utils/transfer_rubikscube.py

#!/usr/bin/env python3
"""
Convert Rubik's Cube DQN trajectories to SFT JSON.

Prompt layout follows VAGEN_old/vagen/env/rubikscube/prompt.py (FORMAT_CONFIGS + templates).
By default, actions in <answer> and "Last valid action(s)" use natural language aligned with
the reasoning style in FORMAT_CONFIGS (e.g. "Rotate Up clockwise"); use --action_repr token
for canonical symbols (UpCW, UpCCW, ...).
"""
from __future__ import annotations

import argparse
import importlib.util
import json
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

_REPO_ROOT = Path(__file__).resolve().parents[1]


def _load_prompt_module(rel_path: str) -> Any:
    """Load vagen/env/*/prompt.py without importing vagen.env package __init__ (avoids side effects)."""
    path = _REPO_ROOT / rel_path
    name = "vagen_prompt_" + rel_path.replace("/", "_").replace(".py", "")
    spec = importlib.util.spec_from_file_location(name, path)
    if spec is None or spec.loader is None:
        raise ImportError(f"Cannot load prompt module from {path}")
    mod = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(mod)
    return mod


_rc_prompt = _load_prompt_module("vagen/env/rubikscube/prompt.py")
rc_action_template = _rc_prompt.action_template
rc_format_prompt = _rc_prompt.format_prompt
rc_init_observation_template = _rc_prompt.init_observation_template
rc_system_prompt = _rc_prompt.system_prompt

# Keep transfer robust if prompt.py changes/gets swapped.
# We only append this block when the base system prompt lacks the face-name mapping.
_NET_MAPPING_BLOCK = """\nVision observation (IMPORTANT):\n- The image shows the cube as a 2D unfolded net (a cross) with 6 faces, each face is a 2x2 grid.\n- Face names in the unfolded net are fixed as:\n\n      [U]\n[L] [F] [R] [B]\n      [D]\n\n  Where:\n  - U = Up (top face)\n  - D = Down (bottom face)\n  - F = Front (facing you)\n  - B = Back (opposite of Front)\n  - L = Left (left of Front)\n  - R = Right (right of Front)\n\n- Sticker positions inside each 2x2 face in the image are:\n  - index 0: top-left\n  - index 1: top-right\n  - index 2: bottom-left\n  - index 3: bottom-right\n"""

# --- Same action order as visual_scout/dqn_rubikscube.VagenRubiksCubeVisionWrapper.ACTIONS ---
ACTION_ID_TO_WORD = {
    0: "UpCW",
    1: "UpCCW",
    2: "DownCW",
    3: "DownCCW",
    4: "LeftCW",
    5: "LeftCCW",
    6: "RightCW",
    7: "RightCCW",
    8: "FrontCW",
    9: "FrontCCW",
    10: "BackCW",
    11: "BackCCW",
}

PROMPT_FORMAT_CHOICES = tuple(rc_format_prompt.keys())


def _token_to_natural(token: str) -> str:
    """Natural phrasing consistent with rubikscube/prompt.py worldmodeling example ('Rotate Up clockwise.')."""
    if token.endswith("CCW"):
        face = token[:-3]
        return f"Rotate {face} counter-clockwise"
    if token.endswith("CW"):
        face = token[:-2]
        return f"Rotate {face} clockwise"
    raise ValueError(f"Unexpected action token: {token}")


def _action_text(action_id: int, *, action_repr: str) -> str:
    """Text inside <answer> and in Last valid action(s)."""
    if int(action_id) not in ACTION_ID_TO_WORD:
        raise ValueError(f"Unknown action id: {action_id}")
    token = ACTION_ID_TO_WORD[int(action_id)]
    if action_repr == "token":
        return token
    if action_repr == "natural":
        return _token_to_natural(token)
    raise ValueError(f"Unknown action_repr: {action_repr}")


def build_system_text(prompt_format: str, max_actions_per_step: int, action_sep: str) -> str:
    if prompt_format not in PROMPT_FORMAT_CHOICES:
        raise ValueError(f"Unknown prompt_format: {prompt_format}, expected one of {PROMPT_FORMAT_CHOICES}")
    fmt_block = rc_format_prompt[prompt_format](max_actions_per_step, action_sep, add_example=True)
    base = rc_system_prompt()
    # Append mapping block only if not already present (avoid duplication).
    if "Face names in the unfolded net are fixed as" not in base:
        base = base.rstrip() + "\n" + _NET_MAPPING_BLOCK.lstrip("\n")
    return base + "\n" + fmt_block


