



'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    42
					A_         =    42




					def __init__(	self					:						List[str]  ,	UpperCamelCase_					:						UNetaDModel  ,	UpperCamelCase_					:						PNDMScheduler       )	->							int:



						'''simple docstring'''





						super().__init__()

						_lowercase       :							int  	=		PNDMScheduler.from_config(scheduler.config       )

						self.register_modules(unet=UpperCamelCase_  ,	scheduler=UpperCamelCase_       )




					@torch.no_grad()
					def __call__(	self					:						List[Any]  ,	UpperCamelCase_					:						int = 1  ,	UpperCamelCase_					:						int = 50  ,	UpperCamelCase_					:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,	UpperCamelCase_					:						Optional[str] = "pil"  ,	UpperCamelCase_					:						bool = True  ,	**UpperCamelCase_					:						Any  ,	)	->							Union[ImagePipelineOutput, Tuple]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		randn_tensor(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  ,	generator=UpperCamelCase_  ,	device=self.device  ,	)

						self.scheduler.set_timesteps(UpperCamelCase_       )
						for t in self.progress_bar(self.scheduler.timesteps       ):
							_lowercase       :							int  	=		self.unet(UpperCamelCase_  ,	UpperCamelCase_       ).sample

							_lowercase       :							str  	=		self.scheduler.step(UpperCamelCase_  ,	UpperCamelCase_  ,	UpperCamelCase_       ).prev_sample

						_lowercase       :							Any  	=		(image / 2 + 0.5).clamp(0  ,	1       )
						_lowercase       :							Optional[Any]  	=		image.cpu().permute(0  ,	2  ,	3  ,	1       ).numpy()
						if output_type == "pil":
							_lowercase       :							List[str]  	=		self.numpy_to_pil(UpperCamelCase_       )

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=UpperCamelCase_       )





'''simple docstring'''

from __future__ import annotations



class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						str  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						_lowercase       :							Any  	=		value
						_lowercase       :							Node | None  	=		None
						_lowercase       :							Node | None  	=		None






class 		lowerCamelCase__   :




					'''simple docstring'''



					A_         =    42




					def __init__(	self					:						List[Any]       )	->							None:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		0




					def     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						if node is None:
							return

						if path_sum == self.target:
							self.paths += 1

						if node.left:
							self.depth_first_search(node.left  ,	path_sum + node.left.value       )
						if node.right:
							self.depth_first_search(node.right  ,	path_sum + node.right.value       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int | None = None       )	->							int:



						'''simple docstring'''





						if node is None:
							return 0
						if target is not None:
							_lowercase       :							Union[str, Any]  	=		target

						self.depth_first_search(UpperCamelCase_  ,	node.value       )
						self.path_sum(node.left       )
						self.path_sum(node.right       )

						return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
