"""run_pipeline ohne LLM: Neustart bei Fremdproblem und Kennzeichnung unzuverlässiger Ergebnisse."""
from benchmark import result_correct
from model_schema import (
    AssumptionReview,
    LLMUsage,
    OptimizationModelSpecification,
    SolverCode,
    SolverStatus,
    SubQuestion,
    TaskUnderstanding,
)
from TextToModel import run_pipeline
from test_model_checks import make_spec
from test_review import make_review
from test_script_export import STRUCTURED_CODE


# Standardverstaendnis: eine Frage, keine Zahlen - reicht fuer Tests, die das Verstehen nicht pruefen
EINFACHES_VERSTAENDNIS = TaskUnderstanding(
    is_optimization_task=True, reason="-", facts=[], ignored_numbers=[],
    sub_questions=[SubQuestion(id="1", quote="-", kind="rechnen", needs_solver=True)])


class FakeSession:
    """Liefert vorgegebene Antworten je Antwort-Typ in fester Reihenfolge."""

    def __init__(self, specs, reviews, codes=(), understandings=None):
        self.queues = {
            OptimizationModelSpecification: list(specs),
            AssumptionReview: list(reviews),
            SolverCode: list(codes),
            TaskUnderstanding: list(understandings if understandings is not None else []),
        }
        self.usage = LLMUsage(model="fake", provider="fake")
        self.calls = {kind: 0 for kind in self.queues}
        self.steps = []
        self.tasks = []
        self.letzter_prompt = ""

    def overrides(self):
        return {}

    def create(self, response_model, messages, step="unbenannt"):
        self.letzter_prompt = messages[-1]["content"]
        if step == "Extraktion":
            self.tasks.append(messages[-1]["content"])
        self.calls[response_model] += 1
        self.usage.calls += 1
        self.steps.append(step)
        warteschlange = self.queues[response_model]
        if response_model is TaskUnderstanding and not warteschlange:
            return EINFACHES_VERSTAENDNIS      # Standard, solange ein Test nichts anderes vorgibt
        return warteschlange.pop(0)


HALLUCINATED = make_spec(executive_summary="Produktionsplanung mit Drehen und Fräsen")  # Fremdproblem
GOOD = make_spec(executive_summary="Rohstoffeinkauf mit minimalen Kosten")
MISMATCH = make_review(matches=False)
OK = make_review()


def test_clean_first_attempt_needs_no_restart():
    session = FakeSession([GOOD], [OK], [SolverCode(code=STRUCTURED_CODE)])
    result = run_pipeline("Aufgabe", session=session, max_revisions=2, max_restarts=1)
    assert result.reliable and result.restarts == 0 and result.revisions == 0
    assert result.solver_run.status == SolverStatus.OPTIMAL


def test_restart_recovers_from_hallucinated_problem():
    # Versuch 1: Fremdproblem, beide Korrekturrunden erfolglos; Neustart liefert das richtige Modell
    session = FakeSession(
        specs=[HALLUCINATED, HALLUCINATED, HALLUCINATED, GOOD],
        reviews=[MISMATCH, MISMATCH, MISMATCH, OK],
        codes=[SolverCode(code=STRUCTURED_CODE)],
    )
    result = run_pipeline("Aufgabe", session=session, max_revisions=2, max_restarts=1)
    assert result.restarts == 1
    assert result.reliable and result.reliability_issues == []
    assert result.specification.executive_summary == GOOD.executive_summary
    assert result.solver_run.status == SolverStatus.OPTIMAL and result.solver_run.objective_value == 9


def test_persistent_mismatch_is_unreliable_and_not_solved():
    # beobachtet im Benchmark: Fremdproblem wurde als "optimal, 2550" ausgegeben
    session = FakeSession(specs=[HALLUCINATED] * 6, reviews=[MISMATCH] * 6)
    result = run_pipeline("Aufgabe", session=session, max_revisions=2, max_restarts=1)
    assert result.restarts == 1
    assert not result.reliable
    assert any("Zielfunktion passt nicht" in issue for issue in result.reliability_issues)
    assert result.solver_run is None and result.solver_code is None
    assert session.calls[SolverCode] == 0  # kein Solver-Code für ein falsches Problem
    assert not result_correct(result.solver_run, {"objective_value": 9, "tolerance": 1e-6})


