


"""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


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



class 						snake_case_(				a__	):
     __UpperCamelCase     =    ['''input_features''', '''is_longer''']






     def __init__(		self     :    int    , UpperCamelCase_     :    Any=6_4    , UpperCamelCase_     :    Dict=4_8_0_0_0    , UpperCamelCase_     :    Dict=4_8_0    , UpperCamelCase_     :    Optional[int]=1_0    , UpperCamelCase_     :    str=1_0_2_4    , UpperCamelCase_     :    Any=0.0    , UpperCamelCase_     :    Union[str, Any]=False    , UpperCamelCase_     :    float = 0    , UpperCamelCase_     :    float = 1_4_0_0_0    , UpperCamelCase_     :    int = None    , UpperCamelCase_     :    str = "fusion"    , UpperCamelCase_     :    str = "repeatpad"    , **UpperCamelCase_     :    List[str]    , ):
           super().__init__(
               feature_size=UpperCamelCase_    , sampling_rate=UpperCamelCase_    , padding_value=UpperCamelCase_    , return_attention_mask=UpperCamelCase_    , **UpperCamelCase_    , )
           lowerCAmelCase    :		str				=      top_db
           lowerCAmelCase    :		str				=      truncation
           lowerCAmelCase    :		Optional[Any]				=      padding
           lowerCAmelCase    :		List[Any]				=      fft_window_size
           lowerCAmelCase    :		Any				=      (fft_window_size >> 1) + 1
           lowerCAmelCase    :		Union[str, Any]				=      hop_length
           lowerCAmelCase    :		int				=      max_length_s
           lowerCAmelCase    :		str				=      max_length_s * sampling_rate
           lowerCAmelCase    :		Any				=      sampling_rate
           lowerCAmelCase    :		str				=      frequency_min
           lowerCAmelCase    :		List[str]				=      frequency_max
           lowerCAmelCase    :		Optional[int]				=      mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins    , num_mel_filters=UpperCamelCase_    , min_frequency=UpperCamelCase_    , max_frequency=UpperCamelCase_    , sampling_rate=UpperCamelCase_    , norm=UpperCamelCase_    , mel_scale='''htk'''    , )
           lowerCAmelCase    :		Union[str, Any]				=      mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins    , num_mel_filters=UpperCamelCase_    , min_frequency=UpperCamelCase_    , max_frequency=UpperCamelCase_    , sampling_rate=UpperCamelCase_    , norm='''slaney'''    , mel_scale='''slaney'''    , )






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		Optional[int]				=      copy.deepcopy(self.__dict__ )
           lowerCAmelCase    :		Optional[int]				=      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    , UpperCamelCase_     :    np.array    , UpperCamelCase_     :    Optional[np.array] = None ):
           lowerCAmelCase    :		List[str]				=      spectrogram(
               UpperCamelCase_    , window_function(self.fft_window_size    , '''hann''' )    , frame_length=self.fft_window_size    , hop_length=self.hop_length    , power=2.0    , mel_filters=UpperCamelCase_    , log_mel='''dB'''    , )
           return log_mel_spectrogram.T






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

           lowerCAmelCase    :		Any				=      mel[idx_front : idx_front + chunk_frames, :]
           lowerCAmelCase    :		int				=      mel[idx_middle : idx_middle + chunk_frames, :]
           lowerCAmelCase    :		Optional[int]				=      mel[idx_back : idx_back + chunk_frames, :]

           lowerCAmelCase    :		List[Any]				=      torch.tensor(mel[None, None, :] )
           lowerCAmelCase    :		Optional[int]				=      torch.nn.functional.interpolate(
               UpperCamelCase_    , size=[chunk_frames, 6_4]    , mode='''bilinear'''    , align_corners=UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      mel_shrink[0][0].numpy()
           lowerCAmelCase    :		List[str]				=      np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]    , axis=0 )
           return mel_fusion






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

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

                 if truncation == "fusion":
                       lowerCAmelCase    :		Optional[Any]				=      self._np_extract_fbank_features(UpperCamelCase_    , self.mel_filters )
                       lowerCAmelCase    :		List[str]				=      np.stack([input_mel, input_mel, input_mel, input_mel]    , axis=0 )
                 else:
                       lowerCAmelCase    :		Union[str, Any]				=      self._np_extract_fbank_features(UpperCamelCase_    , self.mel_filters_slaney )[None, :]

           return input_mel, longer






     def __call__(		self     :    Optional[int]    , UpperCamelCase_     :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    , UpperCamelCase_     :    str = None    , UpperCamelCase_     :    Optional[str] = None    , UpperCamelCase_     :    Optional[int] = None    , UpperCamelCase_     :    Optional[int] = None    , UpperCamelCase_     :    Optional[Union[str, TensorType]] = None    , **UpperCamelCase_     :    Optional[Any]    , ):
           lowerCAmelCase    :		Optional[int]				=      truncation if truncation is not None else self.truncation
           lowerCAmelCase    :		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.''' )

           lowerCAmelCase    :		Tuple				=      isinstance(UpperCamelCase_    , 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    :		Any				=      is_batched_numpy or (
               isinstance(UpperCamelCase_    , (list, tuple) ) and (isinstance(raw_speech[0]    , (np.ndarray, tuple, list) ))
           )

           if is_batched:
                 lowerCAmelCase    :		Tuple				=      [np.asarray(UpperCamelCase_    , dtype=np.floataa ) for speech in raw_speech]
           elif not is_batched and not isinstance(UpperCamelCase_    , np.ndarray ):
                 lowerCAmelCase    :		List[str]				=      np.asarray(UpperCamelCase_    , dtype=np.floataa )
           elif isinstance(UpperCamelCase_    , 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    :		Tuple				=      [np.asarray(UpperCamelCase_ )]

