



'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

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


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


class 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           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:
               lowercase__		       =						self.unet.config.sample_size / self.unet.config.sample_rate

           lowercase__		       =						audio_length_in_s * self.unet.config.sample_rate

           lowercase__		       =						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}.'''      )

           lowercase__		       =						int(UpperCAmelCase_      )
           if sample_size % down_scale_factor != 0:
               lowercase__		       =						(
                   (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."      )
           lowercase__		       =						int(UpperCAmelCase_      )

           lowercase__		       =						next(iter(self.unet.parameters()      )      ).dtype
           lowercase__		       =						(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.'''      )

           lowercase__		       =						randn_tensor(UpperCAmelCase_     ,generator=UpperCAmelCase_     ,device=self.device     ,dtype=UpperCAmelCase_      )

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

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

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

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

           lowercase__		       =						audio[:, :, :original_sample_size]

           if not return_dict:
               return (audio,)

           return AudioPipelineOutput(audios=UpperCAmelCase_      )









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float(moles / volume       ) * nfactor       )




def        lowerCamelCase  (				_snake_case  :						float     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float((moles * 0.0_821 * temperature) / (volume)       )       )




def        lowerCamelCase  (				_snake_case  :						float     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float((moles * 0.0_821 * temperature) / (pressure)       )       )




def        lowerCamelCase  (				_snake_case  :						float     ,_snake_case  :						float     ,_snake_case  :						float       ):





    '''simple docstring'''


    return round(float((pressure * volume) / (0.0_821 * moles)       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




