
"""simple docstring"""


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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      =     logging.get_logger(__name__)



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					List[str]   =			['''input_features''', '''is_longer''']






       def __init__(  self :							str       ,      UpperCAmelCase_ :							List[str]=64       ,      UpperCAmelCase_ :							List[Any]=48_000       ,      UpperCAmelCase_ :							List[Any]=480       ,      UpperCAmelCase_ :							Optional[Any]=10       ,      UpperCAmelCase_ :							Union[str, Any]=1_024       ,      UpperCAmelCase_ :							Tuple=0.0       ,      UpperCAmelCase_ :							Any=False       ,      UpperCAmelCase_ :							float = 0       ,      UpperCAmelCase_ :							float = 14_000       ,      UpperCAmelCase_ :							int = None       ,      UpperCAmelCase_ :							str = "fusion"       ,      UpperCAmelCase_ :							str = "repeatpad"       ,      **UpperCAmelCase_ :							Tuple       ,      )->  Tuple:







              """simple docstring"""
              super().__init__(
                  feature_size=UpperCAmelCase_       ,      sampling_rate=UpperCAmelCase_       ,      padding_value=UpperCAmelCase_       ,      return_attention_mask=UpperCAmelCase_       ,      **UpperCAmelCase_       ,      )
              UpperCamelCase					 =		top_db
              UpperCamelCase					 =		truncation
              UpperCamelCase					 =		padding
              UpperCamelCase					 =		fft_window_size
              UpperCamelCase					 =		(fft_window_size >> 1) + 1
              UpperCamelCase					 =		hop_length
              UpperCamelCase					 =		max_length_s
              UpperCamelCase					 =		max_length_s * sampling_rate
              UpperCamelCase					 =		sampling_rate
              UpperCamelCase					 =		frequency_min
              UpperCamelCase					 =		frequency_max
              UpperCamelCase					 =		mel_filter_bank(
                  num_frequency_bins=self.nb_frequency_bins       ,      num_mel_filters=UpperCAmelCase_       ,      min_frequency=UpperCAmelCase_       ,      max_frequency=UpperCAmelCase_       ,      sampling_rate=UpperCAmelCase_       ,      norm=UpperCAmelCase_       ,      mel_scale="htk"       ,      )
              UpperCamelCase					 =		mel_filter_bank(
                  num_frequency_bins=self.nb_frequency_bins       ,      num_mel_filters=UpperCAmelCase_       ,      min_frequency=UpperCAmelCase_       ,      max_frequency=UpperCAmelCase_       ,      sampling_rate=UpperCAmelCase_       ,      norm="slaney"       ,      mel_scale="slaney"       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Dict[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		copy.deepcopy(self.__dict__			)
              UpperCamelCase					 =		self.__class__.__name__
              if "mel_filters" in output:
                     del output["mel_filters"]
              if "mel_filters_slaney" in output:
                     del output["mel_filters_slaney"]
              return output






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							np.array       ,      UpperCAmelCase_ :							Optional[np.array] = None			)->  np.ndarray:







              """simple docstring"""
              UpperCamelCase					 =		spectrogram(
                  UpperCAmelCase_       ,      window_function(self.fft_window_size       ,      "hann"			)       ,      frame_length=self.fft_window_size       ,      hop_length=self.hop_length       ,      power=2.0       ,      mel_filters=UpperCAmelCase_       ,      log_mel="dB"       ,      )
              return log_mel_spectrogram.T






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							List[str]			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		np.array_split(list(range(0       ,      total_frames - chunk_frames + 1			)			)       ,      3			)
              if len(ranges[1]			) == 0:
                     # if the audio is too short, we just use the first chunk
                     UpperCamelCase					 =		[0]
              if len(ranges[2]			) == 0:
                     # if the audio is too short, we just use the first chunk
                     UpperCamelCase					 =		[0]
              # randomly choose index for each part
              UpperCamelCase					 =		np.random.choice(ranges[0]			)
              UpperCamelCase					 =		np.random.choice(ranges[1]			)
              UpperCamelCase					 =		np.random.choice(ranges[2]			)

              UpperCamelCase					 =		mel[idx_front : idx_front + chunk_frames, :]
              UpperCamelCase					 =		mel[idx_middle : idx_middle + chunk_frames, :]
              UpperCamelCase					 =		mel[idx_back : idx_back + chunk_frames, :]

