code: import numpy as np  # noqa: E402
  
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...schedulers.scheduling_utils import SchedulerMixin
  
  
  try:
   import librosa  # noqa: E402
  
   a     :			Union[str, Any]					    =						True
   a     :			List[str]					    =						""""""
  except Exception as e:
   a     :			int					    =						False
   a     :			str					    =						(
       f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
   )
  
  
  from PIL import Image  # noqa: E402
  
  class 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							List[str]        =						'mel_config.json'
  
  
  
  
      @register_to_config
      def __init__(						self					,	lowerCamelCase__ = 256					,	lowerCamelCase__ = 256					,	lowerCamelCase__ = 22_050					,	lowerCamelCase__ = 2_048					,	lowerCamelCase__ = 512					,	lowerCamelCase__ = 80					,	lowerCamelCase__ = 32					,	):
         lowerCAmelCase_:       Optional[int]         =  hop_length
         lowerCAmelCase_:       List[str]         =  sample_rate
         lowerCAmelCase_:       Any         =  n_fft
         lowerCAmelCase_:       List[str]         =  top_db
         lowerCAmelCase_:       Union[str, Any]         =  n_iter
         self.set_resolution(lowerCamelCase__					,	lowerCamelCase__   )
         lowerCAmelCase_:       Union[str, Any]         =  None
  
         if not _librosa_can_be_imported:
            raise ValueError(_import_error   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Union[str, Any]         =  x_res
         lowerCAmelCase_:       Any         =  y_res
         lowerCAmelCase_:       List[str]         =  self.y_res
         lowerCAmelCase_:       Optional[int]         =  self.x_res * self.hop_length - 1
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__ = None					,	lowerCamelCase__ = None   ):
         if audio_file is not None:
            lowerCAmelCase_:       List[Any]         =  librosa.load(lowerCamelCase__					,	mono=lowerCamelCase__					,	sr=self.sr   )
         else:
            lowerCAmelCase_:       Tuple         =  raw_audio
  
         # Pad with silence if necessary.
         if len(self.audio   ) < self.x_res * self.hop_length:
            lowerCAmelCase_:       Tuple         =  np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio   ),)   )]   )
  
  
  
  
      def     _a						(						self   ):
         return len(self.audio   ) // self.slice_size
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__ = 0   ):
         return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
  
  
  
  
      def     _a						(						self   ):
         return self.sr
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Optional[Any]         =  librosa.feature.melspectrogram(
             y=self.get_audio_slice(lowerCamelCase__   )					,	sr=self.sr					,	n_fft=self.n_fft					,	hop_length=self.hop_length					,	n_mels=self.n_mels   )
         lowerCAmelCase_:       int         =  librosa.power_to_db(lowerCamelCase__					,	ref=np.max					,	top_db=self.top_db   )
         lowerCAmelCase_:       int         =  (((log_S + self.top_db) * 255 / self.top_db).clip(0					,	255   ) + 0.5).astype(np.uinta   )
         lowerCAmelCase_:       Optional[Any]         =  Image.fromarray(lowerCamelCase__   )
         return image
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Optional[int]         =  np.frombuffer(image.tobytes()					,	dtype="uint8"   ).reshape((image.height, image.width)   )
         lowerCAmelCase_:       Tuple         =  bytedata.astype("float"   ) * self.top_db / 255 - self.top_db
         lowerCAmelCase_:       Optional[int]         =  librosa.db_to_power(lowerCamelCase__   )
         lowerCAmelCase_:       Tuple         =  librosa.feature.inverse.mel_to_audio(
             lowerCamelCase__					,	sr=self.sr					,	n_fft=self.n_fft					,	hop_length=self.hop_length					,	n_iter=self.n_iter   )
         return audio
style_context: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float
  
  
  
  
      @property
      def     _a						(						self   ):
         return sizeof(self.value   )
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
     # < 0 is possible if using a dynamic axis
     if dimension <= 0:
        lowerCAmelCase_:       int         =  fixed_dimension
  
     dimension -= num_token_to_add
     return dimension
  def 		snake_case__  (		lowercase       ,							lowercase				):
     return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
     try:
        return AutoProcessor.from_pretrained(lowercase				)
     except (ValueError, OSError, KeyError):
        lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
        try:
           lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
        try:
           lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
  
        if tokenizer is not None and feature_extractor is not None:
           raise ValueError(
               F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
        elif tokenizer is None and feature_extractor is None:
           return None
        elif tokenizer is not None:
           return tokenizer
        else:
           return feature_extractor