoppingCriteriaList",
    ]
    _import_structure["generation_utils"] = ["top_k_top_p_filtering"]
    _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.auto"].extend(
        [
            "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
            "MODEL_FOR_AUDIO_XVECTOR_MAPPING",
            "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
            "MODEL_FOR_CAUSAL_LM_MAPPING",
            "MODEL_FOR_CTC_MAPPING",
            "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
            "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
            "MODEL_FOR_INSTANCE_SEGMENTATION_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_TOKEN_CLASSIFICATION_MAPPING",
            "MODEL_FOR_VISION_2_SEQ_MAPPING",
            "MODEL_MAPPING",
            "MODEL_WITH_LM_HEAD_MAPPING",
            "AutoModel",
            "AutoModelForAudioClassification",
            "AutoModelForAudioFrameClassification",
            "AutoModelForAudioXVector",
            "AutoModelForCausalLM",
            "AutoModelForCTC",
            "AutoModelForImageClassification",
            "AutoModelForImageSegmentation",
            "AutoModelForInstanceSegmentation",
            "AutoModelForMaskedImageModeling",
            "AutoModelForMaskedLM",
            "AutoModelForMultipleChoice",
            "AutoModelForNextSentencePrediction",
            "AutoModelForObjectDetection",
            "AutoModelForPreTraining",
            "AutoModelForQuestionAnswering",
            "AutoModelForSemanticSegmentation",
            "AutoModelForSeq2SeqLM",
            "AutoModelForSequenceClassification",
            "AutoModelForSpeechSeq2Seq",
            "AutoModelForTableQuestionAnswering",
            "AutoModelForTokenClassification",
            "AutoModelForVision2Seq",
            "AutoModelWithLMHead",
        ]
    )
    _import_structure["models.bart"].extend(
        [
            "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
            "BartForCausalLM",
            "BartForConditionalGeneration",
            "BartForQuestionAnswering",
            "BartForSequenceClassification",
            "BartModel",
            "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.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.camembert"].extend(
        [
            "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "CamembertForCausalLM",
            "CamembertForMaskedLM",
            "CamembertForMultipleChoice",
            "CamembertForQuestionAnswering",
            "CamembertForSequenceClassification",
            "CamembertForTokenClassification",
            "CamembertModel",
        ]
    )
    _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.clip"].extend(
        [
            "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
            "CLIPModel",
            "CLIPPreTrainedModel",
            "CLIPTextModel",
            "CLIPVisionModel",
        ]
    )
    _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",
            "ConvNextForImageClassification",
            "ConvNextModel",
            "ConvNextPreTrainedModel",
        ]
    )
    _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.deit"].extend(
        [
            "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "DeiTForImageClassification",
            "DeiTForImageClassificationWithTeacher",
            "DeiTForMaskedImageModeling",
            "DeiTModel",
            "DeiTPreTrainedModel",
        ]
    )
    _import_structure["models.distilbert"].extend(
        [
            "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "DistilBertForMaskedLM",
            "DistilBertForMultipleChoice",
            "DistilBertForQuestionAnswering",
            "DistilBertForSequenceClassification",
            "DistilBertForTokenClassification",
            "DistilBertModel",
            "DistilBertPreTrainedModel",
        ]
    )
    _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.electra"].extend(
        [
            "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "ElectraForCausalLM",
            "ElectraForMaskedLM",
            "ElectraForMultipleChoice",
            "ElectraForPreTraining",
            "ElectraForQuestionAnswering",
            "ElectraForSequenceClassification",
            "ElectraForTokenClassification",
            "ElectraModel",
            "ElectraPreTrainedModel",
            "load_tf_weights_in_electra",
        ]
    )
    _import_structure["models.encoder_decoder"].append("EncoderDecoderModel")
    _import_structure["models.flaubert"].extend(
        [
            "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "FlaubertForMultipleChoice",
            "FlaubertForQuestionAnswering",
            "FlaubertForQuestionAnsweringSimple",
            "FlaubertForSequenceClassification",
            "FlaubertForTokenClassification",
            "FlaubertModel",
            "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.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.glpn"].extend(
        [
            "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
            "GLPNForDepthEstimation",
            "GLPNModel",
            "GLPNPreTrainedModel",
        ]
    )
    _import_structure["models.gpt2"].extend(
        [
            "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
            "GPT2DoubleHeadsModel",
            "GPT2ForSequenceClassification",
            "GPT2ForTokenClassification",
            "GPT2LMHeadModel",
            "GPT2Model",
            "GPT2PreTrainedModel",
            "load_tf_weights_in_gpt2",
        ]
    )
    _import_structure["models.gpt_neo"].extend(
        [
            "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
            "GPTNeoForCausalLM",
            "GPTNeoForSequenceClassification",
            "GPTNeoModel",
            "GPTNeoPreTrainedModel",
            "load_tf_weights_in_gpt_neo",
        ]
    )
    _import_structure["models.gptj"].extend(
        [
            "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
            "GPTJForCausalLM",
            "GPTJForQuestionAnswering",
            "GPTJForSequenceClassification",
            "GPTJModel",
            "GPTJPreTrainedModel",
        ]
    )
    _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.imagegpt"].extend(
        [
            "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "ImageGPTForCausalImageModeling",
            "ImageGPTForImageClassification",
            "ImageGPTModel",
            "ImageGPTPreTrainedModel",
            "load_tf_weights_in_imagegpt",
        ]
    )
    _import_structure["models.layoutlm"].extend(
        [
            "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
            "LayoutLMForMaskedLM",
            "LayoutLMForSequenceClassification",
            "LayoutLMForTokenClassification",
            "LayoutLMModel",
            "LayoutLMPreTrainedModel",
        ]
    )
    _import_structure["models.layoutlmv2"].extend(
        [
            "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
