



"""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


lowerCamelCase				:     int								=  logging.get_logger(__name__)




class    __snake_case(					_UpperCAmelCase       ):
    _A       = ['''input_values''', '''attention_mask''']




    def __init__(   self    ,							A_=1    ,							A_=16_000    ,							A_=128    ,							A_=1_024    ,							A_=0.0    ,							A_=True    ,							A_=-4.2_677_393    ,							A_=4.5_689_974    ,							A_=False    ,							**A_    ,							):





      '''simple docstring'''



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




    def   A   (   self    ,							A_    ,							A_    ,							):





      '''simple docstring'''



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

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

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

      _SCREAMING_SNAKE_CASE        =			fbank.numpy()

      return fbank




    def   A   (   self    ,							A_			):





      '''simple docstring'''



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




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





      '''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.'''			)

      _SCREAMING_SNAKE_CASE        =			isinstance(lowercase_    ,							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}'''			)
      _SCREAMING_SNAKE_CASE        =			is_batched_numpy or (
          isinstance(lowercase_    ,							(list, tuple)			) and (isinstance(raw_speech[0]    ,							(np.ndarray, tuple, list)			))
      )

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

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

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

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

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

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

      if return_tensors is not None:
        _SCREAMING_SNAKE_CASE        =			padded_inputs.convert_to_tensors(lowercase_			)

      return padded_inputs



import timeit


class 					A		:







    """simple docstring"""





    def __init__( self					:			Optional[int],lowercase_					:			Union[str, Any]						)->       List[str]:





        '''simple docstring'''







        if isinstance(lowercase_,lowercase_						):  # Initializes a matrix identical to the one provided.
            A__        =  arg
            A__        =  len(lowercase_						)
        else:  # Initializes a square matrix of the given size and set values to zero.
            A__        =  arg
            A__        =  [[0 for _ in range(self.n						)] for _ in range(self.n						)]


    def __mul__( self					:			Union[str, Any],lowercase_					:			List[Any]						)->       List[str]:





        '''simple docstring'''







        A__        =  Matrix(self.n						)
        for i in range(self.n						):
            for j in range(self.n						):
                for k in range(self.n						):
                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix




def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any] )				-> Union[str, Any]:





    '''simple docstring'''






    A__        =  Matrix([[1, 0], [0, 1]] )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Any )				-> List[str]:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa
    A__        =  Matrix([[1, 1], [1, 0]] )
    A__        =  modular_exponentiation(SCREAMING_SNAKE_CASE__       ,     n - 2 )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      Tuple )				-> Dict:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa

    A__        =  fa
    A__        =  fa
    n -= 2

    while n > 0:
        A__ ,		A__        =  fn_a + fn_a, fn_a
        n -= 1

    return fn_a
def 		_snake_case(       )				-> Optional[Any]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n'
    A__        =  'fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print('With matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Optional[int]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'
    A__        =  'simple_fibonacci(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print(
        'Without matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Union[str, Any]:





    '''simple docstring'''






    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
     main()

