

lowerCamelCase_    =					{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

lowerCamelCase_    =					{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}



def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_							)	->				list:





  '''simple docstring'''





  snake_case_       						=      True
  snake_case_       						=      []

  for neighbour in graph[vert]:
    if not visited[neighbour]:
      connected_verts += dfs(lowercase_			,					lowercase_			,					lowercase_							)

  return [vert, *connected_verts]



def    UpperCamelCase(					lowercase_							)	->				list:





  '''simple docstring'''





  snake_case_       						=      len(lowercase_							)
  snake_case_       						=      graph_size * [False]
  snake_case_       						=      []

  for i in range(lowercase_							):
    if not visited[i]:
      snake_case_       						=      dfs(lowercase_			,					lowercase_			,					lowercase_							)
      components_list.append(lowercase_							)

  return components_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()







"""simple docstring"""


import unittest

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


if is_torch_available():
   import torch

   from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class 						snake_case  (  unittest.TestCase       ):





    @slow
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			int				=   CamembertModel.from_pretrained('camembert-base'			)
      model.to(A			)

      a     :			List[Any]				=   torch.tensor(
          [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]				,		device=A				,		dtype=torch.long				,		)  # J'aime le camembert !
      with torch.no_grad():
        a     :			int				=   model(A			)['last_hidden_state']
      a     :			Union[str, Any]				=   torch.Size((1, 1_0, 7_6_8)			)
      self.assertEqual(output.shape				,		A			)
      # compare the actual values for a slice.
      a     :			List[str]				=   torch.tensor(
          [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]				,		device=A				,		dtype=torch.float				,		)
      # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
      # camembert.eval()
      # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

      self.assertTrue(torch.allclose(output[:, :3, :3]				,		A				,		atol=1E-4			)			)