            "LayoutLMv2ForQuestionAnswering",
            "LayoutLMv2ForSequenceClassification",
            "LayoutLMv2ForTokenClassification",
            "LayoutLMv2Model",
            "LayoutLMv2PreTrainedModel",
        ]
    )
    _import_structure["models.led"].extend(
        [
            "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
            "LEDForConditionalGeneration",
            "LEDForQuestionAnswering",
            "LEDForSequenceClassification",
            "LEDModel",
            "LEDPreTrainedModel",
        ]
    )
    _import_structure["models.longformer"].extend(
        [
            "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
            "LongformerForMaskedLM",
            "LongformerForMultipleChoice",
            "LongformerForQuestionAnswering",
            "LongformerForSequenceClassification",
            "LongformerForTokenClassification",
            "LongformerModel",
            "LongformerPreTrainedModel",
            "LongformerSelfAttention",
        ]
    )
    _import_structure["models.luke"].extend(
        [
            "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
            "LukeForEntityClassification",
            "LukeForEntityPairClassification",
            "LukeForEntitySpanClassification",
            "LukeForMaskedLM",
            "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.maskformer"].extend(
        [
            "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
            "MaskFormerForInstanceSegmentation",
            "MaskFormerModel",
            "MaskFormerPreTrainedModel",
        ]
    )
    _import_structure["models.mbart"].extend(
        [
            "MBartForCausalLM",
            "MBartForConditionalGeneration",
            "MBartForQuestionAnswering",
            "MBartForSequenceClassification",
            "MBartModel",
            "MBartPreTrainedModel",
        ]
    )
    _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.mmbt"].extend(["MMBTForClassification", "MMBTModel", "ModalEmbeddings"])
    _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.mpnet"].extend(
        [
            "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
            "MPNetForMaskedLM",
            "MPNetForMultipleChoice",
            "MPNetForQuestionAnswering",
            "MPNetForSequenceClassification",
            "MPNetForTokenClassification",
            "MPNetLayer",
            "MPNetModel",
            "MPNetPreTrainedModel",
        ]
    )
    _import_structure["models.mt5"].extend(["MT5EncoderModel", "MT5ForConditionalGeneration", "MT5Model"])
    _import_structure["models.nystromformer"].extend(
        [
            "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
            "NystromformerForMaskedLM",
            "NystromformerForMultipleChoice",
            "NystromformerForQuestionAnswering",
            "NystromformerForSequenceClassification",
            "NystromformerForTokenClassification",
            "NystromformerLayer",
            "NystromformerModel",
            "NystromformerPreTrainedModel",
        ]
    )
    _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",
            "OPTModel",
            "OPTPreTrainedModel",
        ]
    )
    _import_structure["models.pegasus"].extend(
        ["PegasusForCausalLM", "PegasusForConditionalGeneration", "PegasusModel", "PegasusPreTrainedModel"]
    )
    _import_structure["models.perceiver"].extend(
        [
            "PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST",
            "PerceiverForImageClassificationConvProcessing",
            "PerceiverForImageClassificationFourier",
            "PerceiverForImageClassificationLearned",
            "PerceiverForMaskedLM",
            "PerceiverForMultimodalAutoencoding",
            "PerceiverForOpticalFlow",
            "PerceiverForSequenceClassification",
            "PerceiverLayer",
            "PerceiverModel",
            "PerceiverPreTrainedModel",
        ]
    )
    _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.prophetnet"].extend(
        [
            "PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
            "ProphetNetDecoder",
            "ProphetNetEncoder",
            "ProphetNetForCausalLM",
            "ProphetNetForConditionalGeneration",
            "ProphetNetModel",
            "ProphetNetPreTrainedModel",
        ]
    )
    _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",
            "ResNetForImageClassification",
            "ResNetModel",
            "ResNetPreTrainedModel",
        ]
    )
    _import_structure["models.retribert"].extend(
        ["RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RetriBertModel", "RetriBertPreTrainedModel"]
    )
    _import_structure["models.roberta"].extend(
        [
            "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "RobertaForCausalLM",
            "RobertaForMaskedLM",
            "RobertaForMultipleChoice",
            "RobertaForQuestionAnswering",
            "RobertaForSequenceClassification",
            "RobertaForTokenClassification",
            "RobertaModel",
            "RobertaPreTrainedModel",
        ]
    )
    _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.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.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.swin"].extend(
        [
            "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
            "SwinForImageClassification",
            "SwinForMaskedImageModeling",
            "SwinModel",
            "SwinPreTrainedModel",
        ]
    )
    _import_structure["models.t5"].extend(
        [
            "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
            "T5EncoderModel",
            "T5ForConditionalGeneration",
            "T5Model",
            "T5PreTrainedModel",
            "load_tf_weights_in_t5",
        ]
    )
    _import_structure["models.trajectory_transformer"].extend(
        [
            "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
            "TrajectoryTransformerModel",
            "TrajectoryTransformerPreTrainedModel",
        ]
    )
    _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.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.van"].extend(
        [
            "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
            "VanForImageClassification",
            "VanModel",
            "VanPreTrainedModel",
        ]
    )
    _import_structure["models.vilt"].extend(
        [
            "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "ViltForImageAndTextRetrieval",
            "ViltForImagesAndTextClassification",
            "ViltForMaskedLM",
            "ViltForQuestionAnswering",
            "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_mae"].extend(
        [
            "VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST",
            "ViTMAEForPreTraining",
            "ViTMAELayer",
            "ViTMAEModel",
            "ViTMAEPreTrainedModel",
        ]
    )
    _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.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",
