





from __future__ import annotations

from typing import TypedDict



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :str
      _UpperCAmelCase      :int



def 						A_							(	_lowerCAmelCase	)		->					list[str]:
 if not isinstance(_lowerCAmelCase				,  _lowerCAmelCase	):
  raise TypeError("The parameter s type must be str."	)

 return [s[i:] + s[:i] for i in range(len(_lowerCAmelCase	)	)]



def 						A_							(	_lowerCAmelCase	)		->					BWTTransformDict:
 if not isinstance(_lowerCAmelCase				,  _lowerCAmelCase	):
  raise TypeError("The parameter s type must be str."	)
 if not s:
  raise ValueError("The parameter s must not be empty."	)

 UpperCamelCase      :   Tuple			      =  all_rotations(_lowerCAmelCase	)
 rotations.sort()  # sort the list of rotations in alphabetically order
 # make a string composed of the last char of each rotation
 UpperCamelCase      :   BWTTransformDict			      =  {
     "bwt_string": "".join([word[-1] for word in rotations]	),
     "idx_original_string": rotations.index(_lowerCAmelCase	),
 }
 return response



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					str:
 if not isinstance(_lowerCAmelCase				,  _lowerCAmelCase	):
  raise TypeError("The parameter bwt_string type must be str."	)
 if not bwt_string:
  raise ValueError("The parameter bwt_string must not be empty."	)
 try:
  UpperCamelCase      :   Union[str, Any]			      =  int(_lowerCAmelCase	)
 except ValueError:
  raise TypeError(
      "The parameter idx_original_string type must be int or passive"
      " of cast to int."	)
 if idx_original_string < 0:
  raise ValueError("The parameter idx_original_string must not be lower than 0."	)
 if idx_original_string >= len(_lowerCAmelCase	):
  raise ValueError(
      "The parameter idx_original_string must be lower than" " len(bwt_string)."	)

 UpperCamelCase      :   int			      =  [""] * len(_lowerCAmelCase	)
 for _ in range(len(_lowerCAmelCase	)	):
  for i in range(len(_lowerCAmelCase	)	):
   UpperCamelCase      :   int			      =  bwt_string[i] + ordered_rotations[i]
  ordered_rotations.sort()
 return ordered_rotations[idx_original_string]


if __name__ == "__main__":
    __lowerCamelCase			:	int					    =	"""Provide a string that I will generate its BWT transform: """
    __lowerCamelCase			:	Optional[Any]					    =	input(entry_msg).strip()
    __lowerCamelCase			:	List[str]					    =	bwt_transform(s)
    print(
        f"""Burrows Wheeler transform for string '{s}' results """
        f"""in '{result['bwt_string']}'"""
    )
    __lowerCamelCase			:	Optional[int]					    =	reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
    print(
        f"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
        f"""we get original string '{original_string}'"""
    )







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


__lowerCamelCase			:	List[str]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :List[str]		       =  ['input_features', 'attention_mask']





      def __init__(					self			,				A_=80			,				A_=1_6000			,				A_=80			,				A_=0.0			,				A_=True			,				A_=True			,				A_=True			,				**A_			,				):
       '''simple docstring'''




       super().__init__(feature_size=A_			,				sampling_rate=A_			,				padding_value=A_			,				**A_  )
       UpperCamelCase      :   List[Any]			      =  num_mel_bins
       UpperCamelCase      :   Any			      =  do_ceptral_normalize
       UpperCamelCase      :   int			      =  normalize_means
       UpperCamelCase      :   Tuple			      =  normalize_vars
       UpperCamelCase      :   List[Any]			      =  True





      def 			__UpperCamelCase(					self			,				A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
       UpperCamelCase      :   Optional[Any]			      =  torch.from_numpy(A_  ).unsqueeze(0  )
       UpperCamelCase      :   Dict			      =  ta_kaldi.fbank(A_			,				num_mel_bins=self.num_mel_bins			,				sample_frequency=self.sampling_rate  )
       return features.numpy()





      @staticmethod
      def 			__UpperCamelCase(					A_			,				A_			,				A_ = True			,				A_ = True			,				A_ = 0.0			,				):
       '''simple docstring'''




       if normalize_means:
        UpperCamelCase      :   str			      =  x[:input_length].mean(axis=0  )
        UpperCamelCase      :   Any			      =  np.subtract(A_			,				A_  )
       if normalize_vars:
        UpperCamelCase      :   List[str]			      =  x[:input_length].std(axis=0  )
        UpperCamelCase      :   Optional[int]			      =  np.divide(A_			,				A_  )

       if input_length < x.shape[0]:
        UpperCamelCase      :   Dict			      =  padding_value

       # make sure array is in float32
       UpperCamelCase      :   Union[str, Any]			      =  x.astype(np.floataa  )

       return x





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




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





      def __call__(					self			,				A_			,				A_ = False			,				A_ = None			,				A_ = False			,				A_ = None			,				A_ = None			,				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."  )

       UpperCamelCase      :   str			      =  isinstance(A_			,				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}"""  )
       UpperCamelCase      :   List[Any]			      =  is_batched_numpy or (
           isinstance(A_			,				(list, tuple)  ) and (isinstance(raw_speech[0]			,				(np.ndarray, tuple, list)  ))
       )

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

       # always return batch
       if not is_batched:
        UpperCamelCase      :   Optional[int]			      =  [raw_speech]

       # extract fbank features
       UpperCamelCase      :   Optional[int]			      =  [self._extract_fbank_features(A_  ) for waveform in raw_speech]

       # convert into correct format for padding
       UpperCamelCase      :   List[Any]			      =  BatchFeature({"input_features": features}  )

       UpperCamelCase      :   Any			      =  self.pad(
           A_			,				padding=A_			,				max_length=A_			,				truncation=A_			,				pad_to_multiple_of=A_			,				return_attention_mask=A_			,				**A_			,				)

       # make sure list is in array format
       UpperCamelCase      :   Optional[int]			      =  padded_inputs.get("input_features"  )
       if isinstance(input_features[0]			,				A_  ):
        UpperCamelCase      :   str			      =  [np.asarray(A_			,				dtype=np.floataa  ) for feature in input_features]

       UpperCamelCase      :   Optional[int]			      =  padded_inputs.get("attention_mask"  )
       if attention_mask is not None:
        UpperCamelCase      :   Optional[int]			      =  [np.asarray(A_			,				dtype=np.intaa  ) for array in attention_mask]

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

       if return_tensors is not None:
        UpperCamelCase      :   List[str]			      =  padded_inputs.convert_to_tensors(A_  )

       return padded_inputs
