"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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


__lowerCAmelCase					: List[str]        =logging.get_logger(__name__)  # pylint: disable=invalid-name





class 				_A		( __snake_case			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



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







      @torch.no_grad()
      def __call__(		self	, __lowerCAmelCase = 1	, __lowerCAmelCase = 100	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = True	, ):







            """simple docstring"""



            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     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :int	)				->					list:
      '''simple docstring'''


      lowercase														=			word.split()

      def justify(lowerCAmelCase__  :list      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int	) -> str:
            lowercase														=			max_width - width
            lowercase														=			len(lowerCAmelCase__	)
            if len(lowerCAmelCase__	) == 1:
                  # if there is only word in line
                  # just insert overall_spaces_count for the remainder of line
                  return line[0] + " " * overall_spaces_count
            else:
                  lowercase														=			words_count - 1
                  # num_spaces_between_words_list[i] : tells you to insert
                  # num_spaces_between_words_list[i] spaces
                  # after word on line[i]
                  lowercase														=			spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  lowercase														=			(
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(lowerCAmelCase__	):
                        num_spaces_between_words_list[i] += 1
                  lowercase														=			[]
                  for i in range(lowerCAmelCase__	):
                        # add the word
                        aligned_words_list.append(line[i]	)
                        # add the spaces to insert
                        aligned_words_list.append(num_spaces_between_words_list[i] * """ """	)
                  # just add the last word to the sentence
                  aligned_words_list.append(line[-1]	)
                  # join the aligned words list to form a justified line
                  return "".join(lowerCAmelCase__	)

      lowercase														=			[]
      lowercase														=			[]
      lowercase														=			0
      for word in words:
            if width + len(lowerCAmelCase__	) + len(lowerCAmelCase__	) <= max_width:
                  # keep adding words until we can fill out max_width
                  # width = sum of length of all words (without overall_spaces_count)
                  # len(word) = length of current word
                  # len(line) = number of overall_spaces_count to insert between words
                  line.append(lowerCAmelCase__	)
                  width += len(lowerCAmelCase__	)
            else:
                  # justify the line and add it to result
                  answer.append(justify(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)	)
                  # reset new line and new width
                  lowercase							,							lowercase														=			[word], len(lowerCAmelCase__	)
      lowercase														=			max_width - width - len(lowerCAmelCase__	)
      answer.append(""" """.join(lowerCAmelCase__	) + (remaining_spaces + 1) * """ """	)
      return answer


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

       testmod()



