




"""simple docstring"""




from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


a_        =       logging.get_logger(__name__)

class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=["audio_values", "audio_mask"]
		def __init__(						self				,						UpperCamelCase_=20_48				,						UpperCamelCase_=1				,						UpperCamelCase_=[16, 16]				,						UpperCamelCase_=1_28				,						UpperCamelCase_=4_41_00				,						UpperCamelCase_=86				,						UpperCamelCase_=20_48				,						UpperCamelCase_=0.0				,						**UpperCamelCase_				,						)						->       Dict:
					super().__init__(
					    feature_size=UpperCamelCase_				,						sampling_rate=UpperCamelCase_				,						padding_value=UpperCamelCase_				,						**UpperCamelCase_				,						)

					__lowercase			:  str								=       spectrogram_length
					__lowercase			:  int								=       num_channels
					__lowercase			:  List[str]								=       patch_size
					__lowercase			:  Any								=       feature_size // self.patch_size[1]
					__lowercase			:  int								=       n_fft
					__lowercase			:  Optional[int]								=       sampling_rate // hop_length_to_sampling_rate
					__lowercase			:  int								=       sampling_rate
					__lowercase			:  Optional[Any]								=       padding_value
					__lowercase			:  List[Any]								=       mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2				,						num_mel_filters=UpperCamelCase_				,						min_frequency=0.0				,						max_frequency=2_2_0_5_0.0				,						sampling_rate=UpperCamelCase_				,						norm='''slaney'''				,						mel_scale='''slaney'''				,						).T
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       np.ndarray:
					__lowercase			:  List[str]								=       spectrogram(
					    UpperCamelCase_				,						window_function(self.n_fft				,						'''hann'''       )				,						frame_length=self.n_fft				,						hop_length=self.hop_length				,						power=2.0				,						mel_filters=self.mel_filters.T				,						log_mel='''dB'''				,						db_range=8_0.0				,						)
					__lowercase			:  int								=       log_spec[:, :-1]
					__lowercase			:  Dict								=       log_spec - 2_0.0
					__lowercase			:  List[str]								=       np.clip(log_spec / 4_0.0				,						-2.0				,						0.0       ) + 1.0
					return log_spec
		def __call__(						self				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						UpperCamelCase_ = True				,						UpperCamelCase_ = None				,						UpperCamelCase_ = False				,						UpperCamelCase_ = False				,						**UpperCamelCase_				,						)						->       BatchFeature:

					if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
											raise ValueError(
											    '''This feature extractor is set to support sampling rate'''
											    F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
											    F""" with {self.sampling_rate} and not {sampling_rate}."""       )
					else:
								logger.warning(
								    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
								    '''Failing to do so can result in silent errors that might be hard to debug.'''       )

					__lowercase			:  Any								=       isinstance(UpperCamelCase_				,						np.ndarray       ) and len(raw_speech.shape       ) > 1
					if is_batched_numpy and len(raw_speech.shape       ) > 2:
								raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""       )
					__lowercase			:  Tuple								=       is_batched_numpy or (
					    isinstance(UpperCamelCase_				,						(list, tuple)       ) and (isinstance(raw_speech[0]				,						(np.ndarray, tuple, list)       ))
					)
					if is_batched:
								__lowercase			:  Union[str, Any]								=       [np.asarray([speech]				,						dtype=np.floataa       ).T for speech in raw_speech]
					elif not is_batched and not isinstance(UpperCamelCase_				,						np.ndarray       ):
								__lowercase			:  List[str]								=       np.asarray(UpperCamelCase_				,						dtype=np.floataa       )
					elif isinstance(UpperCamelCase_				,						np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
								__lowercase			:  int								=       raw_speech.astype(np.floataa       )
					# always return batch
					if not is_batched:
								__lowercase			:  Any								=       [np.asarray([raw_speech]       ).T]

					# Convert audio signals to log mel spectrograms, truncate by time axis
					__lowercase			:  Optional[int]								=       [
					    self._np_extract_fbank_features(waveform.squeeze()       ).T[: self.spectrogram_length] for waveform in raw_speech
					]
					if isinstance(audio_features[0]				,						UpperCamelCase_       ):
								__lowercase			:  List[Any]								=       [np.asarray(UpperCamelCase_				,						dtype=np.floataa       ) for feature in audio_features]

					# Create audio attention mask
					__lowercase			:  Optional[int]								=       max(
					    [ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len for feature in audio_features]       )  # The maximum number of audio patches in a batch
					if return_attention_mask:
								__lowercase			:  str								=       [
								    (ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [1]
								    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [0]
								    for feature in audio_features
								]
								__lowercase			:  Tuple								=       np.array(UpperCamelCase_       ).astype(np.floataa       )

					# convert into correct format for padding
					__lowercase			:  List[str]								=       max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
					__lowercase			:  Optional[Any]								=       np.ones([len(UpperCamelCase_       ), 1, max_time_len, self.feature_size]       ).astype(np.floataa       )
					__lowercase			:  int								=       padded_audio_features * self.padding_value
					for i in range(len(UpperCamelCase_       )       ):
								__lowercase			:  Tuple								=       audio_features[i]
								__lowercase			:  int								=       feature

					# return as BatchFeature
					if return_attention_mask:
								__lowercase			:  List[str]								=       {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
					else:
								__lowercase			:  Union[str, Any]								=       {'''audio_values''': padded_audio_features}

					__lowercase			:  Any								=       BatchFeature(data=UpperCamelCase_				,						tensor_type=UpperCamelCase_       )
					return encoded_inputs











import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	str   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     warnings.warn(
         """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use BeitImageProcessor instead."""   ,				lowerCamelCase__   ,				)
     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)



