# /// script
# dependencies = ["matplotlib", "pandas", "scipy", "inspect-ai"]
# ///
"""Generate evaluation visualisations for multilingual-gsm-symbolic.

Produces two figures saved to figures/ (alongside this script) by default:
  1. distribution.png  — 20 set-level accuracy dots + KDE, with memorisation gap arrow
  2. by_steps.png      — synthetic accuracy by reasoning-step count with shaded CI

Usage:
    uv run hf_dataset/visualize_results.py --log-dir hf_dataset/logs/
"""

import argparse
import re
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from inspect_ai.log import read_eval_log
from scipy.stats import gaussian_kde

# ── colours: flag / football kit inspired ────────────────────────────────────
# England: Union Jack navy   Denmark: Danish red   Norwegian: Norwegian blue
_LANG_COLOR = {"eng": "#012169", "dan": "#C60C30", "nob": "#002868", "deu": "#FFCE00", "isl": "#003897"}
_LANG_LABEL = {"eng": "English", "dan": "Danish", "nob": "Norwegian Bokmål", "deu": "German", "isl": "Icelandic"}

plt.rcParams.update(
    {
        "font.family": "sans-serif",
        "axes.spines.top": False,
        "axes.spines.right": False,
        "figure.dpi": 150,
    }
)


# ── data loading ──────────────────────────────────────────────────────────────


def _score(s) -> float:
    return 1.0 if s.scores["pattern"].value == "C" else 0.0


def _steps(answer: str) -> int:
    return len(re.findall(r"<<", answer))


def load_logs(log_dir: Path) -> dict[str, pd.DataFrame]:
    dfs = {}
    for path in sorted(log_dir.glob("*.eval")):
        if path.stat().st_size < 10_000:  # skip Git LFS pointer stubs (~133 B)
            continue
        log = read_eval_log(str(path))
        if not log.samples:
            continue
        rows = [
            {
                "sample_id": s.id,
                "epoch": s.epoch,
                "source_id": s.metadata.get("source_id"),
                "language": s.metadata.get("language", "?"),
                "correct": _score(s),
                "steps": _steps(s.metadata.get("answer", "")),
            }
            for s in log.samples
        ]
        split = log.eval.task.split("/")[-1]
        dfs[split] = pd.DataFrame(rows)
    return dfs


def per_problem(df: pd.DataFrame) -> pd.DataFrame:
    """Collapse epochs → one row per (sample_id, source_id)."""
    return df.groupby(["sample_id", "source_id", "language", "steps"])["correct"].mean().reset_index()


def build_tables(splits: dict[str, pd.DataFrame]) -> dict[str, dict]:
    """Return {lang: {synthetic: df, original: df}} keyed by language."""
    from collections import defaultdict

    tables: dict = defaultdict(dict)
    for split, df in splits.items():
        lang = df["language"].iloc[0]
        kind = "synthetic" if split.startswith("synthetic") else "original"
        tables[lang][kind] = per_problem(df)
    return dict(tables)


# ── Figure 1: distribution with memorisation gap ─────────────────────────────


def plot_distribution(tables: dict, out: Path) -> None:
    langs = [l for l in tables if "synthetic" in tables[l]]
    fig, axes = plt.subplots(1, len(langs), figsize=(5.5 * len(langs), 4.5), sharey=False)
    if len(langs) == 1:
        axes = [axes]

    rng = np.random.default_rng(0)
    n_sets = 500

    for ax, lang in zip(axes, langs):
        color = _LANG_COLOR.get(lang, "steelblue")
        syn = tables[lang]["synthetic"].copy()

        # Build lookup: source_id → array of per-problem accuracies
        by_template = {sid: grp["correct"].values for sid, grp in syn.groupby("source_id")}
        templates = sorted(by_template)

        # Sample n_sets sets: each picks one variant per template uniformly at random
        set_means = np.array([np.mean([rng.choice(by_template[t]) for t in templates]) for _ in range(n_sets)])

        # Histogram of set means (behind KDE)
        ax.hist(set_means, bins=20, color=color, alpha=0.25, edgecolor="none", density=True, zorder=1)

        # KDE line (no fill)
        kde = gaussian_kde(set_means, bw_method=0.3)
        x = np.linspace(set_means.min() - 0.02, set_means.max() + 0.02, 400)
        y = kde(x)
        ax.plot(x, y, color=color, linewidth=2, zorder=3, label="Synthetic")

        # Single dot at KDE peak
        peak_x = x[np.argmax(y)]
        peak_y = y.max()
        ax.scatter(peak_x, peak_y, color=color, s=70, zorder=4)

