

UpperCamelCase__		 =     [
    [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 						lowerCamelCase__      ( __A    :List[str]	,__A    :Tuple	,__A    :int	,__A    :List[Any]		):


       """simple docstring"""




       __snake_case       	=							[False] * len(__A		)
       __snake_case       	=							[s]
       __snake_case       	=							True

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

       return visited[t]







def 						lowerCamelCase__      ( __A    :Any	,__A    :Any	,__A    :Dict		):


       """simple docstring"""




       __snake_case       	=							[-1] * (len(__A		))
       __snake_case       	=							0
       __snake_case       	=							[]
       __snake_case       	=							[i[:] for i in graph]  # Record original cut, copy.
       while bfs(__A	,__A	,__A	,__A		):
              __snake_case       	=							float("""Inf"""		)
              __snake_case       	=							sink

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

              max_flow += path_flow
              __snake_case       	=							sink

              while v != source:
                     __snake_case       	=							parent[v]
                     graph[u][v] -= path_flow
                     graph[v][u] += path_flow
                     __snake_case       	=							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,
    is_vision_available,
)


UpperCamelCase__		 =     {
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     ['''ConvNextFeatureExtractor''']
    UpperCamelCase__		 =     ['''ConvNextImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextForImageClassification''',
        '''ConvNextModel''',
        '''ConvNextPreTrainedModel''',
        '''ConvNextBackbone''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''TFConvNextForImageClassification''',
        '''TFConvNextModel''',
        '''TFConvNextPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_convnext import ConvNextFeatureExtractor
        from .image_processing_convnext import ConvNextImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnext import (
            CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextBackbone,
            ConvNextForImageClassification,
            ConvNextModel,
            ConvNextPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
    import sys

    UpperCamelCase__		 =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

