




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def      __lowerCAmelCase			(					)     ->	List[Any]:
  lowerCAmelCase__				:  Optional[int]		       =     ArgumentParser(
      description=(
          '''PyTorch TPU distributed training launch '''
          '''helper utility that will spawn up '''
          '''multiple distributed processes'''
      )      )

  # Optional arguments for the launch helper
  parser.add_argument('''--num_cores'''	,      type=UpperCamelCase	,      default=1	,      help='''Number of TPU cores to use (1 or 8).'''      )

  # positional
  parser.add_argument(
      '''training_script'''	,      type=UpperCamelCase	,      help=(
          '''The full path to the single TPU training '''
          '''program/script to be launched in parallel, '''
          '''followed by all the arguments for the '''
          '''training script'''
      )	,      )

  # rest from the training program
  parser.add_argument('''training_script_args'''	,      nargs=UpperCamelCase      )

  return parser.parse_args()



def      __lowerCAmelCase			(					)     ->	Union[str, Any]:
  lowerCAmelCase__				:  Optional[Any]		       =     parse_args()

  # Import training_script as a module.
  lowerCAmelCase__				:  Union[str, Any]		       =     Path(args.training_script      )
  sys.path.append(str(script_fpath.parent.resolve()      )      )
  lowerCAmelCase__				:  Dict		       =     script_fpath.stem
  lowerCAmelCase__				:  Any		       =     importlib.import_module(UpperCamelCase      )

  # Patch sys.argv
  lowerCAmelCase__				:  Tuple		       =     [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores      )]
  xmp.spawn(mod._mp_fn	,      args=()	,      nprocs=args.num_cores      )


if __name__ == "__main__":
     main()







lowerCAmelCase_					    = [
    [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			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	int:
  # Return True if there is node that has not iterated.
  lowerCAmelCase__				:  List[str]		       =     [False] * len(UpperCamelCase      )
  lowerCAmelCase__				:  int		       =     [s]
  lowerCAmelCase__				:  Dict		       =     True

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

  return visited[t]



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[Any]:
  lowerCAmelCase__				:  Dict		       =     [-1] * (len(UpperCamelCase      ))
  lowerCAmelCase__				:  Dict		       =     0
  lowerCAmelCase__				:  List[str]		       =     []
  lowerCAmelCase__				:  Tuple		       =     [i[:] for i in graph]  # Record original cut, copy.
  while bfs(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      ):
    lowerCAmelCase__				:  Optional[Any]		       =     float('''Inf'''      )
    lowerCAmelCase__				:  Optional[Any]		       =     sink

    while s != source:
      # Find the minimum value in select path
      lowerCAmelCase__				:  Optional[Any]		       =     min(UpperCamelCase	,      graph[parent[s]][s]      )
      lowerCAmelCase__				:  Union[str, Any]		       =     parent[s]

    max_flow += path_flow
    lowerCAmelCase__				:  List[str]		       =     sink

    while v != source:
      lowerCAmelCase__				:  Union[str, Any]		       =     parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowerCAmelCase__				:  Dict		       =     parent[v]

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

