



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 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		["""input_features""", """is_longer"""]







					def __init__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     str=64				,       SCREAMING_SNAKE_CASE :     List[Any]=48_000				,       SCREAMING_SNAKE_CASE :     Optional[Any]=480				,       SCREAMING_SNAKE_CASE :     int=10				,       SCREAMING_SNAKE_CASE :     str=1_024				,       SCREAMING_SNAKE_CASE :     List[str]=0.0				,       SCREAMING_SNAKE_CASE :     Tuple=False				,       SCREAMING_SNAKE_CASE :     float = 0				,       SCREAMING_SNAKE_CASE :     float = 14_000				,       SCREAMING_SNAKE_CASE :     int = None				,       SCREAMING_SNAKE_CASE :     str = "fusion"				,       SCREAMING_SNAKE_CASE :     str = "repeatpad"				,       **SCREAMING_SNAKE_CASE :     Any				,       ):
									super().__init__(
									    feature_size=SCREAMING_SNAKE_CASE				,       sampling_rate=SCREAMING_SNAKE_CASE				,       padding_value=SCREAMING_SNAKE_CASE				,       return_attention_mask=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE				,       )
									lowercase__					:   List[Any]													=					top_db
									lowercase__					:   List[str]													=					truncation
									lowercase__					:   Union[str, Any]													=					padding
									lowercase__					:   str													=					fft_window_size
									lowercase__					:   Optional[int]													=					(fft_window_size >> 1) + 1
									lowercase__					:   Optional[int]													=					hop_length
									lowercase__					:   Dict													=					max_length_s
									lowercase__					:   Dict													=					max_length_s * sampling_rate
									lowercase__					:   Any													=					sampling_rate
									lowercase__					:   Any													=					frequency_min
									lowercase__					:   Optional[Any]													=					frequency_max
									lowercase__					:   Tuple													=					mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins				,       num_mel_filters=SCREAMING_SNAKE_CASE				,       min_frequency=SCREAMING_SNAKE_CASE				,       max_frequency=SCREAMING_SNAKE_CASE				,       sampling_rate=SCREAMING_SNAKE_CASE				,       norm=SCREAMING_SNAKE_CASE				,       mel_scale="htk"				,       )
									lowercase__					:   Union[str, Any]													=					mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins				,       num_mel_filters=SCREAMING_SNAKE_CASE				,       min_frequency=SCREAMING_SNAKE_CASE				,       max_frequency=SCREAMING_SNAKE_CASE				,       sampling_rate=SCREAMING_SNAKE_CASE				,       norm="slaney"				,       mel_scale="slaney"				,       )







					def 			snake_case					(							self :     List[str]					):
									lowercase__					:   Optional[Any]													=					copy.deepcopy(self.__dict__					)
									lowercase__					:   Dict													=					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 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     np.array				,       SCREAMING_SNAKE_CASE :     Optional[np.array] = None					):
									lowercase__					:   Optional[Any]													=					spectrogram(
									    SCREAMING_SNAKE_CASE				,       window_function(self.fft_window_size				,       "hann"					)				,       frame_length=self.fft_window_size				,       hop_length=self.hop_length				,       power=2.0				,       mel_filters=SCREAMING_SNAKE_CASE				,       log_mel="dB"				,       )
									return log_mel_spectrogram.T







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     List[Any]					):
									lowercase__					:   Optional[Any]													=					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__					:   List[Any]													=					[0]
									if len(ranges[2]					) == 0:
													# if the audio is too short, we just use the first chunk
													lowercase__					:   List[str]													=					[0]
									# randomly choose index for each part
									lowercase__					:   Tuple													=					np.random.choice(ranges[0]					)
									lowercase__					:   Dict													=					np.random.choice(ranges[1]					)
									lowercase__					:   Optional[int]													=					np.random.choice(ranges[2]					)

									lowercase__					:   Tuple													=					mel[idx_front : idx_front + chunk_frames, :]
									lowercase__					:   Optional[int]													=					mel[idx_middle : idx_middle + chunk_frames, :]
									lowercase__					:   int													=					mel[idx_back : idx_back + chunk_frames, :]

									lowercase__					:   Dict													=					torch.tensor(mel[None, None, :]					)
									lowercase__					:   int													=					torch.nn.functional.interpolate(
									    SCREAMING_SNAKE_CASE				,       size=[chunk_frames, 64]				,       mode="bilinear"				,       align_corners=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					mel_shrink[0][0].numpy()
									lowercase__					:   List[Any]													=					np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]				,       axis=0					)
									return mel_fusion







