





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 							__lowercase    ( UpperCAmelCase_  ):







	'''simple docstring'''

	a      :			Any     =    ["""input_features""", """is_longer"""]


	def __init__(self       ,_lowerCamelCase=64       ,_lowerCamelCase=48000       ,_lowerCamelCase=480       ,_lowerCamelCase=10       ,_lowerCamelCase=1024       ,_lowerCamelCase=0.0       ,_lowerCamelCase=False       ,_lowerCamelCase = 0       ,_lowerCamelCase = 14000       ,_lowerCamelCase = None       ,_lowerCamelCase = "fusion"       ,_lowerCamelCase = "repeatpad"       ,**_lowerCamelCase       ,)							->							int:







				'''simple docstring'''





				super().__init__(
				    feature_size=__lowercase       ,sampling_rate=__lowercase       ,padding_value=__lowercase       ,return_attention_mask=__lowercase       ,**__lowercase       ,)
				__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=__lowercase       ,min_frequency=__lowercase       ,max_frequency=__lowercase       ,sampling_rate=__lowercase       ,norm=__lowercase       ,mel_scale='''htk'''       ,)
				__lowercase			  =							mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins       ,num_mel_filters=__lowercase       ,min_frequency=__lowercase       ,max_frequency=__lowercase       ,sampling_rate=__lowercase       ,norm='''slaney'''       ,mel_scale='''slaney'''       ,)


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







				'''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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							np.ndarray:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							List[str]:







				'''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(
				    __lowercase       ,size=[chunk_frames, 64]       ,mode='''bilinear'''       ,align_corners=__lowercase	)
				__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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							np.array:







				'''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(__lowercase	) - max_length
										__lowercase			  =							np.random.randint(0       ,overflow + 1	)
										__lowercase			  =							waveform[idx : idx + max_length]
										__lowercase			  =							self._np_extract_fbank_features(__lowercase       ,self.mel_filters_slaney	)[None, :]
							elif truncation == "fusion":
										__lowercase			  =							self._np_extract_fbank_features(__lowercase       ,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(__lowercase       ,__lowercase       ,__lowercase	)
													__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(__lowercase	)	)
													__lowercase			  =							np.stack(np.tile(__lowercase       ,n_repeat + 1	)	)[:max_length]
										if padding == "repeatpad":
													__lowercase			  =							int(max_length / len(__lowercase	)	)
													__lowercase			  =							np.stack(np.tile(__lowercase       ,__lowercase	)	)
										__lowercase			  =							np.pad(__lowercase       ,(0, max_length - waveform.shape[0])       ,mode='''constant'''       ,constant_values=0	)

							if truncation == "fusion":
										__lowercase			  =							self._np_extract_fbank_features(__lowercase       ,self.mel_filters	)
										__lowercase			  =							np.stack([input_mel, input_mel, input_mel, input_mel]       ,axis=0	)
							else:
										__lowercase			  =							self._np_extract_fbank_features(__lowercase       ,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'''





				__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(__lowercase       ,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(__lowercase       ,(list, tuple)	) and (isinstance(raw_speech[0]       ,(np.ndarray, tuple, list)	))
				)

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

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

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

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

				if isinstance(input_mel[0]       ,__lowercase	):
							__lowercase			  =							[np.asarray(__lowercase       ,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(__lowercase	)

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

				return input_features












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					int = 6_0_0_8_5_1_4_7_5_1_4_3			):

			try:
						__lowercase			  =							int(lowerCamelCase_			)
			except (TypeError, ValueError):
						raise TypeError('''Parameter n must be int or castable to int.'''			)
			if n <= 0:
						raise ValueError('''Parameter n must be greater than or equal to one.'''			)
			__lowercase			  =							1
			__lowercase			  =							2
			while i * i <= n:
						while n % i == 0:
									__lowercase			  =							i
									n //= i
						i += 1
			if n > 1:
						__lowercase			  =							n
			return int(lowerCamelCase_			)


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





