




from __future__ import annotations

class 		lowercase_       :




					def __init__(   self:		Optional[int],     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  value
									__lowerCAmelCase					=  None
									__lowerCAmelCase					=  None





class 		lowercase_       :
					__UpperCamelCase     	= 42




					def __init__(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  0




					def     _lowercase						(   self:		int,     _lowercase:		Dict,     _lowercase:		str):


									'''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     _lowercase						(   self:		Optional[int],     _lowercase:		Dict,     _lowercase:		Any = None):


									'''simple docstring'''


									if node is None:
													return 0
									if target is not None:
													__lowerCAmelCase					=  target

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

									return self.paths


if __name__ == "__main__":
	import doctest

	doctest.testmod()







'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )