from model_schema import AssumptionReview, CheckIssue, CheckSeverity
from TextToModel import select_best


def make_review(matches=True, verdict="justified", missing=(), units_consistent=True):
    return AssumptionReview.model_validate({
        "objective_check": {
            "task_evidence": "Bestimme die Aufteilung mit der geringsten Volatilität",
            "asked_quantity": "Standardabweichung der Portfoliorendite",
            "modeled_quantity": "Standardabweichung" if matches else "Varianz",
            "matches": matches,
            "units_consistent": units_consistent,
            "unit_explanation": "Rendite €, Risiko €, Kosten €" if units_consistent else "Rendite €, Risiko dimensionslos, Kosten €",
            "explanation": "-",
        },
        "checks": [{"unclear_point": "-", "verdict": verdict, "task_evidence": "-", "explanation": "-"}],
        "missing_assumptions": list(missing),
    })


def test_clean_review_needs_no_revision():
    assert not make_review().needs_revision


def test_objective_mismatch_triggers_revision():
    assert make_review(matches=False).needs_revision


def test_faulty_assumption_triggers_revision():
    assert make_review(verdict="stricter_than_task").needs_revision


def test_missing_assumption_triggers_revision():
    missing = [{"unclear_point": "-", "assumption": "-", "alternative": "-", "impact": "-"}]
    assert make_review(missing=missing).needs_revision


# --- Auswahl der besten Korrekturrunde (TextToModel.select_best) ---

ERROR = CheckIssue(severity=CheckSeverity.ERROR, code="CPSAT_WITH_CONTINUOUS_VARS", message="-")
WARNING = CheckIssue(severity=CheckSeverity.WARNING, code="UNDECLARED_SYMBOL", message="-")


def test_later_hallucinated_round_is_discarded():
    # beobachtet in portfolio_qp: Runde 1 sauber, Runde 2 beschreibt ein Fremdproblem mit Check-Error
    candidates = [
        (None, [], make_review(matches=False)),
        (None, [WARNING], make_review()),
        (None, [ERROR, WARNING], make_review(matches=False)),
    ]
    assert select_best(candidates) == 1


def test_clean_later_round_wins_tie():
    candidates = [(None, [], make_review()), (None, [WARNING], make_review())]
    assert select_best(candidates) == 1


def test_fewer_errors_preferred_when_all_have_errors():
    candidates = [(None, [ERROR], make_review(matches=False)), (None, [ERROR, ERROR], make_review(matches=False))]
    assert select_best(candidates) == 0


def test_mixed_units_trigger_revision():
    # beobachtet: Rendite/Kosten in Euro, Risiko als dimensionslose Summe w_i^2 sigma_i^2 (Faktor 100.000 zu klein)
    assert make_review(units_consistent=False).needs_revision