from __future__ import annotations

from engine.models import ConstraintSpec, DecisionResult, ObjectiveSpec, RunResult, ScoreResult


def _constraint_failed(candidate: ScoreResult, constraint: ConstraintSpec) -> bool:
    value = candidate.metrics.get(constraint.metric)
    if value is None:
        return True

    if constraint.op == "<=":
        return value > constraint.value
    if constraint.op == ">=":
        return value < constraint.value
    if constraint.op == "==":
        return value != constraint.value
    raise ValueError(f"unsupported constraint operator: {constraint.op}")


def decide_candidate(
    baseline: float | None,
    candidate: ScoreResult,
    objective: ObjectiveSpec,
    constraints: list[ConstraintSpec],
    tie_breakers: list[dict[str, str]],
    run_result: RunResult,
) -> DecisionResult:
    if run_result.exit_code != 0:
        return DecisionResult(
            status="crash",
            reason=f"command failed with exit code {run_result.exit_code}",
            baseline_score=baseline,
            candidate_score=None,
        )

    failed_constraints = [
        constraint.metric
        for constraint in constraints
        if _constraint_failed(candidate, constraint)
    ]
    if failed_constraints:
        return DecisionResult(
            status="discard",
            reason=f"constraint failure: {', '.join(failed_constraints)}",
            baseline_score=baseline,
            candidate_score=candidate.primary_score,
            constraint_failures=failed_constraints,
        )

    if baseline is None:
        return DecisionResult(
            status="keep",
            reason="no baseline available",
            baseline_score=None,
            candidate_score=candidate.primary_score,
        )

    if objective.direction == "maximize":
        better = candidate.primary_score > baseline
    else:
        better = candidate.primary_score < baseline

    if better:
        return DecisionResult(
            status="keep",
            reason="candidate improved primary score",
            baseline_score=baseline,
            candidate_score=candidate.primary_score,
        )

    return DecisionResult(
        status="discard",
        reason="candidate did not improve primary score",
        baseline_score=baseline,
        candidate_score=candidate.primary_score,
    )