


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

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


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


class 		_snake_case       (  UpperCamelCase__						):




					def __init__(   self				,				_lowerCamelCase				,				_lowerCamelCase):
								super().__init__()
								self.register_modules(unet=__lowerCamelCase				,				scheduler=__lowerCamelCase)




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

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

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

								UpperCAmelCase__	:       List[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}.''')

								UpperCAmelCase__	:       Optional[Any]   =  int(__lowerCamelCase)
								if sample_size % down_scale_factor != 0:
											UpperCAmelCase__	:       Dict   =  (
											    (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(__lowerCamelCase)

								UpperCAmelCase__	:       Any   =  next(iter(self.unet.parameters())).dtype
								UpperCAmelCase__	:       Dict   =  (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__	:       int   =  randn_tensor(__lowerCamelCase				,				generator=__lowerCamelCase				,				device=self.device				,				dtype=__lowerCamelCase)

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

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

											# 2. compute previous image: x_t -> t_t-1
											UpperCAmelCase__	:       Optional[int]   =  self.scheduler.step(__lowerCamelCase				,				__lowerCamelCase				,				__lowerCamelCase).prev_sample

								UpperCAmelCase__	:       Union[str, Any]   =  audio.clamp(-1				,				1).float().cpu().numpy()

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

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=__lowerCamelCase)






import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)





