





from typing import TYPE_CHECKING

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


__lowercase       :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:
    __lowercase       :List[Any]       =  [
        "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Data2VecAudioForAudioFrameClassification",
        "Data2VecAudioForCTC",
        "Data2VecAudioForSequenceClassification",
        "Data2VecAudioForXVector",
        "Data2VecAudioModel",
        "Data2VecAudioPreTrainedModel",
    ]
    __lowercase       :Dict       =  [
        "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Data2VecTextForCausalLM",
        "Data2VecTextForMaskedLM",
        "Data2VecTextForMultipleChoice",
        "Data2VecTextForQuestionAnswering",
        "Data2VecTextForSequenceClassification",
        "Data2VecTextForTokenClassification",
        "Data2VecTextModel",
        "Data2VecTextPreTrainedModel",
    ]
    __lowercase       :Union[str, Any]       =  [
        "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Data2VecVisionForImageClassification",
        "Data2VecVisionForMaskedImageModeling",
        "Data2VecVisionForSemanticSegmentation",
        "Data2VecVisionModel",
        "Data2VecVisionPreTrainedModel",
    ]

if is_tf_available():
    __lowercase       :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

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






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list[int | float]			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if len(_lowerCamelCase     ) == 0:
												raise ValueError("find_max() arg is an empty sequence"     )
						if (
						    left >= len(_lowerCamelCase     )
						    or left < -len(_lowerCamelCase     )
						    or right >= len(_lowerCamelCase     )
						    or right < -len(_lowerCamelCase     )
						):
												raise IndexError("list index out of range"     )
						if left == right:
												return nums[left]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					(left + right) >> 1  # the middle
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					find_max(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )  # find max in range[left, mid]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					find_max(_lowerCamelCase			,   mid + 1			,   _lowerCamelCase     )  # find max in range[mid + 1, right]

						return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)