import socket



def 	__SCREAMING_SNAKE_CASE  (					)							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      socket.socket(socket.AF_INET ,     socket.SOCK_STREAM	)
  SCREAMING_SNAKE_CASE__										=      socket.gethostname()
  SCREAMING_SNAKE_CASE__										=      1_23_12

  sock.connect((host, port)	)
  sock.send(B"""Hello server!"""	)

  with open("""Received_file""" ,     """wb"""	) as out_file:
    print("""File opened"""	)
    print("""Receiving data..."""	)
    while True:
      SCREAMING_SNAKE_CASE__										=      sock.recv(10_24	)
      if not data:
        break
      out_file.write(__UpperCamelCase	)

  print("""Successfully received the file"""	)
  sock.close()
  print("""Connection closed"""	)


if __name__ == "__main__":
   main()

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()
