
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__     :    int      							=					True
					SCREAMING_SNAKE_CASE__     :    Dict      							=					''
except Exception as e:
					SCREAMING_SNAKE_CASE__     :    str      							=					False
					SCREAMING_SNAKE_CASE__     :    Optional[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      lowerCAmelCase__  (			lowercase_						,			lowercase_    ):
		a__ :		int   						=	"""mel_config.json"""






		@register_to_config
		def __init__(	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							int = 2_56							,    SCREAMING_SNAKE_CASE__	:							int = 2_56							,    SCREAMING_SNAKE_CASE__	:							int = 2_20_50							,    SCREAMING_SNAKE_CASE__	:							int = 20_48							,    SCREAMING_SNAKE_CASE__	:							int = 5_12							,    SCREAMING_SNAKE_CASE__	:							int = 80							,    SCREAMING_SNAKE_CASE__	:							int = 32							,    )  ->							int:
						__lowerCamelCase					      =			hop_length
						__lowerCamelCase					      =			sample_rate
						__lowerCamelCase					      =			n_fft
						__lowerCamelCase					      =			top_db
						__lowerCamelCase					      =			n_iter
						self.set_resolution(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			None

						if not _librosa_can_be_imported:
										raise ValueError(_import_error						)






		def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							int						)  ->							Dict:
						__lowerCamelCase					      =			x_res
						__lowerCamelCase					      =			y_res
						__lowerCamelCase					      =			self.y_res
						__lowerCamelCase					      =			self.x_res * self.hop_length - 1






		def 				__A (	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							str = None							,    SCREAMING_SNAKE_CASE__	:							np.ndarray = None						)  ->							Dict:







						if audio_file is not None:
										__lowerCamelCase      ,					__lowerCamelCase					      =			librosa.load(SCREAMING_SNAKE_CASE__							,    mono=SCREAMING_SNAKE_CASE__							,    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 				__A (	self	:							Optional[int]						)  ->							Optional[int]:
						return len(self.audio						) // self.slice_size






		def 				__A (	self	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							int = 0						)  ->							Tuple:
						return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






		def 				__A (	self	:							int						)  ->							Any:
						return self.sr






		def 				__A (	self	:							str							,    SCREAMING_SNAKE_CASE__	:							int						)  ->							List[Any]:
						__lowerCamelCase					      =			librosa.feature.melspectrogram(
						    y=self.get_audio_slice(SCREAMING_SNAKE_CASE__						)							,    sr=self.sr							,    n_fft=self.n_fft							,    hop_length=self.hop_length							,    n_mels=self.n_mels						)
						__lowerCamelCase					      =			librosa.power_to_db(SCREAMING_SNAKE_CASE__							,    ref=np.max							,    top_db=self.top_db						)
						__lowerCamelCase					      =			(((log_S + self.top_db) * 2_55 / self.top_db).clip(0							,    2_55						) + 0.5).astype(np.uinta						)
						__lowerCamelCase					      =			Image.fromarray(SCREAMING_SNAKE_CASE__						)
						return image







		def 				__A (	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							Image.Image						)  ->							Any:
						__lowerCamelCase					      =			np.frombuffer(image.tobytes()							,    dtype='''uint8'''						).reshape((image.height, image.width)						)
						__lowerCamelCase					      =			bytedata.astype('''float'''						) * self.top_db / 2_55 - self.top_db
						__lowerCamelCase					      =			librosa.db_to_power(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			librosa.feature.inverse.mel_to_audio(
						    SCREAMING_SNAKE_CASE__							,    sr=self.sr							,    n_fft=self.n_fft							,    hop_length=self.hop_length							,    n_iter=self.n_iter						)
						return audio




def 				__magic_name__				(     __lowerCAmelCase			:		list	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int							)    ->     int:
				__lowerCamelCase					      =			int((lower_bound + upper_bound) // 2							)
				if array[r] == value:
								return r
				if lower_bound >= upper_bound:
								return -1
				if array[r] < value:
								return binary_search(__lowerCAmelCase	,						r + 1	,						__lowerCAmelCase	,						__lowerCAmelCase							)
				else:
								return binary_search(__lowerCAmelCase	,						__lowerCAmelCase	,						r - 1	,						__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		list							)    ->     list:
				__lowerCamelCase					      =			0
				if matrix[index][0] == value:
								return [index, 0]
				while index < len(__lowerCAmelCase							) and matrix[index][0] < value:
								__lowerCamelCase					      =			binary_search(matrix[index]	,						0	,						len(matrix[index]							) - 1	,						__lowerCAmelCase							)
								if r != -1:
												return [index, r]
								index += 1
				return [-1, -1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()



