


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 _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						XLMRobertaModel.from_pretrained("xlm-roberta-base")
				SCREAMING_SNAKE_CASE_				:Any				     =						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

				SCREAMING_SNAKE_CASE_				:str				     =						torch.Size((1, 12, 7_68))  # batch_size, sequence_length, embedding_vector_dim
				SCREAMING_SNAKE_CASE_				:List[str]				     =						torch.tensor(
				    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])
				#  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():
						SCREAMING_SNAKE_CASE_				:List[str]				     =						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 _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						XLMRobertaModel.from_pretrained("xlm-roberta-large")
				SCREAMING_SNAKE_CASE_				:str				     =						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

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.Size((1, 12, 10_24))  # batch_size, sequence_length, embedding_vector_dim
				SCREAMING_SNAKE_CASE_				:str				     =						torch.tensor(
				    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]])
				#  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():
						SCREAMING_SNAKE_CASE_				:Any				     =						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))










def     lowercase	(    a     , a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[False] * len(a )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						[]
		queue.append(a )
		SCREAMING_SNAKE_CASE_				:int				     =						True

		while queue:
				SCREAMING_SNAKE_CASE_				:int				     =						queue.pop(0 )
				for ind in range(len(graph[u] ) ):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(a )
								SCREAMING_SNAKE_CASE_				:Tuple				     =						True
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						u

		return visited[t]


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						[-1] * (len(a ))
		SCREAMING_SNAKE_CASE_				:Tuple				     =						0
		while bfs(a     , a     , a     , a ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						float("Inf" )
				SCREAMING_SNAKE_CASE_				:str				     =						sink

				while s != source:
						# Find the minimum value in select path
						SCREAMING_SNAKE_CASE_				:str				     =						min(a     , graph[parent[s]][s] )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						parent[s]

				max_flow += path_flow
				SCREAMING_SNAKE_CASE_				:Dict				     =						sink

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


SCREAMING_SNAKE_CASE__     					=			[
    [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],
]

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






