





from typing import List, Optional, Tuple, Union

import torch

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


lowerCAmelCase     :   Tuple						     =  logging.get_logger(__name__)  # pylint: disable=invalid-name
class  _A		(  _snake_case):

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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



  @torch.no_grad()
  def __call__(				self ,					_SCREAMING_SNAKE_CASE = 1 ,					_SCREAMING_SNAKE_CASE = 100 ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = True ,					):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     audio_length_in_s * self.unet.config.sample_rate

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     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}."					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     int(_SCREAMING_SNAKE_CASE					)
        if sample_size % down_scale_factor != 0:
              SCREAMING_SNAKE_CASE_				:  Dict           =     (
                  (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.'					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     int(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     next(iter(self.unet.parameters()					)					).dtype
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     (batch_size, self.unet.config.in_channels, 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."					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     randn_tensor(_SCREAMING_SNAKE_CASE ,					generator=_SCREAMING_SNAKE_CASE ,					device=self.device ,					dtype=_SCREAMING_SNAKE_CASE					)

        # set step values
        self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE ,					device=audio.device					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.scheduler.timesteps.to(_SCREAMING_SNAKE_CASE					)

        for t in self.progress_bar(self.scheduler.timesteps					):
              # 1. predict noise model_output
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.unet(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).sample

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

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

        SCREAMING_SNAKE_CASE_				:  int           =     audio[:, :, :original_sample_size]

        if not return_dict:
              return (audio,)

        return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE					)









def    A_  (							a					,   a  ):

      """simple docstring"""



      return int((input_a, input_a).count(0  ) != 0  )

def    A_  (							):

      """simple docstring"""



      assert nand_gate(0					,   0  ) == 1
      assert nand_gate(0					,   1  ) == 1
      assert nand_gate(1					,   0  ) == 1
      assert nand_gate(1					,   1  ) == 0


if __name__ == "__main__":
    print(nand_gate(0, 0))
    print(nand_gate(0, 1))
    print(nand_gate(1, 0))
    print(nand_gate(1, 1))






