

"""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

	__A	:       Tuple        =						True
	__A	:       Optional[Any]        =						''
except Exception as e:
	__A	:       str        =						False
	__A	:       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      __UpperCamelCase					(  _A	,						_A		):
		SCREAMING_SNAKE_CASE														=       "mel_config.json"


		@register_to_config
		def __init__(self					:						int	,	__SCREAMING_SNAKE_CASE					:						int = 2_5_6	,	__SCREAMING_SNAKE_CASE					:						int = 2_5_6	,	__SCREAMING_SNAKE_CASE					:						int = 2_2_0_5_0	,	__SCREAMING_SNAKE_CASE					:						int = 2_0_4_8	,	__SCREAMING_SNAKE_CASE					:						int = 5_1_2	,	__SCREAMING_SNAKE_CASE					:						int = 8_0	,	__SCREAMING_SNAKE_CASE					:						int = 3_2	,	):
							A     =   hop_length
							A     =   sample_rate
							A     =   n_fft
							A     =   top_db
							A     =   n_iter
							self.set_resolution(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							A     =   None

							if not _librosa_can_be_imported:
												raise ValueError(_import_error)


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						int):
							A     =   x_res
							A     =   y_res
							A     =   self.y_res
							A     =   self.x_res * self.hop_length - 1


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						str = None	,	__SCREAMING_SNAKE_CASE					:						np.ndarray = None):
							if audio_file is not None:
												A     ,       A     =   librosa.load(__SCREAMING_SNAKE_CASE	,	mono=__SCREAMING_SNAKE_CASE	,	sr=self.sr)
							else:
												A     =   raw_audio

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


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


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	__SCREAMING_SNAKE_CASE					:						int = 0):
							return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							return self.sr


		def    SCREAMING_SNAKE_CASE__  (self					:						str	,	__SCREAMING_SNAKE_CASE					:						int):
							A     =   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)
							A     =   librosa.power_to_db(__SCREAMING_SNAKE_CASE	,	ref=np.max	,	top_db=self.top_db)
							A     =   (((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0	,	2_5_5) + 0.5).astype(np.uinta)
							A     =   Image.fromarray(__SCREAMING_SNAKE_CASE)
							return image


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Image.Image):
							A     =   np.frombuffer(image.tobytes()	,	dtype="uint8").reshape((image.height, image.width))
							A     =   bytedata.astype("float") * self.top_db / 2_5_5 - self.top_db
							A     =   librosa.db_to_power(__SCREAMING_SNAKE_CASE)
							A     =   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"""







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


__lowercase          = logging.get_logger(__name__)






class 		_A    (    _a    ):







    """simple docstring"""




    def __init__(   self							:       Optional[int]							,					*__UpperCAmelCase							:       Dict							,					**__UpperCAmelCase							:       Any):
      warnings.warn(
          "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use LevitImageProcessor instead."							,					__UpperCAmelCase							,					)
      super().__init__(*__UpperCAmelCase							,					**__UpperCAmelCase)






