code: 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					=				logging.getLogger(__name__)
  
  
  
  
  @dataclass
  class    __lowerCAmelCase  :
  
  
  
   '''simple docstring'''
   _lowerCamelCase						:	str				    = field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
   _lowerCamelCase						:	Optional[str]				    = field(
       default=a_,	metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
   _lowerCamelCase						:	Optional[str]				    = field(
       default=a_,	metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
   _lowerCamelCase						:	bool				    = field(default=a_,	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.
   _lowerCamelCase						:	Optional[str]				    = field(
       default=a_,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'},	)
  
  
  
  
  
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         # 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     :       Dict  						=  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     :       Union[str, Any]  						=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
         else:
                _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'		,						lowercase_				)
  
         # 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     :       Optional[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     :       Dict  						=  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=lowercase_		,						)
         _UpperCamelCase     :       List[Any]  						=  AutoModelForQuestionAnswering.from_pretrained(
             model_args.model_name_or_path		,						from_tf=bool('.ckpt' in model_args.model_name_or_path				)		,						config=lowercase_		,						cache_dir=model_args.cache_dir		,						)
  
         # Get datasets
         _UpperCamelCase     :       List[Any]  						=  hasattr(model.config		,						'lang2id'				)
         _UpperCamelCase     :       int  						=  (
             SquadDataset(
                 lowercase_		,						tokenizer=lowercase_		,						is_language_sensitive=lowercase_		,						cache_dir=model_args.cache_dir				)
             if training_args.do_train
             else None
         )
         _UpperCamelCase     :       Dict  						=  (
             SquadDataset(
                 lowercase_		,						tokenizer=lowercase_		,						mode='dev'		,						is_language_sensitive=lowercase_		,						cache_dir=model_args.cache_dir		,						)
             if training_args.do_eval
             else None
         )
  
         # Data collator
         _UpperCamelCase     :       Tuple  						=  DataCollatorWithPadding(lowercase_		,						pad_to_multiple_of=8				) if training_args.fpaa else None
  
         # Initialize our Trainer
         _UpperCamelCase     :       List[Any]  						=  Trainer(
             model=lowercase_		,						args=lowercase_		,						train_dataset=lowercase_		,						eval_dataset=lowercase_		,						data_collator=lowercase_		,						)
  
         # 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 SCREAMING_SNAKE_CASE				(     lowercase_:	Dict				):
         # For xla_spawn (TPUs)
         main()
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
style_context: from ..utils import requires_backends
  
  
  UpperCAmelCase					=				None
  
  
  
  
  class    __lowerCAmelCase  :
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Optional[int]       ,			*lowerCamelCase       :		Dict       ,			**lowerCamelCase       :		int  ) ->							Tuple:
          requires_backends(self       ,			['detectron2']  )
  
   @classmethod
   def 					__snake_case					(			cls       :		Optional[Any]       ,			*lowerCamelCase       :		Optional[int]       ,			**lowerCamelCase       :		Tuple  ) ->							List[str]:
          requires_backends(cls       ,			['detectron2']  )
  
  
  
  