


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

import torch_xla.distributed.xla_multiprocessing as xmp




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[Any]            =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=SCREAMING_SNAKE_CASE__						,     default=1						,     help='Number of TPU cores to use (1 or 8).'	)

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

       return parser.parse_args()




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						List[str]            =parse_args()

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

       # Patch sys.argv
       _lowerCamelCase			:						Optional[Any]            =[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()



def _lowerCamelCase  (		snake_case  ):
							assert (
							    isinstance(snake_case     , snake_case  ) and number_of_steps > 0
							), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
							if number_of_steps == 1:
														return 1
							_lowerCAmelCase			,		_lowerCAmelCase							=							1, 1
							for _ in range(number_of_steps - 1  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							current + previous, current
							return current


if __name__ == "__main__":
				import doctest

				doctest.testmod()






