





'''simple docstring'''




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


lowercase__   :				int      =   logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE   (a__ ,		a__							):
     lowerCAmelCase		      =	'''maskformer-swin'''

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


     def __init__(						self						,     _UpperCAmelCase=224						,     _UpperCAmelCase=4						,     _UpperCAmelCase=3						,     _UpperCAmelCase=96						,     _UpperCAmelCase=[2, 2, 6, 2]						,     _UpperCAmelCase=[3, 6, 12, 24]						,     _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						,     ):

          '''simple docstring'''






          super().__init__(**_UpperCAmelCase)

          __A						:			Optional[int]								=	image_size
          __A						:			Union[str, Any]								=	patch_size
          __A						:			Optional[int]								=	num_channels
          __A						:			str								=	embed_dim
          __A						:			Tuple								=	depths
          __A						:			Any								=	len(_UpperCAmelCase)
          __A						:			int								=	num_heads
          __A						:			List[str]								=	window_size
          __A						:			Tuple								=	mlp_ratio
          __A						:			Union[str, Any]								=	qkv_bias
          __A						:			Optional[Any]								=	hidden_dropout_prob
          __A						:			str								=	attention_probs_dropout_prob
          __A						:			Dict								=	drop_path_rate
          __A						:			Any								=	hidden_act
          __A						:			Union[str, Any]								=	use_absolute_embeddings
          __A						:			List[str]								=	layer_norm_eps
          __A						:			List[str]								=	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
          __A						:			int								=	int(embed_dim * 2 ** (len(_UpperCAmelCase) - 1))
          __A						:			str								=	['stem'] + [F'stage{idx}' for idx in range(1						,     len(_UpperCAmelCase) + 1)]
          __A			,__A						:			Union[str, Any]								=	get_aligned_output_features_output_indices(
              out_features=_UpperCAmelCase						,     out_indices=_UpperCAmelCase						,     stage_names=self.stage_names)






'''simple docstring'''




lowercase__   :				Any      =   {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
lowercase__   :				List[Any]      =   ['''a''', '''b''', '''c''', '''d''', '''e''']






def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			Tuple			,							__snake_case  :			int      )					->					Tuple:
     __A						:			List[str]								=	start
     # add current to visited
     visited.append(__snake_case      )
     __A						:			Optional[int]								=	edges[current]
     for neighbor in neighbors:
          # if neighbor not in visited, visit
          if neighbor not in visited:
               __A						:			int								=	topological_sort(__snake_case			,							__snake_case			,							__snake_case      )
    # if all neighbors visited add current to sort
     sort.append(__snake_case      )
     # if all vertices haven't been visited select a new one to visit
     if len(__snake_case      ) != len(__snake_case      ):
          for vertice in vertices:
               if vertice not in visited:
                    __A						:			Dict								=	topological_sort(__snake_case			,							__snake_case			,							__snake_case      )
    # return sort
     return sort


if __name__ == "__main__":
  lowercase__   :				Tuple      =   topological_sort('''a''', [], [])
  print(sort)