

from functools import reduce

_SCREAMING_SNAKE_CASE					:  Any             =      (
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)
def     UpperCAmelCase_       (						_A = N    ):





     '''simple docstring'''




     return max(
         # mypy cannot properly interpret reduce
         int(reduce(lambda _A				,		_A    : str(int(_A    ) * int(_A    )    )				,		n[i : i + 13]    )    )
         for i in range(len(_A    ) - 12    )    )


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





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


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= ["input_features"]






  def __init__(							self						:		Dict     ,       __lowerCamelCase						:		Tuple=80     ,       __lowerCamelCase						:		List[Any]=1_6000     ,       __lowerCamelCase						:		Optional[int]=160     ,       __lowerCamelCase						:		List[str]=30     ,       __lowerCamelCase						:		List[Any]=400     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		str=False     ,       **__lowerCamelCase						:		List[str]     ,       )		->							Any:
       super().__init__(
           feature_size=__lowerCamelCase     ,       sampling_rate=__lowerCamelCase     ,       padding_value=__lowerCamelCase     ,       return_attention_mask=__lowerCamelCase     ,       **__lowerCamelCase     ,       )
       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=__lowerCamelCase     ,       min_frequency=0.0     ,       max_frequency=8000.0     ,       sampling_rate=__lowerCamelCase     ,       norm='''slaney'''     ,       mel_scale='''slaney'''     ,       )






  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		np.array )		->							np.ndarray:
       SCREAMING_SNAKE_CASE__   =   spectrogram(
           __lowerCamelCase     ,       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(__lowerCamelCase     ,       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 				lowercase_		(							__lowerCamelCase						:		List[np.ndarray]     ,       __lowerCamelCase						:		List[np.ndarray]     ,       __lowerCamelCase						:		float = 0.0 )		->							List[np.ndarray]:
       if attention_mask is not None:
            SCREAMING_SNAKE_CASE__   =   np.array(__lowerCamelCase     ,       np.intaa )
            SCREAMING_SNAKE_CASE__   =   []

            for vector, length in zip(__lowerCamelCase     ,       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(__lowerCamelCase )
       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						:		List[str]     ,       __lowerCamelCase						:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[Union[str, TensorType]] = None     ,       __lowerCamelCase						:		Optional[bool] = None     ,       __lowerCamelCase						:		Optional[str] = "max_length"     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[bool] = None     ,       **__lowerCamelCase						:		List[str]     ,       )		->							BatchFeature:

       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(__lowerCamelCase     ,       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(__lowerCamelCase     ,       (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(__lowerCamelCase     ,       np.ndarray ):
            SCREAMING_SNAKE_CASE__   =   np.asarray(__lowerCamelCase     ,       dtype=np.floataa )
       elif isinstance(__lowerCamelCase     ,       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(
           __lowerCamelCase     ,       padding=__lowerCamelCase     ,       max_length=max_length if max_length else self.n_samples     ,       truncation=__lowerCamelCase     ,       pad_to_multiple_of=__lowerCamelCase     ,       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(__lowerCamelCase ) for waveform in input_features[0]]

       if isinstance(input_features[0]     ,       __lowerCamelCase ):
            SCREAMING_SNAKE_CASE__   =   [np.asarray(__lowerCamelCase     ,       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(__lowerCamelCase )

       return padded_inputs







  def 				lowercase_		(							self						:		str )		->							Dict[str, Any]:
       SCREAMING_SNAKE_CASE__   =   copy.deepcopy(self.__dict__ )
       SCREAMING_SNAKE_CASE__   =   self.__class__.__name__
       if "mel_filters" in output:
            del output["mel_filters"]
       return output


