


"""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             =   logging.get_logger(__name__)







class _lowerCAmelCase    (				snake_case_						):
   __UpperCAmelCase			:	Any							  =  ['''input_features''', '''is_longer''']







   def __init__(     self						,     UpperCamelCase__=64						,     UpperCamelCase__=4_8000						,     UpperCamelCase__=480						,     UpperCamelCase__=10						,     UpperCamelCase__=1024						,     UpperCamelCase__=0.0						,     UpperCamelCase__=False						,     UpperCamelCase__ = 0						,     UpperCamelCase__ = 1_4000						,     UpperCamelCase__ = None						,     UpperCamelCase__ = "fusion"						,     UpperCamelCase__ = "repeatpad"						,     **UpperCamelCase__						,     )     ->		Dict:

       '''simple docstring'''




       super().__init__(
           feature_size=UpperCamelCase__						,     sampling_rate=UpperCamelCase__						,     padding_value=UpperCamelCase__						,     return_attention_mask=UpperCamelCase__						,     **UpperCamelCase__						,     )
       snake_case   :				Union[str, Any]				 =   top_db
       snake_case   :				Optional[int]				 =   truncation
       snake_case   :				Any				 =   padding
       snake_case   :				Tuple				 =   fft_window_size
       snake_case   :				List[str]				 =   (fft_window_size >> 1) + 1
       snake_case   :				Optional[int]				 =   hop_length
       snake_case   :				Any				 =   max_length_s
       snake_case   :				List[str]				 =   max_length_s * sampling_rate
       snake_case   :				List[str]				 =   sampling_rate
       snake_case   :				Optional[Any]				 =   frequency_min
       snake_case   :				str				 =   frequency_max
       snake_case   :				List[str]				 =   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"						,     )
       snake_case   :				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="slaney"						,     mel_scale="slaney"						,     )







   def 			lowerCamelCase		(     self						)     ->		Dict[str, Any]:

       '''simple docstring'''




       snake_case   :				Dict				 =   copy.deepcopy(self.__dict__						)
       snake_case   :				List[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 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		np.ndarray:

       '''simple docstring'''




       snake_case   :				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						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[int]:

       '''simple docstring'''




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

       snake_case   :				Any				 =   mel[idx_front : idx_front + chunk_frames, :]
       snake_case   :				Optional[Any]				 =   mel[idx_middle : idx_middle + chunk_frames, :]
       snake_case   :				List[str]				 =   mel[idx_back : idx_back + chunk_frames, :]

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







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		np.array:

       '''simple docstring'''




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

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

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

       return input_mel, longer







   def __call__(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		BatchFeature:

       '''simple docstring'''




       snake_case   :				Any				 =   truncation if truncation is not None else self.truncation
       snake_case   :				int				 =   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."						)

       snake_case   :				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}'						)
       snake_case   :				Tuple				 =   is_batched_numpy or (
           isinstance(UpperCamelCase__						,     (list, tuple)						) and (isinstance(raw_speech[0]						,     (np.ndarray, tuple, list)						))
       )

       if is_batched:
           snake_case   :				str				 =   [np.asarray(UpperCamelCase__						,     dtype=np.floataa						) for speech in raw_speech]
       elif not is_batched and not isinstance(UpperCamelCase__						,     np.ndarray						):
           snake_case   :				List[str]				 =   np.asarray(UpperCamelCase__						,     dtype=np.floataa						)
       elif isinstance(UpperCamelCase__						,     np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
           snake_case   :				Any				 =   raw_speech.astype(np.floataa						)

       # always return batch
       if not is_batched:
           snake_case   :				int				 =   [np.asarray(UpperCamelCase__						)]

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

       snake_case   :				str				 =   []
       snake_case   :				Tuple				 =   []
       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
           snake_case   :				Optional[int]				 =   np.random.randint(0						,     len(UpperCamelCase__						)						)
           snake_case   :				Union[str, Any]				 =   True

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

       # is_longer is a list of bool
       snake_case   :				Tuple				 =   [[longer] for longer in is_longer]

       snake_case   :				List[str]				 =   {"input_features": input_mel, "is_longer": is_longer}
       snake_case   :				Optional[int]				 =   BatchFeature(UpperCamelCase__						)

       if return_tensors is not None:
           snake_case   :				str				 =   input_features.convert_to_tensors(UpperCamelCase__						)

       return input_features









"""simple docstring"""



import math
import sys







def   __lowerCAmelCase						(						lowercase  :  int			)     ->						int:


    """simple docstring"""





    if number != int(lowercase			):
        raise ValueError("the value of input must be a natural number"			)
    if number < 0:
        raise ValueError("the value of input must not be a negative number"			)
    if number == 0:
        return 1
    snake_case   :				Optional[Any]				 =   [-1] * (number + 1)
    snake_case   :				str				 =   0
    for i in range(1						,							number + 1			):
        snake_case   :				List[Any]				 =   sys.maxsize
        snake_case   :				Union[str, Any]				 =   int(math.sqrt(lowercase			)			)
        for j in range(1						,							root + 1			):
            snake_case   :				List[str]				 =   1 + answers[i - (j**2)]
            snake_case   :				Optional[Any]				 =   min(lowercase						,							lowercase			)
        snake_case   :				Any				 =   answer
    return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()





