
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp




def 					_UpperCamelCase		(    )   ->				Tuple:


		'''simple docstring'''


		snake_case   :    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 					_UpperCamelCase		(    )   ->				Tuple:


		'''simple docstring'''


		snake_case   :    str			       =		parse_args()

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

		# Patch sys.argv
		snake_case   :    List[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()









from __future__ import annotations

import math
import random






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							int    ,      UpperCamelCase__:							int    ,      UpperCamelCase__:							int			):
		SCREAMING_SNAKE_CASE__								=					d * e - 1
		SCREAMING_SNAKE_CASE__								=					0
		SCREAMING_SNAKE_CASE__								=					0
		while p == 0:
				SCREAMING_SNAKE_CASE__								=					random.randint(2    ,      n - 1			)
				SCREAMING_SNAKE_CASE__								=					k
				while True:
						if t % 2 == 0:
								SCREAMING_SNAKE_CASE__								=					t // 2
								SCREAMING_SNAKE_CASE__								=					(g**t) % n
								SCREAMING_SNAKE_CASE__								=					math.gcd(x - 1    ,      UpperCamelCase__			)
								if x > 1 and y > 1:
										SCREAMING_SNAKE_CASE__								=					y
										SCREAMING_SNAKE_CASE__								=					n // y
										break  # find the correct factors
						else:
								break  # t is not divisible by 2, break and choose another g
		return sorted([p, q]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()