



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				1



       @register_to_config
       def __init__(     self     ,lowercase=2000     ,lowercase=0.1     ,lowercase=20     ,lowercase=1E-3):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		None
             UpperCAmelCase_  :	int				      =		None
             UpperCAmelCase_  :	Any				      =		None



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		torch.linspace(1     ,self.config.sampling_eps     ,lowercase     ,device=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase=None):

             """simple docstring"""





             if self.timesteps is None:
                   raise ValueError(
                       "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             UpperCAmelCase_  :	Union[str, Any]				      =		(
                 -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             UpperCAmelCase_  :	List[str]				      =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
             UpperCAmelCase_  :	List[Any]				      =		std.flatten()
             while len(std.shape) < len(score.shape):
                   UpperCAmelCase_  :	List[Any]				      =		std.unsqueeze(-1)
             UpperCAmelCase_  :	List[Any]				      =		-score / std

             # compute
             UpperCAmelCase_  :	List[Any]				      =		-1.0 / len(self.timesteps)

             UpperCAmelCase_  :	Any				      =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             UpperCAmelCase_  :	Optional[int]				      =		beta_t.flatten()
             while len(beta_t.shape) < len(x.shape):
                   UpperCAmelCase_  :	Dict				      =		beta_t.unsqueeze(-1)
             UpperCAmelCase_  :	str				      =		-0.5 * beta_t * x

             UpperCAmelCase_  :	Any				      =		torch.sqrt(lowercase)
             UpperCAmelCase_  :	Dict				      =		drift - diffusion**2 * score
             UpperCAmelCase_  :	Union[str, Any]				      =		x + drift * dt

             # add noise
             UpperCAmelCase_  :	int				      =		randn_tensor(x.shape     ,layout=x.layout     ,generator=lowercase     ,device=x.device     ,dtype=x.dtype)
             UpperCAmelCase_  :	str				      =		x_mean + diffusion * math.sqrt(-dt) * noise

             return x, x_mean



       def __len__(     self):

             """simple docstring"""





             return self.config.num_train_timesteps





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""ClapFeatureExtractor"""
       _lowerCamelCase			    =				("""RobertaTokenizer""", """RobertaTokenizerFast""")



       def __init__(     self     ,lowercase     ,lowercase):

             """simple docstring"""





             super().__init__(lowercase     ,lowercase)



       def __call__(     self     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		kwargs.pop("sampling_rate"     ,lowercase)

             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:
                   UpperCAmelCase_  :	List[str]				      =		self.tokenizer(lowercase     ,return_tensors=lowercase     ,**lowercase)

             if audios is not None:
                   UpperCAmelCase_  :	str				      =		self.feature_extractor(
                       lowercase     ,sampling_rate=lowercase     ,return_tensors=lowercase     ,**lowercase)

             if text is not None and audios is not None:
                   UpperCAmelCase_  :	Optional[int]				      =		audio_features.input_features
                   return encoding
             elif text is not None:
                   return encoding
             else:
                   return BatchEncoding(data=dict(**lowercase)     ,tensor_type=lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





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



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





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



       @property
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.tokenizer.model_input_names
             UpperCAmelCase_  :	str				      =		self.feature_extractor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
