






import numpy as np
import qiskit
def    a__    (	A		,					A       ):
   lowercase__    :						List[str]       =			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:
      lowercase__    :						Optional[Any]       =			np.random.randint(1		,					2**num_qubits       )
      # Next, format 'b' as a binary string of length 'n', padded with zeros:
      lowercase__    :						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)
      lowercase__    :						int       =			np.random.randint(2       )
      if output == 1:
         oracle_qc.x(A       )

   lowercase__    :						Optional[Any]       =			oracle_qc.to_gate()
   lowercase__    :						str       =			"""Oracle"""  # To show when we display the circuit
   return oracle_gate
def    a__    (	A		,					A       ):
   lowercase__    :						Dict       =			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    a__    (	A		,					A       ):
   lowercase__    :						Optional[Any]       =			qiskit.Aer.get_backend("""aer_simulator"""       )

   lowercase__    :						Any       =			dj_oracle(A		,					A       )
   lowercase__    :						Optional[int]       =			dj_algorithm(A		,					A       )

   # Execute the circuit on the simulator
   lowercase__    :						Tuple       =			qiskit.execute(A		,					A		,					shots=1_000       )

   # 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)}')










from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
a 			=							[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
def    a__    (	A       ):
   return [int(A       ) for c in F'''{ruleset:08}'''[:8]]
def    a__    (	A		,					A		,					A       ):
   lowercase__    :						List[Any]       =			len(cells[0]       )  # 31
   lowercase__    :						int       =			[]
   for i in range(A       ):
      # Get the neighbors of each cell
      # Handle neighbours outside bounds by using 0 as their value
      lowercase__    :						Optional[Any]       =			0 if i == 0 else cells[time][i - 1]
      lowercase__    :						Dict       =			0 if i == population - 1 else cells[time][i + 1]
      # Define a new cell and add it to the new generation
      lowercase__    :						str       =			7 - int(F'''{left_neighbor}{cells[time][i]}{right_neighbor}'''		,					2       )
      next_generation.append(rule[situation]       )
   return next_generation
def    a__    (	A       ):
   lowercase__    :						Tuple       =			Image.new("""RGB"""		,					(len(cells[0]       ), len(A       ))       )
   lowercase__    :						Optional[Any]       =			img.load()
   # Generates image
   for w in range(img.width       ):
      for h in range(img.height       ):
         lowercase__    :						Union[str, Any]       =			255 - int(255 * cells[h][w]       )
         lowercase__    :						int       =			(color, color, color)
   return img


if __name__ == "__main__":
 a 			=							bin(int(input('Rule:\n').strip()))[2:]
 a 			=							format_ruleset(int(rule_num))
 for time in range(16):
  CELLS.append(new_generation(CELLS, rule, time))
 a 			=							generate_image(CELLS)
 # Uncomment to save the image
 # img.save(f"rule_{rule_num}.png")
 img.show()


