


'''simple docstring'''






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


a_   :      Dict				    =      logging.get_logger(__name__)

a_   :      List[str]				    =      {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}




class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""bit"""
 _lowerCAmelCase	   =		["""preactivation""", """bottleneck"""]
 _lowerCAmelCase	   =		["""SAME""", """VALID"""]
 def __init__(	self ,			__magic_name__=3 ,			__magic_name__=64 ,			__magic_name__=[2_56, 5_12, 10_24, 20_48] ,			__magic_name__=[3, 4, 6, 3] ,			__magic_name__="preactivation" ,			__magic_name__="relu" ,			__magic_name__=None ,			__magic_name__=32 ,			__magic_name__=0.0 ,			__magic_name__=False ,			__magic_name__=32 ,			__magic_name__=1 ,			__magic_name__=None ,			__magic_name__=None ,			**__magic_name__ ,			)   ->							Dict:
      super().__init__(**__magic_name__  )
      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      =							global_padding.upper()
           else:
                raise ValueError(f'Padding strategy {global_padding} not supported'  )
      _a      =							num_channels
      _a      =							embedding_size
      _a      =							hidden_sizes
      _a      =							depths
      _a      =							layer_type
      _a      =							hidden_act
      _a      =							global_padding
      _a      =							num_groups
      _a      =							drop_path_rate
      _a      =							embedding_dynamic_padding
      _a      =							output_stride
      _a      =							width_factor

      _a      =							['stem'] + [f'stage{idx}' for idx in range(1 ,			len(__magic_name__  ) + 1  )]
      _a ,				_a      =							get_aligned_output_features_output_indices(
          out_features=__magic_name__ ,			out_indices=__magic_name__ ,			stage_names=self.stage_names  )





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list						)				->					float:




     '''simple docstring'''



     _a      =							0
     while len(lowerCAmelCase__						) > 1:
          _a      =							0
          # Consider two files with minimum cost to be merged
          for _ in range(2						):
               _a      =							files.index(min(lowerCAmelCase__						)						)
               temp += files[min_index]
               files.pop(lowerCAmelCase__						)
          files.append(lowerCAmelCase__						)
          optimal_merge_cost += temp
     return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()

