

"""simple docstring"""


import heapq



def   _A					(	_a	:						dict				):



      """simple docstring"""






      A				  =		[]

      # 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
      A				  =		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				  =		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]:
                        A				  =		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()

  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)}""")







"""simple docstring"""


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

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





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



    '''simple docstring'''




    _lowerCamelCase					=   field(default='''summarization'''     ,   metadata={'''include_in_asdict_even_if_is_default''': True}    )
    _lowerCamelCase					=   Features({'''text''': Value('''string'''    )}    )
    _lowerCamelCase					=   Features({'''summary''': Value('''string'''    )}    )
    _lowerCamelCase					=   "text"
    _lowerCamelCase					=   "summary"




    @property
    def UpperCamelCase__		( self    )    ->      Dict[str, str]:
          return {self.text_column: "text", self.summary_column: "summary"}




