"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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


lowerCAmelCase__						:						str		  =			logging.get_logger(__name__)  # pylint: disable=invalid-name
class        snake_case (				UpperCamelCase__       ):




					"""simple docstring"""

					def __init__(       self     :			Optional[Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			str    ):
									super().__init__()
									self.register_modules(unet=lowerCamelCase__					,scheduler=lowerCamelCase__    )







					@torch.no_grad()
					def __call__(       self     :			Union[str, Any]					,lowerCamelCase__     :			str = 1					,lowerCamelCase__     :			Union[str, Any] = 100					,lowerCamelCase__     :			Dict = None					,lowerCamelCase__     :			int = None					,lowerCamelCase__     :			int = True					,):

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

									UpperCAmelCase__    =							audio_length_in_s * self.unet.config.sample_rate

									UpperCAmelCase__    =							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__    =							int(lowerCamelCase__    )
									if sample_size % down_scale_factor != 0:
													UpperCAmelCase__    =							(
													    (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__    =							int(lowerCamelCase__    )

									UpperCAmelCase__    =							next(iter(self.unet.parameters()    )    ).dtype
									UpperCAmelCase__    =							(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.'''    )

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

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

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

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

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

									UpperCAmelCase__    =							audio[:, :, :original_sample_size]

									if not return_dict:
													return (audio,)

									return AudioPipelineOutput(audios=lowerCamelCase__    )



"""simple docstring"""






import warnings
from functools import wraps
from typing import Callable



def 						a_		(      lowerCamelCase ):

				@wraps(lowerCamelCase )
				def _inner_fn(*lowerCamelCase      ,    **lowerCamelCase ):
								warnings.warn(
								    (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,    lowerCamelCase      ,    )
								return fn(*lowerCamelCase      ,    **lowerCamelCase )

				return _inner_fn



