code: def main():
      # Setup configuration
      parser = HfArgumentParser(HumanEvalArguments)
      args = parser.parse_args()
  
      transformers.logging.set_verbosity_error()
      # enables code execution in code_eval metric
      os.environ["HF_ALLOW_CODE_EVAL"] = args.HF_ALLOW_CODE_EVAL
      # make sure tokenizer plays nice with multiprocessing
      os.environ["TOKENIZERS_PARALLELISM"] = "false"
  
      if args.num_workers is None:
          args.num_workers = multiprocessing.cpu_count()
  
      # Use dataset load to feed to accelerate
      accelerator = Accelerator()
      set_seed(args.seed, device_specific=True)
  
      # Load model and tokenizer
      tokenizer = AutoTokenizer.from_pretrained(args.model_ckpt)
      tokenizer.pad_token = tokenizer.eos_token
      model = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
  
      # Generation settings
      gen_kwargs = {
          "do_sample": args.do_sample,
          "temperature": args.temperature,
          "max_new_tokens": args.max_new_tokens,
          "top_p": args.top_p,
          "top_k": args.top_k,
          "stopping_criteria": StoppingCriteriaList(
              [EndOfFunctionCriteria(0, EOF_STRINGS, tokenizer)]
          ),
      }
  
      # Load evaluation dataset and metric
      human_eval = load_dataset("openai_humaneval")
      code_eval_metric = load_metric("code_eval")
  
      n_tasks = (
          args.num_tasks
          if args.num_tasks is not None
          else len(human_eval["test"])
      )
      n_copies = args.n_samples // args.batch_size
  
      human_eval_tokenized = TokenizedDataset(
          tokenizer,
          human_eval["test"],
          n_copies=n_copies,
          n_tasks=n_tasks,
      )
      # do not confuse args.batch_size, which is actually the num_return_sequences
      human_eval_loader = DataLoader(human_eval_tokenized, batch_size=1)
  
      # Run a quick test to see if code evaluation is enabled
      try:
          _ = code_eval_metric.compute(
              references=[""], predictions=[[""]]
          )
      except ValueError as exception:
          print(
              'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
              " flag to enable code evaluation."
          )
          raise exception
  
      model, human_eval_loader = accelerator.prepare(
          model, human_eval_loader
      )
  
      generations = complete_code(
          accelerator,
          model,
          tokenizer,
          human_eval_loader,
          n_tasks=n_tasks,
          batch_size=args.batch_size,
          **gen_kwargs,
      )
  
      if accelerator.is_main_process:
          references = []
  
          for task in tqdm(range(n_tasks)):
              test_func = human_eval["test"][task]["test"]
              entry_point = (
                  f"check({human_eval['test'][task]['entry_point']})"
              )
              references.append("\n" + test_func + "\n" + entry_point)
  
          # Evaluate completions with "code_eval" metric
          pass_at_k, _ = code_eval_metric.compute(
              references=references,
              predictions=generations,
              num_workers=args.num_workers,
          )
          print(f"Results: {pass_at_k}")
  
          # Save results to json file
          with open(args.output_file, "w") as fp:
              json.dump(pass_at_k, fp)
  
imports: import json
  import multiprocessing
  import os
  import re
  from collections import defaultdict
  import torch
  from datasets import load_dataset, load_metric
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from tqdm import tqdm
  import transformers
  from accelerate import Accelerator
  from accelerate.utils import set_seed
  from arguments import HumanEvalArguments
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types