#!/usr/bin/env python3
"""Reproduce Matrix-Game 2.0 self-consistency metrics from released videos."""

from __future__ import annotations

import argparse
import csv
import json
import math
import re
import sys
from collections import defaultdict
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Sequence

import cv2
import numpy as np


EXPECTED_COUNTS = {"inverse": 448, "loop": 445, "equivalence": 239}
PAPER_RESULTS = {
    "inverse": {"lpips": 0.71, "psnr": 10.45},
    "loop": {"lpips": 0.72, "psnr": 10.62},
    "equivalence": {"lpips": 0.59, "psnr": 12.57},
}
EQUIVALENCE_RE = re.compile(
    r"(?P<run>run_\d+_\d+)__pair_(?P<pair>\d+)_(?P<branch>[AB])_traj_\d+\.mp4$",
    re.IGNORECASE,
)


@dataclass(frozen=True)
class EvaluationUnit:
    relation: str
    unit: str
    video_a: Path
    video_b: Path | None = None


@dataclass(frozen=True)
class MetricRow:
    relation: str
    unit: str
    video_a: str
    video_b: str
    frame_a: int
    frame_b: int
    width: int
    height: int
    psnr: float
    lpips: float | None
    psnr_exact_match: bool


def is_downloaded_video(path: Path) -> bool:
    if not path.is_file() or path.stat().st_size <= 200:
        return False
    with path.open("rb") as handle:
        return not handle.read(64).startswith(b"version https://git-lfs.github.com/spec/v1")


def discover_units(root: Path, strict_counts: bool = True) -> list[EvaluationUnit]:
    units: list[EvaluationUnit] = []
    for relation in ("inverse", "loop"):
        for difficulty in ("easy", "hard"):
            folder = root / f"{relation}_{difficulty}"
            if not folder.is_dir():
                raise FileNotFoundError(f"missing dataset folder: {folder}")
            for path in sorted(folder.glob("*.mp4")):
                if not is_downloaded_video(path):
                    raise RuntimeError(f"missing Git LFS video object: {path}")
                units.append(EvaluationUnit(relation, path.stem, path))

    equivalence_folder = root / "equivalence"
    if not equivalence_folder.is_dir():
        raise FileNotFoundError(f"missing dataset folder: {equivalence_folder}")
    pairs: dict[str, dict[str, Path]] = defaultdict(dict)
    for path in sorted(equivalence_folder.glob("*.mp4")):
        if not is_downloaded_video(path):
            raise RuntimeError(f"missing Git LFS video object: {path}")
        match = EQUIVALENCE_RE.fullmatch(path.name)
        if not match:
            raise ValueError(f"unrecognized Equivalence filename: {path.name}")
        unit = f"{match.group('run')}__pair_{match.group('pair')}"
        branch = match.group("branch").upper()
        if branch in pairs[unit]:
            raise ValueError(f"duplicate Equivalence branch {branch}: {unit}")
        pairs[unit][branch] = path
    for unit, branches in sorted(pairs.items()):
        if set(branches) != {"A", "B"}:
            raise ValueError(f"incomplete Equivalence pair {unit}: {sorted(branches)}")
        units.append(EvaluationUnit("equivalence", unit, branches["A"], branches["B"]))

    counts = {relation: sum(unit.relation == relation for unit in units) for relation in EXPECTED_COUNTS}
    if strict_counts and counts != EXPECTED_COUNTS:
        raise RuntimeError(f"unexpected graph counts: found {counts}, expected {EXPECTED_COUNTS}")
    return units


def read_endpoint(path: Path, endpoint: str) -> tuple[np.ndarray, int]:
    capture = cv2.VideoCapture(str(path))
    if not capture.isOpened():
        raise RuntimeError(f"failed to open video: {path}")
    frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
    if frame_count < 2:
        capture.release()
        raise RuntimeError(f"video has fewer than two frames: {path}")
    frame_index = 0 if endpoint == "first" else frame_count - 1
    capture.set(cv2.CAP_PROP_POS_FRAMES, frame_index)
    ok, frame = capture.read()
    capture.release()
    if not ok:
        raise RuntimeError(f"failed to decode {endpoint} frame: {path}")
    return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), frame_index


def psnr(reference: np.ndarray, prediction: np.ndarray) -> float:
    if reference.shape != prediction.shape:
        raise ValueError(f"frame shape mismatch: {reference.shape} versus {prediction.shape}")
    mse = np.mean((reference.astype(np.float64) - prediction.astype(np.float64)) ** 2)
    if mse == 0:
        return float("inf")
    return 10.0 * math.log10((255.0**2) / mse)


