





'''simple docstring'''


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE			=							logging.getLogger(__name__)

@dataclass
class 				lowerCAmelCase_						:
 __lowerCamelCase  :							str    					=   field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
 __lowerCamelCase  :							Optional[str]    					=   field(
     default=__magic_name__  ,metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
 __lowerCamelCase  :							Optional[str]    					=   field(
     default=__magic_name__  ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
 __lowerCamelCase  :							Optional[str]    					=   field(
     default=__magic_name__  ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,)







@dataclass
class 				lowerCAmelCase_						:
 __lowerCamelCase  :							str    					=   field(
     metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()						)}						)
 __lowerCamelCase  :							str    					=   field(
     metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
 __lowerCamelCase  :							int    					=   field(
     default=128  ,metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }  ,)
 __lowerCamelCase  :							bool    					=   field(
     default=__magic_name__  ,metadata={"help": "Overwrite the cached training and evaluation sets"}						)







def 		__a(SCREAMING_SNAKE_CASE_					:   List[InputFeatures]      ):



    '''simple docstring'''





    _lowerCAmelCase		     = default_data_collator(SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = batch.pop("pairID"						,    SCREAMING_SNAKE_CASE_      )
    return batch


def 		__a():



    '''simple docstring'''





    _lowerCAmelCase		     = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
    _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir      )
        and os.listdir(training_args.output_dir      )
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
            " --overwrite_output_dir to overcome."      )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,    datefmt="%m/%d/%Y %H:%M:%S"						,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"						,    training_args.local_rank						,    training_args.device						,    training_args.n_gpu						,    bool(training_args.local_rank != -1      )						,    training_args.fpaa						,    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank      ):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s"						,    SCREAMING_SNAKE_CASE_      )

    # Set seed
    set_seed(training_args.seed      )

    try:
        _lowerCAmelCase		     = hans_tasks_num_labels[data_args.task_name]
    except KeyError:
        raise ValueError("Task not found: %s" % (data_args.task_name)      )

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    _lowerCAmelCase		     = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path						,    num_labels=SCREAMING_SNAKE_CASE_						,    finetuning_task=data_args.task_name						,    cache_dir=model_args.cache_dir						,    )
    _lowerCAmelCase		     = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,    cache_dir=model_args.cache_dir						,    )
    _lowerCAmelCase		     = AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path						,    from_tf=bool(".ckpt" in model_args.model_name_or_path      )						,    config=SCREAMING_SNAKE_CASE_						,    cache_dir=model_args.cache_dir						,    )

    # Get datasets
    _lowerCAmelCase		     = (
        HansDataset(
            data_dir=data_args.data_dir						,    tokenizer=SCREAMING_SNAKE_CASE_						,    task=data_args.task_name						,    max_seq_length=data_args.max_seq_length						,    overwrite_cache=data_args.overwrite_cache						,    )
        if training_args.do_train
        else None
    )
    _lowerCAmelCase		     = (
        HansDataset(
            data_dir=data_args.data_dir						,    tokenizer=SCREAMING_SNAKE_CASE_						,    task=data_args.task_name						,    max_seq_length=data_args.max_seq_length						,    overwrite_cache=data_args.overwrite_cache						,    evaluate=SCREAMING_SNAKE_CASE_						,    )
        if training_args.do_eval
        else None
    )

    # Initialize our Trainer
    _lowerCAmelCase		     = Trainer(
        model=SCREAMING_SNAKE_CASE_						,    args=SCREAMING_SNAKE_CASE_						,    train_dataset=SCREAMING_SNAKE_CASE_						,    eval_dataset=SCREAMING_SNAKE_CASE_						,    data_collator=SCREAMING_SNAKE_CASE_						,    )

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***"      )

        _lowerCAmelCase		     = trainer.predict(SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = output.predictions
        _lowerCAmelCase		     = np.argmax(SCREAMING_SNAKE_CASE_						,    axis=1      )

        _lowerCAmelCase		     = [ex.pairID for ex in eval_dataset]
        _lowerCAmelCase		     = os.path.join(training_args.output_dir						,    "hans_predictions.txt"      )
        _lowerCAmelCase		     = eval_dataset.get_labels()
        if trainer.is_world_master():
            with open(SCREAMING_SNAKE_CASE_						,    "w"      ) as writer:
                writer.write("pairID,gold_label\n"      )
                for pid, pred in zip(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      ):
                    writer.write("ex" + str(SCREAMING_SNAKE_CASE_      ) + "," + label_list[int(SCREAMING_SNAKE_CASE_      )] + "\n"      )

        trainer._log(output.metrics      )


def 		__a(SCREAMING_SNAKE_CASE_					:   Union[str, Any]      ):



    '''simple docstring'''





    main()


if __name__ == "__main__":
       main()









'''simple docstring'''


_SCREAMING_SNAKE_CASE			=							{
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}


def 		__a(SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    return [alphabet[char] for char in key.upper()]


def 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase		     = ""
    _lowerCAmelCase		     = 0
    _lowerCAmelCase		     = generate_table(SCREAMING_SNAKE_CASE_      )
    for char in words.upper():
        cipher += get_opponent(table[count]						,    SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = (count + 1) % len(SCREAMING_SNAKE_CASE_      )
    return cipher


def 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    return encrypt(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )


def 		__a(SCREAMING_SNAKE_CASE_					:   tuple[str, str]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase		     = 0 if char in table[0] else 1
    _lowerCAmelCase		     = table[row].index(SCREAMING_SNAKE_CASE_      )
    return row, col


def 		__a(SCREAMING_SNAKE_CASE_					:   tuple[str, str]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase     ,     _lowerCAmelCase		     = get_position(SCREAMING_SNAKE_CASE_						,    char.upper()      )
    if row == 1:
        return table[0][col]
    else:
        return table[1][col] if row == 0 else char


if __name__ == "__main__":
       import doctest

       doctest.testmod()  # Fist ensure that all our tests are passing...
       _SCREAMING_SNAKE_CASE			=							input("Enter key: ").strip()
       _SCREAMING_SNAKE_CASE			=							input("Enter text to encrypt: ").strip()
       _SCREAMING_SNAKE_CASE			=							encrypt(key, text)

       print(f'''Encrypted: {cipher_text}''')
       print(f'''Decrypted with key: {decrypt(key, cipher_text)}''')


