



from typing import Any





class    __lowerCAmelCase					:




				def __init__(				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       []
									_lowerCAmelCase         =       0




				def __str__(				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       """<""" + str(self.stack						)[1:-1] + """>"""
									return printed




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									self.stack.append(_snake_case						)
									_lowerCAmelCase         =       self.length + 1




				def 				snake_case  (				self						):



									"""simple docstring"""





									self.rotate(1						)
									_lowerCAmelCase         =       self.stack[self.length - 1]
									_lowerCAmelCase         =       self.stack[:-1]
									self.rotate(self.length - 1						)
									_lowerCAmelCase         =       self.length - 1
									return dequeued




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									for _ in range(_snake_case						):
														_lowerCAmelCase         =       self.stack[0]
														_lowerCAmelCase         =       self.stack[1:]
														self.put(_snake_case						)
														_lowerCAmelCase         =       self.length - 1




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get()
									self.put(_snake_case						)
									self.rotate(self.length - 1						)
									return front




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.length




"""simple docstring"""







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

import torch_xla.distributed.xla_multiprocessing as xmp




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					int								=		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 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					List[str]								=		parse_args()

     # Import training_script as a module.
     __A					:					Dict								=		Path(args.training_script						)
     sys.path.append(str(script_fpath.parent.resolve()						)						)
     __A					:					List[Any]								=		script_fpath.stem
     __A					:					Optional[Any]								=		importlib.import_module(_SCREAMING_SNAKE_CASE						)

     # Patch sys.argv
     __A					:					Union[str, 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()



