





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     :   List[str]						=   logging.get_logger(__name__)






class       __A					(					__lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		['''input_features''', '''is_longer''']





			def __init__(					self ,						__lowerCAmelCase=6_4 ,						__lowerCAmelCase=4_8_0_0_0 ,						__lowerCAmelCase=4_8_0 ,						__lowerCAmelCase=1_0 ,						__lowerCAmelCase=1_0_2_4 ,						__lowerCAmelCase=0.0 ,						__lowerCAmelCase=False ,						__lowerCAmelCase = 0 ,						__lowerCAmelCase = 1_4_0_0_0 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = "fusion" ,						__lowerCAmelCase = "repeatpad" ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				super().__init__(
				    feature_size=lowerCAmelCase_ ,						sampling_rate=lowerCAmelCase_ ,						padding_value=lowerCAmelCase_ ,						return_attention_mask=lowerCAmelCase_ ,						**lowerCAmelCase_ ,						)
				lowerCamelCase__              =	top_db
				lowerCamelCase__              =	truncation
				lowerCamelCase__              =	padding
				lowerCamelCase__              =	fft_window_size
				lowerCamelCase__              =	(fft_window_size >> 1) + 1
				lowerCamelCase__              =	hop_length
				lowerCamelCase__              =	max_length_s
				lowerCamelCase__              =	max_length_s * sampling_rate
				lowerCamelCase__              =	sampling_rate
				lowerCamelCase__              =	frequency_min
				lowerCamelCase__              =	frequency_max
				lowerCamelCase__              =	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''' ,						)
				lowerCamelCase__              =	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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	copy.deepcopy(self.__dict__							)
				lowerCamelCase__              =	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 ,						__lowerCAmelCase ,						__lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__              =	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 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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

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

				lowerCamelCase__              =	torch.tensor(mel[None, None, :]							)
				lowerCamelCase__              =	torch.nn.functional.interpolate(
				    lowerCAmelCase_ ,						size=[chunk_frames, 6_4] ,						mode='''bilinear''' ,						align_corners=lowerCAmelCase_							)
				lowerCamelCase__              =	mel_shrink[0][0].numpy()
				lowerCamelCase__              =	np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,						axis=0							)
				return mel_fusion





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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

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

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

				return input_mel, longer





			def __call__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







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

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

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

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

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

				lowerCamelCase__              =	[]
				lowerCamelCase__              =	[]
				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
					lowerCamelCase__              =	np.random.randint(0 ,						len(lowerCAmelCase_							)							)
					lowerCamelCase__              =	True

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

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

				lowerCamelCase__              =	{'''input_features''': input_mel, '''is_longer''': is_longer}
				lowerCamelCase__              =	BatchFeature(lowerCAmelCase_							)

				if return_tensors is not None:
					lowerCamelCase__              =	input_features.convert_to_tensors(lowerCAmelCase_							)

				return input_features










_a						=   "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_a						=   [{"type": "code", "content": INSTALL_CONTENT}]
_a						=   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



