





'''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


_A:       int						    =					logging.get_logger(__name__)






class UpperCAmelCase				(		UpperCAmelCase_				):
    _A			:      str     =							["""input_features""", """attention_mask"""]



    def __init__(	self  ,      __A=80  ,      __A=16_000  ,      __A=80  ,      __A=0.0  ,      __A=True  ,      __A=True  ,      __A=True  ,      **__A  ,      ):
     super().__init__(feature_size=UpperCamelCase_  ,      sampling_rate=UpperCamelCase_  ,      padding_value=UpperCamelCase_  ,      **UpperCamelCase_       )
     __UpperCAmelCase       = num_mel_bins
     __UpperCAmelCase       = do_ceptral_normalize
     __UpperCAmelCase       = normalize_means
     __UpperCAmelCase       = normalize_vars
     __UpperCAmelCase       = True



    def        __lowerCamelCase    (	self  ,      __A  ,      ):
     __UpperCAmelCase       = waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
     __UpperCAmelCase       = torch.from_numpy(UpperCamelCase_       ).unsqueeze(0       )
     __UpperCAmelCase       = ta_kaldi.fbank(UpperCamelCase_  ,      num_mel_bins=self.num_mel_bins  ,      sample_frequency=self.sampling_rate       )
     return features.numpy()



    @staticmethod
    def        __lowerCamelCase    (	__A  ,      __A  ,      __A = True  ,      __A = True  ,      __A = 0.0  ,      ):
     # make sure we normalize float32 arrays
     if normalize_means:
      __UpperCAmelCase       = x[:input_length].mean(axis=0       )
      __UpperCAmelCase       = np.subtract(UpperCamelCase_  ,      UpperCamelCase_       )
     if normalize_vars:
      __UpperCAmelCase       = x[:input_length].std(axis=0       )
      __UpperCAmelCase       = np.divide(UpperCamelCase_  ,      UpperCamelCase_       )

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

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

     return x



    def        __lowerCamelCase    (	self  ,      __A  ,      __A = None       ):
     __UpperCAmelCase       = attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [
         self.utterance_cmvn(UpperCamelCase_  ,      UpperCamelCase_  ,      self.normalize_means  ,      self.normalize_vars  ,      self.padding_value       )
         for x, n in zip(UpperCamelCase_  ,      UpperCamelCase_       )
     ]




    def __call__(	self  ,      __A  ,      __A = False  ,      __A = None  ,      __A = False  ,      __A = None  ,      __A = None  ,      __A = None  ,      __A = None  ,      **__A  ,      ):

     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.'       )

     __UpperCAmelCase       = isinstance(UpperCamelCase_  ,      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(UpperCamelCase_  ,      (list, tuple)       ) and (isinstance(raw_speech[0]  ,      (np.ndarray, tuple, list)       ))
     )

     if is_batched:
      __UpperCAmelCase       = [np.asarray(UpperCamelCase_  ,      dtype=np.floataa       ) for speech in raw_speech]
     elif not is_batched and not isinstance(UpperCamelCase_  ,      np.ndarray       ):
      __UpperCAmelCase       = np.asarray(UpperCamelCase_  ,      dtype=np.floataa       )
     elif isinstance(UpperCamelCase_  ,      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       = [raw_speech]

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

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

     __UpperCAmelCase       = self.pad(
         UpperCamelCase_  ,      padding=UpperCamelCase_  ,      max_length=UpperCamelCase_  ,      truncation=UpperCamelCase_  ,      pad_to_multiple_of=UpperCamelCase_  ,      return_attention_mask=UpperCamelCase_  ,      **UpperCamelCase_  ,      )

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

     __UpperCAmelCase       = padded_inputs.get('attention_mask'       )
     if attention_mask is not None:
      __UpperCAmelCase       = [np.asarray(UpperCamelCase_  ,      dtype=np.intaa       ) for array in attention_mask]

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

     if return_tensors is not None:
      __UpperCAmelCase       = padded_inputs.convert_to_tensors(UpperCamelCase_       )

     return padded_inputs







"""simple docstring"""



from __future__ import annotations

UpperCamelCase__          =	tuple[int, int, int]
UpperCamelCase__          =	tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
UpperCamelCase__          =	'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

# -------------------------- default selection --------------------------
# rotors --------------------------
UpperCamelCase__          =	'EGZWVONAHDCLFQMSIPJBYUKXTR'
UpperCamelCase__          =	'FOBHMDKEXQNRAULPGSJVTYICZW'
UpperCamelCase__          =	'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
UpperCamelCase__          =	{
    'A': 'N',
    'N': 'A',
    'B': 'O',
    'O': 'B',
    'C': 'P',
    'P': 'C',
    'D': 'Q',
    'Q': 'D',
    'E': 'R',
    'R': 'E',
    'F': 'S',
    'S': 'F',
    'G': 'T',
    'T': 'G',
    'H': 'U',
    'U': 'H',
    'I': 'V',
    'V': 'I',
    'J': 'W',
    'W': 'J',
    'K': 'X',
    'X': 'K',
    'L': 'Y',
    'Y': 'L',
    'M': 'Z',
    'Z': 'M',
}

# -------------------------- extra rotors --------------------------
UpperCamelCase__          =	'RMDJXFUWGISLHVTCQNKYPBEZOA'
UpperCamelCase__          =	'SGLCPQWZHKXAREONTFBVIYJUDM'
UpperCamelCase__          =	'HVSICLTYKQUBXDWAJZOMFGPREN'
UpperCamelCase__          =	'RZWQHFMVDBKICJLNTUXAGYPSOE'
UpperCamelCase__          =	'LFKIJODBEGAMQPXVUHYSTCZRWN'
UpperCamelCase__          =	'KOAEGVDHXPQZMLFTYWJNBRCIUS'
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    # Checks if there are 3 unique rotors

    if (unique_rotsel := len(set(_snake_case ) )) < 3:
        UpperCAmelCase__	:      int     							=					F'''Please use 3 unique rotors (not {unique_rotsel})'''
        raise Exception(_snake_case )

