





from collections.abc import Callable

UpperCAmelCase			:			str	  = Callable[[float], float]  # type alias for a real -> real function
def   _A (	SCREAMING_SNAKE_CASE  :     RealFunc , SCREAMING_SNAKE_CASE  :     RealFunc , SCREAMING_SNAKE_CASE  :     int , ):





  """simple docstring"""



  a__			:      Any            =float(SCREAMING_SNAKE_CASE		)
  while True:
    try:
      a__			:      List[str]            =prev_guess - function(SCREAMING_SNAKE_CASE		) / derivative(SCREAMING_SNAKE_CASE		)
    except ZeroDivisionError:
      raise ZeroDivisionError("Could not find root"		) from None
    if abs(prev_guess - next_guess		) < 10**-5:
      return next_guess
    a__			:      int            =next_guess
def   _A (	SCREAMING_SNAKE_CASE  :     float		):





  """simple docstring"""



  return (x**3) - (2 * x) - 5
def   _A (	SCREAMING_SNAKE_CASE  :     float		):





  """simple docstring"""



  return 3 * (x**2) - 2


if __name__ == "__main__":
     print(newton(f, fa, 3))













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__			:      Optional[int]            =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=SCREAMING_SNAKE_CASE , default=1 , help="Number of TPU cores to use (1 or 8)."		)

  # positional
  parser.add_argument(
      "training_script" , type=SCREAMING_SNAKE_CASE , 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=SCREAMING_SNAKE_CASE		)

  return parser.parse_args()
def   _A (	):





  """simple docstring"""



  a__			:      Optional[Any]            =parse_args()

  # Import training_script as a module.
  a__			:      Tuple            =Path(args.training_script		)
  sys.path.append(str(script_fpath.parent.resolve()		)		)
  a__			:      Tuple            =script_fpath.stem
  a__			:      Any            =importlib.import_module(SCREAMING_SNAKE_CASE		)

  # Patch sys.argv
  a__			:      List[str]            =[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()






