


'''simple docstring'''







from __future__ import annotations

lowerCAmelCase    :     Tuple					=						tuple[int, int, int]
lowerCAmelCase    :     Optional[Any]					=						tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowerCAmelCase    :     List[str]					=						"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""

# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCAmelCase    :     str					=						"""EGZWVONAHDCLFQMSIPJBYUKXTR"""
lowerCAmelCase    :     List[Any]					=						"""FOBHMDKEXQNRAULPGSJVTYICZW"""
lowerCAmelCase    :     Tuple					=						"""ZJXESIUQLHAVRMDOYGTNFWPBKC"""
# reflector --------------------------
lowerCAmelCase    :     int					=						{
    """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    :     Optional[Any]					=						"""RMDJXFUWGISLHVTCQNKYPBEZOA"""
lowerCAmelCase    :     str					=						"""SGLCPQWZHKXAREONTFBVIYJUDM"""
lowerCAmelCase    :     List[Any]					=						"""HVSICLTYKQUBXDWAJZOMFGPREN"""
lowerCAmelCase    :     List[str]					=						"""RZWQHFMVDBKICJLNTUXAGYPSOE"""
lowerCAmelCase    :     int					=						"""LFKIJODBEGAMQPXVUHYSTCZRWN"""
lowerCAmelCase    :     Optional[int]					=						"""KOAEGVDHXPQZMLFTYWJNBRCIUS"""






def 			lowercase				(_A   ,						_A   ,						_A					):







       """simple docstring"""







       if (unique_rotsel := len(set(_A					)					)) < 3:
              _lowerCAmelCase    :							Tuple   			=       f'Please use 3 unique rotors (not {unique_rotsel})'
              raise Exception(_A					)

       # Checks if rotor positions are valid
       _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       rotpos
       if not 0 < rotorposa <= len(_A					):
              _lowerCAmelCase    :							Optional[int]   			=       f'First rotor position is not within range of 1..26 ({rotorposa}'
              raise ValueError(_A					)
       if not 0 < rotorposa <= len(_A					):
              _lowerCAmelCase    :							Tuple   			=       f'Second rotor position is not within range of 1..26 ({rotorposa})'
              raise ValueError(_A					)
       if not 0 < rotorposa <= len(_A					):
              _lowerCAmelCase    :							Optional[Any]   			=       f'Third rotor position is not within range of 1..26 ({rotorposa})'
              raise ValueError(_A					)

       # Validates string and returns dict
       _lowerCAmelCase    :							str   			=       _plugboard(_A					)

       return rotpos, rotsel, pbdict






def 			lowercase				(_A					):







       """simple docstring"""







       if not isinstance(_A   ,						_A					):
              _lowerCAmelCase    :							Dict   			=       f'Plugboard setting isn\'t type string ({type(_A					)})'
              raise TypeError(_A					)
       elif len(_A					) % 2 != 0:
              _lowerCAmelCase    :							Dict   			=       f'Odd number of symbols ({len(_A					)})'
              raise Exception(_A					)
       elif pbstring == "":
              return {}

       pbstring.replace(' '   ,						''					)

       # Checks if all characters are unique
       _lowerCAmelCase    :							Any   			=       set()
       for i in pbstring:
              if i not in abc:
                     _lowerCAmelCase    :							Any   			=       f'\'{i}\' not in list of symbols'
                     raise Exception(_A					)
              elif i in tmppbl:
                     _lowerCAmelCase    :							int   			=       f'Duplicate symbol ({i})'
                     raise Exception(_A					)
              else:
                     tmppbl.add(_A					)
       del tmppbl

       # Created the dictionary
       _lowerCAmelCase    :							List[Any]   			=       {}
       for j in range(0   ,						len(_A					) - 1   ,						2					):
              _lowerCAmelCase    :							Optional[int]   			=       pbstring[j + 1]
              _lowerCAmelCase    :							Optional[Any]   			=       pbstring[j]

       return pb






def 			lowercase				(_A   ,						_A   ,						_A = (rotora, rotora, rotora)   ,						_A = ""   ,						):







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       text.upper()
       _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Tuple   			=       _validator(
           _A   ,						_A   ,						plugb.upper()					)

       _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Any   			=       rotor_position
       _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Tuple   			=       rotor_selection
       rotorposa -= 1
       rotorposa -= 1
       rotorposa -= 1

       _lowerCAmelCase    :							Optional[Any]   			=       []

