"""测试 fixtures — 内存 SQLite、TestClient、样例数据。"""

from __future__ import annotations

import json
from datetime import date

import pytest
from fastapi.testclient import TestClient
from sqlalchemy import create_engine, event
from sqlalchemy.orm import sessionmaker
from sqlalchemy.pool import StaticPool

from app.database import get_db
from app.main import create_app
from app.database import init_db
from app.models import (
    Paper,
    PaperAuthor,
    PaperSummary,
    PaperTag,
    SummaryStatus,
)
from app.utils import utc_now


# ── 内存数据库 ──────────────────────────────────────────────────────────


@pytest.fixture
def db_engine():
    """创建内存 SQLite 引擎 + FTS5。"""
    engine = create_engine(
        "sqlite:///:memory:",
        connect_args={"check_same_thread": False},
        poolclass=StaticPool,
    )

    @event.listens_for(engine, "connect")
    def _pragma(dbapi_connection, _record):
        cursor = dbapi_connection.cursor()
        cursor.execute("PRAGMA foreign_keys=ON")
        cursor.close()

    init_db(engine)
    return engine


@pytest.fixture
def db_session(db_engine):
    """提供事务隔离的数据库 session。"""
    Session = sessionmaker(bind=db_engine, autoflush=False, autocommit=False)
    session = Session()
    try:
        yield session
    finally:
        session.close()


@pytest.fixture
def client(db_engine, db_session):
    """FastAPI TestClient，override get_db。"""
    app = create_app()

    def _override_get_db():
        yield db_session

    app.dependency_overrides[get_db] = _override_get_db

    with TestClient(app, raise_server_exceptions=False) as c:
        yield c

    app.dependency_overrides.clear()


# ── 样例数据 ────────────────────────────────────────────────────────────

SAMPLE_ARXIV_ID = "2401.12345"
_TEST_ADMIN_USERNAME = "admin"
_TEST_ADMIN_PASSWORD = "test-password-12345"


@pytest.fixture
def sample_paper(db_session):
    """插入一篇测试论文 + 作者 + 标签 + summary_status(pending)。"""
    now = utc_now()
    paper = Paper(
        arxiv_id=SAMPLE_ARXIV_ID,
        title_en="Test Paper Title",
        abstract="This is a test abstract for the paper.",
        published_at=date(2024, 1, 15),
        paper_date=date(2024, 1, 15),
        crawled_at=now,
        upvotes=42,
        hf_url=f"https://huggingface.co/papers/{SAMPLE_ARXIV_ID}",
        arxiv_url=f"https://arxiv.org/abs/{SAMPLE_ARXIV_ID}",
        pdf_url=f"https://arxiv.org/pdf/{SAMPLE_ARXIV_ID}.pdf",
    )
    db_session.add(paper)
    db_session.flush()

    db_session.add(PaperAuthor(paper_id=paper.id, name="Alice Smith", position=0))
    db_session.add(PaperAuthor(paper_id=paper.id, name="Bob Jones", position=1))
    db_session.add(PaperTag(paper_id=paper.id, tag="NLP", source="hf"))
    db_session.add(PaperTag(paper_id=paper.id, tag="LLM", source="hf"))

    db_session.add(SummaryStatus(paper_id=paper.id, status="pending"))

    # FTS5 初始行（与 crawler 一致）
    db_session.execute(
        __import__("sqlalchemy").text(
            "INSERT INTO papers_fts(rowid, title_en, abstract, authors, tags) "
            "VALUES (:id, :title, :abstract, :authors, :tags)"
        ),
        {
            "id": paper.id,
            "title": paper.title_en,
            "abstract": paper.abstract or "",
            "authors": "Alice Smith, Bob Jones",
            "tags": "NLP, LLM",
        },
    )
    db_session.commit()
    return paper


