




"""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  __lowercase    (     __lowerCAmelCase  ,				__lowerCAmelCase		):


				'''simple docstring'''


				_lowerCAmelCase		     ='''mel_config.json'''

				@register_to_config
				def __init__(				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] = 2_5_6 ,SCREAMING_SNAKE_CASE_				:       Optional[int] = 2_5_6 ,SCREAMING_SNAKE_CASE_				:       Dict = 2_2_0_5_0 ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] = 2_0_4_8 ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] = 5_1_2 ,SCREAMING_SNAKE_CASE_				:       List[Any] = 8_0 ,SCREAMING_SNAKE_CASE_				:       Tuple = 3_2 ,):
										snake_case__    :   Optional[Any]							     =   hop_length
										snake_case__    :   List[Any]							     =   sample_rate
										snake_case__    :   Tuple							     =   n_fft
										snake_case__    :   Optional[int]							     =   top_db
										snake_case__    :   Union[str, Any]							     =   n_iter
										self.set_resolution(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
										snake_case__    :   int							     =   None

										if not _librosa_can_be_imported:
																raise ValueError(_import_error )

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       List[str] ):
										snake_case__    :   str							     =   x_res
										snake_case__    :   Tuple							     =   y_res
										snake_case__    :   Optional[Any]							     =   self.y_res
										snake_case__    :   List[str]							     =   self.x_res * self.hop_length - 1

				def  _lowerCamelCase   (				self				:       int ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,SCREAMING_SNAKE_CASE_				:       str = None ):




										if audio_file is not None:
																snake_case__     ,		snake_case__    :   Optional[int]							     =   librosa.load(__SCREAMING_SNAKE_CASE ,mono=__SCREAMING_SNAKE_CASE ,sr=self.sr )
										else:
																snake_case__    :   Dict							     =   raw_audio

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

				def  _lowerCamelCase   (				self				:       List[str] ):
										return len(self.audio ) // self.slice_size

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[int] = 0 ):
										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]

				def  _lowerCamelCase   (				self				:       Tuple ):
										return self.sr

				def  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       str ):
										snake_case__    :   List[Any]							     =   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 )
										snake_case__    :   List[str]							     =   librosa.power_to_db(__SCREAMING_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__    :   List[Any]							     =   Image.fromarray(__SCREAMING_SNAKE_CASE )
										return image





				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       Tuple ):
										snake_case__    :   Optional[Any]							     =   np.frombuffer(image.tobytes() ,dtype='''uint8''' ).reshape((image.height, image.width) )
										snake_case__    :   Optional[Any]							     =   bytedata.astype('''float''' ) * self.top_db / 2_5_5 - self.top_db
										snake_case__    :   List[Any]							     =   librosa.db_to_power(__SCREAMING_SNAKE_CASE )
										snake_case__    :   Tuple							     =   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








"""simple docstring"""





def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						return int(lowerCamelCase						) if x - int(lowerCamelCase						) <= 0 else int(lowerCamelCase						) + 1


if __name__ == "__main__":
							import doctest

							doctest.testmod()


