






import numpy as np






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		Tuple:







							"""simple docstring"""





							_a  :    str			=						np.exp(UpperCAmelCase     )

							# Add up the all the exponentials
							_a  :    Tuple			=						np.sum(UpperCAmelCase     )

							# Divide every exponent by the sum of all exponents
							_a  :    List[Any]			=						exponent_vector / sum_of_exponents

							return softmax_vector


if __name__ == "__main__":
						print(softmax((0,)))







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      UpperCamelCase_							(				UpperCamelCase ,       UpperCamelCase       ):
						lowercase									=  '''mel_config.json'''




						@register_to_config
						def __init__(    self		,	lowercase = 256		,	lowercase = 256		,	lowercase = 22_050		,	lowercase = 2_048		,	lowercase = 512		,	lowercase = 80		,	lowercase = 32		,	)			->						Optional[Any]:
													_a  :    Tuple			=						hop_length
													_a  :    Dict			=						sample_rate
													_a  :    Dict			=						n_fft
													_a  :    List[str]			=						top_db
													_a  :    Optional[int]			=						n_iter
													self.set_resolution(lowercase		,	lowercase       )
													_a  :    Optional[int]			=						None

													if not _librosa_can_be_imported:
																				raise ValueError(_import_error       )




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						Any:
													_a  :    Any			=						x_res
													_a  :    Optional[Any]			=						y_res
													_a  :    List[Any]			=						self.y_res
													_a  :    Tuple			=						self.x_res * self.hop_length - 1




						def      snake_case__(    self		,	lowercase = None		,	lowercase = None       )			->						Union[str, Any]:
													if audio_file is not None:
																				_a		,					_a  :    str			=						librosa.load(lowercase		,	mono=lowercase		,	sr=self.sr       )
													else:
																				_a  :    Any			=						raw_audio

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




						def      snake_case__(    self       )			->						int:
													return len(self.audio       ) // self.slice_size




						def      snake_case__(    self		,	lowercase = 0       )			->						np.ndarray:
													return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




						def      snake_case__(    self       )			->						int:
													return self.sr




						def      snake_case__(    self		,	lowercase       )			->						Image.Image:
													_a  :    int			=						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       )
													_a  :    List[str]			=						librosa.power_to_db(lowercase		,	ref=np.max		,	top_db=self.top_db       )
													_a  :    List[str]			=						(((log_S + self.top_db) * 255 / self.top_db).clip(0		,	255       ) + 0.5).astype(np.uinta       )
													_a  :    Any			=						Image.fromarray(lowercase       )
													return image







						def      snake_case__(    self		,	lowercase       )			->						np.ndarray:
													_a  :    Any			=						np.frombuffer(image.tobytes()		,	dtype='''uint8'''       ).reshape((image.height, image.width)       )
													_a  :    Any			=						bytedata.astype('''float'''       ) * self.top_db / 255 - self.top_db
													_a  :    Dict			=						librosa.db_to_power(lowercase       )
													_a  :    List[str]			=						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