






import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


a__       =     logging.get_logger(__name__)


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  ['''input_values''', '''attention_mask''']



				def __init__(      self    :    Tuple			,		UpperCamelCase__    :    int = 1			,		UpperCamelCase__    :    int = 1_6_0_0_0			,		UpperCamelCase__    :    float = 0.0			,		UpperCamelCase__    :    bool = False			,		UpperCamelCase__    :    int = 8_0			,		UpperCamelCase__    :    int = 1_6			,		UpperCamelCase__    :    int = 6_4			,		UpperCamelCase__    :    str = "hann_window"			,		UpperCamelCase__    :    float = 1.0			,		UpperCamelCase__    :    float = 8_0			,		UpperCamelCase__    :    float = 7_6_0_0			,		UpperCamelCase__    :    float = 1E-10			,		UpperCamelCase__    :    int = 2			,		UpperCamelCase__    :    bool = True			,		**UpperCamelCase__    :    Optional[int]			,		):






											'''simple docstring'''







											super().__init__(feature_size=UpperCamelCase__			,		sampling_rate=UpperCamelCase__			,		padding_value=UpperCamelCase__			,		**UpperCamelCase__)
											snake_case__	  =       do_normalize
											snake_case__	  =       return_attention_mask

											snake_case__	  =       num_mel_bins
											snake_case__	  =       hop_length
											snake_case__	  =       win_length
											snake_case__	  =       win_function
											snake_case__	  =       frame_signal_scale
											snake_case__	  =       fmin
											snake_case__	  =       fmax
											snake_case__	  =       mel_floor
											snake_case__	  =       reduction_factor

											snake_case__	  =       win_length * sampling_rate // 1_0_0_0
											snake_case__	  =       hop_length * sampling_rate // 1_0_0_0
											snake_case__	  =       optimal_fft_length(self.sample_size)
											snake_case__	  =       (self.n_fft // 2) + 1

											snake_case__	  =       window_function(window_length=self.sample_size			,		name=self.win_function			,		periodic=UpperCamelCase__)

											snake_case__	  =       mel_filter_bank(
											    num_frequency_bins=self.n_freqs			,		num_mel_filters=self.num_mel_bins			,		min_frequency=self.fmin			,		max_frequency=self.fmax			,		sampling_rate=self.sampling_rate			,		norm="""slaney"""			,		mel_scale="""slaney"""			,		)

											if frame_signal_scale != 1.0:
																		warnings.warn(
																		    """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""			,		UpperCamelCase__			,		)
											if reduction_factor != 2.0:
																		warnings.warn(
																		    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""			,		UpperCamelCase__			,		)



				@staticmethod
				# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
				def 					__magic_name__							(      UpperCamelCase__    :    List[np.ndarray]			,		UpperCamelCase__    :    List[np.ndarray]			,		UpperCamelCase__    :    float = 0.0):






											'''simple docstring'''







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

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

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

											return normed_input_values



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    np.ndarray			,		):






											'''simple docstring'''







											snake_case__	  =       spectrogram(
											    UpperCamelCase__			,		window=self.window			,		frame_length=self.sample_size			,		hop_length=self.sample_stride			,		fft_length=self.n_fft			,		mel_filters=self.mel_filters			,		mel_floor=self.mel_floor			,		log_mel="""log10"""			,		)
											return log_mel_spec.T



				def __call__(      self    :    List[str]			,		UpperCamelCase__    :    Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,		UpperCamelCase__    :    Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,		UpperCamelCase__    :    Union[bool, str, PaddingStrategy] = False			,		UpperCamelCase__    :    Optional[int] = None			,		UpperCamelCase__    :    bool = False			,		UpperCamelCase__    :    Optional[int] = None			,		UpperCamelCase__    :    Optional[bool] = None			,		UpperCamelCase__    :    Optional[Union[str, TensorType]] = None			,		UpperCamelCase__    :    Optional[int] = None			,		**UpperCamelCase__    :    List[str]			,		):






											'''simple docstring'''







											if audio is None and audio_target is None:
																		raise ValueError("""You must provide either `audio` or `audio_target` values.""")

