

"""simple docstring"""
import numpy as np
import qiskit







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					int   )						->     qiskit.QuantumCircuit:



       """simple docstring"""







       __magic_name__			:		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:
              __magic_name__			:		int        =     np.random.randint(1     , 2**num_qubits   )
              # Next, format 'b' as a binary string of length 'n', padded with zeros:
              __magic_name__			:		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)
              __magic_name__			:		Any        =     np.random.randint(2   )
              if output == 1:
                     oracle_qc.x(lowerCamelCase   )

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







def       UpperCamelCase_					(     lowerCamelCase							:					qiskit.QuantumCircuit     , lowerCamelCase							:					int   )						->     qiskit.QuantumCircuit:



       """simple docstring"""







       __magic_name__			:		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       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					int   )						->     qiskit.result.counts.Counts:



       """simple docstring"""







       __magic_name__			:		List[str]        =     qiskit.Aer.get_backend('''aer_simulator'''   )

       __magic_name__			:		Tuple        =     dj_oracle(lowerCamelCase     , lowerCamelCase   )
       __magic_name__			:		int        =     dj_algorithm(lowerCamelCase     , lowerCamelCase   )

       # Execute the circuit on the simulator
       __magic_name__			:		List[str]        =     qiskit.execute(lowerCamelCase     , lowerCamelCase     , shots=1000   )

       # 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)