def test_better_earlier_attempt_wins_over_worse_restart():
    # Neustart ist schlechter (Zielgröße falsch) als Versuch 1 (nur Check-Error) -> Versuch 1 verwenden
    stack_error = make_spec(executive_summary="richtig, aber Stack falsch", problem_class="Quadratic Programming (QP)")
    session = FakeSession(
        specs=[stack_error, stack_error, stack_error, HALLUCINATED, HALLUCINATED, HALLUCINATED],
        reviews=[OK, OK, OK, MISMATCH, MISMATCH, MISMATCH],
        codes=[SolverCode(code=STRUCTURED_CODE)],
    )
    result = run_pipeline("Aufgabe", session=session, max_revisions=2, max_restarts=1)
    assert result.specification.executive_summary == "richtig, aber Stack falsch"
    assert not result.reliable  # STACK_MISMATCH bleibt bestehen
    assert any("STACK_MISMATCH" in issue for issue in result.reliability_issues)
    assert result.solver_run is not None  # Zielgröße passt -> Solver läuft, Ergebnis aber als unzuverlässig markiert


def test_no_restart_when_disabled():
    session = FakeSession(specs=[HALLUCINATED] * 3, reviews=[MISMATCH] * 3)
    result = run_pipeline("Aufgabe", session=session, max_revisions=2, max_restarts=0)
    assert result.restarts == 0 and not result.reliable


def test_report_shows_prominent_warning_for_unreliable_result():
    from report import build_document, to_html, to_markdown
    session = FakeSession(specs=[HALLUCINATED] * 3, reviews=[MISMATCH] * 3)
    result = run_pipeline("Aufgabe", session=session, max_revisions=2, max_restarts=0)
    doc = build_document(result)
    assert doc[1][0] == "warn"  # direkt unter der Überschrift
    assert '<div class="warn">⚠ Ergebnis unzuverlässig' in to_html(doc)
    assert "> **⚠ Ergebnis unzuverlässig" in to_markdown(doc)
    assert "Der Solver wurde nicht gestartet." in doc[1][1]


def test_report_shows_the_rejected_reading_of_each_assumption():
    from report import build_document, to_markdown
    spec = make_spec(open_assumptions=[{
        "unclear_point": "„aufeinanderfolgend“",
        "assumption": "Kalendertage, auch über das Wochenende",
        "alternative": "Nur Werktage; verworfen, weil der Text durchgehenden Betrieb nennt",
        "impact": "Restriktion Ruhezeit greift auch am Wochenende",
    }])
    session = FakeSession([spec], [OK], [SolverCode(code=STRUCTURED_CODE)])
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, solve_model=False)
    markdown = to_markdown(build_document(result))
    assert "Verworfene Lesart" in markdown
    assert "verworfen, weil der Text durchgehenden Betrieb nennt" in markdown


def test_pipeline_labels_every_llm_call_for_the_timing_breakdown():
    session = FakeSession([GOOD], [OK], [SolverCode(code=STRUCTURED_CODE)])
    run_pipeline("Aufgabe", session=session, max_revisions=2, max_restarts=1)
    assert session.steps == ["Verstehen", "Extraktion", "Prüfung", "Solver-Code"]


def test_revision_and_repair_steps_are_labelled():
    session = FakeSession(specs=[HALLUCINATED, GOOD], reviews=[MISMATCH, OK],
                          codes=[SolverCode(code="x = 1"), SolverCode(code=STRUCTURED_CODE)])
    run_pipeline("Aufgabe", session=session, max_revisions=1, max_restarts=0)
    assert session.steps == ["Verstehen", "Extraktion", "Prüfung", "Korrektur", "Prüfung", "Solver-Code", "Solver-Reparatur"]


