"""simple docstring"""





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  _snake_case     (					unittest.TestCase		):






						@property
						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													torch.manual_seed(0    )
													__lowerCamelCase					:		List[Any]		   =     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      :			Optional[Any]    ):
													__lowerCamelCase					:		Tuple		   =     self.dummy_uncond_unet
													__lowerCamelCase					:		Any		   =     ScoreSdeVeScheduler()

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

													__lowerCamelCase					:		int		   =     torch.manual_seed(0    )
													__lowerCamelCase					:		Tuple		   =     sde_ve(num_inference_steps=2							,					output_type="numpy"							,					generator=UpperCAmelCase    ).images

													__lowerCamelCase					:		Tuple		   =     torch.manual_seed(0    )
													__lowerCamelCase					:		Optional[Any]		   =     sde_ve(num_inference_steps=2							,					output_type="numpy"							,					generator=UpperCAmelCase							,					return_dict=UpperCAmelCase    )[
													    0
													]

													__lowerCamelCase					:		Any		   =     image[0, -3:, -3:, -1]
													__lowerCamelCase					:		Dict		   =     image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													__lowerCamelCase					:		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  _snake_case     (					unittest.TestCase		):






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Dict		   =     "google/ncsnpp-church-256"
													__lowerCamelCase					:		Optional[Any]		   =     UNetaDModel.from_pretrained(UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     ScoreSdeVeScheduler.from_pretrained(UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     ScoreSdeVePipeline(unet=UpperCAmelCase							,					scheduler=UpperCAmelCase    )
													sde_ve.to(UpperCAmelCase    )
													sde_ve.set_progress_bar_config(disable=UpperCAmelCase    )

													__lowerCamelCase					:		int		   =     torch.manual_seed(0    )
													__lowerCamelCase					:		Any		   =     sde_ve(num_inference_steps=10							,					output_type="numpy"							,					generator=UpperCAmelCase    ).images

													__lowerCamelCase					:		str		   =     image[0, -3:, -3:, -1]

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

													__lowerCamelCase					:		Tuple		   =     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
"""simple docstring"""





import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class  _snake_case     :
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			None
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			True
						snake_case__			  =			None
						snake_case__			  =			1
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			None
						snake_case__			  =			None






						def       lowerCamelCase__     (       self      :			Any    ):
													return self.__class__(**{k: copy.deepcopy(UpperCAmelCase    ) for k, v in self.__dict__.items()}    )