        ]
    )
    _import_structure["models.xlm_roberta"].extend(
        [
            "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "XLMRobertaForCausalLM",
            "XLMRobertaForMaskedLM",
            "XLMRobertaForMultipleChoice",
            "XLMRobertaForQuestionAnswering",
            "XLMRobertaForSequenceClassification",
            "XLMRobertaForTokenClassification",
            "XLMRobertaModel",
        ]
    )
    _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.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_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["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_tf_logits_process"] = [
        "TFForcedBOSTokenLogitsProcessor",
        "TFForcedEOSTokenLogitsProcessor",
        "TFLogitsProcessor",
        "TFLogitsProcessorList",
        "TFLogitsWarper",
        "TFMinLengthLogitsProcessor",
        "TFNoBadWordsLogitsProcessor",
        "TFNoRepeatNGramLogitsProcessor",
        "TFRepetitionPenaltyLogitsProcessor",
        "TFTemperatureLogitsWarper",
        "TFTopKLogitsWarper",
        "TFTopPLogitsWarper",
    ]
    _import_structure["generation_tf_utils"] = ["tf_top_k_top_p_filtering"]
    _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_CAUSAL_LM_MAPPING",
            "TF_MODEL_FOR_IMAGE_CLASSIFICATION_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_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_TOKEN_CLASSIFICATION_MAPPING",
            "TF_MODEL_FOR_VISION_2_SEQ_MAPPING",
            "TF_MODEL_MAPPING",
            "TF_MODEL_WITH_LM_HEAD_MAPPING",
            "TFAutoModel",
            "TFAutoModelForCausalLM",
            "TFAutoModelForImageClassification",
            "TFAutoModelForMaskedLM",
            "TFAutoModelForMultipleChoice",
            "TFAutoModelForNextSentencePrediction",
            "TFAutoModelForPreTraining",
            "TFAutoModelForQuestionAnswering",
            "TFAutoModelForSeq2SeqLM",
            "TFAutoModelForSequenceClassification",
            "TFAutoModelForSpeechSeq2Seq",
            "TFAutoModelForTableQuestionAnswering",
            "TFAutoModelForTokenClassification",
            "TFAutoModelForVision2Seq",
            "TFAutoModelWithLMHead",
        ]
    )
    _import_structure["models.bart"].extend(["TFBartForConditionalGeneration", "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.camembert"].extend(
        [
            "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "TFCamembertForCausalLM",
            "TFCamembertForMaskedLM",
            "TFCamembertForMultipleChoice",
            "TFCamembertForQuestionAnswering",
            "TFCamembertForSequenceClassification",
            "TFCamembertForTokenClassification",
            "TFCamembertModel",
        ]
    )
    _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.data2vec"].extend(
        [
            "TFData2VecVisionForImageClassification",
            "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",
            "TFDebertaV2ForQuestionAnswering",
            "TFDebertaV2ForSequenceClassification",
            "TFDebertaV2ForTokenClassification",
            "TFDebertaV2Model",
            "TFDebertaV2PreTrainedModel",
        ]
    )
    _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.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.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.hubert"].extend(
        [
            "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "TFHubertForCTC",
            "TFHubertModel",
            "TFHubertPreTrainedModel",
        ]
    )
    _import_structure["models.layoutlm"].extend(
        [
            "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
            "TFLayoutLMForMaskedLM",
            "TFLayoutLMForSequenceClassification",
            "TFLayoutLMForTokenClassification",
            "TFLayoutLMMainLayer",
            "TFLayoutLMModel",
            "TFLayoutLMPreTrainedModel",
        ]
    )
    _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.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.pegasus"].extend(
        ["TFPegasusForConditionalGeneration", "TFPegasusModel", "TFPegasusPreTrainedModel"]
    )
    _import_structure["models.rag"].extend(
        [
            "TFRagModel",
            "TFRagPreTrainedModel",
            "TFRagSequenceForGeneration",
            "TFRagTokenForGeneration",
        ]
    )
    _import_structure["models.rembert"].extend(
        [
            "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "TFRemBertForCausalLM",
            "TFRemBertForMaskedLM",
            "TFRemBertForMultipleChoice",
            "TFRemBertForQuestionAnswering",
            "TFRemBertForSequenceClassification",
            "TFRemBertForTokenClassification",
            "TFRemBertLayer",
            "TFRemBertModel",
            "TFRemBertPreTrainedModel",
        ]
    )
    _import_structure["models.roberta"].extend(
        [
            "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "TFRobertaForCausalLM",
            "TFRobertaForMaskedLM",
            "TFRobertaForMultipleChoice",
            "TFRobertaForQuestionAnswering",
            "TFRobertaForSequenceClassification",
            "TFRobertaForTokenClassification",
            "TFRobertaMainLayer",
            "TFRobertaModel",
            "TFRobertaPreTrainedModel",
        ]
    )
    _import_structure["models.roformer"].extend(
        [
            "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
            "TFRoFormerForCausalLM",
            "TFRoFormerForMaskedLM",
            "TFRoFormerForMultipleChoice",
            "TFRoFormerForQuestionAnswering",
            "TFRoFormerForSequenceClassification",
            "TFRoFormerForTokenClassification",
            "TFRoFormerLayer",
            "TFRoFormerModel",
            "TFRoFormerPreTrainedModel",
        ]
    )
    _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.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",
            "TFWav2Vec2Model",
            "TFWav2Vec2PreTrainedModel",
        ]
    )
    _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",
            "TFXLMRobertaForMaskedLM",
            "TFXLMRobertaForMultipleChoice",
            "TFXLMRobertaForQuestionAnswering",
            "TFXLMRobertaForSequenceClassification",
            "TFXLMRobertaForTokenClassification",
            "TFXLMRobertaModel",
        ]
    )
    _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"]


# 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_flax_logits_process"] = [
        "FlaxForcedBOSTokenLogitsProcessor",
        "FlaxForcedEOSTokenLogitsProcessor",
        "FlaxLogitsProcessor",
        "FlaxLogitsProcessorList",
        "FlaxLogitsWarper",
        "FlaxMinLengthLogitsProcessor",
        "FlaxTemperatureLogitsWarper",
        "FlaxTopKLogitsWarper",
        "FlaxTopPLogitsWarper",
    ]
    _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_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_TOKEN_CLASSIFICATION_MAPPING",
