from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowerCAmelCase__     						=			logging.get_logger(__name__)
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  Union[str, Any]      ,   lowercase__ :  int      ,   lowercase__ :  int      ,   lowercase__ :  float      ,   **lowercase__ :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = feature_size
        lowerCAmelCase__					     = sampling_rate
        lowerCAmelCase__					     = padding_value

        lowerCAmelCase__					     = kwargs.pop('padding_side'      ,   'right')
        lowerCAmelCase__					     = kwargs.pop('return_attention_mask'      ,   lowercase__)

        super().__init__(**lowercase__)




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  Union[
         BatchFeature,
         List[BatchFeature],
         Dict[str, BatchFeature],
         Dict[str, List[BatchFeature]],
         List[Dict[str, BatchFeature]],
     ]      ,   lowercase__ :  Union[bool, str, PaddingStrategy] = True      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  bool = False      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   ):







        '''simple docstring'''



        if isinstance(lowercase__      ,   (list, tuple)) and isinstance(processed_features[0]      ,   (dict, BatchFeature)):
               lowerCAmelCase__					     = {
                   key: [example[key] for example in processed_features] for key in processed_features[0].keys()
               }

        # The model's main input name, usually `input_values`, has be passed for padding
        if self.model_input_names[0] not in processed_features:
               raise ValueError(
                   'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                   F""" to this method that includes {self.model_input_names[0]}, but you provided"""
                   F""" {list(processed_features.keys())}""")

        lowerCAmelCase__					     = processed_features[self.model_input_names[0]]
        lowerCAmelCase__					     = (
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(lowercase__) == 0:
               if return_attention_mask:
                      lowerCAmelCase__					     = []
               return processed_features

        # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
        # and rebuild them afterwards if no return_tensors is specified
        # Note that we lose the specific device the tensor may be on for PyTorch

        lowerCAmelCase__					     = required_input[0]
        if isinstance(lowercase__      ,   (list, tuple)):
               # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
               lowerCAmelCase__					     = 0
               while len(required_input[index]) == 0:
                      index += 1
               if index < len(lowercase__):
                      lowerCAmelCase__					     = required_input[index][0]

        if return_tensors is None:
               if is_tf_tensor(lowercase__):
                      lowerCAmelCase__					     = 'tf'
               elif is_torch_tensor(lowercase__):
                      lowerCAmelCase__					     = 'pt'
               elif isinstance(lowercase__      ,   (int, float, list, tuple, np.ndarray)):
                      lowerCAmelCase__					     = 'np'
               else:
                      raise ValueError(
                          F"""type of {first_element} unknown: {type(lowercase__)}. """
                          'Should be one of a python, numpy, pytorch or tensorflow object.')

        for key, value in processed_features.items():
               if isinstance(value[0]      ,   (int, float)):
                      lowerCAmelCase__					     = to_numpy(lowercase__)
               else:
                      lowerCAmelCase__					     = [to_numpy(lowercase__) for v in value]

        # Convert padding_strategy in PaddingStrategy
        lowerCAmelCase__					     = self._get_padding_strategies(padding=lowercase__      ,   max_length=lowercase__)

        lowerCAmelCase__					     = processed_features[self.model_input_names[0]]

        lowerCAmelCase__					     = len(lowercase__)
        if not all(len(lowercase__) == batch_size for v in processed_features.values()):
               raise ValueError('Some items in the output dictionary have a different batch size than others.')

        lowerCAmelCase__					     = []
        for i in range(lowercase__):
               lowerCAmelCase__					     = {k: v[i] for k, v in processed_features.items()}
               # truncation
               lowerCAmelCase__					     = self._truncate(
                   lowercase__      ,   max_length=lowercase__      ,   pad_to_multiple_of=lowercase__      ,   truncation=lowercase__      ,   )
               truncated_inputs.append(lowercase__)

        if padding_strategy == PaddingStrategy.LONGEST:
               # make sure that `max_length` cannot be longer than the longest truncated length
               lowerCAmelCase__					     = max(len(input_slice[self.model_input_names[0]]) for input_slice in truncated_inputs)
               lowerCAmelCase__					     = PaddingStrategy.MAX_LENGTH

        lowerCAmelCase__					     = {}
        for i in range(lowercase__):
               # padding
               lowerCAmelCase__					     = self._pad(
                   truncated_inputs[i]      ,   max_length=lowercase__      ,   padding_strategy=lowercase__      ,   pad_to_multiple_of=lowercase__      ,   return_attention_mask=lowercase__      ,   )

               for key, value in outputs.items():
                      if key not in batch_outputs:
                             lowerCAmelCase__					     = []
                      if value.dtype is np.dtype(np.floataa):
                             lowerCAmelCase__					     = value.astype(np.floataa)
                      batch_outputs[key].append(lowercase__)

        return BatchFeature(lowercase__      ,   tensor_type=lowercase__)




