




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


UpperCAmelCase     :     Tuple							=	logging.get_logger(__name__)







class     __lowerCAmelCase	(   A__):
      _lowercase    : Optional[int]            =      ["""input_features""", """attention_mask"""]







      def __init__(   self		,				lowerCAmelCase__=8_0		,				lowerCAmelCase__=1_6_0_0_0		,				lowerCAmelCase__=8_0		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				**lowerCAmelCase__		,				)       ->						Tuple:



        '''simple docstring'''




        super().__init__(feature_size=__snake_case		,				sampling_rate=__snake_case		,				padding_value=__snake_case		,				**__snake_case							)
        a__     : Tuple  =num_mel_bins
        a__     : List[str]  =do_ceptral_normalize
        a__     : str  =normalize_means
        a__     : Optional[Any]  =normalize_vars
        a__     : List[str]  =True







      def        _lowercase  (   self		,				lowerCAmelCase__		,				)       ->						np.ndarray:



        '''simple docstring'''




        a__     : Dict  =waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
        a__     : List[str]  =torch.from_numpy(__snake_case							).unsqueeze(0							)
        a__     : List[str]  =ta_kaldi.fbank(__snake_case		,				num_mel_bins=self.num_mel_bins		,				sample_frequency=self.sampling_rate							)
        return features.numpy()







      @staticmethod
      def        _lowercase  (   lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = 0.0		,				)       ->						np.ndarray:



        '''simple docstring'''




        if normalize_means:
          a__     : Tuple  =x[:input_length].mean(axis=0							)
          a__     : str  =np.subtract(__snake_case		,				__snake_case							)
        if normalize_vars:
          a__     : str  =x[:input_length].std(axis=0							)
          a__     : Dict  =np.divide(__snake_case		,				__snake_case							)

        if input_length < x.shape[0]:
          a__     : Optional[Any]  =padding_value

        # make sure array is in float32
        a__     : Optional[int]  =x.astype(np.floataa							)

        return x







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						List[np.ndarray]:



        '''simple docstring'''




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







      def __call__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						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."							)

        a__     : Union[str, Any]  =isinstance(__snake_case		,				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}'''							)
        a__     : str  =is_batched_numpy or (
            isinstance(__snake_case		,				(list, tuple)							) and (isinstance(raw_speech[0]		,				(np.ndarray, tuple, list)							))
        )

        if is_batched:
          a__     : Optional[int]  =[np.asarray(__snake_case		,				dtype=np.floataa							) for speech in raw_speech]
        elif not is_batched and not isinstance(__snake_case		,				np.ndarray							):
          a__     : Tuple  =np.asarray(__snake_case		,				dtype=np.floataa							)
        elif isinstance(__snake_case		,				np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
          a__     : Union[str, Any]  =raw_speech.astype(np.floataa							)

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

        # extract fbank features
        a__     : Dict  =[self._extract_fbank_features(__snake_case							) for waveform in raw_speech]

        # convert into correct format for padding
        a__     : Any  =BatchFeature({"input_features": features}							)

        a__     : Any  =self.pad(
            __snake_case		,				padding=__snake_case		,				max_length=__snake_case		,				truncation=__snake_case		,				pad_to_multiple_of=__snake_case		,				return_attention_mask=__snake_case		,				**__snake_case		,				)

        # make sure list is in array format
        a__     : List[Any]  =padded_inputs.get("input_features"							)
        if isinstance(input_features[0]		,				__snake_case							):
          a__     : Tuple  =[np.asarray(__snake_case		,				dtype=np.floataa							) for feature in input_features]

        a__     : Tuple  =padded_inputs.get("attention_mask"							)
        if attention_mask is not None:
          a__     : Union[str, Any]  =[np.asarray(__snake_case		,				dtype=np.intaa							) for array in attention_mask]

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

        if return_tensors is not None:
          a__     : Optional[int]  =padded_inputs.convert_to_tensors(__snake_case							)

        return padded_inputs













import os
def 					_A	(		):





  """simple docstring"""



  a__     : Optional[int]  =os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE						)	,	"num.txt"						)
  with open(SCREAMING_SNAKE_CASE						) as file_hand:
    return str(sum(int(SCREAMING_SNAKE_CASE						) for line in file_hand						)						)[:10]


if __name__ == "__main__":
     print(solution())






