
"""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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







class 							__magic_name__     (		__lowercase   ):
					_SCREAMING_SNAKE_CASE       :					int     							=   ['input_features', 'is_longer']





					def __init__(				self   :							Tuple							,						snake_case_   :							Tuple=64							,						snake_case_   :							Any=48000							,						snake_case_   :							List[str]=480							,						snake_case_   :							Optional[int]=10							,						snake_case_   :							List[Any]=1024							,						snake_case_   :							str=0.0							,						snake_case_   :							Union[str, Any]=False							,						snake_case_   :							float = 0							,						snake_case_   :							float = 14000							,						snake_case_   :							int = None							,						snake_case_   :							str = "fusion"							,						snake_case_   :							str = "repeatpad"							,						**snake_case_   :							Union[str, Any]							,						):
												super().__init__(
												    feature_size=_A							,						sampling_rate=_A							,						padding_value=_A							,						return_attention_mask=_A							,						**_A							,						)
												__snake_case     			=       top_db
												__snake_case     			=       truncation
												__snake_case     			=       padding
												__snake_case     			=       fft_window_size
												__snake_case     			=       (fft_window_size >> 1) + 1
												__snake_case     			=       hop_length
												__snake_case     			=       max_length_s
												__snake_case     			=       max_length_s * sampling_rate
												__snake_case     			=       sampling_rate
												__snake_case     			=       frequency_min
												__snake_case     			=       frequency_max
												__snake_case     			=       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"							,						)
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							int    ):
												__snake_case     			=       copy.deepcopy(self.__dict__    )
												__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   :							List[Any]							,						snake_case_   :							np.array							,						snake_case_   :							Optional[np.array] = None    ):
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							List[Any]							,						snake_case_   :							Optional[Any]							,						snake_case_   :							List[Any]    ):
												__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
																			__snake_case     			=       [0]
												if len(ranges[2]    ) == 0:
																			# if the audio is too short, we just use the first chunk
																			__snake_case     			=       [0]
												# randomly choose index for each part
												__snake_case     			=       np.random.choice(ranges[0]    )
												__snake_case     			=       np.random.choice(ranges[1]    )
												__snake_case     			=       np.random.choice(ranges[2]    )

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

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





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

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

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

												return input_mel, longer





					def __call__(				self   :							str							,						snake_case_   :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,						snake_case_   :							str = None							,						snake_case_   :							Optional[str] = None							,						snake_case_   :							Optional[int] = None							,						snake_case_   :							Optional[int] = None							,						snake_case_   :							Optional[Union[str, TensorType]] = None							,						**snake_case_   :							str							,						):
												__snake_case     			=       truncation if truncation is not None else self.truncation
												__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."    )

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

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

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

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

												__snake_case     			=       []
												__snake_case     			=       []
												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
																			__snake_case     			=       np.random.randint(0							,						len(_A    )    )
																			__snake_case     			=       True

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

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

												__snake_case     			=       {"input_features": input_mel, "is_longer": is_longer}
												__snake_case     			=       BatchFeature(_A    )

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

												return input_features


"""simple docstring"""







from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def   __UpperCamelCase   (					)  ->     tuple[list[int], int]:







							"""simple docstring"""


							__snake_case     			=       [randint(-10_00							,    10_00 ) for i in range(10 )]
							__snake_case     			=       randint(-50_00							,    50_00 )
							return (arr, r)


_SCREAMING_SNAKE_CASE	       =      make_dataset()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     tuple[int, ...]:







							"""simple docstring"""


							for triplet in permutations(SCREAMING_SNAKE_CASE							,    3 ):
														if sum(SCREAMING_SNAKE_CASE ) == target:
																					return tuple(sorted(SCREAMING_SNAKE_CASE ) )
							return (0, 0, 0)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     tuple[int, int, int]:







							"""simple docstring"""


							arr.sort()
							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							for i in range(n - 1 ):
														__snake_case			,   __snake_case     			=       i + 1, n - 1
														while left < right:
																					if arr[i] + arr[left] + arr[right] == target:
																												return (arr[i], arr[left], arr[right])
																					elif arr[i] + arr[left] + arr[right] < target:
																												left += 1
																					elif arr[i] + arr[left] + arr[right] > target:
																												right -= 1
							return (0, 0, 0)
def   __UpperCamelCase   (					)  ->     tuple[float, float]:







							"""simple docstring"""


							__snake_case     			=       "\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n"
							__snake_case     			=       "\ntriplet_sum1(*dataset)\n"
							__snake_case     			=       "\ntriplet_sum2(*dataset)\n"
							__snake_case     			=       repeat(setup=SCREAMING_SNAKE_CASE							,    stmt=SCREAMING_SNAKE_CASE							,    repeat=5							,    number=1_00_00 )
							__snake_case     			=       repeat(setup=SCREAMING_SNAKE_CASE							,    stmt=SCREAMING_SNAKE_CASE							,    repeat=5							,    number=1_00_00 )
							return (min(SCREAMING_SNAKE_CASE ), min(SCREAMING_SNAKE_CASE ))


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_SCREAMING_SNAKE_CASE	       =      solution_times()
		print(F"""The time for naive implementation is {times[0]}.""")
		print(F"""The time for optimized implementation is {times[1]}.""")
