cat-shark
8a715a8064
按"测试规则"重写 tests/：一个模块一个目录、用例按数据→过程→验证三段书写、 不保留全局 conftest.py、测试过程只调用真实生产代码。 结构（73 个文件、30 个模块目录、477 用例）： - tests/nodes/ 15 个模块目录（srt/whisper/ass/ffmpeg/frame_extract/vlm/ subtitle_ocr/llm/llm_filter/subtitle_cleanup/subtitle_correction/ proper_nouns/adaptive_pool/vad_profiler/echo）； - tests/app/ 11 个模块目录（db/scheduler/batch/maintenance/registry/seed/ storage/config/logging/main/routers 三组 API）； - tests/sdk/test_models、tests/web/test_crop、tests/shared（公共设施）。 测试数据随模块目录入库（tests/**/data/），删除根级 testdata/；.gitignore 的 data/ 改为 /data/，否则会连带忽略 tests/**/data/ 导致测试数据无法入库。 顺带发现并修复三个真实缺陷： - nodes/srt.py：相邻条目缺少空行时把下一条时间轴吞进正文（静默错位）， 改为正文行遇时间戳行即报错； - src/wov_app/scheduler.py：_file_size 只捕获 OSError，含 \x00 的产物 URI 抛 ValueError 导致任务误判失败，改为同时捕获； - nodes/subtitle_correction.py：生产代码依赖测试包解析 SRT， 改用生产模块 nodes/srt.py。 真实模型/服务集成测试按外部状态跳过：新增 tests/shared/gpu_memory.py （运行时探测显存、CUDA OOM 转跳过）与 tests/shared/llm_service.py （无 Key / 余额 / 限流转跳过）。全量 477 passed。
474 lines
17 KiB
Python
474 lines
17 KiB
Python
"""nodes/llm.py 的模块级测试（数据 → 测试过程 → 验证结果）。
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被测模块：`nodes/llm.py`（LLM 翻译节点：分批请求 + 按 ID 回填），可独立调用。
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网络属于允许 mock 的 I/O 边界：单元用例注入假 HTTP 响应验证请求体、ID 校验、
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重试与回填；集成用例调用真实 LLM 验证真实字幕翻译质量。
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"""
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from __future__ import annotations
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import json
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import urllib.error
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import urllib.request
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from pathlib import Path
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import pytest
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from nodes.llm import (
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CHUNK_SIZE,
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MAX_BATCH_RETRIES,
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_parse_translations,
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_system_prompt,
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invoke,
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translate_lines,
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)
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from wov_sdk.models import InvokeRequest
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# 模块专用数据目录（真实字幕产物缺失时相关用例跳过）。
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DATA_DIR = Path(__file__).resolve().parent / "data"
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class _FakeHTTPResponse:
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"""假的 HTTP 响应：返回预置 JSON 体（供 urlopen mock 使用）。"""
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def __init__(self, payload: dict) -> None:
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self._data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
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def read(self) -> bytes:
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return self._data
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def __enter__(self):
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return self
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def __exit__(self, *exc) -> None:
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return None
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def _llm_reply(translations: list[tuple[int, str]], total_tokens: int = 42) -> _FakeHTTPResponse:
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"""构造 OpenAI 兼容接口的响应体（content 为 {id,text} JSON 数组）。"""
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content = json.dumps(
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[{"id": i, "text": t} for i, t in translations], ensure_ascii=False
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)
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return _FakeHTTPResponse({
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"choices": [{"message": {"content": content}}],
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"usage": {"total_tokens": total_tokens},
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})
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def _capture_urlopen(calls: list[dict], responses: list[_FakeHTTPResponse]):
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"""返回一个假 urlopen：记录请求体，按顺序返回预置响应。"""
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def fake_urlopen(http_request, timeout=None):
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calls.append({
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"url": http_request.full_url,
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"body": json.loads(http_request.data.decode("utf-8")),
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"headers": dict(http_request.headers),
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"timeout": timeout,
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})
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return responses.pop(0) if responses else _llm_reply([])
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return fake_urlopen
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# ---------------------------------------------------------------------------
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# 提示词与响应解析（纯函数）
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# ---------------------------------------------------------------------------
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def test_system_prompt_mentions_target_language_and_json_contract() -> None:
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"""系统提示词说明目标语言与 JSON 条目契约（时间戳不进入模型）。"""
