


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


UpperCAmelCase__   	=							logging.get_logger(__name__)







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''maskformer-swin'''

     SCREAMING_SNAKE_CASE__      			=			{
         '''num_attention_heads''': '''num_heads''',
         '''num_hidden_layers''': '''num_layers''',
     }





     def __init__(self		,     UpperCAmelCase=2_2_4		,     UpperCAmelCase=4		,     UpperCAmelCase=3		,     UpperCAmelCase=9_6		,     UpperCAmelCase=[2, 2, 6, 2]		,     UpperCAmelCase=[3, 6, 1_2, 2_4]		,     UpperCAmelCase=7		,     UpperCAmelCase=4.0		,     UpperCAmelCase=True		,     UpperCAmelCase=0.0		,     UpperCAmelCase=0.0		,     UpperCAmelCase=0.1		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=False		,     UpperCAmelCase=0.02		,     UpperCAmelCase=1e-5		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   Any:
      super().__init__(**UpperCAmelCase       )

      _lowercase							      =image_size
      _lowercase							      =patch_size
      _lowercase							      =num_channels
      _lowercase							      =embed_dim
      _lowercase							      =depths
      _lowercase							      =len(UpperCAmelCase       )
      _lowercase							      =num_heads
      _lowercase							      =window_size
      _lowercase							      =mlp_ratio
      _lowercase							      =qkv_bias
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =drop_path_rate
      _lowercase							      =hidden_act
      _lowercase							      =use_absolute_embeddings
      _lowercase							      =layer_norm_eps
      _lowercase							      =initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _lowercase							      =int(embed_dim * 2 ** (len(UpperCAmelCase       ) - 1)       )
      _lowercase							      =['''stem'''] + [f"stage{idx}" for idx in range(1		,     len(UpperCAmelCase       ) + 1       )]
      _lowercase      ,    _lowercase							      =get_aligned_output_features_output_indices(
          out_features=UpperCAmelCase		,     out_indices=UpperCAmelCase		,     stage_names=self.stage_names       )





'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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


if __name__ == "__main__":
				import doctest

				doctest.testmod()




