



"""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


A    =			logging.get_logger(__name__)






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     ["""input_features""", """is_longer"""]



		def __init__( self  :    Optional[Any]      ,							snake_case  :    Optional[int]=64      ,							snake_case  :    str=48_000      ,							snake_case  :    int=480      ,							snake_case  :    Dict=10      ,							snake_case  :    Optional[int]=1_024      ,							snake_case  :    Optional[Any]=0.0      ,							snake_case  :    Union[str, Any]=False      ,							snake_case  :    float = 0      ,							snake_case  :    float = 14_000      ,							snake_case  :    int = None      ,							snake_case  :    str = "fusion"      ,							snake_case  :    str = "repeatpad"      ,							**snake_case  :    int      ,							)			->						List[Any]:






									'''simple docstring'''

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



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






									'''simple docstring'''

									A         =					copy.deepcopy(self.__dict__				)
									A         =					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 					A_		( self  :    Tuple      ,							snake_case  :    np.array      ,							snake_case  :    Optional[np.array] = None				)			->						np.ndarray:






									'''simple docstring'''

									A         =					spectrogram(
									    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=snake_case      ,							log_mel='dB'      ,							)
									return log_mel_spectrogram.T



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    Optional[int]      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    List[Any]				)			->						Any:






									'''simple docstring'''

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

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

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



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.array      ,							snake_case  :    Optional[int]      ,							snake_case  :    List[Any]      ,							snake_case  :    str				)			->						np.array:






									'''simple docstring'''

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

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

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

									return input_mel, longer



		def __call__( self  :    Tuple      ,							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  :    int      ,							)			->						BatchFeature:






									'''simple docstring'''

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

									A         =					isinstance(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}"""				)
									A         =					is_batched_numpy or (
									    isinstance(snake_case      ,							(list, tuple)				) and (isinstance(raw_speech[0]      ,							(np.ndarray, tuple, list)				))
									)

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

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

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

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

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

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

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

									A         =					{'input_features': input_mel, 'is_longer': is_longer}
									A         =					BatchFeature(snake_case				)

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

									return input_features








"""simple docstring"""







from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A    =			TypeVar('T')






class      UpperCAmelCase__		(      Generic[T]     ):
		lowerCAmelCase_       :	deque[T]  # Cache store of keys
		lowerCAmelCase_       :	set[T]  # References of the keys in cache
		lowerCAmelCase_       :	int 							=     10  # Maximum capacity of cache



		def __init__( self  :    int      ,							snake_case  :    int				)			->						None:






									'''simple docstring'''

									A         =					deque()
									A         =					set()
									if not n:
																A         =					sys.maxsize
									elif n < 0:
																raise ValueError('n should be an integer greater than 0.'				)
									else:
																A         =					n



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    T				)			->						None:






									'''simple docstring'''

									if x not in self.key_reference:
																if len(self.dq_store				) == LRUCache._MAX_CAPACITY:
																							A         =					self.dq_store.pop()
																							self.key_reference.remove(snake_case				)
									else:
																self.dq_store.remove(snake_case				)

									self.dq_store.appendleft(snake_case				)
									self.key_reference.add(snake_case				)



		def 					A_		( self  :    Dict				)			->						None:






									'''simple docstring'''

									for k in self.dq_store:
																print(snake_case				)



		def __repr__( self  :    int				)			->						str:






									'''simple docstring'''

									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store				)}"""


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							A    =			LRUCache(4)
							lru_cache.refer('A')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('A')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



