"""Evaluation and training plot generation."""

from __future__ import annotations

import json
from pathlib import Path

import matplotlib

matplotlib.use("Agg")
import matplotlib.pyplot as plt


def _load_json(path: Path) -> dict:
    if not path.exists():
        return {}
    return json.loads(path.read_text(encoding="utf-8"))


def _policy_stack_label(label: str) -> str:
    labels = {
        "bandit-only": "Bandits only",
        "bandit_only": "Bandits only",
        "llm-only": "Baseline LLM only",
        "llm_only": "Baseline LLM only",
        "llm+bandit": "LLM + Bandits",
        "llm_bandit": "LLM + Bandits",
    }
    return labels.get(label, label)


def generate_training_plots(report_dir: Path, plot_dir: Path) -> list[str]:
    plot_dir.mkdir(parents=True, exist_ok=True)
    planner = _load_json(report_dir / "planner_grpo.json")
    supervisor = _load_json(report_dir / "supervisor_grpo.json")
    dosing = _load_json(report_dir / "dosing_grpo.json")

    series_names = ["avg_reward", "legality_rate", "success_rate", "avg_process_fidelity"]
    labels = ["supervisor", "planner", "dosing"]
    payloads = [supervisor, planner, dosing]
    output_paths: list[str] = []

    for metric in series_names:
        values = [float(item.get(metric, 0.0)) for item in payloads]
        fig, ax = plt.subplots(figsize=(6.2, 3.6))
        ax.bar(labels, values, color=["#2f855a", "#2b6cb0", "#d69e2e"])
        ax.set_ylim(0.0, 1.0)
        ax.set_title(metric)
        ax.grid(alpha=0.2, axis="y")
        path = plot_dir / f"{metric}.png"
        fig.tight_layout()
        fig.savefig(path)
        plt.close(fig)
        output_paths.append(str(path))

    baselines = _load_json(report_dir / "baselines.json")
    ablations = baselines.get("policy_stack_ablations", {}) if isinstance(baselines, dict) else {}
    if isinstance(ablations, dict) and ablations:
        keys = list(ablations.keys())
        labels = [_policy_stack_label(label) for label in keys]
        values = [float((ablations.get(label) or {}).get("avg_reward", 0.0)) for label in keys]
        fig, ax = plt.subplots(figsize=(7.0, 3.8))
        ax.bar(labels, values, color=["#805ad5", "#2c5282", "#2f855a"][: len(labels)])
        ax.set_ylim(0.0, 1.0)
        ax.set_title("Without Bandits vs With Bandits average reward")
        ax.grid(alpha=0.2, axis="y")
        path = plot_dir / "policy_stack_avg_reward.png"
        fig.tight_layout()
        fig.savefig(path)
        plt.close(fig)
        output_paths.append(str(path))

    # Primary reward channel comparison from planner summary when present.
    planner_channels = ((planner or {}).get("primary_reward_channels", {}) if isinstance(planner, dict) else {}) or {}
    if planner_channels:
        labels = list(planner_channels.keys())
        values = [float(planner_channels[key]) for key in labels]
        fig, ax = plt.subplots(figsize=(7.0, 3.8))
        ax.bar(labels, values, color=["#276749", "#2b6cb0", "#dd6b20", "#4a5568"][: len(labels)])
        ax.set_ylim(0.0, 1.0)
        ax.set_title("planner_primary_reward_channels")
        ax.grid(alpha=0.2, axis="y")
        path = plot_dir / "planner_primary_reward_channels.png"
        fig.tight_layout()
        fig.savefig(path)
        plt.close(fig)
        output_paths.append(str(path))
    return output_paths