

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase__					: Any								=      logging.get_logger(__name__)



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		Union[str, Any]  	=					["input_values", "attention_mask"]

  def __init__(   self  ,						_A=1  ,						_A=16000  ,						_A=0.0  ,						_A=False  ,						_A=True  ,						**_A  ,						):
      super().__init__(feature_size=__A  ,						sampling_rate=__A  ,						padding_value=__A  ,						**__A)
      SCREAMING_SNAKE_CASE_													=  return_attention_mask
      SCREAMING_SNAKE_CASE_													=  do_normalize

  @staticmethod
  def   lowerCAmelCase__       (   _A  ,						_A  ,						_A = 0.0):





      if attention_mask is not None:
          SCREAMING_SNAKE_CASE_													=  np.array(__A  ,						np.intaa)
          SCREAMING_SNAKE_CASE_													=  []

          for vector, length in zip(__A  ,						attention_mask.sum(-1)):
              SCREAMING_SNAKE_CASE_													=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
              if length < normed_slice.shape[0]:
                  SCREAMING_SNAKE_CASE_													=  padding_value

              normed_input_values.append(__A)
      else:
          SCREAMING_SNAKE_CASE_													=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

      return normed_input_values


  def __call__(   self  ,						_A  ,						_A = False  ,						_A = None  ,						_A = False  ,						_A = None  ,						_A = None  ,						_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.')

      SCREAMING_SNAKE_CASE_													=  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}""")
      SCREAMING_SNAKE_CASE_													=  is_batched_numpy or (
          isinstance(__A  ,						(list, tuple)) and (isinstance(raw_speech[0]  ,						(np.ndarray, tuple, list)))
      )

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

      # convert into correct format for padding
      SCREAMING_SNAKE_CASE_													=  BatchFeature({'input_values': raw_speech})

      SCREAMING_SNAKE_CASE_													=  self.pad(
          __A  ,						padding=__A  ,						max_length=__A  ,						truncation=__A  ,						pad_to_multiple_of=__A  ,						return_attention_mask=__A  ,						)

      # convert input values to correct format
      SCREAMING_SNAKE_CASE_													=  padded_inputs["input_values"]
      if not isinstance(input_values[0]  ,						np.ndarray):
          SCREAMING_SNAKE_CASE_													=  [np.asarray(__A  ,						dtype=np.floataa) for array in input_values]
      elif (
          not isinstance(__A  ,						np.ndarray)
          and isinstance(input_values[0]  ,						np.ndarray)
          and input_values[0].dtype is np.dtype(np.floataa)
      ):
          SCREAMING_SNAKE_CASE_													=  [array.astype(np.floataa) for array in input_values]
      elif isinstance(__A  ,						np.ndarray) and input_values.dtype is np.dtype(np.floataa):
          SCREAMING_SNAKE_CASE_													=  input_values.astype(np.floataa)

      # convert attention_mask to correct format
      SCREAMING_SNAKE_CASE_													=  padded_inputs.get('attention_mask')
      if attention_mask is not None:
          SCREAMING_SNAKE_CASE_													=  [np.asarray(__A  ,						dtype=np.intaa) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if self.do_normalize:
          SCREAMING_SNAKE_CASE_													=  (
              attention_mask
              if self._get_padding_strategies(__A  ,						max_length=__A) is not PaddingStrategy.DO_NOT_PAD
              else None
          )
          SCREAMING_SNAKE_CASE_													=  self.zero_mean_unit_var_norm(
              padded_inputs['input_values']  ,						attention_mask=__A  ,						padding_value=self.padding_value)

      if return_tensors is not None:
          SCREAMING_SNAKE_CASE_													=  padded_inputs.convert_to_tensors(__A)

      return padded_inputs





def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						float	,				_SCREAMING_SNAKE_CASE   :						float    ):





    """simple docstring"""


    return a + b


if __name__ == "__main__":
       UpperCamelCase__					: Union[str, Any]								=      5
       UpperCamelCase__					: List[Any]								=      6
       print(F'The sum of {a} + {b} is {add(a, b)}')


