






'''simple docstring'''






import heapq


def 				snake_case_  (      _lowerCAmelCase						:		dict						)   ->     List[str]:
							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				: Tuple											=	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				: List[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__:     Dict										=    {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)}")










'''simple docstring'''






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=A__    )
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= field(default="""language-modeling"""							,	metadata={"""include_in_asdict_even_if_is_default""": True}    )

	lowerCamelCase__    				= Features({"""text""": Value("""string"""    )}    )
	lowerCamelCase__    				= Features({}    )
	lowerCamelCase__    				= "text"







	@property
	def   A			(		self    :  Optional[Any]	)    ->	Dict[str, str]:
								return {self.text_column: "text"}





