



'''simple docstring'''





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


a__    =	logging.get_logger(__name__)

a__    =	{
    """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 		snake_case						(	__A					,__A  ):



   '''simple docstring'''






   snake_case_					: int          =		"""nat"""

   snake_case_					: Tuple          =		{
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }

   def __init__(					self							:			List[str]				,		lowerCAmelCase							:			Tuple=4				,		lowerCAmelCase							:			Optional[int]=3				,		lowerCAmelCase							:			List[str]=64				,		lowerCAmelCase							:			List[str]=[3, 4, 6, 5]				,		lowerCAmelCase							:			Optional[int]=[2, 4, 8, 16]				,		lowerCAmelCase							:			int=7				,		lowerCAmelCase							:			Any=3.0				,		lowerCAmelCase							:			List[Any]=True				,		lowerCAmelCase							:			Optional[int]=0.0				,		lowerCAmelCase							:			Tuple=0.0				,		lowerCAmelCase							:			List[str]=0.1				,		lowerCAmelCase							:			Optional[Any]="gelu"				,		lowerCAmelCase							:			Any=0.02				,		lowerCAmelCase							:			List[str]=1E-5				,		lowerCAmelCase							:			str=0.0				,		lowerCAmelCase							:			Optional[int]=None				,		lowerCAmelCase							:			Optional[Any]=None				,		**lowerCAmelCase							:			Optional[int]				,		)				->				List[Any]:

    """simple docstring"""







    super().__init__(**UpperCamelCase__)

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





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Union[str, Any]:
 _snake_case    :  str					=					[False] * len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					[-1] * len(SCREAMING_SNAKE_CASE__				)

 def dfs(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  _snake_case    :  Optional[Any]					=					True
  _snake_case    :  Tuple					=					c
  for u in graph[v]:
   if not visited[u]:
    dfs(SCREAMING_SNAKE_CASE__				,       1 - c				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  if not visited[i]:
   dfs(SCREAMING_SNAKE_CASE__				,       0				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  for j in graph[i]:
   if color[i] == color[j]:
    return False

 return True


# Adjacency list of graph
a__    =	{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}

print(check_bipartite_dfs(graph))
