
"""simple docstring"""




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


A__   :						str 							=     logging.getLogger(__name__)







@dataclass
class 	_lowercase    :


     '''simple docstring'''





     _A		    =				field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
     _A		    =				field(default=lowerCAmelCase_						, 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.
     _A		    =				field(
         default=lowerCAmelCase_						, metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						, )





def 				a__			(					):






     '''simple docstring'''
     # 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.

     UpperCAmelCase__				:			int							=    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.
          UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[Any]							=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
     else:
          UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			List[str]							=    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.

     UpperCAmelCase__				:			List[Any]							=    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							,     )
     UpperCAmelCase__				:			Any							=    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							,     )
     UpperCAmelCase__				:			str							=    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
     UpperCAmelCase__				:			Union[str, Any]							=    hasattr(model.config							,     "lang2id"		)
     UpperCAmelCase__				:			Any							=    (
         SquadDataset(
             lowerCAmelCase							,     tokenizer=lowerCAmelCase							,     is_language_sensitive=lowerCAmelCase							,     cache_dir=model_args.cache_dir		)
         if training_args.do_train
         else None
     )
     UpperCAmelCase__				:			Optional[Any]							=    (
         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
     UpperCAmelCase__				:			Optional[int]							=    DataCollatorWithPadding(lowerCAmelCase							,     pad_to_multiple_of=8		) if training_args.fpaa else None

     # Initialize our Trainer
     UpperCAmelCase__				:			Any							=    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						:							Union[str, Any]		):






     '''simple docstring'''
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
    main()





"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							str							,     lowerCAmelCase						:							int = 13		):






     '''simple docstring'''
     UpperCAmelCase__				:			Any							=    ""
     for c in s:
          if "A" <= c <= "Z":
               out += chr(ord("A"		) + (ord(lowerCAmelCase		) - ord("A"		) + n) % 26		)
          elif "a" <= c <= "z":
               out += chr(ord("a"		) + (ord(lowerCAmelCase		) - ord("a"		) + n) % 26		)
          else:
               out += c
     return out



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[int]							=    input("Enter message: "		)

     UpperCAmelCase__				:			Optional[Any]							=    dencrypt(lowerCAmelCase							,     13		)
     print("Encryption:"							,     lowerCAmelCase		)

     UpperCAmelCase__				:			List[str]							=    dencrypt(lowerCAmelCase							,     13		)
     print("Decryption: "							,     lowerCAmelCase		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



