





from typing import List, Optional, Tuple, Union

import torch

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


lowercase_					     =	logging.get_logger(__name__)  # pylint: disable=invalid-name
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


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





     """simple docstring"""







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


   @torch.no_grad()
   def __call__(					self    :       Optional[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:
       lowercase__			    =						self.unet.config.sample_size / self.unet.config.sample_rate

     lowercase__			    =						audio_length_in_s * self.unet.config.sample_rate

     lowercase__			    =						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}.""" )

     lowercase__			    =						int(a )
     if sample_size % down_scale_factor != 0:
       lowercase__			    =						(
           (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.' )
     lowercase__			    =						int(a )

     lowercase__			    =						next(iter(self.unet.parameters() ) ).dtype
     lowercase__			    =						(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.""" )

     lowercase__			    =						randn_tensor(a    ,  generator=a    ,  device=self.device    ,  dtype=a )

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

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

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

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

     lowercase__			    =						audio[:, :, :original_sample_size]

     if not return_dict:
       return (audio,)

     return AudioPipelineOutput(audios=a )









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Tuple				      = 'ClapFeatureExtractor'
   _UpperCamelCase					:	Union[str, Any]				      = ('RobertaTokenizer', 'RobertaTokenizerFast')


   def __init__(					self    :       List[Any]    ,  a    :       int    ,  a    :       str )->     Any:





     """simple docstring"""







     super().__init__(a    ,  a )


   def __call__(					self    :       Any    ,  a    :       Tuple=None    ,  a    :       Optional[int]=None    ,  a    :       int=None    ,  **a    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )

     if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.' )

     if text is not None:
       lowercase__			    =						self.tokenizer(a    ,  return_tensors=a    ,  **a )

     if audios is not None:
       lowercase__			    =						self.feature_extractor(
           a    ,  sampling_rate=a    ,  return_tensors=a    ,  **a )

     if text is not None and audios is not None:
       lowercase__			    =						audio_features.input_features
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**a )    ,  tensor_type=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  *a    :       Dict    ,  **a    :       int )->     Optional[int]:





     """simple docstring"""







     return self.tokenizer.batch_decode(*a    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  *a    :       int    ,  **a    :       Dict )->     Dict:





     """simple docstring"""







     return self.tokenizer.decode(*a    ,  **a )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.tokenizer.model_input_names
     lowercase__			    =						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )


