






"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

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


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






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Optional[Any]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						Tuple							)					->       int:
   super().__init__()
   self.register_modules(unet=UpperCAmelCase__							,						scheduler=UpperCAmelCase__							)







 @torch.no_grad()
 def __call__(     self		:						List[str]							,						UpperCAmelCase__		:						int = 1							,						UpperCAmelCase__		:						int = 100							,						UpperCAmelCase__		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None							,						UpperCAmelCase__		:						Optional[float] = None							,						UpperCAmelCase__		:						bool = True							,						)					->       Union[AudioPipelineOutput, Tuple]:

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

   _a  :       List[Any]             =	audio_length_in_s * self.unet.config.sample_rate

   _a  :       Optional[int]             =	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}."""							)

   _a  :       int             =	int(UpperCAmelCase__							)
   if sample_size % down_scale_factor != 0:
     _a  :       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."""							)
   _a  :       Any             =	int(UpperCAmelCase__							)

   _a  :       List[str]             =	next(iter(self.unet.parameters()							)							).dtype
   _a  :       List[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."""							)

   _a  :       Optional[int]             =	randn_tensor(UpperCAmelCase__							,						generator=UpperCAmelCase__							,						device=self.device							,						dtype=UpperCAmelCase__							)

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

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

     # 2. compute previous image: x_t -> t_t-1
     _a  :       Union[str, Any]             =	self.scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

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

   _a  :       Optional[Any]             =	audio[:, :, :original_sample_size]

   if not return_dict:
     return (audio,)

   return AudioPipelineOutput(audios=UpperCAmelCase__							)









"""simple docstring"""




_snake_case          =   '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_snake_case          =   [{'type': 'code', 'content': INSTALL_CONTENT}]
_snake_case          =   {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

