


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  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     # Checks if there are 3 unique rotors

     if (unique_rotsel := len(set(__UpperCamelCase ) )) < 3:
          a_    :     List[str]										=    F'''Please use 3 unique rotors (not {unique_rotsel})'''
          raise Exception(__UpperCamelCase )

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

     # Validates string and returns dict
     a_    :     List[Any]										=    _plugboard(__UpperCamelCase )

     return rotpos, rotsel, pbdict



def  _a			(						__UpperCamelCase ):

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

     pbstring.replace(""" """  ,  """""" )

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

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

     return pb



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase = (rotora, rotora, rotora)  ,  __UpperCamelCase = ""  ,  ):
     a_    :     str										=    text.upper()
     a_			,			a_			,			a_    :     Any										=    _validator(
         __UpperCamelCase  ,  __UpperCamelCase  ,  plugb.upper() )

     a_			,			a_			,			a_    :     List[Any]										=    rotor_position
     a_			,			a_			,			a_    :     int										=    rotor_selection
     rotorposa -= 1
     rotorposa -= 1
     rotorposa -= 1

     a_    :     List[str]										=    []

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

               # rotor ra --------------------------
               a_    :     Union[str, Any]										=    abc.index(__UpperCamelCase ) + rotorposa
               a_    :     Union[str, Any]										=    rotora[index % len(__UpperCamelCase )]

               # rotor rb --------------------------
               a_    :     Any										=    abc.index(__UpperCamelCase ) + rotorposa
               a_    :     Optional[Any]										=    rotora[index % len(__UpperCamelCase )]

               # rotor rc --------------------------
               a_    :     Optional[int]										=    abc.index(__UpperCamelCase ) + rotorposa
               a_    :     Optional[int]										=    rotora[index % len(__UpperCamelCase )]

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

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

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

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

               # moves/resets rotor positions
               rotorposa += 1
               if rotorposa >= len(__UpperCamelCase ):
                    a_    :     str										=    0
                    rotorposa += 1
               if rotorposa >= len(__UpperCamelCase ):
                    a_    :     List[Any]										=    0
                    rotorposa += 1
               if rotorposa >= len(__UpperCamelCase ):
                    a_    :     Tuple										=    0

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

     return "".join(__UpperCamelCase )


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))







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
       raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCamelCase        =     False
__lowerCamelCase        =     False



def  _a			(						__UpperCamelCase ):
     return TrainCommand(__UpperCamelCase )


class   a__	(						lowerCAmelCase_    ):

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     ArgumentParser    ):
            a_    :     List[Any]										=    parser.add_parser("""train"""      ,							help="""CLI tool to train a model on a task."""    )

            train_parser.add_argument(
                """--train_data"""      ,							type=lowerCamelCase_      ,							required=lowerCamelCase_      ,							help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""      ,							)
            train_parser.add_argument(
                """--column_label"""      ,							type=lowerCamelCase_      ,							default=0      ,							help="""Column of the dataset csv file with example labels."""    )
            train_parser.add_argument(
                """--column_text"""      ,							type=lowerCamelCase_      ,							default=1      ,							help="""Column of the dataset csv file with example texts."""    )
            train_parser.add_argument(
                """--column_id"""      ,							type=lowerCamelCase_      ,							default=2      ,							help="""Column of the dataset csv file with example ids."""    )
            train_parser.add_argument(
                """--skip_first_row"""      ,							action="""store_true"""      ,							help="""Skip the first row of the csv file (headers)."""    )

            train_parser.add_argument("""--validation_data"""      ,							type=lowerCamelCase_      ,							default=""""""      ,							help="""path to validation dataset."""    )
            train_parser.add_argument(
                """--validation_split"""      ,							type=lowerCamelCase_      ,							default=0.1      ,							help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""      ,							)

            train_parser.add_argument("""--output"""      ,							type=lowerCamelCase_      ,							default="""./"""      ,							help="""path to saved the trained model."""    )

            train_parser.add_argument(
                """--task"""      ,							type=lowerCamelCase_      ,							default="""text_classification"""      ,							help="""Task to train the model on."""    )
            train_parser.add_argument(
                """--model"""      ,							type=lowerCamelCase_      ,							default="""bert-base-uncased"""      ,							help="""Model's name or path to stored model."""    )
            train_parser.add_argument("""--train_batch_size"""      ,							type=lowerCamelCase_      ,							default=3_2      ,							help="""Batch size for training."""    )
            train_parser.add_argument("""--valid_batch_size"""      ,							type=lowerCamelCase_      ,							default=6_4      ,							help="""Batch size for validation."""    )
            train_parser.add_argument("""--learning_rate"""      ,							type=lowerCamelCase_      ,							default=3E-5      ,							help="""Learning rate."""    )
            train_parser.add_argument("""--adam_epsilon"""      ,							type=lowerCamelCase_      ,							default=1E-08      ,							help="""Epsilon for Adam optimizer."""    )
            train_parser.set_defaults(func=lowerCamelCase_    )

       def __init__(   self      :     int      ,							lowerCamelCase_      :     Namespace    ):
            a_    :     Optional[int]										=    logging.get_logger("""transformers-cli/training"""    )

            a_    :     List[str]										=    """tf""" if is_tf_available() else """torch"""

            os.makedirs(args.output      ,							exist_ok=lowerCamelCase_    )
            a_    :     List[str]										=    args.output

            a_    :     Optional[int]										=    args.column_label
            a_    :     Tuple										=    args.column_text
            a_    :     Optional[int]										=    args.column_id

            self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''    )
            if args.task == "text_classification":
                 a_    :     str										=    TextClassificationPipeline.from_pretrained(args.model    )
            elif args.task == "token_classification":
                 raise NotImplementedError
            elif args.task == "question_answering":
                 raise NotImplementedError

            self.logger.info(F'''Loading dataset from {args.train_data}'''    )
            a_    :     int										=    Processor.create_from_csv(
                args.train_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)
            a_    :     Union[str, Any]										=    None
            if args.validation_data:
                 self.logger.info(F'''Loading validation dataset from {args.validation_data}'''    )
                 a_    :     Tuple										=    Processor.create_from_csv(
                     args.validation_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)

            a_    :     str										=    args.validation_split
            a_    :     Tuple										=    args.train_batch_size
            a_    :     List[Any]										=    args.valid_batch_size
            a_    :     str										=    args.learning_rate
            a_    :     str										=    args.adam_epsilon

       def 						UpperCAmelCase(   self      :     List[str]    ):
            if self.framework == "tf":
                 return self.run_tf()
            return self.run_torch()

       def 						UpperCAmelCase(   self      :     Any    ):
            raise NotImplementedError

       def 						UpperCAmelCase(   self      :     List[str]    ):
            self.pipeline.fit(
                self.train_dataset      ,							validation_data=self.valid_dataset      ,							validation_split=self.validation_split      ,							learning_rate=self.learning_rate      ,							adam_epsilon=self.adam_epsilon      ,							train_batch_size=self.train_batch_size      ,							valid_batch_size=self.valid_batch_size      ,							)

            # Save trained pipeline
            self.pipeline.save_pretrained(self.output    )