        # Original accuracy line + performance degradation arrow at the dot height
        if "original" in tables[lang]:
            orig_acc = tables[lang]["original"]["correct"].mean()
            ax.axvline(
                orig_acc,
                color="black",
                linestyle="--",
                linewidth=1.2,
                alpha=0.8,
                zorder=2,
                label=f"Original ({orig_acc:.1%})",
            )
            arrow_y = peak_y
            ax.annotate(
                "",
                xy=(orig_acc, arrow_y),
                xytext=(peak_x, arrow_y),
                arrowprops=dict(arrowstyle="<->", color="black", lw=1.5),
                zorder=5,
            )
            degradation = peak_x - orig_acc
            mid_x = (orig_acc + peak_x) / 2
            ax.text(
                mid_x,
                arrow_y + peak_y * 0.04,
                f"performance degradation ({degradation:+.1%})",
                ha="center",
                va="bottom",
                fontsize=8,
            )

        ax.set_xlabel("Mean accuracy")
        ax.set_ylabel("Density")
        ax.set_title(_LANG_LABEL.get(lang, lang))
        ax.legend(fontsize=8)

    fig.suptitle("Performance degradation across languages", y=1.02)
    fig.tight_layout()
    fig.savefig(out, bbox_inches="tight")
    plt.close(fig)
    print(f"Saved {out}")


# ── Figure 2: accuracy by steps (synthetic only, shaded CI) ──────────────────


def plot_by_steps(tables: dict, out: Path) -> None:
    fig, ax = plt.subplots(figsize=(7, 4))
    ax2 = ax.twinx()

    # Use the first available synthetic split for the sample-count histogram
    # (step counts are the same across languages as templates are shared)
    count_plotted = False
    all_stats = []

    rng = np.random.default_rng(42)

    for lang, splits in tables.items():
        if "synthetic" not in splits:
            continue
        color = _LANG_COLOR.get(lang, "grey")
        df = splits["synthetic"]
        grouped = df.groupby("steps")["correct"]
        stats = grouped.agg(["mean", "count"]).reset_index()
        stats = stats[stats["count"] >= 5]
        all_stats.append(stats)

        # Bootstrapped 95% CI per step count
        ci_lo, ci_hi = [], []
        for _, row in stats.iterrows():
            vals = df[df["steps"] == row["steps"]]["correct"].values
            boot_means = np.array([rng.choice(vals, size=len(vals), replace=True).mean() for _ in range(1000)])
            ci_lo.append(np.percentile(boot_means, 2.5))
            ci_hi.append(np.percentile(boot_means, 97.5))

        ax.plot(
            stats["steps"],
            stats["mean"],
            color=color,
            linewidth=2,
            marker="o",
            markersize=5,
            label=f"{_LANG_LABEL.get(lang, lang)} (synthetic)",
            zorder=3,
        )
        ax.fill_between(
            stats["steps"],
            ci_lo,
            ci_hi,
            alpha=0.2,
            color=color,
            zorder=2,
            linewidth=0,
        )

        if not count_plotted:
            ax2.bar(
                stats["steps"], stats["count"], color="grey", alpha=0.2, width=0.6, zorder=1, label="Number of problems"
            )
            count_plotted = True

    ax.set_xlabel("Number of reasoning steps (count of ⟨⟨ ⟩⟩ in answer)")
    ax.set_ylabel("Mean accuracy (95% CI)")
    ax.set_ylim(0, 1.05)
    ax.set_zorder(ax2.get_zorder() + 1)
    ax.patch.set_visible(False)

    ax2.set_ylabel("Number of problems", color="grey")
    ax2.tick_params(axis="y", labelcolor="grey")
    ax2.spines["right"].set_color("grey")
    ax2.spines["right"].set_visible(True)
    ax2.spines["top"].set_visible(False)

    lines, labels = ax.get_legend_handles_labels()
    bars, blabels = ax2.get_legend_handles_labels()
    ax.legend(lines + bars, labels + blabels, fontsize=9)
    ax.set_title("Accuracy by problem difficulty")
    fig.tight_layout()
    fig.savefig(out, bbox_inches="tight")
    plt.close(fig)
    print(f"Saved {out}")


