
'''simple docstring'''

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


lowerCAmelCase_			:			Union[str, Any]			   =     logging.get_logger(__name__)







class 	__lowerCAmelCase    (  __a				):
     snake_case   :    List[Any]							      =      ["""input_values""", """attention_mask"""]



     def __init__(self						,	lowerCAmelCase__=1						,	lowerCAmelCase__=1_6_0_0_0						,	lowerCAmelCase__=0.0						,	lowerCAmelCase__=False						,	lowerCAmelCase__=True						,	**lowerCAmelCase__						,	):
          super().__init__(feature_size=lowerCAmelCase__						,	sampling_rate=lowerCAmelCase__						,	padding_value=lowerCAmelCase__						,	**lowerCAmelCase__	)
          _UpperCAmelCase					:  List[Any]         =    return_attention_mask
          _UpperCAmelCase					:  List[Any]         =    do_normalize



     @staticmethod
     def     snake_case_ (lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = 0.0	):
          if attention_mask is not None:
               _UpperCAmelCase					:  int         =    np.array(lowerCAmelCase__						,	np.intaa	)
               _UpperCAmelCase					:  List[Any]         =    []

               for vector, length in zip(lowerCAmelCase__						,	attention_mask.sum(-1	)	):
                    _UpperCAmelCase					:  Tuple         =    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7	)
                    if length < normed_slice.shape[0]:
                         _UpperCAmelCase					:  int         =    padding_value

                    normed_input_values.append(lowerCAmelCase__	)
          else:
               _UpperCAmelCase					:  Optional[int]         =    [(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

          return normed_input_values



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

          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."""	)

          _UpperCAmelCase					:  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}"	)
          _UpperCAmelCase					:  List[str]         =    is_batched_numpy or (
              isinstance(lowerCAmelCase__						,	(list, tuple)	) and (isinstance(raw_speech[0]						,	(np.ndarray, tuple, list)	))
          )

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

          # convert into correct format for padding
          _UpperCAmelCase					:  Optional[Any]         =    BatchFeature({"""input_values""": raw_speech}	)

          _UpperCAmelCase					:  Dict         =    self.pad(
              lowerCAmelCase__						,	padding=lowerCAmelCase__						,	max_length=lowerCAmelCase__						,	truncation=lowerCAmelCase__						,	pad_to_multiple_of=lowerCAmelCase__						,	return_attention_mask=lowerCAmelCase__						,	)

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

          # convert attention_mask to correct format
          _UpperCAmelCase					:  Any         =    padded_inputs.get("""attention_mask"""	)
          if attention_mask is not None:
               _UpperCAmelCase					:  int         =    [np.asarray(lowerCAmelCase__						,	dtype=np.intaa	) for array in attention_mask]

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

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

          return padded_inputs








def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						float       ,     __UpperCamelCase			:						float      )  ->							float:
    return a + b


if __name__ == "__main__":
 _lowerCamelCase			 =						5
 _lowerCamelCase			 =						6
 print(F"The sum of {a} + {b} is {add(a, b)}")





