"""
SSIM + Bootstrap CI computation for all benchmark methods.
Works with existing rendered screenshots and per-sample CLIP scores.

Usage:
    python scripts/step_ssim_bootstrap.py --benchmark_dir results/benchmark --ref_dir data/ref_screenshots
    python scripts/step_ssim_bootstrap.py --benchmark_dir results/benchmark_websight --ref_dir data/ref_screenshots_websight
"""

import argparse
import json
import os
import sys
from pathlib import Path

import numpy as np
from PIL import Image

PROJECT_ROOT = Path(__file__).parent.parent


def compute_ssim_pil(img1, img2, win_size=7):
    """Compute SSIM between two PIL images using numpy (no skimage dependency)."""
    target_size = (min(img1.width, img2.width, 512), min(img1.height, img2.height, 512))
    a = np.array(img1.resize(target_size).convert("RGB"), dtype=np.float64)
    b = np.array(img2.resize(target_size).convert("RGB"), dtype=np.float64)

    C1 = (0.01 * 255) ** 2
    C2 = (0.03 * 255) ** 2

    ssims = []
    for ch in range(3):
        mu1 = uniform_filter(a[:, :, ch], win_size)
        mu2 = uniform_filter(b[:, :, ch], win_size)
        mu1_sq = mu1 ** 2
        mu2_sq = mu2 ** 2
        mu1_mu2 = mu1 * mu2
        sigma1_sq = uniform_filter(a[:, :, ch] ** 2, win_size) - mu1_sq
        sigma2_sq = uniform_filter(b[:, :, ch] ** 2, win_size) - mu2_sq
        sigma12 = uniform_filter(a[:, :, ch] * b[:, :, ch], win_size) - mu1_mu2

        ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / \
                   ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
        ssims.append(ssim_map.mean())

    return float(np.mean(ssims))


def uniform_filter(arr, size):
    """Simple uniform (box) filter."""
    from scipy.ndimage import uniform_filter as _uf
    return _uf(arr, size=size, mode='reflect')


def render_html_to_screenshot(html_path, out_path, width=1280, height=1024, timeout=15000):
    """Render HTML file to PNG screenshot using Playwright."""
    try:
        from playwright.sync_api import sync_playwright
        with sync_playwright() as p:
            browser = p.chromium.launch(headless=True, args=['--no-sandbox', '--disable-gpu'])
            page = browser.new_page(viewport={"width": width, "height": height})
            page.goto(f"file://{html_path}", wait_until="networkidle", timeout=timeout)
            page.wait_for_timeout(1000)
            page.screenshot(path=str(out_path), full_page=True)
            browser.close()
        return True
    except Exception as e:
        print(f"  Render failed for {html_path}: {e}")
        return False


def compute_ssim_for_method(method_dir, ref_dir, render_cache_dir):
    """Compute per-sample SSIM for a method."""
    html_dir = Path(method_dir) / "html_predictions"
    if not html_dir.exists():
        return None

    ref_dir = Path(ref_dir)
    render_dir = Path(render_cache_dir) / Path(method_dir).name
    render_dir.mkdir(parents=True, exist_ok=True)

    html_files = sorted(html_dir.glob("*.html"))
    per_sample = {}

    for html_path in html_files:
        sample_id = html_path.stem
        ref_path = ref_dir / f"{sample_id}.png"
        if not ref_path.exists():
            continue

        rendered_path = render_dir / f"{sample_id}.png"
        if not rendered_path.exists():
            ok = render_html_to_screenshot(str(html_path.resolve()), str(rendered_path))
            if not ok:
                continue

        try:
            ref_img = Image.open(ref_path).convert("RGB")
            rendered_img = Image.open(rendered_path).convert("RGB")
            ssim = compute_ssim_pil(ref_img, rendered_img)
            per_sample[sample_id] = ssim
        except Exception as e:
            print(f"  SSIM error for {sample_id}: {e}")
            continue

    if not per_sample:
        return None

    return {
        "n_samples": len(per_sample),
        "avg_ssim": round(float(np.mean(list(per_sample.values()))), 4),
        "std_ssim": round(float(np.std(list(per_sample.values()))), 4),
        "per_sample": per_sample,
    }


