
import numpy as np
import qiskit


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

   SCREAMING_SNAKE_CASE_	=      oracle_qc.to_gate()
   SCREAMING_SNAKE_CASE_	=      '''Oracle'''  # To show when we display the circuit
   return oracle_gate


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      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  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      qiskit.Aer.get_backend('''aer_simulator'''						)

   SCREAMING_SNAKE_CASE_	=      dj_oracle(__lowerCamelCase,  __lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      dj_algorithm(__lowerCamelCase,  __lowerCamelCase						)

   # Execute the circuit on the simulator
   SCREAMING_SNAKE_CASE_	=      qiskit.execute(__lowerCamelCase,  __lowerCamelCase,  shots=10_00						)

   # 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)}""")




import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =CustomConfig



       def __init__(							self ,     _A	)				->				int:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.hidden_size ,     config.hidden_size	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Optional[int]:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				int:
          pass







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =NoSuperInitConfig



       def __init__(							self ,     _A	)				->				Optional[int]:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.attribute ,     config.attribute	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Any:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          pass


