
'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 		__UpperCAmelCase   (      unittest.TestCase		):


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								torch.manual_seed(0						)
								_snake_case     		=	UNetaDModel(
								    block_out_channels=(32, 64)					,     layers_per_block=2					,     sample_size=32					,     in_channels=3					,     out_channels=3					,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')					,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')					,     )
								return model




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.dummy_uncond_unet
								_snake_case     		=	KarrasVeScheduler()

								_snake_case     		=	KarrasVePipeline(unet=lowerCAmelCase_					,     scheduler=lowerCAmelCase_						)
								pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	torch.manual_seed(0						)
								_snake_case     		=	pipe(num_inference_steps=2					,     generator=lowerCAmelCase_					,     output_type='numpy'						).images

								_snake_case     		=	torch.manual_seed(0						)
								_snake_case     		=	pipe(num_inference_steps=2					,     generator=lowerCAmelCase_					,     output_type='numpy'					,     return_dict=lowerCAmelCase_						)[0]

								_snake_case     		=	image[0, -3:, -3:, -1]
								_snake_case     		=	image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								_snake_case     		=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

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


@slow
@require_torch
class 		__UpperCAmelCase   (      unittest.TestCase		):


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	'google/ncsnpp-celebahq-256'
								_snake_case     		=	UNetaDModel.from_pretrained(lowerCAmelCase_						)
								_snake_case     		=	KarrasVeScheduler()

								_snake_case     		=	KarrasVePipeline(unet=lowerCAmelCase_					,     scheduler=lowerCAmelCase_						)
								pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	torch.manual_seed(0						)
								_snake_case     		=	pipe(num_inference_steps=20					,     generator=lowerCAmelCase_					,     output_type='numpy'						).images

								_snake_case     		=	image[0, -3:, -3:, -1]
								assert image.shape == (1, 2_56, 2_56, 3)
								_snake_case     		=	np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

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



'''simple docstring'''







import math







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			float:
	if (
	    not isinstance(__A    ,       (int, float)			)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError('power_factor must be a valid float value between -1 and 1.'			)
	return apparent_power * power_factor







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			float:
	if (
	    not isinstance(__A    ,       (int, float)			)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError('power_factor must be a valid float value between -1 and 1.'			)
	return apparent_power * math.sqrt(1 - power_factor**2			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