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# 数据：目标语言 zh-CN。
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# 测试过程
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prompt = _system_prompt("zh-CN")
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# 验证结果
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assert "zh-CN" in prompt
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assert "id" in prompt and "text" in prompt
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def test_parse_translations_accepts_out_of_order_ids() -> None:
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"""乱序返回的条目按 ID 回填（不依赖数组顺序）。"""
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# 数据：ID 为 3、1、2 的乱序结果。
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content = json.dumps([
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{"id": 3, "text": "三"},
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{"id": 1, "text": "一"},
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{"id": 2, "text": "二"},
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])
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# 测试过程
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parsed = _parse_translations(content, {1, 2, 3})
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# 验证结果
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assert parsed == {1: "一", 2: "二", 3: "三"}
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def test_parse_translations_rejects_missing_id() -> None:
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"""缺少任一 ID 时明确报错（不允许静默漏译导致时间轴错位）。"""
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# 数据：缺少 ID 2。
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content = json.dumps([{"id": 1, "text": "一"}, {"id": 3, "text": "三"}])
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# 测试过程与验证结果
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with pytest.raises(ValueError, match="missing"):
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_parse_translations(content, {1, 2, 3})
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def test_parse_translations_rejects_duplicate_id() -> None:
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"""重复 ID 报错。"""
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# 数据：ID 1 出现两次。
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content = json.dumps([{"id": 1, "text": "一"}, {"id": 1, "text": "壹"}])
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# 测试过程与验证结果
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with pytest.raises(ValueError, match="duplicate"):
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_parse_translations(content, {1})
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def test_parse_translations_rejects_empty_text() -> None:
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"""空正文或非字符串正文报错。"""
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# 数据：空字符串与数字正文。
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# 测试过程与验证结果
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with pytest.raises(ValueError, match="empty or invalid"):
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_parse_translations(json.dumps([{"id": 1, "text": " "}]), {1})
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with pytest.raises(ValueError, match="empty or invalid"):
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_parse_translations(json.dumps([{"id": 1, "text": 3}]), {1})
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def test_parse_translations_rejects_unexpected_id() -> None:
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"""返回了未请求的 ID 时报错。"""
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# 数据：包含 ID 9（未请求）。
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content = json.dumps([{"id": 9, "text": "九"}])
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# 测试过程与验证结果
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with pytest.raises(ValueError, match="invalid or duplicate"):
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_parse_translations(content, {1})
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def test_parse_translations_rejects_non_array_payload() -> None:
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"""顶层不是数组时报错。"""
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# 数据：对象形式的返回。
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# 测试过程与验证结果
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with pytest.raises(ValueError, match="JSON array"):
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_parse_translations(json.dumps({"id": 1, "text": "一"}), {1})
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def test_parse_translations_keeps_multiline_text_structure() -> None:
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"""译文多行结构保留（去除纯空行，不截断 cue）。"""
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# 数据：含空行的多行译文。
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content = json.dumps([{"id": 1, "text": "第一行\n\n第二行"}])
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# 测试过程
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parsed = _parse_translations(content, {1})
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# 验证结果：空行被去掉但两行都保留。
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assert parsed[1] == "第一行\n第二行"
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# ---------------------------------------------------------------------------
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# translate_lines：请求体、ID 与重试
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# ---------------------------------------------------------------------------
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def test_translate_lines_sends_global_ids_and_maps_back(monkeypatch) -> None:
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"""按全局位置 ID 请求翻译，并把结果按位置回填（空 cue 不请求但占位）。"""
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# 数据：5 行，其中第 3 行为空（占位）。
