



"""simple docstring"""
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def   A_    (     _lowercase = np.pi / 2,   _lowercase = np.pi / 2,   _lowercase = np.pi / 2      ):







						'''simple docstring'''


						snake_case_					:List[Any]		=     QuantumRegister(3,   """qr"""      )  # Define the number of quantum bits
						snake_case_					:Union[str, Any]		=     ClassicalRegister(1,   """cr"""      )  # Define the number of classical bits

						snake_case_					:Optional[int]		=     QuantumCircuit(_lowercase,   _lowercase      )  # Define the quantum circuit.

						# Build the circuit
						quantum_circuit.u(_lowercase,   _lowercase,   _lowercase,   0      )  # Quantum State to teleport
						quantum_circuit.h(1      )  # add hadamard gate
						quantum_circuit.cx(
						    1,   2      )  # add control gate with qubit 1 as control and 2 as target.
						quantum_circuit.cx(0,   1      )
						quantum_circuit.h(0      )
						quantum_circuit.cz(0,   2      )  # add control z gate.
						quantum_circuit.cx(1,   2      )

						quantum_circuit.measure([2],   [0]      )  # measure the qubit.

						# Simulate the circuit using qasm simulator
						snake_case_					:str		=     Aer.get_backend("""aer_simulator"""      )
						snake_case_					:List[str]		=     execute(_lowercase,   _lowercase,   shots=1000      )

						return job.result().get_counts(_lowercase      )


if __name__ == "__main__":
						print(
						    "Total count for teleported state is: "
						    F"""{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}"""
						)





"""simple docstring"""
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'''


						snake_case_					: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=_lowercase,   default=1,   help="""Number of TPU cores to use (1 or 8)."""      )

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

						return parser.parse_args()
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:Any		=     parse_args()

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

						# Patch sys.argv
						snake_case_					: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()
