





"""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 					_UpperCAmelCase   (	unittest.TestCase ):


					@slow
					def 	lowerCamelCase       (					self			:Dict		):
												A		=  XLMRobertaModel.from_pretrained("xlm-roberta-base"		)
												A		=  torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]		)
												# The dog is cute and lives in the garden house

												A		=  torch.Size((1, 12, 7_68)		)  # batch_size, sequence_length, embedding_vector_dim
												A		=  torch.tensor(
												    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]		)
												#  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():
																			A		=  model(__UpperCamelCase		)["last_hidden_state"].detach()
												self.assertEqual(output.shape     ,       __UpperCamelCase		)
												# compare the actual values for a slice of last dim
												self.assertTrue(torch.allclose(output[:, :, -1]     ,       __UpperCamelCase     ,       atol=1e-3		)		)


					@slow
					def 	lowerCamelCase       (					self			:Tuple		):
												A		=  XLMRobertaModel.from_pretrained("xlm-roberta-large"		)
												A		=  torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]		)
												# The dog is cute and lives in the garden house

												A		=  torch.Size((1, 12, 10_24)		)  # batch_size, sequence_length, embedding_vector_dim
												A		=  torch.tensor(
												    [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]		)
												#  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():
																			A		=  model(__UpperCamelCase		)["last_hidden_state"].detach()
												self.assertEqual(output.shape     ,       __UpperCamelCase		)
												# compare the actual values for a slice of last dim
												self.assertTrue(torch.allclose(output[:, :, -1]     ,       __UpperCamelCase     ,       atol=1e-3		)		)









"""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.






def 					A__				(			UpperCamelCase       ):
							A		=  [False] * len(UpperCamelCase       )
							A		=  [-1] * len(UpperCamelCase       )

							def dfs(UpperCamelCase					,  UpperCamelCase       ):
														A		=  True
														A		=  c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase					,  1 - c       )

							for i in range(len(UpperCamelCase       )       ):
														if not visited[i]:
																					dfs(UpperCamelCase					,  0       )

							for i in range(len(UpperCamelCase       )       ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_snake_case     :		str       						= {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))


