
'''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					:    List[Any]           = logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							["""input_features""", """is_longer"""]







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







											'''simple docstring'''






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







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







											'''simple docstring'''






											UpperCamelCase   			=     copy.deepcopy(self.__dict__     )
											UpperCamelCase   			=     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						,				A_						,				A_ = None     )->		np.ndarray:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		Union[str, Any]:







											'''simple docstring'''






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

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

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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_						,				A_     )->		np.array:







											'''simple docstring'''






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

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

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

											return input_mel, longer







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







											'''simple docstring'''






											UpperCamelCase   			=     truncation if truncation is not None else self.truncation
											UpperCamelCase   			=     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.'     )

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

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

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

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

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

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

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

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

											UpperCamelCase   			=     {'input_features': input_mel, 'is_longer': is_longer}
											UpperCamelCase   			=     BatchFeature(A_     )

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

											return input_features










def      lowerCamelCase_						(			)	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       0
				for i in range(1					,	1001 ):
								total += i**i
				return str(UpperCamelCase__ )[-10:]


if __name__ == "__main__":
					print(solution())