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  Union[Dict[str, np.ndarray], BatchFeature]      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[bool] = None      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
               lowerCAmelCase__					     = len(lowercase__)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
               lowerCAmelCase__					     = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        lowerCAmelCase__					     = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowercase__) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
               lowerCAmelCase__					     = np.ones(len(lowercase__)      ,   dtype=np.intaa)

        if needs_to_be_padded:
               lowerCAmelCase__					     = max_length - len(lowercase__)
               if self.padding_side == "right":
                      if return_attention_mask:
                             lowerCAmelCase__					     = np.pad(
                                 processed_features['attention_mask']      ,   (0, difference))
                      lowerCAmelCase__					     = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                      lowerCAmelCase__					     = np.pad(
                          lowercase__      ,   lowercase__      ,   'constant'      ,   constant_values=self.padding_value)
               elif self.padding_side == "left":
                      if return_attention_mask:
                             lowerCAmelCase__					     = np.pad(
                                 processed_features['attention_mask']      ,   (difference, 0))
                      lowerCAmelCase__					     = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                      lowerCAmelCase__					     = np.pad(
                          lowercase__      ,   lowercase__      ,   'constant'      ,   constant_values=self.padding_value)
               else:
                      raise ValueError('Invalid padding strategy:' + str(self.padding_side))

        return processed_features




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  Union[Dict[str, np.ndarray], BatchFeature]      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[bool] = None      ,   ):







        '''simple docstring'''



        if not truncation:
               return processed_features
        elif truncation and max_length is None:
               raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.')

        lowerCAmelCase__					     = processed_features[self.model_input_names[0]]

        # find `max_length` that fits `pad_to_multiple_of`
        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
               lowerCAmelCase__					     = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        lowerCAmelCase__					     = len(lowercase__) > max_length

        if needs_to_be_truncated:
               lowerCAmelCase__					     = processed_features[self.model_input_names[0]][:max_length]
               if "attention_mask" in processed_features:
                      lowerCAmelCase__					     = processed_features['attention_mask'][:max_length]

        return processed_features




 def 		__snake_case						(      self :  int      ,   lowercase__ :  Dict=False      ,   lowercase__ :  Union[str, Any]=None):







        '''simple docstring'''



        if padding is not False:
               if padding is True:
                      lowerCAmelCase__					     = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
               elif not isinstance(lowercase__      ,   lowercase__):
                      lowerCAmelCase__					     = PaddingStrategy(lowercase__)
               elif isinstance(lowercase__      ,   lowercase__):
                      lowerCAmelCase__					     = padding
        else:
               lowerCAmelCase__					     = PaddingStrategy.DO_NOT_PAD

        # Set max length if needed
        if max_length is None:
               if padding_strategy == PaddingStrategy.MAX_LENGTH:
                      raise ValueError(
                          F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""")

        # Test if we have a padding value
        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
               raise ValueError(
                   'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                   ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.')

        return padding_strategy

from __future__ import annotations

lowerCAmelCase__     						=			tuple[int, int, int]
lowerCAmelCase__     						=			tuple[str, str, str]


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

# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCAmelCase__     						=			'EGZWVONAHDCLFQMSIPJBYUKXTR'
lowerCAmelCase__     						=			'FOBHMDKEXQNRAULPGSJVTYICZW'
lowerCAmelCase__     						=			'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
lowerCAmelCase__     						=			{
    '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 --------------------------
lowerCAmelCase__     						=			'RMDJXFUWGISLHVTCQNKYPBEZOA'
lowerCAmelCase__     						=			'SGLCPQWZHKXAREONTFBVIYJUDM'
lowerCAmelCase__     						=			'HVSICLTYKQUBXDWAJZOMFGPREN'
lowerCAmelCase__     						=			'RZWQHFMVDBKICJLNTUXAGYPSOE'
lowerCAmelCase__     						=			'LFKIJODBEGAMQPXVUHYSTCZRWN'
lowerCAmelCase__     						=			'KOAEGVDHXPQZMLFTYWJNBRCIUS'






