code: def main():
      # 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.
  
      parser = 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.
          model_args, data_args, training_args = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1])
          )
      else:
          (
              model_args,
              data_args,
              training_args,
          ) = 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.fp16,
      )
      # 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", training_args)
  
      # 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.
  
      config = 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,
      )
      tokenizer = 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=False,  # SquadDataset is not compatible with Fast tokenizers which have a smarter overflow handeling
      )
      model = AutoModelForQuestionAnswering.from_pretrained(
          model_args.model_name_or_path,
          from_tf=bool(".ckpt" in model_args.model_name_or_path),
          config=config,
          cache_dir=model_args.cache_dir,
      )
  
      # Get datasets
      is_language_sensitive = hasattr(model.config, "lang2id")
      train_dataset = (
          SquadDataset(
              data_args,
              tokenizer=tokenizer,
              is_language_sensitive=is_language_sensitive,
              cache_dir=model_args.cache_dir,
          )
          if training_args.do_train
          else None
      )
      eval_dataset = (
          SquadDataset(
              data_args,
              tokenizer=tokenizer,
              mode="dev",
              is_language_sensitive=is_language_sensitive,
              cache_dir=model_args.cache_dir,
          )
          if training_args.do_eval
          else None
      )
  
      # Data collator
      data_collator = (
          DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
          if training_args.fp16
          else None
      )
  
      # Initialize our Trainer
      trainer = Trainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset,
          eval_dataset=eval_dataset,
          data_collator=data_collator,
      )
  
      # 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)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/legacy/question-answering/run_squad_trainer.py
imports: 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
  from transformers import SquadDataTrainingArguments
  from transformers import Trainer, TrainingArguments
  from transformers.trainer_utils import is_main_process
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types