
import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute






def 		_UpperCAmelCase   (	UpperCamelCase:     int = 3  ):






	"""simple docstring"""


	if isinstance(UpperCamelCase			,							UpperCamelCase  ):
		raise TypeError("number of qubits must be a integer."  )
	if number_of_qubits <= 0:
		raise ValueError("number of qubits must be > 0."  )
	if math.floor(UpperCamelCase  ) != number_of_qubits:
		raise ValueError("number of qubits must be exact integer."  )
	if number_of_qubits > 1_0:
		raise ValueError("number of qubits too large to simulate(>10)."  )

	__lowerCAmelCase         =							QuantumRegister(UpperCamelCase			,							"qr"  )
	__lowerCAmelCase         =							ClassicalRegister(UpperCamelCase			,							"cr"  )

	__lowerCAmelCase         =							QuantumCircuit(UpperCamelCase			,							UpperCamelCase  )

	__lowerCAmelCase         =							number_of_qubits

	for i in range(UpperCamelCase  ):
		quantum_circuit.h(number_of_qubits - i - 1  )
		counter -= 1
		for j in range(UpperCamelCase  ):
			quantum_circuit.cp(np.pi / 2 ** (counter - j)			,							UpperCamelCase			,							UpperCamelCase  )

	for k in range(number_of_qubits // 2  ):
		quantum_circuit.swap(UpperCamelCase			,							number_of_qubits - k - 1  )

	# measure all the qubits
	quantum_circuit.measure(UpperCamelCase			,							UpperCamelCase  )
	# simulate with 10000 shots
	__lowerCAmelCase         =							Aer.get_backend("qasm_simulator"  )
	__lowerCAmelCase         =							execute(UpperCamelCase			,							UpperCamelCase			,							shots=1_0_0_0_0  )

	return job.result().get_counts(UpperCamelCase  )


if __name__ == "__main__":
					print(
					    f'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
					)








import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

@slow
@require_torch_gpu
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

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






