
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   __lowercase						(						unittest.TestCase       ):






	@property
	def       _a(self				: str    ) ->			Any:
							torch.manual_seed(0    )
							_lowercase       :	int           =   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       _a(self				: Any    ) ->			Optional[int]:
							_lowercase       :	Optional[int]           =   self.dummy_uncond_unet
							_lowercase       :	Optional[int]           =   ScoreSdeVeScheduler()

							_lowercase       :	Tuple           =   ScoreSdeVePipeline(unet=_lowercase      ,	scheduler=_lowercase    )
							sde_ve.to(_lowercase    )
							sde_ve.set_progress_bar_config(disable=_lowercase    )

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

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

							_lowercase       :	str           =   image[0, -3:, -3:, -1]
							_lowercase       :	Optional[Any]           =   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_lowercase       :	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   __lowercase						(						unittest.TestCase       ):






	def       _a(self				: str    ) ->			int:
							_lowercase       :	Any           =   "google/ncsnpp-church-256"
							_lowercase       :	Any           =   UNetaDModel.from_pretrained(_lowercase    )

							_lowercase       :	Dict           =   ScoreSdeVeScheduler.from_pretrained(_lowercase    )

							_lowercase       :	int           =   ScoreSdeVePipeline(unet=_lowercase      ,	scheduler=_lowercase    )
							sde_ve.to(_lowercase    )
							sde_ve.set_progress_bar_config(disable=_lowercase    )

							_lowercase       :	List[Any]           =   torch.manual_seed(0    )
							_lowercase       :	Any           =   sde_ve(num_inference_steps=10      ,	output_type="numpy"      ,	generator=_lowercase    ).images

							_lowercase       :	Dict           =   image[0, -3:, -3:, -1]

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

							_lowercase       :	int           =   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







def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[0, 1]
    UpperCamelCase_			  =						0
    while fib[i] <= n:
        fib.append(fib[i] + fib[i + 1]			)
        if fib[i + 2] > n:
            break
        i += 1
    UpperCamelCase_			  =						0
    for j in range(len(SCREAMING_SNAKE_CASE_			) - 1			):
        if fib[j] % 2 == 0:
            total += fib[j]

    return total


if __name__ == "__main__":
 print(F'''{solution() = }''')
