




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__UpperCamelCase      : Tuple					      =   logging.getLogger(__name__)

@dataclass
class 	__lowerCAmelCase			:
							UpperCamelCase__        =						field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
							UpperCamelCase__        =						field(
							    default=__magic_name__   , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}				)
							UpperCamelCase__        =						field(
							    default=__magic_name__   , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}				)
							UpperCamelCase__        =						field(default=__magic_name__   , metadata={'''help''': '''Set this flag to use fast tokenization.'''}				)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							UpperCamelCase__        =						field(
							    default=__magic_name__   , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   , )







def 	__A	(			)   -> Dict:
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	a		       =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)

	if len(sys.argv					) == 2 and sys.argv[1].endswith(""".json"""					):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		a       ,				a       ,				a		       =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
	else:
		a       ,				a       ,				a		       =							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"""							,    __lowerCamelCase					)

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

	a		       =							AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,    cache_dir=model_args.cache_dir							,    )
	a		       =							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							,    use_fast=__lowerCamelCase							,    )
	a		       =							AutoModelForQuestionAnswering.from_pretrained(
	    model_args.model_name_or_path							,    from_tf=bool(""".ckpt""" in model_args.model_name_or_path					)							,    config=__lowerCamelCase							,    cache_dir=model_args.cache_dir							,    )

	# Get datasets
	a		       =							hasattr(model.config							,    """lang2id"""					)
	a		       =							(
	    SquadDataset(
	        __lowerCamelCase							,    tokenizer=__lowerCamelCase							,    is_language_sensitive=__lowerCamelCase							,    cache_dir=model_args.cache_dir					)
	    if training_args.do_train
	    else None
	)
	a		       =							(
	    SquadDataset(
	        __lowerCamelCase							,    tokenizer=__lowerCamelCase							,    mode="""dev"""							,    is_language_sensitive=__lowerCamelCase							,    cache_dir=model_args.cache_dir							,    )
	    if training_args.do_eval
	    else None
	)

	# Data collator
	a		       =							DataCollatorWithPadding(__lowerCamelCase							,    pad_to_multiple_of=8					) if training_args.fpaa else None

	# Initialize our Trainer
	a		       =							Trainer(
	    model=__lowerCamelCase							,    args=__lowerCamelCase							,    train_dataset=__lowerCamelCase							,    eval_dataset=__lowerCamelCase							,    data_collator=__lowerCamelCase							,    )

	# 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					)





def 	__A	(			__lowerCamelCase					)   -> Optional[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
		main()







__UpperCamelCase      : Union[str, Any]					      =   {
    "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	(			__lowerCamelCase					)   -> list[tuple[str, str]]:
	return [alphabet[char] for char in key.upper()]





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							""""""
	a		       =							0
	a		       =							generate_table(__lowerCamelCase					)
	for char in words.upper():
		cipher += get_opponent(table[count]							,    __lowerCamelCase					)
		a		       =							(count + 1) % len(__lowerCamelCase					)
	return cipher





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	return encrypt(__lowerCamelCase							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> tuple[int, int]:
	a		       =							0 if char in table[0] else 1
	a		       =							table[row].index(__lowerCamelCase					)
	return row, col





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a       ,				a		       =							get_position(__lowerCamelCase							,    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...
		__UpperCamelCase      : int					      =   input("Enter key: ").strip()
		__UpperCamelCase      : int					      =   input("Enter text to encrypt: ").strip()
		__UpperCamelCase      : Optional[Any]					      =   encrypt(key, text)

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

