





from typing import List, Optional, Tuple, Union

import torch

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


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





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase)			->						Optional[Any]:







          '''simple docstring'''

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

   @torch.no_grad()
   def __call__(					self					,  lowercase = 1					,  lowercase = 100					,  lowercase = None					,  lowercase = None					,  lowercase = True					,  )			->						Union[AudioPipelineOutput, Tuple]:







          '''simple docstring'''

          if audio_length_in_s is None:
                 a__						:	Optional[int] =				self.unet.config.sample_size / self.unet.config.sample_rate

          a__						:	int =				audio_length_in_s * self.unet.config.sample_rate

          a__						:	Union[str, 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__						:	str =				int(lowercase)
          if sample_size % down_scale_factor != 0:
                 a__						:	List[str] =				(
                     (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__						:	List[Any] =				int(lowercase)

          a__						:	int =				next(iter(self.unet.parameters())).dtype
          a__						:	Tuple =				(batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(lowercase					,  lowercase) and len(lowercase) != batch_size:
                 raise ValueError(
                     F'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
                     F' size of {batch_size}. Make sure the batch size matches the length of the generators.')

          a__						:	Optional[Any] =				randn_tensor(lowercase					,  generator=lowercase					,  device=self.device					,  dtype=lowercase)

          # set step values
          self.scheduler.set_timesteps(lowercase					,  device=audio.device)
          a__						:	Union[str, Any] =				self.scheduler.timesteps.to(lowercase)

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

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

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

          a__						:	List[Any] =				audio[:, :, :original_sample_size]

          if not return_dict:
                 return (audio,)

          return AudioPipelineOutput(audios=lowercase)










'''simple docstring'''







class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				list[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       len(UpperCamelCase__     )
														lowerCamelCase__					:	Union[str, Any]         =       [0] * len_array

														if len_array > 0:
																					lowerCamelCase__					:	Union[str, Any]         =       array[0]

														for i in range(1			,							UpperCamelCase__     ):
																					lowerCamelCase__					:	Tuple         =       self.prefix_sum[i - 1] + array[i]




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														if start == 0:
																					return self.prefix_sum[end]

														return self.prefix_sum[end] - self.prefix_sum[start - 1]




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Dict         =       {0}
														for sum_item in self.prefix_sum:
																					if sum_item - target_sum in sums:
																												return True

																					sums.add(UpperCamelCase__     )

														return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()

