






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

import torch_xla.distributed.xla_multiprocessing as xmp
def      lowerCamelCase					(						)	->			Optional[int]:
							lowerCAmelCase_										=     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=a_ ,						default=1 ,						help='Number of TPU cores to use (1 or 8).'	)

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

							return parser.parse_args()
def      lowerCamelCase					(						)	->			List[str]:
							lowerCAmelCase_										=     parse_args()

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

							# Patch sys.argv
							lowerCAmelCase_										=     [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()











import math
def      lowerCamelCase					(						a_	)	->			Dict:
							lowerCAmelCase_										=     0
							if n % 2 == 0:
														count += 1
														while n % 2 == 0:
																					lowerCAmelCase_										=     int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

							lowerCAmelCase_										=     3

							while i <= int(math.sqrt(a_	)	):
														if n % i == 0:
																					count += 1
																					while n % i == 0:
																												lowerCAmelCase_										=     int(n / i	)
														lowerCAmelCase_										=     i + 2

							# this condition checks the prime
							# number n is greater than 2

							if n > 2:
														count += 1
							return count


if __name__ == "__main__":
		lowerCamelCase_						     =  5_1_2_4_2_1_8_3
		print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
		print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')