@pytest.fixture
def sample_summary_dict() -> dict:
    """完整合法的 summary dict。"""
    return {
        "arxiv_id": "2401.12345",
        "title_zh": "测试论文中文标题",
        "one_line": "这是一篇关于自然语言处理的测试论文的一句话总结。",
        "tags": ["自然语言处理", "大语言模型", "Transformer"],
        "difficulty": "中级",
        "prerequisites": {
            "concepts": [
                {
                    "term": "Transformer",
                    "explanation": "一种基于自注意力机制的序列到序列模型架构，广泛用于NLP任务。",
                    "why_matters": "本文方法基于 Transformer 架构进行改进。",
                },
                {
                    "term": "注意力机制",
                    "explanation": "允许模型在处理序列时动态关注不同位置的信息的机制。",
                    "why_matters": "理解注意力机制是理解本文方法的基础。",
                },
            ],
        },
        "motivation": {
            "problem": "现有模型在长文本理解上存在不足，主要体现在注意力计算复杂度随序列长度二次增长，导致实际应用中无法处理超长文本输入。",
            "goal": "提出一种新的稀疏注意力机制来有效提升长文本建模能力，在保持模型整体性能的同时大幅降低计算开销和显存占用。",
            "gap": "当前方法计算复杂度过高，已有的稀疏注意力方案在保留全局信息方面存在明显不足，导致长距离依赖建模效果不佳。",
        },
        "method": {
            "overview": "提出了一种高效的稀疏注意力机制，通过局部-全局混合的注意力模式，在降低计算复杂度的同时保留了关键的全局信息流动。",
            "key_idea": "使用局部-全局混合的注意力模式来降低计算复杂度，局部窗口捕获短距离依赖，全局采样点维护长距离信息传递。",
            "steps": "首先分析现有注意力机制的计算瓶颈，发现全连接注意力中大部分注意力权重接近于零。然后设计了一种混合稀疏注意力模式，包含局部滑动窗口和全局随机采样两条路径。最后在多个长文本基准数据集上进行了全面的实验验证。",
            "novelty": "首次将局部-全局注意力模式结合应用于长文本建模，通过可学习的采样策略动态调整全局注意力点的位置，而非固定模式。",
        },
        "results": {
            "main_findings": "在长文本基准 LongBench 上取得了 SOTA 结果，平均得分提升 3.2 个百分点。推理速度相比全注意力提升了 2 倍，显存占用降低 60%。在 32k 序列长度下仍保持与全注意力相当的生成质量。",
            "benchmarks": [
                {
                    "task": "长文本摘要",
                    "metric": "ROUGE-L",
                    "this_work": "42.1",
                    "baseline": "38.9",
                    "improvement": "+3.2",
                },
            ],
            "limitations": "在超长文本（>100k tokens）上效果有所下降，主要原因是全局采样点数量不足以覆盖所有关键信息。此外，在小规模数据集上的优势不如大规模数据集明显。",
        },
        "improvements": {
            "weaknesses": "仅验证了英文数据，未在中文等多语言场景下测试。全局采样策略在极端长度的文本上可能需要增加采样点数量，增加了工程复杂度。",
            "future_work": "扩展到多语言场景，研究自适应采样策略，使模型能根据输入内容动态调整全局注意力点的分配。同时探索与 Flash Attention 等底层优化的兼容性。",
            "reproducibility": "代码已在 GitHub 开源，提供了完整的训练脚本和预训练模型权重。实验使用了公开数据集，硬件需求为 8×A100 GPU。",
        },
        "figures": [
            {
                "id": "Figure 1",
                "caption": "稀疏注意力机制的整体架构图",
                "description": "展示了局部窗口注意力和全局采样注意力的组合方式，以及信息如何在两种路径间流动。",
                "reason": "帮助理解本文方法的核心设计思想，直观展示了局部-全局混合模式的工作原理。",
            },
        ],
    }


@pytest.fixture
def sample_summary_json(sample_summary_dict) -> str:
    """合法 summary 的 JSON 字符串。"""
    return json.dumps(sample_summary_dict, ensure_ascii=False, indent=2)


@pytest.fixture
def mock_pi_output(sample_summary_json) -> str:
    """模拟 pi CLI 的完整输出（包含 JSON）。"""
    return f"""以下是论文的深度解读：

```json
{sample_summary_json}
```

希望这个总结对你有帮助！"""


@pytest.fixture
def auth_client(client, monkeypatch):
    """已登录的 TestClient（session cookie 自动携带）。"""
    from app.config import settings

    monkeypatch.setattr(settings, "ADMIN_USERNAME", _TEST_ADMIN_USERNAME)
    monkeypatch.setattr(settings, "ADMIN_PASSWORD", _TEST_ADMIN_PASSWORD)
    monkeypatch.setattr(settings, "CHROMA_ENABLED", False)
    # 登录获取 session cookie
    resp = client.post(
        "/admin/login",
        data={"username": _TEST_ADMIN_USERNAME, "password": _TEST_ADMIN_PASSWORD},
        follow_redirects=False,
    )
    assert resp.status_code == 303
    return client