def bootstrap_ci(scores, n_bootstrap=10000, ci=0.95, seed=42):
    """Compute bootstrap confidence interval."""
    rng = np.random.RandomState(seed)
    scores = np.array(scores)
    n = len(scores)
    boot_means = np.array([
        rng.choice(scores, size=n, replace=True).mean()
        for _ in range(n_bootstrap)
    ])
    alpha = (1 - ci) / 2
    lo = float(np.percentile(boot_means, 100 * alpha))
    hi = float(np.percentile(boot_means, 100 * (1 - alpha)))
    return {
        "mean": float(scores.mean()),
        "ci_lower": round(lo, 4),
        "ci_upper": round(hi, 4),
        "ci_width": round(hi - lo, 4),
        "std": round(float(scores.std()), 4),
        "n": n,
    }


def compute_bootstrap_for_all(benchmark_dir):
    """Compute bootstrap CI for all methods from per-sample CLIP scores."""
    benchmark_dir = Path(benchmark_dir)
    results = {}

    for method_dir in sorted(benchmark_dir.iterdir()):
        if not method_dir.is_dir():
            continue
        clip_file = method_dir / "clip_scores.json"
        if not clip_file.exists():
            continue

        with open(clip_file) as f:
            clip_data = json.load(f)

        per_sample = clip_data.get("per_sample", {})
        if not per_sample:
            continue

        scores = []
        for k, v in per_sample.items():
            if isinstance(v, dict):
                scores.append(v.get("clip_score", 0))
            else:
                scores.append(float(v))

        if not scores:
            continue

        ci_result = bootstrap_ci(scores)
        results[method_dir.name] = ci_result
        print(f"  {method_dir.name}: CLIP={ci_result['mean']:.4f} [{ci_result['ci_lower']:.4f}, {ci_result['ci_upper']:.4f}]")

    return results


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--benchmark_dir", type=str, default=str(PROJECT_ROOT / "results" / "benchmark"))
    parser.add_argument("--ref_dir", type=str, default=str(PROJECT_ROOT / "data" / "ref_screenshots"))
    parser.add_argument("--render_cache", type=str, default=str(PROJECT_ROOT / "results" / "rendered_screenshots"))
    parser.add_argument("--skip_ssim", action="store_true")
    parser.add_argument("--skip_bootstrap", action="store_true")
    args = parser.parse_args()

    benchmark_dir = Path(args.benchmark_dir)
    output = {}

    if not args.skip_bootstrap:
        print("=" * 60)
        print("Computing Bootstrap CI for CLIP scores...")
        print("=" * 60)
        bootstrap_results = compute_bootstrap_for_all(args.benchmark_dir)
        output["bootstrap_ci"] = bootstrap_results

        ci_file = benchmark_dir / "bootstrap_ci.json"
        with open(ci_file, "w") as f:
            json.dump(bootstrap_results, f, indent=2)
        print(f"\nSaved to {ci_file}")

    if not args.skip_ssim:
        print("\n" + "=" * 60)
        print("Computing SSIM scores...")
        print("=" * 60)
        ssim_results = {}
        for method_dir in sorted(benchmark_dir.iterdir()):
            if not method_dir.is_dir():
                continue
            html_dir = method_dir / "html_predictions"
            if not html_dir.exists():
                continue
            print(f"\n  Processing {method_dir.name}...")
            result = compute_ssim_for_method(str(method_dir), args.ref_dir, args.render_cache)
            if result:
                ssim_results[method_dir.name] = {
                    "n_samples": result["n_samples"],
                    "avg_ssim": result["avg_ssim"],
                    "std_ssim": result["std_ssim"],
                }
                print(f"    SSIM={result['avg_ssim']:.4f} ± {result['std_ssim']:.4f} (n={result['n_samples']})")
            output[f"ssim_{method_dir.name}"] = result

        ssim_file = benchmark_dir / "ssim_scores.json"
        with open(ssim_file, "w") as f:
            json.dump(ssim_results, f, indent=2)
        print(f"\nSaved to {ssim_file}")


if __name__ == "__main__":
    main()