



'''simple docstring'''






import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def 				snake_case_  (      )   ->     Optional[int]:
							raise RuntimeError('''CUDA out of memory.'''						)

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






	def __init__(		self    :  Union[str, Any]	)    ->	List[str]:
								super().__init__()
								UpperCAmelCase				: Dict											=	nn.Linear(3	,    4	)
								UpperCAmelCase				: Union[str, Any]											=	nn.BatchNormad(4	)
								UpperCAmelCase				: Tuple											=	nn.Linear(4	,    5	)







	def   A			(		self    :  str	,    __snake_case    :  Union[str, Any]	)    ->	Any:
								return self.lineara(self.batchnorm(self.lineara(__snake_case	)	)	)

class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Optional[Any]	)    ->	str:
								UpperCAmelCase				: Optional[int]											=	[]

								@find_executable_batch_size(starting_batch_size=128	)
								def mock_training_loop_function(__snake_case    :  int	):
															nonlocal batch_sizes
															batch_sizes.append(__snake_case	)
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(__snake_case	,    [128, 64, 32, 16, 8]	)







	def   A			(		self    :  int	)    ->	str:
								UpperCAmelCase				: Any											=	[]

								@find_executable_batch_size(starting_batch_size=128	)
								def mock_training_loop_function(__snake_case    :  Union[str, Any]	,    __snake_case    :  Any	):
															nonlocal batch_sizes
															batch_sizes.append(__snake_case	)
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase				: int											=	mock_training_loop_function('''hello'''	)
								self.assertListEqual(__snake_case	,    [128, 64, 32, 16, 8]	)
								self.assertListEqual([bs, arga]	,    [8, '''hello''']	)







	def   A			(		self    :  Optional[int]	)    ->	List[str]:
								@find_executable_batch_size(starting_batch_size=0	)
								def mock_training_loop_function(__snake_case    :  Optional[Any]	):
															pass

								with self.assertRaises(__snake_case	) as cm:
															mock_training_loop_function()
															self.assertIn('''No executable batch size found, reached zero.'''	,    cm.exception.args[0]	)







	def   A			(		self    :  Tuple	)    ->	List[Any]:
								@find_executable_batch_size(starting_batch_size=16	)
								def mock_training_loop_function(__snake_case    :  Any	):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

								with self.assertRaises(__snake_case	) as cm:
															mock_training_loop_function()
															self.assertIn('''No executable batch size found, reached zero.'''	,    cm.exception.args[0]	)







	def   A			(		self    :  Optional[Any]	)    ->	List[Any]:
								@find_executable_batch_size(starting_batch_size=128	)
								def mock_training_loop_function(__snake_case    :  List[Any]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Optional[Any]	):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(__snake_case	) as cm:
															mock_training_loop_function(128	,    '''hello'''	,    '''world'''	)
															self.assertIn('''Batch size was passed into `f`'''	,    cm.exception.args[0]	)
															self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')'''	,    cm.exception.args[0]	)







	def   A			(		self    :  Dict	)    ->	Optional[int]:
								@find_executable_batch_size(starting_batch_size=16	)
								def mock_training_loop_function(__snake_case    :  Dict	):
															raise ValueError('''Oops, we had an error!'''	)

								with self.assertRaises(__snake_case	) as cm:
															mock_training_loop_function()
															self.assertIn('''Oops, we had an error!'''	,    cm.exception.args[0]	)







	@require_cuda
	def   A			(		self    :  List[str]	)    ->	Optional[Any]:
								UpperCAmelCase				: Union[str, Any]											=	torch.cuda.memory_allocated()
								UpperCAmelCase				: Union[str, Any]											=	ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()	,    __snake_case	)
								UpperCAmelCase				: int											=	release_memory(__snake_case	)
								self.assertEqual(torch.cuda.memory_allocated()	,    __snake_case	)










'''simple docstring'''






def 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Tuple						)   ->     Union[str, Any]:
							# Return True if there is node that has not iterated.
							UpperCAmelCase				: Optional[Any]											=	[False] * len(_lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	[]
							queue.append(_lowerCAmelCase						)
							UpperCAmelCase				: str											=	True

							while queue:
														UpperCAmelCase				: Optional[int]											=	queue.pop(0						)
														for ind in range(len(graph[u]						)						):
																					if visited[ind] is False and graph[u][ind] > 0:
																												queue.append(_lowerCAmelCase						)
																												UpperCAmelCase				: Union[str, Any]											=	True
																												UpperCAmelCase				: Optional[int]											=	u

							return visited[t]


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		List[str]						)   ->     Any:
							# This array is filled by BFS and to store path
							UpperCAmelCase				: Dict											=	[-1] * (len(_lowerCAmelCase						))
							UpperCAmelCase				: Any											=	0
							while bfs(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						):
														UpperCAmelCase				: List[str]											=	float('''Inf'''						)
														UpperCAmelCase				: str											=	sink

														while s != source:
																					# Find the minimum value in select path
																					UpperCAmelCase				: int											=	min(_lowerCAmelCase     ,   graph[parent[s]][s]						)
																					UpperCAmelCase				: Tuple											=	parent[s]

														max_flow += path_flow
														UpperCAmelCase				: Optional[Any]											=	sink

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


UpperCamelCase__:     Tuple										=    [
    [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],
]

UpperCamelCase__			,      UpperCamelCase__:     int										=    0, 5




print(ford_fulkerson(graph, source, sink))





