






'''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      A     (   unittest.TestCase    ):






 """simple docstring"""





 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: List[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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Any            =				self.dummy_uncond_unet
       UpperCamelCase_					: str            =				KarrasVeScheduler()

       UpperCamelCase_					: int            =				KarrasVePipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: List[Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pipe(num_inference_steps=2      ,  generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Union[str, Any]            =				pipe(num_inference_steps=2      ,  generator=__lowerCAmelCase      ,  output_type="""numpy"""      ,  return_dict=__lowerCAmelCase			)[0]

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

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: List[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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[str]            =				"""google/ncsnpp-celebahq-256"""
       UpperCamelCase_					: List[Any]            =				UNetaDModel.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				KarrasVeScheduler()

       UpperCamelCase_					: int            =				KarrasVePipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[Any]            =				pipe(num_inference_steps=20      ,  generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: List[str]            =				image[0, -3:, -3:, -1]
       assert image.shape == (1, 2_56, 2_56, 3)
       UpperCamelCase_					: Optional[int]            =				np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]			)

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














'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    UNetaDModel
 __a      :    ScoreSdeVeScheduler
 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       super().__init__()
       self.register_modules(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 20_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "pil"      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				self.unet.config.sample_size
       UpperCamelCase_					: int            =				(batch_size, 3, img_size, img_size)

       UpperCamelCase_					: List[Any]            =				self.unet

       UpperCamelCase_					: Union[str, Any]            =				randn_tensor(__lowerCAmelCase      ,  generator=__lowerCAmelCase			) * self.scheduler.init_noise_sigma
       UpperCamelCase_					: Union[str, Any]            =				sample.to(self.device			)

       self.scheduler.set_timesteps(__lowerCAmelCase			)
       self.scheduler.set_sigmas(__lowerCAmelCase			)

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
             UpperCamelCase_					: Union[str, Any]            =				self.scheduler.sigmas[i] * torch.ones(shape[0]      ,  device=self.device			)

             # correction step
             for _ in range(self.scheduler.config.correct_steps			):
                   UpperCamelCase_					: List[str]            =				self.unet(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
                   UpperCamelCase_					: List[str]            =				self.scheduler.step_correct(__lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

             # prediction step
             UpperCamelCase_					: List[Any]            =				model(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
             UpperCamelCase_					: Tuple            =				self.scheduler.step_pred(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			)

             UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				output.prev_sample, output.prev_sample_mean

       UpperCamelCase_					: Tuple            =				sample_mean.clamp(0      ,  1			)
       UpperCamelCase_					: str            =				sample.cpu().permute(0      ,  2      ,  3      ,  1			).numpy()
       if output_type == "pil":
             UpperCamelCase_					: Dict            =				self.numpy_to_pil(__lowerCAmelCase			)

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowerCAmelCase			)






