





from typing import List, Optional, Tuple, Union

import torch

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


_A :   Optional[int]  			=  logging.get_logger(__name__)  # pylint: disable=invalid-name


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):







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







       @torch.no_grad()
       def __call__(				self					:     str		, A					:     int = 1		, A					:     int = 1_0_0		, A					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		, A					:     Optional[float] = None		, A					:     bool = True		, )       ->Union[AudioPipelineOutput, Tuple]:

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

          lowerCamelCase__       :						str        =							audio_length_in_s * self.unet.config.sample_rate

          lowerCamelCase__       :						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}."      )

          lowerCamelCase__       :						Dict        =							int(A      )
          if sample_size % down_scale_factor != 0:
             lowerCamelCase__       :						Optional[int]        =							(
                 (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.'''      )
          lowerCamelCase__       :						List[str]        =							int(A      )

          lowerCamelCase__       :						Any        =							next(iter(self.unet.parameters()      )      ).dtype
          lowerCamelCase__       :						Optional[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."      )

          lowerCamelCase__       :						Dict        =							randn_tensor(A		, generator=A		, device=self.device		, dtype=A      )

          # set step values
          self.scheduler.set_timesteps(A		, device=audio.device      )
          lowerCamelCase__       :						Any        =							self.scheduler.timesteps.to(A      )

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

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

          lowerCamelCase__       :						int        =							audio.clamp(-1		, 1      ).float().cpu().numpy()

          lowerCamelCase__       :						Dict        =							audio[:, :, :original_sample_size]

          if not return_dict:
             return (audio,)

          return AudioPipelineOutput(audios=A      )










def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					) or number < 0:
      raise ValueError('''Input must be a non-negative integer'''					)

   lowerCamelCase__       :						List[str]        =							0
   while number:
      # This way we arrive at next set bit (next 1) instead of looping
      # through each bit and checking for 1s hence the
      # loop won't run 32 times it will only run the number of `1` times
      number &= number - 1
      count += 1
   return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()