def format_block_only(prompt_format: str, max_actions_per_step: int, action_sep: str) -> str:
    return rc_format_prompt[prompt_format](max_actions_per_step, action_sep, add_example=False)


def _assistant_action_text(action_word: str, prompt_format: str, think: str = "") -> str:
    if think:
        return f"<think>{think}</think><answer>{action_word}</answer>"
    if prompt_format == "free_think":
        return f"<think> </think><answer>{action_word}</answer>"
    if prompt_format == "grounding":
        return (
            f"<think><observation> </observation><reasoning> </reasoning></think>"
            f"<answer>{action_word}</answer>"
        )
    if prompt_format == "worldmodeling":
        return (
            f"<think><reasoning> </reasoning><prediction> </prediction></think>"
            f"<answer>{action_word}</answer>"
        )
    if prompt_format == "grounding_worldmodeling":
        return (
            f"<think><observation> </observation><reasoning> </reasoning><prediction> </prediction></think>"
            f"<answer>{action_word}</answer>"
        )
    raise ValueError(f"Unhandled prompt_format: {prompt_format}")


def _split_text_by_placeholder(text: str, placeholder: str = "<image>") -> List[Dict[str, Any]]:
    parts = text.split(placeholder)
    if len(parts) == 1:
        return [{"type": "text", "text": text}]

    content: List[Dict[str, Any]] = []
    for i, p in enumerate(parts):
        if p:
            content.append({"type": "text", "text": p})
        if i < len(parts) - 1:
            content.append({"type": "image"})
    return content


def _fill_image_blocks(content: List[Dict[str, Any]], image_path: str) -> List[Dict[str, Any]]:
    out: List[Dict[str, Any]] = []
    for block in content:
        if block.get("type") == "image" and "image" not in block:
            out.append({"type": "image", "image": image_path})
        else:
            out.append(block)
    return out


def _blocks_to_sharegpt_content_and_images(
    blocks: List[Dict[str, Any]], image_placeholder: str = "<image>"
) -> Tuple[str, List[str]]:
    parts: List[str] = []
    images: List[str] = []
    for b in blocks:
        btype = b.get("type")
        if btype == "text":
            parts.append(str(b.get("text", "")))
        elif btype == "image":
            parts.append(image_placeholder)
            img = b.get("image")
            if img is not None:
                images.append(str(img))
        else:
            parts.append(str(b))
    return "".join(parts), images


def messages_to_llamafactory_sharegpt(
    messages: List[Dict[str, Any]], *, image_placeholder: str = "<image>"
) -> Dict[str, Any]:
    out_messages: List[Dict[str, Any]] = []
    out_images: List[str] = []

    for m in messages:
        role = m.get("role")
        content = m.get("content")

        if isinstance(content, list):
            text, imgs = _blocks_to_sharegpt_content_and_images(content, image_placeholder=image_placeholder)
            out_messages.append({"role": role, "content": text})
            out_images.extend(imgs)
        else:
            out_messages.append({"role": role, "content": "" if content is None else str(content)})

    return {"messages": out_messages, "images": out_images}


def build_messages_for_episode(
    frames: List[str],
    actions: List[int],
    rewards: Optional[List[float]] = None,
    *,
    action_repr: str,
    prompt_format: str,
    max_actions_per_step: int,
    action_sep: str,
    include_reward: bool,
    assistant_think: str,
) -> List[Dict[str, Any]]:
    if len(frames) != len(actions) + 1:
        raise ValueError(f"Expected len(frames)=len(actions)+1, got {len(frames)} vs {len(actions)}")

    sys_text = build_system_text(prompt_format, max_actions_per_step, action_sep)
    user_format_suffix = format_block_only(prompt_format, max_actions_per_step, action_sep)

    messages: List[Dict[str, Any]] = [{"role": "system", "content": sys_text}]

    init_text = rc_init_observation_template("<image>") + "\n" + user_format_suffix
    init_content = _fill_image_blocks(_split_text_by_placeholder(init_text), frames[0])
    messages.append({"role": "user", "content": init_content})

