


from typing import List, Optional, Tuple, Union

import torch

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


UpperCAmelCase					:  Optional[Any]			=						logging.get_logger(__name__)  # pylint: disable=invalid-name







class     __lowercase    (				__UpperCAmelCase   ):



		"""simple docstring"""

		def __init__(      self						,	A						,	A     )   ->      List[str]:







					'''simple docstring'''







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







		@torch.no_grad()
		def __call__(      self						,	A = 1						,	A = 1_00						,	A = None						,	A = None						,	A = True						,	)   ->      Optional[int]:







					'''simple docstring'''







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

					lowerCamelCase  						=   audio_length_in_s * self.unet.config.sample_rate

					lowerCamelCase  						=   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}.'     )

					lowerCamelCase  						=   int(lowerCamelCase__     )
					if sample_size % down_scale_factor != 0:
								lowerCamelCase  						=   (
								    (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."""     )
					lowerCamelCase  						=   int(lowerCamelCase__     )

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

					lowerCamelCase  						=   randn_tensor(lowerCamelCase__						,	generator=lowerCamelCase__						,	device=self.device						,	dtype=lowerCamelCase__     )

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

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

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

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

					lowerCamelCase  						=   audio[:, :, :original_sample_size]

					if not return_dict:
								return (audio,)

					return AudioPipelineOutput(audios=lowerCamelCase__     )


"""simple docstring"""






import functools



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							len(lowerCamelCase       )
				UpperCAmelCase__             =							len(lowerCamelCase       )

				@functools.cache
				def min_distance(lowerCamelCase					,			lowerCamelCase       ) -> int:
								# if first word index is overflow - delete all from the second word
								if indexa >= len_worda:
												return len_worda - indexa
								# if second word index is overflow - delete all from the first word
								if indexa >= len_worda:
												return len_worda - indexa
								UpperCAmelCase__             =							int(worda[indexa] != worda[indexa]       )  # current letters not identical
								return min(
								    1 + min_distance(indexa + 1					,			lowerCamelCase       )					,			1 + min_distance(lowerCamelCase					,			indexa + 1       )					,			diff + min_distance(indexa + 1					,			indexa + 1       )					,			)

				return min_distance(0					,			0       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