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lines = ["一", "二", "", "四", "五"]
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calls: list[dict] = []
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monkeypatch.setenv("LLM_API_KEY", "sk-test")
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monkeypatch.setattr(
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urllib.request, "urlopen",
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_capture_urlopen(calls, [_llm_reply([(1, "one"), (2, "two"), (4, "four"), (5, "five")])]),
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)
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# 测试过程
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result = translate_lines(lines, {"target_language": "en"})
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# 验证结果：请求体只含非空行的全局 ID（1,2,4,5），结果按位置回填且空行保留。
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sent = json.loads(calls[0]["body"]["messages"][1]["content"])
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assert [item["id"] for item in sent] == [1, 2, 4, 5]
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assert result == ["one", "two", "", "four", "five"]
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def test_translate_lines_retries_on_structure_error(monkeypatch) -> None:
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"""结构校验失败时重试，最终成功（最多 MAX_BATCH_RETRIES 次）。"""
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# 数据：第一次返回缺 ID，第二次正确。
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lines = ["一", "二"]
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calls: list[dict] = []
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monkeypatch.setenv("LLM_API_KEY", "sk-test")
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monkeypatch.setattr(
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urllib.request, "urlopen",
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_capture_urlopen(calls, [
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_llm_reply([(1, "one")]), # 缺 ID 2 → 触发重试
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_llm_reply([(1, "one"), (2, "two")]), # 正确
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]),
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)
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# 测试过程
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result = translate_lines(lines, {})
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# 验证结果：重试一次后成功，共发起 2 次请求。
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assert result == ["one", "two"]
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assert len(calls) == 2
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def test_translate_lines_raises_after_retries_exhausted(monkeypatch) -> None:
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"""结构错误耗尽重试后抛错（不返回错位译文）。"""
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# 数据：每次都返回错误结构。
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calls: list[dict] = []
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monkeypatch.setenv("LLM_API_KEY", "sk-test")
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monkeypatch.setattr(
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urllib.request, "urlopen",
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_capture_urlopen(calls, [_llm_reply([]) for _ in range(MAX_BATCH_RETRIES)]),
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)
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# 测试过程与验证结果
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with pytest.raises(ValueError, match="alignment failed"):
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translate_lines(["一"], {})
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assert len(calls) == MAX_BATCH_RETRIES
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def test_translate_lines_empty_input_makes_no_request(monkeypatch) -> None:
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"""空输入直接返回空列表，不发请求。"""
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# 数据：空列表。
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calls: list[dict] = []
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monkeypatch.setattr(urllib.request, "urlopen", _capture_urlopen(calls, []))
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# 测试过程与验证结果
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assert translate_lines([], {}) == []
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assert calls == []
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def test_translate_lines_all_empty_cues_make_no_request(monkeypatch) -> None:
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"""全部为空 cue 时不请求模型，返回等长空列表。"""
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# 数据：3 个空行。
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calls: list[dict] = []
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monkeypatch.setattr(urllib.request, "urlopen", _capture_urlopen(calls, []))
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# 测试过程与验证结果
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assert translate_lines(["", " ", ""], {}) == ["", "", ""]
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assert calls == []
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def test_translate_lines_batches_by_chunk_size(monkeypatch) -> None:
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"""超过 CHUNK_SIZE 行时分批请求（每批最多 CHUNK_SIZE 条）。"""
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# 数据：CHUNK_SIZE + 1 行。
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total = CHUNK_SIZE + 1
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lines = [f"行{i}" for i in range(1, total + 1)]
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calls: list[dict] = []
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def fake_urlopen(http_request, timeout=None):
