import heapq


def      a__ (		__UpperCamelCase				):
      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()

      A     :  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)}")



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


