


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


a_ :Any  							=			logging.get_logger(__name__)

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			Optional[Any]      = ['''input_features''']







     def __init__(						self :						List[Any]   ,   _lowercase :						Tuple=80   ,   _lowercase :						Optional[Any]=1_60_00   ,   _lowercase :						int=1_60   ,   _lowercase :						Dict=30   ,   _lowercase :						str=4_00   ,   _lowercase :						Tuple=0.0   ,   _lowercase :						Dict=False   ,   **_lowercase :						Tuple   ,   ):
           super().__init__(
               feature_size=_lowercase   ,   sampling_rate=_lowercase   ,   padding_value=_lowercase   ,   return_attention_mask=_lowercase   ,   **_lowercase   ,   )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		n_fft
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		hop_length
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		chunk_length
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		chunk_length * sampling_rate
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.n_samples // hop_length
           SCREAMING_SNAKE_CASE__       :  int						     =		sampling_rate
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		mel_filter_bank(
               num_frequency_bins=1 + n_fft // 2   ,   num_mel_filters=_lowercase   ,   min_frequency=0.0   ,   max_frequency=8000.0   ,   sampling_rate=_lowercase   ,   norm='''slaney'''   ,   mel_scale='''slaney'''   ,   )







     def 							lowercase__       (						self :						int   ,   _lowercase :						np.array      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		spectrogram(
               _lowercase   ,   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'''   ,   )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		log_spec[:, :-1]
           SCREAMING_SNAKE_CASE__       :  Any						     =		np.maximum(_lowercase   ,   log_spec.max() - 8.0      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		(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 							lowercase__       (						_lowercase :						List[np.ndarray]   ,   _lowercase :						List[np.ndarray]   ,   _lowercase :						float = 0.0      ):
           if attention_mask is not None:
                 SCREAMING_SNAKE_CASE__       :  Any						     =		np.array(_lowercase   ,   np.intaa      )
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		[]

                 for vector, length in zip(_lowercase   ,   attention_mask.sum(-1      )      ):
                       SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7      )
                       if length < normed_slice.shape[0]:
                             SCREAMING_SNAKE_CASE__       :  Tuple						     =		padding_value

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

           return normed_input_values







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

           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.'''      )

           SCREAMING_SNAKE_CASE__       :  Any						     =		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}"""      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		is_batched_numpy or (
               isinstance(_lowercase   ,   (list, tuple)      ) and (isinstance(raw_speech[0]   ,   (np.ndarray, tuple, list)      ))
           )

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

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

           SCREAMING_SNAKE_CASE__       :  Any						     =		BatchFeature({'''input_features''': raw_speech}      )

           # convert into correct format for padding

           SCREAMING_SNAKE_CASE__       :  Any						     =		self.pad(
               _lowercase   ,   padding=_lowercase   ,   max_length=max_length if max_length else self.n_samples   ,   truncation=_lowercase   ,   pad_to_multiple_of=_lowercase   ,   return_attention_mask=return_attention_mask or do_normalize   ,   )

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

           # make sure list is in array format
           SCREAMING_SNAKE_CASE__       :  int						     =		padded_inputs.get('''input_features'''      ).transpose(2   ,   0   ,   1      )

           SCREAMING_SNAKE_CASE__       :  int						     =		[self._np_extract_fbank_features(_lowercase      ) for waveform in input_features[0]]

           if isinstance(input_features[0]   ,   _lowercase      ):
                 SCREAMING_SNAKE_CASE__       :  int						     =		[np.asarray(_lowercase   ,   dtype=np.floataa      ) for feature in input_features]
           else:
                 SCREAMING_SNAKE_CASE__       :  Any						     =		input_features

           if return_attention_mask:
                 # rescale from sample (48000) to feature (3000)
                 SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		padded_inputs['''attention_mask'''][:, :: self.hop_length]

           if return_tensors is not None:
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		padded_inputs.convert_to_tensors(_lowercase      )

           return padded_inputs







     def 							lowercase__       (						self :						int      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		copy.deepcopy(self.__dict__      )
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		self.__class__.__name__
           if "mel_filters" in output:
                 del output["mel_filters"]
           return output








"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					''
    for i in table:
        res += inp[i - 1]
    return res
def 					lowerCamelCase   (     _snake_case ):
    return data[1:] + data[0]
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					''
    for i in range(len(_snake_case ) ):
        if a[i] == b[i]:
            res += "0"
        else:
            res += "1"
    return res
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					int('0b' + data[0] + data[-1]    ,2 )
    UpperCAmelCase__	:      Optional[Any]     							=					int('0b' + data[1:3]    ,2 )
    return bin(s[row][col] )[2:]
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Any     							=					message[:4]
    UpperCAmelCase__	:      Optional[Any]     							=					message[4:]
    UpperCAmelCase__	:      Dict     							=					apply_table(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					xor(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Dict     							=					apply_sbox(_snake_case    ,temp[:4] )  # noqa: E741
    UpperCAmelCase__	:      Tuple     							=					apply_sbox(_snake_case    ,temp[4:] )
    UpperCAmelCase__	:      List[str]     							=					'0' * (2 - len(_snake_case )) + l  # noqa: E741
    UpperCAmelCase__	:      Optional[Any]     							=					'0' * (2 - len(_snake_case )) + r
    UpperCAmelCase__	:      Optional[int]     							=					apply_table(l + r    ,_snake_case )
    UpperCAmelCase__	:      Any     							=					xor(_snake_case    ,_snake_case )
    return temp + right


if __name__ == "__main__":
    UpperCamelCase__          =	input('Enter 10 bit key: ')
    UpperCamelCase__          =	input('Enter 8 bit message: ')

    UpperCamelCase__          =	[6, 3, 7, 4, 8, 5, 10, 9]
    UpperCamelCase__          =	[3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
    UpperCamelCase__          =	[2, 4, 3, 1]
    UpperCamelCase__          =	[2, 6, 3, 1, 4, 8, 5, 7]
    UpperCamelCase__          =	[4, 1, 3, 5, 7, 2, 8, 6]
    UpperCamelCase__          =	[4, 1, 2, 3, 2, 3, 4, 1]
    UpperCamelCase__          =	[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
    UpperCamelCase__          =	[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

    # key generation
    UpperCamelCase__          =	apply_table(key, paa_table)
    UpperCamelCase__          =	temp[:5]
    UpperCamelCase__          =	temp[5:]
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	apply_table(left + right, pa_table)
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	apply_table(left + right, pa_table)

    # encryption
    UpperCamelCase__          =	apply_table(message, IP)
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	temp[4:] + temp[:4]
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	apply_table(temp, IP_inv)
    print('Cipher text is:', CT)

    # decryption
    UpperCamelCase__          =	apply_table(CT, IP)
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	temp[4:] + temp[:4]
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	apply_table(temp, IP_inv)
    print('Plain text after decypting is:', PT)

