




"""simple docstring"""







from ..utils import DummyObject, requires_backends






class       SCREAMING_SNAKE_CASE__					(					metaclass=lowerCamelCase__					):
						_a    						=						['transformers', 'torch', 'note_seq']


						def __init__(							self				:			Optional[int]						,						*lowerCAmelCase				:			Optional[int]						,						**lowerCAmelCase				:			Any					):
										requires_backends(self						,						["""transformers""", """torch""", """note_seq"""]					)


						@classmethod
						def   __lowercase (							cls				:			List[str]						,						*lowerCAmelCase				:			Any						,						**lowerCAmelCase				:			Tuple					):
										requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]					)




						@classmethod
						def   __lowercase (							cls				:			Tuple						,						*lowerCAmelCase				:			Dict						,						**lowerCAmelCase				:			int					):
										requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]					)











"""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       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@property
						def   __lowercase (							self				:			Any					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				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   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.dummy_uncond_unet
										lowerCAmelCase  =				KarrasVeScheduler()

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

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

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

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

										assert image.shape == (1, 32, 32, 3)
										lowerCAmelCase  =				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       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				"""google/ncsnpp-celebahq-256"""
										lowerCAmelCase  =				UNetaDModel.from_pretrained(lowerCAmelCase					)
										lowerCAmelCase  =				KarrasVeScheduler()

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

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=20						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										assert image.shape == (1, 256, 256, 3)
										lowerCAmelCase  =				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





