import datetime as dt
import sqlite3
import pytest
from unittest.mock import AsyncMock, MagicMock, patch

from app.screener.ai_news import pipeline
from app.screener.schema import ensure_screener_schema


@pytest.fixture
def conn():
    c = sqlite3.connect(":memory:")
    c.row_factory = sqlite3.Row
    ensure_screener_schema(c)
    # 시총 상위 3종목 시드
    c.execute("INSERT INTO krx_master (ticker, name, market, market_cap, updated_at) "
              "VALUES (?, ?, 'KOSPI', ?, datetime('now'))", ("005930", "삼성전자", 9_000_000))
    c.execute("INSERT INTO krx_master (ticker, name, market, market_cap, updated_at) "
              "VALUES (?, ?, 'KOSPI', ?, datetime('now'))", ("000660", "SK하이닉스", 8_000_000))
    c.execute("INSERT INTO krx_master (ticker, name, market, market_cap, updated_at) "
              "VALUES (?, ?, 'KOSPI', ?, datetime('now'))", ("373220", "LG에너지솔루션", 7_000_000))
    c.commit()
    yield c
    c.close()


@pytest.mark.asyncio
async def test_refresh_daily_happy_path(conn):
    """3종목 mini integration — articles_source mock + analyzer mock."""
    asof = dt.date(2026, 5, 13)

    fake_articles_by_ticker = {
        "005930": [{"title": "삼성 뉴스", "summary": "", "press": "", "pub_date": ""}],
        "000660": [{"title": "SK 뉴스", "summary": "", "press": "", "pub_date": ""}],
        "373220": [{"title": "LG 뉴스", "summary": "", "press": "", "pub_date": ""}],
    }
    fake_stats = {"total_articles": 3, "matched_pairs": 3, "hit_tickers": 3}

    scores_by_ticker = {
        "005930": 7.5, "000660": 4.0, "373220": -6.0,
    }
    async def fake_score(llm, ticker, news, *, name=None, model="m"):
        return {
            "ticker": ticker, "score_raw": scores_by_ticker[ticker],
            "reason": f"r{ticker}", "news_count": 1,
            "tokens_input": 100, "tokens_output": 20, "model": model,
        }

    with patch.object(pipeline, "articles_source") as mas, \
         patch.object(pipeline, "_analyzer") as ma, \
         patch.object(pipeline, "_make_llm") as ml:
        mas.gather_articles_for_tickers = MagicMock(
            return_value=(fake_articles_by_ticker, fake_stats)
        )
        ma.score_sentiment = fake_score
        ml.return_value.__aenter__.return_value = AsyncMock()
        ml.return_value.__aexit__.return_value = None
        result = await pipeline.refresh_daily(conn, asof, concurrency=3)

    assert result["asof"] == "2026-05-13"
    assert result["updated"] == 3
    assert result["failures"] == []
    assert result["top_pos"][0]["ticker"] == "005930"
    assert result["top_neg"][0]["ticker"] == "373220"
    assert result["mapping"] == fake_stats

    rows = conn.execute("SELECT ticker, score_raw, source FROM news_sentiment "
                        "WHERE date=?", ("2026-05-13",)).fetchall()
    assert len(rows) == 3
    assert all(r["source"] == "articles" for r in rows)


@pytest.mark.asyncio
async def test_refresh_daily_failures_isolated(conn):
    asof = dt.date(2026, 5, 13)

    fake_articles_by_ticker = {
        "005930": [{"title": "h", "summary": "", "press": "", "pub_date": ""}],
        "000660": [{"title": "h", "summary": "", "press": "", "pub_date": ""}],
        "373220": [{"title": "h", "summary": "", "press": "", "pub_date": ""}],
    }
    fake_stats = {"total_articles": 3, "matched_pairs": 3, "hit_tickers": 3}

    async def fake_score(llm, ticker, news, *, name=None, model="m"):
        if ticker == "000660":
            raise RuntimeError("llm exploded")
        return {
            "ticker": ticker, "score_raw": 5.0, "reason": "r", "news_count": 1,
            "tokens_input": 100, "tokens_output": 20, "model": model,
        }

    with patch.object(pipeline, "articles_source") as mas, \
         patch.object(pipeline, "_analyzer") as ma, \
         patch.object(pipeline, "_make_llm") as ml:
        mas.gather_articles_for_tickers = MagicMock(
            return_value=(fake_articles_by_ticker, fake_stats)
        )
        ma.score_sentiment = fake_score
        ml.return_value.__aenter__.return_value = AsyncMock()
        ml.return_value.__aexit__.return_value = None
        result = await pipeline.refresh_daily(conn, asof, concurrency=3)

    assert result["updated"] == 2
    assert len(result["failures"]) == 1


@pytest.mark.asyncio
async def test_refresh_daily_no_match_ticker_skipped(conn):
    """매핑 0인 ticker 는 LLM 호출 skip + news_sentiment 행 미생성."""
    asof = dt.date(2026, 5, 13)

    fake_articles_by_ticker = {
        "005930": [{"title": "삼성", "summary": "", "press": "", "pub_date": ""}],
        "000660": [],  # 매핑 없음
        "373220": [],  # 매핑 없음
    }
    fake_stats = {"total_articles": 1, "matched_pairs": 1, "hit_tickers": 1}

    async def fake_score(llm, ticker, news, *, name=None, model="m"):
        return {
            "ticker": ticker, "score_raw": 5.0, "reason": "r",
            "news_count": 1, "tokens_input": 100, "tokens_output": 20,
            "model": model,
        }

    with patch.object(pipeline, "articles_source") as mas, \
         patch.object(pipeline, "_analyzer") as ma, \
         patch.object(pipeline, "_make_llm") as ml:
        mas.gather_articles_for_tickers = MagicMock(
            return_value=(fake_articles_by_ticker, fake_stats)
        )
        ma.score_sentiment = fake_score
        ml.return_value.__aenter__.return_value = AsyncMock()
        ml.return_value.__aexit__.return_value = None
        result = await pipeline.refresh_daily(conn, asof, concurrency=3)

    assert result["updated"] == 1
    rows = conn.execute("SELECT ticker FROM news_sentiment "
                        "WHERE date=?", ("2026-05-13",)).fetchall()
    assert {r["ticker"] for r in rows} == {"005930"}


def test_top_market_cap_tickers(conn):
    out = pipeline._top_market_cap_tickers(conn, n=2)
    assert out == ["005930", "000660"]