



"""simple docstring"""






def 			snake_case		(		lowerCAmelCase_	)						->				list[list[float]]:
  _snake_case   	=      []
  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 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				list[list[float]]:
  _snake_case   	=      []
  for dlist, weight in zip(lowerCAmelCase_  ,						lowerCAmelCase_	):
    _snake_case   	=      min(lowerCAmelCase_	)
    _snake_case   	=      max(lowerCAmelCase_	)

    _snake_case   	=      []
    # 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:
      _snake_case   	=      f"""Invalid weight of {weight:f} provided"""
      raise ValueError(lowerCAmelCase_	)

    score_lists.append(lowerCAmelCase_	)

  return score_lists




def 			snake_case		(		lowerCAmelCase_	)						->				list[float]:
  _snake_case   	=      [0 for i in range(len(score_lists[0]	)	)]

  for slist in score_lists:
    for j, ele in enumerate(lowerCAmelCase_	):
      _snake_case   	=      final_scores[j] + ele

  return final_scores







def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				list[list[float]]:
  _snake_case   	=      get_data(lowerCAmelCase_	)
  _snake_case   	=      calculate_each_score(lowerCAmelCase_  ,						lowerCAmelCase_	)
  _snake_case   	=      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 ...configuration_utils import PretrainedConfig



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  '''bert-generation'''



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	Optional[int]=5_0_3_5_8							,       __lowerCamelCase    :	List[str]=1_0_2_4							,       __lowerCamelCase    :	Optional[Any]=2_4							,       __lowerCamelCase    :	Any=1_6							,       __lowerCamelCase    :	Union[str, Any]=4_0_9_6							,       __lowerCamelCase    :	Optional[Any]="gelu"							,       __lowerCamelCase    :	str=0.1							,       __lowerCamelCase    :	List[str]=0.1							,       __lowerCamelCase    :	int=5_1_2							,       __lowerCamelCase    :	Dict=0.0_2							,       __lowerCamelCase    :	Tuple=1E-12							,       __lowerCamelCase    :	Any=0							,       __lowerCamelCase    :	Union[str, Any]=2							,       __lowerCamelCase    :	List[Any]=1							,       __lowerCamelCase    :	Optional[Any]="absolute"							,       __lowerCamelCase    :	str=True							,       **__lowerCamelCase    :	List[Any]							,       ):



     """simple docstring"""
     super().__init__(pad_token_id=__lowerCamelCase							,       bos_token_id=__lowerCamelCase							,       eos_token_id=__lowerCamelCase							,       **__lowerCamelCase				)

     _snake_case   	=      vocab_size
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      hidden_act
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      max_position_embeddings
     _snake_case   	=      initializer_range
     _snake_case   	=      layer_norm_eps
     _snake_case   	=      position_embedding_type
     _snake_case   	=      use_cache





