



"""simple docstring"""
import heapq





def  lowerCamelCase_						(UpperCamelCase__		:		dict							):
    _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(UpperCamelCase__     ,       [-1 * len(UpperCamelCase__							), (key, value)]							)

    # chosen_vertices = set of chosen vertices
    _UpperCAmelCase							:  Optional[Any]											=    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							:  Dict											=    heapq.heappop(UpperCamelCase__							)[1][0]
        chosen_vertices.add(UpperCamelCase__							)

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  _lowerCAmelCase    :Union[str, Any]     =					{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"""
import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



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


