





"""simple docstring"""


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      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







  '''simple docstring'''


  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Dict:
        _UpperCamelCase      :	int						=       XLMRobertaModel.from_pretrained("xlm-roberta-base"	)
        _UpperCamelCase      :	List[str]						=       torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]	)
        # The dog is cute and lives in the garden house

        _UpperCamelCase      :	List[str]						=       torch.Size((1, 12, 768)	)  # batch_size, sequence_length, embedding_vector_dim
        _UpperCamelCase      :	List[str]						=       torch.tensor(
            [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]	)
        #  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():
              _UpperCamelCase      :	List[str]						=       model(__a	)["last_hidden_state"].detach()
        self.assertEqual(output.shape					,   __a	)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]					,   __a					,   atol=1e-3	)	)


  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Optional[int]:
        _UpperCamelCase      :	List[str]						=       XLMRobertaModel.from_pretrained("xlm-roberta-large"	)
        _UpperCamelCase      :	Optional[Any]						=       torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]	)
        # The dog is cute and lives in the garden house

        _UpperCamelCase      :	Optional[Any]						=       torch.Size((1, 12, 1024)	)  # batch_size, sequence_length, embedding_vector_dim
        _UpperCamelCase      :	str						=       torch.tensor(
            [[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]]	)
        #  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():
              _UpperCamelCase      :	List[Any]						=       model(__a	)["last_hidden_state"].detach()
        self.assertEqual(output.shape					,   __a	)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]					,   __a					,   atol=1e-3	)	)







"""simple docstring"""


def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       set()
      # edges = list of graph's edges
      _UpperCamelCase      :	Union[str, Any]						=       get_edges(lowercase_			)

      # While there are still elements in edges list, take an arbitrary edge
      # (from_node, to_node) and add his extremity to chosen_vertices and then
      # remove all arcs adjacent to the from_node and to_node
      while edges:
            _UpperCamelCase,  _UpperCamelCase      :	str						=       edges.pop()
            chosen_vertices.add(lowercase_			)
            chosen_vertices.add(lowercase_			)
            for edge in edges.copy():
                  if from_node in edge or to_node in edge:
                        edges.discard(lowercase_			)
      return chosen_vertices




def 					lowercase__    (					lowercase_			)					->		set:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       set()
      for from_node, to_nodes in graph.items():
            for to_node in to_nodes:
                  edges.add((from_node, to_node)			)
      return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