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

           lowerCAmelCase    :		str				=      []
           lowerCAmelCase    :		List[Any]				=      []
           for mel, longer in padded_inputs:
                 input_mel.append(UpperCamelCase_ )
                 is_longer.append(UpperCamelCase_ )

           if truncation == "fusion" and sum(UpperCamelCase_ ) == 0:
                 # if no audio is longer than 10s, then randomly select one audio to be longer
                 lowerCAmelCase    :		List[Any]				=      np.random.randint(0    , len(UpperCamelCase_ ) )
                 lowerCAmelCase    :		Optional[Any]				=      True

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

           # is_longer is a list of bool
           lowerCAmelCase    :		Any				=      [[longer] for longer in is_longer]

           lowerCAmelCase    :		str				=      {'''input_features''': input_mel, '''is_longer''': is_longer}
           lowerCAmelCase    :		int				=      BatchFeature(UpperCamelCase_ )

           if return_tensors is not None:
                 lowerCAmelCase    :		Dict				=      input_features.convert_to_tensors(UpperCamelCase_ )

           return input_features






"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array


def    _snake_case  (  _snake_case :	list[list[float]]	):
      lowerCAmelCase    :		str				=      Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(_snake_case	) == 2 and len(matrix[0]	) == 2 and len(matrix[1]	) == 2:
            # Calculate the determinant of the matrix
            lowerCAmelCase    :		int				=      float(
                d(matrix[0][0]	) * d(matrix[1][1]	) - d(matrix[1][0]	) * d(matrix[0][1]	)	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creates a copy of the matrix with swapped positions of the elements
            lowerCAmelCase    :		Optional[int]				=      [[0.0, 0.0], [0.0, 0.0]]
            lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      matrix[1][1], matrix[0][0]
            lowerCAmelCase,					lowerCAmelCase    :		Union[str, Any]				=      -matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(_snake_case	)	) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(_snake_case	) == 3
          and len(matrix[0]	) == 3
          and len(matrix[1]	) == 3
          and len(matrix[2]	) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            lowerCAmelCase    :		int				=      float(
                (
                    (d(matrix[0][0]	) * d(matrix[1][1]	) * d(matrix[2][2]	))
                    + (d(matrix[0][1]	) * d(matrix[1][2]	) * d(matrix[2][0]	))
                    + (d(matrix[0][2]	) * d(matrix[1][0]	) * d(matrix[2][1]	))
                )
                - (
                    (d(matrix[0][2]	) * d(matrix[1][1]	) * d(matrix[2][0]	))
                    + (d(matrix[0][1]	) * d(matrix[1][0]	) * d(matrix[2][2]	))
                    + (d(matrix[0][0]	) * d(matrix[1][2]	) * d(matrix[2][1]	))
                )	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creating cofactor matrix
            lowerCAmelCase    :		Dict				=      [
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
            ]
            lowerCAmelCase    :		List[str]				=      (d(matrix[1][1]	) * d(matrix[2][2]	)) - (
                d(matrix[1][2]	) * d(matrix[2][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[1][0]	) * d(matrix[2][2]	)) - (d(matrix[1][2]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		str				=      (d(matrix[1][0]	) * d(matrix[2][1]	)) - (
                d(matrix[1][1]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Any				=      -(
                (d(matrix[0][1]	) * d(matrix[2][2]	)) - (d(matrix[0][2]	) * d(matrix[2][1]	))
            )
            lowerCAmelCase    :		Any				=      (d(matrix[0][0]	) * d(matrix[2][2]	)) - (
                d(matrix[0][2]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Optional[int]				=      -(
                (d(matrix[0][0]	) * d(matrix[2][1]	)) - (d(matrix[0][1]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		Optional[int]				=      (d(matrix[0][1]	) * d(matrix[1][2]	)) - (
                d(matrix[0][2]	) * d(matrix[1][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[0][0]	) * d(matrix[1][2]	)) - (d(matrix[0][2]	) * d(matrix[1][0]	))
            )
            lowerCAmelCase    :		List[Any]				=      (d(matrix[0][0]	) * d(matrix[1][1]	)) - (
                d(matrix[0][1]	) * d(matrix[1][0]	)
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            lowerCAmelCase    :		str				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        lowerCAmelCase    :		Optional[Any]				=      cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            lowerCAmelCase    :		Tuple				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        inverse_matrix[i][j] /= d(_snake_case	)

        # Calculate the inverse of the matrix
            return [[float(d(_snake_case	)	) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''	)


