#!/usr/bin/env python3
"""Render the top-three non-DAPO reward curves with base-model references."""

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd


ARTIFACTS = Path(__file__).resolve().parent
INPUT = ARTIFACTS / "top-three-v49-no-dapo-real-reward.csv"
OUTPUT_STEM = ARTIFACTS / "top-three-v49-no-dapo-real-reward"

# Qwen3-Coder-30B-A3B-Instruct TaskTrove v4.6 evaluation. The paired pass@1
# values use the same complete 16-attempt cohorts as the pass@16 estimates.
REFERENCES = {
    "Agent": {"pass@1": 0.48936170212765956, "pass@16": 0.608843537414966},
}


def display_label(job: str) -> str:
    if "entropy3e4-lr2-g16" in job:
        return "RLOO, entropy 0.0003, LR 2e-6, group 16"
    if "rloo-entropy3e3" in job:
        return "RLOO, entropy 0.003, LR 1e-6"
    if "nodapo-lr2" in job:
        return "RLOO, LR 2e-6"
    if "nodapo-lr4" in job:
        return "RLOO, LR 4e-6"
    raise ValueError(f"No concise display label defined for {job}")


def main() -> None:
    frame = pd.read_csv(INPUT)
    run_rows = frame[frame["reward_scope"] != "reported_v46_reference"].copy()
    summaries = (
        run_rows.groupby(["dataset", "job"], as_index=False)
        .agg(step_count=("step", "nunique"), peak=("real_reward", "max"))
        .query("step_count >= 5")
        .sort_values(["dataset", "peak"], ascending=[True, False])
        .groupby("dataset", as_index=False)
        .head(3)
    )
    run_rows = run_rows.merge(summaries[["dataset", "job"]], on=["dataset", "job"], how="inner")
    run_rows["label"] = run_rows["job"].map(display_label)

    reference_rows = []
    for dataset, values in REFERENCES.items():
        for metric, value in values.items():
            reference_rows.append(
                {
                    "dataset": dataset,
                    "job": "Qwen3-Coder-30B-A3B-Instruct",
                    "label": f"Reported v4.6 {metric}",
                    "step": "",
                    "real_reward": value,
                    "reward_scope": "reported_v46_reference",
                }
            )
    pd.concat([run_rows, pd.DataFrame(reference_rows)], ignore_index=True).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)
    colors = ["#0072B2", "#D55E00", "#009E73", "#CC79A7", "#E69F00"]

    for ax, dataset in zip(axes, ("Instruction-following", "Agent"), strict=True):
        panel = run_rows[run_rows["dataset"] == dataset]
        order = (
            panel.groupby(["job", "label"], as_index=False)["real_reward"]
            .max()
            .sort_values("real_reward", ascending=False)
        )
        for color, row in zip(colors, order.itertuples(index=False), strict=False):
            series = panel[panel["job"] == row.job].sort_values("step")
            ax.plot(
                series["step"],
                series["real_reward"],
                color=color,
                marker="o",
                markersize=4.5,
                linewidth=2.2,
                label=f"{row.label} (peak {row.real_reward:.3f})",
                zorder=3,
            )
            peak = series.loc[series["real_reward"].idxmax()]
            ax.scatter(
                [peak["step"]],
                [peak["real_reward"]],
                marker="D",
                s=58,
                color=color,
                edgecolor="white",
                linewidth=0.8,
                zorder=4,
            )

        if dataset in REFERENCES:
            reference_styles = (("pass@1", (0, (6, 4))), ("pass@16", (0, (2, 2))))
            for metric, dash in reference_styles:
                value = REFERENCES[dataset][metric]
                ax.axhline(
                    value,
                    color="#4D4D4D",
                    linestyle=dash,
                    linewidth=1.7,
                    alpha=0.9,
                    label=f"Base model {metric} ({value:.3f}, v4.6)",
                    zorder=2,
                )

        ax.set_title(dataset, pad=12)
        ax.set_xlabel("Training step")
        ax.set_xlim(0.15, max(18.85, float(panel["step"].max()) + 0.85))
        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,
            ncol=1,
        )

    axes[0].set_ylabel("Verifier outcome reward")
    fig.suptitle("Top three v4.9 non-DAPO runs by peak real reward", fontsize=21, fontweight="bold", y=0.98)
    fig.text(
        0.5,
        0.935,
        "Unfiltered batch reward; runs have at least five completed steps. "
        "Agent-panel dashed references are Qwen3-Coder base-model TaskTrove v4.6 evaluations.",
        ha="center",
        fontsize=13,
        color="#444444",
    )
    fig.tight_layout(rect=(0, 0.20, 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()