





"""simple docstring"""
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 TensorType, logging


__UpperCamelCase			:		Tuple		    =   logging.get_logger(__name__)







class 	a				(					a__   ):
 snake_case__    	=    ['''input_values''', '''attention_mask''']



 def __init__(			self							,    _snake_case=1							,    _snake_case=1_60_00							,    _snake_case=1_28							,    _snake_case=10_24							,    _snake_case=0.0							,    _snake_case=True							,    _snake_case=-4.2_677_393							,    _snake_case=4.5_689_974							,    _snake_case=False							,    **_snake_case							,    ):







  """simple docstring"""






  super().__init__(feature_size=_snake_case							,    sampling_rate=_snake_case							,    padding_value=_snake_case							,    **_snake_case			)
  lowerCAmelCase  							=      num_mel_bins
  lowerCAmelCase  							=      max_length
  lowerCAmelCase  							=      do_normalize
  lowerCAmelCase  							=      mean
  lowerCAmelCase  							=      std
  lowerCAmelCase  							=      return_attention_mask



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      torch.from_numpy(_snake_case			).unsqueeze(0			)
  lowerCAmelCase  							=      ta_kaldi.fbank(
      _snake_case							,    htk_compat=_snake_case							,    sample_frequency=self.sampling_rate							,    use_energy=_snake_case							,    window_type='hanning'							,    num_mel_bins=self.num_mel_bins							,    dither=0.0							,    frame_shift=10							,    )

  lowerCAmelCase  							=      fbank.shape[0]
  lowerCAmelCase  							=      max_length - n_frames

  # pad or truncate, depending on difference
  if difference > 0:
   lowerCAmelCase  							=      torch.nn.ZeroPadad((0, 0, 0, difference)			)
   lowerCAmelCase  							=      pad_module(_snake_case			)
  elif difference < 0:
   lowerCAmelCase  							=      fbank[0:max_length, :]

  lowerCAmelCase  							=      fbank.numpy()

  return fbank



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






  return (input_values - (self.mean)) / (self.std * 2)



 def __call__(			self							,    _snake_case							,    _snake_case = None							,    _snake_case = None							,    **_snake_case							,    ):







  """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(_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}'			)
  lowerCAmelCase  							=      is_batched_numpy or (
      isinstance(_snake_case							,    (list, tuple)			) and (isinstance(raw_speech[0]							,    (np.ndarray, tuple, list)			))
  )

  if is_batched:
   lowerCAmelCase  							=      [np.asarray(_snake_case							,    dtype=np.floataa			) for speech in raw_speech]
  elif not is_batched and not isinstance(_snake_case							,    np.ndarray			):
   lowerCAmelCase  							=      np.asarray(_snake_case							,    dtype=np.floataa			)
  elif isinstance(_snake_case							,    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 and pad/truncate to max_length
  lowerCAmelCase  							=      [self._extract_fbank_features(_snake_case							,    max_length=self.max_length			) for waveform in raw_speech]

  # convert into BatchFeature
  lowerCAmelCase  							=      BatchFeature({'input_values': features}			)

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

  # normalization
  if self.do_normalize:
   lowerCAmelCase  							=      [self.normalize(_snake_case			) for feature in input_values]

  if return_tensors is not None:
   lowerCAmelCase  							=      padded_inputs.convert_to_tensors(_snake_case			)

  return padded_inputs












"""simple docstring"""
import os
import sys
import time

from . import transposition_cipher as trans_cipher

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      'Prehistoric Men.txt'
 lowerCAmelCase  							=      'Output.txt'
 lowerCAmelCase  							=      int(input('Enter key: '       )       )
 lowerCAmelCase  							=      input('Encrypt/Decrypt [e/d]: '       )

 if not os.path.exists(_UpperCAmelCase       ):
  print(F'File {input_file} does not exist. Quitting...'       )
  sys.exit()
 if os.path.exists(_UpperCAmelCase       ):
  print(F'Overwrite {output_file}? [y/n]'       )
  lowerCAmelCase  							=      input('> '       )
  if not response.lower().startswith('y'       ):
   sys.exit()

 lowerCAmelCase  							=      time.time()
 if mode.lower().startswith('e'       ):
  with open(_UpperCAmelCase       ) as f:
   lowerCAmelCase  							=      f.read()
  lowerCAmelCase  							=      trans_cipher.encrypt_message(_UpperCAmelCase   ,			_UpperCAmelCase       )
 elif mode.lower().startswith('d'       ):
  with open(_UpperCAmelCase       ) as f:
   lowerCAmelCase  							=      f.read()
  lowerCAmelCase  							=      trans_cipher.decrypt_message(_UpperCAmelCase   ,			_UpperCAmelCase       )

 with open(_UpperCAmelCase   ,			'w'       ) as output_obj:
  output_obj.write(_UpperCAmelCase       )

 lowerCAmelCase  							=      round(time.time() - start_time   ,			2       )
 print(('Done (', total_time, 'seconds )')       )


if __name__ == "__main__":
       main()





