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

    lowerCAmelCase__					=				True
    lowerCAmelCase__					=				''
except Exception as e:
    lowerCAmelCase__					=				False
    lowerCAmelCase__					=				(
        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_				(		SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      'mel_config.json'




 @register_to_config
 def __init__(    self      :						Tuple    ,					lowercase__      :						int = 256    ,					lowercase__      :						int = 256    ,					lowercase__      :						int = 22_050    ,					lowercase__      :						int = 2_048    ,					lowercase__      :						int = 512    ,					lowercase__      :						int = 80    ,					lowercase__      :						int = 32    ,					):







        '''simple docstring'''



        lowerCAmelCase__  			=      hop_length
        lowerCAmelCase__  			=      sample_rate
        lowerCAmelCase__  			=      n_fft
        lowerCAmelCase__  			=      top_db
        lowerCAmelCase__  			=      n_iter
        self.set_resolution(lowercase__    ,					lowercase__)
        lowerCAmelCase__  			=      None

        if not _librosa_can_be_imported:
               raise ValueError(_import_error)




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						int    ,					lowercase__      :						int):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						int    ,					lowercase__      :						str = None    ,					lowercase__      :						np.ndarray = None):







        '''simple docstring'''



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

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




 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						str    ,					lowercase__      :						int = 0):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						str):







        '''simple docstring'''



        return self.sr




 def 	__snake_case		(    self      :						Any    ,					lowercase__      :						int):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						int    ,					lowercase__      :						Image.Image):







        '''simple docstring'''



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

def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       if target == 0:
              answer.append(path.copy()			)
       else:
              for index in range(lowerCAmelCase__			,						len(lowerCAmelCase__			)			):
                     if target >= candidates[index]:
                            path.append(candidates[index]			)
                            backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						target - candidates[index]			,						lowerCAmelCase__			)
                            path.pop(len(lowerCAmelCase__			) - 1			)






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      []  # type: list[int]
       lowerCAmelCase__  			=      []  # type: list[int]
       backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						0			)
       return answer






def    __lowerCamelCase	(		):
       print(combination_sum([-8, 2.3, 0]			,						1			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
