
from typing import List, Optional, Tuple, Union

import torch

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


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






class   UpperCAmelCase		(						__a ):



   def __init__(self		:					List[Any]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Tuple )		->	int:


          '''simple docstring'''


          super().__init__()
          self.register_modules(unet=a_    ,						scheduler=a_ )



   @torch.no_grad()
   def __call__(self		:					Optional[Any]    ,						snake_case__		:					int = 1    ,						snake_case__		:					int = 1_00    ,						snake_case__		:					Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						snake_case__		:					Optional[float] = None    ,						snake_case__		:					bool = True    ,						)		->	List[str]:


          '''simple docstring'''


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

          snake_case       :   Any							=      audio_length_in_s * self.unet.config.sample_rate

          snake_case       :   int							=      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}.""" )

          snake_case       :   str							=      int(a_ )
          if sample_size % down_scale_factor != 0:
                 snake_case       :   Optional[Any]							=      (
                     (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." )
          snake_case       :   List[Any]							=      int(a_ )

          snake_case       :   Dict							=      next(iter(self.unet.parameters() ) ).dtype
          snake_case       :   Any							=      (batch_size, self.unet.config.in_channels, 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       :   Union[str, Any]							=      randn_tensor(a_    ,						generator=a_    ,						device=self.device    ,						dtype=a_ )

          # set step values
          self.scheduler.set_timesteps(a_    ,						device=audio.device )
          snake_case       :   Union[str, Any]							=      self.scheduler.timesteps.to(a_ )

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

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

          snake_case       :   Any							=      audio.clamp(-1    ,						1 ).float().cpu().numpy()

          snake_case       :   str							=      audio[:, :, :original_sample_size]

          if not return_dict:
                 return (audio,)

          return AudioPipelineOutput(audios=a_ )











"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					list    ):
  """simple docstring"""

  if len(snake_case__    ) <= 1:
    return [tuple(snake_case__    )]

  _snake_case							:		List[Any]								=  []

  def generate(snake_case__							:					int		,   snake_case__							:					list    ):
    if k == 1:
      res.append(tuple(arr[:]    )    )
      return

    generate(k - 1		,   snake_case__    )

    for i in range(k - 1    ):
      if k % 2 == 0:  # k is even
        _snake_case		,					_snake_case							:		Optional[Any]								=  arr[k - 1], arr[i]
      else:  # k is odd
        _snake_case		,					_snake_case							:		List[str]								=  arr[k - 1], arr[0]
      generate(k - 1		,   snake_case__    )

  generate(len(snake_case__    )		,   snake_case__    )
  return res


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(heaps(arr))