# --- Zulässigkeitsprüfung im Zusammenspiel ---

VIOLATING_CODE = '''
def solve_model():
    """Liefert eine Lösung, die das Budget um 8 Cent überschreitet (beobachteter Fall)."""
    return 100000.08


def main():
    """Meldet die Lösung samt Restriktionen zur Nachprüfung."""
    invested = solve_model()
    report("optimal", 12041.74, {"x": invested},
           constraints=[{"name": "Bedarf", "lhs": invested, "sense": "<=", "rhs": 100000.0}],
           variables=[{"name": "x", "value": invested, "var_type": "continuous", "lb": 0}])


if __name__ == "__main__":
    main()
'''


def test_infeasible_solution_triggers_repair_and_is_marked_unreliable():
    session = FakeSession([GOOD], [OK], codes=[SolverCode(code=VIOLATING_CODE)] * 3)
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0, solver_retries=2)
    assert session.calls[SolverCode] == 3  # Erstversuch + 2 Reparaturen
    assert result.solver_run.feasibility and "Bedarf" in result.solver_run.feasibility[0]
    assert not result.reliable
    assert any("Zulässigkeitsprüfung" in issue for issue in result.reliability_issues)


def test_feasible_solution_stays_reliable():
    session = FakeSession([GOOD], [OK], codes=[SolverCode(code=STRUCTURED_CODE)])
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0)
    assert result.solver_run.feasibility == []
    assert result.reliable and session.calls[SolverCode] == 1


# --- Gegenprobe bei "unlösbar" ---

INFEASIBLE_CODE = '''
def main():
    """Meldet das Modell als unlösbar."""
    report("infeasible", None, {})


if __name__ == "__main__":
    main()
'''


def test_infeasible_is_rechecked_and_can_be_refuted():
    # beobachtet bei qwen3-235b: "unlösbar" war ein Modellierungsfehler, die Aufgabe ist lösbar
    session = FakeSession(specs=[GOOD, GOOD], reviews=[OK, OK],
                          codes=[SolverCode(code=INFEASIBLE_CODE), SolverCode(code=STRUCTURED_CODE)])
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0)
    assert result.solver_run.status == SolverStatus.OPTIMAL and result.solver_run.objective_value == 9
    assert not result.infeasible_confirmed and result.restarts == 1
    assert "HINWEIS" in session.tasks[1] and "unabhängige Modellierung" in session.tasks[1]


def test_infeasible_confirmed_by_second_modelling():
    session = FakeSession(specs=[GOOD, GOOD], reviews=[OK, OK],
                          codes=[SolverCode(code=INFEASIBLE_CODE)] * 2)
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0)
    assert result.solver_run.status == SolverStatus.INFEASIBLE
    assert result.infeasible_confirmed and result.reliable


def test_infeasible_without_recheck_is_flagged(monkeypatch):
    import TextToModel
    monkeypatch.setattr(TextToModel, "INFEASIBLE_RECHECK", 0)
    session = FakeSession([GOOD], [OK], codes=[SolverCode(code=INFEASIBLE_CODE)])
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0)
    assert not result.reliable and any("ohne Gegenprobe" in i for i in result.reliability_issues)


# --- Wiedereinstieg ab einer Stufe (continue_pipeline / CLI --von) ---

def stored_result(tmp_path, spec=GOOD, review=OK):
    """Ergebnis eines früheren Laufs als Datei – Grundlage für --von."""
    session = FakeSession([spec], [review], [SolverCode(code=STRUCTURED_CODE)])
    result = run_pipeline("Rohstoffeinkauf", session=session, max_revisions=0, max_restarts=0, solve_model=False)
    path = tmp_path / "ergebnis.json"
    path.write_text(result.model_dump_json(indent=2), encoding="utf-8")
    return path


def test_continue_from_solver_skips_extraction_and_review(tmp_path):
    from TextToModel import continue_pipeline
    path = stored_result(tmp_path)
    session = FakeSession([], [], [SolverCode(code=STRUCTURED_CODE)])
    result = continue_pipeline(str(path), "solver", session=session)
    assert session.steps == ["Solver-Code"]  # kein Extraktions-, kein Prüfaufruf
    assert result.specification.executive_summary == GOOD.executive_summary
    assert result.task == "Rohstoffeinkauf"  # Aufgabentext kommt aus dem Ergebnis
    assert result.solver_run.status == SolverStatus.OPTIMAL


