

'''simple docstring'''

from __future__ import annotations

from typing import Any





class 							_snake_case :
   def __init__( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      float = 0							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					row, column
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[[default_value for c in range(UpperCAmelCase_							)] for r in range(UpperCAmelCase_							)]
   def __str__( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

     # Make string identifier
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					0
     for row_vector in self.array:
       for obj in row_vector:
         SCREAMING_SNAKE_CASE:Tuple						      =					max(UpperCAmelCase_     ,len(str(UpperCAmelCase_							)							)							)
     SCREAMING_SNAKE_CASE:Any						      =					F'''%{max_element_length}s'''

     # Make string and return
     def single_line(SCREAMING_SNAKE_CASE__			:      list[float]							) -> str:
       nonlocal string_format_identifier
       SCREAMING_SNAKE_CASE:Tuple						      =					"["
       line += ", ".join(string_format_identifier % (obj,) for obj in row_vector							)
       line += "]"
       return line

     s += "\n".join(single_line(UpperCAmelCase_							) for row_vector in self.array							)
     return s
   def __repr__( self			:      Tuple							):
     return str(self							)
   def     __UpperCamelCase						( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      tuple[int, int]							):



     if not (isinstance(UpperCAmelCase_     ,(list, tuple)							) and len(UpperCAmelCase_							) == 2):
       return False
     elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
       return False
     else:
       return True
   def __getitem__( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      tuple[int, int]							):
     assert self.validate_indicies(UpperCAmelCase_							)
     return self.array[loc[0]][loc[1]]
   def __setitem__( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      tuple[int, int]     ,SCREAMING_SNAKE_CASE__			:      float							):
     assert self.validate_indicies(UpperCAmelCase_							)
     SCREAMING_SNAKE_CASE:List[str]						      =					value
   def __add__( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Matrix							):
     assert isinstance(UpperCAmelCase_     ,UpperCAmelCase_							)
     assert self.row == another.row and self.column == another.column

     # Add
     SCREAMING_SNAKE_CASE:List[Any]						      =					Matrix(self.row     ,self.column							)
     for r in range(self.row							):
       for c in range(self.column							):
         SCREAMING_SNAKE_CASE:int						      =					self[r, c] + another[r, c]
     return result
   def __neg__( self			:      int							):
     SCREAMING_SNAKE_CASE:int						      =					Matrix(self.row     ,self.column							)
     for r in range(self.row							):
       for c in range(self.column							):
         SCREAMING_SNAKE_CASE:Dict						      =					-self[r, c]
     return result
   def __sub__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Matrix							):
     return self + (-another)
   def __mul__( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      int | float | Matrix							):



     if isinstance(UpperCAmelCase_     ,(int, float)							):  # Scalar multiplication
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					Matrix(self.row     ,self.column							)
       for r in range(self.row							):
         for c in range(self.column							):
           SCREAMING_SNAKE_CASE:List[Any]						      =					self[r, c] * another
       return result
     elif isinstance(UpperCAmelCase_     ,UpperCAmelCase_							):  # Matrix multiplication
       assert self.column == another.row
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					Matrix(self.row     ,another.column							)
       for r in range(self.row							):
         for c in range(another.column							):
           for i in range(self.column							):
             result[r, c] += self[r, i] * another[i, c]
       return result
     else:
       SCREAMING_SNAKE_CASE:str						      =					F'''Unsupported type given for another ({type(UpperCAmelCase_							)})'''
       raise TypeError(UpperCAmelCase_							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					Matrix(self.column     ,self.row							)
     for r in range(self.row							):
       for c in range(self.column							):
         SCREAMING_SNAKE_CASE:Any						      =					self[r, c]
     return result







   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Matrix     ,SCREAMING_SNAKE_CASE__			:      Matrix							):
     assert isinstance(UpperCAmelCase_     ,UpperCAmelCase_							) and isinstance(UpperCAmelCase_     ,UpperCAmelCase_							)
     assert self.row == self.column == u.row == v.row  # u, v should be column vector
     assert u.column == v.column == 1  # u, v should be column vector

     # Calculate
     SCREAMING_SNAKE_CASE:str						      =					v.transpose()
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					(v_t * self * u)[0, 0] + 1
     if numerator_factor == 0:
       return None  # It's not invertable
     return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





    def    A_  ( ):
      # a^(-1)
      SCREAMING_SNAKE_CASE:Any						      =					Matrix(3      ,       3      ,       0 )
      for i in range(3 ):
        SCREAMING_SNAKE_CASE:Optional[Any]						      =					1
      print(F'''a^(-1) is {ainv}''' )
      # u, v
      SCREAMING_SNAKE_CASE:int						      =					Matrix(3      ,       1      ,       0 )
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					1, 2, -3
      SCREAMING_SNAKE_CASE:Tuple						      =					Matrix(3      ,       1      ,       0 )
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					4, -2, 5
      print(F'''u is {u}''' )
      print(F'''v is {v}''' )
      print(F'''uv^T is {u * v.transpose()}''' )
      # Sherman Morrison
      print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE )}''' )





    def    A_  ( ):
      import doctest

      doctest.testmod()


    testa()









