



'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Tuple           = 'vit_mae'







							def __init__(							self :	Union[str, Any]     ,_UpperCAmelCase :	List[Any]=768     ,_UpperCAmelCase :	Optional[int]=12     ,_UpperCAmelCase :	Any=12     ,_UpperCAmelCase :	Any=3072     ,_UpperCAmelCase :	List[str]="gelu"     ,_UpperCAmelCase :	Any=0.0     ,_UpperCAmelCase :	Dict=0.0     ,_UpperCAmelCase :	Union[str, Any]=0.02     ,_UpperCAmelCase :	List[str]=1E-12     ,_UpperCAmelCase :	Dict=224     ,_UpperCAmelCase :	Dict=16     ,_UpperCAmelCase :	List[Any]=3     ,_UpperCAmelCase :	Dict=True     ,_UpperCAmelCase :	List[Any]=16     ,_UpperCAmelCase :	int=512     ,_UpperCAmelCase :	Dict=8     ,_UpperCAmelCase :	List[Any]=2048     ,_UpperCAmelCase :	Union[str, Any]=0.75     ,_UpperCAmelCase :	List[str]=False     ,**_UpperCAmelCase :	Tuple     ,):
									super().__init__(**_UpperCAmelCase		)

									_a					:				Any				=							hidden_size
									_a					:				str				=							num_hidden_layers
									_a					:				Dict				=							num_attention_heads
									_a					:				str				=							intermediate_size
									_a					:				Tuple				=							hidden_act
									_a					:				List[Any]				=							hidden_dropout_prob
									_a					:				List[str]				=							attention_probs_dropout_prob
									_a					:				List[Any]				=							initializer_range
									_a					:				int				=							layer_norm_eps
									_a					:				Any				=							image_size
									_a					:				List[str]				=							patch_size
									_a					:				str				=							num_channels
									_a					:				Tuple				=							qkv_bias
									_a					:				Tuple				=							decoder_num_attention_heads
									_a					:				Union[str, Any]				=							decoder_hidden_size
									_a					:				List[str]				=							decoder_num_hidden_layers
									_a					:				Union[str, Any]				=							decoder_intermediate_size
									_a					:				Optional[int]				=							mask_ratio
									_a					:				Optional[int]				=							norm_pix_loss









'''simple docstring'''

import heapq



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				set[int]:
		_a					:				list[list]				=							[]

		# 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
		_a					:				str				=							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
				_a					:				Optional[int]				=							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]:
								_a					:				Dict				=							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()

							__lowerCAmelCase						      =       {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)}""")




