"""
Deterministic task graders for layoutenv benchmark tasks.
"""

from dataclasses import dataclass


TASK_SUCCESS_Q_DELTA = {
    "easy": 0.10,
    "medium": 0.20,
    "hard": 0.32,
}


@dataclass(frozen=True)
class TaskGrade:
    task_id: str
    score: float
    success: bool
    q_delta: float


def _clamp_open01(x: float) -> float:
    # Use a small epsilon to ensure the score is strictly in (0, 1)
    return min(max(x, 0.05), 0.95)


def score_from_q_delta(q_delta: float) -> float:
    """
    Map quality delta to (0, 1) score.

    The linear map is intentionally clamped so large outliers do not
    destabilize reported leaderboard-compatible scores.
    """
    return _clamp_open01((q_delta + 2.0) / 4.0)


def success_from_q_delta(task_id: str, q_delta: float, default_threshold: float) -> bool:
    """
    Determine success using task-specific threshold if available.
    """
    threshold = TASK_SUCCESS_Q_DELTA.get(task_id, default_threshold)
    return q_delta >= threshold


def grade_episode(
    task_id: str,
    initial_quality: float,
    final_quality: float,
    success_q_delta: float = 0.1,
) -> TaskGrade:
    q_delta = final_quality - initial_quality
    return TaskGrade(
        task_id=task_id,
        score=score_from_q_delta(q_delta),
        success=success_from_q_delta(task_id, q_delta, success_q_delta),
        q_delta=q_delta,
    )