






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			:							Tuple       =logging.get_logger(__name__)





class     __a						(					A__ ):
 _lowerCAmelCase : Any 				=				['''input_values''', '''attention_mask''']







 def __init__(	self      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     Tuple=1							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=1_60_00							,     SCREAMING_SNAKE_CASE      :     Dict=0.0							,     SCREAMING_SNAKE_CASE      :     Optional[int]=False							,     SCREAMING_SNAKE_CASE      :     Optional[int]=True							,     **SCREAMING_SNAKE_CASE      :     Optional[int]							,     ):


      '''simple docstring'''






      super().__init__(feature_size=SCREAMING_SNAKE_CASE							,     sampling_rate=SCREAMING_SNAKE_CASE							,     padding_value=SCREAMING_SNAKE_CASE							,     **SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     List[str]        =						return_attention_mask
      UpperCamelCase__    :     Any        =						do_normalize







 @staticmethod
 def       __lowercase      (	SCREAMING_SNAKE_CASE      :     List[np.ndarray]							,     SCREAMING_SNAKE_CASE      :     List[np.ndarray]							,     SCREAMING_SNAKE_CASE      :     float = 0.0		):


      '''simple docstring'''






      if attention_mask is not None:
           UpperCamelCase__    :     List[str]        =						np.array(SCREAMING_SNAKE_CASE							,     np.intaa		)
           UpperCamelCase__    :     Optional[Any]        =						[]

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

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

      return normed_input_values







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


      '''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."		)

      UpperCamelCase__    :     Any        =						isinstance(SCREAMING_SNAKE_CASE							,     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__    :     Union[str, Any]        =						is_batched_numpy or (
          isinstance(SCREAMING_SNAKE_CASE							,     (list, tuple)		) and (isinstance(raw_speech[0]							,     (np.ndarray, tuple, list)		))
      )

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

      # convert into correct format for padding
      UpperCamelCase__    :     Dict        =						BatchFeature({"input_values": raw_speech}		)

      UpperCamelCase__    :     int        =						self.pad(
          SCREAMING_SNAKE_CASE							,     padding=SCREAMING_SNAKE_CASE							,     max_length=SCREAMING_SNAKE_CASE							,     truncation=SCREAMING_SNAKE_CASE							,     pad_to_multiple_of=SCREAMING_SNAKE_CASE							,     return_attention_mask=SCREAMING_SNAKE_CASE							,     )

      # convert input values to correct format
      UpperCamelCase__    :     Optional[int]        =						padded_inputs["input_values"]
      if not isinstance(input_values[0]							,     np.ndarray		):
           UpperCamelCase__    :     Optional[Any]        =						[np.asarray(SCREAMING_SNAKE_CASE							,     dtype=np.floataa		) for array in input_values]
      elif (
          not isinstance(SCREAMING_SNAKE_CASE							,     np.ndarray		)
          and isinstance(input_values[0]							,     np.ndarray		)
          and input_values[0].dtype is np.dtype(np.floataa		)
      ):
           UpperCamelCase__    :     List[str]        =						[array.astype(np.floataa		) for array in input_values]
      elif isinstance(SCREAMING_SNAKE_CASE							,     np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
           UpperCamelCase__    :     Optional[Any]        =						input_values.astype(np.floataa		)

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

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

      if return_tensors is not None:
           UpperCamelCase__    :     Union[str, Any]        =						padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE		)

      return padded_inputs







from __future__ import annotations







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			list[int]:
     assert (
         isinstance(__lowerCAmelCase      ,					__lowerCAmelCase    ) and mask > 0
     ), f'mask needs to be positive integer, your input {mask}'
     UpperCamelCase__    :     Any        =						[]
     UpperCamelCase__    :     int        =						mask

     while submask:
          all_submasks.append(__lowerCAmelCase    )
          UpperCamelCase__    :     List[str]        =						(submask - 1) & mask

     return all_submasks


if __name__ == "__main__":
 import doctest

 doctest.testmod()