


"""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 snake_case      (  unittest.TestCase):







		@slow
		def  a_ (					self      :       Tuple		)       ->						Optional[Any]:





						'''simple docstring'''







						_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_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]		)
						#  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(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  a_ (					self      :       str		)       ->						Tuple:





						'''simple docstring'''







						_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_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]		)
						#  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(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      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	str:
				# Return True if there is node that has not iterated.
				_A					  =			[False] * len(__lowercase   )
				_A					  =			[]
				queue.append(__lowercase   )
				_A					  =			True

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

				return visited[t]






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	int:
				# This array is filled by BFS and to store path
				_A					  =			[-1] * (len(__lowercase   ))
				_A					  =			0
				while bfs(__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   ):
								_A					  =			float("Inf"   )
								_A					  =			sink

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

								max_flow += path_flow
								_A					  =			sink

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


a_       							=    [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

a_							,     a_       							=    0, 5




print(ford_fulkerson(graph, source, sink))