def test_continue_from_review_keeps_specification_but_checks_again(tmp_path):
    from TextToModel import continue_pipeline
    path = stored_result(tmp_path)
    session = FakeSession([], [OK], [SolverCode(code=STRUCTURED_CODE)])
    result = continue_pipeline(str(path), "pruefung", session=session)
    assert session.steps == ["Prüfung", "Solver-Code"]  # Extraktion entfällt
    assert result.specification.executive_summary == GOOD.executive_summary


def test_continue_from_review_can_still_revise(tmp_path):
    from TextToModel import continue_pipeline
    path = stored_result(tmp_path)
    session = FakeSession([GOOD], [MISMATCH, OK], [SolverCode(code=STRUCTURED_CODE)])
    result = continue_pipeline(str(path), "pruefung", session=session, max_revisions=1)
    assert session.steps == ["Prüfung", "Korrektur", "Prüfung", "Solver-Code"]
    assert result.revisions == 1


def test_continue_from_solver_refuses_when_objective_does_not_match(tmp_path):
    # gespeicherte Zielgrößen-Abweichung bleibt bindend: kein Solver, kein LLM-Aufruf
    from TextToModel import continue_pipeline
    path = stored_result(tmp_path, review=MISMATCH)
    session = FakeSession([], [], [])
    result = continue_pipeline(str(path), "solver", session=session)
    assert session.steps == []
    assert result.solver_run is None and not result.reliable


def test_unknown_stage_is_rejected(tmp_path):
    import pytest
    from TextToModel import continue_pipeline
    path = stored_result(tmp_path)
    with pytest.raises(ValueError, match="Unbekannte Stufe"):
        continue_pipeline(str(path), "extraktion", session=FakeSession([], [], []))


def test_warnings_reach_the_revision_prompt():
    # Warnungen loesen keine Runde aus, muessen aber mitkorrigiert werden, wenn ohnehin eine stattfindet
    from model_schema import CheckIssue, CheckSeverity
    from TextToModel import revise_model
    session = FakeSession([GOOD], [], [])
    revise_model(session, "Aufgabe", GOOD, OK, issues=[
        CheckIssue(severity=CheckSeverity.WARNING, code="UNEXPLAINED_JARGON", message="Fachbegriffe: Fluss"),
        CheckIssue(severity=CheckSeverity.ERROR, code="STACK_MISMATCH", message="passt nicht"),
    ])
    prompt = session.letzter_prompt
    assert "STACK_MISMATCH" in prompt.split("HINWEISE")[0]        # Fehler im Fehlerteil
    assert "UNEXPLAINED_JARGON" in prompt.split("HINWEISE")[1]    # Warnung im Hinweisteil


# --- Benchmark haelt fest, womit gemessen wurde ---

def test_benchmark_records_the_prompt_state():
    from benchmark import PROMPT_FILES, prompt_state
    stand = prompt_state()
    assert {"prompts", "commit", "dirty"} <= set(stand)
    assert len(stand["prompts"]) == 12 and stand["commit"]
    assert "Routing-und-Extraktions-Prompt.txt" in PROMPT_FILES
    assert "model_schema.py" in PROMPT_FILES  # Feldbeschreibungen wirken wie Prompt-Anweisungen


def test_deviating_settings_are_recorded(monkeypatch):
    # Ohne die Umgebung liesse sich ein A/B-Lauf (Stufe abgeschaltet, anderes Modell) nicht zuordnen
    import benchmark
    benchmark.prompt_state.cache_clear()
    monkeypatch.setenv("UNDERSTAND_RETRIES", "-1")
    monkeypatch.setenv("LLM_MODEL_SOLVER", "billig/modell")
    stand = benchmark.prompt_state()
    assert stand["config"] == {"UNDERSTAND_RETRIES": "-1", "LLM_MODEL_SOLVER": "billig/modell"}
    benchmark.prompt_state.cache_clear()


def test_prompt_digest_changes_with_the_prompt(tmp_path):
    from benchmark import prompt_digest
    a, b = tmp_path / "eins.txt", tmp_path / "zwei.txt"
    a.write_text("Regel A"), b.write_text("Regel B")
    vorher = prompt_digest([a, b])
    assert prompt_digest([a, b]) == vorher          # gleicher Inhalt, gleiche Kennung
    b.write_text("Regel B, nun schaerfer")
    assert prompt_digest([a, b]) != vorher          # eine geaenderte Regel aendert die Kennung
    assert prompt_digest([b, a]) != prompt_digest([a, b])  # Reihenfolge ist Teil der Kennung