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

									else:
													lowercase__					:   Optional[int]													=					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__					:   Optional[Any]													=					int(max_length / len(SCREAMING_SNAKE_CASE					)					)
																					lowercase__					:   Tuple													=					np.stack(np.tile(SCREAMING_SNAKE_CASE				,       n_repeat + 1					)					)[:max_length]
																	if padding == "repeatpad":
																					lowercase__					:   Dict													=					int(max_length / len(SCREAMING_SNAKE_CASE					)					)
																					lowercase__					:   List[Any]													=					np.stack(np.tile(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)					)
																	lowercase__					:   Dict													=					np.pad(SCREAMING_SNAKE_CASE				,       (0, max_length - waveform.shape[0])				,       mode="constant"				,       constant_values=0					)

													if truncation == "fusion":
																	lowercase__					:   Tuple													=					self._np_extract_fbank_features(SCREAMING_SNAKE_CASE				,       self.mel_filters					)
																	lowercase__					:   str													=					np.stack([input_mel, input_mel, input_mel, input_mel]				,       axis=0					)
													else:
																	lowercase__					:   Optional[int]													=					self._np_extract_fbank_features(SCREAMING_SNAKE_CASE				,       self.mel_filters_slaney					)[None, :]

									return input_mel, longer







					def __call__(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,       SCREAMING_SNAKE_CASE :     str = None				,       SCREAMING_SNAKE_CASE :     Optional[str] = None				,       SCREAMING_SNAKE_CASE :     Optional[int] = None				,       SCREAMING_SNAKE_CASE :     Optional[int] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, TensorType]] = None				,       **SCREAMING_SNAKE_CASE :     List[str]				,       ):
									lowercase__					:   Optional[int]													=					truncation if truncation is not None else self.truncation
									lowercase__					:   Any													=					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__					:   Optional[int]													=					isinstance(SCREAMING_SNAKE_CASE				,       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__					:   List[Any]													=					is_batched_numpy or (
									    isinstance(SCREAMING_SNAKE_CASE				,       (list, tuple)					) and (isinstance(raw_speech[0]				,       (np.ndarray, tuple, list)					))
									)

									if is_batched:
													lowercase__					:   int													=					[np.asarray(SCREAMING_SNAKE_CASE				,       dtype=np.floataa					) for speech in raw_speech]
									elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE				,       np.ndarray					):
													lowercase__					:   Union[str, Any]													=					np.asarray(SCREAMING_SNAKE_CASE				,       dtype=np.floataa					)
									elif isinstance(SCREAMING_SNAKE_CASE				,       np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
													lowercase__					:   str													=					raw_speech.astype(np.floataa					)

									# always return batch
									if not is_batched:
													lowercase__					:   Optional[Any]													=					[np.asarray(SCREAMING_SNAKE_CASE					)]

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

									lowercase__					:   List[Any]													=					[]
									lowercase__					:   int													=					[]
									for mel, longer in padded_inputs:
													input_mel.append(SCREAMING_SNAKE_CASE					)
													is_longer.append(SCREAMING_SNAKE_CASE					)

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

									if isinstance(input_mel[0]				,       SCREAMING_SNAKE_CASE					):
													lowercase__					:   List[Any]													=					[np.asarray(SCREAMING_SNAKE_CASE				,       dtype=np.floataa					) for feature in input_mel]

									# is_longer is a list of bool
									lowercase__					:   Optional[Any]													=					[[longer] for longer in is_longer]

									lowercase__					:   Dict													=					{"input_features": input_mel, "is_longer": is_longer}
									lowercase__					:   Optional[Any]													=					BatchFeature(SCREAMING_SNAKE_CASE					)

									if return_tensors is not None:
													lowercase__					:   List[Any]													=					input_features.convert_to_tensors(SCREAMING_SNAKE_CASE					)

									return input_features









def  __lowerCamelCase (       lowerCamelCase__ = 1_000						):

				"""simple docstring"""


				lowercase__							,							lowercase__					:   int													=					1, 1
				lowercase__					:   List[Any]													=					[]
				for i in range(1					,			n + 1						):
								lowercase__					:   Dict													=					prev_numerator + 2 * prev_denominator
								lowercase__					:   Tuple													=					prev_numerator + prev_denominator
								if len(str(lowerCamelCase__						)						) > len(str(lowerCamelCase__						)						):
												result.append(lowerCamelCase__						)
								lowercase__					:   int													=					numerator
								lowercase__					:   int													=					denominator

				return len(lowerCamelCase__						)


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




