import asyncio


def test_elephant_orchestrator_extracts_fenced_json_object():
    from services.elephant_alpha_orchestrator import ElephantAlphaOrchestrator

    payload = ElephantAlphaOrchestrator._extract_json_object(
        """```json
{"priority":"high","confidence":0.91}
```"""
    )

    assert payload == {"priority": "high", "confidence": 0.91}


def test_elephant_orchestrator_extracts_json_object_from_prefaced_text():
    from services.elephant_alpha_orchestrator import ElephantAlphaOrchestrator

    payload = ElephantAlphaOrchestrator._extract_json_object(
        '先做分析：\n{"priority":"medium","agents_required":["hermes"]}\n以上。'
    )

    assert payload["priority"] == "medium"
    assert payload["agents_required"] == ["hermes"]


def test_elephant_orchestrator_fallback_uses_concrete_evidence_without_plan():
    from services.elephant_alpha_orchestrator import ElephantAlphaOrchestrator

    orchestrator = ElephantAlphaOrchestrator()
    decision = orchestrator._fallback_decision(
        {
            "trigger_type": "market_opportunity",
            "conditions": {
                "_prefetched_hermes_threats": [
                    "[SKU-1] 測試商品｜MOMO $1,200 vs PChome $990｜每件價差 NT$ 210"
                ]
            },
        },
        reason="測試解析失敗",
    )

    assert decision.priority == "high"
    assert decision.confidence == 0.74
    assert decision.execution_plan == []
    assert "1 筆 DB/Hermes 價格比對實證" in decision.reasoning
    assert "自動調價" in decision.reasoning


def test_elephant_orchestrator_fallback_without_evidence_has_no_openclaw_action():
    from services.elephant_alpha_orchestrator import ElephantAlphaOrchestrator

    orchestrator = ElephantAlphaOrchestrator()
    decision = orchestrator._fallback_decision(
        {"trigger_type": "market_opportunity", "conditions": {}},
        reason="測試解析失敗",
    )

    assert decision.confidence == 0.6
    assert decision.execution_plan == []
    assert decision.agents_required == ["elephant_alpha"]
    assert "沒有可稽核的 DB/Hermes 實證" in decision.reasoning


def test_elephant_orchestrator_uses_evidence_fallback_on_non_json_response(monkeypatch):
    from services.elephant_service import ElephantResponse
    from services.elephant_alpha_orchestrator import ElephantAlphaOrchestrator

    class FakeElephant:
        def generate(self, **_kwargs):
            return ElephantResponse(
                success=True,
                content="\n我無法輸出 JSON，但建議先處理。",
                model="fake-model",
            )

    async def _noop_log(*_args, **_kwargs):
        raise AssertionError("parse fallback should not log an LLM decision")

    orchestrator = ElephantAlphaOrchestrator()
    orchestrator.elephant = FakeElephant()
    monkeypatch.setattr(orchestrator, "_get_recent_performance_metrics", lambda: {})
    monkeypatch.setattr(orchestrator, "_get_agent_status", lambda: {})
    monkeypatch.setattr(orchestrator, "_get_system_load", lambda: "normal")
    monkeypatch.setattr(orchestrator, "_get_pending_actions_count", lambda: 0)
    monkeypatch.setattr(orchestrator, "_log_decision", _noop_log)

    decision = asyncio.run(orchestrator.analyze_and_coordinate({
        "trigger_type": "price_drop_alert",
        "conditions": {
            "_prefetched_hermes_threats": [
                "[SKU-9] 實證商品｜MOMO $1,000 vs PChome $800｜每件價差 NT$ 200"
            ]
        },
    }))

    assert decision.priority == "high"
    assert decision.execution_plan == []
    assert "1 筆 DB/Hermes 價格比對實證" in decision.reasoning