from __future__ import annotations

from typing import Any


def _keyword_score(reply: str | None, keywords: list[str]) -> float:
    if not reply or not keywords:
        return 0.0

    lowered = reply.lower()
    hits = sum(1 for keyword in keywords if keyword.lower() in lowered)
    return hits / len(keywords)


def grade_task(
    task: dict[str, Any],
    collected_evidence: list[str],
    drafted_reply: str | None,
    resolution_code: str | None,
    step_count: int,
    repeat_action_count: int,
) -> dict[str, float]:
    required_evidence = task["required_evidence"]
    evidence_hits = sum(1 for key in required_evidence if key in collected_evidence)
    evidence_score = evidence_hits / len(required_evidence)

    resolution_score = 1.0 if resolution_code == task["expected_resolution_code"] else 0.0
    reply_score = _keyword_score(drafted_reply, task["reply_keywords"])

    optimal_steps = task.get("optimal_steps", len(required_evidence) + 2)
    extra_steps = max(0, step_count - optimal_steps)
    efficiency_penalty = min(0.25, (extra_steps * 0.05) + (repeat_action_count * 0.04))
    efficiency_score = max(0.0, 1.0 - efficiency_penalty)

    final_score = (
        0.5 * resolution_score
        + 0.25 * evidence_score
        + 0.2 * reply_score
        + 0.05 * efficiency_score
    )

    return {
        "resolution_score": round(resolution_score, 3),
        "evidence_score": round(evidence_score, 3),
        "reply_score": round(reply_score, 3),
        "efficiency_score": round(efficiency_score, 3),
        "final_score": round(min(max(final_score, 0.0), 1.0), 3),
    }