






import heapq




def      lowercase__      (     __snake_case    :							dict  ):


					'''simple docstring'''


					UpperCAmelCase_    :      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(__snake_case     ,       [-1 * len(__snake_case  ), (key, value)]  )

					# chosen_vertices = set of chosen vertices
					UpperCAmelCase_    :      List[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
										UpperCAmelCase_    :      Any            =						heapq.heappop(__snake_case  )[1][0]
										chosen_vertices.add(__snake_case  )

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__UpperCAmelCase								=						{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)}')














import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__UpperCAmelCase								=						logging.get_logger(__name__)



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							None:
							warnings.warn(
							    'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use ImageGPTImageProcessor instead.'				,		_UpperCamelCase				,		)
							super().__init__(*_UpperCamelCase				,		**_UpperCamelCase   )






