# -*- coding: utf-8 -*-
"""LLM 接入集成测试：规划/标题/意图/禁止降级/长文档两级规划。

用法：python tests/e2e_llm.py
"""
import os
import sys
import time
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
os.environ["PLAN_CHUNK_CHARS"] = "2500"  # 压低阈值，确保长文档用例真正走分段通读

from src.agent import llm  # noqa: E402
from src.agent.perception import GenerateConfig  # noqa: E402
from src.agent import planning as P  # noqa: E402
from src.agent.planning import build_plan, PlanningError  # noqa: E402
from src.agent.chat import parse_intent_llm, parse_intent  # noqa: E402

SAMPLE = """# 项目背景
为提升知识周转效率启动平台建设，前期调研两个月
# 本期成果
部署完成基础服务，导入文档120份
混合检索准确率较基线提升明显
完成三个部门试点接入
# 下一步
扩大试点范围，建立运营机制
"""

failures = []


def check(name, cond, extra=""):
    print(f"  [{'OK' if cond else 'NG'}] {name} {extra}")
    if not cond:
        failures.append(name)


def make_long_doc(sections=40):
    """构造约 1.5-2 万字符的多节长文档（含数字，验证分段通读与页数硬约束）。"""
    parts = []
    for i in range(1, sections + 1):
        parts.append(f"# 第{i}章节 阶段性工作汇报")
        parts.append(f"本阶段完成任务{i}的设计与开发，投入人力3人，周期2周")
        parts.append(f"完成度达到{i*2}%，质量抽检合格率98%")
        parts.append("- 关键交付物已通过评审")
        parts.append("- 遗留问题已登记跟踪")
        parts.append("下一步将推进与业务系统的对接联调")
    return "\n".join(parts)


def main():
    print("== 0. 禁止降级：LLM 失败必须报错而非照搬 ==")
    cfg_nf = GenerateConfig(title="降级测试", user_id=1, content=SAMPLE,
                            page_min=5, page_max=8)
    orig_chat = llm.chat_json
    try:
        llm.chat_json = lambda *a, **k: None
        try:
            build_plan(cfg_nf)
            check("no silent fallback", False, "未抛出 PlanningError")
        except PlanningError as e:
            check("no silent fallback", True, str(e)[:50])
    finally:
        llm.chat_json = orig_chat

    ok = llm.ensure_backend()
    check("llm backend", ok)

    print("== 1. LLM 内容规划（短文档直通） ==")
    t = time.time()
    cfg = GenerateConfig(title="知识平台周报", user_id=1, content=SAMPLE,
                         scene="report", language="zh", page_min=6, page_max=9)
    r = build_plan(cfg)
    plan = r.plan
    slides = plan["slides"]
    print(f"  mode={r.mode} pages={len(slides)} 耗时={time.time()-t:.0f}s")
    check("llm mode used", r.mode == "llm")
    check("page count in range", 6 <= len(slides) <= 9, f"n={len(slides)}")
    check("cover first / end last", slides[0]["type"] == "cover" and slides[-1]["type"] == "end")
    notes_n = sum(1 for s in slides if s.get("notes"))
    check("per-slide notes", notes_n >= len(slides) - 2, f"notes={notes_n}")
    generic = sum(1 for s in slides if s["title"] in ("",) or s["title"].startswith("要点"))
    check("no generic titles", generic == 0)

    def avg_len(sl):
        bl = [len(b) for x in sl if x["type"] == "content" for b in x.get("content", [])]
        return sum(bl) / max(len(bl), 1)
    check("full-sentence bullets (short doc)", avg_len(slides) >= 12,
          f"avg={avg_len(slides):.0f}")
    for s in slides:
        print(f"    - [{s['type']:8s}] {s['title'][:24]} notes={'Y' if s.get('notes') else '-'}")

    print("== 2. 长文档两级规划（分段通读→汇总大纲） ==")
    long_doc = make_long_doc()
    t = time.time()
    cfg2 = GenerateConfig(title="季度工作总结", user_id=1, content=long_doc,
                          scene="report", language="zh", page_min=10, page_max=15)
    r2 = build_plan(cfg2)
    slides2 = r2.plan["slides"]
    n2 = len(slides2)
    print(f"  mode={r2.mode} pages={n2} 源={len(long_doc)}字 耗时={time.time()-t:.0f}s")
    check("long doc page hard cap", n2 <= 15 and n2 >= 4, f"n={n2}")
    notes2 = sum(1 for s in slides2 if s.get("notes"))
    check("long doc has notes", notes2 >= n2 - 2, f"notes={notes2}")
    covered = sum(1 for i in range(1, 41) if str(i) in "".join(
        s["title"] + "".join(s["content"]) for s in slides2))
    check("late sections represented", covered >= 3, f"covered_sections={covered}")
    check("full-sentence bullets (long doc)", avg_len(slides2) >= 12,
          f"avg={avg_len(slides2):.0f}")

    print("== 3. 智能标题 ==")
    t = time.time()
    data = llm.chat_json(
        "根据以下 PPT 内容拟 3 个标题（每个不超过 20 字）。"
        '输出 JSON {"titles": ["..."]}。\n\n' + SAMPLE)
    titles = [str(x).strip() for x in (data or {}).get("titles", []) if str(x).strip()]
    print(f"  titles={titles} 耗时={time.time()-t:.0f}s")
    check("title suggest", len(titles) >= 1)

    print("== 4. LLM 意图解析（规则无法处理的句式） ==")
    msg = '第2页末尾添加一条要点：风险与依赖已同步全组'
    rule = parse_intent(msg, {})
    llm_it = parse_intent_llm(msg, plan)
    check("rule cannot parse (no quotes)", rule["type"] == "unknown", str(rule))
    check("llm parses add", llm_it and llm_it.get("type") == "edit_page"
          and llm_it.get("op") == "add" and llm_it.get("page_no") == 2,
          str(llm_it))
    msg2 = "把标题改成 AI 知识平台建设汇报"
    it2 = parse_intent_llm(msg2, plan)
    check("llm parses title", it2 and it2.get("type") == "change_title"
          and "知识平台" in it2.get("title", ""), str(it2))

    print("== 5. 渲染 LLM plan（含 notes） ==")
    import json
    import tempfile
    from src.engine_bridge import render_plan, verify_output
    tmp = Path(tempfile.mkdtemp(prefix="llm_plan_"))
    p = tmp / "plan.json"
    p.write_text(json.dumps(plan, ensure_ascii=False, indent=2), encoding="utf-8")
    out = tmp / "out.pptx"
    render_plan(p, out)
    vok, detail = verify_output(out, p)
    check("render+verify llm plan", vok, detail[:60])

    print("\n" + ("LLM E2E ALL PASSED" if not failures else "FAILURES: " + "; ".join(failures)))
    sys.exit(1 if failures else 0)


if __name__ == "__main__":
    main()