

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


UpperCAmelCase_							:					str		 =   logging.get_logger(__name__)






class      lowerCamelCase_      (	lowerCamelCase__	):
	_lowercase		:   List[str]     					=				['''input_features''', '''is_longer''']




	def __init__(     self						:     Optional[int]					,      __A						:     str=64					,      __A						:     List[str]=4_8000					,      __A						:     str=480					,      __A						:     Optional[Any]=10					,      __A						:     List[str]=1024					,      __A						:     List[str]=0.0					,      __A						:     int=False					,      __A						:     int = 0					,      __A						:     Dict = 1_4000					,      __A						:     Union[str, Any] = None					,      __A						:     int = "fusion"					,      __A						:     List[Any] = "repeatpad"					,      **__A						:     Union[str, Any]					,      ):
		super().__init__(
		    feature_size=a__					,      sampling_rate=a__					,      padding_value=a__					,      return_attention_mask=a__					,      **a__					,      )
		__A :			int		    =			top_db
		__A :			List[str]		    =			truncation
		__A :			Tuple		    =			padding
		__A :			List[str]		    =			fft_window_size
		__A :			int		    =			(fft_window_size >> 1) + 1
		__A :			int		    =			hop_length
		__A :			Any		    =			max_length_s
		__A :			str		    =			max_length_s * sampling_rate
		__A :			Union[str, Any]		    =			sampling_rate
		__A :			int		    =			frequency_min
		__A :			Dict		    =			frequency_max
		__A :			Union[str, Any]		    =			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"""					,      )
		__A :			Any		    =			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						:     str							):
		__A :			Optional[Any]		    =			copy.deepcopy(self.__dict__							)
		__A :			Union[str, Any]		    =			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						:     Any					,      __A						:     Union[str, Any]					,      __A						:     str = None							):
		__A :			Any		    =			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						:     Any					,      __A						:     List[Any]					,      __A						:     Any					,      __A						:     Union[str, Any]							):
		__A :			Union[str, 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
			__A :			List[str]		    =			[0]
		if len(ranges[2]							) == 0:
			# if the audio is too short, we just use the first chunk
			__A :			Union[str, Any]		    =			[0]
		# randomly choose index for each part
		__A :			List[str]		    =			np.random.choice(ranges[0]							)
		__A :			Tuple		    =			np.random.choice(ranges[1]							)
		__A :			str		    =			np.random.choice(ranges[2]							)

		__A :			Union[str, Any]		    =			mel[idx_front : idx_front + chunk_frames, :]
		__A :			Optional[Any]		    =			mel[idx_middle : idx_middle + chunk_frames, :]
		__A :			Any		    =			mel[idx_back : idx_back + chunk_frames, :]

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




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     List[str]					,      __A						:     int					,      __A						:     List[str]					,      __A						:     int							):





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

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

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

		return input_mel, longer




	def __call__(     self						:     Tuple					,      __A						:     Dict					,      __A						:     Dict = None					,      __A						:     Optional[int] = None					,      __A						:     List[Any] = None					,      __A						:     Optional[Any] = None					,      __A						:     str = None					,      **__A						:     Optional[int]					,      ):
		__A :			str		    =			truncation if truncation is not None else self.truncation
		__A :			Optional[int]		    =			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 :			Any		    =			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}"""							)
		__A :			Optional[Any]		    =			is_batched_numpy or (
		    isinstance(a__					,      (list, tuple)							) and (isinstance(raw_speech[0]					,      (np.ndarray, tuple, list)							))
		)

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

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

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

		__A :			str		    =			[]
		__A :			List[str]		    =			[]
		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
			__A :			List[Any]		    =			np.random.randint(0					,      len(a__							)							)
			__A :			Tuple		    =			True

		if isinstance(input_mel[0]					,      a__							):
			__A :			Dict		    =			[np.asarray(a__					,      dtype=np.floataa							) for feature in input_mel]

		# is_longer is a list of bool
		__A :			List[str]		    =			[[longer] for longer in is_longer]

		__A :			Optional[int]		    =			{'''input_features''': input_mel, '''is_longer''': is_longer}
		__A :			Tuple		    =			BatchFeature(a__							)

		if return_tensors is not None:
			__A :			Any		    =			input_features.convert_to_tensors(a__							)

		return input_features












import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   ["""input_features"""]


