
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


_UpperCAmelCase    			=					logging.get_logger(__name__)

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = ["input_values", "attention_mask"]


  def __init__(					self	,		a__=1	,		a__=1_60_00	,		a__=1_28	,		a__=10_24	,		a__=0.0	,		a__=True	,		a__=-4.2677393	,		a__=4.5689974	,		a__=False	,		**a__	,		):
    super().__init__(feature_size=a__	,		sampling_rate=a__	,		padding_value=a__	,		**a__		)
    _lowerCamelCase      =						num_mel_bins
    _lowerCamelCase      =						max_length
    _lowerCamelCase      =						do_normalize
    _lowerCamelCase      =						mean
    _lowerCamelCase      =						std
    _lowerCamelCase      =						return_attention_mask


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		):
    _lowerCamelCase      =						torch.from_numpy(a__		).unsqueeze(0		)
    _lowerCamelCase      =						ta_kaldi.fbank(
        a__	,		htk_compat=a__	,		sample_frequency=self.sampling_rate	,		use_energy=a__	,		window_type='''hanning'''	,		num_mel_bins=self.num_mel_bins	,		dither=0.0	,		frame_shift=10	,		)

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

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

    _lowerCamelCase      =						fbank.numpy()

    return fbank


  def     _UpperCAmelCase  (					self	,		a__		):
    return (input_values - (self.mean)) / (self.std * 2)


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

    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.'''		)

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

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

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

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

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

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

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

    if return_tensors is not None:
      _lowerCamelCase      =						padded_inputs.convert_to_tensors(a__		)

    return padded_inputs






class 		__magic_name__				:


  """simple docstring"""





  class 		__magic_name__				:


    """simple docstring"""





    _UpperCamelCase						 = "_prev", "_data", "_next"


    def __init__(					self	,		a__	,		a__	,		a__		):
      _lowerCamelCase      =						link_p
      _lowerCamelCase      =						element
      _lowerCamelCase      =						link_n


    def     _UpperCAmelCase  (					self		):
      return (
          f''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
      )







  def __init__(					self		):
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						self._trailer
    _lowerCamelCase      =						self._header
    _lowerCamelCase      =						0


  def __len__(					self		):
    return self._size


  def     _UpperCAmelCase  (					self		):
    return self.__len__() == 0


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__		):
    # Create new_node by setting it's prev.link -> header
    # setting it's next.link -> trailer
    _lowerCamelCase      =						self._Node(a__	,		a__	,		a__		)
    _lowerCamelCase      =						new_node
    _lowerCamelCase      =						new_node
    self._size += 1
    return self


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						node._prev
    _lowerCamelCase      =						node._next

    _lowerCamelCase      =						successor
    _lowerCamelCase      =						predecessor
    self._size -= 1
    _lowerCamelCase      =						node._data
    _lowerCamelCase      =						_lowerCamelCase      =						_lowerCamelCase      =						None
    del node
    return temp







class 		__magic_name__				( _DoublyLinkedBase					):


  """simple docstring"""





  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise Exception('''List is empty'''		)
    return self._header._next._data


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise Exception('''List is empty'''		)
    return self._trailer._prev._data


  def     _UpperCAmelCase  (					self	,		a__		):
    return self._insert(self._header	,		a__	,		self._header._next		)


  def     _UpperCAmelCase  (					self	,		a__		):
    return self._insert(self._trailer._prev	,		a__	,		self._trailer		)


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise IndexError('''remove_first from empty list'''		)
    return self._delete(self._header._next		)


  def     _UpperCAmelCase  (					self		):
    if self.is_empty():
      raise IndexError('''remove_first from empty list'''		)
    return self._delete(self._trailer._prev		)




