





'''simple docstring'''




_A:					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],
]







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[Any]:
 # Return True if there is node that has not iterated.
 __UpperCAmelCase					=   [False] * len(_lowerCAmelCase    )
 __UpperCAmelCase					=   [s]
 __UpperCAmelCase					=   True

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

 return visited[t]







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Tuple:
 __UpperCAmelCase					=   [-1] * (len(_lowerCAmelCase    ))
 __UpperCAmelCase					=   0
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   [i[:] for i in graph]  # Record original cut, copy.
 while bfs(_lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    ):
  __UpperCAmelCase					=   float('Inf'    )
  __UpperCAmelCase					=   sink

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

  max_flow += path_flow
  __UpperCAmelCase					=   sink

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

 for i in range(len(_lowerCAmelCase    )    ):
  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))












'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A:					Tuple							=				logging.get_logger(__name__)






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[Any]						   =       """timm_backbone"""



    def __init__(  self     ,			__A=None     ,			__A=3     ,			__A=True     ,			__A=True     ,			__A=None     ,			**__A     ,			):
     super().__init__(**__A					)
     __UpperCAmelCase					=   backbone
     __UpperCAmelCase					=   num_channels
     __UpperCAmelCase					=   features_only
     __UpperCAmelCase					=   use_pretrained_backbone
     __UpperCAmelCase					=   True
     __UpperCAmelCase					=   out_indices if out_indices is not None else (-1,)





