code: 
  
  
  from __future__ import annotations
  
  from enum import Enum
  
  
  
  
  
  class        lowercase_				( _SCREAMING_SNAKE_CASE):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		24
  							snake_case_        =		21
  							snake_case_        =		18
  							snake_case_        =		15
  							snake_case_        =		12
  							snake_case_        =		9
  							snake_case_        =		6
  							snake_case_        =		3
  							snake_case_        =		2
  							snake_case_        =		1
  							snake_case_        =		-1
  							snake_case_        =		-2
  							snake_case_        =		-3
  							snake_case_        =		-6
  							snake_case_        =		-9
  							snake_case_        =		-12
  							snake_case_        =		-15
  							snake_case_        =		-18
  							snake_case_        =		-21
  							snake_case_        =		-24
  
  
  
  
  
  class        lowercase_				( _SCREAMING_SNAKE_CASE):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		8
  							snake_case_        =		7
  							snake_case_        =		6
  							snake_case_        =		5
  							snake_case_        =		4
  							snake_case_        =		3
  							snake_case_        =		2
  							snake_case_        =		1
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						if isinstance(UpperCAmelCase__       ,    UpperCAmelCase__		):
  												a_					=		SIUnit[known_prefix.lower()]
  						if isinstance(UpperCAmelCase__       ,    UpperCAmelCase__		):
  												a_					=		SIUnit[unknown_prefix.lower()]
  						a_					=		known_amount * (
  						    10 ** (known_prefix.value - unknown_prefix.value)
  						)
  						return unknown_amount
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						if isinstance(UpperCAmelCase__       ,    UpperCAmelCase__		):
  												a_					=		BinaryUnit[known_prefix.lower()]
  						if isinstance(UpperCAmelCase__       ,    UpperCAmelCase__		):
  												a_					=		BinaryUnit[unknown_prefix.lower()]
  						a_					=		known_amount * (
  						    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
  						)
  						return unknown_amount
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
style_context: 
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""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=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_       (					):
  
  
  
  						"""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(UpperCAmelCase__      )
  
  						# 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()