




class 							__lowercase   :
     class 							__lowercase   :
          _lowerCAmelCase		       =						"_prev", "_data", "_next"



          def __init__(  self   :							Optional[int]      ,		lowercase__   :							Dict      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							Tuple       ):
            a_		    =  link_p
            a_		    =  element
            a_		    =  link_n



          def 				__magic_name__		(  self   :							List[Any]       ):
            return (
                f" Prev -> {self._prev is not None}, Next -> {self._next is not None}"
            )



     def __init__(  self   :							Optional[Any]       ):
       a_		    =  self._Node(lowercase__      ,		lowercase__      ,		lowercase__       )
       a_		    =  self._Node(lowercase__      ,		lowercase__      ,		lowercase__       )
       a_		    =  self._trailer
       a_		    =  self._header
       a_		    =  0



     def __len__(  self   :							Union[str, Any]       ):
       return self._size



     def 				__magic_name__		(  self   :							List[str]       ):
       return self.__len__() == 0



     def 				__magic_name__		(  self   :							Union[str, Any]      ,		lowercase__   :							Any      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							List[str]       ):
       # Create new_node by setting it's prev.link -> header
       # setting it's next.link -> trailer
       a_		    =  self._Node(lowercase__      ,		lowercase__      ,		lowercase__       )
       a_		    =  new_node
       a_		    =  new_node
       self._size += 1
       return self



     def 				__magic_name__		(  self   :							Optional[Any]      ,		lowercase__   :							List[Any]       ):
       a_		    =  node._prev
       a_		    =  node._next

       a_		    =  successor
       a_		    =  predecessor
       self._size -= 1
       a_		    =  node._data
       a_		    =  a_		    =  a_		    =  None
       del node
       return temp



class 							__lowercase   (			_DoublyLinkedBase    ):



     def 				__magic_name__		(  self   :							Any       ):
       if self.is_empty():
         raise Exception('''List is empty'''       )
       return self._header._next._data



     def 				__magic_name__		(  self   :							int       ):
       if self.is_empty():
         raise Exception('''List is empty'''       )
       return self._trailer._prev._data



     def 				__magic_name__		(  self   :							List[Any]      ,		lowercase__   :							Union[str, Any]       ):
       return self._insert(self._header      ,		lowercase__      ,		self._header._next       )



     def 				__magic_name__		(  self   :							List[Any]      ,		lowercase__   :							Union[str, Any]       ):
       return self._insert(self._trailer._prev      ,		lowercase__      ,		self._trailer       )



     def 				__magic_name__		(  self   :							str       ):
       if self.is_empty():
         raise IndexError('''remove_first from empty list'''       )
       return self._delete(self._header._next       )



     def 				__magic_name__		(  self   :							Any       ):
       if self.is_empty():
         raise IndexError('''remove_first from empty list'''       )
       return self._delete(self._trailer._prev       )






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__       				=	logging.get_logger(__name__)
class 							__lowercase   (			a__    ):
     _lowerCAmelCase		       =						["input_values", "attention_mask"]



     def __init__(  self   :							Union[str, Any]      ,		lowercase__   :							str=1      ,		lowercase__   :							int=1_6_0_0_0      ,		lowercase__   :							Optional[int]=0.0      ,		lowercase__   :							List[Any]=False      ,		lowercase__   :							Dict=True      ,		**lowercase__   :							Any      ,		):
       super().__init__(feature_size=lowercase__      ,		sampling_rate=lowercase__      ,		padding_value=lowercase__      ,		**lowercase__       )
       a_		    =  return_attention_mask
       a_		    =  do_normalize



     @staticmethod
     def 				__magic_name__		(  lowercase__   :							List[np.ndarray]      ,		lowercase__   :							List[np.ndarray]      ,		lowercase__   :							float = 0.0       ):
       if attention_mask is not None:
         a_		    =  np.array(lowercase__      ,		np.intaa       )
         a_		    =  []

         for vector, length in zip(lowercase__      ,		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(lowercase__       )
       else:
         a_		    =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7       ) for x in input_values]

       return normed_input_values



     def __call__(  self   :							Any      ,		lowercase__   :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,		lowercase__   :							Union[bool, str, PaddingStrategy] = False      ,		lowercase__   :							Optional[int] = None      ,		lowercase__   :							bool = False      ,		lowercase__   :							Optional[int] = None      ,		lowercase__   :							Optional[bool] = None      ,		lowercase__   :							Optional[Union[str, TensorType]] = None      ,		lowercase__   :							Optional[int] = None      ,		**lowercase__   :							List[str]      ,		):

       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(lowercase__      ,		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(lowercase__      ,		(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(
           lowercase__      ,		padding=lowercase__      ,		max_length=lowercase__      ,		truncation=lowercase__      ,		pad_to_multiple_of=lowercase__      ,		return_attention_mask=lowercase__      ,		)

       # convert input values to correct format
       a_		    =  padded_inputs['''input_values''']
       if not isinstance(input_values[0]      ,		np.ndarray       ):
         a_		    =  [np.asarray(lowercase__      ,		dtype=np.floataa       ) for array in input_values]
       elif (
           not isinstance(lowercase__      ,		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(lowercase__      ,		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(lowercase__      ,		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(lowercase__      ,		max_length=lowercase__       ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         a_		    =  self.zero_mean_unit_var_norm(
             padded_inputs['''input_values''']      ,		attention_mask=lowercase__      ,		padding_value=self.padding_value       )

       if return_tensors is not None:
         a_		    =  padded_inputs.convert_to_tensors(lowercase__       )

       return padded_inputs