class LPIPSMetric:
    def __init__(self, device: str) -> None:
        try:
            import lpips
            import torch
        except ImportError as exc:
            raise RuntimeError("install the Python dependencies listed in README.md before using --lpips") from exc
        if device == "auto":
            device = "cuda" if torch.cuda.is_available() else "cpu"
        self.torch = torch
        self.device = torch.device(device)
        self.model = lpips.LPIPS(net="alex").to(self.device).eval()

    def __call__(self, reference: np.ndarray, prediction: np.ndarray) -> float:
        if reference.shape != prediction.shape:
            raise ValueError(f"frame shape mismatch: {reference.shape} versus {prediction.shape}")
        tensors = []
        for image in (reference, prediction):
            tensor = self.torch.from_numpy(np.ascontiguousarray(image)).permute(2, 0, 1).float()
            tensors.append(tensor.div(127.5).sub(1.0).unsqueeze(0).to(self.device))
        with self.torch.inference_mode():
            value = self.model(tensors[0], tensors[1], normalize=False)
        return float(value.item())


def evaluate(units: Sequence[EvaluationUnit], lpips_metric: LPIPSMetric | None) -> list[MetricRow]:
    rows: list[MetricRow] = []
    for index, unit in enumerate(units, start=1):
        if unit.relation in {"inverse", "loop"}:
            frame_a, index_a = read_endpoint(unit.video_a, "first")
            frame_b, index_b = read_endpoint(unit.video_a, "last")
            video_b = unit.video_a
        else:
            if unit.video_b is None:
                raise AssertionError("Equivalence unit is missing branch B")
            frame_a, index_a = read_endpoint(unit.video_a, "last")
            frame_b, index_b = read_endpoint(unit.video_b, "last")
            video_b = unit.video_b
        value_psnr = psnr(frame_a, frame_b)
        value_lpips = lpips_metric(frame_a, frame_b) if lpips_metric else None
        rows.append(
            MetricRow(
                relation=unit.relation,
                unit=unit.unit,
                video_a=str(unit.video_a),
                video_b=str(video_b),
                frame_a=index_a,
                frame_b=index_b,
                width=int(frame_a.shape[1]),
                height=int(frame_a.shape[0]),
                psnr=value_psnr,
                lpips=value_lpips,
                psnr_exact_match=math.isinf(value_psnr),
            )
        )
        if index % 100 == 0 or index == len(units):
            print(f"evaluated {index}/{len(units)} graph units", flush=True)
    return rows


def bootstrap_ci(values: Sequence[float], seed: int, repetitions: int) -> tuple[float, float]:
    array = np.asarray(values, dtype=np.float64)
    if len(array) < 2:
        return float("nan"), float("nan")
    rng = np.random.default_rng(seed)
    means = np.empty(repetitions, dtype=np.float64)
    for start in range(0, repetitions, 500):
        count = min(500, repetitions - start)
        indices = rng.integers(0, len(array), size=(count, len(array)))
        means[start : start + count] = array[indices].mean(axis=1)
    low, high = np.quantile(means, [0.025, 0.975])
    return float(low), float(high)


def summarize(rows: Sequence[MetricRow], seed: int, repetitions: int) -> list[dict[str, object]]:
    output: list[dict[str, object]] = []
    for relation in ("inverse", "loop", "equivalence"):
        group = [row for row in rows if row.relation == relation]
        summary: dict[str, object] = {
            "relation": relation,
            "n_graphs": len(group),
            "psnr_finite_n": sum(math.isfinite(row.psnr) for row in group),
            "psnr_exact_match_n": sum(row.psnr_exact_match for row in group),
        }
        for metric in ("psnr", "lpips"):
            values = [
                float(value)
                for row in group
                if (value := getattr(row, metric)) is not None and math.isfinite(float(value))
            ]
            if not values:
                continue
            low, high = bootstrap_ci(values, seed, repetitions)
            summary.update(
                {
                    metric: float(np.mean(values)),
                    f"{metric}_std": float(np.std(values, ddof=1)) if len(values) > 1 else 0.0,
                    f"{metric}_ci95_low": low,
                    f"{metric}_ci95_high": high,
                }
            )
        output.append(summary)
    return output


def write_csv(path: Path, rows: Sequence[dict[str, object]]) -> None:
    if not rows:
        return
    columns: list[str] = []
    for row in rows:
        for key in row:
            if key not in columns:
                columns.append(key)
    with path.open("w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=columns)
        writer.writeheader()
        writer.writerows(rows)


