

"""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 lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   ['''input_features''']




    def __init__( self ,lowerCamelCase_=8_0 ,lowerCamelCase_=1_6_0_0_0 ,lowerCamelCase_=1_6_0 ,lowerCamelCase_=3_0 ,lowerCamelCase_=4_0_0 ,lowerCamelCase_=0.0 ,lowerCamelCase_=False ,**lowerCamelCase_ ,)    ->      List[Any]:
          super().__init__(
              feature_size=lowerCamelCase_ ,sampling_rate=lowerCamelCase_ ,padding_value=lowerCamelCase_ ,return_attention_mask=lowerCamelCase_ ,**lowerCamelCase_ ,)
          A				  =		n_fft
          A				  =		hop_length
          A				  =		chunk_length
          A				  =		chunk_length * sampling_rate
          A				  =		self.n_samples // hop_length
          A				  =		sampling_rate
          A				  =		mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2 ,num_mel_filters=lowerCamelCase_ ,min_frequency=0.0 ,max_frequency=80_00.0 ,sampling_rate=lowerCamelCase_ ,norm="""slaney""" ,mel_scale="""slaney""" ,)




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      np.ndarray:
          A				  =		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""" ,)
          A				  =		log_spec[:, :-1]
          A				  =		np.maximum(lowerCamelCase_ ,log_spec.max() - 8.0    )
          A				  =		(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 UpperCamelCase__		( lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = 0.0    )    ->      List[np.ndarray]:
          if attention_mask is not None:
                A				  =		np.array(lowerCamelCase_ ,np.intaa    )
                A				  =		[]

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

                      normed_input_values.append(lowerCamelCase_    )
          else:
                A				  =		[(x - x.mean()) / np.sqrt(x.var() + 1E-7    ) for x in input_values]

          return normed_input_values




    def __call__( self ,lowerCamelCase_ ,lowerCamelCase_ = True ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = "max_length" ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,**lowerCamelCase_ ,)    ->      BatchFeature:

          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				  =		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				  =		is_batched_numpy or (
              isinstance(lowerCamelCase_ ,(list, tuple)    ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list)    ))
          )

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

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

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

          # convert into correct format for padding

          A				  =		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:
                A				  =		self.zero_mean_unit_var_norm(
                    padded_inputs["""input_features"""] ,attention_mask=padded_inputs["""attention_mask"""] ,padding_value=self.padding_value ,)
                A				  =		np.stack(padded_inputs["""input_features"""] ,axis=0    )

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

          A				  =		[self._np_extract_fbank_features(lowerCamelCase_    ) for waveform in input_features[0]]

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

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

          if return_tensors is not None:
                A				  =		padded_inputs.convert_to_tensors(lowerCamelCase_    )

          return padded_inputs




    def UpperCamelCase__		( self    )    ->      Dict[str, Any]:
          A				  =		copy.deepcopy(self.__dict__    )
          A				  =		self.__class__.__name__
          if "mel_filters" in output:
                del output["mel_filters"]
          return output







"""simple docstring"""


import random





class lowerCamelCase__   :



    '''simple docstring'''




    @staticmethod
    def UpperCamelCase__		( lowerCamelCase_    )    ->      tuple[list[int], list[int]]:
          A				  =		[ord(lowerCamelCase_    ) for i in text]
          A				  =		[]
          A				  =		[]
          for i in plain:
                A				  =		random.randint(1 ,3_0_0    )
                A				  =		(i + k) * k
                cipher.append(lowerCamelCase_    )
                key.append(lowerCamelCase_    )
          return cipher, key




    @staticmethod
    def UpperCamelCase__		( lowerCamelCase_ ,lowerCamelCase_    )    ->      str:
          A				  =		[]
          for i in range(len(lowerCamelCase_    )    ):
                A				  =		int((cipher[i] - (key[i]) ** 2) / key[i]    )
                plain.append(chr(lowerCamelCase_    )    )
          return "".join(lowerCamelCase_    )


if __name__ == "__main__":
  UpperCAmelCase							,			UpperCAmelCase													=Onepad().encrypt("Hello")
  print(c, k)
  print(Onepad().decrypt(c, k))