    for t, act_id in enumerate(actions):
        act_word = _action_text(act_id, action_repr=action_repr)
        messages.append(
            {
                "role": "assistant",
                "content": _assistant_action_text(act_word, prompt_format, think=assistant_think),
            }
        )

        obs_text = rc_action_template([act_word], "<image>") + "\n" + user_format_suffix
        if include_reward:
            r = 0.0
            if rewards is not None and t < len(rewards):
                try:
                    r = float(rewards[t])
                except Exception:
                    r = 0.0
            obs_text = f"Reward:\n{r}\n\n" + obs_text

        obs_content = _fill_image_blocks(_split_text_by_placeholder(obs_text), frames[t + 1])
        messages.append({"role": "user", "content": obs_content})

    return messages


def extract_system_prefix(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    sys_msgs: List[Dict[str, Any]] = []
    for m in messages:
        if m.get("role") == "system":
            sys_msgs.append(m)
        else:
            break
    return sys_msgs


def collect_user_assistant_pairs(messages: List[Dict[str, Any]], start_idx: int = 0) -> List[List[Dict[str, Any]]]:
    pairs: List[List[Dict[str, Any]]] = []
    i = int(start_idx)
    n = len(messages)
    while i < n:
        while i < n and messages[i].get("role") != "user":
            i += 1
        if i >= n:
            break
        j = i + 1
        if j < n and messages[j].get("role") == "assistant":
            pairs.append([messages[i], messages[j]])
            i = j + 1
        else:
            i += 1
    return pairs


def split_conversation_cumulative(messages: List[Dict[str, Any]], source_id: int) -> List[Dict[str, Any]]:
    sys_prefix = extract_system_prefix(messages)
    start_idx = len(sys_prefix)
    pairs = collect_user_assistant_pairs(messages, start_idx=start_idx)

    outputs: List[Dict[str, Any]] = []
    total_turns = len(pairs)
    for k in range(1, total_turns + 1):
        out_msgs = sys_prefix + [m for pair in pairs[:k] for m in pair]
        outputs.append(
            {
                "messages": out_msgs,
                "meta": {
                    "source_id": int(source_id),
                    "turns": int(k),
                    "total_turns": int(total_turns),
                },
            }
        )
    return outputs


def find_latest_step_dir(traj_root: Path) -> Path:
    step_dirs = [p for p in traj_root.iterdir() if p.is_dir() and p.name.startswith("step_")]
    if not step_dirs:
        raise FileNotFoundError(f"No step_* directories under {traj_root}")
    step_dirs.sort(key=lambda p: int(p.name.split("_")[-1]))
    return step_dirs[-1]


def convert_step_dir(
    step_dir: Path,
    output_dir: Path,
    *,
    include_failed: bool,
    action_repr: str,
    prompt_format: str,
    max_actions_per_step: int,
    action_sep: str,
    include_reward: bool,
    assistant_think: str,
    strip_prefix: Optional[str],
    split_multiturn: bool,
    output_format: str,
) -> Path:
    traj_path = step_dir / "trajectories.jsonl"
    metrics_path = step_dir / "metrics.json"

    if not traj_path.exists():
        raise FileNotFoundError(f"Missing trajectories.jsonl at {traj_path}")

    output_dir.mkdir(parents=True, exist_ok=True)
    out_path = output_dir / f"{step_dir.name}_sft.json"

    global_step = None
    if metrics_path.exists():
        try:
            global_step = json.loads(metrics_path.read_text()).get("global_step")
        except Exception:
            global_step = None

    prefix = str(Path(strip_prefix)) if strip_prefix else None

    out_items: List[Dict[str, Any]] = []
    source_id = 0
    with traj_path.open("r", encoding="utf-8") as fin:
        for line in fin:
            line = line.strip()
            if not line:
                continue
            traj = json.loads(line)
            ep_success = bool(traj.get("episode_success", False))
            if (not include_failed) and (not ep_success):
                continue

            frames = traj.get("frames", [])
            actions = traj.get("actions", [])
            rewards = traj.get("rewards", [])

            if not frames or not actions:
                continue
            frames = [str(Path(p).resolve()) for p in frames]

            if prefix:
                new_frames = []
                for p in frames:
                    ps = str(p)
                    if ps.startswith(prefix):
                        ps = ps[len(prefix) :]
                        if ps.startswith("/"):
                            ps = ps[1:]
                    new_frames.append(ps)
                frames = new_frames

