
def a__    (       A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       int(A_					)

  # Initialize Result
  __magic_name__				     =       []

  # Traverse through all denomination
  for denomination in reversed(A_					):
    # Find denominations
    while int(A_					) >= int(A_					):
      total_value -= int(A_					)
      answer.append(A_					)  # Append the "answers" array

  return answer


# Driver Code
if __name__ == "__main__":
    __lowerCAmelCase			:     Optional[int]           =   []
    __lowerCAmelCase			:     List[Any]           =   '0'

    if (
        input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
        == "y"
    ):
        __lowerCAmelCase			:     Any           =   int(input('Enter the number of denominations you want to add: ').strip())

        for i in range(0, n):
            denominations.append(int(input(F'''Denomination {i}: ''').strip()))
        __lowerCAmelCase			:     Union[str, Any]           =   input('Enter the change you want to make in Indian Currency: ').strip()
    else:
        # All denominations of Indian Currency if user does not enter
        __lowerCAmelCase			:     Optional[Any]           =   [1, 2, 5, 10, 20, 50, 100, 500, 2000]
        __lowerCAmelCase			:     Optional[Any]           =   input('Enter the change you want to make: ').strip()

    if int(value) == 0 or int(value) < 0:
        print('The total value cannot be zero or negative.')

    else:
        print(F'''Following is minimal change for {value}: ''')
        __lowerCAmelCase			:     Tuple           =   find_minimum_change(denominations, value)
        # Print result
        for i in range(len(answer)):
            print(answer[i], end=' ')



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'''





  __magic_name__				     =       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 a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       parse_args()

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

  # Patch sys.argv
  __magic_name__				     =       [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()