      def __init__(		self   ,				a__=80   ,				a__=1_6000   ,				a__=160   ,				a__=30   ,				a__=400   ,				a__=0.0   ,				a__=False   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(
                 feature_size=a__   ,				sampling_rate=a__   ,				padding_value=a__   ,				return_attention_mask=a__   ,				**a__   ,				)
             _lowerCamelCase				:  int						=		n_fft
             _lowerCamelCase				:  List[Any]						=		hop_length
             _lowerCamelCase				:  Tuple						=		chunk_length
             _lowerCamelCase				:  Optional[Any]						=		chunk_length * sampling_rate
             _lowerCamelCase				:  Optional[Any]						=		self.n_samples // hop_length
             _lowerCamelCase				:  List[str]						=		sampling_rate
             _lowerCamelCase				:  Optional[int]						=		mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2   ,				num_mel_filters=a__   ,				min_frequency=0.0   ,				max_frequency=8000.0   ,				sampling_rate=a__   ,				norm='''slaney'''   ,				mel_scale='''slaney'''   ,				)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		spectrogram(
                 a__   ,				window_function(self.n_fft   ,				'''hann''')   ,				frame_length=self.n_fft   ,				hop_length=self.hop_length   ,				power=2.0   ,				mel_filters=self.mel_filters   ,				log_mel='''log10'''   ,				)
             _lowerCamelCase				:  str						=		log_spec[:, :-1]
             _lowerCamelCase				:  Union[str, Any]						=		np.maximum(a__   ,				log_spec.max() - 8.0)
             _lowerCamelCase				:  Optional[int]						=		(log_spec + 4.0) / 4.0
             return log_spec


      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def 					__snake_case      (		a__   ,				a__   ,				a__ = 0.0):



             """simple docstring"""





             if attention_mask is not None:
                    _lowerCamelCase				:  Dict						=		np.array(a__   ,				np.intaa)
                    _lowerCamelCase				:  Any						=		[]

                    for vector, length in zip(a__   ,				attention_mask.sum(-1)):
                           _lowerCamelCase				:  Any						=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
                           if length < normed_slice.shape[0]:
                                  _lowerCamelCase				:  Optional[int]						=		padding_value

                           normed_input_values.append(a__)
             else:
                    _lowerCamelCase				:  Optional[int]						=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]

             return normed_input_values


      def __call__(		self   ,				a__   ,				a__ = True   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				a__ = "max_length"   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             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				:  Dict						=		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}""")
             _lowerCamelCase				:  int						=		is_batched_numpy or (
                 isinstance(a__   ,				(list, tuple)) and (isinstance(raw_speech[0]   ,				(np.ndarray, tuple, list)))
             )

             if is_batched:
                    _lowerCamelCase				:  List[Any]						=		[np.asarray([speech]   ,				dtype=np.floataa).T for speech in raw_speech]
             elif not is_batched and not isinstance(a__   ,				np.ndarray):
                    _lowerCamelCase				:  str						=		np.asarray(a__   ,				dtype=np.floataa)
             elif isinstance(a__   ,				np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
                    _lowerCamelCase				:  str						=		raw_speech.astype(np.floataa)

             # always return batch
             if not is_batched:
                    _lowerCamelCase				:  List[Any]						=		[np.asarray([raw_speech]).T]

             _lowerCamelCase				:  List[str]						=		BatchFeature({'''input_features''': raw_speech})

             # convert into correct format for padding

             _lowerCamelCase				:  Optional[Any]						=		self.pad(
                 a__   ,				padding=a__   ,				max_length=max_length if max_length else self.n_samples   ,				truncation=a__   ,				pad_to_multiple_of=a__   ,				return_attention_mask=return_attention_mask or do_normalize   ,				)

             # zero-mean and unit-variance normalization
             if do_normalize:
                    _lowerCamelCase				:  Dict						=		self.zero_mean_unit_var_norm(
                        padded_inputs['''input_features''']   ,				attention_mask=padded_inputs['''attention_mask''']   ,				padding_value=self.padding_value   ,				)
                    _lowerCamelCase				:  int						=		np.stack(padded_inputs['''input_features''']   ,				axis=0)

             # make sure list is in array format
             _lowerCamelCase				:  Optional[Any]						=		padded_inputs.get('''input_features''').transpose(2   ,				0   ,				1)

             _lowerCamelCase				:  Tuple						=		[self._np_extract_fbank_features(a__) for waveform in input_features[0]]

             if isinstance(input_features[0]   ,				a__):
                    _lowerCamelCase				:  str						=		[np.asarray(a__   ,				dtype=np.floataa) for feature in input_features]
             else:
                    _lowerCamelCase				:  Dict						=		input_features

             if return_attention_mask:
                    # rescale from sample (48000) to feature (3000)
                    _lowerCamelCase				:  Optional[int]						=		padded_inputs['''attention_mask'''][:, :: self.hop_length]

             if return_tensors is not None:
                    _lowerCamelCase				:  str						=		padded_inputs.convert_to_tensors(a__)

             return padded_inputs



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		copy.deepcopy(self.__dict__)
             _lowerCamelCase				:  int						=		self.__class__.__name__
             if "mel_filters" in output:
                    del output["mel_filters"]
             return output