              UpperCamelCase					 =		torch.tensor(mel[None, None, :]			)
              UpperCamelCase					 =		torch.nn.functional.interpolate(
                  UpperCAmelCase_       ,      size=[chunk_frames, 64]       ,      mode="bilinear"       ,      align_corners=UpperCAmelCase_			)
              UpperCamelCase					 =		mel_shrink[0][0].numpy()
              UpperCamelCase					 =		np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]       ,      axis=0			)
              return mel_fusion






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							np.array       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							List[Any]			)->  np.array:







              """simple docstring"""
              if waveform.shape[0] > max_length:
                     if truncation == "rand_trunc":
                            UpperCamelCase					 =		True
                            # random crop to max_length (for compatibility) -> this should be handled by self.pad
                            UpperCamelCase					 =		len(UpperCAmelCase_			) - max_length
                            UpperCamelCase					 =		np.random.randint(0       ,      overflow + 1			)
                            UpperCamelCase					 =		waveform[idx : idx + max_length]
                            UpperCamelCase					 =		self._np_extract_fbank_features(UpperCAmelCase_       ,      self.mel_filters_slaney			)[None, :]
                     elif truncation == "fusion":
                            UpperCamelCase					 =		self._np_extract_fbank_features(UpperCAmelCase_       ,      self.mel_filters			)
                            UpperCamelCase					 =		max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                            UpperCamelCase					 =		mel.shape[0]
                            if chunk_frames == total_frames:
                                   # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                                   # In this case, we just use the whole audio.
                                   UpperCamelCase					 =		np.stack([mel, mel, mel, mel]       ,      axis=0			)
                                   UpperCamelCase					 =		False
                            else:
                                   UpperCamelCase					 =		self._random_mel_fusion(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
                                   UpperCamelCase					 =		True
                     else:
                            raise NotImplementedError(f"data_truncating {truncation} not implemented"			)

              else:
                     UpperCamelCase					 =		False
                     # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
                     if waveform.shape[0] < max_length:
                            if padding == "repeat":
                                   UpperCamelCase					 =		int(max_length / len(UpperCAmelCase_			)			)
                                   UpperCamelCase					 =		np.stack(np.tile(UpperCAmelCase_       ,      n_repeat + 1			)			)[:max_length]
                            if padding == "repeatpad":
                                   UpperCamelCase					 =		int(max_length / len(UpperCAmelCase_			)			)
                                   UpperCamelCase					 =		np.stack(np.tile(UpperCAmelCase_       ,      UpperCAmelCase_			)			)
                            UpperCamelCase					 =		np.pad(UpperCAmelCase_       ,      (0, max_length - waveform.shape[0])       ,      mode="constant"       ,      constant_values=0			)

                     if truncation == "fusion":
                            UpperCamelCase					 =		self._np_extract_fbank_features(UpperCAmelCase_       ,      self.mel_filters			)
                            UpperCamelCase					 =		np.stack([input_mel, input_mel, input_mel, input_mel]       ,      axis=0			)
                     else:
                            UpperCamelCase					 =		self._np_extract_fbank_features(UpperCAmelCase_       ,      self.mel_filters_slaney			)[None, :]

              return input_mel, longer






       def __call__(  self :							Dict       ,      UpperCAmelCase_ :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,      UpperCAmelCase_ :							str = None       ,      UpperCAmelCase_ :							Optional[str] = None       ,      UpperCAmelCase_ :							Optional[int] = None       ,      UpperCAmelCase_ :							Optional[int] = None       ,      UpperCAmelCase_ :							Optional[Union[str, TensorType]] = None       ,      **UpperCAmelCase_ :							Tuple       ,      )->  BatchFeature:







              """simple docstring"""
              UpperCamelCase					 =		truncation if truncation is not None else self.truncation
              UpperCamelCase					 =		padding if padding else self.padding

              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."			)

              UpperCamelCase					 =		isinstance(UpperCAmelCase_       ,      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					 =		is_batched_numpy or (
                  isinstance(UpperCAmelCase_       ,      (list, tuple)			) and (isinstance(raw_speech[0]       ,      (np.ndarray, tuple, list)			))
              )

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

              # always return batch
              if not is_batched:
                     UpperCamelCase					 =		[np.asarray(UpperCAmelCase_			)]

