import qiskit
from qiskit.providers import Backend




def 				_snake_case     (       lowerCAmelCase							:       int       ,			lowerCAmelCase							:       int     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   bin(lowerCAmelCase     )[2:], bin(lowerCAmelCase     )[2:]  # Remove leading '0b'

		# Ensure that both strings are of the same length
		if len(lowerCAmelCase     ) > len(lowerCAmelCase     ):
				SCREAMING_SNAKE_CASE_  :		str    				=   y.zfill(len(lowerCAmelCase     )     )
		else:
				SCREAMING_SNAKE_CASE_  :		List[Any]    				=   x.zfill(len(lowerCAmelCase     )     )

		# 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
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   qiskit.QuantumCircuit((len(lowerCAmelCase     ) * 3) + 1       ,			len(lowerCAmelCase     ) + 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(lowerCAmelCase     )     ):
				if x[::-1][i] == "1":
						circuit.x(lowerCAmelCase     )
		for j in range(len(lowerCAmelCase     )     ):
				if y[::-1][j] == "1":
						circuit.x(len(lowerCAmelCase     ) + j     )

		return circuit, x, y




def 				_snake_case     (       lowerCAmelCase							:       qiskit.QuantumCircuit       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       int       ,			):
		"""simple docstring"""







		circuit.ccx(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		circuit.cx(lowerCAmelCase       ,			lowerCAmelCase     )
		circuit.ccx(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		circuit.cx(lowerCAmelCase       ,			lowerCAmelCase     )
		circuit.cx(lowerCAmelCase       ,			lowerCAmelCase     )




def 				_snake_case     (       lowerCAmelCase							:       int       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Backend = qiskit.Aer.get_backend("aer_simulator"     )       ,			):
		"""simple docstring"""







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

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

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

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   qiskit.execute(lowerCAmelCase       ,			lowerCAmelCase       ,			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 gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCamelCase :     List[Any]          =  False






@nightly
@require_torch_gpu
class 		a__						( unittest.TestCase ):

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							# remove text_unet
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		int    				=   pipe(
							    prompt="first prompt",image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images

							with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_A   )
									SCREAMING_SNAKE_CASE_  :		Dict    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained(_A   )

							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   generator.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		str    				=   pipe(
							    prompt="first prompt",image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images

							assert np.abs(image - new_image   ).sum() < 1E-5, "Models don't have the same forward pass"






					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   "cyberpunk 2077"
							SCREAMING_SNAKE_CASE_  :		Dict    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   pipe(
							    prompt=_A,image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=50,output_type="numpy",).images

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
