


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       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					42
					A__     =					42

					def __init__(self       :		Optional[Any] ,   _UpperCAmelCase       :		UNetaDModel ,   _UpperCAmelCase       :		ScoreSdeVeScheduler						)		->  Union[str, Any]:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase ,   scheduler=_UpperCAmelCase						)


					@torch.no_grad()
					def __call__(self       :		List[Any] ,   _UpperCAmelCase       :		int = 1 ,   _UpperCAmelCase       :		int = 2000 ,   _UpperCAmelCase       :		Optional[Union[torch.Generator, List[torch.Generator]]] = None ,   _UpperCAmelCase       :		Optional[str] = "pil" ,   _UpperCAmelCase       :		bool = True ,   **_UpperCAmelCase       :		Any ,   )		->  Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__	     =						self.unet.config.sample_size
							lowercase__	     =						(batch_size, 3, img_size, img_size)

							lowercase__	     =						self.unet

							lowercase__	     =						randn_tensor(_UpperCAmelCase ,   generator=_UpperCAmelCase						) * self.scheduler.init_noise_sigma
							lowercase__	     =						sample.to(self.device						)

							self.scheduler.set_timesteps(_UpperCAmelCase						)
							self.scheduler.set_sigmas(_UpperCAmelCase						)

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

									# correction step
									for _ in range(self.scheduler.config.correct_steps						):
											lowercase__	     =						self.unet(_UpperCAmelCase ,   _UpperCAmelCase						).sample
											lowercase__	     =						self.scheduler.step_correct(_UpperCAmelCase ,   _UpperCAmelCase ,   generator=_UpperCAmelCase						).prev_sample

									# prediction step
									lowercase__	     =						model(_UpperCAmelCase ,   _UpperCAmelCase						).sample
									lowercase__	     =						self.scheduler.step_pred(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   generator=_UpperCAmelCase						)

									lowercase__     ,      lowercase__	     =						output.prev_sample, output.prev_sample_mean

							lowercase__	     =						sample_mean.clamp(0 ,   1						)
							lowercase__	     =						sample.cpu().permute(0 ,   2 ,   3 ,   1						).numpy()
							if output_type == "pil":
									lowercase__	     =						self.numpy_to_pil(_UpperCAmelCase						)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase						)







from __future__ import annotations

from collections.abc import Iterator





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     value
       SCREAMING_SNAKE_CASE_      		=     None
       SCREAMING_SNAKE_CASE_      		=     None





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						int							,							_lowerCAmelCase    :						Node		):
       SCREAMING_SNAKE_CASE_      		=     tree



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						Node | None		):
       if node is None:
             return 0
       return node.value + (
           self.depth_first_search(node.left		) + self.depth_first_search(node.right		)
       )




 def __iter__(				self    :						Dict		):
       yield self.depth_first_search(self.tree		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()