from __future__ import annotations

import argparse
import csv
import json
from pathlib import Path
from typing import Any

from PIL import Image


WEIGHTS = {"iaa": 0.40, "iqa": 0.20, "ista": 0.40}


def read_tasks(root: Path) -> dict[str, dict[str, Any]]:
    data = json.loads((root / "benchmark" / "tasks.json").read_text(encoding="utf-8"))
    return {item["task_id"]: item for item in data["tasks"] if item.get("enabled", True)}


def read_submission(path: Path) -> list[dict[str, str]]:
    with path.open("r", encoding="utf-8-sig", newline="") as f:
        return list(csv.DictReader(f))


def resolve_image(path_text: str, submission_root: Path) -> Path:
    path = Path(path_text)
    if path.is_absolute():
        return path
    return submission_root / path


def normalize_score(value: Any) -> float:
    if value is None:
        return 0.0
    score = float(value)
    if score > 1.5:
        score = score / 100.0
    return max(0.0, min(1.0, score))


def m_qs(iaa: float, iqa: float, ista: float) -> float:
    return WEIGHTS["iaa"] * iaa + WEIGHTS["iqa"] * iqa + WEIGHTS["ista"] * ista


def is_readable_image(path: Path) -> bool:
    if not path.exists() or not path.is_file():
        return False
    try:
        with Image.open(path) as img:
            img.verify()
        return True
    except Exception:
        return False


def load_unipercept(device: str):
    try:
        from unipercept_reward import UniPerceptRewardInferencer
    except ImportError as exc:
        raise SystemExit(
            "Missing dependency: unipercept-reward. Install it with "
            "`pip install unipercept-reward` and follow the UniPercept "
            "repository instructions for model setup."
        ) from exc
    return UniPerceptRewardInferencer(device=device)


def score_batch(inferencer: Any, image_paths: list[str]) -> list[dict[str, Any]]:
    rewards = inferencer.reward(image_paths=image_paths)
    if len(rewards) != len(image_paths):
        raise RuntimeError(
            f"UniPercept returned {len(rewards)} rewards for {len(image_paths)} images."
        )
    return rewards


def main() -> None:
    parser = argparse.ArgumentParser(description="Score VPhotoBench images with UniPercept.")
    parser.add_argument("--benchmark-root", default=".", help="Path to the VPhotoBench release root.")
    parser.add_argument("--submission", required=True, help="CSV submission file.")
    parser.add_argument(
        "--submission-root",
        default=None,
        help="Root for relative image paths. Defaults to the submission file directory.",
    )
    parser.add_argument("--output", required=True, help="Output score CSV path.")
    parser.add_argument("--device", default="cuda", help="UniPercept device, e.g. cuda or cpu.")
    parser.add_argument("--batch-size", type=int, default=8)
    args = parser.parse_args()

    benchmark_root = Path(args.benchmark_root).resolve()
    submission = Path(args.submission).resolve()
    submission_root = Path(args.submission_root).resolve() if args.submission_root else submission.parent
    output = Path(args.output).resolve()
    output.parent.mkdir(parents=True, exist_ok=True)

    tasks = read_tasks(benchmark_root)
    rows = read_submission(submission)
    row_by_task: dict[str, dict[str, str]] = {}
    for row in rows:
        task_id = row.get("task_id", "").strip()
        if task_id:
            row_by_task[task_id] = row

    records: list[dict[str, Any]] = []
    valid_indices: list[int] = []
    valid_paths: list[str] = []

    for task_id, task in tasks.items():
        submission_row = row_by_task.get(task_id, {})
        image_text = submission_row.get("image_path", "").strip()
        status = submission_row.get("status", "ok").strip() or "ok"
        image_path = resolve_image(image_text, submission_root) if image_text else Path("")
        valid = status == "ok" and bool(image_text) and is_readable_image(image_path)
        record = {
            "task_id": task_id,
            "scene_id": task["scene_id"],
            "mission_type": task["mission_type"],
            "image_path": str(image_path) if image_text else "",
            "status": "ok" if valid else (status if status != "ok" else "missing_or_invalid_image"),
            "iaa": 0.0,
            "iqa": 0.0,
            "ista": 0.0,
            "m_qs": 0.0,
            "succ_at_0_55": 0,
            "error": "" if valid else "Image missing, failed, or unreadable.",
        }
        if valid:
            valid_indices.append(len(records))
            valid_paths.append(str(image_path))
        records.append(record)

    inferencer = load_unipercept(args.device) if valid_paths else None
    for start in range(0, len(valid_paths), args.batch_size):
        batch_paths = valid_paths[start : start + args.batch_size]
        rewards = score_batch(inferencer, batch_paths)
        for offset, reward in enumerate(rewards):
            record = records[valid_indices[start + offset]]
            iaa = normalize_score(reward.get("iaa"))
            iqa = normalize_score(reward.get("iqa"))
            ista = normalize_score(reward.get("ista"))
            score = m_qs(iaa, iqa, ista)
            record.update(
                {
                    "iaa": iaa,
                    "iqa": iqa,
                    "ista": ista,
                    "m_qs": score,
                    "succ_at_0_55": int(score >= 0.55),
                    "error": "",
                }
            )

    fieldnames = [
        "task_id",
        "scene_id",
        "mission_type",
        "image_path",
        "status",
        "iaa",
        "iqa",
        "ista",
        "m_qs",
        "succ_at_0_55",
        "error",
    ]
    with output.open("w", encoding="utf-8", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writeheader()
        writer.writerows(records)

    print(f"Wrote {len(records)} scored task rows to {output}")


if __name__ == "__main__":
    main()