            "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING",
            "FLAX_MODEL_MAPPING",
            "FlaxAutoModel",
            "FlaxAutoModelForCausalLM",
            "FlaxAutoModelForImageClassification",
            "FlaxAutoModelForMaskedLM",
            "FlaxAutoModelForMultipleChoice",
            "FlaxAutoModelForNextSentencePrediction",
            "FlaxAutoModelForPreTraining",
            "FlaxAutoModelForQuestionAnswering",
            "FlaxAutoModelForSeq2SeqLM",
            "FlaxAutoModelForSequenceClassification",
            "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.clip"].extend(
        [
            "FlaxCLIPModel",
            "FlaxCLIPPreTrainedModel",
            "FlaxCLIPTextModel",
            "FlaxCLIPTextPreTrainedModel",
            "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.marian"].extend(
        [
            "FlaxMarianModel",
            "FlaxMarianMTModel",
            "FlaxMarianPreTrainedModel",
        ]
    )
    _import_structure["models.mbart"].extend(
        [
            "FlaxMBartForConditionalGeneration",
            "FlaxMBartForQuestionAnswering",
            "FlaxMBartForSequenceClassification",
            "FlaxMBartModel",
            "FlaxMBartPreTrainedModel",
        ]
    )
    _import_structure["models.mt5"].extend(["FlaxMT5ForConditionalGeneration", "FlaxMT5Model"])
    _import_structure["models.pegasus"].extend(
        [
            "FlaxPegasusForConditionalGeneration",
            "FlaxPegasusModel",
            "FlaxPegasusPreTrainedModel",
        ]
    )
    _import_structure["models.roberta"].extend(
        [
            "FlaxRobertaForCausalLM",
            "FlaxRobertaForMaskedLM",
            "FlaxRobertaForMultipleChoice",
            "FlaxRobertaForQuestionAnswering",
            "FlaxRobertaForSequenceClassification",
            "FlaxRobertaForTokenClassification",
            "FlaxRobertaModel",
            "FlaxRobertaPreTrainedModel",
        ]
    )
    _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(["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.xglm"].extend(
        [
            "FlaxXGLMForCausalLM",
            "FlaxXGLMModel",
            "FlaxXGLMPreTrainedModel",
        ]
    )
    _import_structure["models.xlm_roberta"].extend(
        [
            "FlaxXLMRobertaForMaskedLM",
            "FlaxXLMRobertaForMultipleChoice",
            "FlaxXLMRobertaForQuestionAnswering",
            "FlaxXLMRobertaForSequenceClassification",
            "FlaxXLMRobertaForTokenClassification",
            "FlaxXLMRobertaModel",
        ]
    )


# 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
    from .hf_argparser import HfArgumentParser

    # Integrations
    from .integrations import (
        is_comet_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.auto import (
        ALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CONFIG_MAPPING,
        FEATURE_EXTRACTOR_MAPPING,
        MODEL_NAMES_MAPPING,
        PROCESSOR_MAPPING,
        TOKENIZER_MAPPING,
        AutoConfig,
        AutoFeatureExtractor,
        AutoProcessor,
        AutoTokenizer,
    )
    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.blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig, BlenderbotTokenizer
    from .models.blenderbot_small import (
        BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BlenderbotSmallConfig,
        BlenderbotSmallTokenizer,
    )
    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.clip import (
        CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPConfig,
        CLIPTextConfig,
        CLIPTokenizer,
        CLIPVisionConfig,
    )
    from .models.convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertTokenizer
    from .models.convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig
    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.deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig
    from .models.detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig
    from .models.distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, DistilBertTokenizer
    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.electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraTokenizer
    from .models.encoder_decoder import EncoderDecoderConfig
    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.fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig, FSMTTokenizer
    from .models.funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig, FunnelTokenizer
    from .models.glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
    from .models.gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2Tokenizer
    from .models.gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig
    from .models.gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig
    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.imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig
    from .models.layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMTokenizer
    from .models.layoutlmv2 import (
        LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        LayoutLMv2Config,
        LayoutLMv2FeatureExtractor,
        LayoutLMv2Processor,
        LayoutLMv2Tokenizer,
    )
    from .models.layoutxlm import LayoutXLMProcessor
    from .models.led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig, LEDTokenizer
    from .models.longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig, LongformerTokenizer
    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.maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .models.mbart import MBartConfig
    from .models.megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
    from .models.mmbt import MMBTConfig
    from .models.mobilebert import MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileBertConfig, MobileBertTokenizer
    from .models.mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig, MPNetTokenizer
    from .models.mt5 import MT5Config
    from .models.nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
    from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer
    from .models.opt import OPTConfig
    from .models.pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig, PegasusTokenizer
    from .models.perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverTokenizer
    from .models.phobert import PhobertTokenizer
    from .models.plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
    from .models.poolformer import POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, PoolFormerConfig
    from .models.prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig, ProphetNetTokenizer
    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.retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig, RetriBertTokenizer
    from .models.roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaTokenizer
    from .models.roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerTokenizer
    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
    from .models.speech_to_text_2 import (
        SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        Speech2Text2Config,
        Speech2Text2Processor,
        Speech2Text2Tokenizer,
    )
    from .models.splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig, SplinterTokenizer
    from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer
    from .models.swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig
    from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
    from .models.tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig, TapasTokenizer
    from .models.tapex import TapexTokenizer
    from .models.trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        TrajectoryTransformerConfig,
    )
    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.unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
    from .models.unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig
    from .models.van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
    from .models.vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig, ViltFeatureExtractor, 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_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
    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.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.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,
        FeatureExtractionPipeline,
        FillMaskPipeline,
        ImageClassificationPipeline,
        ImageSegmentationPipeline,
        JsonPipelineDataFormat,
        NerPipeline,
        ObjectDetectionPipeline,
        PipedPipelineDataFormat,
        Pipeline,
        PipelineDataFormat,
        QuestionAnsweringPipeline,
        SummarizationPipeline,
        TableQuestionAnsweringPipeline,
        Text2TextGenerationPipeline,
        TextClassificationPipeline,
        TextGenerationPipeline,
        TokenClassificationPipeline,
        TranslationPipeline,
        ZeroShotClassificationPipeline,
        ZeroShotImageClassificationPipeline,
        pipeline,
    )
    from .processing_utils import ProcessorMixin

    # Tokenization
    from .tokenization_utils import PreTrainedTokenizer
    from .tokenization_utils_base import (
        AddedToken,
        BatchEncoding,
        CharSpan,
        PreTrainedTokenizerBase,
        SpecialTokensMixin,
        TokenSpan,
    )

    # 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,
        cached_path,
        is_apex_available,
        is_datasets_available,
        is_faiss_available,
        is_flax_available,
        is_phonemizer_available,
        is_psutil_available,
        is_py3nvml_available,
        is_pyctcdecode_available,
        is_scipy_available,
        is_sentencepiece_available,
        is_sklearn_available,
        is_speech_available,
        is_tf_available,
        is_timm_available,
        is_tokenizers_available,
        is_torch_available,
        is_torch_tpu_available,
        is_vision_available,
        logging,
    )

    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.cpm import CpmTokenizer
        from .models.deberta_v2 import DebertaV2Tokenizer
        from .models.fnet import FNetTokenizer
        from .models.layoutxlm import LayoutXLMTokenizer
        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.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.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.camembert import CamembertTokenizerFast
        from .models.clip import CLIPTokenizerFast
        from .models.convbert import ConvBertTokenizerFast
        from .models.cpm import CpmTokenizerFast
        from .models.deberta import DebertaTokenizerFast
        from .models.deberta_v2 import DebertaV2TokenizerFast
        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.herbert import HerbertTokenizerFast
        from .models.layoutlm import LayoutLMTokenizerFast
        from .models.layoutlmv2 import LayoutLMv2TokenizerFast
        from .models.layoutxlm import LayoutXLMTokenizerFast
        from .models.led import LEDTokenizerFast
        from .models.longformer import LongformerTokenizerFast
        from .models.lxmert import LxmertTokenizerFast
        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.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.retribert import RetriBertTokenizerFast
        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.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.speech_to_text import Speech2TextFeatureExtractor

    try:
        if not (is_speech_available() and is_sentencepiece_available()):
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        from .utils.dummy_sentencepiece_and_speech_objects import *
    else:
        from .models.speech_to_text import Speech2TextProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        from .utils.dummy_vision_objects import *
    else:
        from .image_utils import ImageFeatureExtractionMixin
        from .models.beit import BeitFeatureExtractor
        from .models.clip import CLIPFeatureExtractor, CLIPProcessor
        from .models.convnext import ConvNextFeatureExtractor
        from .models.deit import DeiTFeatureExtractor
        from .models.detr import DetrFeatureExtractor
        from .models.dpt import DPTFeatureExtractor
        from .models.flava import FlavaFeatureExtractor, FlavaProcessor
        from .models.glpn import GLPNFeatureExtractor
        from .models.imagegpt import ImageGPTFeatureExtractor
        from .models.layoutlmv2 import LayoutLMv2FeatureExtractor, LayoutLMv2Processor
        from .models.layoutxlm import LayoutXLMProcessor
        from .models.maskformer import MaskFormerFeatureExtractor
        from .models.perceiver import PerceiverFeatureExtractor
        from .models.poolformer import PoolFormerFeatureExtractor
        from .models.segformer import SegformerFeatureExtractor
        from .models.vilt import ViltFeatureExtractor, ViltProcessor
        from .models.vit import ViTFeatureExtractor
        from .models.yolos import YolosFeatureExtractor

    # Modeling
    try:
        if not (is_timm_available() and is_vision_available()):
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        from .utils.dummy_timm_objects import *
    else:
        from .models.detr import (
            DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetrForObjectDetection,
            DetrForSegmentation,
            DetrModel,
            DetrPreTrainedModel,
        )

    try:
        if not is_scatter_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        from .utils.dummy_scatter_objects import *
    else:
        from .models.tapas import (
            TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
            TapasForMaskedLM,
            TapasForQuestionAnswering,
            TapasForSequenceClassification,
            TapasModel,
            TapasPreTrainedModel,
            load_tf_weights_in_tapas,
        )

    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_beam_constraints import (
            Constraint,
            ConstraintListState,
            DisjunctiveConstraint,
            PhrasalConstraint,
        )
