






import math

def 			__lowerCamelCase     (						snake_case__						)				->    int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    0
						if n % 2 == 0:
												count += 1
												while n % 2 == 0:
																		_SCREAMING_SNAKE_CASE       =    int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

						_SCREAMING_SNAKE_CASE       =    3

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

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

						if n > 2:
												count += 1
						return count


if __name__ == "__main__":
		UpperCamelCase												=      51_242_183
		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}")





"""simple docstring"""







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"""

						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=_UpperCamelCase						,      default=1						,      help='''Number of TPU cores to use (1 or 8).'''			)

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

						return parser.parse_args()






def        a__			(							):





						"""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(_UpperCamelCase			)

						# 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()