def test_unexplained_jargon_triggers_a_revision_but_stays_reliable():
    # Erklaeren ist Kernziel: die Warnung ist eine Runde wert, macht das Ergebnis aber nicht unzuverlaessig
    from model_checks import check_spec
    unerklaert = make_spec(
        executive_summary="Der Betrieb entscheidet ueber Mengen.",
        constraints=[{"identifier": "Flusserhaltung", "latex_formula": "\\sum_j x_j = 1", "description": "-"}])
    assert "UNEXPLAINED_JARGON" in {i.code for i in check_spec(unerklaert)}

    session = FakeSession(specs=[unerklaert, GOOD], reviews=[OK, OK], codes=[SolverCode(code=STRUCTURED_CODE)])
    result = run_pipeline("Aufgabe", session=session, max_revisions=1, max_restarts=0)
    assert session.steps == ["Verstehen", "Extraktion", "Prüfung", "Korrektur", "Prüfung", "Solver-Code"]
    assert result.revisions == 1
    assert result.reliable  # Warnung, kein Fehler


def test_clean_model_still_needs_no_revision():
    session = FakeSession([GOOD], [OK], [SolverCode(code=STRUCTURED_CODE)])
    result = run_pipeline("Aufgabe", session=session, max_revisions=2, max_restarts=0)
    assert result.revisions == 0


# --- Verstehensstufe im Ablauf ---

def test_understanding_runs_first_and_is_kept_in_the_result():
    from model_schema import Fact
    verstanden = TaskUnderstanding(
        is_optimization_task=True, reason="Kostenminimierung", ignored_numbers=[],
        sub_questions=[SubQuestion(id="a", quote="Wie viel?", kind="rechnen", needs_solver=True)],
        facts=[Fact(value="12", unit="€", meaning="Deckungsbeitrag je Stueck", quote="12 € je Stueck")])
    session = FakeSession([GOOD], [OK], [SolverCode(code=STRUCTURED_CODE)], understandings=[verstanden])
    result = run_pipeline("Der Deckungsbeitrag ist 12 € je Stueck.", session=session,
                          max_revisions=0, max_restarts=0)
    assert session.steps[0] == "Verstehen"
    assert result.understanding.facts[0].value == "12"
    assert [q.id for q in result.understanding.sub_questions] == ["a"]


def test_incomplete_understanding_is_asked_again():
    # erste Antwort uebersieht die Zahl, zweite erfasst sie
    from model_schema import Fact
    luecke = TaskUnderstanding(is_optimization_task=True, reason="-", facts=[], ignored_numbers=[],
                               sub_questions=[SubQuestion(id="1", quote="-", kind="rechnen", needs_solver=True)])
    vollstaendig = luecke.model_copy(update={
        "facts": [Fact(value="12", unit="€", meaning="Deckungsbeitrag", quote="12 € je Stueck")]})
    session = FakeSession([GOOD], [OK], [SolverCode(code=STRUCTURED_CODE)],
                          understandings=[luecke, vollstaendig])
    result = run_pipeline("12 € je Stueck.", session=session, max_revisions=0, max_restarts=0)
    assert session.steps[:2] == ["Verstehen", "Verstehen"]      # genau ein Nachfassen
    assert result.understanding.facts[0].value == "12"


def test_report_shows_questions_and_numbers():
    from model_schema import Fact
    from report import build_document, to_markdown
    verstanden = TaskUnderstanding(
        is_optimization_task=True, reason="-",
        sub_questions=[SubQuestion(id="a", quote="Formulieren Sie das Problem!", kind="formulieren", needs_solver=False),
                       SubQuestion(id="b", quote="Loesen Sie es!", kind="rechnen", needs_solver=True)],
        facts=[Fact(value="8", unit="€", meaning="Kosten im ersten Jahr", quote="Zeile 0: 8 | 18 | 31")],
        ignored_numbers=[])
    session = FakeSession([GOOD], [OK], [SolverCode(code=STRUCTURED_CODE)], understandings=[verstanden])
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0)
    markdown = to_markdown(build_document(result))
    assert "Gestellte Fragen" in markdown and "Formulieren Sie das Problem!" in markdown
    assert "Gegebene Zahlen" in markdown and "Kosten im ersten Jahr" in markdown


