[content] crowd-pilot/crowd_pilot/serialize_dataset_nemo_json.py (python)
    max_docs: Optional[int]
    csv_root: Optional[str]
    val_ratio: float


def to_nemo_jsonl(cfg: SerializeConfig) -> None:
    """Convert CSV sessions to NeMo JSONL format."""
    os.makedirs(cfg.output_dir, exist_ok=True)

    required_cols = ["Sequence", "Time", "File", "RangeOffset", "RangeLength", "Text", "Language", "Type"]

    session_dataframes: List[Tuple[pd.DataFrame, str]] = []
    root = Path(cast(str, cfg.csv_root)).expanduser().resolve()
    csv_files = _discover_local_sessions(root)
    assert csv_files, f"No CSV files found under {root}"
    
    for csv_file in csv_files:
        df = pd.read_csv(csv_file)
        missing_local = [c for c in required_cols if c not in df.columns]
        assert not missing_local, f"Missing required CSV columns in {csv_file}: {missing_local}"
        session_dataframes.append((df, str(csv_file)))

    random.seed(42)
    random.shuffle(session_dataframes)
    
    total_sessions = len(session_dataframes)
    val_count = int(total_sessions * cfg.val_ratio)
    train_count = total_sessions - val_count

    train_conversations = []
    val_conversations = []
    
    turn_counts: List[int] = []
    char_counts: List[int] = []
    docs_written = 0

    for i, (session_df, _) in enumerate(session_dataframes):
        if cfg.max_docs and docs_written >= cfg.max_docs:
            break
            
        session_df = pd.DataFrame(session_df.copy())

        assert cfg.max_chars_per_conversation > 0, "max_chars_per_conversation must be positive"
        assert cfg.max_chars_per_turn > 0, "max_chars_per_turn must be positive"
        conversation_chunks = session_to_nemo_conversation_chunks(
            session_df,
            cfg.max_chars_per_conversation,
            max_chars_per_turn=cfg.max_chars_per_turn,
            min_conversation_turns=cfg.min_conversation_turns,
        )

        # Per-chunk statistics (for reporting)
        per_chunk_turns = [len(chunk) for chunk in conversation_chunks]
        per_chunk_chars = [
            sum(len(turn.get("value", "")) for turn in chunk)
            for chunk in conversation_chunks
        ]
        
        turn_counts.extend(per_chunk_turns)
        char_counts.extend(per_chunk_chars)

        for chunk in conversation_chunks:
            if cfg.max_docs and docs_written >= cfg.max_docs:
                break

            record = {
                "mask": "User",
                "system": "You are a helpful assistant that can interact multiple times with a computer shell to solve programming tasks.
Your response must contain exactly ONE bash code block with ONE command (or commands connected with && or ||).

Format your response as shown in <format_example>.

<format_example>
```bash
your_command_here
```
</format_example>

Failure to follow these rules will cause your response to be rejected.",
                "conversations": chunk,
            }

            if i < train_count:
                train_conversations.append(record)
            else:
                val_conversations.append(record)

            docs_written += 1

        if cfg.max_docs and docs_written >= cfg.max_docs:
            break

    train_path = Path(cfg.output_dir) / "training.jsonl"
    with open(train_path, 'w', encoding='utf-8') as f:
        for record in train_conversations:
            f.write(json.dumps(record, ensure_ascii=False) + '
')
    
    val_path = Path(cfg.output_dir) / "validation.jsonl"
    with open(val_path, 'w', encoding='utf-8') as f:
        for record in val_conversations:
            f.write(json.dumps(record, ensure_ascii=False) + '
')

    def _compute_stats(values: List[int]) -> dict | None:
        if not values:
            return None
        count = len(values)
        total = sum(values)
        avg = total / count if count > 0 else 0.0
        sorted_vals = sorted(values)
        mid = count // 2
        if count % 2 == 1:
            median = float(sorted_vals[mid])
        else:
            median = 0.5 * (sorted_vals[mid - 1] + sorted_vals[mid])
        min_len = min(values)
        max_len = max(values)
        return {
            "count": count,
            "total": total,
            "median": median,
            "avg": avg,
            "min": min_len,
            "max": max_len,
        }

