"""Aggregate pre/post evaluator JSONL into a before/after evidence pack.

Reads ``evidence/pre_eval.jsonl`` + ``evidence/post_eval.jsonl`` (one
JSONL row per episode, written by ``training/evaluate.py``) and emits
the side-by-side artifacts the trainer dashboard surfaces:

* ``evidence/before_after_metrics.json`` — ``{pre, post, delta}``,
  with the headline metrics every reviewer asks for first.
* ``evidence/reward_components.csv`` — one row per (tag, episode) with
  all 8 component columns + total, ready to pivot in any spreadsheet.
* ``evidence/before_after_summary.png`` — grouped bar chart of pre vs
  post for each headline metric.
* ``evidence/reward_distribution.png`` — overlapping histograms of
  cumulative reward, pre vs post.
* ``evidence/reward_components.png`` — grouped bar chart of mean reward
  components, pre vs post.
* ``evidence/sample_trajectories.md`` — pretty-printed worst-3 pre +
  best-3 post episodes, with action sequences and per-step rewards.

CLI::

    python -m training.summarize \\
        --evidence_dir evidence \\
        --pre pre_eval.jsonl --post post_eval.jsonl
"""

from __future__ import annotations

import argparse
import csv
import json
import logging
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence


logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger("training.summarize")


# Mirrors ``training/evaluate.py::_REWARD_COMPONENTS`` (cannot import
# directly because this module must run with only stdlib + matplotlib
# in case the heavy ML deps are not installed at summarisation time).
_REWARD_COMPONENTS = (
    "evidence_coverage",
    "decision_accuracy",
    "credit_efficiency",
    "reasoning_coherence",
    "novelty",
    "penalty",
    "shaping",
    "terminal",
)

_HEADLINE_METRICS = (
    "n",
    "mean_reward",
    "median_reward",
    "success_rate",
    "decision_accuracy",
    "evidence_coverage",
)


def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--evidence_dir", default="evidence")
    parser.add_argument("--pre", default="pre_eval.jsonl",
                        help="Filename of the pre-train JSONL inside evidence_dir.")
    parser.add_argument("--post", default="post_eval.jsonl",
                        help="Filename of the post-train JSONL inside evidence_dir.")
    parser.add_argument("--metrics_out", default="before_after_metrics.json")
    parser.add_argument("--components_csv", default="reward_components.csv")
    parser.add_argument("--summary_png", default="before_after_summary.png")
    parser.add_argument("--distribution_png", default="reward_distribution.png")
    parser.add_argument("--components_png", default="reward_components.png")
    parser.add_argument("--samples_md", default="sample_trajectories.md")
    parser.add_argument("--n_samples", type=int, default=3,
                        help="Number of worst-pre + best-post episodes to dump.")
    return parser.parse_args(argv)


def _load_jsonl(path: Path) -> List[Dict[str, Any]]:
    if not path.exists():
        logger.warning("eval file missing: %s", path)
        return []
    rows: List[Dict[str, Any]] = []
    with open(path, "r", encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            try:
                rows.append(json.loads(line))
            except json.JSONDecodeError as exc:
                logger.warning("skipping unparseable JSONL line in %s: %s", path, exc)
    return rows


def _success(row: Dict[str, Any]) -> bool:
    """Reviewer-facing 'success' (submitted AND right AND positive reward)."""
    return bool(
        row.get("submitted")
        and row.get("submitted_decision") == row.get("correct_decision")
        and float(row.get("cumulative_reward", 0.0)) > 0.0
    )


def _compute_aggregate(rows: Sequence[Dict[str, Any]]) -> Dict[str, float]:
    """Compute the headline metrics for one tag."""
    n = len(rows)
    if n == 0:
        return {k: 0.0 for k in _HEADLINE_METRICS} | {"n": 0}
    rewards = [float(r.get("cumulative_reward", 0.0)) for r in rows]
    rewards_sorted = sorted(rewards)
    median = rewards_sorted[n // 2] if n % 2 else 0.5 * (rewards_sorted[n // 2 - 1] + rewards_sorted[n // 2])
    return {
        "n": n,
        "mean_reward": sum(rewards) / n,
        "median_reward": median,
        "success_rate": sum(1 for r in rows if _success(r)) / n,
        "decision_accuracy": sum(float(r.get("decision_accuracy", 0.0)) for r in rows) / n,
        "evidence_coverage": sum(float(r.get("evidence_coverage", 0.0)) for r in rows) / n,
    }


