
import torch
from torch import autograd


class 							lowercase   (						autograd.Function  ):



		@staticmethod
		def __UpperCamelCase					(					A_							, A_							, A_							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	inputs.numel()
				UpperCamelCase            =	int(0.005 * nb_elems			) + 1
				if sigmoid:
						UpperCamelCase            =	(torch.sigmoid(lowerCAmelCase_			) > threshold).type(inputs.type()			)
				else:
						UpperCamelCase            =	(inputs > threshold).type(inputs.type()			)
				if mask.sum() < nb_min:
						# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
						UpperCamelCase            =	inputs.flatten().kthvalue(max(nb_elems - nb_min							, 1			)			).values
						UpperCamelCase            =	(inputs > k_threshold).type(inputs.type()			)
				return mask



		@staticmethod
		def __UpperCamelCase					(					A_							, A_			)    ->   int:
				"""simple docstring"""
				return gradOutput, None, None






class 							lowercase   (						autograd.Function  ):



		@staticmethod
		def __UpperCamelCase					(					A_							, A_							, A_			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	inputs.clone()
				UpperCamelCase            =	inputs.flatten().sort(descending=lowerCAmelCase_			)
				UpperCamelCase            =	int(threshold * inputs.numel()			)

				# flat_out and mask access the same memory.
				UpperCamelCase            =	mask.flatten()
				UpperCamelCase            =	0
				UpperCamelCase            =	1
				return mask



		@staticmethod
		def __UpperCamelCase					(					A_							, A_			)    ->   int:
				"""simple docstring"""
				return gradOutput, None






class 							lowercase   (						SCREAMING_SNAKE_CASE__  ):



		@staticmethod
		def __UpperCamelCase					(					A_							, A_			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	inputs.clone()
				UpperCamelCase            =	inputs.abs().flatten().sort(descending=lowerCAmelCase_			)
				UpperCamelCase            =	int(threshold * inputs.numel()			)

				# flat_out and mask access the same memory.
				UpperCamelCase            =	mask.flatten()
				UpperCamelCase            =	0
				UpperCamelCase            =	1
				return mask


import numpy as np
import qiskit






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

    # 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)
      _A:    int						     =			np.random.randint(2 )
      if output == 1:
         oracle_qc.x(a )

   _A:    int						     =			oracle_qc.to_gate()
   _A:    str						     =			'''Oracle'''  # To show when we display the circuit
   return oracle_gate






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

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

   return dj_circuit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.result.counts.Counts:
   _A:    int						     =			qiskit.Aer.get_backend('''aer_simulator''' )

   _A:    Optional[Any]						     =			dj_oracle(a						,						a )
   _A:    int						     =			dj_algorithm(a						,						a )

   # Execute the circuit on the simulator
   _A:    int						     =			qiskit.execute(a						,						a						,						shots=10_00 )

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


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



