





'''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 PaddingStrategy, TensorType, logging


lowercase__       : str         =       logging.get_logger(__name__)


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			['''input_features''', '''attention_mask''']


     def __init__(		self		,      _UpperCAmelCase=80		,      _UpperCAmelCase=1_6000		,      _UpperCAmelCase=80		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(feature_size=_UpperCAmelCase		,      sampling_rate=_UpperCAmelCase		,      padding_value=_UpperCAmelCase		,      **_UpperCAmelCase)
          __A		:					str						=			num_mel_bins
          __A		:					Optional[Any]						=			do_ceptral_normalize
          __A		:					Optional[Any]						=			normalize_means
          __A		:					int						=			normalize_vars
          __A		:					int						=			True


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					Any						=			waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
          __A		:					Union[str, Any]						=			torch.from_numpy(_UpperCAmelCase).unsqueeze(0)
          __A		:					List[str]						=			ta_kaldi.fbank(_UpperCAmelCase		,      num_mel_bins=self.num_mel_bins		,      sample_frequency=self.sampling_rate)
          return features.numpy()


     @staticmethod
     def 				SCREAMING_SNAKE_CASE    (		_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = True		,      _UpperCAmelCase = True		,      _UpperCAmelCase = 0.0		,      ):

          '''simple docstring'''






          if normalize_means:
               __A		:					Any						=			x[:input_length].mean(axis=0)
               __A		:					Dict						=			np.subtract(_UpperCAmelCase		,      _UpperCAmelCase)
          if normalize_vars:
               __A		:					Optional[Any]						=			x[:input_length].std(axis=0)
               __A		:					List[Any]						=			np.divide(_UpperCAmelCase		,      _UpperCAmelCase)

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

          # make sure array is in float32
          __A		:					List[str]						=			x.astype(np.floataa)

          return x


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






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


     def __call__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase = False		,      _UpperCAmelCase = None		,      _UpperCAmelCase = False		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''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.')

          __A		:					Tuple						=			isinstance(_UpperCAmelCase		,      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}')
          __A		:					Any						=			is_batched_numpy or (
              isinstance(_UpperCAmelCase		,      (list, tuple)) and (isinstance(raw_speech[0]		,      (np.ndarray, tuple, list)))
          )

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

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

          # extract fbank features
          __A		:					Union[str, Any]						=			[self._extract_fbank_features(_UpperCAmelCase) for waveform in raw_speech]

          # convert into correct format for padding
          __A		:					int						=			BatchFeature({'input_features': features})

          __A		:					List[Any]						=			self.pad(
              _UpperCAmelCase		,      padding=_UpperCAmelCase		,      max_length=_UpperCAmelCase		,      truncation=_UpperCAmelCase		,      pad_to_multiple_of=_UpperCAmelCase		,      return_attention_mask=_UpperCAmelCase		,      **_UpperCAmelCase		,      )

          # make sure list is in array format
          __A		:					Optional[int]						=			padded_inputs.get('input_features')
          if isinstance(input_features[0]		,      _UpperCAmelCase):
               __A		:					Tuple						=			[np.asarray(_UpperCAmelCase		,      dtype=np.floataa) for feature in input_features]

          __A		:					Any						=			padded_inputs.get('attention_mask')
          if attention_mask is not None:
               __A		:					Union[str, Any]						=			[np.asarray(_UpperCAmelCase		,      dtype=np.intaa) for array in attention_mask]

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

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

          return padded_inputs






'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					int					,				__snake_case    :					int     )	->	float:
     __A		:					Dict						=			(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total






def 			_lowerCAmelCase     (    )	->	Union[str, Any]:
     print(sum_of_series(1					,				1					,				10     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()