from __future__ import annotations

import argparse
import csv
import json
import math
from pathlib import Path
from typing import Any

from .audit_benchmark import audit_benchmark_run
from .provenance import require_file
from .validate_fidelity import validate_fidelity


def _read_rows(path: str | Path) -> list[dict[str, str]]:
    with require_file(path, "benchmark table").open("r", encoding="utf-8", newline="") as handle:
        return list(csv.DictReader(handle))


def _float(value: object, default: float | None = None) -> float | None:
    try:
        text = str(value).strip()
        if not text:
            return default
        return float(text)
    except Exception:
        return default


def _score(row: dict[str, Any], *keys: str) -> float | None:
    for key in keys:
        value = _float(row.get(key), None)
        if value is not None and math.isfinite(value):
            return value
    return None


def _mean(values: list[float]) -> float:
    return sum(values) / len(values) if values else 0.0


def _spearman(xs: list[float], ys: list[float]) -> float | None:
    if len(xs) < 2 or len(xs) != len(ys):
        return None

    def _ranks(values: list[float]) -> list[float]:
        order = sorted(range(len(values)), key=lambda i: values[i])
        ranks = [0.0] * len(values)
        for rank, idx in enumerate(order, start=1):
            ranks[idx] = float(rank)
        return ranks

    rx = _ranks(xs)
    ry = _ranks(ys)
    mx = _mean(rx)
    my = _mean(ry)
    num = sum((a - mx) * (b - my) for a, b in zip(rx, ry))
    denx = math.sqrt(sum((a - mx) ** 2 for a in rx))
    deny = math.sqrt(sum((b - my) ** 2 for b in ry))
    if denx == 0.0 or deny == 0.0:
        return None
    return num / (denx * deny)


def _ensure_matplotlib():
    try:
        import matplotlib

        matplotlib.use("Agg")
        import matplotlib.pyplot as plt
    except Exception:
        return None
    return plt


def _write_json(path: Path, payload: dict[str, Any]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(json.dumps(payload, indent=2), encoding="utf-8")


def _validation_plots(run_dir: Path, metrics: dict[str, Any]) -> list[str]:
    plt = _ensure_matplotlib()
    plot_dir = run_dir / "plots"
    plot_dir.mkdir(parents=True, exist_ok=True)
    out: list[str] = []
    if plt is None:
        return out

    trace_rows = _read_rows(run_dir / "tables" / "multifidelity_trace.csv") if (run_dir / "tables" / "multifidelity_trace.csv").exists() else []
    final_filtered = _read_rows(run_dir / "tables" / "final_hits_filtered.csv") if (run_dir / "tables" / "final_hits_filtered.csv").exists() else []
    random_rows = _read_rows(run_dir / "tables" / "random_baseline_scores.csv") if (run_dir / "tables" / "random_baseline_scores.csv").exists() else []
    single_rows = _read_rows(run_dir / "tables" / "single_fidelity_adaptive_scores.csv") if (run_dir / "tables" / "single_fidelity_adaptive_scores.csv").exists() else []

    def save(fig, name: str) -> None:
        path = plot_dir / name
        fig.tight_layout()
        fig.savefig(path, dpi=160)
        plt.close(fig)
        out.append(str(path))

    # cost balance comparison
    fig, ax = plt.subplots(figsize=(7, 4))
    labels = ["adaptive", "random", "single"]
    vals = [
        float(metrics.get("multifidelity_total_runs_spent") or 0.0),
        float(metrics.get("random_total_runs_spent") or 0.0),
        float(metrics.get("single_fidelity_total_runs_spent") or 0.0),
    ]
    ax.bar(labels, vals, color=["#3b6ea8", "#bf7f2f", "#7a4f9d"])
    ax.set_title("Cost balance comparison across benchmark strategies")
    ax.set_xlabel("Strategy")
    ax.set_ylabel("Total rDock runs spent")
    save(fig, "cost_balance_comparison.png")

