





UpperCAmelCase						:   List[Any]			  =     [
    [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],
]







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a  )      ->      List[str]:
					# Return True if there is node that has not iterated.
					__A	:						Optional[int]       =				[False] * len(a  )
					__A	:						List[str]       =				[s]
					__A	:						Dict       =				True

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

					return visited[t]







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      List[str]:
					__A	:						Union[str, Any]       =				[-1] * (len(a  ))
					__A	:						Union[str, Any]       =				0
					__A	:						str       =				[]
					__A	:						int       =				[i[:] for i in graph]  # Record original cut, copy.
					while bfs(a					,						a					,						a					,						a  ):
										__A	:						Dict       =				float('Inf'  )
										__A	:						str       =				sink

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

										max_flow += path_flow
										__A	:						Any       =				sink

										while v != source:
															__A	:						Dict       =				parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															__A	:						List[Any]       =				parent[v]

					for i in range(len(a  )  ):
										for j in range(len(graph[0]  )  ):
															if graph[i][j] == 0 and temp[i][j] > 0:
																				res.append((i, j)  )

					return res


if __name__ == "__main__":
				print(mincut(test_graph, source=0, sink=5))











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase						:   Optional[int]			  =     {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Union[str, Any]			  =     [
				    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''XLMForMultipleChoice''',
				    '''XLMForQuestionAnswering''',
				    '''XLMForQuestionAnsweringSimple''',
				    '''XLMForSequenceClassification''',
				    '''XLMForTokenClassification''',
				    '''XLMModel''',
				    '''XLMPreTrainedModel''',
				    '''XLMWithLMHeadModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Optional[Any]			  =     [
				    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFXLMForMultipleChoice''',
				    '''TFXLMForQuestionAnsweringSimple''',
				    '''TFXLMForSequenceClassification''',
				    '''TFXLMForTokenClassification''',
				    '''TFXLMMainLayer''',
				    '''TFXLMModel''',
				    '''TFXLMPreTrainedModel''',
				    '''TFXLMWithLMHeadModel''',
				]


if TYPE_CHECKING:
				from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
				from .tokenization_xlm import XLMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm import (
								    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMForMultipleChoice,
								    XLMForQuestionAnswering,
								    XLMForQuestionAnsweringSimple,
								    XLMForSequenceClassification,
								    XLMForTokenClassification,
								    XLMModel,
								    XLMPreTrainedModel,
								    XLMWithLMHeadModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlm import (
								    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLMForMultipleChoice,
								    TFXLMForQuestionAnsweringSimple,
								    TFXLMForSequenceClassification,
								    TFXLMForTokenClassification,
								    TFXLMMainLayer,
								    TFXLMModel,
								    TFXLMPreTrainedModel,
								    TFXLMWithLMHeadModel,
								)

else:
				import sys

				UpperCAmelCase						:   str			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