        from .generation_beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
        from .generation_logits_process import (
            ForcedBOSTokenLogitsProcessor,
            ForcedEOSTokenLogitsProcessor,
            HammingDiversityLogitsProcessor,
            InfNanRemoveLogitsProcessor,
            LogitsProcessor,
            LogitsProcessorList,
            LogitsWarper,
            MinLengthLogitsProcessor,
            NoBadWordsLogitsProcessor,
            NoRepeatNGramLogitsProcessor,
            PrefixConstrainedLogitsProcessor,
            RepetitionPenaltyLogitsProcessor,
            TemperatureLogitsWarper,
            TopKLogitsWarper,
            TopPLogitsWarper,
        )
        from .generation_stopping_criteria import (
            MaxLengthCriteria,
            MaxTimeCriteria,
            StoppingCriteria,
            StoppingCriteriaList,
        )
        from .generation_utils import 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.auto import (
            MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_XVECTOR_MAPPING,
            MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
            MODEL_FOR_CAUSAL_LM_MAPPING,
            MODEL_FOR_CTC_MAPPING,
            MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
            MODEL_FOR_INSTANCE_SEGMENTATION_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_TOKEN_CLASSIFICATION_MAPPING,
            MODEL_FOR_VISION_2_SEQ_MAPPING,
            MODEL_MAPPING,
            MODEL_WITH_LM_HEAD_MAPPING,
            AutoModel,
            AutoModelForAudioClassification,
            AutoModelForAudioFrameClassification,
            AutoModelForAudioXVector,
            AutoModelForCausalLM,
            AutoModelForCTC,
            AutoModelForImageClassification,
            AutoModelForImageSegmentation,
            AutoModelForInstanceSegmentation,
            AutoModelForMaskedImageModeling,
            AutoModelForMaskedLM,
            AutoModelForMultipleChoice,
            AutoModelForNextSentencePrediction,
            AutoModelForObjectDetection,
            AutoModelForPreTraining,
            AutoModelForQuestionAnswering,
            AutoModelForSemanticSegmentation,
            AutoModelForSeq2SeqLM,
            AutoModelForSequenceClassification,
            AutoModelForSpeechSeq2Seq,
            AutoModelForTableQuestionAnswering,
            AutoModelForTokenClassification,
            AutoModelForVision2Seq,
            AutoModelWithLMHead,
        )
        from .models.bart import (
            BART_PRETRAINED_MODEL_ARCHIVE_LIST,
            BartForCausalLM,
            BartForConditionalGeneration,
            BartForQuestionAnswering,
            BartForSequenceClassification,
            BartModel,
            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.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.camembert import (
            CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CamembertForCausalLM,
            CamembertForMaskedLM,
            CamembertForMultipleChoice,
            CamembertForQuestionAnswering,
            CamembertForSequenceClassification,
            CamembertForTokenClassification,
            CamembertModel,
        )
        from .models.canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )
        from .models.clip import (
            CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPModel,
            CLIPPreTrainedModel,
            CLIPTextModel,
            CLIPVisionModel,
        )
        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,
            ConvNextForImageClassification,
            ConvNextModel,
            ConvNextPreTrainedModel,
        )
        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.deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )
        from .models.distilbert import (
            DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DistilBertForMaskedLM,
            DistilBertForMultipleChoice,
            DistilBertForQuestionAnswering,
            DistilBertForSequenceClassification,
            DistilBertForTokenClassification,
            DistilBertModel,
            DistilBertPreTrainedModel,
        )
        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.electra import (
            ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            ElectraForCausalLM,
            ElectraForMaskedLM,
            ElectraForMultipleChoice,
            ElectraForPreTraining,
            ElectraForQuestionAnswering,
            ElectraForSequenceClassification,
            ElectraForTokenClassification,
            ElectraModel,
            ElectraPreTrainedModel,
            load_tf_weights_in_electra,
        )
        from .models.encoder_decoder import EncoderDecoderModel
        from .models.flaubert import (
            FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            FlaubertForMultipleChoice,
            FlaubertForQuestionAnswering,
            FlaubertForQuestionAnsweringSimple,
            FlaubertForSequenceClassification,
            FlaubertForTokenClassification,
            FlaubertModel,
            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.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.glpn import (
            GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
            GLPNForDepthEstimation,
            GLPNModel,
            GLPNPreTrainedModel,
        )
        from .models.gpt2 import (
            GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPT2DoubleHeadsModel,
            GPT2ForSequenceClassification,
            GPT2ForTokenClassification,
            GPT2LMHeadModel,
            GPT2Model,
            GPT2PreTrainedModel,
            load_tf_weights_in_gpt2,
        )
        from .models.gpt_neo import (
            GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoForCausalLM,
            GPTNeoForSequenceClassification,
            GPTNeoModel,
            GPTNeoPreTrainedModel,
            load_tf_weights_in_gpt_neo,
        )
        from .models.gptj import (
            GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTJForCausalLM,
            GPTJForQuestionAnswering,
            GPTJForSequenceClassification,
            GPTJModel,
            GPTJPreTrainedModel,
        )
        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.imagegpt import (
            IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ImageGPTForCausalImageModeling,
            ImageGPTForImageClassification,
            ImageGPTModel,
            ImageGPTPreTrainedModel,
            load_tf_weights_in_imagegpt,
        )
        from .models.layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
        from .models.layoutlmv2 import (
            LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMv2ForQuestionAnswering,
            LayoutLMv2ForSequenceClassification,
            LayoutLMv2ForTokenClassification,
            LayoutLMv2Model,
            LayoutLMv2PreTrainedModel,
        )
        from .models.led import (
            LED_PRETRAINED_MODEL_ARCHIVE_LIST,
