code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import math
  
  import qiskit
  from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
  
  
  
  
  def 						_UpperCamelCase			(  A = 1    ,       A = 1			):
  			if isinstance(snake_case_    ,       snake_case_			) or isinstance(snake_case_    ,       snake_case_			):
  						raise TypeError("inputs must be integers."			)
  			if (bit_a < 0) or (bit_a < 0):
  						raise ValueError("inputs must be positive."			)
  			if (math.floor(snake_case_			) != bit_a) or (math.floor(snake_case_			) != bit_a):
  						raise ValueError("inputs must be exact integers."			)
  			if (bit_a > 1) or (bit_a > 1):
  						raise ValueError("inputs must be less or equal to 1."			)
  
  			# build registers
  			UpperCamelCase_     =QuantumRegister(2    ,       "qr"			)
  			UpperCamelCase_     =ClassicalRegister(2    ,       "cr"			)
  
  			UpperCamelCase_     =QuantumCircuit(snake_case_    ,       snake_case_			)
  
  			# entanglement the qubits
  			quantum_circuit.h(1			)
  			quantum_circuit.cx(1    ,       0			)
  
  			# send the information
  			UpperCamelCase_     =str(snake_case_			) + str(snake_case_			)
  
  			if c_information == "11":
  						quantum_circuit.x(1			)
  						quantum_circuit.z(1			)
  			elif c_information == "10":
  						quantum_circuit.z(1			)
  			elif c_information == "01":
  						quantum_circuit.x(1			)
  			else:
  						quantum_circuit.i(1			)
  
  			# unentangled the circuit
  			quantum_circuit.cx(1    ,       0			)
  			quantum_circuit.h(1			)
  
  			# measure the circuit
  			quantum_circuit.measure(snake_case_    ,       snake_case_			)
  
  			UpperCamelCase_     =Aer.get_backend("aer_simulator"			)
  			UpperCamelCase_     =execute(snake_case_    ,       snake_case_    ,       shots=1_000			)
  
  			return job.result().get_counts(snake_case_			)
  
  
  if __name__ == "__main__":
  				print(f'''Counts for classical state send: {superdense_coding(1,1)}''')
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import unittest
  from dataclasses import dataclass
  from typing import List, Union
  
  import numpy as np
  import PIL.Image
  
  from diffusers.utils.outputs import BaseOutput
  @dataclass
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Union[List[PIL.Image.Image], np.ndarray]
  
  
  
  
  class 						__lowerCAmelCase   (			unittest.TestCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def 					UpperCamelCase__   (      self:		Tuple					):
       UpperCamelCase_     =CustomOutput(images=np.random.rand(1				,			3				,			4				,			4					)					)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,			np.ndarray					)
       assert outputs.images.shape == (1, 3, 4, 4)
       assert isinstance(outputs["images"]				,			np.ndarray					)
       assert outputs["images"].shape == (1, 3, 4, 4)
       assert isinstance(outputs[0]				,			np.ndarray					)
       assert outputs[0].shape == (1, 3, 4, 4)
  
       # test with a non-tensor attribute
       UpperCamelCase_     =CustomOutput(images=[PIL.Image.new("RGB"				,			(4, 4)					)]					)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,			UpperCamelCase_					)
       assert isinstance(outputs.images[0]				,			PIL.Image.Image					)
       assert isinstance(outputs["images"]				,			UpperCamelCase_					)
       assert isinstance(outputs["images"][0]				,			PIL.Image.Image					)
       assert isinstance(outputs[0]				,			UpperCamelCase_					)
       assert isinstance(outputs[0][0]				,			PIL.Image.Image					)
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Optional[int]					):
       # test output reinitialization with a `dict` for compatibility with `accelerate`
       UpperCamelCase_     =CustomOutput({"images": np.random.rand(1				,			3				,			4				,			4					)}					)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,			np.ndarray					)
       assert outputs.images.shape == (1, 3, 4, 4)
       assert isinstance(outputs["images"]				,			np.ndarray					)
       assert outputs["images"].shape == (1, 3, 4, 4)
       assert isinstance(outputs[0]				,			np.ndarray					)
       assert outputs[0].shape == (1, 3, 4, 4)
  
       # test with a non-tensor attribute
       UpperCamelCase_     =CustomOutput({"images": [PIL.Image.new("RGB"				,			(4, 4)					)]}					)
  
       # check every way of getting the attribute
       assert isinstance(outputs.images				,			UpperCamelCase_					)
       assert isinstance(outputs.images[0]				,			PIL.Image.Image					)
       assert isinstance(outputs["images"]				,			UpperCamelCase_					)
       assert isinstance(outputs["images"][0]				,			PIL.Image.Image					)
       assert isinstance(outputs[0]				,			UpperCamelCase_					)
       assert isinstance(outputs[0][0]				,			PIL.Image.Image					)
  