


import numpy as np
import qiskit



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			)->			qiskit.QuantumCircuit:
				lowerCAmelCase_			:   List[Any]     					=					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:
								lowerCAmelCase_			:   int     					=					np.random.randint(1			, 2**num_qubits			)
								# Next, format 'b' as a binary string of length 'n', padded with zeros:
								lowerCAmelCase_			:   List[str]     					=					format(lowerCAmelCase_			, 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(lowerCAmelCase_			):
												if bit == "1":
																oracle_qc.x(lowerCAmelCase_			)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
								for index in range(lowerCAmelCase_			):
												oracle_qc.cx(lowerCAmelCase_			, lowerCAmelCase_			)
								# Next, place the final X-gates
								for index, bit in enumerate(lowerCAmelCase_			):
												if bit == "1":
																oracle_qc.x(lowerCAmelCase_			)

    # 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)
								lowerCAmelCase_			:   Any     					=					np.random.randint(2			)
								if output == 1:
												oracle_qc.x(lowerCAmelCase_			)

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



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			)->			qiskit.QuantumCircuit:
				lowerCAmelCase_			:   Dict     					=					qiskit.QuantumCircuit(num_qubits + 1			, lowerCAmelCase_			)
				# Set up the output qubit:
				dj_circuit.x(lowerCAmelCase_			)
				dj_circuit.h(lowerCAmelCase_			)
				# And set up the input register:
				for qubit in range(lowerCAmelCase_			):
								dj_circuit.h(lowerCAmelCase_			)
				# Let's append the oracle gate to our circuit:
				dj_circuit.append(lowerCAmelCase_			, range(num_qubits + 1			)			)
				# Finally, perform the H-gates again and measure:
				for qubit in range(lowerCAmelCase_			):
								dj_circuit.h(lowerCAmelCase_			)

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

				return dj_circuit



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			)->			qiskit.result.counts.Counts:
				lowerCAmelCase_			:   List[str]     					=					qiskit.Aer.get_backend('''aer_simulator'''			)

				lowerCAmelCase_			:   Tuple     					=					dj_oracle(lowerCAmelCase_			, lowerCAmelCase_			)
				lowerCAmelCase_			:   int     					=					dj_algorithm(lowerCAmelCase_			, lowerCAmelCase_			)

				# Execute the circuit on the simulator
				lowerCAmelCase_			:   List[str]     					=					qiskit.execute(lowerCAmelCase_			, lowerCAmelCase_			, shots=1_000			)

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


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


"""simple docstring"""
import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     TaForConditionalGeneration(lowerCamelCase   )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
       model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
 A									=						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 T5 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."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
