






'''simple docstring'''







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

import torch_xla.distributed.xla_multiprocessing as xmp





def  SCREAMING_SNAKE_CASE_       ()      ->       Optional[Any]:
							lowerCamelCase__					:	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=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  SCREAMING_SNAKE_CASE_       ()      ->       Union[str, Any]:
							lowerCamelCase__					:	Optional[Any]         =       parse_args()

							# Import training_script as a module.
							lowerCamelCase__					:	Dict         =       Path(args.training_script      )
							sys.path.append(str(script_fpath.parent.resolve()      )      )
							lowerCamelCase__					:	Union[str, Any]         =       script_fpath.stem
							lowerCamelCase__					:	List[str]         =       importlib.import_module(A_      )

							# Patch sys.argv
							lowerCamelCase__					:	Optional[Any]         =       [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()



from __future__ import annotations

from collections.abc import Iterator




class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Dict			,							UpperCamelCase__			: int    )			->	None:







    """simple docstring"""






    __magic_name__				     =       value
    __magic_name__				     =       None
    __magic_name__				     =       None







class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Union[str, Any]			,							UpperCamelCase__			: Node    )			->	None:







    """simple docstring"""






    __magic_name__				     =       tree
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: Node | None    )			->	int:







    """simple docstring"""






    if node is None:
      return 0
    return node.value + (
        self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
    )





  def __iter__( self			: int    )			->	Iterator[int]:







    """simple docstring"""






    yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

