


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 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	str:




											'''simple docstring'''

											torch.manual_seed(0						)
											__snake_case				=		UNetaDModel(
											    block_out_channels=(3_2, 6_4)     ,    layers_per_block=2     ,    sample_size=3_2     ,    in_channels=3     ,    out_channels=3     ,    down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,    up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,    )
											return model






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		self.dummy_uncond_unet
											__snake_case				=		KarrasVeScheduler()

											__snake_case				=		KarrasVePipeline(unet=SCREAMING_SNAKE_CASE     ,    scheduler=SCREAMING_SNAKE_CASE						)
											pipe.to(SCREAMING_SNAKE_CASE						)
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

											__snake_case				=		torch.manual_seed(0						)
											__snake_case				=		pipe(num_inference_steps=2     ,    generator=SCREAMING_SNAKE_CASE     ,    output_type="numpy"						).images

											__snake_case				=		torch.manual_seed(0						)
											__snake_case				=		pipe(num_inference_steps=2     ,    generator=SCREAMING_SNAKE_CASE     ,    output_type="numpy"     ,    return_dict=SCREAMING_SNAKE_CASE						)[0]

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

											assert image.shape == (1, 3_2, 3_2, 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 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		"google/ncsnpp-celebahq-256"
											__snake_case				=		UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE						)
											__snake_case				=		KarrasVeScheduler()

											__snake_case				=		KarrasVePipeline(unet=SCREAMING_SNAKE_CASE     ,    scheduler=SCREAMING_SNAKE_CASE						)
											pipe.to(SCREAMING_SNAKE_CASE						)
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

											__snake_case				=		torch.manual_seed(0						)
											__snake_case				=		pipe(num_inference_steps=2_0     ,    generator=SCREAMING_SNAKE_CASE     ,    output_type="numpy"						).images

											__snake_case				=		image[0, -3:, -3:, -1]
											assert image.shape == (1, 2_5_6, 2_5_6, 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









import os
from typing import Dict, List, Tuple, TypeVar, Union


A							:					List[Any]     						=     TypeVar('T')

A							:					Dict     						=     Union[List[T], Tuple[T, ...]]
A							:					Any     						=     Union[T, List[T], Dict[str, T]]
A							:					Optional[int]     						=     Union[str, bytes, os.PathLike]