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body = json.loads(http_request.data.decode("utf-8"))
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calls.append(body)
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items = json.loads(body["messages"][1]["content"])
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return _llm_reply([(item["id"], f"t{item['id']}") for item in items])
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monkeypatch.setenv("LLM_API_KEY", "sk-test")
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monkeypatch.setattr(urllib.request, "urlopen", fake_urlopen)
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# 测试过程
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result = translate_lines(lines, {})
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# 验证结果：两批（CHUNK_SIZE + 1），结果等长且顺序正确。
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assert len(calls) == 2
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assert len(result) == total
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assert result[0] == "t1" and result[-1] == f"t{total}"
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def test_translate_lines_injects_proper_noun_rule(monkeypatch) -> None:
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"""本批原文命中专名时，系统提示词追加规则（不被硬译）。"""
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# 数据：含专名 ジンゴ 的一行。
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calls: list[dict] = []
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monkeypatch.setenv("LLM_API_KEY", "sk-test")
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monkeypatch.setattr(
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urllib.request, "urlopen",
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_capture_urlopen(calls, [_llm_reply([(1, "译文")])]),
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)
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# 测试过程
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translate_lines(["ジンゴがここで味わいませんか"], {})
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# 验证结果：系统提示词包含该专名与禁止硬译的说明。
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system_prompt = calls[0]["body"]["messages"][0]["content"]
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assert "ジンゴ" in system_prompt
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assert "芒果" in system_prompt
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def test_translate_lines_default_model_and_env_override(monkeypatch) -> None:
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"""默认模型来自 LLM_MODEL 环境变量（数据驱动，不改代码切换模型）。"""
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# 数据：设置环境变量为自定义模型。
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calls: list[dict] = []
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monkeypatch.setenv("LLM_API_KEY", "sk-test")
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monkeypatch.setenv("LLM_MODEL", "自定义/模型")
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monkeypatch.setattr(
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urllib.request, "urlopen",
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_capture_urlopen(calls, [_llm_reply([(1, "x")])]),
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)
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# 测试过程
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translate_lines(["一"], {})
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# 验证结果：请求体使用环境变量指定的模型。
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assert calls[0]["body"]["model"] == "自定义/模型"
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def test_translate_lines_param_model_wins_over_env(monkeypatch) -> None:
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"""节点参数 model 优先于环境变量。"""
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# 数据：环境变量与参数都设置。
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calls: list[dict] = []
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monkeypatch.setenv("LLM_API_KEY", "sk-test")
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monkeypatch.setenv("LLM_MODEL", "env/模型")
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monkeypatch.setattr(
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urllib.request, "urlopen",
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_capture_urlopen(calls, [_llm_reply([(1, "x")])]),
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)
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# 测试过程
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translate_lines(["一"], {"model": "param/模型"})
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# 验证结果
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assert calls[0]["body"]["model"] == "param/模型"
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def test_translate_lines_uses_timeout_env(monkeypatch) -> None:
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"""LLM_TIMEOUT_SECONDS 决定请求超时（默认 600）。"""
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# 数据：设置 45 秒。
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calls: list[dict] = []
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monkeypatch.setenv("LLM_API_KEY", "sk-test")
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monkeypatch.setenv("LLM_TIMEOUT_SECONDS", "45")
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monkeypatch.setattr(
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urllib.request, "urlopen",
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_capture_urlopen(calls, [_llm_reply([(1, "x")])]),
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)
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# 测试过程
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translate_lines(["一"], {})
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# 验证结果
