


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       A    (   unittest.TestCase ):


							@slow
							def 			_A       (self					):
									__lowercase=			XLMRobertaModel.from_pretrained('xlm-roberta-base'					)
									__lowercase=			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]					)
									# The dog is cute and lives in the garden house

									__lowercase=			torch.Size((1, 1_2, 7_6_8)					)  # batch_size, sequence_length, embedding_vector_dim
									__lowercase=			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():
											__lowercase=			model(lowerCAmelCase					)['last_hidden_state'].detach()
									self.assertEqual(output.shape      ,				lowerCAmelCase					)
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]      ,				lowerCAmelCase      ,				atol=1E-3					)					)







							@slow
							def 			_A       (self					):
									__lowercase=			XLMRobertaModel.from_pretrained('xlm-roberta-large'					)
									__lowercase=			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]					)
									# The dog is cute and lives in the garden house

									__lowercase=			torch.Size((1, 1_2, 1_0_2_4)					)  # batch_size, sequence_length, embedding_vector_dim
									__lowercase=			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():
											__lowercase=			model(lowerCAmelCase					)['last_hidden_state'].detach()
									self.assertEqual(output.shape      ,				lowerCAmelCase					)
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]      ,				lowerCAmelCase      ,				atol=1E-3					)					)










def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			[False] * len(lowercase__	)
		__lowercase=			[]
		queue.append(lowercase__	)
		__lowercase=			True

		while queue:
				__lowercase=			queue.pop(0	)
				for ind in range(len(graph[u]	)	):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(lowercase__	)
								__lowercase=			True
								__lowercase=			u

		return visited[t]







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	int:

		'''simple docstring'''







		__lowercase=			[-1] * (len(lowercase__	))
		__lowercase=			0
		while bfs(lowercase__   , lowercase__   , lowercase__   , lowercase__	):
				__lowercase=			float('Inf'	)
				__lowercase=			sink

				while s != source:
						# Find the minimum value in select path
						__lowercase=			min(lowercase__   , graph[parent[s]][s]	)
						__lowercase=			parent[s]

				max_flow += path_flow
				__lowercase=			sink

				while v != source:
						__lowercase=			parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						__lowercase=			parent[v]
		return max_flow


lowerCAmelCase           =		[
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

lowerCAmelCase       ,lowerCAmelCase           =		0, 5
print(ford_fulkerson(graph, source, sink))






