


from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			['''input_features''', '''attention_mask''']





     def __init__(self		,     UpperCAmelCase=8_0		,     UpperCAmelCase=1_6_0_0_0		,     UpperCAmelCase=8_0		,     UpperCAmelCase=0.0		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     **UpperCAmelCase		,     )     ->   Dict:
      super().__init__(feature_size=UpperCAmelCase		,     sampling_rate=UpperCAmelCase		,     padding_value=UpperCAmelCase		,     **UpperCAmelCase       )
      _lowercase							      =num_mel_bins
      _lowercase							      =do_ceptral_normalize
      _lowercase							      =normalize_means
      _lowercase							      =normalize_vars
      _lowercase							      =True





     def      __A					(self		,     UpperCAmelCase		,     )     ->   np.ndarray:
      _lowercase							      =waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
      _lowercase							      =torch.from_numpy(UpperCAmelCase       ).unsqueeze(0       )
      _lowercase							      =ta_kaldi.fbank(UpperCAmelCase		,     num_mel_bins=self.num_mel_bins		,     sample_frequency=self.sampling_rate       )
      return features.numpy()





     @staticmethod
     def      __A					(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = True		,     UpperCAmelCase = True		,     UpperCAmelCase = 0.0		,     )     ->   np.ndarray:
      # make sure we normalize float32 arrays
      if normalize_means:
       _lowercase							      =x[:input_length].mean(axis=0       )
       _lowercase							      =np.subtract(UpperCAmelCase		,     UpperCAmelCase       )
      if normalize_vars:
       _lowercase							      =x[:input_length].std(axis=0       )
       _lowercase							      =np.divide(UpperCAmelCase		,     UpperCAmelCase       )

      if input_length < x.shape[0]:
       _lowercase							      =padding_value

      # make sure array is in float32
      _lowercase							      =x.astype(np.floataa       )

      return x





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   List[np.ndarray]:
      _lowercase							      =attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
      return [
          self.utterance_cmvn(UpperCAmelCase		,     UpperCAmelCase		,     self.normalize_means		,     self.normalize_vars		,     self.padding_value       )
          for x, n in zip(UpperCAmelCase		,     UpperCAmelCase       )
      ]





     def __call__(self		,     UpperCAmelCase		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     **UpperCAmelCase		,     )     ->   BatchFeature:

      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.'''       )

      _lowercase							      =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}"       )
      _lowercase							      =is_batched_numpy or (
          isinstance(UpperCAmelCase		,     (list, tuple)       ) and (isinstance(raw_speech[0]		,     (np.ndarray, tuple, list)       ))
      )

      if is_batched:
       _lowercase							      =[np.asarray(UpperCAmelCase		,     dtype=np.floataa       ) for speech in raw_speech]
      elif not is_batched and not isinstance(UpperCAmelCase		,     np.ndarray       ):
       _lowercase							      =np.asarray(UpperCAmelCase		,     dtype=np.floataa       )
      elif isinstance(UpperCAmelCase		,     np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
       _lowercase							      =raw_speech.astype(np.floataa       )

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

      # extract fbank features
      _lowercase							      =[self._extract_fbank_features(UpperCAmelCase       ) for waveform in raw_speech]

      # convert into correct format for padding
      _lowercase							      =BatchFeature({'''input_features''': features}       )

      _lowercase							      =self.pad(
          UpperCAmelCase		,     padding=UpperCAmelCase		,     max_length=UpperCAmelCase		,     truncation=UpperCAmelCase		,     pad_to_multiple_of=UpperCAmelCase		,     return_attention_mask=UpperCAmelCase		,     **UpperCAmelCase		,     )

      # make sure list is in array format
      _lowercase							      =padded_inputs.get('''input_features'''       )
      if isinstance(input_features[0]		,     UpperCAmelCase       ):
       _lowercase							      =[np.asarray(UpperCAmelCase		,     dtype=np.floataa       ) for feature in input_features]

      _lowercase							      =padded_inputs.get('''attention_mask'''       )
      if attention_mask is not None:
       _lowercase							      =[np.asarray(UpperCAmelCase		,     dtype=np.intaa       ) for array in attention_mask]

      # Utterance-level cepstral mean and variance normalization
      if self.do_ceptral_normalize:
       _lowercase							      =(
           np.array(UpperCAmelCase		,     dtype=np.intaa       )
           if self._get_padding_strategies(UpperCAmelCase		,     max_length=UpperCAmelCase       ) is not PaddingStrategy.DO_NOT_PAD
           else None
       )
       _lowercase							      =self.normalize(
           padded_inputs['''input_features''']		,     attention_mask=UpperCAmelCase       )

      if return_tensors is not None:
       _lowercase							      =padded_inputs.convert_to_tensors(UpperCAmelCase       )

      return padded_inputs





from math import isqrt
def 				UpperCAmelCase_					(							__snake_case						)     ->			list[int]:







 """simple docstring"""


 _lowercase							      =[True] * max_number
 for i in range(2     ,						isqrt(max_number - 1						) + 1						):
  if is_prime[i]:
   for j in range(i**2     ,						__snake_case     ,						__snake_case						):
    _lowercase							      =False

 return [i for i in range(2     ,						__snake_case						) if is_prime[i]]
def 				UpperCAmelCase_					(							__snake_case = 10**8						)     ->			int:







 """simple docstring"""


 _lowercase							      =calculate_prime_numbers(max_number // 2						)

 _lowercase							      =0
 _lowercase							      =0
 _lowercase							      =len(__snake_case						) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


if __name__ == "__main__":
  print(f'''{solution() = }''')