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

                     # rotor ra --------------------------
                     _lowerCAmelCase    :							Optional[int]   			=       abc.index(_A					) + rotorposa
                     _lowerCAmelCase    :							Dict   			=       rotora[index % len(_A					)]

                     # rotor rb --------------------------
                     _lowerCAmelCase    :							Tuple   			=       abc.index(_A					) + rotorposa
                     _lowerCAmelCase    :							Union[str, Any]   			=       rotora[index % len(_A					)]

                     # rotor rc --------------------------
                     _lowerCAmelCase    :							List[str]   			=       abc.index(_A					) + rotorposa
                     _lowerCAmelCase    :							List[Any]   			=       rotora[index % len(_A					)]

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

                     _lowerCAmelCase    :							Optional[Any]   			=       reflector[symbol]

                     # 2nd rotors
                     _lowerCAmelCase    :							Any   			=       abc[rotora.index(_A					) - rotorposa]
                     _lowerCAmelCase    :							int   			=       abc[rotora.index(_A					) - rotorposa]
                     _lowerCAmelCase    :							Union[str, Any]   			=       abc[rotora.index(_A					) - rotorposa]

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

                     # moves/resets rotor positions
                     rotorposa += 1
                     if rotorposa >= len(_A					):
                            _lowerCAmelCase    :							Tuple   			=       0
                            rotorposa += 1
                     if rotorposa >= len(_A					):
                            _lowerCAmelCase    :							Optional[Any]   			=       0
                            rotorposa += 1
                     if rotorposa >= len(_A					):
                            _lowerCAmelCase    :							Dict   			=       0

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

       return "".join(_A					)


if __name__ == "__main__":
       lowerCAmelCase    :     Tuple					=						"""This is my Python script that emulates the Enigma machine from WWII."""
       lowerCAmelCase    :     Optional[Any]					=						(1, 1, 1)
       lowerCAmelCase    :     Optional[Any]					=						"""pictures"""
       lowerCAmelCase    :     Dict					=						(rotora, rotora, rotora)
       lowerCAmelCase    :     Optional[int]					=						enigma(message, rotor_pos, rotor_sel, pb)

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





'''simple docstring'''







import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase    :     int					=						logging.get_logger(__name__)
lowerCAmelCase    :     Union[str, Any]					=						{"""vocab_file""": """spiece.model"""}

lowerCAmelCase    :     Optional[int]					=						{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

lowerCAmelCase    :     Union[str, Any]					=						{
    """AI-Sweden/gpt-sw3-126m""": 20_48,
    """AI-Sweden/gpt-sw3-350m""": 20_48,
    """AI-Sweden/gpt-sw3-1.6b""": 20_48,
    """AI-Sweden/gpt-sw3-6.7b""": 20_48,
    """AI-Sweden/gpt-sw3-20b""": 20_48,
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    VOCAB_FILES_NAMES
     __magic_name__    			=    PRETRAINED_VOCAB_FILES_MAP
     __magic_name__    			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__    			=    ["input_ids", "attention_mask"]







     def __init__( self      ,	snake_case__      ,	snake_case__=False      ,	snake_case__=False      ,	snake_case__=False      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       {} if sp_model_kwargs is None else sp_model_kwargs

            _lowerCAmelCase    :							List[Any]   			=       kwargs.get('name_or_path'   )
            if name_or_path is None:
                   logger.warning(
                       'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
                       ' you are testing the model, this can safely be ignored'   )
                   _lowerCAmelCase    :							Any   			=       'None'

            # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
            _lowerCAmelCase    :							str   			=       '<|endoftext|>' if eos_token is None else eos_token
            _lowerCAmelCase    :							Tuple   			=       '<unk>' if unk_token is None else unk_token
            if "gpt-sw3-7b" in name_or_path:
                   _lowerCAmelCase    :							List[str]   			=       unk_token if pad_token is None else pad_token
                   _lowerCAmelCase    :							Optional[int]   			=       eos_token if bos_token is None else bos_token
            else:
                   _lowerCAmelCase    :							Tuple   			=       '<pad>' if pad_token is None else pad_token
                   _lowerCAmelCase    :							Union[str, Any]   			=       '<s>' if bos_token is None else bos_token

            super().__init__(
                do_lower_case=snake_case__      ,	remove_space=snake_case__      ,	keep_accents=snake_case__      ,	bos_token=snake_case__      ,	eos_token=snake_case__      ,	unk_token=snake_case__      ,	pad_token=snake_case__      ,	sp_model_kwargs=self.sp_model_kwargs      ,	**snake_case__      ,	)

