




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


lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Dict			=      ['''input_features''', '''is_longer''']






  def __init__(  self		:     int				,						_A		:     List[str]=64				,						_A		:     Union[str, Any]=48_000				,						_A		:     Union[str, Any]=480				,						_A		:     Optional[int]=10				,						_A		:     Union[str, Any]=1_024				,						_A		:     int=0.0				,						_A		:     Union[str, Any]=False				,						_A		:     float = 0				,						_A		:     float = 14_000				,						_A		:     int = None				,						_A		:     str = "fusion"				,						_A		:     str = "repeatpad"				,						**_A		:     Tuple				,						)		->   Tuple:






       """simple docstring"""







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






       """simple docstring"""







       lowercase :       Optional[int]       =			copy.deepcopy(self.__dict__				)
       lowercase :       str       =			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  __a      (  self		:     str				,						_A		:     np.array				,						_A		:     Optional[np.array] = None				)		->   np.ndarray:






       """simple docstring"""







       lowercase :       List[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  __a      (  self		:     int				,						_A		:     List[str]				,						_A		:     Optional[Any]				,						_A		:     List[Any]				)		->   List[str]:






       """simple docstring"""







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

       lowercase :       Optional[int]       =			mel[idx_front : idx_front + chunk_frames, :]
       lowercase :       int       =			mel[idx_middle : idx_middle + chunk_frames, :]
       lowercase :       str       =			mel[idx_back : idx_back + chunk_frames, :]

       lowercase :       Dict       =			torch.tensor(mel[None, None, :]				)
       lowercase :       Optional[Any]       =			torch.nn.functional.interpolate(
           _A				,						size=[chunk_frames, 64]				,						mode='''bilinear'''				,						align_corners=_A				)
       lowercase :       str       =			mel_shrink[0][0].numpy()
       lowercase :       Optional[Any]       =			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]				,						axis=0				)
       return mel_fusion






  def  __a      (  self		:     Optional[int]				,						_A		:     np.array				,						_A		:     List[Any]				,						_A		:     str				,						_A		:     Union[str, Any]				)		->   np.array:






       """simple docstring"""







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

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

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

       return input_mel, longer






  def __call__(  self		:     int				,						_A		:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,						_A		:     str = None				,						_A		:     Optional[str] = None				,						_A		:     Optional[int] = None				,						_A		:     Optional[int] = None				,						_A		:     Optional[Union[str, TensorType]] = None				,						**_A		:     Optional[int]				,						)		->   BatchFeature:






       """simple docstring"""







       lowercase :       str       =			truncation if truncation is not None else self.truncation
       lowercase :       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.'''				)

       lowercase :       Optional[int]       =			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}"""				)
       lowercase :       str       =			is_batched_numpy or (
           isinstance(_A				,						(list, tuple)				) and (isinstance(raw_speech[0]				,						(np.ndarray, tuple, list)				))
       )

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

       # always return batch
       if not is_batched:
            lowercase :       Any       =			[np.asarray(_A				)]

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

       lowercase :       Optional[int]       =			[]
       lowercase :       Optional[Any]       =			[]
       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
            lowercase :       Any       =			np.random.randint(0				,						len(_A				)				)
            lowercase :       int       =			True

       if isinstance(input_mel[0]				,						_A				):
            lowercase :       Optional[int]       =			[np.asarray(_A				,						dtype=np.floataa				) for feature in input_mel]

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

       lowercase :       Dict       =			{'''input_features''': input_mel, '''is_longer''': is_longer}
       lowercase :       Any       =			BatchFeature(_A				)

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

       return input_features





class 						_A :  # Public class to implement a graph






  def __init__(  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     list[list[bool]]				)		->   None:






       """simple docstring"""







       lowercase :       Tuple       =			row
       lowercase :       Union[str, Any]       =			col
       lowercase :       int       =			graph






  def  __a      (  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     list[list[bool]]				)		->   bool:






       """simple docstring"""







       return (
           0 <= i < self.ROW
           and 0 <= j < self.COL
           and not visited[i][j]
           and self.graph[i][j]
       )






  def  __a      (  self		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     list[list[bool]]				)		->   None:






       """simple docstring"""







       lowercase :       List[str]       =			[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
       lowercase :       Dict       =			[-1, 0, 1, -1, 1, -1, 0, 1]
       lowercase :       Dict       =			True  # Make those cells visited
       for k in range(8				):
            if self.is_safe(i + row_nbr[k]				,						j + col_nbr[k]				,						_A				):
                 self.diffs(i + row_nbr[k]				,						j + col_nbr[k]				,						_A				)






  def  __a      (  self		:     List[str]				)		->   int:  # And finally, count all islands.






       """simple docstring"""







       lowercase :       List[str]       =			[[False for j in range(self.COL				)] for i in range(self.ROW				)]
       lowercase :       Optional[Any]       =			0
       for i in range(self.ROW				):
            for j in range(self.COL				):
                 if visited[i][j] is False and self.graph[i][j] == 1:
                      self.diffs(_A				,						_A				,						_A				)
                      count += 1
       return count