"""
GRM Evaluation Suite — Scoring Logic

GRM Score = mean(Roleplay_score, Actions_score, General_score)

Each category score is a weighted average of its benchmarks:
  category_score = sum(score_i * calc_weight_i) / sum(calc_weight_i)
"""

from benchmarks import BENCHMARKS, CATEGORIES, get_benchmarks_by_category
from scores import MODEL_SCORES


def compute_category_score(
    model_scores: dict[str, float | None],
    category: str,
) -> float | None:
    """Weighted average of available benchmark scores in a category."""
    benchmarks = get_benchmarks_by_category(category)
    total_weight = 0.0
    total_value = 0.0
    for b in benchmarks:
        score = model_scores.get(b["name"])
        if score is not None:
            total_weight += b["calc_weight"]
            total_value += score * b["calc_weight"]
    if total_weight == 0:
        return None
    return total_value / total_weight


def compute_grm_score(model_scores: dict[str, float | None]) -> dict:
    """Return GRM Score and per-category scores for a single model.

    All returned values are on a 0–100 scale, or None if no data.
    """
    cat_scores = {}
    for cat in CATEGORIES:
        raw = compute_category_score(model_scores, cat)
        cat_scores[cat] = round(raw * 100, 1) if raw is not None else None

    available = [v for v in cat_scores.values() if v is not None]
    grm = round(sum(available) / len(available), 1) if available else None

    return {
        "GRM Score": grm,
        "Roleplay (33%)": cat_scores["ROLEPLAY"],
        "Actions (33%)": cat_scores["ACTIONS"],
        "General (33%)": cat_scores["GENERAL"],
    }


def build_leaderboard() -> list[dict]:
    """Compute scores for all models and return sorted rows."""
    rows = []
    for model_name, model_scores in MODEL_SCORES.items():
        result = compute_grm_score(model_scores)
        result["Model"] = model_name
        rows.append(result)

    rows.sort(key=lambda r: r["GRM Score"] if r["GRM Score"] is not None else -1, reverse=True)

    for i, row in enumerate(rows, start=1):
        row["Rank"] = i

    return rows