            LEDForConditionalGeneration,
            LEDForQuestionAnswering,
            LEDForSequenceClassification,
            LEDModel,
            LEDPreTrainedModel,
        )
        from .models.longformer import (
            LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            LongformerForMaskedLM,
            LongformerForMultipleChoice,
            LongformerForQuestionAnswering,
            LongformerForSequenceClassification,
            LongformerForTokenClassification,
            LongformerModel,
            LongformerPreTrainedModel,
            LongformerSelfAttention,
        )
        from .models.luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            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.maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .models.mbart import (
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )
        from .models.megatron_bert import (
            MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            MegatronBertForCausalLM,
            MegatronBertForMaskedLM,
            MegatronBertForMultipleChoice,
            MegatronBertForNextSentencePrediction,
            MegatronBertForPreTraining,
            MegatronBertForQuestionAnswering,
            MegatronBertForSequenceClassification,
            MegatronBertForTokenClassification,
            MegatronBertModel,
            MegatronBertPreTrainedModel,
        )
        from .models.mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
        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.mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )
        from .models.mt5 import MT5EncoderModel, MT5ForConditionalGeneration, MT5Model
        from .models.nystromformer import (
            NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            NystromformerForMaskedLM,
            NystromformerForMultipleChoice,
            NystromformerForQuestionAnswering,
            NystromformerForSequenceClassification,
            NystromformerForTokenClassification,
            NystromformerLayer,
            NystromformerModel,
            NystromformerPreTrainedModel,
        )
        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, OPTModel, OPTPreTrainedModel
        from .models.pegasus import (
            PegasusForCausalLM,
            PegasusForConditionalGeneration,
            PegasusModel,
            PegasusPreTrainedModel,
        )
        from .models.perceiver import (
            PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
            PerceiverForImageClassificationConvProcessing,
            PerceiverForImageClassificationFourier,
            PerceiverForImageClassificationLearned,
            PerceiverForMaskedLM,
            PerceiverForMultimodalAutoencoding,
            PerceiverForOpticalFlow,
            PerceiverForSequenceClassification,
            PerceiverLayer,
            PerceiverModel,
            PerceiverPreTrainedModel,
        )
        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.prophetnet import (
            PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            ProphetNetDecoder,
            ProphetNetEncoder,
            ProphetNetForCausalLM,
            ProphetNetForConditionalGeneration,
            ProphetNetModel,
            ProphetNetPreTrainedModel,
        )
        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,
            ResNetForImageClassification,
            ResNetModel,
            ResNetPreTrainedModel,
        )
        from .models.retribert import RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST, RetriBertModel, RetriBertPreTrainedModel
        from .models.roberta import (
            ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaForCausalLM,
            RobertaForMaskedLM,
            RobertaForMultipleChoice,
            RobertaForQuestionAnswering,
            RobertaForSequenceClassification,
            RobertaForTokenClassification,
            RobertaModel,
            RobertaPreTrainedModel,
        )
        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.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.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.swin import (
            SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
            SwinModel,
            SwinPreTrainedModel,
        )
        from .models.t5 import (
            T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            T5EncoderModel,
            T5ForConditionalGeneration,
            T5Model,
            T5PreTrainedModel,
            load_tf_weights_in_t5,
        )
        from .models.trajectory_transformer import (
            TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TrajectoryTransformerModel,
            TrajectoryTransformerPreTrainedModel,
        )
        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.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.van import (
            VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
            VanForImageClassification,
            VanModel,
            VanPreTrainedModel,
        )
        from .models.vilt import (
            VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViltForImageAndTextRetrieval,
            ViltForImagesAndTextClassification,
            ViltForMaskedLM,
            ViltForQuestionAnswering,
            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_mae import (
            VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViTMAEForPreTraining,
            ViTMAELayer,
            ViTMAEModel,
            ViTMAEPreTrainedModel,
        )
        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.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,
        )
        from .models.xlm_roberta import (
            XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMRobertaForCausalLM,
            XLMRobertaForMaskedLM,
            XLMRobertaForMultipleChoice,
            XLMRobertaForQuestionAnswering,
            XLMRobertaForSequenceClassification,
            XLMRobertaForTokenClassification,
            XLMRobertaModel,
        )
        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.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_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_tf_logits_process import (
            TFForcedBOSTokenLogitsProcessor,
            TFForcedEOSTokenLogitsProcessor,
            TFLogitsProcessor,
            TFLogitsProcessorList,
            TFLogitsWarper,
            TFMinLengthLogitsProcessor,
            TFNoBadWordsLogitsProcessor,
            TFNoRepeatNGramLogitsProcessor,
            TFRepetitionPenaltyLogitsProcessor,
            TFTemperatureLogitsWarper,
            TFTopKLogitsWarper,
            TFTopPLogitsWarper,
        )
        from .generation_tf_utils import tf_top_k_top_p_filtering
        from .keras_callbacks import KerasMetricCallback, PushToHubCallback
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )
