import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__lowerCamelCase			: Optional[int]							   =		logging.get_logger(__name__)







class __snake_case				(						snake_case_		):
   lowerCAmelCase_														=							["input_features"]




   def __init__(    self			:				str       ,	_lowercase			:				Dict=80       ,	_lowercase			:				Union[str, Any]=1_60_00       ,	_lowercase			:				Optional[Any]=1_60       ,	_lowercase			:				Tuple=30       ,	_lowercase			:				str=4_00       ,	_lowercase			:				str=0.0       ,	_lowercase			:				Optional[Any]=False       ,	**_lowercase			:				Tuple       ,	):



     """simple docstring"""






     super().__init__(
         feature_size=_lowercase       ,	sampling_rate=_lowercase       ,	padding_value=_lowercase       ,	return_attention_mask=_lowercase       ,	**_lowercase       ,	)
     SCREAMING_SNAKE_CASE__										=      n_fft
     SCREAMING_SNAKE_CASE__										=      hop_length
     SCREAMING_SNAKE_CASE__										=      chunk_length
     SCREAMING_SNAKE_CASE__										=      chunk_length * sampling_rate
     SCREAMING_SNAKE_CASE__										=      self.n_samples // hop_length
     SCREAMING_SNAKE_CASE__										=      sampling_rate
     SCREAMING_SNAKE_CASE__										=      mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2       ,	num_mel_filters=_lowercase       ,	min_frequency=0.0       ,	max_frequency=80_00.0       ,	sampling_rate=_lowercase       ,	norm="""slaney"""       ,	mel_scale="""slaney"""       ,	)




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      spectrogram(
         _lowercase       ,	window_function(self.n_fft       ,	"""hann"""      )       ,	frame_length=self.n_fft       ,	hop_length=self.hop_length       ,	power=2.0       ,	mel_filters=self.mel_filters       ,	log_mel="""log10"""       ,	)
     SCREAMING_SNAKE_CASE__										=      log_spec[:, :-1]
     SCREAMING_SNAKE_CASE__										=      np.maximum(_lowercase       ,	log_spec.max() - 8.0      )
     SCREAMING_SNAKE_CASE__										=      (log_spec + 4.0) / 4.0
     return log_spec




   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 	__a						(    _lowercase			:				Union[str, Any]       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				str = 0.0      ):



     """simple docstring"""






     if attention_mask is not None:
       SCREAMING_SNAKE_CASE__										=      np.array(_lowercase       ,	np.intaa      )
       SCREAMING_SNAKE_CASE__										=      []

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

         normed_input_values.append(_lowercase      )
     else:
       SCREAMING_SNAKE_CASE__										=      [(x - x.mean()) / np.sqrt(x.var() + 1E-7      ) for x in input_values]

     return normed_input_values




   def __call__(    self			:				Optional[Any]       ,	_lowercase			:				Dict       ,	_lowercase			:				Tuple = True       ,	_lowercase			:				List[str] = None       ,	_lowercase			:				int = None       ,	_lowercase			:				Optional[Any] = None       ,	_lowercase			:				Dict = "max_length"       ,	_lowercase			:				List[str] = None       ,	_lowercase			:				int = None       ,	_lowercase			:				List[Any] = None       ,	**_lowercase			:				int       ,	):



     """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.__class__.__name__} was trained using a"""
             f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
             f""" was sampled with {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."""      )

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

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

     # always return batch
     if not is_batched:
       SCREAMING_SNAKE_CASE__										=      [np.asarray([raw_speech]      ).T]

     SCREAMING_SNAKE_CASE__										=      BatchFeature({"""input_features""": raw_speech}      )

     # convert into correct format for padding

     SCREAMING_SNAKE_CASE__										=      self.pad(
         _lowercase       ,	padding=_lowercase       ,	max_length=max_length if max_length else self.n_samples       ,	truncation=_lowercase       ,	pad_to_multiple_of=_lowercase       ,	return_attention_mask=return_attention_mask or do_normalize       ,	)

     # zero-mean and unit-variance normalization
     if do_normalize:
       SCREAMING_SNAKE_CASE__										=      self.zero_mean_unit_var_norm(
           padded_inputs["""input_features"""]       ,	attention_mask=padded_inputs["""attention_mask"""]       ,	padding_value=self.padding_value       ,	)
       SCREAMING_SNAKE_CASE__										=      np.stack(padded_inputs["""input_features"""]       ,	axis=0      )

     # make sure list is in array format
     SCREAMING_SNAKE_CASE__										=      padded_inputs.get("""input_features"""      ).transpose(2       ,	0       ,	1      )

     SCREAMING_SNAKE_CASE__										=      [self._np_extract_fbank_features(_lowercase      ) for waveform in input_features[0]]

     if isinstance(input_features[0]       ,	_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      [np.asarray(_lowercase       ,	dtype=np.floataa      ) for feature in input_features]
     else:
       SCREAMING_SNAKE_CASE__										=      input_features

     if return_attention_mask:
       # rescale from sample (48000) to feature (3000)
       SCREAMING_SNAKE_CASE__										=      padded_inputs["""attention_mask"""][:, :: self.hop_length]

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

     return padded_inputs




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      copy.deepcopy(self.__dict__      )
     SCREAMING_SNAKE_CASE__										=      self.__class__.__name__
     if "mel_filters" in output:
       del output["mel_filters"]
     return output







'''simple docstring'''

from __future__ import annotations

import math





def lowerCamelCase						(     __lowerCamelCase :    int    )						->list[int]:

    if num <= 0:
        _SCREAMING_SNAKE_CASE									=			F'{num}: Invalid input, please enter a positive integer.'
        raise ValueError(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			[True] * (num + 1)
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			2
    _SCREAMING_SNAKE_CASE									=			int(math.sqrt(__lowerCamelCase    )    )

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(__lowerCamelCase    )

            # Set multiples of start be False
            for i in range(start * start					,       num + 1					,       __lowerCamelCase    ):
                if sieve[i] is True:
                    _SCREAMING_SNAKE_CASE									=			False

        start += 1

    for j in range(end + 1					,       num + 1    ):
        if sieve[j] is True:
            prime.append(__lowerCamelCase    )

    return prime


if __name__ == "__main__":
      print(prime_sieve(int(input("""Enter a positive integer: """).strip())))