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       # Checks if there are 3 unique rotors

       if (unique_rotsel := len(set(lowerCAmelCase__     )     )) < 3:
              lowerCAmelCase__					     = F"""Please use 3 unique rotors (not {unique_rotsel})"""
              raise Exception(lowerCAmelCase__     )

       # Checks if rotor positions are valid
       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = rotpos
       if not 0 < rotorposa <= len(lowerCAmelCase__     ):
              lowerCAmelCase__					     = F"""First rotor position is not within range of 1..26 ({rotorposa}"""
              raise ValueError(lowerCAmelCase__     )
       if not 0 < rotorposa <= len(lowerCAmelCase__     ):
              lowerCAmelCase__					     = F"""Second rotor position is not within range of 1..26 ({rotorposa})"""
              raise ValueError(lowerCAmelCase__     )
       if not 0 < rotorposa <= len(lowerCAmelCase__     ):
              lowerCAmelCase__					     = F"""Third rotor position is not within range of 1..26 ({rotorposa})"""
              raise ValueError(lowerCAmelCase__     )

       # Validates string and returns dict
       lowerCAmelCase__					     = _plugboard(lowerCAmelCase__     )

       return rotpos, rotsel, pbdict






def 				__lowerCamelCase		(						lowerCAmelCase__     ):

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

       pbstring.replace(' '     ,  ''     )

       # Checks if all characters are unique
       lowerCAmelCase__					     = set()
       for i in pbstring:
              if i not in abc:
                     lowerCAmelCase__					     = F"""'{i}' not in list of symbols"""
                     raise Exception(lowerCAmelCase__     )
              elif i in tmppbl:
                     lowerCAmelCase__					     = F"""Duplicate symbol ({i})"""
                     raise Exception(lowerCAmelCase__     )
              else:
                     tmppbl.add(lowerCAmelCase__     )
       del tmppbl

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

       return pb






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = (rotora, rotora, rotora)     ,  lowerCAmelCase__ = ""     ,  ):
       lowerCAmelCase__					     = text.upper()
       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = _validator(
           lowerCAmelCase__     ,  lowerCAmelCase__     ,  plugb.upper()     )

       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = rotor_position
       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = rotor_selection
       rotorposa -= 1
       rotorposa -= 1
       rotorposa -= 1

       lowerCAmelCase__					     = []

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

                     # rotor ra --------------------------
                     lowerCAmelCase__					     = abc.index(lowerCAmelCase__     ) + rotorposa
                     lowerCAmelCase__					     = rotora[index % len(lowerCAmelCase__     )]

                     # rotor rb --------------------------
                     lowerCAmelCase__					     = abc.index(lowerCAmelCase__     ) + rotorposa
                     lowerCAmelCase__					     = rotora[index % len(lowerCAmelCase__     )]

                     # rotor rc --------------------------
                     lowerCAmelCase__					     = abc.index(lowerCAmelCase__     ) + rotorposa
                     lowerCAmelCase__					     = rotora[index % len(lowerCAmelCase__     )]

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

                     lowerCAmelCase__					     = reflector[symbol]

                     # 2nd rotors
                     lowerCAmelCase__					     = abc[rotora.index(lowerCAmelCase__     ) - rotorposa]
                     lowerCAmelCase__					     = abc[rotora.index(lowerCAmelCase__     ) - rotorposa]
                     lowerCAmelCase__					     = abc[rotora.index(lowerCAmelCase__     ) - rotorposa]

                     # 2nd plugboard
                     if symbol in plugboard:
                            lowerCAmelCase__					     = plugboard[symbol]

                     # moves/resets rotor positions
                     rotorposa += 1
                     if rotorposa >= len(lowerCAmelCase__     ):
                            lowerCAmelCase__					     = 0
                            rotorposa += 1
                     if rotorposa >= len(lowerCAmelCase__     ):
                            lowerCAmelCase__					     = 0
                            rotorposa += 1
                     if rotorposa >= len(lowerCAmelCase__     ):
                            lowerCAmelCase__					     = 0

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

       return "".join(lowerCAmelCase__     )


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

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