
"""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

					_lowercase							 =							True
					_lowercase							 =							''''''
except Exception as e:
					_lowercase							 =							False
					_lowercase							 =							(
					    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					):



							'''simple docstring'''







							_lowerCamelCase				: List[Any]					      =      """mel_config.json"""
							@register_to_config
							def __init__(    self     ,						_lowerCamelCase = 256     ,						_lowerCamelCase = 256     ,						_lowerCamelCase = 22_050     ,						_lowerCamelCase = 2_048     ,						_lowerCamelCase = 512     ,						_lowerCamelCase = 80     ,						_lowerCamelCase = 32     ,						) ->       Union[str, Any]:




										'''simple docstring'''
										__lowercase						      =					hop_length
										__lowercase						      =					sample_rate
										__lowercase						      =					n_fft
										__lowercase						      =					top_db
										__lowercase						      =					n_iter
										self.set_resolution(_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					None

										if not _librosa_can_be_imported:
													raise ValueError(_import_error					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					x_res
										__lowercase						      =					y_res
										__lowercase						      =					self.y_res
										__lowercase						      =					self.x_res * self.hop_length - 1
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase = None     ,						_lowerCamelCase = None					) ->       Any:




										'''simple docstring'''
										if audio_file is not None:
													__lowercase      ,				__lowercase						      =					librosa.load(_lowerCamelCase     ,						mono=_lowerCamelCase     ,						sr=self.sr					)
										else:
													__lowercase						      =					raw_audio

										# Pad with silence if necessary.
										if len(self.audio					) < self.x_res * self.hop_length:
													__lowercase						      =					np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio					),)					)]					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										return len(self.audio					) // self.slice_size
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase = 0					) ->       np.ndarray:




										'''simple docstring'''
										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										return self.sr
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Image.Image:




										'''simple docstring'''
										__lowercase						      =					librosa.feature.melspectrogram(
										    y=self.get_audio_slice(_lowerCamelCase					)     ,						sr=self.sr     ,						n_fft=self.n_fft     ,						hop_length=self.hop_length     ,						n_mels=self.n_mels					)
										__lowercase						      =					librosa.power_to_db(_lowerCamelCase     ,						ref=np.max     ,						top_db=self.top_db					)
										__lowercase						      =					(((log_S + self.top_db) * 255 / self.top_db).clip(0     ,						255					) + 0.5).astype(np.uinta					)
										__lowercase						      =					Image.fromarray(_lowerCamelCase					)
										return image





							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       np.ndarray:




										'''simple docstring'''
										__lowercase						      =					np.frombuffer(image.tobytes()     ,						dtype="uint8"					).reshape((image.height, image.width)					)
										__lowercase						      =					bytedata.astype("float"					) * self.top_db / 255 - self.top_db
										__lowercase						      =					librosa.db_to_power(_lowerCamelCase					)
										__lowercase						      =					librosa.feature.inverse.mel_to_audio(
										    _lowerCamelCase     ,						sr=self.sr     ,						n_fft=self.n_fft     ,						hop_length=self.hop_length     ,						n_iter=self.n_iter					)
										return audio







"""simple docstring"""






from scipy.constants import g



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->float:
			if mass < 0:
						# handling of negative values of mass
						raise ValueError("The mass of a body cannot be negative"			)
			if height < 0:
						# handling of negative values of height
						raise ValueError("The height above the ground cannot be negative"			)
			return mass * g * height


if __name__ == "__main__":
					from doctest import testmod

					testmod(name='''potential_energy''')





