





'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}






class 							__lowercase    ( SCREAMING_SNAKE_CASE__	, SCREAMING_SNAKE_CASE__  ):







	'''simple docstring'''

	a     =    "focalnet"


	def __init__(self       ,_lowerCamelCase=224       ,_lowerCamelCase=4       ,_lowerCamelCase=3       ,_lowerCamelCase=96       ,_lowerCamelCase=False       ,_lowerCamelCase=[192, 384, 768, 768]       ,_lowerCamelCase=[2, 2, 6, 2]       ,_lowerCamelCase=[2, 2, 2, 2]       ,_lowerCamelCase=[3, 3, 3, 3]       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=4.0       ,_lowerCamelCase=0.0       ,_lowerCamelCase=0.1       ,_lowerCamelCase=False       ,_lowerCamelCase=1E-4       ,_lowerCamelCase=False       ,_lowerCamelCase=False       ,_lowerCamelCase=False       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-5       ,_lowerCamelCase=32       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							Optional[Any]:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase	)

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












'''simple docstring'''



import heapq




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict			):
			__lowercase			  =							[]

			# 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(lowerCamelCase_						,    [-1 * len(lowerCamelCase_			), (key, value)]			)

			# chosen_vertices = set of chosen vertices
			__lowercase			  =							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
						__lowercase			  =							heapq.heappop(lowerCamelCase_			)[1][0]
						chosen_vertices.add(lowerCamelCase_			)

						# 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]:
												__lowercase			  =							elem[1][1].index(lowerCamelCase_			)
												del elem[1][1][index]
												elem[0] += 1
        # re-order the queue
						heapq.heapify(lowerCamelCase_			)
			return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_SCREAMING_SNAKE_CASE   =      {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)}''')





