





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


snake_case_							=			logging.get_logger(__name__)
class        snake_case_		(			_UpperCAmelCase):
							lowerCamelCase    :Optional[int]						=     ["input_features"]







							def __init__(		self			,     __lowercase=8_0			,     __lowercase=1_6_0_0_0			,     __lowercase=1_6_0			,     __lowercase=3_0			,     __lowercase=4_0_0			,     __lowercase=0.0			,     __lowercase=False			,     **__lowercase			,     )				->   Optional[Any]:
									super().__init__(
									    feature_size=lowercase__			,     sampling_rate=lowercase__			,     padding_value=lowercase__			,     return_attention_mask=lowercase__			,     **lowercase__			,     )
									lowerCamelCase   : Optional[int]								=n_fft
									lowerCamelCase   : int								=hop_length
									lowerCamelCase   : Any								=chunk_length
									lowerCamelCase   : List[Any]								=chunk_length * sampling_rate
									lowerCamelCase   : Optional[Any]								=self.n_samples // hop_length
									lowerCamelCase   : Optional[Any]								=sampling_rate
									lowerCamelCase   : Any								=mel_filter_bank(
									    num_frequency_bins=1 + n_fft // 2			,     num_mel_filters=lowercase__			,     min_frequency=0.0			,     max_frequency=8_0_0_0.0			,     sampling_rate=lowercase__			,     norm='''slaney'''			,     mel_scale='''slaney'''			,     )







							def 			__lowercase   (		self			,     __lowercase   )				->   Optional[int]:
									lowerCamelCase   : Tuple								=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'''			,     )
									lowerCamelCase   : Dict								=log_spec[:, :-1]
									lowerCamelCase   : List[Any]								=np.maximum(lowercase__			,     log_spec.max() - 8.0   )
									lowerCamelCase   : Optional[Any]								=(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   (		__lowercase			,     __lowercase			,     __lowercase = 0.0   )				->   List[Any]:






									if attention_mask is not None:
											lowerCamelCase   : List[str]								=np.array(lowercase__			,     np.intaa   )
											lowerCamelCase   : Optional[Any]								=[]

											for vector, length in zip(lowercase__			,     attention_mask.sum(-1   )   ):
													lowerCamelCase   : Dict								=(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7   )
													if length < normed_slice.shape[0]:
															lowerCamelCase   : Tuple								=padding_value

													normed_input_values.append(lowercase__   )
									else:
											lowerCamelCase   : Any								=[(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]

									return normed_input_values







							def __call__(		self			,     __lowercase			,     __lowercase = True			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     __lowercase = "max_length"			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     **__lowercase			,     )				->   Tuple:






									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.'''   )

									lowerCamelCase   : Any								=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}"   )
									lowerCamelCase   : Tuple								=is_batched_numpy or (
									    isinstance(lowercase__			,     (list, tuple)   ) and (isinstance(raw_speech[0]			,     (np.ndarray, tuple, list)   ))
									)

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

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

									lowerCamelCase   : Optional[Any]								=BatchFeature({'''input_features''': raw_speech}   )

									# convert into correct format for padding

									lowerCamelCase   : Any								=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:
											lowerCamelCase   : str								=self.zero_mean_unit_var_norm(
											    padded_inputs['''input_features''']			,     attention_mask=padded_inputs['''attention_mask''']			,     padding_value=self.padding_value			,     )
											lowerCamelCase   : List[str]								=np.stack(padded_inputs['''input_features''']			,     axis=0   )

									# make sure list is in array format
									lowerCamelCase   : Optional[Any]								=padded_inputs.get('''input_features'''   ).transpose(2			,     0			,     1   )

									lowerCamelCase   : str								=[self._np_extract_fbank_features(lowercase__   ) for waveform in input_features[0]]

									if isinstance(input_features[0]			,     lowercase__   ):
											lowerCamelCase   : str								=[np.asarray(lowercase__			,     dtype=np.floataa   ) for feature in input_features]
									else:
											lowerCamelCase   : Optional[int]								=input_features

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

									if return_tensors is not None:
											lowerCamelCase   : Tuple								=padded_inputs.convert_to_tensors(lowercase__   )

									return padded_inputs







							def 			__lowercase   (		self   )				->   List[Any]:
									lowerCamelCase   : str								=copy.deepcopy(self.__dict__   )
									lowerCamelCase   : Tuple								=self.__class__.__name__
									if "mel_filters" in output:
											del output["mel_filters"]
									return output








def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = False		)   ->     bool:
		if n == 2:
				return True
		if not n % 2 or n < 2:
				return False
		if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
				return False
		if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
				raise ValueError(
				    '''Warning: upper bound of deterministic test is exceeded. '''
				    '''Pass allow_probable=True to allow probabilistic test. '''
				    '''A return value of True indicates a probable prime.'''		)
		# array bounds provided by analysis
		lowerCamelCase   : Optional[Any]								=[
		    2_0_4_7,
		    1_3_7_3_6_5_3,
		    2_5_3_2_6_0_0_1,
		    3_2_1_5_0_3_1_7_5_1,
		    2_1_5_2_3_0_2_8_9_8_7_4_7,
		    3_4_7_4_7_4_9_6_6_0_3_8_3,
		    3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
		    1,
		    3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
		    1,
		    1,
		    3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
		    3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
		]

		lowerCamelCase   : Dict								=[2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
		for idx, _p in enumerate(SCREAMING_SNAKE_CASE_							,       1		):
				if n < _p:
						# then we have our last prime to check
						lowerCamelCase   : Any								=primes[:idx]
						break
		lowerCamelCase		,   lowerCamelCase   : Any								=n - 1, 0
		# break up n -1 into a power of 2 (s) and
		# remaining odd component
		# essentially, solve for d * 2 ** s == n - 1
		while d % 2 == 0:
				d //= 2
				s += 1
		for prime in plist:
				lowerCamelCase   : Union[str, Any]								=False
				for r in range(SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : List[str]								=pow(SCREAMING_SNAKE_CASE_							,       d * 2**r							,       SCREAMING_SNAKE_CASE_		)
						# see article for analysis explanation for m
						if (r == 0 and m == 1) or ((m + 1) % n == 0):
								lowerCamelCase   : List[str]								=True
								# this loop will not determine compositeness
								break
				if pr:
						continue
				# if pr is False, then the above loop never evaluated to true,
				# and the n MUST be composite
				return False
		return True


def 					A__	(       )   ->     None:
		assert not miller_rabin(5_6_1		)
		assert miller_rabin(5_6_3		)
		# 2047

		assert not miller_rabin(8_3_8_2_0_1		)
		assert miller_rabin(8_3_8_2_0_7		)
		# 1_373_653

		assert not miller_rabin(1_7_3_1_6_0_0_1		)
		assert miller_rabin(1_7_3_1_6_0_1_7		)
		# 25_326_001

		assert not miller_rabin(3_0_7_8_3_8_6_6_4_1		)
		assert miller_rabin(3_0_7_8_3_8_6_6_5_3		)
		# 3_215_031_751

		assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1		)
		assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9		)
		# 2_152_302_898_747

		assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7		)
		assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7		)
		# 3_474_749_660_383

		assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1		)
		assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7		)
		# 341_550_071_728_321

		assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1		)
		assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1		)
		# 3_825_123_056_546_413_051

		assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7		)
		assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1		)
		# 318_665_857_834_031_151_167_461

		assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3		)
		assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9		)
		# 3_317_044_064_679_887_385_961_981
		# upper limit for probabilistic test


if __name__ == "__main__":
				test_miller_rabin()

