





def 	lowerCAmelCase_					(			__a						)	->						list[list[float]]:







  """simple docstring"""





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





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						list[list[float]]:







  """simple docstring"""





  lowerCamelCase__: list[list[float]]         =[]
  for dlist, weight in zip(__a      ,       __a						):
    lowerCamelCase__: Dict         =min(__a						)
    lowerCamelCase__: List[str]         =max(__a						)

    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__: int         =F"""Invalid weight of {weight:f} provided"""
      raise ValueError(__a						)

    score_lists.append(__a						)

  return score_lists





def 	lowerCAmelCase_					(			__a						)	->						list[float]:







  """simple docstring"""





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

  for slist in score_lists:
    for j, ele in enumerate(__a						):
      lowerCamelCase__: Dict         =final_scores[j] + ele

  return final_scores






def 	lowerCAmelCase_					(			__a      ,       __a						)	->						list[list[float]]:







  """simple docstring"""





  lowerCamelCase__: List[Any]         =get_data(__a						)
  lowerCamelCase__: List[str]         =calculate_each_score(__a      ,       __a						)
  lowerCamelCase__: Union[str, Any]         =generate_final_scores(__a						)

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

  return source_data




'''simple docstring'''




from typing import TYPE_CHECKING

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


_a					:			Dict							=    {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Optional[Any]							=    [
      'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLMForMultipleChoice',
      'XLMForQuestionAnswering',
      'XLMForQuestionAnsweringSimple',
      'XLMForSequenceClassification',
      'XLMForTokenClassification',
      'XLMModel',
      'XLMPreTrainedModel',
      'XLMWithLMHeadModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			int							=    [
      'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLMForMultipleChoice',
      'TFXLMForQuestionAnsweringSimple',
      'TFXLMForSequenceClassification',
      'TFXLMForTokenClassification',
      'TFXLMMainLayer',
      'TFXLMModel',
      'TFXLMPreTrainedModel',
      'TFXLMWithLMHeadModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

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


