
"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

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


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





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          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     ,	):

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

          UpperCAmelCase__	:      Any     							=					audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__	:      str     							=					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}.'''      )

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

          UpperCAmelCase__	:      List[str]     							=					next(iter(self.unet.parameters()      )      ).dtype
          UpperCAmelCase__	:      Union[str, Any]     							=					(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.'''      )

          UpperCAmelCase__	:      str     							=					randn_tensor(UpperCamelCase_     ,	generator=UpperCamelCase_     ,	device=self.device     ,	dtype=UpperCamelCase_      )

          # set step values
          self.scheduler.set_timesteps(UpperCamelCase_     ,	device=audio.device      )
          UpperCAmelCase__	:      List[str]     							=					self.scheduler.timesteps.to(UpperCamelCase_      )

          for t in self.progress_bar(self.scheduler.timesteps      ):
              # 1. predict noise model_output
              UpperCAmelCase__	:      Union[str, Any]     							=					self.unet(UpperCamelCase_     ,	UpperCamelCase_      ).sample

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

          UpperCAmelCase__	:      int     							=					audio.clamp(-1     ,	1      ).float().cpu().numpy()

          UpperCAmelCase__	:      Tuple     							=					audio[:, :, :original_sample_size]

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=UpperCamelCase_      )








'''simple docstring'''




import math
import sys






def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	int:
     if number != int(__snake_case     ):
          raise ValueError('the value of input must be a natural number'     )
     if number < 0:
          raise ValueError('the value of input must not be a negative number'     )
     if number == 0:
          return 1
     __A		:					str						=			[-1] * (number + 1)
     __A		:					Dict						=			0
     for i in range(1					,				number + 1     ):
          __A		:					int						=			sys.maxsize
          __A		:					int						=			int(math.sqrt(__snake_case     )     )
          for j in range(1					,				root + 1     ):
               __A		:					str						=			1 + answers[i - (j**2)]
               __A		:					Dict						=			min(__snake_case					,				__snake_case     )
          __A		:					Union[str, Any]						=			answer
     return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()