





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

import torch_xla.distributed.xla_multiprocessing as xmp






def 					lowerCamelCase_						(    )		->						Any:






				"""simple docstring"""


				snake_case_						:   Any           =  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 					lowerCamelCase_						(    )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   List[Any]           =  parse_args()

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

				# Patch sys.argv
				snake_case_						:   Optional[int]           =  [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()







def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				if number > 0:
								raise ValueError('''input must be a negative integer'''			)
				snake_case_						:   List[str]           =  len(bin(_UpperCamelCase			)[3:]			)
				snake_case_						:   str           =  bin(abs(_UpperCamelCase			) - (1 << binary_number_length)			)[3:]
				snake_case_						:   Dict           =  (
				    (
				        '''1'''
				        + '''0''' * (binary_number_length - len(_UpperCamelCase			))
				        + twos_complement_number
				    )
				    if number < 0
				    else '''0'''
				)
				return "0b" + twos_complement_number


if __name__ == "__main__":
	import doctest

	doctest.testmod()
