
'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

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


lowercase			:				Union[str, Any]    		=			logging.get_logger(__name__)  # pylint: disable=invalid-name





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Any:






         """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						,			) ->				Union[AudioPipelineOutput, Tuple]:






         """simple docstring"""






         if audio_length_in_s is None:
             A       :   Union[str, Any]					=						self.unet.config.sample_size / self.unet.config.sample_rate

         A       :   Dict					=						audio_length_in_s * self.unet.config.sample_rate

         A       :   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}.'	)

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

         A       :   List[Any]					=						next(iter(self.unet.parameters()	)	).dtype
         A       :   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.'	)

         A       :   str					=						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	)
         A       :   Optional[Any]					=						self.scheduler.timesteps.to(SCREAMING_SNAKE_CASE	)

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

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

         A       :   str					=						audio.clamp(-1						,			1	).float().cpu().numpy()

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

         if not return_dict:
             return (audio,)

         return AudioPipelineOutput(audios=SCREAMING_SNAKE_CASE	)






'''simple docstring'''




def 		lowerCAmelCase_				(   snake_case__ = 10						):




    '''simple docstring'''



    if not isinstance(snake_case__		,				snake_case__						) or n < 0:
        raise ValueError('''Invalid input'''						)
    A       :   List[str]					=						10**n
    A       :   Tuple					=						2_8433 * (pow(2		,				783_0457		,				snake_case__						)) + 1
    return str(number % modulus						)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      print(f'''{solution(10) = }''')




