code: def main():
      parser = HfArgumentParser(
          (ModelArguments, TrainingArguments, Args)
      )
      model_args, training_args, args = cast(
          tuple[ModelArguments, TrainingArguments, Args],
          parser.parse_args_into_dataclasses(),
      )
      data_covered = read_jsonl(args.covered_pairs_path)
      preprocessed_covered = preprocess(data_covered)
      print("Length (covered):", len(preprocessed_covered))
      data_all_except_covered = read_jsonl(args.all_pairs_path)
      preprocessed_all = preprocess(data_all_except_covered)
      print("Length (all except coverred):", len(preprocessed_all))
      random.seed(training_args.seed)
      preprocessed_all = random.sample(
          preprocessed_all, k=args.n_samples - len(preprocessed_covered)
      )
      all = preprocessed_covered + preprocessed_all
      assert len(all) == args.n_samples
      random.shuffle(all)
      dataset = Dataset.from_list(all, split="train")
      assert len(dataset) == args.n_samples
      model_key = model_args.model_key
      if (model_name_or_path := model_args.model_name_or_path) is None:
          model_name_or_path = model_key
  
      tokenization_context = TokenizationContext.from_model_key(
          model_key, model_name_or_path
      )
      # if dataset_config.dpo_jsonl_path is None or dataset_config.dpo_sft:
      train_dataset = dataset.map(
          function=map_dataset,
          fn_kwargs=dict(args=args, context=tokenization_context),
          batched=True,
          num_proc=N_CORES,
          remove_columns=dataset.column_names,
          load_from_cache_file=False,  # not args.overwrite_cache
          desc="Running tokenizer on train dataset",
      )
      msg = f"#Examples truncated: {sum(train_dataset['exceeding_length'])} / {len(train_dataset)}"
      print(msg)
  
      train_dataset = train_dataset.shuffle(seed=training_args.seed)
  
      state = get_model_context(
          model_key,
          model_name_or_path,
          tokenization_context,
          inference_mode=False,
          use_flash_attention=args.use_flash_attention,
      )
  
      print("Parallel mode:", training_args.parallel_mode)
      data_collator = get_data_collator(
          args, state.tokenization_context.pad_token_id
      )
  
      # neftune_noise_alpha
      trainer = Trainer(
          model=state.model,
          args=training_args,
          train_dataset=train_dataset,
          # eval_dataset=eval_dataset,
          data_collator=data_collator,
          # eval_dataset=small_eval_dataset,
          # compute_metrics=compute_metrics,
      )
  
      # NOTE: the checkpoint will override the initialized model
      trainer.train(
          resume_from_checkpoint=training_args.resume_from_checkpoint
      )
      trainer.save_state()
      trainer.save_model(training_args.output_dir)
      state.tokenization_context.tokenizer.save_pretrained(
          training_args.output_dir
      )
  
file_path: magicoder/experiments/train_comment_code_pairs.py
imports: import random
  from dataclasses import dataclass, field
  from typing import cast
  import torch
  from datasets import Dataset, load_dataset
  from tqdm.auto import tqdm
  from transformers import HfArgumentParser, Trainer, TrainingArguments
  from magicoder.llm_wrapper import DecodingConfig, EncodingConfig, TokenizationContext, get_model_context, pad_sequences
  from magicoder.prompt_template import MAGICODER_PROMPT
  from magicoder.utils import N_CORES, read_jsonl, write_jsonl
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types