code: 
  
  
  
  
  
  
  """simple docstring"""
  import warnings
  
  from ...utils import logging
  from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
  
  snake_case   :   Tuple    		=		logging.get_logger(__name__)
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Union[str, Any]							,						*UpperCamelCase_   :  Tuple							,						**UpperCamelCase_   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             warnings.warn(
                 'The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                 ' Please use MobileNetV1ImageProcessor instead.'							,						UpperCamelCase_							,						)
             super().__init__(*UpperCamelCase_							,						**UpperCamelCase_   )
style_context: 
  
  
  
  
  
  
  """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   :   Tuple    		=		logging.get_logger(__name__)
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   ["""input_values""", """attention_mask"""]
  
  
  
  
  
  
  
      def __init__(		self   :  Union[str, Any]							,						UpperCamelCase_   :  int=1							,						UpperCamelCase_   :  Optional[int]=1_6_0_0_0							,						UpperCamelCase_   :  List[str]=0.0							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  Tuple=True							,						**UpperCamelCase_   :  Tuple							,						):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__(feature_size=UpperCamelCase_							,						sampling_rate=UpperCamelCase_							,						padding_value=UpperCamelCase_							,						**UpperCamelCase_   )
             __magic_name__										=						return_attention_mask
             __magic_name__										=						do_normalize
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  List[np.ndarray]							,						UpperCamelCase_   :  List[np.ndarray]							,						UpperCamelCase_   :  float = 0.0   ):
  
  
  
  
  
             '''simple docstring'''
  
             if attention_mask is not None:
                    __magic_name__										=						np.array(UpperCamelCase_							,						np.intaa   )
                    __magic_name__										=						[]
  
                    for vector, length in zip(UpperCamelCase_							,						attention_mask.sum(-1   )   ):
                           __magic_name__										=						(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7   )
                           if length < normed_slice.shape[0]:
                                  __magic_name__										=						padding_value
  
                           normed_input_values.append(UpperCamelCase_   )
             else:
                    __magic_name__										=						[(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]
  
             return normed_input_values
  
  
  
  
  
  
  
      def __call__(		self   :  Any							,						UpperCamelCase_   :  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,						UpperCamelCase_   :  Union[bool, str, PaddingStrategy] = False							,						UpperCamelCase_   :  Optional[int] = None							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  Optional[int] = None							,						UpperCamelCase_   :  Optional[bool] = None							,						UpperCamelCase_   :  Optional[Union[str, TensorType]] = None							,						UpperCamelCase_   :  Optional[int] = None							,						**UpperCamelCase_   :  Any							,						):
  
  
  
  
  
             '''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.'   )
  
             __magic_name__										=						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}"""   )
             __magic_name__										=						is_batched_numpy or (
                 isinstance(UpperCamelCase_							,						(list, tuple)   ) and (isinstance(raw_speech[0]							,						(np.ndarray, tuple, list)   ))
             )
  
             # always return batch
             if not is_batched:
                    __magic_name__										=						[raw_speech]
  
             # convert into correct format for padding
             __magic_name__										=						BatchFeature({'input_values': raw_speech}   )
  
             __magic_name__										=						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
             __magic_name__										=						padded_inputs['input_values']
             if not isinstance(input_values[0]							,						np.ndarray   ):
                    __magic_name__										=						[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   )
             ):
                    __magic_name__										=						[array.astype(np.floataa   ) for array in input_values]
             elif isinstance(UpperCamelCase_							,						np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
                    __magic_name__										=						input_values.astype(np.floataa   )
  
             # convert attention_mask to correct format
             __magic_name__										=						padded_inputs.get('attention_mask'   )
             if attention_mask is not None:
                    __magic_name__										=						[np.asarray(UpperCamelCase_							,						dtype=np.intaa   ) for array in attention_mask]
  
             # zero-mean and unit-variance normalization
             if self.do_normalize:
                    __magic_name__										=						(
                        attention_mask
                        if self._get_padding_strategies(UpperCamelCase_							,						max_length=UpperCamelCase_   ) is not PaddingStrategy.DO_NOT_PAD
                        else None
                    )
                    __magic_name__										=						self.zero_mean_unit_var_norm(
                        padded_inputs['input_values']							,						attention_mask=UpperCamelCase_							,						padding_value=self.padding_value   )
  
             if return_tensors is not None:
                    __magic_name__										=						padded_inputs.convert_to_tensors(UpperCamelCase_   )
  
             return padded_inputs