





import qiskit
from qiskit.providers import Backend





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   tuple[qiskit.QuantumCircuit, str, str]:




    """simple docstring"""




    _A, _A    =       bin(_SCREAMING_SNAKE_CASE							)[2:], bin(_SCREAMING_SNAKE_CASE							)[2:]  # Remove leading '0b'

    # Ensure that both strings are of the same length
    if len(_SCREAMING_SNAKE_CASE							) > len(_SCREAMING_SNAKE_CASE							):
        _A    =       y.zfill(len(_SCREAMING_SNAKE_CASE							)							)
    else:
        _A    =       x.zfill(len(_SCREAMING_SNAKE_CASE							)							)

    # We need (3 * number of bits in the larger number)+1 qBits
    # The second parameter is the number of classical registers, to measure the result
    _A    =       qiskit.QuantumCircuit((len(_SCREAMING_SNAKE_CASE							) * 3) + 1       ,    len(_SCREAMING_SNAKE_CASE							) + 1							)

    # We are essentially "not-ing" the bits that are 1
    # Reversed because it's easier to perform ops on more significant bits
    for i in range(len(_SCREAMING_SNAKE_CASE							)							):
        if x[::-1][i] == "1":
            circuit.x(_SCREAMING_SNAKE_CASE							)
    for j in range(len(_SCREAMING_SNAKE_CASE							)							):
        if y[::-1][j] == "1":
            circuit.x(len(_SCREAMING_SNAKE_CASE							) + j							)

    return circuit, x, y





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    )       ->   Tuple:




    """simple docstring"""




    circuit.ccx(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    circuit.cx(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    circuit.ccx(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    circuit.cx(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    circuit.cx(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE = qiskit.Aer.get_backend('aer_simulator'							)       ,    )       ->   int:




    """simple docstring"""




    if vala < 0 or vala < 0:
        raise ValueError('Both Integers must be positive!'							)

    # Store the Integers
    _A, _A, _A    =       store_two_classics(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    for i in range(len(_SCREAMING_SNAKE_CASE							)							):
        full_adder(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    len(_SCREAMING_SNAKE_CASE							) + i       ,    len(_SCREAMING_SNAKE_CASE							) + len(_SCREAMING_SNAKE_CASE							) + i       ,    len(_SCREAMING_SNAKE_CASE							) + len(_SCREAMING_SNAKE_CASE							) + i + 1							)
        circuit.barrier()  # Optional, just for aesthetics

    # Measure the resultant qBits
    for i in range(len(_SCREAMING_SNAKE_CASE							) + 1							):
        circuit.measure([(len(_SCREAMING_SNAKE_CASE							) * 2) + i]       ,    [i]							)

    _A    =       qiskit.execute(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    shots=1							).result()

    # The result is in binary. Convert it back to int
    return int(list(res.get_counts()							)[0]       ,    2							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()












import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class     lowerCamelCase(	unittest.TestCase  ):
     '''simple docstring'''


     @property
     def 					lowerCAmelCase__		(						self       ):
         _A    =       1
         _A    =       3
         _A    =       8
         _A    =       8

         _A    =       torch.rand((batch_size, num_channels, height, width)       )

         return sample




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         if isinstance(image[0]							,    PIL.Image.Image       ):
             return np.stack([np.array(snake_case_       ) for i in image]							,    axis=0       )
         elif isinstance(snake_case_							,    torch.Tensor       ):
             return image.cpu().numpy().transpose(0							,    2							,    3							,    1       )
         return image




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       self.to_np(snake_case_       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(
                 image_processor.preprocess(snake_case_       )							,    output_type=snake_case_							,    )
             _A    =       self.to_np(snake_case_       )
             _A    =       (input_np * 255).round() if output_type == 'pil' else input_np
             assert (
                 np.abs(in_np - out_np       ).max() < 1E-6
             ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )
         _A    =       self.dummy_sample.cpu().numpy().transpose(0							,    2							,    3							,    1       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(image_processor.preprocess(snake_case_       )							,    output_type=snake_case_       )

             _A    =       self.to_np(snake_case_       )
             _A    =       (input_np * 255).round() if output_type == 'pil' else input_np
             assert (
                 np.abs(in_np - out_np       ).max() < 1E-6
             ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample.cpu().numpy().transpose(0							,    2							,    3							,    1       )
         _A    =       image_processor.numpy_to_pil(snake_case_       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(image_processor.preprocess(snake_case_       )							,    output_type=snake_case_       )
             for i, o in zip(snake_case_							,    snake_case_       ):
                 _A    =       np.array(snake_case_       )
                 _A    =       self.to_np(snake_case_       ) if output_type == 'pil' else (self.to_np(snake_case_       ) * 255).round()
                 assert (
                     np.abs(in_np - out_np       ).max() < 1E-6
                 ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       input_pt_ad.squeeze(0       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )
         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       self.to_np(self.dummy_sample       )
         _A    =       input_np_ad.squeeze(0       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )
         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         assert np.abs(out_pt_ad - out_pt_ad       ).max() < 1E-6
         assert np.abs(out_np_ad - out_np_ad       ).max() < 1E-6




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       list(snake_case_       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       self.to_np(self.dummy_sample       )
         list(snake_case_       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         assert np.abs(out_pt_ad - out_pt_list       ).max() < 1E-6
         assert np.abs(out_np_ad - out_np_list       ).max() < 1E-6





