






'''simple docstring'''







from typing import List, Optional, Union

import numpy as np

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


__lowerCAmelCase       		=      logging.get_logger(__name__)





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				["input_features", "attention_mask"]






			def __init__( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict=80  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=1_6000  ,						__SCREAMING_SNAKE_CASE      :			Any=0.0  ,						__SCREAMING_SNAKE_CASE      :			str=10  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=25  ,						__SCREAMING_SNAKE_CASE      :			List[str]="hamming_window"  ,						__SCREAMING_SNAKE_CASE      :			Any=3_2768.0  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=0.97  ,						__SCREAMING_SNAKE_CASE      :			Any=1.0  ,						__SCREAMING_SNAKE_CASE      :			Tuple=True  ,						__SCREAMING_SNAKE_CASE      :			Tuple=True  ,						__SCREAMING_SNAKE_CASE      :			List[str]=False  ,						**__SCREAMING_SNAKE_CASE      :			Tuple  ,						)			->			List[Any]:
							super().__init__(feature_size=__SCREAMING_SNAKE_CASE  ,						sampling_rate=__SCREAMING_SNAKE_CASE  ,						padding_value=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)

							a_		:  str											=      feature_size
							a_		:  Optional[Any]											=      sampling_rate
							a_		:  int											=      padding_value
							a_		:  Optional[int]											=      hop_length
							a_		:  Dict											=      win_length
							a_		:  Tuple											=      frame_signal_scale
							a_		:  str											=      preemphasis_coeff
							a_		:  Optional[Any]											=      mel_floor
							a_		:  Tuple											=      normalize_means
							a_		:  int											=      normalize_vars
							a_		:  List[Any]											=      win_function
							a_		:  int											=      return_attention_mask

							a_		:  List[Any]											=      win_length * sampling_rate // 1000
							a_		:  Optional[Any]											=      hop_length * sampling_rate // 1000

							a_		:  Optional[Any]											=      optimal_fft_length(self.sample_size					)
							a_		:  Optional[int]											=      (self.n_fft // 2) + 1






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			np.array					)			->			np.ndarray:
							if self.win_function == "hamming_window":
											a_		:  List[Any]											=      window_function(window_length=self.sample_size  ,						name=self.win_function  ,						periodic=__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  int											=      window_function(window_length=self.sample_size  ,						name=self.win_function					)

							a_		:  List[str]											=      mel_filter_bank(
							    num_frequency_bins=self.n_freqs  ,						num_mel_filters=self.feature_size  ,						min_frequency=0.0  ,						max_frequency=self.sampling_rate / 2.0  ,						sampling_rate=self.sampling_rate  ,						)

							a_		:  int											=      spectrogram(
							    one_waveform * self.frame_signal_scale  ,						window=__SCREAMING_SNAKE_CASE  ,						frame_length=self.sample_size  ,						hop_length=self.sample_stride  ,						fft_length=self.n_fft  ,						center=__SCREAMING_SNAKE_CASE  ,						preemphasis=self.preemphasis_coeff  ,						mel_filters=__SCREAMING_SNAKE_CASE  ,						mel_floor=self.mel_floor  ,						log_mel='''log'''  ,						)
							return msfc_features.T






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			List[str]					)			->			Optional[int]:
							# make sure we normalize float32 arrays
							if self.normalize_means:
											a_		:  Optional[int]											=      x[:input_length].mean(axis=0					)
											a_		:  Tuple											=      np.subtract(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							if self.normalize_vars:
											a_		:  Optional[Any]											=      x[:input_length].std(axis=0					)
											a_		:  List[Any]											=      np.divide(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							if input_length < x.shape[0]:
											a_		:  List[str]											=      padding_value

							# make sure array is in float32
							a_		:  Union[str, Any]											=      x.astype(np.floataa					)

							return x






			def 			SCREAMING_SNAKE_CASE      ( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			List[np.ndarray]  ,						__SCREAMING_SNAKE_CASE      :			Optional[np.ndarray] = None					)			->			List[np.ndarray]:
							a_		:  List[Any]											=      attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
							return [self._normalize_one(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						self.padding_value					) for x, n in zip(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)]







			def __call__( self      :			Union[str, 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      :			Dict  ,						)			->			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.'''					)

							a_		:  str											=      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}'					)
							a_		:  Tuple											=      is_batched_numpy or (
							    isinstance(__SCREAMING_SNAKE_CASE  ,						(list, tuple)					) and (isinstance(raw_speech[0]  ,						(np.ndarray, tuple, list)					))
							)

							if is_batched:
											a_		:  Tuple											=      [np.asarray(__SCREAMING_SNAKE_CASE  ,						dtype=np.floataa					) for speech in raw_speech]
							elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE  ,						np.ndarray					):
											a_		:  Optional[Any]											=      np.asarray(__SCREAMING_SNAKE_CASE  ,						dtype=np.floataa					)
							elif isinstance(__SCREAMING_SNAKE_CASE  ,						np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
											a_		:  int											=      raw_speech.astype(np.floataa					)

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

							# extract fbank features
							a_		:  int											=      [self._extract_mfsc_features(__SCREAMING_SNAKE_CASE					) for one_waveform in raw_speech]

							# convert into correct format for padding
							a_		:  List[Any]											=      BatchFeature({'''input_features''': features}					)

							a_		:  Union[str, Any]											=      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  ,						**__SCREAMING_SNAKE_CASE  ,						)
							# make sure list is in array format
							a_		:  int											=      padded_inputs.get('''input_features'''					)
							if isinstance(input_features[0]  ,						__SCREAMING_SNAKE_CASE					):
											a_		:  Optional[int]											=      [np.asarray(__SCREAMING_SNAKE_CASE  ,						dtype=np.floataa					) for feature in input_features]

							a_		:  str											=      padded_inputs.get('''attention_mask'''					)
							if attention_mask is not None:
											a_		:  List[Any]											=      [np.asarray(__SCREAMING_SNAKE_CASE  ,						dtype=np.intaa					) for array in attention_mask]

							if self.normalize_means or self.normalize_vars:
											a_		:  List[str]											=      (
											    np.array(__SCREAMING_SNAKE_CASE  ,						dtype=np.intaa					)
											    if self._get_padding_strategies(__SCREAMING_SNAKE_CASE  ,						max_length=__SCREAMING_SNAKE_CASE					) is not PaddingStrategy.DO_NOT_PAD
											    and padding
											    else None
											)
											a_		:  int											=      self.normalize(
											    padded_inputs['''input_features''']  ,						attention_mask=__SCREAMING_SNAKE_CASE					)

							if return_tensors is not None:
											a_		:  int											=      padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE					)

							return padded_inputs












'''simple docstring'''







import sys

__lowerCAmelCase       		=      (
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)





def 		_UpperCAmelCase   (							__A							:   str      ):
				a_		:  Tuple											=      1
				for digit in s:
								product *= int(__A      )
				return product





def 		_UpperCAmelCase   (							__A							:   str = N      ):
				a_		:  Dict											=      -sys.maxsize - 1
				a_		:  Optional[int]											=      n[:13]
				a_		:  str											=      13
				while cur_index < len(__A      ) - 13:
								if int(n[cur_index]      ) >= int(substr[0]      ):
												a_		:  Tuple											=      substr[1:] + n[cur_index]
												cur_index += 1
								else:
												a_		:  Dict											=      max(__A						,		str_eval(__A      )      )
												a_		:  List[str]											=      n[cur_index : cur_index + 13]
												cur_index += 13
				return largest_product


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




