

from typing import List, Optional, Tuple, Union

import torch

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


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



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Optional[int]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict   )							->     str:






     """simple docstring"""







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







  @torch.no_grad()
  def __call__(      self			:   int	,							lowerCAmelCase			:   int = 1	,							lowerCAmelCase			:   int = 1_00	,							lowerCAmelCase			:   Optional[Union[torch.Generator, List[torch.Generator]]] = None	,							lowerCAmelCase			:   Optional[float] = None	,							lowerCAmelCase			:   bool = True	,							)							->     Union[AudioPipelineOutput, Tuple]:






     """simple docstring"""







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

     __lowerCAmelCase  :      Any    =						audio_length_in_s * self.unet.config.sample_rate

     __lowerCAmelCase  :      Dict    =						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  :      Tuple    =						int(lowerCAmelCase   )
     if sample_size % down_scale_factor != 0:
        __lowerCAmelCase  :      Optional[int]    =						(
            (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  :      List[Any]    =						int(lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						next(iter(self.unet.parameters()   )   ).dtype
     __lowerCAmelCase  :      Optional[Any]    =						(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  :      int    =						randn_tensor(lowerCAmelCase	,							generator=lowerCAmelCase	,							device=self.device	,							dtype=lowerCAmelCase   )

     # set step values
     self.scheduler.set_timesteps(lowerCAmelCase	,							device=audio.device   )
     __lowerCAmelCase  :      int    =						self.scheduler.timesteps.to(lowerCAmelCase   )

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

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

     __lowerCAmelCase  :      Tuple    =						audio.clamp(-1	,							1   ).float().cpu().numpy()

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

     if not return_dict:
        return (audio,)

     return AudioPipelineOutput(audios=lowerCAmelCase   )





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

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


