






'''simple docstring'''







import qiskit





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       qiskit.result.counts.Counts:
							lowerCamelCase__					:	str         =       qiskit.Aer.get_backend("""aer_simulator"""      )

							# Create a Quantum Circuit acting on the q register
							lowerCamelCase__					:	Union[str, Any]         =       qiskit.QuantumCircuit(UpperCamelCase				,			UpperCamelCase      )

							# Apply X (NOT) Gate to Qubits 0 & 1
							circuit.x(0      )
							circuit.x(1      )

							# Map the quantum measurement to the classical bits
							circuit.measure([0, 1]				,			[0, 1]      )

							# Execute the circuit on the qasm simulator
							lowerCamelCase__					:	int         =       qiskit.execute(UpperCamelCase				,			UpperCamelCase				,			shots=1000      )

							# Return the histogram data of the results of the experiment.
							return job.result().get_counts(UpperCamelCase      )


if __name__ == "__main__":
			_A	:			Tuple         =single_qubit_measure(2, 2)
			print(F'Total count for various states are: {counts}')









'''simple docstring'''







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


_A	:			Union[str, Any]         =False

class _lowercase	(				unittest.TestCase		):
							pass



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




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Tuple         =       VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
														pipe.to(UpperCamelCase__     )
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
														lowerCamelCase__					:	List[Any]         =       torch.manual_seed(0     )
														lowerCamelCase__					:	List[Any]         =       pipe(
														    image=UpperCamelCase__			,							generator=UpperCamelCase__			,							guidance_scale=7.5			,							num_inference_steps=50			,							output_type="""numpy"""			,							).images

														lowerCamelCase__					:	List[str]         =       image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__					:	Tuple         =       np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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

