






"""simple docstring"""





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


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






class 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					Optional[Any]						=			["""input_values""", """attention_mask"""]






    def __init__(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]=1	,						__SCREAMING_SNAKE_CASE					:							Dict=16000	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=128	,						__SCREAMING_SNAKE_CASE					:							Dict=1024	,						__SCREAMING_SNAKE_CASE					:							Tuple=0.0	,						__SCREAMING_SNAKE_CASE					:							Tuple=True	,						__SCREAMING_SNAKE_CASE					:							str=-4.2_677_393	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]=4.5_689_974	,						__SCREAMING_SNAKE_CASE					:							Tuple=False	,						**__SCREAMING_SNAKE_CASE					:							str	,						)					->		int:
      super().__init__(feature_size=__SCREAMING_SNAKE_CASE	,						sampling_rate=__SCREAMING_SNAKE_CASE	,						padding_value=__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						num_mel_bins
      lowerCamelCase_   					=						max_length
      lowerCamelCase_   					=						do_normalize
      lowerCamelCase_   					=						mean
      lowerCamelCase_   					=						std
      lowerCamelCase_   					=						return_attention_mask






    def        UpperCamelCase	(	self					:							Dict	,						__SCREAMING_SNAKE_CASE					:							np.ndarray	,						__SCREAMING_SNAKE_CASE					:							int	,						)					->		np.ndarray:
      lowerCamelCase_   					=						torch.from_numpy(__SCREAMING_SNAKE_CASE ).unsqueeze(0 )
      lowerCamelCase_   					=						ta_kaldi.fbank(
          __SCREAMING_SNAKE_CASE	,						htk_compat=__SCREAMING_SNAKE_CASE	,						sample_frequency=self.sampling_rate	,						use_energy=__SCREAMING_SNAKE_CASE	,						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(__SCREAMING_SNAKE_CASE )
      elif difference < 0:
        lowerCamelCase_   					=						fbank[0:max_length, :]

      lowerCamelCase_   					=						fbank.numpy()

      return fbank






    def        UpperCamelCase	(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							np.ndarray )					->		np.ndarray:
      return (input_values - (self.mean)) / (self.std * 2)






    def __call__(	self					:							Dict	,						__SCREAMING_SNAKE_CASE					:							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,						__SCREAMING_SNAKE_CASE					:							Optional[int] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[Union[str, TensorType]] = None	,						**__SCREAMING_SNAKE_CASE					:							Optional[Any]	,						)					->		BatchFeature:

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

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

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

      if return_tensors is not None:
        lowerCamelCase_   					=						padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE )

      return padded_inputs












"""simple docstring"""





import numpy as np







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Tuple			)				->    str:
  lowerCamelCase_   					=						n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  lowerCamelCase_   					=						np.floor(_lowerCamelCase			)
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  lowerCamelCase_   					=						int(n_draws - floored.sum()			)
  if need_to_add > 0:
    lowerCamelCase_   					=						continuous - floored
    lowerCamelCase_   					=						np.sort(np.unique(_lowerCamelCase			)			)[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((lowerCamelCase_)					,     )   					=						np.where(remainder == value			)
      # if we need_to_add less than what's in inds
      # we draw randomly from them.
      # if we need to add more, we add them all and
      # go to the next value
      lowerCamelCase_   					=						min(len(_lowerCamelCase			)		,    _lowerCamelCase			)
      lowerCamelCase_   					=						rng.choice(_lowerCamelCase		,    size=_lowerCamelCase		,    replace=_lowerCamelCase			)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Optional[int]=10			)				->    Any:
  lowerCamelCase_					,     lowerCamelCase_   					=						np.unique(_lowerCamelCase		,    return_inverse=_lowerCamelCase			)
  lowerCamelCase_   					=						classes.shape[0]
  lowerCamelCase_   					=						np.bincount(_lowerCamelCase			)
  if np.min(_lowerCamelCase			) < 2:
    raise ValueError('Minimum class count error'			)
  if n_train < n_classes:
    raise ValueError(
        'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)			)
  if n_test < n_classes:
    raise ValueError(
        'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)			)
  lowerCamelCase_   					=						np.split(np.argsort(_lowerCamelCase		,    kind='mergesort'			)		,    np.cumsum(_lowerCamelCase			)[:-1]			)
  for _ in range(_lowerCamelCase			):
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						class_counts - n_i
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]

    for i in range(_lowerCamelCase			):
      lowerCamelCase_   					=						rng.permutation(class_counts[i]			)
      lowerCamelCase_   					=						class_indices[i].take(_lowerCamelCase		,    mode='clip'			)
      train.extend(perm_indices_class_i[: n_i[i]]			)
      test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)

    yield train, test




