






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'''







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

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

					return parser.parse_args()




def      __A					(     ):

					'''simple docstring'''







					_A								= parse_args()

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

					# Patch sys.argv
					_A								= [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      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= []
					_A								= 2
					_A								= int(math.sqrt(_lowercase ) )  # Size of every segment
					_A								= [True] * (end + 1)
					_A								= []

					while start <= end:
										if temp[start] is True:
															in_prime.append(_lowercase )
															for i in range(start * start					,			end + 1					,			_lowercase ):
																				_A								= False
										start += 1
					prime += in_prime

					_A								= end + 1
					_A								= min(2 * end					,			_lowercase )

					while low <= n:
										_A								= [True] * (high - low + 1)
										for each in in_prime:
															_A								= math.floor(low / each ) * each
															if t < low:
																				t += each

															for j in range(_lowercase					,			high + 1					,			_lowercase ):
																				_A								= False

										for j in range(len(_lowercase ) ):
															if temp[j] is True:
																				prime.append(j + low )

										_A								= high + 1
										_A								= min(high + end					,			_lowercase )

					return prime


print(sieve(10**6))



