




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       _A				(		__lowercase     ):
  __a					    =       ["""input_features""", """is_longer"""]






  def __init__(    self						,		_SCREAMING_SNAKE_CASE=64						,		_SCREAMING_SNAKE_CASE=4_8000						,		_SCREAMING_SNAKE_CASE=480						,		_SCREAMING_SNAKE_CASE=10						,		_SCREAMING_SNAKE_CASE=1024						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE = 0						,		_SCREAMING_SNAKE_CASE = 1_4000						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = "fusion"						,		_SCREAMING_SNAKE_CASE = "repeatpad"						,		**_SCREAMING_SNAKE_CASE						,		):
        super().__init__(
            feature_size=_SCREAMING_SNAKE_CASE						,		sampling_rate=_SCREAMING_SNAKE_CASE						,		padding_value=_SCREAMING_SNAKE_CASE						,		return_attention_mask=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE						,		)
        _UpperCAmelCase      =			top_db
        _UpperCAmelCase      =			truncation
        _UpperCAmelCase      =			padding
        _UpperCAmelCase      =			fft_window_size
        _UpperCAmelCase      =			(fft_window_size >> 1) + 1
        _UpperCAmelCase      =			hop_length
        _UpperCAmelCase      =			max_length_s
        _UpperCAmelCase      =			max_length_s * sampling_rate
        _UpperCAmelCase      =			sampling_rate
        _UpperCAmelCase      =			frequency_min
        _UpperCAmelCase      =			frequency_max
        _UpperCAmelCase      =			mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins						,		num_mel_filters=_SCREAMING_SNAKE_CASE						,		min_frequency=_SCREAMING_SNAKE_CASE						,		max_frequency=_SCREAMING_SNAKE_CASE						,		sampling_rate=_SCREAMING_SNAKE_CASE						,		norm=_SCREAMING_SNAKE_CASE						,		mel_scale="""htk"""						,		)
        _UpperCAmelCase      =			mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins						,		num_mel_filters=_SCREAMING_SNAKE_CASE						,		min_frequency=_SCREAMING_SNAKE_CASE						,		max_frequency=_SCREAMING_SNAKE_CASE						,		sampling_rate=_SCREAMING_SNAKE_CASE						,		norm="""slaney"""						,		mel_scale="""slaney"""						,		)






  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			copy.deepcopy(self.__dict__			)
        _UpperCAmelCase      =			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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
        _UpperCAmelCase      =			spectrogram(
            _SCREAMING_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=_SCREAMING_SNAKE_CASE						,		log_mel="""dB"""						,		)
        return log_mel_spectrogram.T






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

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

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






  def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):







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

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

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

        return input_mel, longer






  def __call__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		**_SCREAMING_SNAKE_CASE						,		):
        _UpperCAmelCase      =			truncation if truncation is not None else self.truncation
        _UpperCAmelCase      =			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."""			)

        _UpperCAmelCase      =			isinstance(_SCREAMING_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}"			)
        _UpperCAmelCase      =			is_batched_numpy or (
            isinstance(_SCREAMING_SNAKE_CASE						,		(list, tuple)			) and (isinstance(raw_speech[0]						,		(np.ndarray, tuple, list)			))
        )

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

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

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

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

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

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

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

        _UpperCAmelCase      =			{'input_features': input_mel, 'is_longer': is_longer}
        _UpperCAmelCase      =			BatchFeature(_SCREAMING_SNAKE_CASE			)

        if return_tensors is not None:
              _UpperCAmelCase      =			input_features.convert_to_tensors(_SCREAMING_SNAKE_CASE			)

        return input_features





import math




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * power_factor




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * math.sqrt(1 - power_factor**2       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()