"""AI 讲解/知识点，以及用户 AI 配置。判分相关见 test_grading.py。"""
import json
from unittest.mock import patch

from app.services.llm.client import LLMResult

SUBJECTIVE = {
    "type": "short_answer",
    "stem_markdown": "简述牛顿第一定律",
    "correct_answer": ["惯性定律"],
}


def _fake_text(content: str):
    async def _inner(self, system, user, json_mode=False, model=None):
        return LLMResult(content, "text-model", 10, 20)

    return _inner


class TestSettings:
    async def test_api_key_never_returned(self, client, auth_headers, configure_ai):
        h = await auth_headers()
        out = await configure_ai(h, api_key="sk-secret-value")
        assert out["has_api_key"] is True
        assert "sk-secret-value" not in json.dumps(out)

        r = await client.get("/api/settings", headers=h)
        assert "sk-secret-value" not in r.text
        assert r.json()["has_api_key"] is True

    async def test_api_key_stored_encrypted(self, client, auth_headers, configure_ai):
        """库里存的必须是密文，且能正确解密回原值。"""
        from sqlalchemy import select

        from app.db import SessionLocal
        from app.models.user import UserSettings
        from app.services import crypto

        h = await auth_headers()
        await configure_ai(h, api_key="sk-secret-value")

        async with SessionLocal() as db:
            row = (await db.scalars(select(UserSettings))).first()
            assert row.llm_api_key_encrypted != "sk-secret-value"
            assert crypto.decrypt(row.llm_api_key_encrypted) == "sk-secret-value"

    async def test_empty_api_key_leaves_existing(self, client, auth_headers, configure_ai):
        """留空表示不修改，不该把已配置的 key 清掉。"""
        h = await auth_headers()
        await configure_ai(h)
        r = await client.put(
            "/api/settings", json={"llm_text_model": "new-model"}, headers=h
        )
        assert r.json()["has_api_key"] is True
        assert r.json()["llm_text_model"] == "new-model"

    async def test_settings_isolated_per_user(self, client, auth_headers, configure_ai):
        ha = await auth_headers("alice")
        hb = await auth_headers("bob")
        await configure_ai(ha)
        assert (
            await client.get("/api/settings", headers=hb)
        ).json()["has_api_key"] is False


class TestAiExplain:
    async def test_explain_persists(self, client, auth_headers, configure_ai):
        h = await auth_headers()
        await configure_ai(h)
        qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=h)).json()["id"]

        with patch(
            "app.services.llm.client.LLMClient.chat_text",
            _fake_text("## 讲解\n\n物体在无外力时 $v$ 恒定。"),
        ):
            r = await client.post(f"/api/questions/{qid}/ai/explain", headers=h)
        assert r.status_code == 200
        assert "$v$" in r.json()["explanation_markdown"]

        q = (await client.get(f"/api/questions/{qid}", headers=h)).json()
        assert "讲解" in q["explanation_markdown"]

    async def test_explain_never_touches_my_note(self, client, auth_headers, configure_ai):
        """我自己写的笔记是我的，AI 讲解不得覆盖它。"""
        h = await auth_headers()
        await configure_ai(h)
        qid = (
            await client.post(
                "/api/questions",
                json={**SUBJECTIVE, "my_note_markdown": "我当时错在没考虑摩擦力"},
                headers=h,
            )
        ).json()["id"]

        with patch(
            "app.services.llm.client.LLMClient.chat_text", _fake_text("AI 的讲解内容")
        ):
            await client.post(f"/api/questions/{qid}/ai/explain", headers=h)

        q = (await client.get(f"/api/questions/{qid}", headers=h)).json()
        assert q["my_note_markdown"] == "我当时错在没考虑摩擦力"
        assert q["explanation_markdown"] == "AI 的讲解内容"

    async def test_unconfigured_ai_returns_400(self, client, auth_headers):
        h = await auth_headers()
        qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=h)).json()["id"]
        r = await client.post(f"/api/questions/{qid}/ai/explain", headers=h)
        assert r.status_code == 400
        assert "设置" in r.json()["detail"]

    async def test_cannot_explain_others_note(self, client, auth_headers, configure_ai):
        ha = await auth_headers("alice")
        hb = await auth_headers("bob")
        await configure_ai(hb)
        qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=ha)).json()["id"]
        r = await client.post(f"/api/questions/{qid}/ai/explain", headers=hb)
        assert r.status_code == 404


class TestAiTags:
    async def test_summarize_tags_merges(self, client, auth_headers, configure_ai):
        h = await auth_headers()
        await configure_ai(h)
        qid = (
            await client.post(
                "/api/questions", json={**SUBJECTIVE, "tags": ["物理"]}, headers=h
            )
        ).json()["id"]

        with patch(
            "app.services.llm.client.LLMClient.chat_text",
            _fake_text(json.dumps({"tags": ["力学", "牛顿定律"]})),
        ):
            r = await client.post(f"/api/questions/{qid}/ai/summarize-tags", headers=h)
        assert r.status_code == 200
        assert set(r.json()["tags"]) == {"物理", "力学", "牛顿定律"}

    async def test_malformed_tags_json_yields_no_tags(
        self, client, auth_headers, configure_ai
    ):
        h = await auth_headers()
        await configure_ai(h)
        qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=h)).json()["id"]

        with patch("app.services.llm.client.LLMClient.chat_text", _fake_text("not json")):
            r = await client.post(f"/api/questions/{qid}/ai/summarize-tags", headers=h)
        assert r.status_code == 200
        assert r.json()["tags"] == []

    async def test_tags_on_unclassified_note(self, client, auth_headers, configure_ai):
        """拍照记下的未分类笔记，也能让 AI 归类知识点。"""
        h = await auth_headers()
        await configure_ai(h)
        qid = (
            await client.post(
                "/api/questions",
                json={"type": "unclassified", "stem_markdown": "求导数"},
                headers=h,
            )
        ).json()["id"]

        with patch(
            "app.services.llm.client.LLMClient.chat_text",
            _fake_text(json.dumps({"tags": ["导数"]})),
        ):
            r = await client.post(f"/api/questions/{qid}/ai/summarize-tags", headers=h)
        assert r.json()["tags"] == ["导数"]


class TestAiAudit:
    async def test_ai_generation_is_audited(self, client, auth_headers, configure_ai):
        """每次 AI 调用都要留审计记录，便于观察成本。"""
        from sqlalchemy import select

        from app.db import SessionLocal
        from app.models.practice import AiGeneration

        h = await auth_headers()
        await configure_ai(h)
        qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=h)).json()["id"]

        with patch("app.services.llm.client.LLMClient.chat_text", _fake_text("讲解内容")):
            await client.post(f"/api/questions/{qid}/ai/explain", headers=h)

        async with SessionLocal() as db:
            rows = (await db.scalars(select(AiGeneration))).all()
            assert len(rows) == 1
            assert rows[0].task == "explain"
            assert rows[0].model == "text-model"
            assert rows[0].request_tokens == 10
            assert rows[0].response_tokens == 20

    async def test_recording_attempt_makes_no_ai_call(self, client, auth_headers):
        """记录作答不该产生任何 AI 调用（也就不消耗额度）。"""
        from sqlalchemy import select

        from app.db import SessionLocal
        from app.models.practice import AiGeneration

        h = await auth_headers()
        qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=h)).json()["id"]
        await client.post(
            f"/api/questions/{qid}/attempts", json={"user_answer": ["x"]}, headers=h
        )

        async with SessionLocal() as db:
            assert (await db.scalars(select(AiGeneration))).all() == []