'''simple docstring'''





from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


lowercase_     :   List[Any] 				=				logging.get_logger(__name__)






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      ["input_features", "attention_mask"]
    def __init__(  self , lowerCAmelCase=80 , lowerCAmelCase=16000 , lowerCAmelCase=0.0 , lowerCAmelCase=10 , lowerCAmelCase=25 , lowerCAmelCase="hamming_window" , lowerCAmelCase=32768.0 , lowerCAmelCase=0.97 , lowerCAmelCase=1.0 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=False , **lowerCAmelCase , )       ->					Any:
     super().__init__(feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   str=      feature_size
     SCREAMING_SNAKE_CASE__:   Dict=      sampling_rate
     SCREAMING_SNAKE_CASE__:   Optional[int]=      padding_value
     SCREAMING_SNAKE_CASE__:   str=      hop_length
     SCREAMING_SNAKE_CASE__:   List[Any]=      win_length
     SCREAMING_SNAKE_CASE__:   Optional[int]=      frame_signal_scale
     SCREAMING_SNAKE_CASE__:   Any=      preemphasis_coeff
     SCREAMING_SNAKE_CASE__:   List[str]=      mel_floor
     SCREAMING_SNAKE_CASE__:   int=      normalize_means
     SCREAMING_SNAKE_CASE__:   List[str]=      normalize_vars
     SCREAMING_SNAKE_CASE__:   str=      win_function
     SCREAMING_SNAKE_CASE__:   Optional[int]=      return_attention_mask

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      win_length * sampling_rate // 1000
     SCREAMING_SNAKE_CASE__:   Any=      hop_length * sampling_rate // 1000

     SCREAMING_SNAKE_CASE__:   List[str]=      optimal_fft_length(self.sample_size   )
     SCREAMING_SNAKE_CASE__:   Dict=      (self.n_fft // 2) + 1
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					str:







     if self.win_function == "hamming_window":
      SCREAMING_SNAKE_CASE__:   Any=      window_function(window_length=self.sample_size , name=self.win_function , periodic=__lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      window_function(window_length=self.sample_size , name=self.win_function   )

     SCREAMING_SNAKE_CASE__:   Tuple=      mel_filter_bank(
         num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      spectrogram(
         one_waveform * self.frame_signal_scale , window=__lowerCAmelCase , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=__lowerCAmelCase , preemphasis=self.preemphasis_coeff , mel_filters=__lowerCAmelCase , mel_floor=self.mel_floor , log_mel='''log''' , )
     return msfc_features.T
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					List[Any]:







     if self.normalize_means:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      x[:input_length].mean(axis=0   )
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      np.subtract(__lowerCAmelCase , __lowerCAmelCase   )
     if self.normalize_vars:
      SCREAMING_SNAKE_CASE__:   str=      x[:input_length].std(axis=0   )
      SCREAMING_SNAKE_CASE__:   Optional[int]=      np.divide(__lowerCAmelCase , __lowerCAmelCase   )

     if input_length < x.shape[0]:
      SCREAMING_SNAKE_CASE__:   List[str]=      padding_value

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

     return x
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Any=      attention_mask.sum(-1   ) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [self._normalize_one(__lowerCAmelCase , __lowerCAmelCase , self.padding_value   ) for x, n in zip(__lowerCAmelCase , __lowerCAmelCase   )]



    def __call__(  self , lowerCAmelCase , lowerCAmelCase = False , lowerCAmelCase = None , lowerCAmelCase = False , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = 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.'''   )

     SCREAMING_SNAKE_CASE__:   List[str]=      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}'   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      is_batched_numpy or (
         isinstance(__lowerCAmelCase , (list, tuple)   ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)   ))
     )

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

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

     # extract fbank features
     SCREAMING_SNAKE_CASE__:   Dict=      [self._extract_mfsc_features(__lowerCAmelCase   ) for one_waveform in raw_speech]

