

UpperCAmelCase_							:					str		 =   [
    [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__    :   Union[str, Any]			,a__    :   str			,a__    :   Any			,a__    :   List[str]       )					->				str:
	# Return True if there is node that has not iterated.
	__A :			str		    =			[False] * len(a__       )
	__A :			Tuple		    =			[s]
	__A :			Any		    =			True

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

	return visited[t]
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]			,a__    :   int			,a__    :   Any       )					->				int:
	__A :			Optional[Any]		    =			[-1] * (len(a__       ))
	__A :			List[Any]		    =			0
	__A :			Optional[Any]		    =			[]
	__A :			int		    =			[i[:] for i in graph]  # Record original cut, copy.
	while bfs(a__			,a__			,a__			,a__       ):
		__A :			int		    =			float("""Inf"""       )
		__A :			Optional[int]		    =			sink

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

		max_flow += path_flow
		__A :			Union[str, Any]		    =			sink

		while v != source:
			__A :			Tuple		    =			parent[v]
			graph[u][v] -= path_flow
			graph[v][u] += path_flow
			__A :			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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_							:					int		 =   {
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[str]		 =   [
			    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''InformerForPrediction''',
			    '''InformerModel''',
			    '''InformerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_informer import (
						    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InformerForPrediction,
						    InformerModel,
						    InformerPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_							:					Optional[int]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





