





"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      ['input_features', 'attention_mask']





      def __init__(       self      ,						lowercase=80      ,						lowercase=16_000      ,						lowercase=80      ,						lowercase=0.0      ,						lowercase=True      ,						lowercase=True      ,						lowercase=True      ,						**lowercase      ,						)		->     Optional[Any]:
            super().__init__(feature_size=lowercase      ,						sampling_rate=lowercase      ,						padding_value=lowercase      ,						**lowercase )
            lowerCAmelCase  						=       num_mel_bins
            lowerCAmelCase  						=       do_ceptral_normalize
            lowerCAmelCase  						=       normalize_means
            lowerCAmelCase  						=       normalize_vars
            lowerCAmelCase  						=       True





      def     _snake_case			(       self      ,						lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
            lowerCAmelCase  						=       torch.from_numpy(lowercase ).unsqueeze(0 )
            lowerCAmelCase  						=       ta_kaldi.fbank(lowercase      ,						num_mel_bins=self.num_mel_bins      ,						sample_frequency=self.sampling_rate )
            return features.numpy()





      @staticmethod
      def     _snake_case			(       lowercase      ,						lowercase      ,						lowercase = True      ,						lowercase = True      ,						lowercase = 0.0      ,						)		->     np.ndarray:
            # make sure we normalize float32 arrays
            if normalize_means:
                  lowerCAmelCase  						=       x[:input_length].mean(axis=0 )
                  lowerCAmelCase  						=       np.subtract(lowercase      ,						lowercase )
            if normalize_vars:
                  lowerCAmelCase  						=       x[:input_length].std(axis=0 )
                  lowerCAmelCase  						=       np.divide(lowercase      ,						lowercase )

            if input_length < x.shape[0]:
                  lowerCAmelCase  						=       padding_value

            # make sure array is in float32
            lowerCAmelCase  						=       x.astype(np.floataa )

            return x





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     List[np.ndarray]:
            lowerCAmelCase  						=       attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
            return [
                self.utterance_cmvn(lowercase      ,						lowercase      ,						self.normalize_means      ,						self.normalize_vars      ,						self.padding_value )
                for x, n in zip(lowercase      ,						lowercase )
            ]





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

            if sampling_rate is not None:
                  if sampling_rate != self.sampling_rate:
                        raise ValueError(
                            f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                            f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                            f' {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  						=       isinstance(lowercase      ,						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  						=       is_batched_numpy or (
                isinstance(lowercase      ,						(list, tuple) ) and (isinstance(raw_speech[0]      ,						(np.ndarray, tuple, list) ))
            )

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

            # always return batch
            if not is_batched:
                  lowerCAmelCase  						=       [raw_speech]

            # extract fbank features
            lowerCAmelCase  						=       [self._extract_fbank_features(lowercase ) for waveform in raw_speech]

            # convert into correct format for padding
            lowerCAmelCase  						=       BatchFeature({"""input_features""": features} )

            lowerCAmelCase  						=       self.pad(
                lowercase      ,						padding=lowercase      ,						max_length=lowercase      ,						truncation=lowercase      ,						pad_to_multiple_of=lowercase      ,						return_attention_mask=lowercase      ,						**lowercase      ,						)

            # make sure list is in array format
            lowerCAmelCase  						=       padded_inputs.get("""input_features""" )
            if isinstance(input_features[0]      ,						lowercase ):
                  lowerCAmelCase  						=       [np.asarray(lowercase      ,						dtype=np.floataa ) for feature in input_features]

            lowerCAmelCase  						=       padded_inputs.get("""attention_mask""" )
            if attention_mask is not None:
                  lowerCAmelCase  						=       [np.asarray(lowercase      ,						dtype=np.intaa ) for array in attention_mask]

            # Utterance-level cepstral mean and variance normalization
            if self.do_ceptral_normalize:
                  lowerCAmelCase  						=       (
                      np.array(lowercase      ,						dtype=np.intaa )
                      if self._get_padding_strategies(lowercase      ,						max_length=lowercase ) is not PaddingStrategy.DO_NOT_PAD
                      else None
                  )
                  lowerCAmelCase  						=       self.normalize(
                      padded_inputs["""input_features"""]      ,						attention_mask=lowercase )

            if return_tensors is not None:
                  lowerCAmelCase  						=       padded_inputs.convert_to_tensors(lowercase )

            return padded_inputs














def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[1]

				lowercase__ ,						lowercase__ ,						lowercase__    =			0, 0, 0
				lowercase__    =			ugly_nums[ia] * 2
				lowercase__    =			ugly_nums[ia] * 3
				lowercase__    =			ugly_nums[ia] * 5

				for _ in range(1		,   lowerCamelCase_				):
								lowercase__    =			min(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				)
								ugly_nums.append(lowerCamelCase_				)
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 2
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 3
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 5
				return ugly_nums[-1]


if __name__ == "__main__":
							from doctest import testmod

							testmod(verbose=True)
							print(F"{ugly_numbers(2_00) = }")




