





'''simple docstring'''







import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def    _lowerCAmelCase						(						lowercase = np.pi / 2					,	lowercase = np.pi / 2					,	lowercase = np.pi / 2	)					->					qiskit.result.counts.Counts:
			__lowerCAmelCase											=				QuantumRegister(3					,	"""qr"""	)  # Define the number of quantum bits
			__lowerCAmelCase											=				ClassicalRegister(1					,	"""cr"""	)  # Define the number of classical bits

			__lowerCAmelCase											=				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
			__lowerCAmelCase											=				Aer.get_backend("""aer_simulator"""	)
			__lowerCAmelCase											=				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    _lowerCAmelCase						(						)					->					List[str]:
			__lowerCAmelCase											=				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    _lowerCAmelCase						(						)					->					Any:
			__lowerCAmelCase											=				parse_args()

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

			# Patch sys.argv
			__lowerCAmelCase											=				[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()

