


'''simple docstring'''







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

		_SCREAMING_SNAKE_CASE       :   List[Any]        = True
		_SCREAMING_SNAKE_CASE       :   Dict        = ""
except Exception as e:
		_SCREAMING_SNAKE_CASE       :   Tuple        = False
		_SCREAMING_SNAKE_CASE       :   Dict        = (
		    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 		_snake_case    (				snake_case_  ,  snake_case_		):






						'''simple docstring'''







						__snake_case   =      "mel_config.json"







						@register_to_config
						def __init__(  self:	int      ,							__UpperCamelCase:	int = 256      ,							__UpperCamelCase:	int = 256      ,							__UpperCamelCase:	int = 2_2050      ,							__UpperCamelCase:	int = 2048      ,							__UpperCamelCase:	int = 512      ,							__UpperCamelCase:	int = 80      ,							__UpperCamelCase:	int = 32      ,							)							->	int:
										__magic_name__				:					Optional[int]						       =       hop_length
										__magic_name__				:					str						       =       sample_rate
										__magic_name__				:					List[str]						       =       n_fft
										__magic_name__				:					str						       =       top_db
										__magic_name__				:					Tuple						       =       n_iter
										self.set_resolution(__UpperCamelCase      ,							__UpperCamelCase     )
										__magic_name__				:					Any						       =       None

										if not _librosa_can_be_imported:
														raise ValueError(_import_error     )







						def lowerCAmelCase__ (  self:	List[Any]      ,							__UpperCamelCase:	int      ,							__UpperCamelCase:	int     )							->	Optional[Any]:
										__magic_name__				:					Union[str, Any]						       =       x_res
										__magic_name__				:					List[str]						       =       y_res
										__magic_name__				:					Any						       =       self.y_res
										__magic_name__				:					str						       =       self.x_res * self.hop_length - 1







						def lowerCAmelCase__ (  self:	int      ,							__UpperCamelCase:	str = None      ,							__UpperCamelCase:	np.ndarray = None     )							->	str:
										if audio_file is not None:
														__magic_name__       ,     __magic_name__				:					List[Any]						       =       librosa.load(__UpperCamelCase      ,							mono=__UpperCamelCase      ,							sr=self.sr     )
										else:
														__magic_name__				:					Optional[Any]						       =       raw_audio

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







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	int:
										return len(self.audio     ) // self.slice_size







						def lowerCAmelCase__ (  self:	Dict      ,							__UpperCamelCase:	int = 0     )							->	np.ndarray:
										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	int:
										return self.sr







						def lowerCAmelCase__ (  self:	List[str]      ,							__UpperCamelCase:	int     )							->	Image.Image:
										__magic_name__				:					int						       =       librosa.feature.melspectrogram(
										    y=self.get_audio_slice(__UpperCamelCase     )      ,							sr=self.sr      ,							n_fft=self.n_fft      ,							hop_length=self.hop_length      ,							n_mels=self.n_mels     )
										__magic_name__				:					Any						       =       librosa.power_to_db(__UpperCamelCase      ,							ref=np.max      ,							top_db=self.top_db     )
										__magic_name__				:					Optional[Any]						       =       (((log_S + self.top_db) * 255 / self.top_db).clip(0      ,							255     ) + 0.5).astype(np.uinta     )
										__magic_name__				:					List[Any]						       =       Image.fromarray(__UpperCamelCase     )
										return image







						def lowerCAmelCase__ (  self:	int      ,							__UpperCamelCase:	Image.Image     )							->	np.ndarray:
										__magic_name__				:					List[str]						       =       np.frombuffer(image.tobytes()      ,							dtype="uint8"     ).reshape((image.height, image.width)     )
										__magic_name__				:					str						       =       bytedata.astype("float"     ) * self.top_db / 255 - self.top_db
										__magic_name__				:					List[str]						       =       librosa.db_to_power(__UpperCamelCase     )
										__magic_name__				:					Any						       =       librosa.feature.inverse.mel_to_audio(
										    __UpperCamelCase      ,							sr=self.sr      ,							n_fft=self.n_fft      ,							hop_length=self.hop_length      ,							n_iter=self.n_iter     )
										return audio



'''simple docstring'''







def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				if not isinstance(UpperCamelCase__     ,   UpperCamelCase__		) or number < 0:
								raise ValueError("Input must be a non-negative integer"		)

				__magic_name__				:					str						       =       number & ~(number - 1)
				__magic_name__				:					Optional[int]						       =       0
				while intermediate:
								intermediate >>= 1
								index += 1
				return index - 1


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)