code: 
  
  
  """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
  
  
  UpperCAmelCase   =	logging.getLogger(__name__)
  
  
  
  
  @dataclass
  class   lowercase__							:
     __UpperCAmelCase       					=				field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}							)
     __UpperCAmelCase       					=				field(
         default=A_   ,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}							)
     __UpperCAmelCase       					=				field(
         default=A_   ,metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}							)
     __UpperCAmelCase       					=				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.
     __UpperCAmelCase       					=				field(
         default=A_   ,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,)
  
  
  
  
  
  
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Optional[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.
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				Optional[Any]      =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
     else:
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		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"""	,   __snake_case      )
  
     # 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.
  
     _lowerCamelCase  :				int      =		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	,   )
     _lowerCamelCase  :				Tuple      =		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=__snake_case	,   )
     _lowerCamelCase  :				Union[str, Any]      =		AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path	,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )	,   config=__snake_case	,   cache_dir=model_args.cache_dir	,   )
  
     # Get datasets
     _lowerCamelCase  :				Any      =		hasattr(model.config	,   """lang2id"""      )
     _lowerCamelCase  :				Tuple      =		(
         SquadDataset(
             __snake_case	,   tokenizer=__snake_case	,   is_language_sensitive=__snake_case	,   cache_dir=model_args.cache_dir      )
         if training_args.do_train
         else None
     )
     _lowerCamelCase  :				Tuple      =		(
         SquadDataset(
             __snake_case	,   tokenizer=__snake_case	,   mode="""dev"""	,   is_language_sensitive=__snake_case	,   cache_dir=model_args.cache_dir	,   )
         if training_args.do_eval
         else None
     )
  
     # Data collator
     _lowerCamelCase  :				Optional[Any]      =		DataCollatorWithPadding(__snake_case	,   pad_to_multiple_of=8      ) if training_args.fpaa else None
  
     # Initialize our Trainer
     _lowerCamelCase  :				int      =		Trainer(
         model=__snake_case	,   args=__snake_case	,   train_dataset=__snake_case	,   eval_dataset=__snake_case	,   data_collator=__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      )
  
  def 							_snake_case			(      __snake_case       :			str      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     main()
  
  
  if __name__ == "__main__":
   main()
  
  
style_context: 
  
  
  """simple docstring"""
  from itertools import permutations
  
  def 							_snake_case			(      __snake_case       :			int = 5      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if gon_side < 3 or gon_side > 5:
        raise ValueError("""gon_side must be in the range [3, 5]"""      )
  
     # Since it's 16, we know 10 is on the outer ring
     # Put the big numbers at the end so that they are never the first number
     _lowerCamelCase  :				Dict      =		list(range(gon_side + 1	,   0	,   -1      )      )
     _lowerCamelCase  :				Optional[int]      =		list(range(gon_side + 2	,   gon_side * 2 + 1      )      )
  
     for perm in permutations(small_numbers + big_numbers      ):
        _lowerCamelCase  :				List[str]      =		generate_gon_ring(__snake_case	,   list(__snake_case      )      )
        if is_magic_gon(__snake_case      ):
           return int("""""".join(str(__snake_case      ) for n in numbers      )      )
  
     _lowerCamelCase  :				Union[str, Any]      =		F'Magic {gon_side}-gon ring is impossible'
     raise ValueError(__snake_case      )
  
  def 							_snake_case			(      __snake_case       :			int	,   __snake_case       :			list[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Union[str, Any]      =		[0] * (gon_side * 3)
     _lowerCamelCase  :				Dict      =		perm[0:3]
     perm.append(perm[1]      )
  
     _lowerCamelCase  :				Tuple      =		1 if gon_side < 5 else 2
  
     for i in range(1	,   len(__snake_case      ) // 3 + magic_number      ):
        _lowerCamelCase  :				Dict      =		perm[2 * i + 1]
        _lowerCamelCase  :				str      =		result[3 * i - 1]
        _lowerCamelCase  :				Dict      =		perm[2 * i + 2]
  
     return result
  
  def 							_snake_case			(      __snake_case       :			list[int]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if len(__snake_case      ) % 3 != 0:
        raise ValueError("""a gon ring should have a length that is a multiple of 3"""      )
  
     if min(numbers[::3]      ) != numbers[0]:
        return False
  
     _lowerCamelCase  :				str      =		sum(numbers[:3]      )
  
     return all(sum(numbers[i : i + 3]      ) == total for i in range(3	,   len(__snake_case      )	,   3      )      )
  
  
  if __name__ == "__main__":
   print(solution())
  
  