def test_report_puts_the_answers_before_the_numbers():
    from report import build_document, to_markdown
    code = '''
def main():
    """Loest und beantwortet."""
    report("optimal", 9.0, {"x": 1.0},
           headline="Die Kosten betragen 9 Geldeinheiten.",
           constraints=[{"name": "Bedarf", "lhs": 4.0, "sense": ">=", "rhs": 4.0}],
           variables=[{"name": "x", "value": 1.0, "var_type": "continuous", "lb": 0}],
           answers=[{"question_id": "a", "text": "Das Modell ist ein lineares Programm."},
                    {"question_id": "b", "text": "Die minimalen Kosten betragen 9 Geldeinheiten."}])


if __name__ == "__main__":
    main()
'''
    verstanden = TaskUnderstanding(
        is_optimization_task=True, reason="-", facts=[], ignored_numbers=[],
        sub_questions=[SubQuestion(id="a", quote="Formulieren Sie das Modell!", kind="formulieren", needs_solver=False),
                       SubQuestion(id="b", quote="Berechnen Sie die Kosten!", kind="rechnen", needs_solver=True)])
    session = FakeSession([GOOD], [OK], [SolverCode(code=code)], understandings=[verstanden])
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0)
    markdown = to_markdown(build_document(result))
    assert "Antworten auf die Aufgabe" in markdown
    assert markdown.index("Formulieren Sie das Modell!") < markdown.index("| Status |")
    assert result.solver_run.warnings == []          # Abnahme ohne Befund


def test_unanswered_sub_question_is_a_warning_not_a_wrong_number():
    code = '''
def main():
    """Beantwortet nur die zweite Frage."""
    report("optimal", 9.0, {"x": 1.0},
           constraints=[{"name": "Bedarf", "lhs": 4.0, "sense": ">=", "rhs": 4.0}],
           variables=[{"name": "x", "value": 1.0, "var_type": "continuous", "lb": 0}],
           answers=[{"question_id": "b", "text": "Die minimalen Kosten betragen 9 Geldeinheiten."}])


if __name__ == "__main__":
    main()
'''
    verstanden = TaskUnderstanding(
        is_optimization_task=True, reason="-", facts=[], ignored_numbers=[],
        sub_questions=[SubQuestion(id="a", quote="Zeichnen Sie den Graphen!", kind="zeichnen", needs_solver=False),
                       SubQuestion(id="b", quote="Berechnen Sie die Kosten!", kind="rechnen", needs_solver=True)])
    session = FakeSession([GOOD], [OK], [SolverCode(code=code)] * 3, understandings=[verstanden])
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0)
    assert any("Teilfragen ohne Antwort" in w for w in result.solver_run.warnings)
    assert result.solver_run.objective_value == 9.0   # die Zahl bleibt richtig


def test_report_shows_data_profile_and_scenarios(tmp_path):
    from model_schema import Scenario
    from report import build_document, to_markdown
    datei = tmp_path / "werte.csv"
    datei.write_text("name,menge\nA,3\nB,4\n")
    code = '''
DATA = {"faktor": 1.0}


def main():
    """Meldet zwei Datenvarianten."""
    report("optimal", 7.0, {"x": 1.0},
           constraints=[{"name": "Bedarf", "lhs": 4.0, "sense": ">=", "rhs": 4.0}],
           variables=[{"name": "x", "value": 1.0, "var_type": "continuous", "lb": 0}],
           scenarios=[{"name": "Doppelt", "description": "Faktor verdoppelt",
                       "data_override": {"faktor": 2.0}, "objective_value": 7.0}])


if __name__ == "__main__":
    main()
'''
    session = FakeSession([GOOD], [OK], [SolverCode(code=code)])
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0, data_files=[datei])
    markdown = to_markdown(build_document(result))
    assert "Beigelegte Daten" in markdown and "werte.csv (2 Zeilen)" in markdown
    assert "Verglichene Konfigurationen" in markdown and "Doppelt" in markdown
    assert result.data_files[0].rows == 2
    assert result.solver_run.scenarios[0].verified_value == 7.0   # ohne LLM nachgerechnet


