"""simple docstring"""


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Tuple     							=							['input_features']


    def __init__(					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict=80   ,				__lowerCAmelCase	:  str=1_6000   ,				__lowerCAmelCase	:  int=160   ,				__lowerCAmelCase	:  Any=30   ,				__lowerCAmelCase	:  Dict=400   ,				__lowerCAmelCase	:  Optional[Any]=0.0   ,				__lowerCAmelCase	:  List[str]=False   ,				**__lowerCAmelCase	:  str   ,				):
      super().__init__(
          feature_size=__lowerCAmelCase   ,				sampling_rate=__lowerCAmelCase   ,				padding_value=__lowerCAmelCase   ,				return_attention_mask=__lowerCAmelCase   ,				**__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      n_fft
      _UpperCAmelCase						 =      hop_length
      _UpperCAmelCase						 =      chunk_length
      _UpperCAmelCase						 =      chunk_length * sampling_rate
      _UpperCAmelCase						 =      self.n_samples // hop_length
      _UpperCAmelCase						 =      sampling_rate
      _UpperCAmelCase						 =      mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2   ,				num_mel_filters=__lowerCAmelCase   ,				min_frequency=0.0   ,				max_frequency=8000.0   ,				sampling_rate=__lowerCAmelCase   ,				norm="""slaney"""   ,				mel_scale="""slaney"""   ,				)


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  np.array					):
      _UpperCAmelCase						 =      spectrogram(
          __lowerCAmelCase   ,				window_function(self.n_fft   ,				"""hann"""					)   ,				frame_length=self.n_fft   ,				hop_length=self.hop_length   ,				power=2.0   ,				mel_filters=self.mel_filters   ,				log_mel="""log10"""   ,				)
      _UpperCAmelCase						 =      log_spec[:, :-1]
      _UpperCAmelCase						 =      np.maximum(__lowerCAmelCase   ,				log_spec.max() - 8.0					)
      _UpperCAmelCase						 =      (log_spec + 4.0) / 4.0
      return log_spec


    @staticmethod
    # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  List[np.ndarray]   ,				__lowerCAmelCase	:  List[np.ndarray]   ,				__lowerCAmelCase	:  float = 0.0					):
      if attention_mask is not None:
        _UpperCAmelCase						 =      np.array(__lowerCAmelCase   ,				np.intaa					)
        _UpperCAmelCase						 =      []

        for vector, length in zip(__lowerCAmelCase   ,				attention_mask.sum(-1					)					):
          _UpperCAmelCase						 =      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7					)
          if length < normed_slice.shape[0]:
            _UpperCAmelCase						 =      padding_value

          normed_input_values.append(__lowerCAmelCase					)
      else:
        _UpperCAmelCase						 =      [(x - x.mean()) / np.sqrt(x.var() + 1e-7					) for x in input_values]

      return normed_input_values


    def __call__(					self	:  str   ,				__lowerCAmelCase	:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,				__lowerCAmelCase	:  bool = True   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  Optional[Union[str, TensorType]] = None   ,				__lowerCAmelCase	:  Optional[bool] = None   ,				__lowerCAmelCase	:  Optional[str] = "max_length"   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  Optional[int] = None   ,				__lowerCAmelCase	:  Optional[bool] = None   ,				**__lowerCAmelCase	:  Optional[int]   ,				):

      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."""					)

      _UpperCAmelCase						 =      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}'''					)
      _UpperCAmelCase						 =      is_batched_numpy or (
          isinstance(__lowerCAmelCase   ,				(list, tuple)					) and (isinstance(raw_speech[0]   ,				(np.ndarray, tuple, list)					))
      )

      if is_batched:
        _UpperCAmelCase						 =      [np.asarray([speech]   ,				dtype=np.floataa					).T for speech in raw_speech]
      elif not is_batched and not isinstance(__lowerCAmelCase   ,				np.ndarray					):
        _UpperCAmelCase						 =      np.asarray(__lowerCAmelCase   ,				dtype=np.floataa					)
      elif isinstance(__lowerCAmelCase   ,				np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
        _UpperCAmelCase						 =      raw_speech.astype(np.floataa					)

      # always return batch
      if not is_batched:
        _UpperCAmelCase						 =      [np.asarray([raw_speech]					).T]

      _UpperCAmelCase						 =      BatchFeature({"""input_features""": raw_speech}					)

      # convert into correct format for padding

      _UpperCAmelCase						 =      self.pad(
          __lowerCAmelCase   ,				padding=__lowerCAmelCase   ,				max_length=max_length if max_length else self.n_samples   ,				truncation=__lowerCAmelCase   ,				pad_to_multiple_of=__lowerCAmelCase   ,				return_attention_mask=return_attention_mask or do_normalize   ,				)

      # zero-mean and unit-variance normalization
      if do_normalize:
        _UpperCAmelCase						 =      self.zero_mean_unit_var_norm(
            padded_inputs["""input_features"""]   ,				attention_mask=padded_inputs["""attention_mask"""]   ,				padding_value=self.padding_value   ,				)
        _UpperCAmelCase						 =      np.stack(padded_inputs["""input_features"""]   ,				axis=0					)

      # make sure list is in array format
      _UpperCAmelCase						 =      padded_inputs.get("""input_features"""					).transpose(2   ,				0   ,				1					)

      _UpperCAmelCase						 =      [self._np_extract_fbank_features(__lowerCAmelCase					) for waveform in input_features[0]]

      if isinstance(input_features[0]   ,				__lowerCAmelCase					):
        _UpperCAmelCase						 =      [np.asarray(__lowerCAmelCase   ,				dtype=np.floataa					) for feature in input_features]
      else:
        _UpperCAmelCase						 =      input_features

      if return_attention_mask:
        # rescale from sample (48000) to feature (3000)
        _UpperCAmelCase						 =      padded_inputs["""attention_mask"""][:, :: self.hop_length]

      if return_tensors is not None:
        _UpperCAmelCase						 =      padded_inputs.convert_to_tensors(__lowerCAmelCase					)

      return padded_inputs


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      copy.deepcopy(self.__dict__					)
      _UpperCAmelCase						 =      self.__class__.__name__
      if "mel_filters" in output:
        del output["mel_filters"]
      return output

"""simple docstring"""


import unittest

from knapsack import knapsack as k


class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      0
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)

      _UpperCAmelCase						 =      [60]
      _UpperCAmelCase						 =      [10]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      3
      _UpperCAmelCase						 =      [1, 2, 3]
      _UpperCAmelCase						 =      [3, 2, 1]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				5					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      50
      _UpperCAmelCase						 =      [60, 100, 120]
      _UpperCAmelCase						 =      [10, 20, 30]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				220					)


if __name__ == "__main__":
    unittest.main()