'''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


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     Any       =					['''input_features''', '''is_longer''']
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      str=64     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=48_000     ,SCREAMING_SNAKE_CASE__			:      List[str]=480     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=10     ,SCREAMING_SNAKE_CASE__			:      List[Any]=1_024     ,SCREAMING_SNAKE_CASE__			:      List[str]=0.0     ,SCREAMING_SNAKE_CASE__			:      Dict=False     ,SCREAMING_SNAKE_CASE__			:      float = 0     ,SCREAMING_SNAKE_CASE__			:      float = 14_000     ,SCREAMING_SNAKE_CASE__			:      int = None     ,SCREAMING_SNAKE_CASE__			:      str = "fusion"     ,SCREAMING_SNAKE_CASE__			:      str = "repeatpad"     ,**SCREAMING_SNAKE_CASE__			:      int     ,):
     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__     ,)
     SCREAMING_SNAKE_CASE:Dict						      =					top_db
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					truncation
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					padding
     SCREAMING_SNAKE_CASE:int						      =					fft_window_size
     SCREAMING_SNAKE_CASE:Tuple						      =					(fft_window_size >> 1) + 1
     SCREAMING_SNAKE_CASE:Dict						      =					hop_length
     SCREAMING_SNAKE_CASE:List[Any]						      =					max_length_s
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					max_length_s * sampling_rate
     SCREAMING_SNAKE_CASE:int						      =					sampling_rate
     SCREAMING_SNAKE_CASE:Optional[int]						      =					frequency_min
     SCREAMING_SNAKE_CASE:List[str]						      =					frequency_max
     SCREAMING_SNAKE_CASE:int						      =					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"     ,)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					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			:      str							):
     SCREAMING_SNAKE_CASE:List[str]						      =					copy.deepcopy(self.__dict__							)
     SCREAMING_SNAKE_CASE: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     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      np.array     ,SCREAMING_SNAKE_CASE__			:      Optional[np.array] = None							):
     SCREAMING_SNAKE_CASE:Dict						      =					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			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					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
       SCREAMING_SNAKE_CASE:Tuple						      =					[0]
     if len(ranges[2]							) == 0:
       # if the audio is too short, we just use the first chunk
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					[0]
     # randomly choose index for each part
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					np.random.choice(ranges[0]							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					np.random.choice(ranges[1]							)
     SCREAMING_SNAKE_CASE:Any						      =					np.random.choice(ranges[2]							)

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

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.tensor(mel[None, None, :]							)
     SCREAMING_SNAKE_CASE:List[str]						      =					torch.nn.functional.interpolate(
         SCREAMING_SNAKE_CASE__     ,size=[chunk_frames, 64]     ,mode="bilinear"     ,align_corners=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					mel_shrink[0][0].numpy()
     SCREAMING_SNAKE_CASE:Any						      =					np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,axis=0							)
     return mel_fusion
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      np.array     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     if waveform.shape[0] > max_length:
       if truncation == "rand_trunc":
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					True
         # random crop to max_length (for compatibility) -> this should be handled by self.pad
         SCREAMING_SNAKE_CASE:Dict						      =					len(SCREAMING_SNAKE_CASE__							) - max_length
         SCREAMING_SNAKE_CASE:List[str]						      =					np.random.randint(0     ,overflow + 1							)
         SCREAMING_SNAKE_CASE:int						      =					waveform[idx : idx + max_length]
         SCREAMING_SNAKE_CASE:Optional[int]						      =					self._np_extract_fbank_features(SCREAMING_SNAKE_CASE__     ,self.mel_filters_slaney							)[None, :]
       elif truncation == "fusion":
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self._np_extract_fbank_features(SCREAMING_SNAKE_CASE__     ,self.mel_filters							)
         SCREAMING_SNAKE_CASE:Optional[int]						      =					max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
         SCREAMING_SNAKE_CASE: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.
           SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.stack([mel, mel, mel, mel]     ,axis=0							)
           SCREAMING_SNAKE_CASE:Dict						      =					False
         else:
           SCREAMING_SNAKE_CASE:Optional[Any]						      =					self._random_mel_fusion(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
           SCREAMING_SNAKE_CASE:Optional[Any]						      =					True
       else:
         raise NotImplementedError(F'''data_truncating {truncation} not implemented'''							)

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

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

     return input_mel, longer







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

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					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}'''							)
     SCREAMING_SNAKE_CASE:Dict						      =					is_batched_numpy or (
         isinstance(SCREAMING_SNAKE_CASE__     ,(list, tuple)							) and (isinstance(raw_speech[0]     ,(np.ndarray, tuple, list)							))
     )

     if is_batched:
       SCREAMING_SNAKE_CASE:Any						      =					[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							):
       SCREAMING_SNAKE_CASE:str						      =					np.asarray(SCREAMING_SNAKE_CASE__     ,dtype=np.floataa							)
     elif isinstance(SCREAMING_SNAKE_CASE__     ,np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					raw_speech.astype(np.floataa							)

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

     # convert to mel spectrogram, truncate and pad if needed.
     SCREAMING_SNAKE_CASE:List[str]						      =					[
         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
     ]

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[]
     SCREAMING_SNAKE_CASE:Dict						      =					[]
     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
       SCREAMING_SNAKE_CASE:str						      =					np.random.randint(0     ,len(SCREAMING_SNAKE_CASE__							)							)
       SCREAMING_SNAKE_CASE:str						      =					True

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

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

     SCREAMING_SNAKE_CASE:Tuple						      =					{"input_features": input_mel, "is_longer": is_longer}
     SCREAMING_SNAKE_CASE:str						      =					BatchFeature(SCREAMING_SNAKE_CASE__							)

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

     return input_features






