




from typing import List, Optional, Tuple, Union

import torch

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


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







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						Dict		,							A_      :						Dict		,							A_      :						Optional[int]						)     ->			List[str]:

       """simple docstring"""






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





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

       """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(A_						)
       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(A_						)

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

       lowerCamelCase_	  =					randn_tensor(A_		,							generator=A_		,							device=self.device		,							dtype=A_						)

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

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

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

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

       lowerCamelCase_	  =					audio[:, :, :original_sample_size]

       if not return_dict:
             return (audio,)

       return AudioPipelineOutput(audios=A_						)









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



