
'''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


__lowerCAmelCase         =    logging.get_logger(__name__)







class UpperCAmelCase__	( SCREAMING_SNAKE_CASE__	):





  """simple docstring"""

  __UpperCAmelCase				:				Union[str, Any]   =					["input_features", "is_longer"]


  def __init__(		self      :     Any							,_a      :     Tuple=64							,_a      :     int=4_8000							,_a      :     List[Any]=480							,_a      :     Optional[int]=10							,_a      :     Any=1024							,_a      :     Optional[Any]=0.0							,_a      :     Dict=False							,_a      :     Optional[int] = 0							,_a      :     Optional[int] = 1_4000							,_a      :     List[str] = None							,_a      :     Optional[int] = "fusion"							,_a      :     Optional[Any] = "repeatpad"							,**_a      :     Tuple							,):


     '''simple docstring'''






     super().__init__(
         feature_size=lowerCamelCase__							,sampling_rate=lowerCamelCase__							,padding_value=lowerCamelCase__							,return_attention_mask=lowerCamelCase__							,**lowerCamelCase__							,)
     _a				:	Tuple			=   top_db
     _a				:	Dict			=   truncation
     _a				:	int			=   padding
     _a				:	Optional[Any]			=   fft_window_size
     _a				:	Optional[Any]			=   (fft_window_size >> 1) + 1
     _a				:	List[Any]			=   hop_length
     _a				:	Tuple			=   max_length_s
     _a				:	str			=   max_length_s * sampling_rate
     _a				:	List[str]			=   sampling_rate
     _a				:	Dict			=   frequency_min
     _a				:	str			=   frequency_max
     _a				:	Union[str, Any]			=   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				:	Union[str, Any]			=   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   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	Any			=   copy.deepcopy(self.__dict__       )
     _a				:	Tuple			=   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   __lowercase		(		self      :     Any							,_a      :     Union[str, Any]							,_a      :     Tuple = 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   __lowercase		(		self      :     str							,_a      :     str							,_a      :     Optional[Any]							,_a      :     Dict       ):


     '''simple docstring'''






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

     _a				:	List[str]			=   mel[idx_front : idx_front + chunk_frames, :]
     _a				:	Union[str, Any]			=   mel[idx_middle : idx_middle + chunk_frames, :]
     _a				:	Any			=   mel[idx_back : idx_back + chunk_frames, :]

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


  def   __lowercase		(		self      :     Dict							,_a      :     int							,_a      :     Tuple							,_a      :     List[Any]							,_a      :     int       ):


     '''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				:	int			=   len(lowerCamelCase__       ) - max_length
           _a				:	Optional[int]			=   np.random.randint(0							,overflow + 1       )
           _a				:	List[Any]			=   waveform[idx : idx + max_length]
           _a				:	Optional[Any]			=   self._np_extract_fbank_features(lowerCamelCase__							,self.mel_filters_slaney       )[None, :]
        elif truncation == "fusion":
           _a				:	List[str]			=   self._np_extract_fbank_features(lowerCamelCase__							,self.mel_filters       )
           _a				:	List[str]			=   max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
           _a				:	Optional[Any]			=   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				:	str			=   np.stack([mel, mel, mel, mel]							,axis=0       )
              _a				:	Union[str, Any]			=   False
           else:
              _a				:	Dict			=   self._random_mel_fusion(lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__       )
              _a				:	Tuple			=   True
        else:
           raise NotImplementedError(F"""data_truncating {truncation} not implemented"""       )

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

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

     return input_mel, longer


  def __call__(		self      :     List[str]							,_a      :     Tuple							,_a      :     Union[str, Any] = None							,_a      :     Tuple = None							,_a      :     List[Any] = None							,_a      :     Optional[int] = None							,_a      :     Optional[Any] = None							,**_a      :     int							,):


     '''simple docstring'''






     _a				:	Any			=   truncation if truncation is not None else self.truncation
     _a				:	Union[str, 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.'       )

     _a				:	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				:	Any			=   is_batched_numpy or (
         isinstance(lowerCamelCase__							,(list, tuple)       ) and (isinstance(raw_speech[0]							,(np.ndarray, tuple, list)       ))
     )

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

     # always return batch
     if not is_batched:
        _a				:	List[Any]			=   [np.asarray(lowerCamelCase__       )]

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

     _a				:	Optional[Any]			=   []
     _a				:	Optional[Any]			=   []
     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				:	Tuple			=   np.random.randint(0							,len(lowerCamelCase__       )       )
        _a				:	str			=   True

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

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

     _a				:	Union[str, Any]			=   {"""input_features""": input_mel, """is_longer""": is_longer}
     _a				:	Tuple			=   BatchFeature(lowerCamelCase__       )

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

     return input_features








def 	__UpperCamelCase	(  _lowerCAmelCase = 100_0000		)       ->  int:



    """simple docstring"""






    A :       str          =   limit + 1
    A :       Tuple          =   [0] * limit
    for first_term in range(1      ,     _lowerCAmelCase		):
        for n in range(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		):
            A :       Any          =   first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    A :       Optional[int]          =   sum(1 for x in frequency[1:limit] if x == 10		)

    return count


if __name__ == "__main__":
     print(F"""{solution() = }""")


