




import math

import qiskit







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[Any] = 1      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any] = 1      ,      SCREAMING_SNAKE_CASE_							:				Dict = 1     ):
					if (
					    isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     )
					    or isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     )
					    or isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     )
					):
										raise TypeError("inputs must be integers."     )

					if (input_a < 0) or (input_a < 0) or (carry_in < 0):
										raise ValueError("inputs must be positive."     )

					if (
					    (math.floor(lowerCAmelCase__     ) != input_a)
					    or (math.floor(lowerCAmelCase__     ) != input_a)
					    or (math.floor(lowerCAmelCase__     ) != carry_in)
					):
										raise ValueError("inputs must be exact integers."     )

					if (input_a > 2) or (input_a > 2) or (carry_in > 2):
										raise ValueError("inputs must be less or equal to 2."     )

					# build registers
					__lowerCAmelCase				   =       qiskit.QuantumRegister(4      ,      "qr"     )
					__lowerCAmelCase				   =       qiskit.ClassicalRegister(2      ,      "cr"     )
					# list the entries
					__lowerCAmelCase				   =       [input_a, input_a, carry_in]

					__lowerCAmelCase				   =       qiskit.QuantumCircuit(lowerCAmelCase__      ,      lowerCAmelCase__     )

					for i in range(0      ,      3     ):
										if entry[i] == 2:
															quantum_circuit.h(lowerCAmelCase__     )  # for hadamard entries
										elif entry[i] == 1:
															quantum_circuit.x(lowerCAmelCase__     )  # for 1 entries
										elif entry[i] == 0:
															quantum_circuit.i(lowerCAmelCase__     )  # for 0 entries

    # build the circuit
					quantum_circuit.ccx(0      ,      1      ,      3     )  # ccx = toffoli gate
					quantum_circuit.cx(0      ,      1     )
					quantum_circuit.ccx(1      ,      2      ,      3     )
					quantum_circuit.cx(1      ,      2     )
					quantum_circuit.cx(0      ,      1     )

					quantum_circuit.measure([2, 3]      ,      lowerCAmelCase__     )  # measure the last two qbits

					__lowerCAmelCase				   =       qiskit.Aer.get_backend("aer_simulator"     )
					__lowerCAmelCase				   =       qiskit.execute(lowerCAmelCase__      ,      lowerCAmelCase__      ,      shots=10_00     )

					return job.result().get_counts(lowerCAmelCase__     )


if __name__ == "__main__":
					print(f'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')








'''simple docstring'''
import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
  # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
  # setup instructions, if using on-demand hardware

  # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
  # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
  # Throw an error if user passes both BYO and on-demand cluster args
  # Otherwise, use default values
  UpperCamelCase__    					=      argparse.ArgumentParser()
  parser.add_argument('''--user''', type=str, default='''ubuntu''')
  parser.add_argument('''--host''', type=str, default='''localhost''')
  parser.add_argument('''--key_path''', type=str, default=None)
  parser.add_argument('''--instance''', type=str, default='''V100:1''')
  parser.add_argument('''--provider''', type=str, default='''cheapest''')
  parser.add_argument('''--use_spot''', type=bool, default=False)
  parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
  UpperCamelCase__					,   UpperCamelCase__    					=      parser.parse_known_args()
  if args.host != "localhost":
    if args.instance != "V100:1" or args.provider != "cheapest":
      raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
    UpperCamelCase__    					=      rh.cluster(
        name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
    )
  else:
    UpperCamelCase__    					=      rh.cluster(
        name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
    )
  UpperCamelCase__    					=      args.example.rsplit('''/''', 1)[0]

  # Set up remote environment
  cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
  # Note transformers is copied into the home directory on the remote machine, so we can install from there
  cluster.run([F"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
  cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

  # Run example. You can bypass the CLI wrapper and paste your own code here.
  cluster.run([F"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])

  # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
  # from my_script... import train
  # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
  # launch_train_gpu = rh.function(fn=train,
  #                                system=gpu,
  #                                reqs=reqs,
  #                                name='train_bert_glue')
  #
  # We can pass in arguments just like we would to a function:
  # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
  #                  stream_logs=True)
