



'''simple docstring'''
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 TensorType, logging


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






class 							lowercase__			(							lowercase     ):
	lowercase__						       =    ["""input_values""", """attention_mask"""]





	def __init__(				self      :       Optional[int]				,lowerCamelCase__      :       int=1				,lowerCamelCase__      :       Optional[int]=16000				,lowerCamelCase__      :       Dict=128				,lowerCamelCase__      :       Dict=1024				,lowerCamelCase__      :       List[str]=0.0				,lowerCamelCase__      :       Optional[Any]=True				,lowerCamelCase__      :       Optional[int]=-4.2_6_7_7_3_9_3				,lowerCamelCase__      :       Union[str, Any]=4.5_6_8_9_9_7_4				,lowerCamelCase__      :       List[str]=False				,**lowerCamelCase__      :       Optional[int]				,):







							'''simple docstring'''
							super().__init__(feature_size=lowerCamelCase__				,sampling_rate=lowerCamelCase__				,padding_value=lowerCamelCase__				,**lowerCamelCase__				)
							_UpperCamelCase      :	Optional[int]				       =     num_mel_bins
							_UpperCamelCase      :	Any				       =     max_length
							_UpperCamelCase      :	Tuple				       =     do_normalize
							_UpperCamelCase      :	Tuple				       =     mean
							_UpperCamelCase      :	str				       =     std
							_UpperCamelCase      :	List[Any]				       =     return_attention_mask





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       np.ndarray				,lowerCamelCase__      :       int				,):







							'''simple docstring'''
							_UpperCamelCase      :	Union[str, Any]				       =     torch.from_numpy(lowerCamelCase__				).unsqueeze(0				)
							_UpperCamelCase      :	Dict				       =     ta_kaldi.fbank(
							    lowerCamelCase__				,htk_compat=lowerCamelCase__				,sample_frequency=self.sampling_rate				,use_energy=lowerCamelCase__				,window_type='hanning'				,num_mel_bins=self.num_mel_bins				,dither=0.0				,frame_shift=10				,)

							_UpperCamelCase      :	List[Any]				       =     fbank.shape[0]
							_UpperCamelCase      :	Optional[int]				       =     max_length - n_frames

							# pad or truncate, depending on difference
							if difference > 0:
													_UpperCamelCase      :	Tuple				       =     torch.nn.ZeroPadad((0, 0, 0, difference)				)
													_UpperCamelCase      :	Any				       =     pad_module(lowerCamelCase__				)
							elif difference < 0:
													_UpperCamelCase      :	List[str]				       =     fbank[0:max_length, :]

							_UpperCamelCase      :	Optional[int]				       =     fbank.numpy()

							return fbank





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       np.ndarray				):







							'''simple docstring'''
							return (input_values - (self.mean)) / (self.std * 2)





	def __call__(				self      :       List[str]				,lowerCamelCase__      :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,lowerCamelCase__      :       Optional[int] = None				,lowerCamelCase__      :       Optional[Union[str, TensorType]] = None				,**lowerCamelCase__      :       Dict				,):







							'''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.'				)

							_UpperCamelCase      :	Dict				       =     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}'				)
							_UpperCamelCase      :	int				       =     is_batched_numpy or (
							    isinstance(lowerCamelCase__				,(list, tuple)				) and (isinstance(raw_speech[0]				,(np.ndarray, tuple, list)				))
							)

							if is_batched:
													_UpperCamelCase      :	List[Any]				       =     [np.asarray(lowerCamelCase__				,dtype=np.floataa				) for speech in raw_speech]
							elif not is_batched and not isinstance(lowerCamelCase__				,np.ndarray				):
													_UpperCamelCase      :	Tuple				       =     np.asarray(lowerCamelCase__				,dtype=np.floataa				)
							elif isinstance(lowerCamelCase__				,np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
													_UpperCamelCase      :	List[str]				       =     raw_speech.astype(np.floataa				)

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

							# extract fbank features and pad/truncate to max_length
							_UpperCamelCase      :	Optional[Any]				       =     [self._extract_fbank_features(lowerCamelCase__				,max_length=self.max_length				) for waveform in raw_speech]

							# convert into BatchFeature
							_UpperCamelCase      :	Dict				       =     BatchFeature({'input_values': features}				)

							# make sure list is in array format
							_UpperCamelCase      :	Any				       =     padded_inputs.get('input_values'				)
							if isinstance(input_values[0]				,lowerCamelCase__				):
													_UpperCamelCase      :	List[Any]				       =     [np.asarray(lowerCamelCase__				,dtype=np.floataa				) for feature in input_values]

							# normalization
							if self.do_normalize:
													_UpperCamelCase      :	List[Any]				       =     [self.normalize(lowerCamelCase__				) for feature in input_values]

							if return_tensors is not None:
													_UpperCamelCase      :	Union[str, Any]				       =     padded_inputs.convert_to_tensors(lowerCamelCase__				)

							return padded_inputs







'''simple docstring'''
import math






def A__					(  UpperCAmelCase_							):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5						,						int(math.sqrt(UpperCAmelCase_							) + 1							)						,						6							):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True






def A__					(  UpperCAmelCase_ = 1_0_0_0						,						UpperCAmelCase_ = 1_0_0_0							):
						_UpperCamelCase      :	str				       =     [0, 0, 0]  # length, a, b
						for a in range((a_limit * -1) + 1						,						UpperCAmelCase_							):
												for b in range(2						,						UpperCAmelCase_							):
																		if is_prime(UpperCAmelCase_							):
																								_UpperCamelCase      :	List[str]				       =     0
																								_UpperCamelCase      :	Union[str, Any]				       =     0
																								while is_prime((n**2) + (a * n) + b							):
																														count += 1
																														n += 1
																								if count > longest[0]:
																														_UpperCamelCase      :	Any				       =     [count, a, b]
						_UpperCamelCase      :	Dict				       =     longest[1] * longest[2]
						return ans


if __name__ == "__main__":
					print(solution(1000, 1000))


