





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			:Optional[Any]	 =     True
							__lowerCAmelCase			:Dict	 =     ''
except Exception as e:
							__lowerCAmelCase			:int	 =     False
							__lowerCAmelCase			:Union[str, 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 							_a(     __A						, __A     ):
		lowerCamelCase__  :str 					=		'mel_config.json'







		@register_to_config
		def __init__(     self			,    __snake_case = 2_5_6			,    __snake_case = 2_5_6			,    __snake_case = 2_2_0_5_0			,    __snake_case = 2_0_4_8			,    __snake_case = 5_1_2			,    __snake_case = 8_0			,    __snake_case = 3_2			,    )   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       List[Any]												= hop_length
							_snake_case							:       Dict												= sample_rate
							_snake_case							:       Optional[int]												= n_fft
							_snake_case							:       Optional[int]												= top_db
							_snake_case							:       Optional[int]												= n_iter
							self.set_resolution(__snake_case			,    __snake_case					)
							_snake_case							:       List[Any]												= None

							if not _librosa_can_be_imported:
												raise ValueError(_import_error					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case					)   ->   List[Any]:


							'''simple docstring'''

							_snake_case							:       Union[str, Any]												= x_res
							_snake_case							:       str												= y_res
							_snake_case							:       List[Any]												= self.y_res
							_snake_case							:       int												= self.x_res * self.hop_length - 1







		def 							lowercase							(     self			,    __snake_case = None			,    __snake_case = None					)   ->   Optional[int]:


							'''simple docstring'''

							if audio_file is not None:
												_snake_case							,			_snake_case							:       List[str]												= librosa.load(__snake_case			,    mono=__snake_case			,    sr=self.sr					)
							else:
												_snake_case							:       List[str]												= raw_audio

							# Pad with silence if necessary.
							if len(self.audio					) < self.x_res * self.hop_length:
												_snake_case							:       List[str]												= 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			,    __snake_case = 0					)   ->   np.ndarray:


							'''simple docstring'''

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







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							return self.sr







		def 							lowercase							(     self			,    __snake_case					)   ->   Image.Image:


							'''simple docstring'''

							_snake_case							:       Dict												= librosa.feature.melspectrogram(
							    y=self.get_audio_slice(__snake_case					)			,    sr=self.sr			,    n_fft=self.n_fft			,    hop_length=self.hop_length			,    n_mels=self.n_mels					)
							_snake_case							:       int												= librosa.power_to_db(__snake_case			,    ref=np.max			,    top_db=self.top_db					)
							_snake_case							:       List[str]												= (((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0			,    2_5_5					) + 0.5).astype(np.uinta					)
							_snake_case							:       Tuple												= Image.fromarray(__snake_case					)
							return image







		def 							lowercase							(     self			,    __snake_case					)   ->   np.ndarray:


							'''simple docstring'''

							_snake_case							:       Any												= np.frombuffer(image.tobytes()			,    dtype="uint8"					).reshape((image.height, image.width)					)
							_snake_case							:       List[str]												= bytedata.astype("float"					) * self.top_db / 2_5_5 - self.top_db
							_snake_case							:       Tuple												= librosa.db_to_power(__snake_case					)
							_snake_case							:       List[str]												= librosa.feature.inverse.mel_to_audio(
							    __snake_case			,    sr=self.sr			,    n_fft=self.n_fft			,    hop_length=self.hop_length			,    n_iter=self.n_iter					)
							return audio






def  A					(				UpperCAmelCase					):
					if not isinstance(UpperCAmelCase	,	UpperCAmelCase					):
										_snake_case							:       Optional[Any]												= F"""Input value of [number={number}] must be an integer"""
										raise TypeError(UpperCAmelCase					)
					if number < 1:
										raise ValueError("Input must be a positive integer"					)
					return number * (2 * number - 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()