# --- Benchmark misst "Aufgabe erfuellt", nicht nur "Zahl richtig" ---

def test_benchmark_counts_unanswered_questions_as_not_fulfilled(monkeypatch):
    import benchmark
    from model_schema import Answer, SubQuestion, TaskUnderstanding

    def ergebnis(antworten, warnungen=()):
        session = FakeSession([GOOD], [OK], [SolverCode(code=STRUCTURED_CODE)], understandings=[TaskUnderstanding(
            is_optimization_task=True, reason="-", facts=[], ignored_numbers=[],
            sub_questions=[SubQuestion(id=f, quote="-", kind="rechnen", needs_solver=True) for f in ("a", "b")])])
        r = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0)
        r.solver_run.answers = [Answer(question_id=k, text="-") for k in antworten]
        r.solver_run.warnings = list(warnungen)
        return r

    fall = {"name": "test", "task": "Aufgabe", "expected": {"objective_value": 9, "tolerance": 1e-6}}
    for antworten, warnungen, erwartet in [
        (["a", "b"], [], True),                                        # alles beantwortet
        (["b"], ["Teilfragen ohne Antwort: a"], False),                # eine Frage offen
        (["a", "b"], ["Antwort zu 'b' nennt Zahlen …"], False),        # unbelegte Zahl
    ]:
        monkeypatch.setattr(benchmark, "run_pipeline", lambda *a, **k: ergebnis(antworten, warnungen))
        zeile = benchmark.run_case(fall, "fake", 1)
        assert zeile["objective_ok"] is True                           # die Zahl stimmt immer
        assert zeile["task_ok"] is erwartet, (antworten, warnungen)


def test_benchmark_case_data_files_are_collected(tmp_path, monkeypatch):
    import benchmark
    fall = tmp_path / "cases" / "mit_daten"
    fall.mkdir(parents=True)
    (fall / "task.txt").write_text("Aufgabe")
    (fall / "expected.json").write_text('{"objective_value": 1, "tolerance": 1e-6}')
    (fall / "werte.csv").write_text("a,b\n1,2\n")
    monkeypatch.setattr(benchmark, "CASES_DIR", tmp_path / "cases")
    faelle = benchmark.load_cases()
    assert [p.split("/")[-1] for p in faelle[0]["data_files"]] == ["werte.csv"]


# Spezifikation ohne Kostendaten, Programm erfindet sie per Zufallsgenerator (Muster "Portfolio Nr. 2")
OHNE_DATEN = make_spec(parameters=[
    {"symbol": "c_j", "dimension": "Vektor", "unit": "€/t", "description": "Kosten", "extracted_values": ""},
    {"symbol": "b", "dimension": "Skalar", "unit": "t", "description": "Bedarf", "extracted_values": "4"}])
ERFUNDENE_DATEN = STRUCTURED_CODE.replace(
    "COSTS = [3, 2]", "import numpy as np\nCOSTS = [float(v) for v in np.random.default_rng(0).integers(2, 4, 2)]")


def test_undeclared_generated_data_is_repaired():
    session = FakeSession([OHNE_DATEN], [OK], [
        SolverCode(code=ERFUNDENE_DATEN),
        SolverCode(code=ERFUNDENE_DATEN, data_assumptions=["c_j: zufällig erzeugt (Seed 0), keine Daten im Text"])])
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0, solver_retries=1)
    assert session.calls[SolverCode] == 2                    # der Befund hat eine Reparatur ausgelöst
    assert result.reliable and result.solver_run.undeclared_data == []


def test_persistently_undeclared_data_is_unreliable():
    # Nachmessung "Portfolio Nr. 2": data_assumptions leer, Antwort behauptete geladene Kursdaten
    session = FakeSession([OHNE_DATEN], [OK], [SolverCode(code=ERFUNDENE_DATEN)] * 2)
    result = run_pipeline("Aufgabe", session=session, max_revisions=0, max_restarts=0, solver_retries=1)
    assert not result.reliable
    assert any(p.startswith("Datenherkunft") for p in result.reliability_issues)