def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Any	)							->					Any:







  """simple docstring"""





  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       str=0	)							->					Optional[Any]:







  """simple docstring"""





  return sorted(__UpperCamelCase ,     key=lambda __UpperCamelCase	: x[column]	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Union[str, Any]=float("""inf"""	)	)							->					str:







  """simple docstring"""





  for i in range(points_counts - 1	):
    for j in range(i + 1 ,     __UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      euclidean_distance_sqr(points[i] ,     points[j]	)
      if current_dis < min_dis:
        SCREAMING_SNAKE_CASE__										=      current_dis
  return min_dis



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       List[Any]=float("""inf"""	)	)							->					int:







  """simple docstring"""





  for i in range(min(6 ,     points_counts - 1	) ,     __UpperCamelCase	):
    for j in range(max(0 ,     i - 6	) ,     __UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      euclidean_distance_sqr(points[i] ,     points[j]	)
      if current_dis < min_dis:
        SCREAMING_SNAKE_CASE__										=      current_dis
  return min_dis



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       Any ,     __UpperCamelCase    :       Any	)							->					Optional[Any]:







  """simple docstring"""





  if points_counts <= 3:
    return dis_between_closest_pair(__UpperCamelCase ,     __UpperCamelCase	)

  # recursion
  SCREAMING_SNAKE_CASE__										=      points_counts // 2
  SCREAMING_SNAKE_CASE__										=      closest_pair_of_points_sqr(
      __UpperCamelCase ,     points_sorted_on_y[:mid] ,     __UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      closest_pair_of_points_sqr(
      __UpperCamelCase ,     points_sorted_on_y[mid:] ,     points_counts - mid	)
  SCREAMING_SNAKE_CASE__										=      min(__UpperCamelCase ,     __UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      []
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]	) < closest_pair_dis:
      cross_strip.append(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      dis_between_closest_in_strip(
      __UpperCamelCase ,     len(__UpperCamelCase	) ,     __UpperCamelCase	)
  return min(__UpperCamelCase ,     __UpperCamelCase	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       str	)							->					List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      column_based_sort(__UpperCamelCase ,     column=0	)
  SCREAMING_SNAKE_CASE__										=      column_based_sort(__UpperCamelCase ,     column=1	)
  return (
      closest_pair_of_points_sqr(
          __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
  ) ** 0.5


if __name__ == "__main__":
   __lowerCamelCase			: Dict							   =		[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
   print('''Distance:''', closest_pair_of_points(points, len(points)))

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()
