



import qiskit
from qiskit.providers import Backend


def       UpperCamelCase(  __UpperCamelCase							:		int					,__UpperCamelCase							:		int			):
		lowerCAmelCase_ ,  lowerCAmelCase_      :					Any     =			bin(__UpperCamelCase			)[2:], bin(__UpperCamelCase			)[2:]  # Remove leading '0b'

		# Ensure that both strings are of the same length
		if len(__UpperCamelCase			) > len(__UpperCamelCase			):
				lowerCAmelCase_      :					Optional[Any]     =			y.zfill(len(__UpperCamelCase			)			)
		else:
				lowerCAmelCase_      :					Union[str, Any]     =			x.zfill(len(__UpperCamelCase			)			)

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

		return circuit, x, y


def       UpperCamelCase(  __UpperCamelCase							:		qiskit.QuantumCircuit					,__UpperCamelCase							:		int					,__UpperCamelCase							:		int					,__UpperCamelCase							:		int					,__UpperCamelCase							:		int					,):
		circuit.ccx(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			)
		circuit.cx(__UpperCamelCase					,__UpperCamelCase			)
		circuit.ccx(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			)
		circuit.cx(__UpperCamelCase					,__UpperCamelCase			)
		circuit.cx(__UpperCamelCase					,__UpperCamelCase			)


def       UpperCamelCase(  __UpperCamelCase							:		int					,__UpperCamelCase							:		int					,__UpperCamelCase							:		Backend = qiskit.Aer.get_backend('''aer_simulator'''			)					,):

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

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

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

		lowerCAmelCase_      :					str     =			qiskit.execute(__UpperCamelCase					,__UpperCamelCase					,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 random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class     __snake_case						(					UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a      				=			IFImgaImgPipeline
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_a      				=			PipelineTesterMixin.required_optional_params - {'''latents'''}




							def        UpperCAmelCase__      (	self					:	Any):
									return self._get_dummy_components()




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	Optional[int]    ,    A_					:	List[Any]=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_      :					Dict     =			torch.manual_seed(A_)
									else:
											lowerCAmelCase_      :					List[Any]     =			torch.Generator(device=A_).manual_seed(A_)

									lowerCAmelCase_      :					List[Any]     =			floats_tensor((1, 3, 3_2, 3_2)    ,    rng=random.Random(A_)).to(A_)

									lowerCAmelCase_      :					Any     =			{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''numpy''',
									}

									return inputs




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_save_load_optional_components()




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()    ,    reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,    )
							def        UpperCAmelCase__      (	self					:	List[str]):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Dict):
									# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
									super().test_save_load_floataa(expected_max_diff=1e-1)




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Tuple):
									super().test_floataa_inference(expected_max_diff=1e-1)




							def        UpperCAmelCase__      (	self					:	str):
									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




							def        UpperCAmelCase__      (	self					:	int):
									self._test_save_load_local()







							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1e-2    ,    )





