



"""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


__snake_case	:				Dict  					=			logging.get_logger(__name__)

class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: Union[str, Any]				   =["input_values", "attention_mask"]





     def __init__(					self :							Optional[Any]	,      _lowerCamelCase :							List[str]=1	,      _lowerCamelCase :							List[str]=1_6_0_0_0	,      _lowerCamelCase :							List[Any]=1_2_8	,      _lowerCamelCase :							str=1_0_2_4	,      _lowerCamelCase :							List[str]=0.0	,      _lowerCamelCase :							List[str]=True	,      _lowerCamelCase :							str=-4.2_677_393	,      _lowerCamelCase :							Any=4.5_689_974	,      _lowerCamelCase :							List[Any]=False	,      **_lowerCamelCase :							Optional[Any]	,      ):
          super().__init__(feature_size=_lowerCamelCase	,      sampling_rate=_lowerCamelCase	,      padding_value=_lowerCamelCase	,      **_lowerCamelCase )
          A__				=   num_mel_bins
          A__				=   max_length
          A__				=   do_normalize
          A__				=   mean
          A__				=   std
          A__				=   return_attention_mask





     def 						A__  (					self :							Any	,      _lowerCamelCase :							np.ndarray	,      _lowerCamelCase :							int	,      ):
          A__				=   torch.from_numpy(_lowerCamelCase ).unsqueeze(0 )
          A__				=   ta_kaldi.fbank(
              _lowerCamelCase	,      htk_compat=_lowerCamelCase	,      sample_frequency=self.sampling_rate	,      use_energy=_lowerCamelCase	,      window_type='''hanning'''	,      num_mel_bins=self.num_mel_bins	,      dither=0.0	,      frame_shift=1_0	,      )

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

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

          A__				=   fbank.numpy()

          return fbank





     def 						A__  (					self :							str	,      _lowerCamelCase :							np.ndarray ):
          return (input_values - (self.mean)) / (self.std * 2)





     def __call__(					self :							Any	,      _lowerCamelCase :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,      _lowerCamelCase :							Optional[int] = None	,      _lowerCamelCase :							Optional[Union[str, TensorType]] = None	,      **_lowerCamelCase :							int	,      ):

          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__				=   isinstance(_lowerCamelCase	,      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__				=   is_batched_numpy or (
              isinstance(_lowerCamelCase	,      (list, tuple) ) and (isinstance(raw_speech[0]	,      (np.ndarray, tuple, list) ))
          )

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

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

          # extract fbank features and pad/truncate to max_length
          A__				=   [self._extract_fbank_features(_lowerCamelCase	,      max_length=self.max_length ) for waveform in raw_speech]

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

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

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

          if return_tensors is not None:
               A__				=   padded_inputs.convert_to_tensors(_lowerCamelCase )

          return padded_inputs








"""simple docstring"""







def   a_	(			__a      ):
     if not isinstance(__a		,							__a      ):
          raise TypeError('''recursive_lucas_number accepts only integer arguments.'''      )
     if n_th_number == 0:
          return 2
     if n_th_number == 1:
          return 1

     return recursive_lucas_number(n_th_number - 1      ) + recursive_lucas_number(
         n_th_number - 2      )



def   a_	(			__a      ):
     if not isinstance(__a		,							__a      ):
          raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''      )
     A__	,     A__				=   2, 1
     for _ in range(__a      ):
          A__	,     A__				=   b, a + b
     return a


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

 testmod()
 __snake_case	:				Dict  					=			int(input('Enter the number of terms in lucas series:\n').strip())
 print('Using recursive function to calculate lucas series:')
 print(' '.join(str(recursive_lucas_number(i)) for i in range(n)))
 print('\nUsing dynamic function to calculate lucas series:')
 print(' '.join(str(dynamic_lucas_number(i)) for i in range(n)))



