






'''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        a_		(    lowerCamelCase       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        super().__init__()

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

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


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



        """simple docstring"""






        if isinstance(self.unet.config.sample_size    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      (
                  batch_size,
                  self.unet.config.in_channels,
                  self.unet.config.sample_size,
                  self.unet.config.sample_size,
              )
        else:
              UpperCamelCase       						=      (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

        UpperCamelCase       						=      randn_tensor(_SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE    ,	device=self.device    ,	dtype=self.unet.dtype   )

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

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

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

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE   )








'''simple docstring'''

from math import factorial







def 					lowercase__				(   __UpperCamelCase = 20					)->				int:
      UpperCamelCase       						=      2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
      # 2, 3,...
      UpperCamelCase       						=      n // 2

      return int(factorial(__UpperCamelCase					) / (factorial(__UpperCamelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          print(solution(2_0))
     else:
          try:
               SCREAMING_SNAKE_CASE__											=     int(sys.argv[1])
               print(solution(n))
          except ValueError:
               print('Invalid entry - please enter a number.')
