mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-11-14 09:51:28 +08:00
f3ad81b39e
Run `ruff check app tests scripts --fix` with the configured rule set (E4/E7/E9/F/I): import sorting (I001), f-strings without placeholders (F541), lambda/variable/unused-import cleanups that ruff marks safe. Nine files whose only ruff debt sits next to pre-existing pylint import-resolution errors (lazy exports, sys.path script imports) or whose "unused" imports are reached through attribute access by tests are left untouched so the changed-file pylint gate and the benchmark tests stay green; their diagnostics remain in the baseline. app/startup/lifecycle/__init__.py and app/main.py import business modules after cache composition, import-path and stdio setup on purpose (the source comments document the contract), so declare E402 per-file-ignores for them instead of adding per-line noqa. Ruff ratchet watermark drops from 498 to 100; sync the baseline fixture and the optimization checklist figure. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
import asyncio
|
|
from unittest.mock import AsyncMock, Mock
|
|
|
|
from app.domain.meta.metabase import MetaBase
|
|
from app.modules.douban import DoubanModule
|
|
from app.schemas.types import MediaSource, MediaType
|
|
|
|
|
|
def test_douban_recognize_does_not_keep_dedicated_mapping_cache():
|
|
"""豆瓣识别应每次执行匹配，不再保留专用标题映射缓存。"""
|
|
module = DoubanModule()
|
|
meta = MetaBase("测试电影")
|
|
meta.name = "测试电影"
|
|
meta.type = MediaType.MOVIE
|
|
meta.year = "2024"
|
|
match_doubaninfo = Mock(return_value={"id": "200"})
|
|
douban_info = Mock(return_value={
|
|
"id": "200",
|
|
"title": "测试电影",
|
|
"type": "movie",
|
|
"year": "2024",
|
|
})
|
|
|
|
first_result = module._recognize_media_core(
|
|
meta=meta,
|
|
media_source=MediaSource.Douban,
|
|
match_doubaninfo_func=match_doubaninfo,
|
|
douban_info_func=douban_info,
|
|
)
|
|
second_result = module._recognize_media_core(
|
|
meta=meta,
|
|
media_source=MediaSource.Douban,
|
|
match_doubaninfo_func=match_doubaninfo,
|
|
douban_info_func=douban_info,
|
|
)
|
|
|
|
assert first_result.douban_id == "200"
|
|
assert second_result.douban_id == "200"
|
|
assert match_doubaninfo.call_count == 2
|
|
assert douban_info.call_count == 2
|
|
|
|
|
|
def test_async_douban_recognize_does_not_keep_dedicated_mapping_cache():
|
|
"""异步豆瓣识别也应每次执行匹配，不使用专用标题映射缓存。"""
|
|
module = DoubanModule()
|
|
meta = MetaBase("测试剧集")
|
|
meta.name = "测试剧集"
|
|
meta.type = MediaType.TV
|
|
meta.year = "2024"
|
|
match_doubaninfo = AsyncMock(return_value={"id": "201"})
|
|
douban_info = AsyncMock(return_value={
|
|
"id": "201",
|
|
"title": "测试剧集",
|
|
"type": "tv",
|
|
"year": "2024",
|
|
})
|
|
|
|
async def recognize_twice():
|
|
"""连续执行两次异步豆瓣识别。"""
|
|
first_result = await module._async_recognize_media_core(
|
|
meta=meta,
|
|
media_source=MediaSource.Douban,
|
|
async_match_doubaninfo_func=match_doubaninfo,
|
|
async_douban_info_func=douban_info,
|
|
)
|
|
second_result = await module._async_recognize_media_core(
|
|
meta=meta,
|
|
media_source=MediaSource.Douban,
|
|
async_match_doubaninfo_func=match_doubaninfo,
|
|
async_douban_info_func=douban_info,
|
|
)
|
|
return first_result, second_result
|
|
|
|
first_result, second_result = asyncio.run(recognize_twice())
|
|
|
|
assert first_result.douban_id == "201"
|
|
assert second_result.douban_id == "201"
|
|
assert match_doubaninfo.await_count == 2
|
|
assert douban_info.await_count == 2
|