# ── Figure 3: overlapping language distributions with language gap arrow ──────


def plot_language_gap(tables: dict, out: Path) -> None:
    langs = [l for l in tables if "synthetic" in tables[l]]
    fig, ax = plt.subplots(figsize=(6, 4.5))

    rng = np.random.default_rng(0)
    n_sets = 500
    peaks: dict[str, tuple[float, float]] = {}  # lang → (peak_x, peak_y)

    for lang in langs:
        color = _LANG_COLOR.get(lang, "steelblue")
        syn = tables[lang]["synthetic"].copy()
        by_template = {sid: grp["correct"].values for sid, grp in syn.groupby("source_id")}
        templates = sorted(by_template)
        set_means = np.array([np.mean([rng.choice(by_template[t]) for t in templates]) for _ in range(n_sets)])

        ax.hist(set_means, bins=20, color=color, alpha=0.2, edgecolor="none", density=True, zorder=1)
        kde = gaussian_kde(set_means, bw_method=0.3)
        x = np.linspace(set_means.min() - 0.02, set_means.max() + 0.02, 400)
        y = kde(x)
        ax.plot(x, y, color=color, linewidth=2, zorder=3, label=f"{_LANG_LABEL.get(lang, lang)} (synthetic)")

        peak_x = x[np.argmax(y)]
        peak_y = y.max()
        ax.scatter(peak_x, peak_y, color=color, s=70, zorder=4)
        peaks[lang] = (peak_x, peak_y)

    # Arrow between highest and lowest peak_x language, raised above distributions
    if len(peaks) >= 2:
        sorted_langs = sorted(peaks, key=lambda l: peaks[l][0])  # noqa
        lo_lang, hi_lang = sorted_langs[0], sorted_langs[-1]
        lo_x, lo_py = peaks[lo_lang]
        hi_x, hi_py = peaks[hi_lang]
        max_peak_y = max(p[1] for p in peaks.values())
        arrow_y = max_peak_y * 1.15

        ax.annotate(
            "",
            xy=(hi_x, arrow_y),
            xytext=(lo_x, arrow_y),
            arrowprops=dict(arrowstyle="<->", color="black", lw=1.5),
            zorder=5,
        )
        gap = hi_x - lo_x
        mid_x = (lo_x + hi_x) / 2
        ax.text(mid_x, arrow_y + max_peak_y * 0.05, f"language gap ({gap:+.1%})", ha="center", va="bottom", fontsize=8)

    ax.set_xlabel("Mean accuracy")
    ax.set_ylabel("Density")
    ax.legend(fontsize=9)
    ax.set_title("Language gap")
    fig.tight_layout()
    fig.savefig(out, bbox_inches="tight")
    plt.close(fig)
    print(f"Saved {out}")


# ── Figure 4: speakers (log) vs synthetic accuracy ───────────────────────────

# Approximate L1 speaker counts (Wikipedia, rounded)
_SPEAKERS = {
    "eng": 380_000_000,
    "deu": 100_000_000,
    "dan": 6_000_000,
    "nob": 5_000_000,
    "isl": 370_000,
}


def plot_speakers(tables: dict, out: Path) -> None:
    rng = np.random.default_rng(0)
    langs = [l for l in tables if "synthetic" in tables[l] and l in _SPEAKERS]

    fig, ax = plt.subplots(figsize=(6, 4.5))
    for l in langs:
        color = _LANG_COLOR.get(l, "steelblue")
        x = _SPEAKERS[l]
        vals = tables[l]["synthetic"]["correct"].values
        y = vals.mean()
        boot = np.array([rng.choice(vals, size=len(vals), replace=True).mean() for _ in range(1000)])
        ci_lo, ci_hi = np.percentile(boot, 2.5), np.percentile(boot, 97.5)

        ax.errorbar(x, y, yerr=[[y - ci_lo], [ci_hi - y]], fmt="o", color=color, capsize=4, markersize=7, zorder=3)
        ax.annotate(
            _LANG_LABEL.get(l, l),
            (x, y),
            textcoords="offset points",
            xytext=(6, 4),
            fontsize=9,
            color=color,
        )

    ax.set_xscale("log")
    ax.set_xlabel("Number of native speakers (log scale)")
    ax.set_ylabel("Mean accuracy (95% CI)")
    ax.set_ylim(0, 1)
    ax.set_title("Speakers vs. accuracy")
    fig.tight_layout()
    fig.savefig(out, bbox_inches="tight")
    plt.close(fig)
    print(f"Saved {out}")


# ── main ──────────────────────────────────────────────────────────────────────


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--log-dir", default=Path(__file__).parent / "logs", type=Path)
    parser.add_argument("--out-dir", default=Path(__file__).parent / "figures", type=Path)
    args = parser.parse_args()

    args.out_dir.mkdir(exist_ok=True)
    splits = load_logs(args.log_dir)
    if not splits:
        raise SystemExit("No completed eval logs found.")

    print("Loaded splits:", list(splits.keys()))
    tables = build_tables(splits)
    print("Languages:", list(tables.keys()))

    plot_distribution(tables, args.out_dir / "distribution.png")
    plot_by_steps(tables, args.out_dir / "by_steps.png")
    plot_language_gap(tables, args.out_dir / "language_gap.png")
    plot_speakers(tables, args.out_dir / "speakers.png")


if __name__ == "__main__":
    main()