


"""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


__snake_case												=							logging.get_logger(__name__)


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						str     =		['input_values', 'attention_mask']


     def __init__(   self     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=16000     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					**__UpperCAmelCase     ,					)							->				Optional[Any]:
          super().__init__(feature_size=__UpperCAmelCase     ,					sampling_rate=__UpperCAmelCase     ,					padding_value=__UpperCAmelCase     ,					**__UpperCAmelCase				)
          _a     						=					return_attention_mask
          _a     						=					do_normalize


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = 0.0				)							->				List[np.ndarray]:
          if attention_mask is not None:
               _a     						=					np.array(__UpperCAmelCase     ,					np.intaa				)
               _a     						=					[]

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

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

          return normed_input_values





     def __call__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					)							->				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.'''				)

          _a     						=					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     						=					is_batched_numpy or (
              isinstance(__UpperCAmelCase     ,					(list, tuple)				) and (isinstance(raw_speech[0]     ,					(np.ndarray, tuple, list)				))
          )

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

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

          _a     						=					self.pad(
              __UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					max_length=__UpperCAmelCase     ,					truncation=__UpperCAmelCase     ,					pad_to_multiple_of=__UpperCAmelCase     ,					return_attention_mask=__UpperCAmelCase     ,					)

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

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

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

          if return_tensors is not None:
               _a     						=					padded_inputs.convert_to_tensors(__UpperCAmelCase				)

          return padded_inputs



"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str							):






     """simple docstring"""

     return "".join(chr(ord(_lowerCAmelCase							) + 32							) if '''A''' <= char <= '''Z''' else char for char in word							)


if __name__ == "__main__":
   from doctest import testmod

   testmod()