

"""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					(     __UpperCAmelCase     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      super().__init__()

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

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







     @torch.no_grad()
     def __call__(      self ,						UpperCamelCase_ = 1 ,						UpperCamelCase_ = None ,						UpperCamelCase_ = 0.0 ,						UpperCamelCase_ = 50 ,						UpperCamelCase_ = None ,						UpperCamelCase_ = "pil" ,						UpperCamelCase_ = True ,						):

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

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

      __magic_name__     =      randn_tensor(UpperCamelCase_ ,						generator=UpperCamelCase_ ,						device=self.device ,						dtype=self.unet.dtype       )

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

      for t in self.progress_bar(self.scheduler.timesteps       ):
       # 1. predict noise model_output
       __magic_name__     =      self.unet(UpperCamelCase_ ,						UpperCamelCase_       ).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
       __magic_name__     =      self.scheduler.step(
           UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						eta=UpperCamelCase_ ,						use_clipped_model_output=UpperCamelCase_ ,						generator=UpperCamelCase_       ).prev_sample

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCamelCase_       )




"""simple docstring"""






class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_       ):
      __magic_name__     =      size
      __magic_name__     =      [0] * size
      __magic_name__     =      [0] * size







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return index | (index + 1)







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return (index & (index + 1)) - 1







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      value
      while index < self.size:
       __magic_name__     =      self.get_prev(UpperCamelCase_       ) + 1
       if current_left_border == index:
        __magic_name__     =      value
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      self.get_next(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      right -= 1  # Because of right is exclusive
      __magic_name__     =      0
      while left <= right:
       __magic_name__     =      self.get_prev(UpperCamelCase_       )
       if left <= current_left:
        __magic_name__     =      max(UpperCamelCase_ ,						self.tree[right]       )
        __magic_name__     =      current_left
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						self.arr[right]       )
        right -= 1
      return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()

