






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


snake_case__   :						Tuple													=   logging.get_logger(__name__)  # pylint: disable=invalid-name


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[Any]:
     super().__init__()
     self.register_modules(unet=_UpperCAmelCase		,		scheduler=_UpperCAmelCase			)




  @torch.no_grad()
  def __call__(  self		,		_UpperCAmelCase = 1		,		_UpperCAmelCase = 100		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		)      ->			Union[AudioPipelineOutput, Tuple]:

     if audio_length_in_s is None:
        UpperCamelCase_						=  self.unet.config.sample_size / self.unet.config.sample_rate

     UpperCamelCase_						=  audio_length_in_s * self.unet.config.sample_rate

     UpperCamelCase_						=  2 ** len(self.unet.up_blocks			)
     if sample_size < 3 * down_scale_factor:
        raise ValueError(
            f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
            f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""			)

     UpperCamelCase_						=  int(_UpperCAmelCase			)
     if sample_size % down_scale_factor != 0:
        UpperCamelCase_						=  (
            (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
        ) * down_scale_factor
        logger.info(
            f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
            f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
            ' process.'			)
     UpperCamelCase_						=  int(_UpperCAmelCase			)

     UpperCamelCase_						=  next(iter(self.unet.parameters()			)			).dtype
     UpperCamelCase_						=  (batch_size, self.unet.config.in_channels, 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."""			)

     UpperCamelCase_						=  randn_tensor(_UpperCAmelCase		,		generator=_UpperCAmelCase		,		device=self.device		,		dtype=_UpperCAmelCase			)

     # set step values
     self.scheduler.set_timesteps(_UpperCAmelCase		,		device=audio.device			)
     UpperCamelCase_						=  self.scheduler.timesteps.to(_UpperCAmelCase			)

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

        # 2. compute previous image: x_t -> t_t-1
        UpperCamelCase_						=  self.scheduler.step(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			).prev_sample

     UpperCamelCase_						=  audio.clamp(-1		,		1			).float().cpu().numpy()

     UpperCamelCase_						=  audio[:, :, :original_sample_size]

     if not return_dict:
        return (audio,)

     return AudioPipelineOutput(audios=_UpperCAmelCase			)













def      _snake_case     (__lowercase):
   UpperCamelCase_						=  1
   for i in range(1     ,   num + 1):
      fact *= i
   return fact






def      _snake_case     (__lowercase):
   UpperCamelCase_						=  0
   while number > 0:
      UpperCamelCase_						=  number % 10
      sum_of_digits += last_digit
      UpperCamelCase_						=  number // 10  # Removing the last_digit from the given number
   return sum_of_digits






def      _snake_case     (__lowercase = 100):
   UpperCamelCase_						=  factorial(__lowercase)
   UpperCamelCase_						=  split_and_add(__lowercase)
   return result


if __name__ == "__main__":
 print(solution(int(input("""Enter the Number: """).strip())))





