

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					:			str  				=			logging.get_logger(__name__)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						['input_features', 'is_longer']





			def __init__(		self					,  __snake_case=64					,  __snake_case=4_8000					,  __snake_case=480					,  __snake_case=10					,  __snake_case=1024					,  __snake_case=0.0					,  __snake_case=False					,  __snake_case = 0					,  __snake_case = 1_4000					,  __snake_case = None					,  __snake_case = "fusion"					,  __snake_case = "repeatpad"					,  **__snake_case					,  )   -> Tuple:




						'''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 		__magic_name__    (		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 		__magic_name__    (		self					,  __snake_case					,  __snake_case = 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 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					)   -> int:




						'''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 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)   -> 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					,  __snake_case					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  **__snake_case					,  )   -> 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












from math import sqrt







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       bool:





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







def 			lowerCamelCase_		(       UpperCamelCase__				:      int = 1_0001      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    0
				__lowerCamelCase		       =    1
				while count != nth and number < 3:
								number += 1
								if is_prime(UpperCamelCase__      ):
												count += 1
				while count != nth:
								number += 2
								if is_prime(UpperCamelCase__      ):
												count += 1
				return number


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


