
"""simple docstring"""





from typing import TYPE_CHECKING

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


_a		       =							{
    '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		       =							[
        'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Data2VecAudioForAudioFrameClassification',
        'Data2VecAudioForCTC',
        'Data2VecAudioForSequenceClassification',
        'Data2VecAudioForXVector',
        'Data2VecAudioModel',
        'Data2VecAudioPreTrainedModel',
    ]
    _a		       =							[
        'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Data2VecTextForCausalLM',
        'Data2VecTextForMaskedLM',
        'Data2VecTextForMultipleChoice',
        'Data2VecTextForQuestionAnswering',
        'Data2VecTextForSequenceClassification',
        'Data2VecTextForTokenClassification',
        'Data2VecTextModel',
        'Data2VecTextPreTrainedModel',
    ]
    _a		       =							[
        'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Data2VecVisionForImageClassification',
        'Data2VecVisionForMaskedImageModeling',
        'Data2VecVisionForSemanticSegmentation',
        'Data2VecVisionModel',
        'Data2VecVisionPreTrainedModel',
    ]

if is_tf_available():
    _a		       =							[
        '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		       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  list )	->						int:


    """simple docstring"""





    if not grid or not grid[0]:
        raise TypeError("The grid does not contain the appropriate information" )

    for cell_n in range(1						,							len(grid[0] ) ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    snake_case    :      int      					=    grid[0]

    for row_n in range(1						,							len(lowercase ) ):
        snake_case    :      Optional[Any]      					=    grid[row_n]
        snake_case    :      List[str]      					=    fill_row(lowercase						,							lowercase )
        snake_case    :      Optional[int]      					=    grid[row_n]

    return grid[-1][-1]







def   __lowerCAmelCase						(						lowercase  :  list						,							lowercase  :  list )	->						list:


    """simple docstring"""





    current_row[0] += row_above[0]
    for cell_n in range(1						,							len(lowercase ) ):
        current_row[cell_n] += min(current_row[cell_n - 1]						,							row_above[cell_n] )

    return current_row


if __name__ == "__main__":
  import doctest

  doctest.testmod()





