"""simple docstring"""




def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: list[list[float]]			) ->   Optional[int]:
  _lowerCAmelCase		: list[list[float]]						    =  []
  for data in source_data:
    for i, el in enumerate(_lowerCamelCase			):
      if len(_lowerCamelCase			) < i + 1:
        data_lists.append([]			)
      data_lists[i].append(float(_lowerCamelCase			)			)
  return data_lists





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: list[list[float]]    ,_lowerCamelCase		: list[int]			) ->   Dict:
  _lowerCAmelCase		: list[list[float]]						    =  []
  for dlist, weight in zip(_lowerCamelCase    ,_lowerCamelCase			):
    _lowerCAmelCase		: Optional[Any]						    =  min(_lowerCamelCase			)
    _lowerCAmelCase		: Tuple						    =  max(_lowerCamelCase			)

    _lowerCAmelCase		: 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:
      _lowerCAmelCase		: Optional[int]						    =  f"Invalid weight of {weight:f} provided"
      raise ValueError(_lowerCamelCase			)

    score_lists.append(_lowerCamelCase			)

  return score_lists





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: list[list[float]]			) ->   str:
  _lowerCAmelCase		: list[float]						    =  [0 for i in range(len(score_lists[0]			)			)]

  for slist in score_lists:
    for j, ele in enumerate(_lowerCamelCase			):
      _lowerCAmelCase		: Union[str, Any]						    =  final_scores[j] + ele

  return final_scores






def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: list[list[float]]    ,_lowerCamelCase		: list[int]			) ->   List[Any]:
  _lowerCAmelCase		: List[str]						    =  get_data(_lowerCamelCase			)
  _lowerCAmelCase		: List[Any]						    =  calculate_each_score(_lowerCamelCase    ,_lowerCamelCase			)
  _lowerCAmelCase		: Optional[Any]						    =  generate_final_scores(_lowerCamelCase			)

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

  return source_data


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   :	Tuple 					=     {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	str 					=     [
         'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'WavLMForAudioFrameClassification',
         'WavLMForCTC',
         'WavLMForSequenceClassification',
         'WavLMForXVector',
         'WavLMModel',
         'WavLMPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_wavlm import (
              WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              WavLMForAudioFrameClassification,
              WavLMForCTC,
              WavLMForSequenceClassification,
              WavLMForXVector,
              WavLMModel,
              WavLMPreTrainedModel,
          )

else:
     import sys

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




