





'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ) ->	str:
      super().__init__()

      # make sure scheduler can always be converted to DDIM
      snake_case				=			DDIMScheduler.from_config(scheduler.config    )

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







     @torch.no_grad()
     def __call__( self    ,   A__ = 1    ,   A__ = None    ,   A__ = 0.0    ,   A__ = 50    ,   A__ = None    ,   A__ = "pil"    ,   A__ = True    ,   ) ->	Union[ImagePipelineOutput, Tuple]:

      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size    ,   A__    ):
       snake_case				=			(
           batch_size,
           self.unet.config.in_channels,
           self.unet.config.sample_size,
           self.unet.config.sample_size,
       )
      else:
       snake_case				=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

      if isinstance(A__    ,   A__    ) and len(A__    ) != batch_size:
       raise ValueError(
           F"""You have passed a list of generators of length {len(A__    )}, but requested an effective batch"""
           F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

      snake_case				=			randn_tensor(A__    ,   generator=A__    ,   device=self.device    ,   dtype=self.unet.dtype    )

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

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

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       snake_case				=			self.scheduler.step(
           A__    ,   A__    ,   A__    ,   eta=A__    ,   use_clipped_model_output=A__    ,   generator=A__    ).prev_sample

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=A__    )








'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			value
      snake_case				=			None
      snake_case				=			None




class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			tree







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      if node is None:
       return 0
      return node.value + (
          self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
      )







     def __iter__( self    ) ->	Iterator[int]:
      yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

