


'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowercase	:						Union[str, Any]       =	logging.get_logger(__name__)

__lowercase	:						Tuple       =	{
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}





class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_					):
	A_				=    "bit"
	A_				=    ["preactivation", "bottleneck"]
	A_				=    ["SAME", "VALID"]




	def __init__(   self						,				__a=3						,				__a=64						,				__a=[256, 512, 1024, 2048]						,				__a=[3, 4, 6, 3]						,				__a="preactivation"						,				__a="relu"						,				__a=None						,				__a=32						,				__a=0.0						,				__a=False						,				__a=32						,				__a=1						,				__a=None						,				__a=None						,				**__a						,				):

						'''simple docstring'''





						super().__init__(**__a							)
						if layer_type not in self.layer_types:
											raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types							)}"""							)
						if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
																__a			:       Tuple					    =  global_padding.upper()
											else:
																raise ValueError(f"""Padding strategy {global_padding} not supported"""							)
						__a			:       str					    =  num_channels
						__a			:       List[str]					    =  embedding_size
						__a			:       str					    =  hidden_sizes
						__a			:       Optional[int]					    =  depths
						__a			:       Dict					    =  layer_type
						__a			:       Union[str, Any]					    =  hidden_act
						__a			:       int					    =  global_padding
						__a			:       int					    =  num_groups
						__a			:       str					    =  drop_path_rate
						__a			:       Any					    =  embedding_dynamic_padding
						__a			:       List[Any]					    =  output_stride
						__a			:       Dict					    =  width_factor

						__a			:       List[Any]					    =  ['stem'] + [f"""stage{idx}""" for idx in range(1						,				len(__a							) + 1							)]
						__a    ,				__a			:       str					    =  get_aligned_output_features_output_indices(
						    out_features=__a						,				out_indices=__a						,				stage_names=self.stage_names							)







'''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



