




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

   lowercase__      :			Any           =					True
   lowercase__      :			Optional[int]           =					""
except Exception as e:
   lowercase__      :			int           =					False
   lowercase__      :			Tuple           =					(
       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 			a__       (					UpperCamelCase__   ,					UpperCamelCase__      ):
     a		:			List[Any]							=	"""mel_config.json"""







     @register_to_config
     def __init__(	self  ,		A = 256  ,		A = 256  ,		A = 22050  ,		A = 2048  ,		A = 512  ,		A = 80  ,		A = 32  ,		)  ->    Dict:



          '''simple docstring'''



          a      =      hop_length
          a      =      sample_rate
          a      =      n_fft
          a      =      top_db
          a      =      n_iter
          self.set_resolution(A  ,		A )
          a      =      None

          if not _librosa_can_be_imported:
               raise ValueError(_import_error )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    int:



          '''simple docstring'''



          a      =      x_res
          a      =      y_res
          a      =      self.y_res
          a      =      self.x_res * self.hop_length - 1







     def 			lowerCAmelCase_				(	self  ,		A = None  ,		A = None )  ->    Optional[int]:



          '''simple docstring'''



          if audio_file is not None:
               a  ,      a      =      librosa.load(A  ,		mono=A  ,		sr=self.sr )
          else:
               a      =      raw_audio

          # Pad with silence if necessary.
          if len(self.audio ) < self.x_res * self.hop_length:
               a      =      np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio ),) )] )







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          return len(self.audio ) // self.slice_size







     def 			lowerCAmelCase_				(	self  ,		A = 0 )  ->    np.ndarray:



          '''simple docstring'''



          return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          return self.sr







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Image.Image:



          '''simple docstring'''



          a      =      librosa.feature.melspectrogram(
              y=self.get_audio_slice(A )  ,		sr=self.sr  ,		n_fft=self.n_fft  ,		hop_length=self.hop_length  ,		n_mels=self.n_mels )
          a      =      librosa.power_to_db(A  ,		ref=np.max  ,		top_db=self.top_db )
          a      =      (((log_S + self.top_db) * 255 / self.top_db).clip(0  ,		255 ) + 0.5).astype(np.uinta )
          a      =      Image.fromarray(A )
          return image







     def 			lowerCAmelCase_				(	self  ,		A )  ->    np.ndarray:



          '''simple docstring'''



          a      =      np.frombuffer(image.tobytes()  ,		dtype="uint8" ).reshape((image.height, image.width) )
          a      =      bytedata.astype("float" ) * self.top_db / 255 - self.top_db
          a      =      librosa.db_to_power(A )
          a      =      librosa.feature.inverse.mel_to_audio(
              A  ,		sr=self.sr  ,		n_fft=self.n_fft  ,		hop_length=self.hop_length  ,		n_iter=self.n_iter )
          return audio







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     list:
     a      =      []
     a      =      1

     for i in range(len(__UpperCamelCase)):
          if i + 1 < len(__UpperCamelCase) and text[i] == text[i + 1]:
               count += 1
          else:
               encoded.append((text[i], count))
               a      =      1

     return encoded

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     str:
     return "".join(char * length for char, length in encoded)


if __name__ == "__main__":
   from doctest import testmod

   testmod(name="run_length_encode", verbose=True)
   testmod(name="run_length_decode", verbose=True)

