"""simple docstring"""




_a   :	Optional[int] 					=     [
    [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  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Optional[Any]			) ->   int:
  # Return True if there is node that has not iterated.
  _lowerCAmelCase		: str						    =  [False] * len(snake_case_			)
  _lowerCAmelCase		: Optional[int]						    =  [s]
  _lowerCAmelCase		: str						    =  True

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

  return visited[t]





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: int    ,_lowerCamelCase		: Tuple			) ->   Union[str, Any]:
  _lowerCAmelCase		: List[str]						    =  [-1] * (len(snake_case_			))
  _lowerCAmelCase		: List[Any]						    =  0
  _lowerCAmelCase		: Dict						    =  []
  _lowerCAmelCase		: Any						    =  [i[:] for i in graph]  # Record original cut, copy.
  while bfs(snake_case_    ,snake_case_    ,snake_case_    ,snake_case_			):
    _lowerCAmelCase		: Optional[int]						    =  float("""Inf"""			)
    _lowerCAmelCase		: Dict						    =  sink

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

    max_flow += path_flow
    _lowerCAmelCase		: Tuple						    =  sink

    while v != source:
      _lowerCAmelCase		: Optional[int]						    =  parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      _lowerCAmelCase		: Union[str, Any]						    =  parent[v]

  for i in range(len(snake_case_			)			):
    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"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  torch.nn.Linear(10       ,  10						)
       _lowerCAmelCase		: Optional[Any]						    =  torch.optim.SGD(model.parameters()       ,  0.1						)
       _lowerCAmelCase		: Optional[Any]						    =  Accelerator()
       _lowerCAmelCase		: Tuple						    =  accelerator.prepare(a__						)
       try:
         pickle.loads(pickle.dumps(a__						)						)
       except Exception as e:
         self.fail(F"Accelerated optimizer pickling failed with {e}"						)
       AcceleratorState._reset_state()




