


'''simple docstring'''





def    _UpperCAmelCase	(   __lowerCamelCase = "mytext.txt" ,     __lowerCamelCase = False       )		->None:


 '''simple docstring'''
 import socket

 snake_case   : Optional[Any]	  =      12312  # Reserve a port for your service.
 snake_case   : int	  =      socket.socket()  # Create a socket object
 snake_case   : List[Any]	  =      socket.gethostname()  # Get local machine name
 sock.bind((host, port)       )  # Bind to the port
 sock.listen(5       )  # Now wait for client connection.

 print('''Server listening....'''       )

 while True:
  snake_case   : str	  =      sock.accept()  # Establish connection with client.
  print(f'''Got connection from {addr}'''       )
  snake_case   : Dict	  =      conn.recv(1024       )
  print(f'''Server received: {data = }'''       )

  with open(__lowerCamelCase ,     '''rb'''       ) as in_file:
   snake_case   : Dict	  =      in_file.read(1024       )
   while data:
    conn.send(__lowerCamelCase       )
    print(f'''Sent {data!r}'''       )
    snake_case   : Any	  =      in_file.read(1024       )

  print('''Done sending'''       )
  conn.close()
  if testing:  # Allow the test to complete
   break

 sock.shutdown(1       )
 sock.close()


if __name__ == "__main__":
  send_file()










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

import torch_xla.distributed.xla_multiprocessing as xmp






def    _UpperCAmelCase	(   )		->str:


 '''simple docstring'''
 snake_case   : List[str]	  =      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=__lowerCamelCase ,     default=1 ,     help='''Number of TPU cores to use (1 or 8).'''       )

 # positional
 parser.add_argument(
     '''training_script''' ,     type=__lowerCamelCase ,     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=__lowerCamelCase       )

 return parser.parse_args()






def    _UpperCAmelCase	(   )		->Optional[int]:


 '''simple docstring'''
 snake_case   : Optional[Any]	  =      parse_args()

 # Import training_script as a module.
 snake_case   : str	  =      Path(args.training_script       )
 sys.path.append(str(script_fpath.parent.resolve()       )       )
 snake_case   : str	  =      script_fpath.stem
 snake_case   : Optional[int]	  =      importlib.import_module(__lowerCamelCase       )

 # Patch sys.argv
 snake_case   : int	  =      [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()






