






'''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


__UpperCamelCase   :							Tuple         =   logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["input_features", "is_longer"]




      def __init__(	self			, lowerCamelCase=64			, lowerCamelCase=4_8000			, lowerCamelCase=480			, lowerCamelCase=10			, lowerCamelCase=1024			, lowerCamelCase=0.0			, lowerCamelCase=False			, lowerCamelCase = 0			, lowerCamelCase = 1_4000			, lowerCamelCase = None			, lowerCamelCase = "fusion"			, lowerCamelCase = "repeatpad"			, **lowerCamelCase			, )				->List[Any]:



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




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



         '''simple docstring'''
         __a					  =     copy.deepcopy(self.__dict__   )
         __a					  =     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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None   )				->np.ndarray:



         '''simple docstring'''
         __a					  =     spectrogram(
             lowerCamelCase			, window_function(self.fft_window_size			, 'hann'   )			, frame_length=self.fft_window_size			, hop_length=self.hop_length			, power=2.0			, mel_filters=lowerCamelCase			, log_mel='dB'			, )
         return log_mel_spectrogram.T




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->List[str]:



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

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

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




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->np.array:



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

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

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

         return input_mel, longer






      def __call__(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = None			, **lowerCamelCase			, )				->BatchFeature:



         '''simple docstring'''
         __a					  =     truncation if truncation is not None else self.truncation
         __a					  =     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.'   )

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

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

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

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

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

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

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

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

         __a					  =     {'input_features': input_mel, 'is_longer': is_longer}
         __a					  =     BatchFeature(lowerCamelCase   )

         if return_tensors is not None:
            __a					  =     input_features.convert_to_tensors(lowerCamelCase   )

         return input_features







'''simple docstring'''



from math import isqrt



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int     )	->      bool:

   """simple docstring"""
   return all(number % divisor != 0 for divisor in range(2,       isqrt(SCREAMING_SNAKE_CASE__     ) + 1     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int = 10**6     )	->      int:

   """simple docstring"""
   __a					  =     0
   __a					  =     1
   __a					  =     7
   while prime_candidate < max_prime:
      primes_count += is_prime(SCREAMING_SNAKE_CASE__     )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


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