def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       dict	)							->					set:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      set()
  # edges = list of graph's edges
  SCREAMING_SNAKE_CASE__										=      get_edges(__UpperCamelCase	)

  # While there are still elements in edges list, take an arbitrary edge
  # (from_node, to_node) and add his extremity to chosen_vertices and then
  # remove all arcs adjacent to the from_node and to_node
  while edges:
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      edges.pop()
    chosen_vertices.add(__UpperCamelCase	)
    chosen_vertices.add(__UpperCamelCase	)
    for edge in edges.copy():
      if from_node in edge or to_node in edge:
        edges.discard(__UpperCamelCase	)
  return chosen_vertices



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       dict	)							->					set:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      set()
  for from_node, to_nodes in graph.items():
    for to_node in to_nodes:
      edges.add((from_node, to_node)	)
  return edges


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
   # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")

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

import torch_xla.distributed.xla_multiprocessing as xmp



def 	__SCREAMING_SNAKE_CASE  (					)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      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 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      parse_args()

  # Import training_script as a module.
  SCREAMING_SNAKE_CASE__										=      Path(args.training_script	)
  sys.path.append(str(script_fpath.parent.resolve()	)	)
  SCREAMING_SNAKE_CASE__										=      script_fpath.stem
  SCREAMING_SNAKE_CASE__										=      importlib.import_module(__UpperCamelCase	)

  # Patch sys.argv
  SCREAMING_SNAKE_CASE__										=      [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()