        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_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_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_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_TOKEN_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
            TF_MODEL_MAPPING,
            TF_MODEL_WITH_LM_HEAD_MAPPING,
            TFAutoModel,
            TFAutoModelForCausalLM,
            TFAutoModelForImageClassification,
            TFAutoModelForMaskedLM,
            TFAutoModelForMultipleChoice,
            TFAutoModelForNextSentencePrediction,
            TFAutoModelForPreTraining,
            TFAutoModelForQuestionAnswering,
            TFAutoModelForSeq2SeqLM,
            TFAutoModelForSequenceClassification,
            TFAutoModelForSpeechSeq2Seq,
            TFAutoModelForTableQuestionAnswering,
            TFAutoModelForTokenClassification,
            TFAutoModelForVision2Seq,
            TFAutoModelWithLMHead,
        )
        from .models.bart import TFBartForConditionalGeneration, 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.camembert import (
            TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCamembertForCausalLM,
            TFCamembertForMaskedLM,
            TFCamembertForMultipleChoice,
            TFCamembertForQuestionAnswering,
            TFCamembertForSequenceClassification,
            TFCamembertForTokenClassification,
            TFCamembertModel,
        )
        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.data2vec import (
            TFData2VecVisionForImageClassification,
            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,
            TFDebertaV2ForQuestionAnswering,
            TFDebertaV2ForSequenceClassification,
            TFDebertaV2ForTokenClassification,
            TFDebertaV2Model,
            TFDebertaV2PreTrainedModel,
        )
        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.electra import (
            TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFElectraForMaskedLM,
            TFElectraForMultipleChoice,
            TFElectraForPreTraining,
            TFElectraForQuestionAnswering,
            TFElectraForSequenceClassification,
            TFElectraForTokenClassification,
            TFElectraModel,
            TFElectraPreTrainedModel,
        )
        from .models.encoder_decoder import TFEncoderDecoderModel
        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.hubert import (
            TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFHubertForCTC,
            TFHubertModel,
            TFHubertPreTrainedModel,
        )
        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.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.pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel
        from .models.rag import TFRagModel, TFRagPreTrainedModel, TFRagSequenceForGeneration, TFRagTokenForGeneration
        from .models.rembert import (
            TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRemBertForCausalLM,
            TFRemBertForMaskedLM,
            TFRemBertForMultipleChoice,
            TFRemBertForQuestionAnswering,
            TFRemBertForSequenceClassification,
            TFRemBertForTokenClassification,
            TFRemBertLayer,
            TFRemBertModel,
            TFRemBertPreTrainedModel,
        )
        from .models.roberta import (
            TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaForCausalLM,
            TFRobertaForMaskedLM,
            TFRobertaForMultipleChoice,
            TFRobertaForQuestionAnswering,
            TFRobertaForSequenceClassification,
            TFRobertaForTokenClassification,
            TFRobertaMainLayer,
            TFRobertaModel,
            TFRobertaPreTrainedModel,
        )
        from .models.roformer import (
            TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForMultipleChoice,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerLayer,
            TFRoFormerModel,
            TFRoFormerPreTrainedModel,
        )
        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.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,
            TFWav2Vec2Model,
            TFWav2Vec2PreTrainedModel,
        )
        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,
            TFXLMRobertaForMaskedLM,
            TFXLMRobertaForMultipleChoice,
            TFXLMRobertaForQuestionAnswering,
            TFXLMRobertaForSequenceClassification,
            TFXLMRobertaForTokenClassification,
            TFXLMRobertaModel,
        )
        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_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_flax_logits_process import (
            FlaxForcedBOSTokenLogitsProcessor,
            FlaxForcedEOSTokenLogitsProcessor,
            FlaxLogitsProcessor,
            FlaxLogitsProcessorList,
            FlaxLogitsWarper,
            FlaxMinLengthLogitsProcessor,
            FlaxTemperatureLogitsWarper,
            FlaxTopKLogitsWarper,
            FlaxTopPLogitsWarper,
        )
        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_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_TOKEN_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
            FLAX_MODEL_MAPPING,
            FlaxAutoModel,
            FlaxAutoModelForCausalLM,
            FlaxAutoModelForImageClassification,
            FlaxAutoModelForMaskedLM,
            FlaxAutoModelForMultipleChoice,
            FlaxAutoModelForNextSentencePrediction,
            FlaxAutoModelForPreTraining,
            FlaxAutoModelForQuestionAnswering,
            FlaxAutoModelForSeq2SeqLM,
            FlaxAutoModelForSequenceClassification,
            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.clip import (
            FlaxCLIPModel,
            FlaxCLIPPreTrainedModel,
            FlaxCLIPTextModel,
            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.marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
        from .models.mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )
        from .models.mt5 import FlaxMT5ForConditionalGeneration, FlaxMT5Model
        from .models.pegasus import FlaxPegasusForConditionalGeneration, FlaxPegasusModel, FlaxPegasusPreTrainedModel
        from .models.roberta import (
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaModel,
            FlaxRobertaPreTrainedModel,
        )
        from .models.roformer import (
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerModel,
            FlaxRoFormerPreTrainedModel,
        )
        from .models.speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
        from .models.t5 import 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.xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
        from .models.xlm_roberta import (
            FlaxXLMRobertaForMaskedLM,
            FlaxXLMRobertaForMultipleChoice,
            FlaxXLMRobertaForQuestionAnswering,
            FlaxXLMRobertaForSequenceClassification,
            FlaxXLMRobertaForTokenClassification,
            FlaxXLMRobertaModel,
        )

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."
    )