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)
      )
      (
          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)
  
      # Set seed
      set_seed(training_args.seed)
  
      try:
          num_labels = hans_tasks_num_labels[data_args.task_name]
      except KeyError:
          raise ValueError("Task not found: %s" % (data_args.task_name))
  
      # 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,
          num_labels=num_labels,
          finetuning_task=data_args.task_name,
          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,
      )
      model = AutoModelForSequenceClassification.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
      train_dataset = (
          HansDataset(
              data_dir=data_args.data_dir,
              tokenizer=tokenizer,
              task=data_args.task_name,
              max_seq_length=data_args.max_seq_length,
              overwrite_cache=data_args.overwrite_cache,
          )
          if training_args.do_train
          else None
      )
      eval_dataset = (
          HansDataset(
              data_dir=data_args.data_dir,
              tokenizer=tokenizer,
              task=data_args.task_name,
              max_seq_length=data_args.max_seq_length,
              overwrite_cache=data_args.overwrite_cache,
              evaluate=True,
          )
          if training_args.do_eval
          else None
      )
  
      # Initialize our Trainer
      trainer = Trainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset,
          eval_dataset=eval_dataset,
          data_collator=hans_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)
  
      # Evaluation
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
  
          output = trainer.predict(eval_dataset)
          preds = output.predictions
          preds = np.argmax(preds, axis=1)
  
          pair_ids = [ex.pairID for ex in eval_dataset]
          output_eval_file = os.path.join(
              training_args.output_dir, "hans_predictions.txt"
          )
          label_list = eval_dataset.get_labels()
          if trainer.is_world_master():
              with open(output_eval_file, "w") as writer:
                  writer.write("pairID,gold_label\n")
                  for pid, pred in zip(pair_ids, preds):
                      writer.write(
                          "ex"
                          + str(pid)
                          + ","
                          + label_list[int(pred)]
                          + "\n"
                      )
  
          trainer._log(output.metrics)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/adversarial/run_hans.py
imports: import logging
  import os
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional
  import numpy
  import torch
  import transformers
  from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed
  from transformers.trainer_utils import is_main_process
  from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types