def _delta(pre: Dict[str, float], post: Dict[str, float]) -> Dict[str, float]:
    out: Dict[str, float] = {}
    for k in _HEADLINE_METRICS:
        try:
            out[k] = float(post.get(k, 0.0)) - float(pre.get(k, 0.0))
        except (TypeError, ValueError):
            out[k] = 0.0
    return out


def _write_metrics_json(
    *,
    pre: Dict[str, float],
    post: Dict[str, float],
    delta: Dict[str, float],
    path: Path,
) -> None:
    payload = {"pre": pre, "post": post, "delta": delta}
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
    logger.info("wrote %s", path)


def _write_components_csv(
    *,
    pre_rows: Sequence[Dict[str, Any]],
    post_rows: Sequence[Dict[str, Any]],
    path: Path,
) -> None:
    """One row per (tag, episode) with all 8 components + total."""
    fields = ["tag", "episode", "scenario", "cumulative_reward", *_REWARD_COMPONENTS, "total"]
    path.parent.mkdir(parents=True, exist_ok=True)
    with open(path, "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=fields)
        writer.writeheader()
        for tag_rows, tag in ((pre_rows, "pre"), (post_rows, "post")):
            for r in tag_rows:
                comps = r.get("reward_components_total") or {}
                row = {
                    "tag": tag,
                    "episode": r.get("episode"),
                    "scenario": r.get("scenario"),
                    "cumulative_reward": round(float(r.get("cumulative_reward", 0.0)), 6),
                    "total": round(sum(float(comps.get(c, 0.0)) for c in _REWARD_COMPONENTS), 6),
                }
                for c in _REWARD_COMPONENTS:
                    row[c] = round(float(comps.get(c, 0.0)), 6)
                writer.writerow(row)
    logger.info("wrote %s", path)


def _try_matplotlib():
    """Returns (plt, np) or (None, None) when matplotlib is unavailable."""
    try:
        import matplotlib  # type: ignore
        matplotlib.use("Agg")
        import matplotlib.pyplot as plt  # type: ignore
    except Exception as exc:  # pragma: no cover - plotting best-effort
        logger.warning("matplotlib unavailable (%s); skipping plots", exc)
        return None, None
    try:
        import numpy as np  # type: ignore
    except Exception:
        np = None  # type: ignore
    return plt, np


def _write_summary_png(
    *,
    pre: Dict[str, float],
    post: Dict[str, float],
    path: Path,
) -> None:
    plt, np = _try_matplotlib()
    if plt is None:
        return
    metrics = [
        ("mean_reward", "Mean reward"),
        ("median_reward", "Median reward"),
        ("success_rate", "Success rate"),
        ("decision_accuracy", "Decision accuracy"),
        ("evidence_coverage", "Evidence coverage"),
    ]
    labels = [lbl for _, lbl in metrics]
    pre_vals = [float(pre.get(k, 0.0)) for k, _ in metrics]
    post_vals = [float(post.get(k, 0.0)) for k, _ in metrics]
    n = len(metrics)
    x = list(range(n))
    width = 0.36

    fig, ax = plt.subplots(figsize=(9, 5))
    bar_pre = ax.bar([xi - width / 2 for xi in x], pre_vals, width=width,
                     label=f"pre (n={int(pre.get('n', 0))})", color="#94a3b8")
    bar_post = ax.bar([xi + width / 2 for xi in x], post_vals, width=width,
                      label=f"post (n={int(post.get('n', 0))})", color="#1d4ed8")
    ax.set_xticks(x)
    ax.set_xticklabels(labels, rotation=14, ha="right")
    ax.set_ylabel("metric value")
    ax.set_title("DrugEnv before vs after — headline metrics")
    ax.grid(alpha=0.25, axis="y")
    ax.axhline(0, color="black", lw=0.8)
    for bar_group in (bar_pre, bar_post):
        for rect in bar_group:
            h = rect.get_height()
            ax.text(rect.get_x() + rect.get_width() / 2, h, f"{h:.2f}",
                    ha="center", va="bottom" if h >= 0 else "top", fontsize=8)
    ax.legend(loc="best")
    fig.tight_layout()
    path.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(path, dpi=150)
    plt.close(fig)
    logger.info("wrote %s", path)