    # Checks if rotor positions are valid
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					rotpos
    if not 0 < rotorposa <= len(_snake_case ):
        UpperCAmelCase__	:      List[Any]     							=					F'''First rotor position is not within range of 1..26 ({rotorposa}'''
        raise ValueError(_snake_case )
    if not 0 < rotorposa <= len(_snake_case ):
        UpperCAmelCase__	:      Tuple     							=					F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
        raise ValueError(_snake_case )
    if not 0 < rotorposa <= len(_snake_case ):
        UpperCAmelCase__	:      Tuple     							=					F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
        raise ValueError(_snake_case )

    # Validates string and returns dict
    UpperCAmelCase__	:      Optional[int]     							=					_plugboard(_snake_case )

    return rotpos, rotsel, pbdict
def 					lowerCamelCase   (     _snake_case ):

    # tests the input string if it
    # a) is type string
    # b) has even length (so pairs can be made)
    if not isinstance(_snake_case    ,_snake_case ):
        UpperCAmelCase__	:      Union[str, Any]     							=					F'''Plugboard setting isn\'t type string ({type(_snake_case )})'''
        raise TypeError(_snake_case )
    elif len(_snake_case ) % 2 != 0:
        UpperCAmelCase__	:      Dict     							=					F'''Odd number of symbols ({len(_snake_case )})'''
        raise Exception(_snake_case )
    elif pbstring == "":
        return {}

    pbstring.replace(' '    ,'' )

    # Checks if all characters are unique
    UpperCAmelCase__	:      List[Any]     							=					set()
    for i in pbstring:
        if i not in abc:
            UpperCAmelCase__	:      List[Any]     							=					F'''\'{i}\' not in list of symbols'''
            raise Exception(_snake_case )
        elif i in tmppbl:
            UpperCAmelCase__	:      Optional[Any]     							=					F'''Duplicate symbol ({i})'''
            raise Exception(_snake_case )
        else:
            tmppbl.add(_snake_case )
    del tmppbl

    # Created the dictionary
    UpperCAmelCase__	:      str     							=					{}
    for j in range(0    ,len(_snake_case ) - 1    ,2 ):
        UpperCAmelCase__	:      Dict     							=					pbstring[j + 1]
        UpperCAmelCase__	:      int     							=					pbstring[j]

    return pb
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case = (rotora, rotora, rotora)    ,_snake_case = ""    ,):
    UpperCAmelCase__	:      str     							=					text.upper()
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					_validator(
        _snake_case    ,_snake_case    ,plugb.upper() )

    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					rotor_position
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[Any]     							=					rotor_selection
    rotorposa -= 1
    rotorposa -= 1
    rotorposa -= 1

    UpperCAmelCase__	:      Any     							=					[]

    # encryption/decryption process --------------------------
    for symbol in text:
        if symbol in abc:
            # 1st plugboard --------------------------
            if symbol in plugboard:
                UpperCAmelCase__	:      Dict     							=					plugboard[symbol]

            # rotor ra --------------------------
            UpperCAmelCase__	:      List[str]     							=					abc.index(_snake_case ) + rotorposa
            UpperCAmelCase__	:      Optional[Any]     							=					rotora[index % len(_snake_case )]

            # rotor rb --------------------------
            UpperCAmelCase__	:      Optional[int]     							=					abc.index(_snake_case ) + rotorposa
            UpperCAmelCase__	:      List[Any]     							=					rotora[index % len(_snake_case )]

            # rotor rc --------------------------
            UpperCAmelCase__	:      Optional[Any]     							=					abc.index(_snake_case ) + rotorposa
            UpperCAmelCase__	:      Any     							=					rotora[index % len(_snake_case )]

            # reflector --------------------------
            # this is the reason you don't need another machine to decipher

            UpperCAmelCase__	:      Union[str, Any]     							=					reflector[symbol]

            # 2nd rotors
            UpperCAmelCase__	:      List[str]     							=					abc[rotora.index(_snake_case ) - rotorposa]
            UpperCAmelCase__	:      Optional[Any]     							=					abc[rotora.index(_snake_case ) - rotorposa]
            UpperCAmelCase__	:      List[Any]     							=					abc[rotora.index(_snake_case ) - rotorposa]

            # 2nd plugboard
            if symbol in plugboard:
                UpperCAmelCase__	:      int     							=					plugboard[symbol]

            # moves/resets rotor positions
            rotorposa += 1
            if rotorposa >= len(_snake_case ):
                UpperCAmelCase__	:      Dict     							=					0
                rotorposa += 1
            if rotorposa >= len(_snake_case ):
                UpperCAmelCase__	:      Any     							=					0
                rotorposa += 1
            if rotorposa >= len(_snake_case ):
                UpperCAmelCase__	:      int     							=					0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
        result.append(_snake_case )

    return "".join(_snake_case )


if __name__ == "__main__":
    UpperCamelCase__          =	'This is my Python script that emulates the Enigma machine from WWII.'
    UpperCamelCase__          =	(1, 1, 1)
    UpperCamelCase__          =	'pictures'
    UpperCamelCase__          =	(rotora, rotora, rotora)
    UpperCamelCase__          =	enigma(message, rotor_pos, rotor_sel, pb)

    print('Encrypted message:', en)
    print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))

