
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a      :			Tuple					=							{
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			List[Any]					=							[
        "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Data2VecAudioForAudioFrameClassification",
        "Data2VecAudioForCTC",
        "Data2VecAudioForSequenceClassification",
        "Data2VecAudioForXVector",
        "Data2VecAudioModel",
        "Data2VecAudioPreTrainedModel",
    ]
    _a      :			Dict					=							[
        "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Data2VecTextForCausalLM",
        "Data2VecTextForMaskedLM",
        "Data2VecTextForMultipleChoice",
        "Data2VecTextForQuestionAnswering",
        "Data2VecTextForSequenceClassification",
        "Data2VecTextForTokenClassification",
        "Data2VecTextModel",
        "Data2VecTextPreTrainedModel",
    ]
    _a      :			Union[str, Any]					=							[
        "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Data2VecVisionForImageClassification",
        "Data2VecVisionForMaskedImageModeling",
        "Data2VecVisionForSemanticSegmentation",
        "Data2VecVisionModel",
        "Data2VecVisionPreTrainedModel",
    ]

if is_tf_available():
    _a      :			Union[str, Any]					=							[
        "TFData2VecVisionForImageClassification",
        "TFData2VecVisionForSemanticSegmentation",
        "TFData2VecVisionModel",
        "TFData2VecVisionPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
    from .configuration_dataavec_text import (
        DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DataaVecTextConfig,
        DataaVecTextOnnxConfig,
    )
    from .configuration_dataavec_vision import (
        DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DataaVecVisionConfig,
        DataaVecVisionOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_dataavec_audio import (
            DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
            DataaVecAudioForAudioFrameClassification,
            DataaVecAudioForCTC,
            DataaVecAudioForSequenceClassification,
            DataaVecAudioForXVector,
            DataaVecAudioModel,
            DataaVecAudioPreTrainedModel,
        )
        from .modeling_dataavec_text import (
            DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DataaVecTextForCausalLM,
            DataaVecTextForMaskedLM,
            DataaVecTextForMultipleChoice,
            DataaVecTextForQuestionAnswering,
            DataaVecTextForSequenceClassification,
            DataaVecTextForTokenClassification,
            DataaVecTextModel,
            DataaVecTextPreTrainedModel,
        )
        from .modeling_dataavec_vision import (
            DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
            DataaVecVisionForImageClassification,
            DataaVecVisionForMaskedImageModeling,
            DataaVecVisionForSemanticSegmentation,
            DataaVecVisionModel,
            DataaVecVisionPreTrainedModel,
        )
    if is_tf_available():
        from .modeling_tf_dataavec_vision import (
            TFDataaVecVisionForImageClassification,
            TFDataaVecVisionForSemanticSegmentation,
            TFDataaVecVisionModel,
            TFDataaVecVisionPreTrainedModel,
        )

else:
    import sys

    _a      :			int					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_a      :			str					=							{"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Optional[Any]					=							["""YolosFeatureExtractor"""]
    _a      :			List[Any]					=							["""YolosImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Union[str, Any]					=							[
        """YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """YolosForObjectDetection""",
        """YolosModel""",
        """YolosPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_yolos import YolosFeatureExtractor
        from .image_processing_yolos import YolosImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_yolos import (
            YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
            YolosForObjectDetection,
            YolosModel,
            YolosPreTrainedModel,
        )


else:
    import sys

    _a      :			Union[str, Any]					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