     # convert into correct format for padding
     SCREAMING_SNAKE_CASE__:   List[str]=      BatchFeature({'''input_features''': features}   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.pad(
         __lowerCAmelCase , padding=__lowerCAmelCase , max_length=__lowerCAmelCase , truncation=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , **__lowerCAmelCase , )
     # make sure list is in array format
     SCREAMING_SNAKE_CASE__:   Any=      padded_inputs.get('''input_features'''   )
     if isinstance(input_features[0] , __lowerCAmelCase   ):
      SCREAMING_SNAKE_CASE__:   str=      [np.asarray(__lowerCAmelCase , dtype=np.floataa   ) for feature in input_features]

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      padded_inputs.get('''attention_mask'''   )
     if attention_mask is not None:
      SCREAMING_SNAKE_CASE__:   Tuple=      [np.asarray(__lowerCAmelCase , dtype=np.intaa   ) for array in attention_mask]

     if self.normalize_means or self.normalize_vars:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      (
          np.array(__lowerCAmelCase , dtype=np.intaa   )
          if self._get_padding_strategies(__lowerCAmelCase , max_length=__lowerCAmelCase   ) is not PaddingStrategy.DO_NOT_PAD
          and padding
          else None
      )
      SCREAMING_SNAKE_CASE__:   List[Any]=      self.normalize(
          padded_inputs['''input_features'''] , attention_mask=__lowerCAmelCase   )

     if return_tensors is not None:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      padded_inputs.convert_to_tensors(__lowerCAmelCase   )

     return padded_inputs


def 			A__      (	snake_case_ :		str      ):
 if not head:
  return True
 # split the list to two parts
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      head.next, head
 while fast and fast.next:
  SCREAMING_SNAKE_CASE__:   Dict=      fast.next.next
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      slow.next
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      slow.next
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      None  # Don't forget here! But forget still works!
 # reverse the second part
 SCREAMING_SNAKE_CASE__:   Optional[int]=      None
 while second:
  SCREAMING_SNAKE_CASE__:   Any=      second.next
  SCREAMING_SNAKE_CASE__:   int=      node
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      second
  SCREAMING_SNAKE_CASE__:   Any=      nxt
 # compare two parts
 # second part has the same or one less node
 while node:
  if node.val != head.val:
   return False
  SCREAMING_SNAKE_CASE__:   Tuple=      node.next
  SCREAMING_SNAKE_CASE__:   Optional[int]=      head.next
 return True







def 			A__      (	snake_case_ :		Optional[Any]      ):
 if not head or not head.next:
  return True

 # 1. Get the midpoint (slow)
 SCREAMING_SNAKE_CASE__:   List[Any]=      head
 while fast and fast.next:
  SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Any=      fast.next.next, slow.next

 # 2. Push the second half into the stack
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      [slow.val]
 while slow.next:
  SCREAMING_SNAKE_CASE__:   Optional[int]=      slow.next
  stack.append(slow.val      )

 # 3. Comparison
 while stack:
  if stack.pop() != cur.val:
   return False
  SCREAMING_SNAKE_CASE__:   Tuple=      cur.next

 return True







def 			A__      (	snake_case_ :		Any      ):
 if not head or not head.next:
  return True
 SCREAMING_SNAKE_CASE__:   Optional[int]=      {}
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      0
 while head:
  if head.val in d:
   d[head.val].append(snake_case_      )
  else:
   SCREAMING_SNAKE_CASE__:   Optional[int]=      [pos]
  SCREAMING_SNAKE_CASE__:   Dict=      head.next
  pos += 1
 SCREAMING_SNAKE_CASE__:   Dict=      pos - 1
 SCREAMING_SNAKE_CASE__:   str=      0
 for v in d.values():
  if len(snake_case_      ) % 2 != 0:
   middle += 1
  else:
   SCREAMING_SNAKE_CASE__:   List[Any]=      0
   for i in range(0       ,				len(snake_case_      )      ):
    if v[i] + v[len(snake_case_      ) - 1 - step] != checksum:
     return False
    step += 1
  if middle > 1:
   return False
 return True