            messages = build_messages_for_episode(
                frames=frames,
                actions=actions,
                rewards=rewards,
                action_repr=action_repr,
                prompt_format=prompt_format,
                max_actions_per_step=max_actions_per_step,
                action_sep=action_sep,
                include_reward=include_reward,
                assistant_think=assistant_think,
            )

            meta: Dict[str, Any] = {
                "episode_return": traj.get("episode_return", None),
                "episode_success": ep_success,
                "global_step": global_step,
            }

            if split_multiturn:
                split_records = split_conversation_cumulative(messages, source_id=source_id)
                for sr in split_records:
                    merged_meta = dict(meta)
                    merged_meta.update(sr.get("meta", {}))

                    if output_format == "llamafactory_sharegpt":
                        rec = messages_to_llamafactory_sharegpt(sr["messages"])
                        out_items.append({**rec, "meta": merged_meta})
                    else:
                        out_items.append({"messages": sr["messages"], "meta": merged_meta})
            else:
                if output_format == "llamafactory_sharegpt":
                    rec = messages_to_llamafactory_sharegpt(messages)
                    out_items.append({**rec, "meta": meta})
                else:
                    out_items.append({"messages": messages, "meta": meta})

            source_id += 1

    out_path.write_text(json.dumps(out_items, ensure_ascii=False, indent=2), encoding="utf-8")
    return out_path


def main():
    parser = argparse.ArgumentParser(
        description="Convert visual Rubik's Cube eval trajectories to SFT JSON (vagen/env/rubikscube/prompt.py)."
    )
    parser.add_argument("run_dir", help="Path to the run directory (contains trajectories/)")
    parser.add_argument("--step", default=None, help="Specific step directory name (e.g., step_400000)")
    parser.add_argument("--include_failed", action="store_true", help="Include failed episodes")
    parser.add_argument(
        "--action_repr",
        type=str,
        default="natural",
        choices=["natural", "token"],
        help='How to write each action in <answer> and "Last valid action(s)": '
        'natural = "Rotate Up clockwise" style (matches rubikscube/prompt.py examples); '
        "token = UpCW, UpCCW, ... (canonical symbols from system prompt).",
    )
    parser.add_argument(
        "--prompt_format",
        type=str,
        default="grounding_worldmodeling",
        choices=PROMPT_FORMAT_CHOICES,
        help="Must match a key in rubikscube/prompt.py FORMAT_CONFIGS",
    )
    parser.add_argument("--max_actions_per_step", type=int, default=1, help="max_actions_per_step (format block)")
    parser.add_argument("--action_sep", type=str, default=",", help="Action separator in multi-action examples")
    parser.add_argument("--include_reward", action="store_true", help="Prefix each observation turn with reward")
    parser.add_argument("--assistant_think", type=str, default="", help="Assistant </think> content (optional)")
    parser.add_argument("--strip_prefix", type=str, default=None, help="Optional path prefix to strip from frame paths")
    parser.add_argument(
        "--output_format",
        type=str,
        default="llamafactory_sharegpt",
        choices=["llamafactory_sharegpt", "internal_blocks"],
        help="Output JSON format. Use llamafactory_sharegpt for LLaMAFactory (messages+images).",
    )
    parser.add_argument(
        "--no_split_multiturn",
        action="store_true",
        help="Disable cumulative multi-turn splitting; output one sample per episode.",
    )

    args = parser.parse_args()

    run_dir = Path(args.run_dir)
    traj_root = run_dir / "trajectories"
    if not traj_root.exists():
        raise FileNotFoundError(f"Not found trajectories directory: {traj_root}")

    step_dir = traj_root / args.step if args.step else find_latest_step_dir(traj_root)
    output_dir = run_dir / "sft"

    out_path = convert_step_dir(
        step_dir=step_dir,
        output_dir=output_dir,
        include_failed=args.include_failed,
        action_repr=args.action_repr,
        prompt_format=args.prompt_format,
        max_actions_per_step=args.max_actions_per_step,
        action_sep=args.action_sep,
        include_reward=args.include_reward,
        assistant_think=args.assistant_think,
        strip_prefix=args.strip_prefix,
        split_multiturn=(not args.no_split_multiturn),
        output_format=args.output_format,
    )

    print(f"SFT data written to: {out_path}")


if __name__ == "__main__":
    main()