#!/usr/bin/env python3
"""Render DAPO reward curves with at least five recorded steps."""

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd


ARTIFACTS = Path(__file__).resolve().parent
INPUT = ARTIFACTS / "top-five-v49-dapo-real-reward.csv"
OUTPUT_STEM = ARTIFACTS / "top-five-v49-dapo-real-reward"


def display_label(job: str) -> str:
    if "if-v49-dapo-b32" in job:
        return "DAPO, batch 32"
    if "agent-v49-sync-r5" in job:
        return "DAPO, batch 64, zero staleness"
    raise ValueError(f"No concise display label defined for {job}")


def main() -> None:
    frame = pd.read_csv(INPUT)
    eligible = (
        frame.groupby(["dataset", "job"], as_index=False)
        .agg(step_count=("step", "nunique"))
        .query("step_count >= 5")
    )
    frame = frame.merge(eligible[["dataset", "job"]], on=["dataset", "job"], how="inner")
    frame["label"] = frame["job"].map(display_label)
    frame.to_csv(INPUT, index=False)

    plt.rcParams.update(
        {
            "font.family": "DejaVu Sans",
            "font.size": 12,
            "axes.titlesize": 18,
            "axes.titleweight": "bold",
            "axes.labelsize": 13,
            "legend.fontsize": 10.5,
        }
    )
    fig, axes = plt.subplots(1, 2, figsize=(15.5, 11.5), sharey=True)

    for ax, dataset in zip(axes, ("Instruction-following", "Agent"), strict=True):
        panel = frame[frame["dataset"] == dataset]
        for row in panel[["job", "label"]].drop_duplicates().itertuples(index=False):
            series = panel[panel["job"] == row.job].sort_values("step")
            peak = series.loc[series["real_reward"].idxmax()]
            ax.plot(
                series["step"],
                series["real_reward"],
                color="#0072B2",
                marker="o",
                markersize=5,
                linewidth=2.3,
                label=f"{row.label} (peak {peak['real_reward']:.3f})",
                zorder=3,
            )
            ax.scatter(
                [peak["step"]],
                [peak["real_reward"]],
                marker="D",
                s=62,
                color="#0072B2",
                edgecolor="white",
                linewidth=0.8,
                zorder=4,
            )

        ax.set_title(dataset, pad=12)
        ax.set_xlabel("Training step")
        ax.set_xlim(0.65, max(7.35, float(panel["step"].max()) + 0.35))
        ax.set_ylim(0, 0.82)
        ax.grid(True, color="#CFD5DC", linewidth=0.7, alpha=0.65)
        ax.set_axisbelow(True)
        ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.13), frameon=False)

    axes[0].set_ylabel("Verifier outcome reward")
    fig.suptitle("v4.9 DAPO runs with at least five steps", fontsize=21, fontweight="bold", y=0.98)
    fig.text(
        0.5,
        0.935,
        "Admission-conditioned reward over mixed groups; not an unbiased policy-evaluation score.",
        ha="center",
        fontsize=13,
        color="#444444",
    )
    fig.tight_layout(rect=(0, 0.12, 1, 0.90), w_pad=3.0)
    fig.savefig(OUTPUT_STEM.with_suffix(".png"), dpi=180, bbox_inches="tight")
    fig.savefig(OUTPUT_STEM.with_suffix(".svg"), bbox_inches="tight")


if __name__ == "__main__":
    main()