    # top-k filtered score comparison
    fig, ax = plt.subplots(figsize=(8, 4))
    ks = [1, 5, 10, 20]
    series = {}
    for name, rows in {
        "adaptive": final_filtered,
        "random": random_rows,
        "single": single_rows,
    }.items():
        ranked = sorted(
            [row for row in rows if _score(row, "final_score", "SCORE") is not None],
            key=lambda row: _score(row, "final_score", "SCORE") or float("inf"),
        )
        series[name] = [_mean([_score(row, "final_score", "SCORE") or 0.0 for row in ranked[:k]]) if ranked[:k] else math.nan for k in ks]
    for idx, (name, vals_) in enumerate(series.items()):
        ax.plot(ks, vals_, marker="o", linewidth=2, label=name)
    ax.set_title("Top-k filtered score comparison by strategy")
    ax.set_xlabel("k")
    ax.set_ylabel("Mean filtered docking SCORE")
    ax.legend()
    save(fig, "topk_filtered_score_comparison.png")

    # surrogate calibration and uncertainty
    pred_obs = []
    unc_err = []
    for row in trace_rows:
        pred = _score(row, "pre_docking_predicted_score", "predicted_filtered_score")
        obs = _score(row, "ranking_score", "SCORE")
        unc = _score(row, "pre_docking_predicted_uncertainty", "predicted_uncertainty")
        if pred is not None and obs is not None:
            if _score(row, "pre_docking_predicted_score") is None and _score(row, "predicted_uncertainty") == 0.0:
                continue
            pred_obs.append((pred, obs))
            if unc is not None:
                unc_err.append((unc, abs(pred - obs)))
    if pred_obs:
        fig, ax = plt.subplots(figsize=(5, 5))
        xs = [item[0] for item in pred_obs]
        ys = [item[1] for item in pred_obs]
        ax.scatter(xs, ys, alpha=0.7, color="#3b6ea8")
        lo = min(xs + ys)
        hi = max(xs + ys)
        ax.plot([lo, hi], [lo, hi], linestyle="--", color="gray")
        ax.set_title("Surrogate calibration: predicted vs observed score")
        ax.set_xlabel("Predicted filtered score")
        ax.set_ylabel("Observed docking score")
        save(fig, "surrogate_calibration.png")
    if unc_err:
        fig, ax = plt.subplots(figsize=(5, 4))
        xs = [item[0] for item in unc_err]
        ys = [item[1] for item in unc_err]
        ax.scatter(xs, ys, alpha=0.7, color="#7a4f9d")
        ax.set_title("Surrogate uncertainty versus absolute error")
        ax.set_xlabel("Predicted uncertainty")
        ax.set_ylabel("Absolute prediction error")
        save(fig, "surrogate_uncertainty_vs_error.png")

    # cluster diversity over time
    if trace_rows:
        fig, ax = plt.subplots(figsize=(7, 4))
        seen = set()
        xs: list[int] = []
        ys: list[int] = []
        for idx, row in enumerate(sorted(trace_rows, key=lambda r: (int(float(r.get("batch_id") or 0.0)), int(float(r.get("selected_fidelity_runs") or 0.0)))), start=1):
            seen.add(str(row.get("cluster_id", "")))
            xs.append(idx)
            ys.append(len(seen))
        ax.plot(xs, ys, color="#3b6ea8")
        ax.set_title("Cluster diversity over adaptive screening time")
        ax.set_xlabel("Processed multifidelity records")
        ax.set_ylabel("Unique clusters seen")
        save(fig, "cluster_diversity_over_time.png")

    return out


def validate_benchmark_model(run_dir: str | Path) -> dict[str, Any]:
    root = Path(run_dir)
    audit = audit_benchmark_run(root)
    fidelity = validate_fidelity(root)
    metrics = dict(audit["metrics"])
    comparability = dict(audit["comparability_audit"])
    trace_rows = _read_rows(root / "tables" / "multifidelity_trace.csv") if (root / "tables" / "multifidelity_trace.csv").exists() else []
    raw_rows = _read_rows(root / "tables" / "final_hits_raw.csv") if (root / "tables" / "final_hits_raw.csv").exists() else []
    filtered_rows = _read_rows(root / "tables" / "final_hits_filtered.csv") if (root / "tables" / "final_hits_filtered.csv").exists() else []

