
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      :					List[Any]   	=       logging.get_logger(__name__)




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""input_values""", """attention_mask"""]
  def __init__(			self      :			List[str]      ,							UpperCamelCase__      :			Tuple=1      ,							UpperCamelCase__      :			List[str]=1_6000      ,							UpperCamelCase__      :			Optional[int]=0.0      ,							UpperCamelCase__      :			Union[str, Any]=False      ,							UpperCamelCase__      :			Tuple=True      ,							**UpperCamelCase__      :			Tuple      ,							)      ->     Any:







    """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 							_lowercase							(			UpperCamelCase__      :			List[np.ndarray]      ,							UpperCamelCase__      :			List[np.ndarray]      ,							UpperCamelCase__      :			float = 0.0   )      ->     List[np.ndarray]:







    """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      :			Dict      ,							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__      :			Tuple      ,							)      ->     BatchFeature:







    """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



from itertools import compress, repeat
from math import ceil, sqrt

def a__   (	A_ ):






  '''simple docstring'''





  if num <= 2:
    return []
  if num == 3:
    return [2]

  # Odd sieve for numbers in range [3, num - 1]
  __magic_name__    					=	bytearray(b"""\x01""" ) * ((num >> 1) - 1)

  for i in range(3,					int(sqrt(A_ ) ) + 1,					2 ):
    if sieve[(i >> 1) - 1]:
      __magic_name__    					=	i**2
      __magic_name__    					=	repeat(
          0,					ceil((num - i_squared) / (i << 1) ) )

  return [2] + list(compress(range(3,					A_,					2 ),					A_ ) )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

