




from typing import List, Optional, Tuple, Union

import torch

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


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







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    List[Any]		,				A_   :    List[Any]		,				A_   :    Any    )  ->      List[str]:

       """simple docstring"""






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





 @torch.no_grad()
 def __call__(							self   :    List[Any]		,				A_   :    int = 1		,				A_   :    int = 100		,				A_   :    Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				A_   :    Optional[float] = None		,				A_   :    bool = True		,				)  ->      Union[AudioPipelineOutput, Tuple]:

       """simple docstring"""






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

       lowerCamelCase_							=       audio_length_in_s * self.unet.config.sample_rate

       lowerCamelCase_							=       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_							=       int(A_    )
       if sample_size % down_scale_factor != 0:
             lowerCamelCase_							=       (
                 (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_							=       int(A_    )

       lowerCamelCase_							=       next(iter(self.unet.parameters()    )    ).dtype
       lowerCamelCase_							=       (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_							=       randn_tensor(A_		,				generator=A_		,				device=self.device		,				dtype=A_    )

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

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

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

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

       lowerCamelCase_							=       audio[:, :, :original_sample_size]

       if not return_dict:
             return (audio,)

       return AudioPipelineOutput(audios=A_    )










"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]   )					->float:






	'''simple docstring'''






	if not nums:  # Makes sure that the list is not empty
		raise ValueError("List is empty"   )

	a :						Dict      =							sum(_lowercase   ) / len(_lowercase   )  # Calculate the average
	return sum(abs(x - average   ) for x in nums   ) / len(_lowercase   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