def _write_distribution_png(
    *,
    pre_rows: Sequence[Dict[str, Any]],
    post_rows: Sequence[Dict[str, Any]],
    path: Path,
) -> None:
    plt, _np = _try_matplotlib()
    if plt is None:
        return
    pre_rewards = [float(r.get("cumulative_reward", 0.0)) for r in pre_rows]
    post_rewards = [float(r.get("cumulative_reward", 0.0)) for r in post_rows]
    if not pre_rewards and not post_rewards:
        logger.warning("no rewards to plot — skipping reward_distribution.png")
        return

    fig, ax = plt.subplots(figsize=(9, 5))
    bins = 12
    if pre_rewards:
        ax.hist(pre_rewards, bins=bins, color="#94a3b8", alpha=0.55,
                label=f"pre (n={len(pre_rewards)}, μ={sum(pre_rewards) / len(pre_rewards):.2f})")
    if post_rewards:
        ax.hist(post_rewards, bins=bins, color="#1d4ed8", alpha=0.55,
                label=f"post (n={len(post_rewards)}, μ={sum(post_rewards) / len(post_rewards):.2f})")
    ax.set_xlabel("cumulative reward (per episode)")
    ax.set_ylabel("episode count")
    ax.set_title("DrugEnv per-episode reward distribution — pre vs post")
    ax.grid(alpha=0.25, axis="y")
    ax.legend(loc="best")
    fig.tight_layout()
    path.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(path, dpi=150)
    plt.close(fig)
    logger.info("wrote %s", path)


def _write_components_png(
    *,
    pre_rows: Sequence[Dict[str, Any]],
    post_rows: Sequence[Dict[str, Any]],
    path: Path,
) -> None:
    plt, _np = _try_matplotlib()
    if plt is None:
        return

    def _means(rows: Sequence[Dict[str, Any]]) -> List[float]:
        if not rows:
            return [0.0] * len(_REWARD_COMPONENTS)
        out: List[float] = []
        for c in _REWARD_COMPONENTS:
            vals = [float((r.get("reward_components_total") or {}).get(c, 0.0)) for r in rows]
            out.append(sum(vals) / len(vals))
        return out

    pre_means = _means(pre_rows)
    post_means = _means(post_rows)
    n = len(_REWARD_COMPONENTS)
    x = list(range(n))
    width = 0.36

    fig, ax = plt.subplots(figsize=(11, 5.5))
    ax.bar([xi - width / 2 for xi in x], pre_means, width=width,
           color="#94a3b8", label=f"pre (n={len(pre_rows)})")
    ax.bar([xi + width / 2 for xi in x], post_means, width=width,
           color="#1d4ed8", label=f"post (n={len(post_rows)})")
    ax.set_xticks(x)
    ax.set_xticklabels(list(_REWARD_COMPONENTS), rotation=18, ha="right")
    ax.set_ylabel("per-episode component sum (mean)")
    ax.set_title("DrugEnv reward component breakdown — pre vs post")
    ax.grid(alpha=0.25, axis="y")
    ax.axhline(0, color="black", lw=0.8)
    ax.legend(loc="best")
    fig.tight_layout()
    path.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(path, dpi=150)
    plt.close(fig)
    logger.info("wrote %s", path)


def _format_episode_md(row: Dict[str, Any], rank_label: str) -> str:
    actions = row.get("action_sequence") or []
    rewards = row.get("step_rewards") or []
    lines = [
        f"### {rank_label} — episode {row.get('episode')} "
        f"(scenario={row.get('scenario')}, target={row.get('target_gene')})",
        "",
        f"- tag: `{row.get('tag')}`",
        f"- seed: `{row.get('seed')}`",
        f"- cumulative_reward: **{float(row.get('cumulative_reward', 0.0)):+.3f}**",
        f"- n_steps: {row.get('n_steps')} (invalid: {row.get('invalid_actions', 0)})",
        f"- submitted: `{row.get('submitted')}`  "
        f"submitted_decision: `{row.get('submitted_decision')}`  "
        f"correct_decision: `{row.get('correct_decision')}`",
        f"- decision_accuracy: {float(row.get('decision_accuracy', 0.0)):.3f}  "
        f"evidence_coverage: {float(row.get('evidence_coverage', 0.0)):.3f}",
        "",
        "| step | action | reward |",
        "|-----:|:-------|-------:|",
    ]
    pad = max(len(actions), len(rewards))
    actions = list(actions) + ["(no-op)"] * (pad - len(actions))
    rewards = list(rewards) + [0.0] * (pad - len(rewards))
    for i, (a, r) in enumerate(zip(actions, rewards)):
        lines.append(f"| {i} | `{a}` | {float(r):+.3f} |")
    comps = row.get("reward_components_total") or {}
    if comps:
        lines.append("")
        lines.append("**Reward component totals**")
        lines.append("")
        lines.append("| component | total |")
        lines.append("|:----------|------:|")
        for c in _REWARD_COMPONENTS:
            lines.append(f"| `{c}` | {float(comps.get(c, 0.0)):+.3f} |")
    lines.append("")
    return "\n".join(lines)


