#!/usr/bin/env python3
"""Plot per-class multimodal isolated AUPRO comparison across models."""
import argparse
import json
import sys
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns


def find_data_file(explicit_path=None):
    if explicit_path:
        p = Path(explicit_path)
        if p.is_file():
            return p
        raise FileNotFoundError(f"Specified data file not found: {explicit_path}")

    candidates = [
        Path("checkpoint_data/multimodal_isolated_metrics.json"),
        Path("../checkpoint_data/multimodal_isolated_metrics.json"),
        Path(__file__).resolve().parent.parent / "checkpoint_data" / "multimodal_isolated_metrics.json",
        Path(__file__).resolve().parent / "multimodal_isolated_metrics.json",
        Path("multimodal_isolated_metrics.json"),
    ]
    for c in candidates:
        if c.is_file():
            return c
    raise FileNotFoundError(
        "Could not find 'multimodal_isolated_metrics.json'. Looked in: "
        + ", ".join(str(c) for c in candidates)
    )


def main():
    parser = argparse.ArgumentParser(description="Plot per-class multimodal isolated AUPRO")
    parser.add_argument(
        "--data",
        type=Path,
        default=None,
        help="Path to multimodal_isolated_metrics.json (auto-detected if omitted)",
    )
    parser.add_argument(
        "--out-dir",
        type=Path,
        default=None,
        help="Output folder for figures (default: checkpoint_data/ or current dir)",
    )
    parser.add_argument(
        "--stem",
        default="performance_aupro_multimodal",
        help="Output filename stem without extension",
    )
    parser.add_argument(
        "--show",
        action="store_true",
        help="Show interactive plot window",
    )
    args = parser.parse_args()

    data_file = find_data_file(args.data)
    print(f"Loading data from: {data_file}")

    with open(data_file, "r", encoding="utf-8") as f:
        data = json.load(f)

    # 2. Flatten the data
    rows = []
    for model, cameras in data.items():
        if model == "CMDR":
            model = "CMDR-IAD"
        for camera, scenes in cameras.items():
            for scene, classes in scenes.items():
                for cls, metrics in classes.items():
                    rows.append({
                        "Model": model,
                        "Class": cls,
                        "AUPRO": metrics.get("Pixel_AUPRO", metrics.get("aupro_30", 0.0)),
                    })

    if not rows:
        raise SystemExit("No metric records found in input file.")

    df = pd.DataFrame(rows)

    # 3. Aggregate: Mean performance per Model and Class
    df_agg = df.groupby(["Model", "Class"])["AUPRO"].mean().reset_index()

    # 4. Calculate Mean per Class (across all models)
    class_means = df_agg.groupby("Class")["AUPRO"].mean()

    # 5. Visualization
    sns.set_theme(style="whitegrid", font_scale=1.1)
    fig = plt.figure(figsize=(14, 5))

    # Create barplot
    chart = sns.barplot(
        data=df_agg,
        x="Class",
        y="AUPRO",
        hue="Model",
        hue_order=["M3DM", "CMDR-IAD", "CFM", "WinCLIP", "UniVAD"],
        palette="viridis",
    )

    # 6. Add dotted line segments and text labels
    classes = df_agg["Class"].unique()
    for i, cls in enumerate(classes):
        mean_val = class_means[cls]
        plt.hlines(
            y=mean_val,
            xmin=i - 0.4,
            xmax=i + 0.4,
            colors="red",
            linestyles=":",
            linewidth=3,
            label="Class Mean" if i == 0 else "",
        )
        plt.text(
            i + 0.3,
            mean_val + 0.018,
            f"{mean_val:.2f}",
            color="red",
            ha="left",
            va="center",
            fontsize=9,
            fontweight="bold",
        )

    plt.xticks(rotation=15, ha="right")
    plt.xlabel("")
    plt.ylabel("Mean Pixel AUPRO Score")

    plt.legend(
        loc="upper left",
        ncol=3,
        frameon=True,
        framealpha=0.2,
        facecolor="white",
        edgecolor="black",
    )

    plt.tight_layout()

    if args.out_dir is not None:
        out_dir = args.out_dir
    elif Path("checkpoint_data").is_dir():
        out_dir = Path("checkpoint_data")
    elif (Path(__file__).resolve().parent.parent / "checkpoint_data").is_dir():
        out_dir = Path(__file__).resolve().parent.parent / "checkpoint_data"
    else:
        out_dir = Path(".")

    out_dir.mkdir(parents=True, exist_ok=True)
    pdf_path = out_dir / f"{args.stem}.pdf"
    png_path = out_dir / f"{args.stem}.png"

    plt.savefig(pdf_path, bbox_inches="tight")
    plt.savefig(png_path, dpi=300, bbox_inches="tight")
    print(f"Saved: {pdf_path}")
    print(f"Saved: {png_path}")

    if args.show:
        plt.show()
    plt.close(fig)


if __name__ == "__main__":
    main()