"""simple docstring"""



import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


SCREAMING_SNAKE_CASE__:Dict									=			logging.get_logger(__name__)






class  snake_case__      (       lowerCamelCase__       ):
 _snake_case       :					List[Any]							  =						["""input_values""", """attention_mask"""]


 def __init__( self    ,		lowerCamelCase = 1    ,		lowerCamelCase = 16000    ,		lowerCamelCase = 0.0    ,		lowerCamelCase = False    ,		lowerCamelCase = 80    ,		lowerCamelCase = 16    ,		lowerCamelCase = 64    ,		lowerCamelCase = "hann_window"    ,		lowerCamelCase = 1.0    ,		lowerCamelCase = 80    ,		lowerCamelCase = 7600    ,		lowerCamelCase = 1E-10    ,		lowerCamelCase = 2    ,		lowerCamelCase = True    ,		**lowerCamelCase    ,		):
        super().__init__(feature_size=a__    ,		sampling_rate=a__    ,		padding_value=a__    ,		**a__						)
        __a       =    do_normalize
        __a       =    return_attention_mask

        __a       =    num_mel_bins
        __a       =    hop_length
        __a       =    win_length
        __a       =    win_function
        __a       =    frame_signal_scale
        __a       =    fmin
        __a       =    fmax
        __a       =    mel_floor
        __a       =    reduction_factor

        __a       =    win_length * sampling_rate // 1000
        __a       =    hop_length * sampling_rate // 1000
        __a       =    optimal_fft_length(self.sample_size						)
        __a       =    (self.n_fft // 2) + 1

        __a       =    window_function(window_length=self.sample_size    ,		name=self.win_function    ,		periodic=a__						)

        __a       =    mel_filter_bank(
            num_frequency_bins=self.n_freqs    ,		num_mel_filters=self.num_mel_bins    ,		min_frequency=self.fmin    ,		max_frequency=self.fmax    ,		sampling_rate=self.sampling_rate    ,		norm="slaney"    ,		mel_scale="slaney"    ,		)

        if frame_signal_scale != 1.0:
               warnings.warn(
                   "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"    ,		a__    ,		)
        if reduction_factor != 2.0:
               warnings.warn(
                   "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"    ,		a__    ,		)


 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 						a__   ( lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = 0.0						):





        if attention_mask is not None:
               __a       =    np.array(a__    ,		np.intaa						)
               __a       =    []

               for vector, length in zip(a__    ,		attention_mask.sum(-1						)						):
                      __a       =    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7						)
                      if length < normed_slice.shape[0]:
                             __a       =    padding_value

                      normed_input_values.append(a__						)
        else:
               __a       =    [(x - x.mean()) / np.sqrt(x.var() + 1E-7						) for x in input_values]

        return normed_input_values


 def 						a__   ( self    ,		lowerCamelCase    ,		):
        __a       =    spectrogram(
            a__    ,		window=self.window    ,		frame_length=self.sample_size    ,		hop_length=self.sample_stride    ,		fft_length=self.n_fft    ,		mel_filters=self.mel_filters    ,		mel_floor=self.mel_floor    ,		log_mel="log10"    ,		)
        return log_mel_spec.T


 def __call__( self    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):





        if audio is None and audio_target is None:
               raise ValueError("You must provide either `audio` or `audio_target` values."						)

        if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                      raise ValueError(
                          F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                          F" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
                          F" {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."						)

        if audio is not None:
               __a       =    self._process_audio(
                   a__    ,		a__    ,		a__    ,		a__    ,		a__    ,		a__    ,		a__    ,		a__    ,		**a__    ,		)
        else:
               __a       =    None

        if audio_target is not None:
               __a       =    self._process_audio(
                   a__    ,		a__    ,		a__    ,		a__    ,		a__    ,		a__    ,		a__    ,		a__    ,		**a__    ,		)

               if inputs is None:
                      return inputs_target
               else:
                      __a       =    inputs_target['''input_values''']
                      __a       =    inputs_target.get("attention_mask"						)
                      if decoder_attention_mask is not None:
                             __a       =    decoder_attention_mask

        return inputs


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = False    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    isinstance(a__    ,		np.ndarray						) and len(speech.shape						) > 1
        if is_batched_numpy and len(speech.shape						) > 2:
               raise ValueError(F"Only mono-channel audio is supported for input to {self}"						)
        __a       =    is_batched_numpy or (
            isinstance(a__    ,		(list, tuple)						) and (isinstance(speech[0]    ,		(np.ndarray, tuple, list)						))
        )

        if is_batched:
               __a       =    [np.asarray(a__    ,		dtype=np.floataa						) for speech in speech]
        elif not is_batched and not isinstance(a__    ,		np.ndarray						):
               __a       =    np.asarray(a__    ,		dtype=np.floataa						)
        elif isinstance(a__    ,		np.ndarray						) and speech.dtype is np.dtype(np.floataa						):
               __a       =    speech.astype(np.floataa						)

        # always return batch
        if not is_batched:
               __a       =    [speech]

        # needed to make pad() work on spectrogram inputs
        __a       =    self.feature_size

        # convert into correct format for padding
        if is_target:
               __a       =    [self._extract_mel_features(a__						) for waveform in speech]
               __a       =    BatchFeature({"input_values": features}						)
               __a       =    self.num_mel_bins
        else:
               __a       =    BatchFeature({"input_values": speech}						)

        __a       =    self.pad(
            a__    ,		padding=a__    ,		max_length=a__    ,		truncation=a__    ,		pad_to_multiple_of=a__    ,		return_attention_mask=a__    ,		**a__    ,		)

        __a       =    feature_size_hack

        # convert input values to correct format
        __a       =    padded_inputs['''input_values''']
        if not isinstance(input_values[0]    ,		np.ndarray						):
               __a       =    [np.asarray(a__    ,		dtype=np.floataa						) for array in input_values]
        elif (
            not isinstance(a__    ,		np.ndarray						)
            and isinstance(input_values[0]    ,		np.ndarray						)
            and input_values[0].dtype is np.dtype(np.floataa						)
        ):
               __a       =    [array.astype(np.floataa						) for array in input_values]
        elif isinstance(a__    ,		np.ndarray						) and input_values.dtype is np.dtype(np.floataa						):
               __a       =    input_values.astype(np.floataa						)

        # convert attention_mask to correct format
        __a       =    padded_inputs.get("attention_mask"						)
        if attention_mask is not None:
               __a       =    [np.asarray(a__    ,		dtype=np.intaa						) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if not is_target and self.do_normalize:
               __a       =    (
                   attention_mask
                   if self._get_padding_strategies(a__    ,		max_length=a__						) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               __a       =    self.zero_mean_unit_var_norm(
                   padded_inputs["input_values"]    ,		attention_mask=a__    ,		padding_value=self.padding_value						)

        if return_tensors is not None:
               __a       =    padded_inputs.convert_to_tensors(a__						)

        return padded_inputs






 def 						a__   ( self						):
        __a       =    super().to_dict()

        # Don't serialize these as they are derived from the other properties.
        __a       =    ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
        for name in names:
               if name in output:
                      del output[name]

        return output







import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DeiTImageProcessor instead.'''   ,				a__   ,				)
             super().__init__(*a__   ,				**a__)

