



"""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


lowercase__       =	logging.get_logger(__name__)




class SCREAMING_SNAKE_CASE__       (		lowerCAmelCase__   ):
						_lowerCAmelCase    						=       ["input_features", "is_longer"]



						def __init__(self    ,  _lowercase=64    ,  _lowercase=48000    ,  _lowercase=480    ,  _lowercase=10    ,  _lowercase=1024    ,  _lowercase=0.0    ,  _lowercase=False    ,  _lowercase = 0    ,  _lowercase = 14000    ,  _lowercase = None    ,  _lowercase = "fusion"    ,  _lowercase = "repeatpad"    ,  **_lowercase    ,  ):





													'''simple docstring'''

													super().__init__(
													    feature_size=_lowerCamelCase    ,  sampling_rate=_lowerCamelCase    ,  padding_value=_lowerCamelCase    ,  return_attention_mask=_lowerCamelCase    ,  **_lowerCamelCase    ,  )
													__a		:		Union[str, Any]						=							top_db
													__a		:		Dict						=							truncation
													__a		:		Optional[int]						=							padding
													__a		:		str						=							fft_window_size
													__a		:		Optional[int]						=							(fft_window_size >> 1) + 1
													__a		:		Any						=							hop_length
													__a		:		Any						=							max_length_s
													__a		:		List[str]						=							max_length_s * sampling_rate
													__a		:		List[str]						=							sampling_rate
													__a		:		Optional[int]						=							frequency_min
													__a		:		int						=							frequency_max
													__a		:		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"""    ,  )
													__a		:		Dict						=							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'''

													__a		:		int						=							copy.deepcopy(self.__dict__				)
													__a		:		Dict						=							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    ,  _lowercase    ,  _lowercase = None				):





													'''simple docstring'''

													__a		:		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      lowerCAmelCase__(self    ,  _lowercase    ,  _lowercase    ,  _lowercase				):





													'''simple docstring'''

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

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

													__a		:		str						=							torch.tensor(mel[None, None, :]				)
													__a		:		Optional[Any]						=							torch.nn.functional.interpolate(
													    _lowerCamelCase    ,  size=[chunk_frames, 64]    ,  mode="""bilinear"""    ,  align_corners=_lowerCamelCase				)
													__a		:		List[str]						=							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    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  _lowercase				):





													'''simple docstring'''

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

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

																				if truncation == "fusion":
																											__a		:		int						=							self._np_extract_fbank_features(_lowerCamelCase    ,  self.mel_filters				)
																											__a		:		List[Any]						=							np.stack([input_mel, input_mel, input_mel, input_mel]    ,  axis=0				)
																				else:
																											__a		:		Dict						=							self._np_extract_fbank_features(_lowerCamelCase    ,  self.mel_filters_slaney				)[None, :]

													return input_mel, longer





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





													'''simple docstring'''

													__a		:		List[str]						=							truncation if truncation is not None else self.truncation
													__a		:		List[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."""				)

													__a		:		List[str]						=							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}'''				)
													__a		:		List[str]						=							is_batched_numpy or (
													    isinstance(_lowerCamelCase    ,  (list, tuple)				) and (isinstance(raw_speech[0]    ,  (np.ndarray, tuple, list)				))
													)

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

													# always return batch
													if not is_batched:
																				__a		:		str						=							[np.asarray(_lowerCamelCase				)]

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

													__a		:		List[str]						=							[]
													__a		:		int						=							[]
													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
																				__a		:		Dict						=							np.random.randint(0    ,  len(_lowerCamelCase				)				)
																				__a		:		Union[str, Any]						=							True

													if isinstance(input_mel[0]    ,  _lowerCamelCase				):
																				__a		:		str						=							[np.asarray(_lowerCamelCase    ,  dtype=np.floataa				) for feature in input_mel]

													# is_longer is a list of bool
													__a		:		int						=							[[longer] for longer in is_longer]

													__a		:		Dict						=							{"""input_features""": input_mel, """is_longer""": is_longer}
													__a		:		Any						=							BatchFeature(_lowerCamelCase				)

													if return_tensors is not None:
																				__a		:		Optional[int]						=							input_features.convert_to_tensors(_lowerCamelCase				)

													return input_features




"""simple docstring"""
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      if a < 0 or b < 0:
            raise ValueError("""the value of both inputs must be positive"""    )

      UpperCAmelCase__			:							Dict							=    str(bin(lowerCAmelCase    )    )[2:]  # remove the leading "0b"
      UpperCAmelCase__			:							Any							=    str(bin(lowerCAmelCase    )    )[2:]  # remove the leading "0b"

      UpperCAmelCase__			:							Optional[Any]							=    max(len(lowerCAmelCase    )			,	len(lowerCAmelCase    )    )

      return "0b" + "".join(
          str(int(char_a != char_b    )    )
          for char_a, char_b in zip(a_binary.zfill(lowerCAmelCase    )			,	b_binary.zfill(lowerCAmelCase    )    )    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

