from __future__ import annotations

_lowercase							:      int      							=tuple[int, int, int]
_lowercase							:      Any      							=tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
_lowercase							:      List[Any]      							='''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

# -------------------------- default selection --------------------------
# rotors --------------------------
_lowercase							:      Dict      							='''EGZWVONAHDCLFQMSIPJBYUKXTR'''
_lowercase							:      Tuple      							='''FOBHMDKEXQNRAULPGSJVTYICZW'''
_lowercase							:      int      							='''ZJXESIUQLHAVRMDOYGTNFWPBKC'''
# reflector --------------------------
_lowercase							:      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 --------------------------
_lowercase							:      List[str]      							='''RMDJXFUWGISLHVTCQNKYPBEZOA'''
_lowercase							:      Union[str, Any]      							='''SGLCPQWZHKXAREONTFBVIYJUDM'''
_lowercase							:      Dict      							='''HVSICLTYKQUBXDWAJZOMFGPREN'''
_lowercase							:      str      							='''RZWQHFMVDBKICJLNTUXAGYPSOE'''
_lowercase							:      List[str]      							='''LFKIJODBEGAMQPXVUHYSTCZRWN'''
_lowercase							:      int      							='''KOAEGVDHXPQZMLFTYWJNBRCIUS'''



def A__  (			lowercase:     RotorPositionT,		lowercase:     RotorSelectionT,		lowercase:     str					)				->		tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
       # Checks if there are 3 unique rotors

       if (unique_rotsel := len(set(lowercase					)					)) < 3:
              A						:     Any         =F'Please use 3 unique rotors (not {unique_rotsel})'
              raise Exception(lowercase					)

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

       # Validates string and returns dict
       A						:     Any         =_plugboard(lowercase					)

       return rotpos, rotsel, pbdict



def A__  (			lowercase:     str					)				->		dict[str, str]:

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

       pbstring.replace(' ',		''					)

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

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

       return pb



def A__  (			lowercase:     str,		lowercase:     RotorPositionT,		lowercase:     RotorSelectionT = (rotora, rotora, rotora),		lowercase:     str = "",		)				->		str:
       A						:     Optional[Any]         =text.upper()
       A						:     Union[str, Any]         =_validator(
           lowercase,		lowercase,		plugb.upper()					)

       A						:     Any         =rotor_position
       A						:     Dict         =rotor_selection
       rotorposa -= 1
       rotorposa -= 1
       rotorposa -= 1

       A						:     Union[str, Any]         =[]

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

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

                     # rotor rb --------------------------
                     A						:     Tuple         =abc.index(lowercase					) + rotorposa
                     A						:     Tuple         =rotora[index % len(lowercase					)]

                     # rotor rc --------------------------
                     A						:     Optional[Any]         =abc.index(lowercase					) + rotorposa
                     A						:     Union[str, Any]         =rotora[index % len(lowercase					)]

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

                     A						:     int         =reflector[symbol]

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

                     # 2nd plugboard
                     if symbol in plugboard:
                            A						:     Union[str, Any]         =plugboard[symbol]

                     # moves/resets rotor positions
                     rotorposa += 1
                     if rotorposa >= len(lowercase					):
                            A						:     Optional[Any]         =0
                            rotorposa += 1
                     if rotorposa >= len(lowercase					):
                            A						:     Union[str, Any]         =0
                            rotorposa += 1
                     if rotorposa >= len(lowercase					):
                            A						:     Any         =0

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

       return "".join(lowercase					)


if __name__ == "__main__":
    _lowercase							:      List[str]      							='''This is my Python script that emulates the Enigma machine from WWII.'''
    _lowercase							:      Optional[int]      							=(1, 1, 1)
    _lowercase							:      Optional[int]      							='''pictures'''
    _lowercase							:      int      							=(rotora, rotora, rotora)
    _lowercase							:      Any      							=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
_lowercase							:      str      							=False
_lowercase							:      Optional[Any]      							=False



def A__  (			lowercase:     Namespace					)				->		Optional[int]:
       return TrainCommand(lowercase					)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				SCREAMING_SNAKE_CASE__    :					ArgumentParser     )    ->				Dict:
         A						:     Optional[Any]         =parser.add_parser('train'      ,	help='CLI tool to train a model on a task.'     )

         train_parser.add_argument(
             '--train_data'      ,	type=SCREAMING_SNAKE_CASE__      ,	required=SCREAMING_SNAKE_CASE__      ,	help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'      ,	)
         train_parser.add_argument(
             '--column_label'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=0      ,	help='Column of the dataset csv file with example labels.'     )
         train_parser.add_argument(
             '--column_text'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1      ,	help='Column of the dataset csv file with example texts.'     )
         train_parser.add_argument(
             '--column_id'      ,	type=SCREAMING_SNAKE_CASE__      ,	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=SCREAMING_SNAKE_CASE__      ,	default=''      ,	help='path to validation dataset.'     )
         train_parser.add_argument(
             '--validation_split'      ,	type=SCREAMING_SNAKE_CASE__      ,	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=SCREAMING_SNAKE_CASE__      ,	default='./'      ,	help='path to saved the trained model.'     )

         train_parser.add_argument(
             '--task'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='text_classification'      ,	help='Task to train the model on.'     )
         train_parser.add_argument(
             '--model'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='bert-base-uncased'      ,	help='Model\'s name or path to stored model.'     )
         train_parser.add_argument('--train_batch_size'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=32      ,	help='Batch size for training.'     )
         train_parser.add_argument('--valid_batch_size'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=64      ,	help='Batch size for validation.'     )
         train_parser.add_argument('--learning_rate'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=3e-5      ,	help='Learning rate.'     )
         train_parser.add_argument('--adam_epsilon'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1e-08      ,	help='Epsilon for Adam optimizer.'     )
         train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__     )







  def __init__(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Namespace     )    ->				List[Any]:
         A						:     Optional[int]         =logging.get_logger('transformers-cli/training'     )

         A						:     Dict         ='tf' if is_tf_available() else 'torch'

         os.makedirs(args.output      ,	exist_ok=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =args.output

         A						:     List[str]         =args.column_label
         A						:     int         =args.column_text
         A						:     Union[str, Any]         =args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'     )
         if args.task == "text_classification":
                A						:     Optional[Any]         =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						:     Tuple         =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						:     Dict         =None
         if args.validation_data:
                self.logger.info(f'Loading validation dataset from {args.validation_data}'     )
                A						:     List[Any]         =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						:     Optional[Any]         =args.validation_split
         A						:     str         =args.train_batch_size
         A						:     Any         =args.valid_batch_size
         A						:     Dict         =args.learning_rate
         A						:     List[str]         =args.adam_epsilon







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Union[str, Any]:
         if self.framework == "tf":
                return self.run_tf()
         return self.run_torch()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[str]:
         raise NotImplementedError







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				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     )






