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_     ,  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: def 		snake_case__  (		lowercase = 998001				):
  
     # fetches the next number
     for number in range(n - 1       ,							9999       ,							-1				):
        lowerCAmelCase_:       str         =  str(lowercase				)
  
        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
           lowerCAmelCase_:       List[str]         =  999
  
           # if 'number' is a product of two 3-digit numbers
           # then number is the answer otherwise fetch next number.
           while divisor != 99:
              if (number % divisor == 0) and (len(str(number // divisor				)				) == 3.0):
                 return number
              divisor -= 1
     raise ValueError("That number is larger than our acceptable range."				)
  
  
  if __name__ == "__main__":
   print(f'''{solution() = }''')