
"""simple docstring"""

import heapq




def      lowercase__    (       lowerCAmelCase					:       dict			)			->   set[int]:






    """simple docstring"""




    UpperCAmelCase          =       []

    # 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(lowerCAmelCase			,					[-1 * len(lowerCAmelCase			), (key, value)]			)

    # chosen_vertices = set of chosen vertices
    UpperCAmelCase          =       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          =       heapq.heappop(lowerCAmelCase			)[1][0]
        chosen_vertices.add(lowerCAmelCase			)

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   SCREAMING_SNAKE_CASE_            =    {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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						["image_processor", "tokenizer"]
      __SCREAMING_SNAKE_CASE :				Dict				   =						"AutoImageProcessor"
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						"AutoTokenizer"




      def __init__(				self ,							lowercase_ ,							lowercase_				)    ->       List[str]:
          super().__init__(lowercase_ ,							lowercase_				)
          UpperCAmelCase          =       self.image_processor




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Any:

          if text is None and images is None:
              raise ValueError('You have to specify either text or images. Both cannot be none.'				)

          if text is not None:
              UpperCAmelCase          =       self.tokenizer(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if images is not None:
              UpperCAmelCase          =       self.image_processor(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if text is not None and images is not None:
              UpperCAmelCase          =       image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          else:
              return BatchEncoding(data=dict(**lowercase_				) ,							tensor_type=lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[Any]:
          return self.tokenizer.batch_decode(*lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[int]:
          return self.tokenizer.decode(*lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Optional[int]:
          return ["input_ids", "attention_mask", "pixel_values"]