    def _print_turn_stats(label: str, values: List[int]) -> None:
        if not values:
            print(f"[debug] No {label} chunks for turn count stats.")
            return
        stats = _compute_stats(values)
        assert stats is not None
        print(
            f"[debug] {label.capitalize()} chunks turn stats: "
            f"count={stats['count']}, median_turns={stats['median']:.1f}, avg_turns={stats['avg']:.1f}, "
            f"min_turns={stats['min']}, max_turns={stats['max']}"
        )

    def _print_char_stats(label: str, values: List[int]) -> None:
        if not values:
            print(f"[debug] No {label} chunks for character stats.")
            return
        stats = _compute_stats(values)
        assert stats is not None
        print(
            f"[debug] {label.capitalize()} chunks char stats: "
            f"count={stats['count']}, median_chars={stats['median']:.1f}, avg_chars={stats['avg']:.1f}, "
            f"min_chars={stats['min']}, max_chars={stats['max']}"
        )

    print(f"
[summary]")
    print(f"  Total sessions processed: {total_sessions}")
    print(f"  Train conversations: {len(train_conversations)}")
    print(f"  Val conversations: {len(val_conversations)}")
    print(f"  Output: {cfg.output_dir}/{{training,validation}}.jsonl")

    metadata = {
        "config": {
            "csv_root": cfg.csv_root,
            "output_dir": cfg.output_dir,
            "min_conversation_turns": cfg.min_conversation_turns,
            "max_docs": cfg.max_docs,
            "val_ratio": cfg.val_ratio,
            "max_chars_per_conversation": cfg.max_chars_per_conversation,
            "max_chars_per_turn": cfg.max_chars_per_turn,
        },
        "counts": {
            "total_sessions": total_sessions,
            "train_conversations": len(train_conversations),
            "val_conversations": len(val_conversations),
            "docs_written": docs_written,
        },
        "turn_stats": _compute_stats(turn_counts),
        "char_stats": _compute_stats(char_counts),
        "files": {
            "train_path": str(train_path),
            "val_path": str(val_path),
        },
    }
    metadata_path = Path(cfg.output_dir) / "metadata.json"
    with open(metadata_path, "w", encoding="utf-8") as mf:
        json.dump(metadata, mf, ensure_ascii=False, indent=2)
    print(f"  Metadata: {metadata_path}")


def parse_args() -> SerializeConfig:
    p = argparse.ArgumentParser(
        description="Serialize CSV sessions to JSONL for NeMo 2.0 SFT function calling"
    )
    p.add_argument("--csv_root", type=str, required=True, 
                   help="Root directory containing per-session CSV files")
    p.add_argument("--output_dir", type=str, required=True, 
                   help="Output directory for JSONL files")
    p.add_argument("--min_conversation_turns", type=int, default=5, 
                   help="Minimum number of turns to keep a conversation chunk")
    p.add_argument("--max_docs", type=int, default=None, 
                   help="Stop after writing this many unique docs")
    p.add_argument("--val_ratio", type=float, default=0.10, 
                   help="Fraction of sessions to route to validation [0,1)")
    p.add_argument(
        "--max_chars_per_conversation",
        type=int,
        default=8192*16*3,
        help="Maximum characters per conversation chunk",
    )
    p.add_argument("--max_chars_per_turn", type=int, default=8192*3, help="Maximum characters per turn")
    args = p.parse_args()
    return SerializeConfig(
        output_dir=args.output_dir,
        max_chars_per_conversation=args.max_chars_per_conversation,
        max_chars_per_turn=args.max_chars_per_turn,
        min_conversation_turns=args.min_conversation_turns,
        max_docs=args.max_docs,