


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


_snake_case     :		Tuple						=				logging.get_logger(__name__)

class 				a		(lowercase_      ):




							"""simple docstring"""


							__UpperCAmelCase    :							Optional[Any]       =  ['''input_features''', '''is_longer''']

							def __init__(							self       :  int    ,	lowerCamelCase       :  Tuple=64    ,	lowerCamelCase       :  str=48000    ,	lowerCamelCase       :  Dict=480    ,	lowerCamelCase       :  List[Any]=10    ,	lowerCamelCase       :  Dict=1024    ,	lowerCamelCase       :  Any=0.0    ,	lowerCamelCase       :  Optional[Any]=False    ,	lowerCamelCase       :  List[str] = 0    ,	lowerCamelCase       :  Any = 14000    ,	lowerCamelCase       :  int = None    ,	lowerCamelCase       :  Tuple = "fusion"    ,	lowerCamelCase       :  List[Any] = "repeatpad"    ,	**lowerCamelCase       :  Dict    ,	)	->    List[str]:
														super().__init__(
														    feature_size=lowerCamelCase    ,	sampling_rate=lowerCamelCase    ,	padding_value=lowerCamelCase    ,	return_attention_mask=lowerCamelCase    ,	**lowerCamelCase    ,	)
														__snake_case		:			int							 =   top_db
														__snake_case		:			Optional[int]							 =   truncation
														__snake_case		:			List[Any]							 =   padding
														__snake_case		:			int							 =   fft_window_size
														__snake_case		:			Union[str, Any]							 =   (fft_window_size >> 1) + 1
														__snake_case		:			str							 =   hop_length
														__snake_case		:			Dict							 =   max_length_s
														__snake_case		:			str							 =   max_length_s * sampling_rate
														__snake_case		:			Tuple							 =   sampling_rate
														__snake_case		:			Union[str, Any]							 =   frequency_min
														__snake_case		:			List[str]							 =   frequency_max
														__snake_case		:			int							 =   mel_filter_bank(
														    num_frequency_bins=self.nb_frequency_bins    ,	num_mel_filters=lowerCamelCase    ,	min_frequency=lowerCamelCase    ,	max_frequency=lowerCamelCase    ,	sampling_rate=lowerCamelCase    ,	norm=lowerCamelCase    ,	mel_scale="htk"    ,	)
														__snake_case		:			Optional[int]							 =   mel_filter_bank(
														    num_frequency_bins=self.nb_frequency_bins    ,	num_mel_filters=lowerCamelCase    ,	min_frequency=lowerCamelCase    ,	max_frequency=lowerCamelCase    ,	sampling_rate=lowerCamelCase    ,	norm="slaney"    ,	mel_scale="slaney"    ,	)

							def     __snake_case					(							self       :  Tuple				)	->    int:
														__snake_case		:			int							 =   copy.deepcopy(self.__dict__				)
														__snake_case		:			Tuple							 =   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       :  Dict    ,	lowerCamelCase       :  List[Any]    ,	lowerCamelCase       :  Union[str, Any] = None				)	->    Dict:
														__snake_case		:			Optional[Any]							 =   spectrogram(
														    lowerCamelCase    ,	window_function(self.fft_window_size    ,	"hann"				)    ,	frame_length=self.fft_window_size    ,	hop_length=self.hop_length    ,	power=2.0    ,	mel_filters=lowerCamelCase    ,	log_mel="dB"    ,	)
														return log_mel_spectrogram.T

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

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

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

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

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

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

														return input_mel, longer






							def __call__(							self       :  Optional[int]    ,	lowerCamelCase       :  Union[str, Any]    ,	lowerCamelCase       :  Optional[Any] = None    ,	lowerCamelCase       :  Union[str, Any] = None    ,	lowerCamelCase       :  Union[str, Any] = None    ,	lowerCamelCase       :  List[Any] = None    ,	lowerCamelCase       :  Tuple = None    ,	**lowerCamelCase       :  Any    ,	)	->    int:
														__snake_case		:			List[Any]							 =   truncation if truncation is not None else self.truncation
														__snake_case		:			str							 =   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		:			int							 =   isinstance(lowerCamelCase    ,	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		:			Optional[int]							 =   is_batched_numpy or (
														    isinstance(lowerCamelCase    ,	(list, tuple)				) and (isinstance(raw_speech[0]    ,	(np.ndarray, tuple, list)				))
														)

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

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

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

														__snake_case		:			Tuple							 =   []
														__snake_case		:			Any							 =   []
														for mel, longer in padded_inputs:
																					input_mel.append(lowerCamelCase				)
																					is_longer.append(lowerCamelCase				)

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

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

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

														__snake_case		:			List[Any]							 =   {"input_features": input_mel, "is_longer": is_longer}
														__snake_case		:			int							 =   BatchFeature(lowerCamelCase				)

														if return_tensors is not None:
																					__snake_case		:			List[str]							 =   input_features.convert_to_tensors(lowerCamelCase				)

														return input_features




from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  lowercase				(       lowercase_			):




				@staticmethod
				@abstractmethod
				def     a							(					snake_case       ):
											raise NotImplementedError()




				@abstractmethod
				def     a							(					self       ):
											raise NotImplementedError()




