


"""simple docstring"""
import logging

from transformers import PretrainedConfig


__SCREAMING_SNAKE_CASE							:			List[Any]						=			logging.getLogger(__name__)


__SCREAMING_SNAKE_CASE							:			str						=			{
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}





class    __A   (_SCREAMING_SNAKE_CASE):





							'''simple docstring'''

							__lowercase:						Union[str, Any]												=			"""bertabs"""



							def __init__( self							:					str						,				UpperCAmelCase_							:					List[Any]=30_522						,				UpperCAmelCase_							:					Optional[int]=512						,				UpperCAmelCase_							:					str=6						,				UpperCAmelCase_							:					Optional[int]=512						,				UpperCAmelCase_							:					Optional[Any]=8						,				UpperCAmelCase_							:					List[Any]=512						,				UpperCAmelCase_							:					Any=0.2						,				UpperCAmelCase_							:					Optional[Any]=6						,				UpperCAmelCase_							:					Tuple=768						,				UpperCAmelCase_							:					Union[str, Any]=8						,				UpperCAmelCase_							:					Optional[int]=2_048						,				UpperCAmelCase_							:					List[Any]=0.2						,				**UpperCAmelCase_							:					Tuple						,				)    ->Dict:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_				)

														snake_case_							=						vocab_size
														snake_case_							=						max_pos

														snake_case_							=						enc_layers
														snake_case_							=						enc_hidden_size
														snake_case_							=						enc_heads
														snake_case_							=						enc_ff_size
														snake_case_							=						enc_dropout

														snake_case_							=						dec_layers
														snake_case_							=						dec_hidden_size
														snake_case_							=						dec_heads
														snake_case_							=						dec_ff_size
														snake_case_							=						dec_dropout







'''simple docstring'''






def 				UpperCamelCase_			(    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 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for dlist, weight in zip(A__							,    A__					):
    lowerCAmelCase_			:      Tuple			=   min(A__					)
    lowerCAmelCase_			:      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_			:      List[Any]			=   f'Invalid weight of {weight:f} provided'
      raise ValueError(A__					)

    score_lists.append(A__					)

  return score_lists







def 				UpperCamelCase_			(    A__					:   list[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_			:      List[Any]			=   final_scores[j] + ele

  return final_scores







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   get_data(A__					)
  lowerCAmelCase_			:      Tuple			=   calculate_each_score(A__							,    A__					)
  lowerCAmelCase_			:      Optional[int]			=   generate_final_scores(A__					)

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

  return source_data