            _lowerCAmelCase    :							Union[str, Any]   			=       do_lower_case
            _lowerCAmelCase    :							Optional[int]   			=       remove_space
            _lowerCAmelCase    :							Any   			=       keep_accents
            _lowerCAmelCase    :							Optional[int]   			=       vocab_file

            _lowerCAmelCase    :							Dict   			=       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(snake_case__   )

            # Used for whitespace normalization in input texts
            # fmt : off
            _lowerCAmelCase    :							Optional[Any]   			=       {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
            # fmt : on

            # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
            _lowerCAmelCase    :							Optional[Any]   			=       re.compile(
                F'[{"".join(map(snake_case__      ,	list(range(0      ,	9   )   ) + list(range(11      ,	32   )   ) + list(range(127      ,	160   )   ) + [160, 173, 8203]   )   )}]'   )







     def __getstate__( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       self.__dict__.copy()
            _lowerCAmelCase    :							int   			=       None
            return state







     def __setstate__( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       d

            # for backward compatibility
            if not hasattr(self      ,	'sp_model_kwargs'   ):
                   _lowerCAmelCase    :							int   			=       {}

            _lowerCAmelCase    :							List[str]   			=       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(self.vocab_file   )







     @property
     # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
     def a	( self   ):

            '''simple docstring'''

            return len(self.sp_model   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.non_printing_characters_re.sub(''      ,	snake_case__   )

            # Normalize whitespaces
            _lowerCAmelCase    :							Tuple   			=       ''.join([char if char not in self.whitespaces else ' ' for char in text]   )

            # NFC Unicode normalization
            _lowerCAmelCase    :							Union[str, Any]   			=       unicodedata.normalize('NFC'      ,	snake_case__   )
            return text







     def a	( self      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.preprocess_text(snake_case__   )
            return self.sp_model.encode(snake_case__      ,	out_type=snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.sp_model.PieceToId(snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.sp_model.IdToPiece(snake_case__   )







     @staticmethod
     def a	( snake_case__   ):

            '''simple docstring'''

            return out_string







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       []
            _lowerCAmelCase    :							Optional[Any]   			=       ''
            _lowerCAmelCase    :							Tuple   			=       False
            for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                          # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                          if not prev_is_special:
                                 out_string += " "

                          out_string += self.sp_model.decode(snake_case__   ) + token
                          _lowerCAmelCase    :							Union[str, Any]   			=       True
                          _lowerCAmelCase    :							List[Any]   			=       []
                   else:
                          current_sub_tokens.append(snake_case__   )
                          _lowerCAmelCase    :							List[Any]   			=       False
            out_string += self.sp_model.decode(snake_case__   )

            return out_string







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       {self.convert_ids_to_tokens(snake_case__   ): i for i in range(self.vocab_size   )}
            vocab.update(self.added_tokens_encoder   )
            return vocab







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            if not os.path.isdir(snake_case__   ):
                   logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
                   return
            _lowerCAmelCase    :							int   			=       os.path.join(
                snake_case__      ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

            if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case__   ) and os.path.isfile(self.vocab_file   ):
                   copyfile(self.vocab_file      ,	snake_case__   )
            elif not os.path.isfile(self.vocab_file   ):
                   with open(snake_case__      ,	'wb'   ) as fi:
                          _lowerCAmelCase    :							Any   			=       self.sp_model.serialized_model_proto()
                          fi.write(snake_case__   )

            return (out_vocab_file,)







     def a	( self      ,	snake_case__      ,	snake_case__ = False   ):

            '''simple docstring'''

            if isinstance(snake_case__      ,	snake_case__   ):
                   _lowerCAmelCase    :							Optional[Any]   			=       self.preprocess_text(snake_case__   )
                   _lowerCAmelCase    :							List[str]   			=       self.sp_model.encode(snake_case__   )
            else:
                   _lowerCAmelCase    :							Tuple   			=       [self.preprocess_text(snake_case__   ) for t in text]
                   _lowerCAmelCase    :							List[str]   			=       self.sp_model.encode(snake_case__   )

            if return_tensors is True or return_tensors == "pt":
                   _lowerCAmelCase    :							int   			=       torch.tensor(snake_case__   )

            return token_ids







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return self.sp_model.decode(snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       [F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
            _lowerCAmelCase    :							str   			=       (
                F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(snake_case__   ) + F'{self.bos_token}Bot:'
            )
            return self.encode(text=snake_case__   )