											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 audio 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.""")

											if audio is not None:
																		snake_case__	  =       self._process_audio(
																		    UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__			,		)
											else:
																		snake_case__	  =       None

											if audio_target is not None:
																		snake_case__	  =       self._process_audio(
																		    UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__			,		)

																		if inputs is None:
																									return inputs_target
																		else:
																									snake_case__	  =       inputs_target["""input_values"""]
																									snake_case__	  =       inputs_target.get("""attention_mask""")
																									if decoder_attention_mask is not None:
																																snake_case__	  =       decoder_attention_mask

											return inputs



				def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,		UpperCamelCase__    :    bool = False			,		UpperCamelCase__    :    Union[bool, str, PaddingStrategy] = False			,		UpperCamelCase__    :    Optional[int] = None			,		UpperCamelCase__    :    bool = False			,		UpperCamelCase__    :    Optional[int] = None			,		UpperCamelCase__    :    Optional[bool] = None			,		UpperCamelCase__    :    Optional[Union[str, TensorType]] = None			,		**UpperCamelCase__    :    List[str]			,		):






											'''simple docstring'''







											snake_case__	  =       isinstance(UpperCamelCase__			,		np.ndarray) and len(speech.shape) > 1
											if is_batched_numpy and len(speech.shape) > 2:
																		raise ValueError(F'''Only mono-channel audio is supported for input to {self}''')
											snake_case__	  =       is_batched_numpy or (
											    isinstance(UpperCamelCase__			,		(list, tuple)) and (isinstance(speech[0]			,		(np.ndarray, tuple, list)))
											)

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

											# always return batch
											if not is_batched:
																		snake_case__	  =       [speech]

											# needed to make pad() work on spectrogram inputs
											snake_case__	  =       self.feature_size

											# convert into correct format for padding
											if is_target:
																		snake_case__	  =       [self._extract_mel_features(UpperCamelCase__) for waveform in speech]
																		snake_case__	  =       BatchFeature({"""input_values""": features})
																		snake_case__	  =       self.num_mel_bins
											else:
																		snake_case__	  =       BatchFeature({"""input_values""": speech})

											snake_case__	  =       self.pad(
											    UpperCamelCase__			,		padding=UpperCamelCase__			,		max_length=UpperCamelCase__			,		truncation=UpperCamelCase__			,		pad_to_multiple_of=UpperCamelCase__			,		return_attention_mask=UpperCamelCase__			,		**UpperCamelCase__			,		)

											snake_case__	  =       feature_size_hack

											# convert input values to correct format
											snake_case__	  =       padded_inputs["""input_values"""]
											if not isinstance(input_values[0]			,		np.ndarray):
																		snake_case__	  =       [np.asarray(UpperCamelCase__			,		dtype=np.floataa) for array in input_values]
											elif (
											    not isinstance(UpperCamelCase__			,		np.ndarray)
											    and isinstance(input_values[0]			,		np.ndarray)
											    and input_values[0].dtype is np.dtype(np.floataa)
											):
																		snake_case__	  =       [array.astype(np.floataa) for array in input_values]
											elif isinstance(UpperCamelCase__			,		np.ndarray) and input_values.dtype is np.dtype(np.floataa):
																		snake_case__	  =       input_values.astype(np.floataa)

											# convert attention_mask to correct format
											snake_case__	  =       padded_inputs.get("""attention_mask""")
											if attention_mask is not None:
																		snake_case__	  =       [np.asarray(UpperCamelCase__			,		dtype=np.intaa) for array in attention_mask]

											# zero-mean and unit-variance normalization
											if not is_target and self.do_normalize:
																		snake_case__	  =       (
																		    attention_mask
																		    if self._get_padding_strategies(UpperCamelCase__			,		max_length=UpperCamelCase__) is not PaddingStrategy.DO_NOT_PAD
																		    else None
																		)
																		snake_case__	  =       self.zero_mean_unit_var_norm(
																		    padded_inputs["""input_values"""]			,		attention_mask=UpperCamelCase__			,		padding_value=self.padding_value)

											if return_tensors is not None:
																		snake_case__	  =       padded_inputs.convert_to_tensors(UpperCamelCase__)

											return padded_inputs



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       super().to_dict()

											# Don't serialize these as they are derived from the other properties.
											snake_case__	  =       ["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
											for name in names:
																		if name in output:
																									del output[name]

											return output








import math
from collections.abc import Iterator
from itertools import takewhile

def _UpperCAmelCase   (	a						:	int ):

							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(a ) + 1 )  ,					6 ):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True

def _UpperCAmelCase   (	):
							snake_case__	  =       2
							while True:
														if is_prime(a ):
																					yield num
														num += 1

def _UpperCAmelCase   (	a						:	int = 200_0000 ):
							return sum(takewhile(lambda a : x < n  ,					prime_generator() ) )


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