


import qiskit
from qiskit.providers import Backend






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  lowerCamelCase			: Optional[int]   =    bin(SCREAMING_SNAKE_CASE_     )[2:], bin(SCREAMING_SNAKE_CASE_     )[2:]  # Remove leading '0b'

  # Ensure that both strings are of the same length
  if len(SCREAMING_SNAKE_CASE_     ) > len(SCREAMING_SNAKE_CASE_     ):
    lowerCamelCase			: Optional[int]   =    y.zfill(len(SCREAMING_SNAKE_CASE_     )     )
  else:
    lowerCamelCase			: str   =    x.zfill(len(SCREAMING_SNAKE_CASE_     )     )

  # We need (3 * number of bits in the larger number)+1 qBits
  # The second parameter is the number of classical registers, to measure the result
  lowerCamelCase			: Optional[int]   =    qiskit.QuantumCircuit((len(SCREAMING_SNAKE_CASE_     ) * 3) + 1				,						len(SCREAMING_SNAKE_CASE_     ) + 1     )

  # We are essentially "not-ing" the bits that are 1
  # Reversed because it's easier to perform ops on more significant bits
  for i in range(len(SCREAMING_SNAKE_CASE_     )     ):
    if x[::-1][i] == "1":
      circuit.x(SCREAMING_SNAKE_CASE_     )
  for j in range(len(SCREAMING_SNAKE_CASE_     )     ):
    if y[::-1][j] == "1":
      circuit.x(len(SCREAMING_SNAKE_CASE_     ) + j     )

  return circuit, x, y






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						):


  '''simple docstring'''

  circuit.ccx(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     )
  circuit.cx(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     )
  circuit.ccx(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     )
  circuit.cx(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     )
  circuit.cx(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     )






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_ = qiskit.Aer.get_backend("aer_simulator"     )				,						):


  '''simple docstring'''

  if vala < 0 or vala < 0:
    raise ValueError("Both Integers must be positive!"     )

  # Store the Integers
  lowerCamelCase			: Any   =    store_two_classics(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     )
  for i in range(len(SCREAMING_SNAKE_CASE_     )     ):
    full_adder(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						len(SCREAMING_SNAKE_CASE_     ) + i				,						len(SCREAMING_SNAKE_CASE_     ) + len(SCREAMING_SNAKE_CASE_     ) + i				,						len(SCREAMING_SNAKE_CASE_     ) + len(SCREAMING_SNAKE_CASE_     ) + i + 1     )
    circuit.barrier()  # Optional, just for aesthetics

  # Measure the resultant qBits
  for i in range(len(SCREAMING_SNAKE_CASE_     ) + 1     ):
    circuit.measure([(len(SCREAMING_SNAKE_CASE_     ) * 2) + i]				,						[i]     )

  lowerCamelCase			: List[str]   =    qiskit.execute(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						shots=1     ).result()

  # The result is in binary. Convert it back to int
  return int(list(res.get_counts()     )[0]				,						2     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()








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

import torch_xla.distributed.xla_multiprocessing as xmp






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: str   =    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 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: Optional[Any]   =    parse_args()

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

  # Patch sys.argv
  lowerCamelCase			: Union[str, 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()



