




'''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   _SCREAMING_SNAKE_CASE   (   lowerCamelCase__     ):



 def __init__(   self		:   Optional[int]			,				a__		:   Union[str, Any]			,				a__		:   Union[str, Any]      ):
      super().__init__()

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

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



 @torch.no_grad()
 def __call__(   self		:   int			,				a__		:   int = 1			,				a__		:   Any = None			,				a__		:   Dict = 0.0			,				a__		:   Optional[Any] = 50			,				a__		:   str = None			,				a__		:   Any = "pil"			,				a__		:   List[str] = True			,				):







      if isinstance(self.unet.config.sample_size			,				a__      ):
           __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(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.'''      )

      __magic_name__						       =    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
           __magic_name__						       =    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
           __magic_name__						       =    self.scheduler.step(
               a__			,				a__			,				a__			,				eta=a__			,				use_clipped_model_output=a__			,				generator=a__      ).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(a__      )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=a__      )









'''simple docstring'''




from __future__ import annotations

from fractions import Fraction




def 						UpperCamelCase	(			a		,						a			)				->     bool:






     '''simple docstring'''
     return (
         num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
     )




def 						UpperCamelCase	(			a			)				->     list[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     __magic_name__						       =    11
     __magic_name__						       =    int('''1''' + '''0''' * digit_len			)
     for num in range(a		,						a			):
          while den <= 99:
               if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                    if is_digit_cancelling(a		,						a			):
                         solutions.append(F'''{num}/{den}'''			)
               den += 1
          num += 1
          __magic_name__						       =    10
     return solutions




def 						UpperCamelCase	(			a = 2			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    1.0
     for fraction in fraction_list(a			):
          __magic_name__						       =    Fraction(a			)
          result *= frac.denominator / frac.numerator
     return int(a			)


if __name__ == "__main__":
      print(solution())






