from __future__ import annotations

import math
from typing import Any


def query_metrics(candidates: list[dict[str, Any]], gold_ids: set[str] | None, cutoffs: tuple[int, ...] = (5, 10, 30, 100)) -> dict[str, Any]:
    if gold_ids is None:
        return {
            "has_gold": None,
            **{f"gold_at_{k}": None for k in cutoffs},
            "mrr": None,
            "ndcg_at_10": None,
        }
    has_gold = bool(gold_ids)
    ranks = [idx + 1 for idx, row in enumerate(candidates) if row["candidate_id"] in gold_ids]
    first_rank = min(ranks) if ranks else None
    metrics = {"has_gold": has_gold}
    for cutoff in cutoffs:
        metrics[f"gold_at_{cutoff}"] = bool(first_rank is not None and first_rank <= cutoff)
    metrics["mrr"] = 1.0 / first_rank if first_rank else 0.0
    dcg = 0.0
    for idx, row in enumerate(candidates[:10], start=1):
        if row["candidate_id"] in gold_ids:
            dcg += 1.0 / math.log2(idx + 1)
    ideal_hits = min(len(gold_ids), 10)
    idcg = sum(1.0 / math.log2(idx + 1) for idx in range(1, ideal_hits + 1))
    metrics["ndcg_at_10"] = dcg / idcg if idcg > 0 else 0.0
    return metrics


def aggregate_metrics(dataset: str, split: str, method: str, rows: list[dict[str, Any]], notes: str) -> dict[str, Any]:
    evaluable = [row for row in rows if row["metrics"].get("has_gold") is not None]
    if not evaluable:
        return {
            "Dataset": dataset,
            "Split": split,
            "Method": method,
            "R@5": "N/A",
            "R@10": "N/A",
            "R@30": "N/A",
            "R@100": "N/A",
            "MRR": "N/A",
            "nDCG@10": "N/A",
            "candidate_count_avg": round(sum(len(row["candidates"]) for row in rows) / max(1, len(rows)), 4),
            "gold_coverage": "N/A",
            "Notes": notes,
        }
    with_gold = [row for row in evaluable if row["metrics"].get("has_gold")]
    denom = max(1, len(with_gold))

    def avg_bool(key: str) -> float:
        return sum(1 for row in with_gold if row["metrics"].get(key)) / denom

    return {
        "Dataset": dataset,
        "Split": split,
        "Method": method,
        "R@5": round(avg_bool("gold_at_5"), 6),
        "R@10": round(avg_bool("gold_at_10"), 6),
        "R@30": round(avg_bool("gold_at_30"), 6),
        "R@100": round(avg_bool("gold_at_100"), 6),
        "MRR": round(sum(float(row["metrics"].get("mrr", 0.0)) for row in with_gold) / denom, 6),
        "nDCG@10": round(sum(float(row["metrics"].get("ndcg_at_10", 0.0)) for row in with_gold) / denom, 6),
        "candidate_count_avg": round(sum(len(row["candidates"]) for row in rows) / max(1, len(rows)), 4),
        "gold_coverage": round(len(with_gold) / max(1, len(evaluable)), 6),
        "Notes": notes,
    }