    pred = []
    obs = []
    unc = []
    abs_err = []
    for row in trace_rows:
        predicted = _score(row, "pre_docking_predicted_score", "predicted_filtered_score")
        observed = _score(row, "ranking_score", "SCORE")
        uncertainty = _score(row, "pre_docking_predicted_uncertainty", "predicted_uncertainty")
        if predicted is not None and observed is not None:
            if _score(row, "pre_docking_predicted_score") is None and _score(row, "predicted_uncertainty") == 0.0:
                continue
            pred.append(predicted)
            obs.append(observed)
            abs_err.append(abs(predicted - observed))
            if uncertainty is not None:
                unc.append((uncertainty, abs(predicted - observed)))

    mae = _mean(abs_err) if abs_err else None
    spearman = _spearman(pred, obs)
    uncertainty_spearman = _spearman([x for x, _ in unc], [y for _, y in unc]) if unc else None
    raw_ids = {str(row.get("ligand_id", "")) for row in raw_rows}
    filtered_ids = {str(row.get("ligand_id", "")) for row in filtered_rows}
    dropped_raw = len(raw_ids - filtered_ids)

    validation_metrics = {
        "run_dir": str(root),
        "benchmark_status": metrics.get("benchmark_status"),
        "comparable": comparability.get("comparable"),
        "cost_ratio_random_vs_multifidelity": comparability.get("cost_ratio_random_vs_multifidelity"),
        "cost_ratio_single_vs_multifidelity": comparability.get("cost_ratio_single_vs_multifidelity"),
        "filter_retention_fraction": (len(filtered_rows) / len(raw_rows)) if raw_rows else 0.0,
        "raw_hits_dropped_after_filtering": dropped_raw,
        "surrogate_mae": mae,
        "surrogate_spearman": spearman,
        "uncertainty_vs_error_spearman": uncertainty_spearman,
        "n_surrogate_points": len(pred),
        "fidelity_reliability": fidelity,
        "reasons": comparability.get("reasons", []),
    }
    if spearman is None or spearman <= 0.0:
        validation_metrics.setdefault("warnings", []).append("MODEL DOES NOT PROVIDE USEFUL RANKING SIGNAL YET")
    if not fidelity.get("low_fidelity_reliable", True):
        validation_metrics.setdefault("warnings", []).append("LOW FIDELITY IS NOT RELIABLE ENOUGH FOR AGGRESSIVE PRUNING")
    plots = _validation_plots(root, metrics)
    _write_json(root / "metrics" / "validation_metrics.json", validation_metrics)
    report_lines = [
        f"# validation_report: {root.name}",
        "",
        f"- comparable: `{validation_metrics['comparable']}`",
        f"- benchmark_status: `{validation_metrics['benchmark_status']}`",
        f"- cost_ratio_random_vs_multifidelity: `{validation_metrics['cost_ratio_random_vs_multifidelity']}`",
        f"- cost_ratio_single_vs_multifidelity: `{validation_metrics['cost_ratio_single_vs_multifidelity']}`",
        f"- filter_retention_fraction: `{validation_metrics['filter_retention_fraction']}`",
        f"- raw_hits_dropped_after_filtering: `{validation_metrics['raw_hits_dropped_after_filtering']}`",
        f"- surrogate_mae: `{validation_metrics['surrogate_mae']}`",
        f"- surrogate_spearman: `{validation_metrics['surrogate_spearman']}`",
        f"- uncertainty_vs_error_spearman: `{validation_metrics['uncertainty_vs_error_spearman']}`",
        f"- low_fidelity_reliable: `{fidelity.get('low_fidelity_reliable')}`",
    ]
    for warning in validation_metrics.get("warnings", []):
        report_lines.append(f"- warning: `{warning}`")
    for reason in validation_metrics["reasons"]:
        report_lines.append(f"- reason: `{reason}`")
    report_lines.extend(["", "## Diagnostic Plots"])
    for path in plots:
        report_lines.append(f"- `{path}`")
    (root / "validation_report.md").write_text("\n".join(report_lines) + "\n", encoding="utf-8")
    return {"validation_metrics": validation_metrics, "validation_report": str(root / "validation_report.md"), "plots": plots}


def build_arg_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(description="Validate an existing adaptive benchmark run and generate diagnostic plots.")
    parser.add_argument("--run-dir", required=True)
    return parser


def run_from_args(args: argparse.Namespace) -> dict[str, Any]:
    return validate_benchmark_model(args.run_dir)


def main() -> int:
    parser = build_arg_parser()
    args = parser.parse_args()
    print(json.dumps(run_from_args(args), indent=2))
    return 0