


'''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__)

_lowercase    :    Union[str, Any]			     =  {
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}






class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase ):
				__magic_name__       :   List[str]							= "focalnet"




				def __init__(	self    :				List[Any]   ,					lowerCAmelCase    :				Any=224   ,					lowerCAmelCase    :				Any=4   ,					lowerCAmelCase    :				Optional[int]=3   ,					lowerCAmelCase    :				str=96   ,					lowerCAmelCase    :				List[str]=False   ,					lowerCAmelCase    :				int=[192, 384, 768, 768]   ,					lowerCAmelCase    :				List[Any]=[2, 2, 6, 2]   ,					lowerCAmelCase    :				Tuple=[2, 2, 2, 2]   ,					lowerCAmelCase    :				Optional[Any]=[3, 3, 3, 3]   ,					lowerCAmelCase    :				Dict="gelu"   ,					lowerCAmelCase    :				str=4.0   ,					lowerCAmelCase    :				int=0.0   ,					lowerCAmelCase    :				Dict=0.1   ,					lowerCAmelCase    :				Optional[int]=False   ,					lowerCAmelCase    :				Optional[int]=1E-4   ,					lowerCAmelCase    :				Optional[int]=False   ,					lowerCAmelCase    :				Any=False   ,					lowerCAmelCase    :				Tuple=False   ,					lowerCAmelCase    :				Any=0.02   ,					lowerCAmelCase    :				Tuple=1E-5   ,					lowerCAmelCase    :				Dict=32   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				Any=None   ,					**lowerCAmelCase    :				Optional[Any]   ,					)->  Dict:

										"""simple docstring"""




										super().__init__(**lowerCAmelCase   )

										UpperCAmelCase         =			image_size
										UpperCAmelCase         =			patch_size
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			embed_dim
										UpperCAmelCase         =			use_conv_embed
										UpperCAmelCase         =			hidden_sizes
										UpperCAmelCase         =			depths
										UpperCAmelCase         =			focal_levels
										UpperCAmelCase         =			focal_windows
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			mlp_ratio
										UpperCAmelCase         =			hidden_dropout_prob
										UpperCAmelCase         =			drop_path_rate
										UpperCAmelCase         =			use_layerscale
										UpperCAmelCase         =			layerscale_value
										UpperCAmelCase         =			use_post_layernorm
										UpperCAmelCase         =			use_post_layernorm_in_modulation
										UpperCAmelCase         =			normalize_modulator
										UpperCAmelCase         =			initializer_range
										UpperCAmelCase         =			layer_norm_eps
										UpperCAmelCase         =			encoder_stride
										UpperCAmelCase         =			['''stem'''] + [F"""stage{idx}""" for idx in range(1   ,					len(self.depths   ) + 1   )]
										UpperCAmelCase       ,       UpperCAmelCase         =			get_aligned_output_features_output_indices(
										    out_features=lowerCAmelCase   ,					out_indices=lowerCAmelCase   ,					stage_names=self.stage_names   )









'''simple docstring'''



import heapq


def    lowerCamelCase__   ( A     :    dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			[]

						# for each node and his adjacency list add them and the rank of the node to queue
						# using heapq module the queue will be filled like a Priority Queue
						# heapq works with a min priority queue, so I used -1*len(v) to build it
						for key, value in graph.items():
												# O(log(n))
												heapq.heappush(A   ,     [-1 * len(A  ), (key, value)]  )

						# chosen_vertices = set of chosen vertices
						UpperCAmelCase         =			set()

						# while queue isn't empty and there are still edges
						#   (queue[0][0] is the rank of the node with max rank)
						while queue and queue[0][0] != 0:
												# extract vertex with max rank from queue and add it to chosen_vertices
												UpperCAmelCase         =			heapq.heappop(A  )[1][0]
												chosen_vertices.add(A  )

												# Remove all arcs adjacent to argmax
												for elem in queue:
																		# if v haven't adjacent node, skip
																		if elem[0] == 0:
																								continue
																		# if argmax is reachable from elem
																		# remove argmax from elem's adjacent list and update his rank
																		if argmax in elem[1][1]:
																								UpperCAmelCase         =			elem[1][1].index(A  )
																								del elem[1][1][index]
																								elem[0] += 1
        # re-order the queue
												heapq.heapify(A  )
						return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowercase    :    Optional[int]			     =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")





