





'''simple docstring'''



# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue




def       _lowerCAmelCase  ( lowerCamelCase_	:     Union[str, Any] ):
			__lowercase   =							Queue()
			__lowercase   =							[False] * len(lowerCamelCase_ )
			__lowercase   =							[-1] * len(lowerCamelCase_ )

			def bfs():
						while not queue.empty():
									__lowercase   =							queue.get()
									__lowercase   =							True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															__lowercase   =							1 - color[u]
															queue.put(lowerCamelCase_ )

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(lowerCamelCase_ ) ):
						if not visited[i]:
									queue.put(lowerCamelCase_ )
									__lowercase   =							0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
	# Adjacency List of graph
	print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))









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        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.tensor(
           [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,      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				)				)

