from __future__ import annotations



from .models import CommitGuardAction





def compute_reward(

    *,

    action: CommitGuardAction,

    is_vulnerable: bool | None,

    cwe: str | None,

    target_file: str | None,

    cwe_keywords: dict[str, list[str]] | None,

    context_requests: int,

) -> float:

    """

    Tiered RLVR reward (PRD 5.3, architecture contract).



    Notes:

    - Ground truth must remain server-only; caller passes it in.

    - Reward is a scalar only; no label debug info.

    """

    # Per-context-request penalty applies regardless of verdict.

    reward = -0.05 * float(max(0, context_requests))



    if action.parse_error:

        return reward - 0.5



    # Small CoT bonus: reward 'analyze' steps that provide substantial reasoning.

    # This provides a tiny positive float signal to encourage thinking.

    if action.action_type == "analyze":

        reasoning_len = len(action.reasoning or "")

        if reasoning_len > 50:

            reward += min(0.05, 0.001 * (reasoning_len // 10))

        return reward



    if action.action_type != "verdict":

        return reward



    if is_vulnerable is None:

        return reward



    pred = bool(action.is_vulnerable) if action.is_vulnerable is not None else None

    if pred is None:

        return reward - 0.5



    if pred is True and is_vulnerable is True:

        reward += 1.0

        # Correct CWE (Discrete 0.5)

        if cwe and action.vuln_type and action.vuln_type.strip().upper() == cwe.strip().upper():

            reward += 0.5



        # Proportional Keyword Match (Continuous Float up to 0.5)

        kws = (cwe_keywords or {}).get(cwe or "", []) if cwe else []

        if kws:

            sketch = (action.exploit_sketch or "").lower()

            matches = sum(1 for k in kws if k.lower() in sketch)

            # Continuous signal: reward is proportional to percentage of keywords found.

            reward += 0.5 * (matches / len(kws))

        return reward



    if pred is True and is_vulnerable is False:

        return reward - 1.0



    if pred is False and is_vulnerable is True:

        return reward - 0.5



    if pred is False and is_vulnerable is False:

        return reward + 1.0



    return reward