# ── 多样例数据 ────────────────────────────────────────────────────────────


@pytest.fixture
def sample_papers_range(db_session):
    """插入 5 篇不同日期的论文（用于 admin / cleaner 测试）。"""
    now = utc_now()
    papers = []
    for i, (arxiv_id, paper_date_str) in enumerate(
        [
            ("2401.10001", "2024-01-10"),
            ("2401.10002", "2024-01-11"),
            ("2401.10003", "2024-01-12"),
            ("2401.10004", "2024-01-13"),
            ("2401.10005", "2024-01-14"),
        ]
    ):
        paper_date = date.fromisoformat(paper_date_str)
        p = Paper(
            arxiv_id=arxiv_id,
            title_en=f"Test Paper {i + 1}",
            abstract=f"Abstract for paper {i + 1}.",
            paper_date=paper_date,
            crawled_at=now,
            upvotes=i * 10,
        )
        db_session.add(p)
        db_session.flush()
        db_session.add(PaperAuthor(paper_id=p.id, name=f"Author {i + 1}", position=0))
        db_session.add(PaperTag(paper_id=p.id, tag=f"Tag{i + 1}", source="hf"))
        db_session.add(SummaryStatus(paper_id=p.id, status="pending"))
        # FTS5
        db_session.execute(
            __import__("sqlalchemy").text(
                "INSERT INTO papers_fts(rowid, title_en, abstract, authors, tags) "
                "VALUES (:id, :title, :abstract, :authors, :tags)"
            ),
            {
                "id": p.id,
                "title": p.title_en,
                "abstract": p.abstract,
                "authors": f"Author {i + 1}",
                "tags": f"Tag{i + 1}",
            },
        )
        papers.append(p)
    db_session.commit()
    return papers


@pytest.fixture
def sample_papers_with_summary(db_session):
    """插入 5 篇带总结的论文（用于 search / pages / trends 测试）。"""
    now = utc_now()
    papers = []
    for i, (arxiv_id, paper_date_str) in enumerate(
        [
            ("2401.20001", "2024-01-10"),
            ("2401.20002", "2024-01-11"),
            ("2401.20003", "2024-01-12"),
            ("2401.20004", "2024-01-13"),
            ("2401.20005", "2024-01-14"),
        ]
    ):
        paper_date = date.fromisoformat(paper_date_str)
        p = Paper(
            arxiv_id=arxiv_id,
            title_en=f"Test Paper {i + 1}",
            title_zh=f"测试论文 {i + 1}",
            abstract=f"Abstract for paper {i + 1}.",
            paper_date=paper_date,
            crawled_at=now,
            upvotes=i * 10 + 5,
        )
        db_session.add(p)
        db_session.flush()

        db_session.add(PaperAuthor(paper_id=p.id, name=f"Author {i + 1}", position=0))
        db_session.add(PaperTag(paper_id=p.id, tag="NLP", source="hf"))
        db_session.add(PaperTag(paper_id=p.id, tag=f"Tag{i + 1}", source="hf"))

        db_session.add(
            SummaryStatus(
                paper_id=p.id,
                status="done" if i < 4 else "pending",
                quality="normal",
            )
        )

        # 添加总结（前 4 篇）
        if i < 4:
            summary = PaperSummary(
                paper_id=p.id,
                one_line=f"这是论文{i + 1}的一句话摘要",
                difficulty="中级",
                motivation_problem=f"论文{i + 1}的研究问题",
                motivation_goal=f"论文{i + 1}的研究目标",
                method_key_idea=f"论文{i + 1}的关键思路",
                method_overview=f"论文{i + 1}的方法概述",
                updated_at=now,
                full_json=json.dumps({"title_zh": f"测试论文 {i + 1}"}),
            )
            db_session.add(summary)

        # FTS5
        db_session.execute(
            __import__("sqlalchemy").text(
                "INSERT INTO papers_fts(rowid, title_en, title_zh, abstract, authors, tags) "
                "VALUES (:id, :title_en, :title_zh, :abstract, :authors, :tags)"
            ),
            {
                "id": p.id,
                "title_en": p.title_en,
                "title_zh": p.title_zh or "",
                "abstract": p.abstract or "",
                "authors": f"Author {i + 1}",
                "tags": f"NLP, Tag{i + 1}",
            },
        )
        papers.append(p)
    db_session.commit()
    return papers