




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase    :		Any			   =       logging.get_logger(__name__)







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''input_features''', '''attention_mask''']

 def __init__(    self :  Union[str, Any]						,							A_ :  Tuple=80						,							A_ :  Any=16000						,							A_ :  Optional[int]=80						,							A_ :  str=0.0						,							A_ :  Union[str, Any]=True						,							A_ :  str=True						,							A_ :  List[str]=True						,							**A_ :  Union[str, Any]						,							)       ->     List[Any]:

       """simple docstring"""






       super().__init__(feature_size=A_						,							sampling_rate=A_						,							padding_value=A_						,							**A_  )
       lowerCamelCase_							=   num_mel_bins
       lowerCamelCase_							=   do_ceptral_normalize
       lowerCamelCase_							=   normalize_means
       lowerCamelCase_							=   normalize_vars
       lowerCamelCase_							=   True

 def     a__			(    self :  List[Any]						,							A_ :  np.ndarray						,							)       ->     np.ndarray:

       """simple docstring"""






       lowerCamelCase_							=   waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
       lowerCamelCase_							=   torch.from_numpy(A_  ).unsqueeze(0  )
       lowerCamelCase_							=   ta_kaldi.fbank(A_						,							num_mel_bins=self.num_mel_bins						,							sample_frequency=self.sampling_rate  )
       return features.numpy()

 @staticmethod
 def     a__			(    A_ :  np.ndarray						,							A_ :  int						,							A_ :  Optional[bool] = True						,							A_ :  Optional[bool] = True						,							A_ :  float = 0.0						,							)       ->     np.ndarray:

       """simple docstring"""






       if normalize_means:
             lowerCamelCase_							=   x[:input_length].mean(axis=0  )
             lowerCamelCase_							=   np.subtract(A_						,							A_  )
       if normalize_vars:
             lowerCamelCase_							=   x[:input_length].std(axis=0  )
             lowerCamelCase_							=   np.divide(A_						,							A_  )

       if input_length < x.shape[0]:
             lowerCamelCase_							=   padding_value

       # make sure array is in float32
       lowerCamelCase_							=   x.astype(np.floataa  )

       return x

 def     a__			(    self :  Any						,							A_ :  List[np.ndarray]						,							A_ :  Optional[np.ndarray] = None  )       ->     List[np.ndarray]:

       """simple docstring"""






       lowerCamelCase_							=   attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [
           self.utterance_cmvn(A_						,							A_						,							self.normalize_means						,							self.normalize_vars						,							self.padding_value  )
           for x, n in zip(A_						,							A_  )
       ]





 def __call__(    self :  Any						,							A_ :  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,							A_ :  Union[bool, str, PaddingStrategy] = False						,							A_ :  Optional[int] = None						,							A_ :  bool = False						,							A_ :  Optional[int] = None						,							A_ :  Optional[Union[str, TensorType]] = None						,							A_ :  Optional[int] = None						,							A_ :  Optional[bool] = None						,							**A_ :  Tuple						,							)       ->     BatchFeature:

       """simple docstring"""






       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 `raw_speech` 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.'  )

       lowerCamelCase_							=   isinstance(A_						,							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}"""  )
       lowerCamelCase_							=   is_batched_numpy or (
           isinstance(A_						,							(list, tuple)  ) and (isinstance(raw_speech[0]						,							(np.ndarray, tuple, list)  ))
       )

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

       # always return batch
       if not is_batched:
             lowerCamelCase_							=   [raw_speech]

       # extract fbank features
       lowerCamelCase_							=   [self._extract_fbank_features(A_  ) for waveform in raw_speech]

       # convert into correct format for padding
       lowerCamelCase_							=   BatchFeature({'input_features': features}  )

       lowerCamelCase_							=   self.pad(
           A_						,							padding=A_						,							max_length=A_						,							truncation=A_						,							pad_to_multiple_of=A_						,							return_attention_mask=A_						,							**A_						,							)

       # make sure list is in array format
       lowerCamelCase_							=   padded_inputs.get('input_features'  )
       if isinstance(input_features[0]						,							A_  ):
             lowerCamelCase_							=   [np.asarray(A_						,							dtype=np.floataa  ) for feature in input_features]

       lowerCamelCase_							=   padded_inputs.get('attention_mask'  )
       if attention_mask is not None:
             lowerCamelCase_							=   [np.asarray(A_						,							dtype=np.intaa  ) for array in attention_mask]

       # Utterance-level cepstral mean and variance normalization
       if self.do_ceptral_normalize:
             lowerCamelCase_							=   (
                 np.array(A_						,							dtype=np.intaa  )
                 if self._get_padding_strategies(A_						,							max_length=A_  ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             lowerCamelCase_							=   self.normalize(
                 padded_inputs['input_features']						,							attention_mask=A_  )

       if return_tensors is not None:
             lowerCamelCase_							=   padded_inputs.convert_to_tensors(A_  )

       return padded_inputs









import math

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	float					):


      '''simple docstring'''



      return math.pow(lowercase     ,							2					) - a

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      return 2 * x

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      lowerCamelCase_							=   2.0

      while start <= a:
            lowerCamelCase_							=   math.pow(lowercase     ,							2					)

      return start

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	int = 99_99     ,							lowercase     :	float = 0.00_0000_0000_0001					):


      '''simple docstring'''



      if a < 0:
            raise ValueError('math domain error'					)

      lowerCamelCase_							=   get_initial_point(lowercase					)

      for _ in range(lowercase					):
            lowerCamelCase_							=   value
            lowerCamelCase_							=   value - fx(lowercase     ,							lowercase					) / fx_derivative(lowercase					)
            if abs(prev_value - value					) < tolerance:
                  return value

      return value


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

 testmod()