              # convert to mel spectrogram, truncate and pad if needed.
              UpperCamelCase					 =		[
                  self._get_input_mel(UpperCAmelCase_       ,      max_length if max_length else self.nb_max_samples       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
                  for waveform in raw_speech
              ]

              UpperCamelCase					 =		[]
              UpperCamelCase					 =		[]
              for mel, longer in padded_inputs:
                     input_mel.append(UpperCAmelCase_			)
                     is_longer.append(UpperCAmelCase_			)

              if truncation == "fusion" and sum(UpperCAmelCase_			) == 0:
                     # if no audio is longer than 10s, then randomly select one audio to be longer
                     UpperCamelCase					 =		np.random.randint(0       ,      len(UpperCAmelCase_			)			)
                     UpperCamelCase					 =		True

              if isinstance(input_mel[0]       ,      UpperCAmelCase_			):
                     UpperCamelCase					 =		[np.asarray(UpperCAmelCase_       ,      dtype=np.floataa			) for feature in input_mel]

              # is_longer is a list of bool
              UpperCamelCase					 =		[[longer] for longer in is_longer]

              UpperCamelCase					 =		{"input_features": input_mel, "is_longer": is_longer}
              UpperCamelCase					 =		BatchFeature(UpperCAmelCase_			)

              if return_tensors is not None:
                     UpperCamelCase					 =		input_features.convert_to_tensors(UpperCAmelCase_			)

              return input_features







"""simple docstring"""


def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_ = False				)->							bool:







       """simple docstring"""
       if n == 2:
              return True
       if not n % 2 or n < 2:
              return False
       if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
              return False
       if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 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
       UpperCamelCase					 =		[
           20_47,
           1_37_36_53,
           25_32_60_01,
           32_15_03_17_51,
           2_15_23_02_89_87_47,
           3_47_47_49_66_03_83,
           3_41_55_00_71_72_83_21,
           1,
           3_82_51_23_05_65_46_41_30_51,
           1,
           1,
           31_86_65_85_78_34_03_11_51_16_74_61,
           3_31_70_44_06_46_79_88_73_85_96_19_81,
       ]

       UpperCamelCase					 =		[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
       for idx, _p in enumerate(UpperCAmelCase_		,      1				):
              if n < _p:
                     # then we have our last prime to check
                     UpperCamelCase					 =		primes[:idx]
                     break
       UpperCamelCase ,       UpperCamelCase					 =		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:
              UpperCamelCase					 =		False
              for r in range(UpperCAmelCase_				):
                     UpperCamelCase					 =		pow(UpperCAmelCase_		,      d * 2**r		,      UpperCAmelCase_				)
                     # see article for analysis explanation for m
                     if (r == 0 and m == 1) or ((m + 1) % n == 0):
                            UpperCamelCase					 =		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 	lowerCamelCase__			(							)->							None:







       """simple docstring"""
       assert not miller_rabin(5_61				)
       assert miller_rabin(5_63				)
       # 2047

       assert not miller_rabin(83_82_01				)
       assert miller_rabin(83_82_07				)
       # 1_373_653

       assert not miller_rabin(17_31_60_01				)
       assert miller_rabin(17_31_60_17				)
       # 25_326_001

       assert not miller_rabin(30_78_38_66_41				)
       assert miller_rabin(30_78_38_66_53				)
       # 3_215_031_751

       assert not miller_rabin(1_71_30_45_57_48_01				)
       assert miller_rabin(1_71_30_45_57_48_19				)
       # 2_152_302_898_747

       assert not miller_rabin(2_77_97_99_72_83_07				)
       assert miller_rabin(2_77_97_99_72_83_27				)
       # 3_474_749_660_383

       assert not miller_rabin(1_13_85_00_23_90_94_41				)
       assert miller_rabin(1_13_85_00_23_90_95_27				)
       # 341_550_071_728_321

       assert not miller_rabin(1_27_50_41_01_88_48_80_43_51				)
       assert miller_rabin(1_27_50_41_01_88_48_80_43_91				)
       # 3_825_123_056_546_413_051

       assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67				)
       assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51				)
       # 318_665_857_834_031_151_167_461

       assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33				)
       assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59				)
       # 3_317_044_064_679_887_385_961_981
       # upper limit for probabilistic test


if __name__ == "__main__":
      test_miller_rabin()





