


'''simple docstring'''



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


__lowercase     :						str	=     logging.get_logger(__name__)

__lowercase     :						List[Any]	=     {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}





class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_ ):
	A_           =   "nat"

	A_           =   {
	    "num_attention_heads": "num_heads",
	    "num_hidden_layers": "num_layers",
	}




	def __init__(       self						,    __a=4						,    __a=3						,    __a=64						,    __a=[3, 4, 6, 5]						,    __a=[2, 4, 8, 16]						,    __a=7						,    __a=3.0						,    __a=True						,    __a=0.0						,    __a=0.0						,    __a=0.1						,    __a="gelu"						,    __a=0.02						,    __a=1E-5						,    __a=0.0						,    __a=None						,    __a=None						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(**__a							)

						__a      :	Optional[Any]    =  patch_size
						__a      :	Union[str, Any]    =  num_channels
						__a      :	List[str]    =  embed_dim
						__a      :	Dict    =  depths
						__a      :	List[Any]    =  len(__a							)
						__a      :	Optional[Any]    =  num_heads
						__a      :	Union[str, Any]    =  kernel_size
						__a      :	List[str]    =  mlp_ratio
						__a      :	List[Any]    =  qkv_bias
						__a      :	List[str]    =  hidden_dropout_prob
						__a      :	Dict    =  attention_probs_dropout_prob
						__a      :	Any    =  drop_path_rate
						__a      :	Any    =  hidden_act
						__a      :	Dict    =  layer_norm_eps
						__a      :	int    =  initializer_range
						# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
						# this indicates the channel dimension after the last stage of the model
						__a      :	str    =  int(embed_dim * 2 ** (len(__a							) - 1)							)
						__a      :	Optional[int]    =  layer_scale_init_value
						__a      :	Dict    =  ['stem'] + [f"""stage{idx}""" for idx in range(1						,    len(__a							) + 1							)]
						__a   ,    __a      :	Optional[Any]    =  get_aligned_output_features_output_indices(
						    out_features=__a						,    out_indices=__a						,    stage_names=self.stage_names							)







'''simple docstring'''



__lowercase     :						Optional[Any]	=     {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase     :						List[str]	=     ['a', 'b', 'c', 'd', 'e']






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       List[str]       ):
					__a      :	Any    =  start
					# add current to visited
					visited.append(_SCREAMING_SNAKE_CASE       )
					__a      :	Union[str, Any]    =  edges[current]
					for neighbor in neighbors:
										# if neighbor not in visited, visit
										if neighbor not in visited:
															__a      :	Dict    =  topological_sort(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
    # if all neighbors visited add current to sort
					sort.append(_SCREAMING_SNAKE_CASE       )
					# if all vertices haven't been visited select a new one to visit
					if len(_SCREAMING_SNAKE_CASE       ) != len(_SCREAMING_SNAKE_CASE       ):
										for vertice in vertices:
															if vertice not in visited:
																				__a      :	List[Any]    =  topological_sort(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
    # return sort
					return sort


if __name__ == "__main__":
	__lowercase     :						Union[str, Any]	=     topological_sort('a', [], [])
	print(sort)



