def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :list[list[float]]			  =		[]
    for data in source_data:
        for i, el in enumerate(SCREAMING_SNAKE_CASE	):
            if len(SCREAMING_SNAKE_CASE	) < i + 1:
                data_lists.append([]	)
            data_lists[i].append(float(SCREAMING_SNAKE_CASE	)	)
    return data_lists



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :list[list[float]]			  =		[]
    for dlist, weight in zip(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[int]			  =		min(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Dict			  =		max(SCREAMING_SNAKE_CASE	)

        __UpperCamelCase     :list[float]			  =		[]
        # for weight 0 score is 1 - actual score
        if weight == 0:
            for item in dlist:
                try:
                    score.append(1 - ((item - mind) / (maxd - mind))	)
                except ZeroDivisionError:
                    score.append(1	)

        elif weight == 1:
            for item in dlist:
                try:
                    score.append((item - mind) / (maxd - mind)	)
                except ZeroDivisionError:
                    score.append(0	)

        # weight not 0 or 1
        else:
            __UpperCamelCase     :int			  =		f"""Invalid weight of {weight:f} provided"""
            raise ValueError(SCREAMING_SNAKE_CASE	)

        score_lists.append(SCREAMING_SNAKE_CASE	)

    return score_lists



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :list[float]			  =		[0 for i in range(len(score_lists[0]	)	)]

    for slist in score_lists:
        for j, ele in enumerate(SCREAMING_SNAKE_CASE	):
            __UpperCamelCase     :Optional[Any]			  =		final_scores[j] + ele

    return final_scores



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Dict			  =		get_data(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Optional[Any]			  =		calculate_each_score(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[str]			  =		generate_final_scores(SCREAMING_SNAKE_CASE	)

    # append scores to source data
    for i, ele in enumerate(SCREAMING_SNAKE_CASE	):
        source_data[i].append(SCREAMING_SNAKE_CASE	)

    return source_data







from typing import TYPE_CHECKING

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


__lowerCAmelCase			:     Any           =   {
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     int           =   [
        'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MCTCTForCTC',
        'MCTCTModel',
        'MCTCTPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
    from .feature_extraction_mctct import MCTCTFeatureExtractor
    from .processing_mctct import MCTCTProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
    import sys

    __lowerCAmelCase			:     List[Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

