import heapq



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: dict			)							->      set[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   []

  # 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
  SCREAMING_SNAKE_CASE__    							=   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
    SCREAMING_SNAKE_CASE__    							=   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]:
        SCREAMING_SNAKE_CASE__    							=   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    :				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)}""")

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase    :				List[Any]							     =	{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase    :				Union[str, Any]							     =	[
       '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''InformerForPrediction''',
       '''InformerModel''',
       '''InformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase    :				Dict							     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
