


import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							@property
							def 					__snake_case      (							self :						Union[str, Any]       )			->					Optional[Any]:
									torch.manual_seed(0       )
									__snake_case	:    str											=     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 					__snake_case      (							self :						Optional[int]       )			->					str:
									__snake_case	:    List[str]											=     self.dummy_uncond_unet
									__snake_case	:    List[str]											=     ScoreSdeVeScheduler()

									__snake_case	:    str											=     ScoreSdeVePipeline(unet=lowerCamelCase					,			scheduler=lowerCamelCase       )
									sde_ve.to(lowerCamelCase       )
									sde_ve.set_progress_bar_config(disable=lowerCamelCase       )

									__snake_case	:    Union[str, Any]											=     torch.manual_seed(0       )
									__snake_case	:    int											=     sde_ve(num_inference_steps=2					,			output_type="numpy"					,			generator=lowerCamelCase       ).images

									__snake_case	:    List[str]											=     torch.manual_seed(0       )
									__snake_case	:    Union[str, Any]											=     sde_ve(num_inference_steps=2					,			output_type="numpy"					,			generator=lowerCamelCase					,			return_dict=lowerCamelCase       )[
									    0
									]

									__snake_case	:    Optional[int]											=     image[0, -3:, -3:, -1]
									__snake_case	:    List[Any]											=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									__snake_case	:    Optional[Any]											=     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        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Union[str, Any]       )			->					Tuple:
									__snake_case	:    Optional[int]											=     "google/ncsnpp-church-256"
									__snake_case	:    List[str]											=     UNetaDModel.from_pretrained(lowerCamelCase       )

									__snake_case	:    str											=     ScoreSdeVeScheduler.from_pretrained(lowerCamelCase       )

									__snake_case	:    Dict											=     ScoreSdeVePipeline(unet=lowerCamelCase					,			scheduler=lowerCamelCase       )
									sde_ve.to(lowerCamelCase       )
									sde_ve.set_progress_bar_config(disable=lowerCamelCase       )

									__snake_case	:    Union[str, Any]											=     torch.manual_seed(0       )
									__snake_case	:    Dict											=     sde_ve(num_inference_steps=10					,			output_type="numpy"					,			generator=lowerCamelCase       ).images

									__snake_case	:    List[str]											=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)

									__snake_case	:    Union[str, Any]											=     np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]       )

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





import math







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):

							if initial_intensity < 0:
														raise ValueError("The value of intensity cannot be negative"			)
														# handling of negative values of initial intensity
							if angle < 0 or angle > 3_6_0:
														raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"			)
														# handling of values out of allowed range
							return initial_intensity * (math.cos(math.radians(__lowerCamelCase			)			) ** 2)


if __name__ == "__main__":
			import doctest

			doctest.testmod(name="malus_law")

