




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							=      logging.get_logger(__name__)


class        UpperCAmelCase__				(							a__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Any     					=    ['input_values', 'attention_mask']
     def __init__(				self ,    _lowerCAmelCase=1 ,    _lowerCAmelCase=16_000 ,    _lowerCAmelCase=128 ,    _lowerCAmelCase=1_024 ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=True ,    _lowerCAmelCase=-4.2_67_73_93 ,    _lowerCAmelCase=4.5_68_99_74 ,    _lowerCAmelCase=False ,    **_lowerCAmelCase ,    ):
            super().__init__(feature_size=__UpperCAmelCase ,    sampling_rate=__UpperCAmelCase ,    padding_value=__UpperCAmelCase ,    **__UpperCAmelCase	)
            a			   =num_mel_bins
            a			   =max_length
            a			   =do_normalize
            a			   =mean
            a			   =std
            a			   =return_attention_mask
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    ):
            a			   =torch.from_numpy(__UpperCAmelCase	).unsqueeze(0	)
            a			   =ta_kaldi.fbank(
                __UpperCAmelCase ,    htk_compat=__UpperCAmelCase ,    sample_frequency=self.sampling_rate ,    use_energy=__UpperCAmelCase ,    window_type="""hanning""" ,    num_mel_bins=self.num_mel_bins ,    dither=0.0 ,    frame_shift=10 ,    )

            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(__UpperCAmelCase	)
            elif difference < 0:
                   a			   =fbank[0:max_length, :]

            a			   =fbank.numpy()

            return fbank
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            return (input_values - (self.mean)) / (self.std * 2)






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

            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(__UpperCAmelCase ,    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(__UpperCAmelCase ,    (list, tuple)	) and (isinstance(raw_speech[0] ,    (np.ndarray, tuple, list)	))
            )

            if is_batched:
                   a			   =[np.asarray(__UpperCAmelCase ,    dtype=np.floataa	) for speech in raw_speech]
            elif not is_batched and not isinstance(__UpperCAmelCase ,    np.ndarray	):
                   a			   =np.asarray(__UpperCAmelCase ,    dtype=np.floataa	)
            elif isinstance(__UpperCAmelCase ,    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(__UpperCAmelCase ,    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] ,    __UpperCAmelCase	):
                   a			   =[np.asarray(__UpperCAmelCase ,    dtype=np.floataa	) for feature in input_values]

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

            if return_tensors is not None:
                   a			   =padded_inputs.convert_to_tensors(__UpperCAmelCase	)

            return padded_inputs




"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if len(_lowerCAmelCase							) < len(_lowerCAmelCase							):
          return levenshtein_distance(_lowerCAmelCase,  _lowerCAmelCase							)

     if len(_lowerCAmelCase							) == 0:
          return len(_lowerCAmelCase							)

     _a     						=					list(range(len(_lowerCAmelCase							) + 1							)							)

     for i, ca in enumerate(_lowerCAmelCase							):
          _a     						=					[i + 1]

          for j, ca in enumerate(_lowerCAmelCase							):
               # Calculate insertions, deletions and substitutions
               _a     						=					previous_row[j + 1] + 1
               _a     						=					current_row[j] + 1
               _a     						=					previous_row[j] + (ca != ca)

               # Get the minimum to append to the current row
               current_row.append(min(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)							)

          # Store the previous row
          _a     						=					current_row

     # Returns the last element (distance)
     return previous_row[-1]


if __name__ == "__main__":
   __snake_case												=							input('''Enter the first word:\n''').strip()
   __snake_case												=							input('''Enter the second word:\n''').strip()

   __snake_case												=							levenshtein_distance(first_word, second_word)
   print(f'Levenshtein distance between {first_word} and {second_word} is {result}')