"""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


lowerCAmelCase_:					Optional[int]     =					logging.get_logger(__name__)






class    a__    (   _a	):
   snake_case_							=      ["input_features", "is_longer"]


   def __init__(     self,				_UpperCAmelCase=64,				_UpperCAmelCase=4_8000,				_UpperCAmelCase=480,				_UpperCAmelCase=10,				_UpperCAmelCase=1024,				_UpperCAmelCase=0.0,				_UpperCAmelCase=False,				_UpperCAmelCase = 0,				_UpperCAmelCase = 1_4000,				_UpperCAmelCase = None,				_UpperCAmelCase = "fusion",				_UpperCAmelCase = "repeatpad",				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            feature_size=_UpperCAmelCase,				sampling_rate=_UpperCAmelCase,				padding_value=_UpperCAmelCase,				return_attention_mask=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							top_db
        lowercase__				 =							truncation
        lowercase__				 =							padding
        lowercase__				 =							fft_window_size
        lowercase__				 =							(fft_window_size >> 1) + 1
        lowercase__				 =							hop_length
        lowercase__				 =							max_length_s
        lowercase__				 =							max_length_s * sampling_rate
        lowercase__				 =							sampling_rate
        lowercase__				 =							frequency_min
        lowercase__				 =							frequency_max
        lowercase__				 =							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",				)
        lowercase__				 =							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 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							copy.deepcopy(self.__dict__  )
        lowercase__				 =							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 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = None  ):
        '''simple docstring'''



        lowercase__				 =							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 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							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
             lowercase__				 =							[0]
        if len(ranges[2]  ) == 0:
             # if the audio is too short, we just use the first chunk
             lowercase__				 =							[0]
        # randomly choose index for each part
        lowercase__				 =							np.random.choice(ranges[0]  )
        lowercase__				 =							np.random.choice(ranges[1]  )
        lowercase__				 =							np.random.choice(ranges[2]  )

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

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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        if waveform.shape[0] > max_length:
             if truncation == "rand_trunc":
                  lowercase__				 =							True
                  # random crop to max_length (for compatibility) -> this should be handled by self.pad
                  lowercase__				 =							len(_UpperCAmelCase  ) - max_length
                  lowercase__				 =							np.random.randint(0,				overflow + 1  )
                  lowercase__				 =							waveform[idx : idx + max_length]
                  lowercase__				 =							self._np_extract_fbank_features(_UpperCAmelCase,				self.mel_filters_slaney  )[None, :]
             elif truncation == "fusion":
                  lowercase__				 =							self._np_extract_fbank_features(_UpperCAmelCase,				self.mel_filters  )
                  lowercase__				 =							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                  lowercase__				 =							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.
                       lowercase__				 =							np.stack([mel, mel, mel, mel],				axis=0  )
                       lowercase__				 =							False
                  else:
                       lowercase__				 =							self._random_mel_fusion(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )
                       lowercase__				 =							True
             else:
                  raise NotImplementedError(F'''data_truncating {truncation} not implemented'''  )

        else:
             lowercase__				 =							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":
                       lowercase__				 =							int(max_length / len(_UpperCAmelCase  )  )
                       lowercase__				 =							np.stack(np.tile(_UpperCAmelCase,				n_repeat + 1  )  )[:max_length]
                  if padding == "repeatpad":
                       lowercase__				 =							int(max_length / len(_UpperCAmelCase  )  )
                       lowercase__				 =							np.stack(np.tile(_UpperCAmelCase,				_UpperCAmelCase  )  )
                  lowercase__				 =							np.pad(_UpperCAmelCase,				(0, max_length - waveform.shape[0]),				mode="constant",				constant_values=0  )

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

        return input_mel, longer


   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        lowercase__				 =							truncation if truncation is not None else self.truncation
        lowercase__				 =							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."  )

        lowercase__				 =							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}'''  )
        lowercase__				 =							is_batched_numpy or (
            isinstance(_UpperCAmelCase,				(list, tuple)  ) and (isinstance(raw_speech[0],				(np.ndarray, tuple, list)  ))
        )

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

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

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

        lowercase__				 =							[]
        lowercase__				 =							[]
        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
             lowercase__				 =							np.random.randint(0,				len(_UpperCAmelCase  )  )
             lowercase__				 =							True

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

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

        lowercase__				 =							{"input_features": input_mel, "is_longer": is_longer}
        lowercase__				 =							BatchFeature(_UpperCAmelCase  )

        if return_tensors is not None:
             lowercase__				 =							input_features.convert_to_tensors(_UpperCAmelCase  )

        return input_features





"""simple docstring"""


from __future__ import annotations

import math







def 					__a   (		A      ):






     '''simple docstring'''



     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


lowerCAmelCase_:					Optional[Any]     =					[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ):
          raise ValueError("n must be an integer"      )
     if n <= 0:
          raise ValueError("n must be >= 0"      )

     lowercase__				 =							[]
     for num in range(len(A      )      ):
          lowercase__				 =							0
          while 2 * i * i <= odd_composites[num]:
               lowercase__				 =							odd_composites[num] - 2 * i * i
               if is_prime(A      ):
                    break
               i += 1
          else:
               list_nums.append(odd_composites[num]      )
               if len(A      ) == n:
                    return list_nums

     return []







def 					__a   (		):






     '''simple docstring'''



     return compute_nums(1      )[0]


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




