



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

import torch_xla.distributed.xla_multiprocessing as xmp







def 				A			(		)       ->       Dict:



						'''simple docstring'''






						UpperCAmelCase_     							=     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			(		)       ->       Tuple:



						'''simple docstring'''






						UpperCAmelCase_     							=     parse_args()

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

						# Patch sys.argv
						UpperCAmelCase_     							=     [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()








'''simple docstring'''
from __future__ import annotations

import re


def       a__    (  _SCREAMING_SNAKE_CASE					:    list[str]							)			-> list[str]:


	"""simple docstring"""





	def alphanum_key(_SCREAMING_SNAKE_CASE					:    Optional[Any]							):
		return [int(_SCREAMING_SNAKE_CASE							) if s.isdigit() else s.lower() for s in re.split("([0-9]+)"     ,							_SCREAMING_SNAKE_CASE							)]

	return sorted(_SCREAMING_SNAKE_CASE     ,							key=_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