def _write_samples_md(
    *,
    pre_rows: Sequence[Dict[str, Any]],
    post_rows: Sequence[Dict[str, Any]],
    n_samples: int,
    path: Path,
) -> None:
    """Worst-N pre-train + best-N post-train, sorted by cumulative reward."""
    pre_sorted = sorted(pre_rows, key=lambda r: float(r.get("cumulative_reward", 0.0)))
    post_sorted = sorted(post_rows, key=lambda r: float(r.get("cumulative_reward", 0.0)), reverse=True)
    worst_pre = pre_sorted[:n_samples]
    best_post = post_sorted[:n_samples]

    parts: List[str] = [
        "# Sample trajectories — DrugEnv before/after",
        "",
        "Generated by `training/summarize.py`. Worst pre-train episodes show "
        "what the warm-started model failed at; best post-train episodes show "
        "the trajectories GRPO actually reinforced.",
        "",
        f"## Worst {len(worst_pre)} pre-train episodes (lowest cumulative reward)",
        "",
    ]
    if not worst_pre:
        parts.append("_(no pre-train episodes recorded)_\n")
    for i, row in enumerate(worst_pre):
        parts.append(_format_episode_md(row, f"Worst-pre #{i + 1}"))
    parts.append(f"## Best {len(best_post)} post-train episodes (highest cumulative reward)")
    parts.append("")
    if not best_post:
        parts.append("_(no post-train episodes recorded)_\n")
    for i, row in enumerate(best_post):
        parts.append(_format_episode_md(row, f"Best-post #{i + 1}"))

    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text("\n".join(parts), encoding="utf-8")
    logger.info("wrote %s", path)


def main(argv: Optional[List[str]] = None) -> int:
    args = _parse_args(argv)
    evidence_dir = Path(args.evidence_dir)
    evidence_dir.mkdir(parents=True, exist_ok=True)

    pre_rows = _load_jsonl(evidence_dir / args.pre)
    post_rows = _load_jsonl(evidence_dir / args.post)
    logger.info("loaded pre=%d post=%d episodes", len(pre_rows), len(post_rows))

    pre_metrics = _compute_aggregate(pre_rows)
    post_metrics = _compute_aggregate(post_rows)
    delta_metrics = _delta(pre_metrics, post_metrics)

    _write_metrics_json(
        pre=pre_metrics, post=post_metrics, delta=delta_metrics,
        path=evidence_dir / args.metrics_out,
    )
    _write_components_csv(
        pre_rows=pre_rows, post_rows=post_rows,
        path=evidence_dir / args.components_csv,
    )
    _write_summary_png(
        pre=pre_metrics, post=post_metrics,
        path=evidence_dir / args.summary_png,
    )
    _write_distribution_png(
        pre_rows=pre_rows, post_rows=post_rows,
        path=evidence_dir / args.distribution_png,
    )
    _write_components_png(
        pre_rows=pre_rows, post_rows=post_rows,
        path=evidence_dir / args.components_png,
    )
    _write_samples_md(
        pre_rows=pre_rows, post_rows=post_rows,
        n_samples=int(args.n_samples),
        path=evidence_dir / args.samples_md,
    )

    logger.info(
        "summary: pre_mean=%.3f post_mean=%.3f Δmean=%+.3f "
        "pre_success=%.2f post_success=%.2f Δsuccess=%+.2f",
        pre_metrics.get("mean_reward", 0.0),
        post_metrics.get("mean_reward", 0.0),
        delta_metrics.get("mean_reward", 0.0),
        pre_metrics.get("success_rate", 0.0),
        post_metrics.get("success_rate", 0.0),
        delta_metrics.get("success_rate", 0.0),
    )
    return 0


if __name__ == "__main__":
    sys.exit(main())