


"""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     =  logging.get_logger(__name__)


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      int      							=					["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__       ,		):
								super().__init__(
								    feature_size=UpperCAmelCase__       ,		sampling_rate=UpperCAmelCase__       ,		padding_value=UpperCAmelCase__       ,		return_attention_mask=UpperCAmelCase__       ,		**UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				top_db
								SCREAMING_SNAKE_CASE__     							=				truncation
								SCREAMING_SNAKE_CASE__     							=				padding
								SCREAMING_SNAKE_CASE__     							=				fft_window_size
								SCREAMING_SNAKE_CASE__     							=				(fft_window_size >> 1) + 1
								SCREAMING_SNAKE_CASE__     							=				hop_length
								SCREAMING_SNAKE_CASE__     							=				max_length_s
								SCREAMING_SNAKE_CASE__     							=				max_length_s * sampling_rate
								SCREAMING_SNAKE_CASE__     							=				sampling_rate
								SCREAMING_SNAKE_CASE__     							=				frequency_min
								SCREAMING_SNAKE_CASE__     							=				frequency_max
								SCREAMING_SNAKE_CASE__     							=				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"       ,		)
								SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				copy.deepcopy(self.__dict__     )
								SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				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
												SCREAMING_SNAKE_CASE__     							=				[0]
								if len(ranges[2]     ) == 0:
												# if the audio is too short, we just use the first chunk
												SCREAMING_SNAKE_CASE__     							=				[0]
								# randomly choose index for each part
								SCREAMING_SNAKE_CASE__     							=				np.random.choice(ranges[0]     )
								SCREAMING_SNAKE_CASE__     							=				np.random.choice(ranges[1]     )
								SCREAMING_SNAKE_CASE__     							=				np.random.choice(ranges[2]     )

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

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



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

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

												if truncation == "fusion":
																SCREAMING_SNAKE_CASE__     							=				self._np_extract_fbank_features(UpperCAmelCase__       ,		self.mel_filters     )
																SCREAMING_SNAKE_CASE__     							=				np.stack([input_mel, input_mel, input_mel, input_mel]       ,		axis=0     )
												else:
																SCREAMING_SNAKE_CASE__     							=				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__       ,		):
								SCREAMING_SNAKE_CASE__     							=				truncation if truncation is not None else self.truncation
								SCREAMING_SNAKE_CASE__     							=				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."     )

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

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

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

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

								SCREAMING_SNAKE_CASE__     							=				[]
								SCREAMING_SNAKE_CASE__     							=				[]
								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
												SCREAMING_SNAKE_CASE__     							=				np.random.randint(0       ,		len(UpperCAmelCase__     )     )
												SCREAMING_SNAKE_CASE__     							=				True

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

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

								SCREAMING_SNAKE_CASE__     							=				{"input_features": input_mel, "is_longer": is_longer}
								SCREAMING_SNAKE_CASE__     							=				BatchFeature(UpperCAmelCase__     )

								if return_tensors is not None:
												SCREAMING_SNAKE_CASE__     							=				input_features.convert_to_tensors(UpperCAmelCase__     )

								return input_features




"""simple docstring"""






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors


def __lowercase     (      lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				prime_factors(lowerCamelCase_  )
				if is_square_free(lowerCamelCase_  ):
								return -1 if len(lowerCamelCase_  ) % 2 else 1
				return 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()
