'''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


a=	logging.get_logger(__name__)






class       __lowercase		(    A_    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    ["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=_A  ,      sampling_rate=_A  ,      padding_value=_A  ,      **_A     )
         __UpperCamelCase						:						Optional[int]		     =	return_attention_mask
         __UpperCamelCase						:						Union[str, Any]		     =	do_normalize







     @staticmethod
     def lowerCAmelCase  (		_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = 0.0     ):
         if attention_mask is not None:
             __UpperCamelCase						:						Any		     =	np.array(_A  ,      np.intaa     )
             __UpperCamelCase						:						Dict		     =	[]

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

                 normed_input_values.append(_A     )
         else:
             __UpperCamelCase						:						Optional[Any]		     =	[(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						:						Optional[int]		     =	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}"""     )
         __UpperCamelCase						:						Tuple		     =	is_batched_numpy or (
             isinstance(_A  ,      (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						:						List[Any]		     =	BatchFeature({'input_values': raw_speech}     )

         __UpperCamelCase						:						Any		     =	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
         __UpperCamelCase						:						Dict		     =	padded_inputs['input_values']
         if not isinstance(input_values[0]  ,      np.ndarray     ):
             __UpperCamelCase						:						List[str]		     =	[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     )
         ):
             __UpperCamelCase						:						str		     =	[array.astype(np.floataa     ) for array in input_values]
         elif isinstance(_A  ,      np.ndarray     ) and input_values.dtype is np.dtype(np.floataa     ):
             __UpperCamelCase						:						List[Any]		     =	input_values.astype(np.floataa     )

         # convert attention_mask to correct format
         __UpperCamelCase						:						Dict		     =	padded_inputs.get('attention_mask'     )
         if attention_mask is not None:
             __UpperCamelCase						:						int		     =	[np.asarray(_A  ,      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(_A  ,      max_length=_A     ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             __UpperCamelCase						:						Optional[int]		     =	self.zero_mean_unit_var_norm(
                 padded_inputs['input_values']  ,      attention_mask=_A  ,      padding_value=self.padding_value     )

         if return_tensors is not None:
             __UpperCamelCase						:						Dict		     =	padded_inputs.convert_to_tensors(_A     )

         return padded_inputs





import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


_A       =					logging.get_logger(__name__)







class 	lowerCamelCase						(    A_    ):





     def __init__(self :       Any							,       *_A :       Optional[Any]							,       **_A :       List[Any]  )		->		None:
         warnings.warn(
             "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use LevitImageProcessor instead."							,       _A							,       )
         super().__init__(*_A							,       **_A  )