def paper_check(summaries: Sequence[dict[str, object]]) -> tuple[bool, list[dict[str, object]]]:
    checks: list[dict[str, object]] = []
    passed = True
    for summary in summaries:
        relation = str(summary["relation"])
        for metric in ("lpips", "psnr"):
            value = summary.get(metric)
            expected = PAPER_RESULTS[relation][metric]
            metric_passed = value is not None and round(float(value), 2) == expected
            checks.append(
                {
                    "relation": relation,
                    "metric": metric,
                    "computed": value,
                    "paper_rounded": expected,
                    "pass_at_2_decimals": metric_passed,
                }
            )
            passed = passed and metric_passed
    return passed, checks


def report(summaries: Sequence[dict[str, object]], check_passed: bool | None) -> str:
    lines = [
        "# Matrix-Game 2.0 SC Reproduction",
        "",
        "| Relation | Graph N | LPIPS (95% CI) | PSNR dB (95% CI) | Exact PSNR pairs |",
        "| --- | ---: | ---: | ---: | ---: |",
    ]
    for row in summaries:
        lpips_text = "not computed"
        if "lpips" in row:
            lpips_text = (
                f"{row['lpips']:.4f} [{row['lpips_ci95_low']:.4f}, "
                f"{row['lpips_ci95_high']:.4f}]"
            )
        psnr_text = f"{row['psnr']:.4f} [{row['psnr_ci95_low']:.4f}, {row['psnr_ci95_high']:.4f}]"
        lines.append(
            f"| {str(row['relation']).title()} | {row['n_graphs']} | {lpips_text} | "
            f"{psnr_text} | {row['psnr_exact_match_n']} |"
        )
    if check_passed is not None:
        lines.extend(["", f"Paper rounded-value check: **{'PASS' if check_passed else 'FAIL'}**."])
    lines.extend(
        [
            "",
            "Inverse/Loop compare the generated first and final frames. Equivalence compares",
            "the generated final frames of paired A/B rollouts. Confidence intervals use",
            "10,000 graph-level bootstrap resamples by default.",
            "",
        ]
    )
    return "\n".join(lines)


def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--data", type=Path, default=Path("data/Nips_WM_Eval_qzf"))
    parser.add_argument("--output", type=Path, default=Path("results/matrix_game_sc"))
    parser.add_argument("--lpips", action="store_true", help="compute LPIPS 0.1.4 with AlexNet")
    parser.add_argument("--device", default="auto", help="auto, cpu, cuda, or cuda:<index>")
    parser.add_argument("--seed", type=int, default=2026)
    parser.add_argument("--bootstrap-repetitions", type=int, default=10_000)
    parser.add_argument("--allow-partial", action="store_true", help="disable published-count checks")
    parser.add_argument("--check-paper", action="store_true", help="check values at paper precision")
    return parser.parse_args(argv)


def main(argv: Sequence[str] | None = None) -> int:
    args = parse_args(argv)
    if not args.data.is_dir():
        raise SystemExit(f"dataset directory does not exist: {args.data}")
    if args.check_paper and not args.lpips:
        raise SystemExit("--check-paper requires --lpips")
    args.output.mkdir(parents=True, exist_ok=True)

    units = discover_units(args.data, strict_counts=not args.allow_partial)
    metric = LPIPSMetric(args.device) if args.lpips else None
    rows = evaluate(units, metric)
    summaries = summarize(rows, args.seed, args.bootstrap_repetitions)
    check_passed: bool | None = None
    checks: list[dict[str, object]] = []
    if args.check_paper:
        check_passed, checks = paper_check(summaries)

    write_csv(args.output / "per_graph.csv", [asdict(row) for row in rows])
    write_csv(args.output / "summary.csv", summaries)
    if checks:
        write_csv(args.output / "paper_check.csv", checks)
    audit = {
        "data": str(args.data.resolve()),
        "definitions": {
            "inverse": "generated first frame versus generated final frame",
            "loop": "generated first frame versus generated final frame",
            "equivalence": "generated branch-A final frame versus generated branch-B final frame",
        },
        "expected_counts": EXPECTED_COUNTS,
        "observed_counts": {row["relation"]: row["n_graphs"] for row in summaries},
        "lpips": "lpips==0.1.4, AlexNet, RGB in [-1,1]" if args.lpips else "not computed",
        "psnr": "RGB uint8, MAX=255; exact matches excluded from finite PSNR mean and counted separately",
        "bootstrap_seed": args.seed,
        "bootstrap_repetitions": args.bootstrap_repetitions,
        "paper_check_passed": check_passed,
    }
    (args.output / "audit.json").write_text(
        json.dumps(audit, indent=2, ensure_ascii=True) + "\n", encoding="utf-8"
    )
    (args.output / "report.md").write_text(report(summaries, check_passed), encoding="utf-8")
    print(report(summaries, check_passed))
    print(f"Outputs: {args.output.resolve()}")
    return 0 if check_passed is not False else 1


if __name__ == "__main__":
    raise SystemExit(main())