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assert calls[0]["timeout"] == 45.0
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def test_translate_lines_sends_bearer_key(monkeypatch) -> None:
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"""请求头带 Bearer Key（来自 LLM_API_KEY）。"""
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# 数据：设置 key。
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calls: list[dict] = []
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monkeypatch.setenv("LLM_API_KEY", "sk-abc")
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monkeypatch.setattr(
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urllib.request, "urlopen",
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_capture_urlopen(calls, [_llm_reply([(1, "x")])]),
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)
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# 测试过程
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translate_lines(["一"], {})
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# 验证结果
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headers = {k.lower(): v for k, v in calls[0]["headers"].items()}
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assert headers.get("authorization") == "Bearer sk-abc"
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# ---------------------------------------------------------------------------
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# invoke 全流程
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# ---------------------------------------------------------------------------
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def test_invoke_translates_srt_and_writes_artifact(monkeypatch, tmp_path: Path) -> None:
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"""invoke 解析 SRT → 翻译 → 写出 cn_srt，时间轴保持原样。"""
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# 数据：两条真实 SRT。
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srt = "1\n00:00:01,000 --> 00:00:02,000\nこんにちは\n\n2\n00:00:03,000 --> 00:00:04,000\nさようなら\n"
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srt_path = tmp_path / "in.srt"
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srt_path.write_text(srt, encoding="utf-8")
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calls: list[dict] = []
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monkeypatch.setenv("LLM_API_KEY", "sk-test")
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monkeypatch.setattr(
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urllib.request, "urlopen",
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_capture_urlopen(calls, [_llm_reply([(1, "你好"), (2, "再见")])]),
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)
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request = InvokeRequest(
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run_id="r", node_instance_id="n", params={},
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inputs={"srt_uri": str(srt_path)}, output_dir=str(tmp_path / "out"),
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)
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||
|
||
# 测试过程
|
||
response = invoke(request)
|
||
|
||
# 验证结果：状态、产物时间轴与译文顺序。
|
||
assert response.status == "completed", response.error
|
||
content = Path(response.outputs["cn_srt_uri"]).read_text(encoding="utf-8")
|
||
assert "00:00:01,000 --> 00:00:02,000" in content
|
||
assert "你好" in content and "再见" in content
|
||
assert content.index("你好") < content.index("再见")
|
||
|
||
|
||
def test_invoke_fails_without_input(tmp_path: Path) -> None:
|
||
"""缺少 srt_uri 时失败。"""
|
||
# 数据：空输入。
|
||
request = InvokeRequest(
|
||
run_id="r", node_instance_id="n", params={}, inputs={}, output_dir=str(tmp_path)
|
||
)
|
||
|
||
# 测试过程
|
||
response = invoke(request)
|
||
|
||
# 验证结果
|
||
assert response.status == "failed"
|
||
assert "srt_uri" in (response.error or "")
|
||
|
||
|
||
def test_invoke_fails_when_input_missing(tmp_path: Path) -> None:
|
||
"""输入文件不存在时失败。"""
|
||
# 数据：不存在的路径。
|
||
request = InvokeRequest(
|
||
run_id="r", node_instance_id="n", params={},
|
||
inputs={"srt_uri": str(tmp_path / "nope.srt")}, output_dir=str(tmp_path),
|
||
)
|
||
|
||
# 测试过程
|
||
response = invoke(request)
|
||
|
||
# 验证结果
|
||
assert response.status == "failed"
|
||
assert "not found" in (response.error or "")
|
||
|
||
|
||
def test_invoke_reports_failure_on_llm_error(monkeypatch, tmp_path: Path) -> None:
|
||
"""LLM 报错时节点返回 failed（不产出半成品产物）。"""
|
||
# 数据：urlopen 抛 HTTPError。
|
||
srt_path = tmp_path / "in.srt"
|
||
srt_path.write_text("1\n00:00:01,000 --> 00:00:02,000\nこんにちは\n", encoding="utf-8")
|
||
monkeypatch.setenv("LLM_API_KEY", "sk-test")
|
||
|
||
def fake_urlopen(http_request, timeout=None):
|
||
raise urllib.error.HTTPError(http_request.full_url, 500, "server error", {}, None)
|
||
|
||
monkeypatch.setattr(urllib.request, "urlopen", fake_urlopen)
|
||
request = InvokeRequest(
|
||
run_id="r", node_instance_id="n", params={},
|
||
inputs={"srt_uri": str(srt_path)}, output_dir=str(tmp_path / "out"),
|
||
)
|
||
|
||
# 测试过程
|
||
response = invoke(request)
|
||
|
||
# 验证结果
|
||
assert response.status == "failed"
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 真实 LLM 集成（需要 LLM_API_KEY 与网络）
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
@pytest.mark.integration
|
||
def test_real_llm_translates_short_lines_with_domain_rules() -> None:
|
||
"""真实 LLM 校准：短句专名/隐语不按字面直译（真实 API，外部状态缺失则跳过）。"""
|
||
# 数据：含专名 ジンゴ 的短句；Key 与账号可用性由共享判定处理。
|
||
from nodes.llm import translate_lines as real_translate
|
||
from tests.shared.llm_service import require_llm_credentials, skip_on_service_unavailable
|
||
|
||
require_llm_credentials()
|
||
|
||
# 测试过程
|
||
with skip_on_service_unavailable():
|
||
out = real_translate(["ジンゴがここで味わいませんか"], {"target_language": "zh-CN"})
|
||
|
||
# 验证结果：有译文且未把专名硬译成"芒果"。
|
||
assert out and out[0].strip()
|
||
assert "芒果" not in out[0]
|