




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						:		Optional[int]          =						True
	__A						:		Optional[int]          =						""
except Exception as e:
	__A						:		Dict          =						False
	__A						:		Any          =						(
	    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_       (	lowerCAmelCase__						,							lowerCAmelCase__				):
					__UpperCamelCase     	= "mel_config.json"




					@register_to_config
					def __init__(   self:		List[str],     _lowercase:		int = 256,     _lowercase:		int = 256,     _lowercase:		int = 22050,     _lowercase:		int = 2048,     _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     _lowercase						(   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     _lowercase						(   self:		Optional[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     _lowercase						(   self:		int):


									'''simple docstring'''


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




					def     _lowercase						(   self:		Optional[int],     _lowercase:		int = 0):


									'''simple docstring'''


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




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									return self.sr




					def     _lowercase						(   self:		Dict,     _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     _lowercase						(   self:		Union[str, Any],     _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











'''simple docstring'''


def 					SCREAMING_SNAKE_CASE(   __lowercase	)			-> int:
     if not isinstance(__lowercase		,				__lowercase	):
          raise ValueError('''double_factorial() only accepts integral values'''	)
     if n < 0:
          raise ValueError('''double_factorial() not defined for negative values'''	)
     return 1 if n <= 1 else n * double_factorial(n - 2	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






