



"""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 TensorType, logging


_UpperCamelCase:					Optional[Any]     				=	logging.get_logger(__name__)


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					['input_values', 'attention_mask']


   def __init__(				self   :				List[str],  lowerCAmelCase   :				Any=1,  lowerCAmelCase   :				Optional[Any]=16000,  lowerCAmelCase   :				Any=128,  lowerCAmelCase   :				Any=1024,  lowerCAmelCase   :				List[str]=0.0,  lowerCAmelCase   :				Union[str, Any]=True,  lowerCAmelCase   :				Tuple=-4.267_7393,  lowerCAmelCase   :				Optional[int]=4.568_9974,  lowerCAmelCase   :				Optional[int]=False,  **lowerCAmelCase   :				Tuple,  )     ->							int:
         super().__init__(feature_size=lowerCAmelCase,  sampling_rate=lowerCAmelCase,  padding_value=lowerCAmelCase,  **lowerCAmelCase      )
         lowercase	:   Optional[int]											= num_mel_bins
         lowercase	:   Dict											= max_length
         lowercase	:   Any											= do_normalize
         lowercase	:   Dict											= mean
         lowercase	:   Any											= std
         lowercase	:   int											= return_attention_mask


   def 							lowercase					(				self   :				Optional[int],  lowerCAmelCase   :				np.ndarray,  lowerCAmelCase   :				int,  )     ->							np.ndarray:
         lowercase	:   List[Any]											= torch.from_numpy(lowerCAmelCase      ).unsqueeze(0      )
         lowercase	:   Union[str, Any]											= ta_kaldi.fbank(
             lowerCAmelCase,  htk_compat=lowerCAmelCase,  sample_frequency=self.sampling_rate,  use_energy=lowerCAmelCase,  window_type='hanning',  num_mel_bins=self.num_mel_bins,  dither=0.0,  frame_shift=10,  )

         lowercase	:   List[Any]											= fbank.shape[0]
         lowercase	:   Tuple											= max_length - n_frames

         # pad or truncate, depending on difference
         if difference > 0:
               lowercase	:   Tuple											= torch.nn.ZeroPadad((0, 0, 0, difference)      )
               lowercase	:   str											= pad_module(lowerCAmelCase      )
         elif difference < 0:
               lowercase	:   Union[str, Any]											= fbank[0:max_length, :]

         lowercase	:   List[Any]											= fbank.numpy()

         return fbank


   def 							lowercase					(				self   :				Optional[int],  lowerCAmelCase   :				np.ndarray      )     ->							np.ndarray:
         return (input_values - (self.mean)) / (self.std * 2)






   def __call__(				self   :				Tuple,  lowerCAmelCase   :				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],  lowerCAmelCase   :				Optional[int] = None,  lowerCAmelCase   :				Optional[Union[str, TensorType]] = None,  **lowerCAmelCase   :				str,  )     ->							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.'      )

         lowercase	:   List[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}'''      )
         lowercase	:   List[Any]											= is_batched_numpy or (
             isinstance(lowerCAmelCase,  (list, tuple)      ) and (isinstance(raw_speech[0],  (np.ndarray, tuple, list)      ))
         )

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

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

         # extract fbank features and pad/truncate to max_length
         lowercase	:   List[str]											= [self._extract_fbank_features(lowerCAmelCase,  max_length=self.max_length      ) for waveform in raw_speech]

         # convert into BatchFeature
         lowercase	:   Dict											= BatchFeature({'input_values': features}      )

         # make sure list is in array format
         lowercase	:   Optional[Any]											= padded_inputs.get('input_values'      )
         if isinstance(input_values[0],  lowerCAmelCase      ):
               lowercase	:   List[str]											= [np.asarray(lowerCAmelCase,  dtype=np.floataa      ) for feature in input_values]

         # normalization
         if self.do_normalize:
               lowercase	:   Any											= [self.normalize(lowerCAmelCase      ) for feature in input_values]

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

         return padded_inputs





"""simple docstring"""




from __future__ import annotations


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      lowercase	:   Optional[Any]											= [0] * max_one

      for i in range(0						,   _UpperCAmelCase					):
            lowercase	:   List[Any]											= i

      for i in range(2						,   _UpperCAmelCase					):
            if totients[i] == i:
                  for j in range(_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					):
                        totients[j] -= totients[j] // i

      return totients


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase					)  ->      bool:






      '''simple docstring'''


      return sorted(str(_UpperCAmelCase					)					) == sorted(str(_UpperCAmelCase					)					)


def 	lowercase__  (      _UpperCAmelCase = 10_00_00_00					)  ->      int:






      '''simple docstring'''


      lowercase	:   Union[str, Any]											= 1  # i
      lowercase	:   Any											= 0  # φ(i)
      lowercase	:   int											= get_totients(max_n + 1					)

      for i in range(2						,   max_n + 1					):
            lowercase	:   Any											= totients[i]

            if i * min_denominator < min_numerator * t and has_same_digits(_UpperCAmelCase						,   _UpperCAmelCase					):
                  lowercase	:   List[Any]											= i
                  lowercase	:   Optional[Any]											= t

      return min_numerator


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