code: def main():
      args = cast(
          Args, HfArgumentParser(Args).parse_args_into_dataclasses()[0]
      )
      if args.key == "combine":
          assert (
              args.dataset_path == "json"
              and args.data_files is not None
          )
          all_data: list[dict] = []
          for data_file in args.data_files:
              data = read_jsonl(data_file)
              all_data.extend(
                  dict(
                      instruction=item["instruction"],
                      response=item["response"],
                  )
                  for item in data
              )
          write_jsonl(args.output_file, all_data)
      else:
          dataset = load_dataset(
              args.dataset_path,
              data_files=args.data_files,
              split=args.split,
              num_proc=N_CORES,
          )
          dataset = dataset.map(
              map_fn,
              fn_kwargs=dict(key=args.key),
              batched=True,
              num_proc=N_CORES,
              remove_columns=dataset.column_names,
          )
          write_jsonl(args.output_file, dataset)
  
file_path: magicoder/src/magicoder/preprocess_data.py
imports: from dataclasses import dataclass, field
  from typing import Literal, cast
  from datasets import load_dataset
  from transformers import HfArgumentParser
  from magicoder.prompt_template import SRC_INSTRUCT_INSTRUCTION_PROMPT
  from magicoder.utils import N_CORES, read_jsonl, write_jsonl
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types