import json
import subprocess
import textwrap
import unittest
from pathlib import Path


REPO_ROOT = Path(__file__).resolve().parents[1]
EXTRACTOR_PATH = (
    REPO_ROOT.parent / "skill_lib" / "skills" / "zhihu-hotlist" / "scripts" /
    "extract_hotlist.js"
)


def run_extractor(*, body_text: str, selectors: dict[str, list[dict]] | None = None) -> dict:
    selector_map = selectors or {}
    node_script = textwrap.dedent(
        f"""
        import fs from 'node:fs';
        import vm from 'node:vm';

        const extractorPath = {json.dumps(str(EXTRACTOR_PATH))};
        const selectorMap = {json.dumps(selector_map, ensure_ascii=False)};
        const bodyText = {json.dumps(body_text, ensure_ascii=False)};
        const source = fs.readFileSync(extractorPath, 'utf8');

        function createNode(spec) {{
          const text = String(spec?.text ?? '');
          const children = spec?.selectors ?? {{}};
          return {{
            textContent: text,
            innerText: text,
            querySelector(selector) {{
              const value = children[selector];
              if (!value) {{
                return null;
              }}
              return createNode(value);
            }},
          }};
        }}

        const bodyNode = createNode({{text: bodyText}});
        const context = {{
          args: {{top_n: '10'}},
          location: {{origin: 'https://www.zhihu.com', pathname: '/hot'}},
          document: {{
            body: bodyNode,
            querySelector(selector) {{
              if (selector === 'body' || selector === '#root' || selector === 'main') {{
                return bodyNode;
              }}
              return null;
            }},
            querySelectorAll(selector) {{
              return (selectorMap[selector] || []).map((item) => createNode(item));
            }},
          }},
          console,
          JSON,
          Math,
          Number,
          Object,
          RegExp,
          Set,
          String,
          Array,
          Error,
        }};

        try {{
          const result = vm.runInNewContext(`(function(){{\\n${{source}}\\n}})()`, context);
          process.stdout.write(JSON.stringify({{ok: true, result}}));
        }} catch (error) {{
          process.stdout.write(JSON.stringify({{
            ok: false,
            error: String(error && error.message ? error.message : error),
          }}));
          process.exitCode = 1;
        }}
        """
    )
    completed = subprocess.run(
        ["node", "--input-type=module", "-e", node_script],
        check=False,
        capture_output=True,
        text=True,
    )
    payload = json.loads(completed.stdout)
    if completed.returncode != 0:
        raise AssertionError(payload["error"])
    return payload["result"]


class SkillScriptHotlistExtractorTest(unittest.TestCase):
    def test_extracts_hotlist_from_page_text_when_legacy_dom_classes_are_missing(self):
        result = run_extractor(
            body_text=textwrap.dedent(
                """
                知乎热榜
                1
                如何看待张雪机车在 2026 年 WSBK 葡萄牙站夺冠？
                1707 万热度
                2
                李荣浩摆证据 4 连质问单依纯
                1150 万热度
                3
                日本拟动用外储做空国际原油
                601 万热度
                """
            ),
        )

        self.assertEqual(result["sheet_name"], "知乎热榜")
        self.assertEqual(result["columns"], ["rank", "title", "heat"])
        self.assertEqual(
            result["rows"][:3],
            [
                [1, "如何看待张雪机车在 2026 年 WSBK 葡萄牙站夺冠？", "1707万"],
                [2, "李荣浩摆证据 4 连质问单依纯", "1150万"],
                [3, "日本拟动用外储做空国际原油", "601万"],
            ],
        )


if __name__ == "__main__":
    unittest.main()