





"""simple docstring"""




import numpy as np
import qiskit





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   int      =       qiskit.QuantumCircuit(num_qubits + 1  )

						# First, let's deal with the case in which oracle is balanced
						if case == "balanced":
												# First generate a random number that tells us which CNOTs to
												# wrap in X-gates:
												_UpperCamelCase     :   List[str]      =       np.random.randint(1			,		2**num_qubits  )
												# Next, format 'b' as a binary string of length 'n', padded with zeros:
												_UpperCamelCase     :   int      =       format(_lowercase			,		F"""0{num_qubits}b"""  )
												# Next, we place the first X-gates. Each digit in our binary string
												# corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
												# we apply an X-gate to that qubit:
												for index, bit in enumerate(_lowercase  ):
																		if bit == "1":
																								oracle_qc.x(_lowercase  )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
												for index in range(_lowercase  ):
																		oracle_qc.cx(_lowercase			,		_lowercase  )
												# Next, place the final X-gates
												for index, bit in enumerate(_lowercase  ):
																		if bit == "1":
																								oracle_qc.x(_lowercase  )

    # Case in which oracle is constant
						if case == "constant":
												# First decide what the fixed output of the oracle will be
												# (either always 0 or always 1)
												_UpperCamelCase     :   str      =       np.random.randint(2  )
												if output == 1:
																		oracle_qc.x(_lowercase  )

						_UpperCamelCase     :   Union[str, Any]      =       oracle_qc.to_gate()
						_UpperCamelCase     :   Dict      =       '''Oracle'''  # To show when we display the circuit
						return oracle_gate





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Union[str, Any]      =       qiskit.QuantumCircuit(num_qubits + 1			,		_lowercase  )
						# Set up the output qubit:
						dj_circuit.x(_lowercase  )
						dj_circuit.h(_lowercase  )
						# And set up the input register:
						for qubit in range(_lowercase  ):
												dj_circuit.h(_lowercase  )
						# Let's append the oracle gate to our circuit:
						dj_circuit.append(_lowercase			,		range(num_qubits + 1  )  )
						# Finally, perform the H-gates again and measure:
						for qubit in range(_lowercase  ):
												dj_circuit.h(_lowercase  )

						for i in range(_lowercase  ):
												dj_circuit.measure(_lowercase			,		_lowercase  )

						return dj_circuit





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   str      =       qiskit.Aer.get_backend('''aer_simulator'''  )

						_UpperCamelCase     :   Tuple      =       dj_oracle(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   Optional[Any]      =       dj_algorithm(_lowercase			,		_lowercase  )

						# Execute the circuit on the simulator
						_UpperCamelCase     :   Dict      =       qiskit.execute(_lowercase			,		_lowercase			,		shots=1000  )

						# Return the histogram data of the results of the experiment.
						return job.result().get_counts(_lowercase  )


if __name__ == "__main__":
						print(F"Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}")
						print(F"Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}")











import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		str:







							"""simple docstring"""





							_a  :    Union[str, Any]			=						ElectraConfig.from_json_file(UpperCAmelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )

							if discriminator_or_generator == "discriminator":
														_a  :    int			=						ElectraForPreTraining(UpperCAmelCase     )
							elif discriminator_or_generator == "generator":
														_a  :    Any			=						ElectraForMaskedLM(UpperCAmelCase     )
							else:
														raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )

							# Load weights from tf checkpoint
							load_tf_weights_in_electra(
							    UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	discriminator_or_generator=UpperCAmelCase     )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'     )
							torch.save(model.state_dict()    ,	UpperCAmelCase     )


if __name__ == "__main__":
						__lowerCamelCase						= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
						)
						parser.add_argument(
						    '--config_file',
						    default=None,
						    type=str,
						    required=True,
						    help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						parser.add_argument(
						    '--discriminator_or_generator',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '
						        '\'generator\'.'
						    ),
						)
						__lowerCamelCase						= parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
						)