





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


_snake_case           =			logging.get_logger(__name__)





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      ['input_features', 'attention_mask']







  def __init__(      self  :Union[str, Any]   ,							_lowercase  :List[str]=80   ,							_lowercase  :str=1_60_00   ,							_lowercase  :Tuple=80   ,							_lowercase  :int=0.0   ,							_lowercase  :Union[str, Any]=True   ,							_lowercase  :Optional[Any]=True   ,							_lowercase  :Optional[Any]=True   ,							**_lowercase  :Tuple   ,							):






     '''simple docstring'''





     super().__init__(feature_size=_lowercase   ,							sampling_rate=_lowercase   ,							padding_value=_lowercase   ,							**_lowercase )
     lowercase__			       =  num_mel_bins
     lowercase__			       =  do_ceptral_normalize
     lowercase__			       =  normalize_means
     lowercase__			       =  normalize_vars
     lowercase__			       =  True







  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :np.ndarray   ,							):






     '''simple docstring'''





     lowercase__			       =  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
     lowercase__			       =  torch.from_numpy(_lowercase ).unsqueeze(0 )
     lowercase__			       =  ta_kaldi.fbank(_lowercase   ,							num_mel_bins=self.num_mel_bins   ,							sample_frequency=self.sampling_rate )
     return features.numpy()







  @staticmethod
  def   UpperCAmelCase					(      _lowercase  :np.ndarray   ,							_lowercase  :int   ,							_lowercase  :Optional[bool] = True   ,							_lowercase  :Optional[bool] = True   ,							_lowercase  :float = 0.0   ,							):






     '''simple docstring'''





     if normalize_means:
        lowercase__			       =  x[:input_length].mean(axis=0 )
        lowercase__			       =  np.subtract(_lowercase   ,							_lowercase )
     if normalize_vars:
        lowercase__			       =  x[:input_length].std(axis=0 )
        lowercase__			       =  np.divide(_lowercase   ,							_lowercase )

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

     # make sure array is in float32
     lowercase__			       =  x.astype(np.floataa )

     return x







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :List[np.ndarray]   ,							_lowercase  :Optional[np.ndarray] = None ):






     '''simple docstring'''





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







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






     '''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." )

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

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

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

     # extract fbank features
     lowercase__			       =  [self._extract_fbank_features(_lowercase ) for waveform in raw_speech]

     # convert into correct format for padding
     lowercase__			       =  BatchFeature({"input_features": features} )

     lowercase__			       =  self.pad(
         _lowercase   ,							padding=_lowercase   ,							max_length=_lowercase   ,							truncation=_lowercase   ,							pad_to_multiple_of=_lowercase   ,							return_attention_mask=_lowercase   ,							**_lowercase   ,							)

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

     lowercase__			       =  padded_inputs.get("attention_mask" )
     if attention_mask is not None:
        lowercase__			       =  [np.asarray(_lowercase   ,							dtype=np.intaa ) for array in attention_mask]

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

     if return_tensors is not None:
        lowercase__			       =  padded_inputs.convert_to_tensors(_lowercase )

     return padded_inputs













def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	):
   if height >= 1:
      move_tower(height - 1  , __magic_name__  , __magic_name__  , __magic_name__	)
      move_disk(__magic_name__  , __magic_name__	)
      move_tower(height - 1  , __magic_name__  , __magic_name__  , __magic_name__	)


def _A       (   __magic_name__  , __magic_name__	):
   print("moving disk from"  , __magic_name__  , "to"  , __magic_name__	)


def _A       (   ):
   lowercase__			       =  int(input("Height of hanoi: "	).strip()	)
   move_tower(__magic_name__  , "A"  , "B"  , "C"	)


if __name__ == "__main__":
  main()






