mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-08-29 03:56:43 +08:00
277 lines
9.1 KiB
Python
277 lines
9.1 KiB
Python
import asyncio
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import json
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from pathlib import Path
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from types import SimpleNamespace
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from unittest.mock import AsyncMock, Mock
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from app.runtime.config import ConfigModel
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from app.modules.acoustid import AcoustIdModule
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from app.adapters.network.http import AsyncRequestUtils, RequestUtils
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RECORDING_ID = "38035858-f990-4fbb-b3b2-f2f8b958eeba"
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class FakeResponse:
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"""提供 AcoustID 模块测试所需的最小 HTTP 响应接口。"""
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def __init__(self, payload: dict, status_code: int = 200) -> None:
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"""保存响应数据并记录资源是否被关闭。"""
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self.payload = payload
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self.status_code = status_code
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self.closed = False
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def json(self) -> dict:
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"""返回预设 JSON 响应。"""
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return self.payload
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def close(self) -> None:
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"""记录响应资源已释放。"""
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self.closed = True
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class FakeAsyncResponse(FakeResponse):
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"""提供异步 AcoustID 查询所需的响应关闭接口。"""
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async def aclose(self) -> None:
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"""记录异步响应资源已释放。"""
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self.closed = True
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def test_acoustid_api_key_has_built_in_default():
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"""系统应内置可用 AcoustID 应用 Key，同时允许运行配置覆盖。"""
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assert ConfigModel.model_fields["ACOUSTID_API_KEY"].default == "b1auxfOzAg"
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def test_identify_music_by_fingerprint_queries_acoustid_and_caches_result(
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tmp_path,
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monkeypatch,
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):
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"""有效指纹应请求 recordingids，并按文件状态缓存 MusicBrainz ID。"""
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audio_path = tmp_path / "track.flac"
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audio_path.write_bytes(b"audio")
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module = AcoustIdModule()
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module._fpcalc_path = "/usr/bin/fpcalc"
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fpcalc = Mock(return_value=SimpleNamespace(
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returncode=0,
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stdout=json.dumps({"duration": 243.4, "fingerprint": "AQADtM..."}),
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))
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response = FakeResponse({
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"status": "ok",
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"results": [{
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"score": 0.98,
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"recordings": [{"id": RECORDING_ID}],
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}],
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})
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post_res = Mock(return_value=response)
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monkeypatch.setattr("app.modules.acoustid.subprocess.run", fpcalc)
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monkeypatch.setattr(RequestUtils, "post_res", post_res)
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monkeypatch.setattr(module, "_wait_for_rate_limit", lambda: None)
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monkeypatch.setattr("app.modules.acoustid.settings.ACOUSTID_API_KEY", "client-key")
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first = module.identify_music_by_fingerprint(audio_path)
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second = module.identify_music_by_fingerprint(audio_path)
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assert first == RECORDING_ID
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assert second == RECORDING_ID
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fpcalc.assert_called_once_with(
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["/usr/bin/fpcalc", "-json", str(audio_path)],
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capture_output=True,
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text=True,
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check=False,
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timeout=60,
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)
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post_res.assert_called_once()
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assert post_res.call_args.kwargs["url"] == "https://api.acoustid.org/v2/lookup"
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assert post_res.call_args.kwargs["data"] == {
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"client": "client-key",
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"duration": 243,
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"fingerprint": "AQADtM...",
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"meta": "recordingids",
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"format": "json",
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}
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assert response.closed is True
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def test_generate_fingerprint_accepts_valid_output_on_nonfatal_exit(
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tmp_path,
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monkeypatch,
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):
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"""fpcalc 报告非致命解码错误时仍应使用通过校验的指纹。"""
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audio_path = tmp_path / "track.mp3"
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audio_path.write_bytes(b"audio")
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module = AcoustIdModule()
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module._fpcalc_path = "/usr/bin/fpcalc"
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monkeypatch.setattr(
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"app.modules.acoustid.subprocess.run",
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Mock(return_value=SimpleNamespace(
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returncode=3,
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stdout=json.dumps({
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"duration": 243.4,
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"fingerprint": "AQADtM...",
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}),
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)),
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)
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assert module._generate_fingerprint(audio_path) == (243, "AQADtM...")
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def test_generate_fingerprint_rejects_invalid_output_on_nonfatal_exit(
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tmp_path,
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monkeypatch,
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):
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"""非致命退出码不能绕过时长和指纹内容校验。"""
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audio_path = tmp_path / "track.mp3"
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audio_path.write_bytes(b"audio")
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module = AcoustIdModule()
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module._fpcalc_path = "/usr/bin/fpcalc"
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monkeypatch.setattr(
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"app.modules.acoustid.subprocess.run",
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Mock(return_value=SimpleNamespace(returncode=3, stdout="{}")),
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)
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assert module._generate_fingerprint(audio_path) is None
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def test_select_recording_id_requires_high_score_and_valid_uuid():
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"""低置信结果和异常外部 ID 不得进入 MusicBrainz 详情查询。"""
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payload = {
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"status": "ok",
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"results": [
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{"score": 0.99, "recordings": [{"id": "not-a-uuid"}]},
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{"score": 0.91, "recordings": [{"id": RECORDING_ID}]},
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{
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"score": 0.89,
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"recordings": [{"id": "b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d"}],
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},
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],
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}
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assert AcoustIdModule._select_recording_id(payload) == RECORDING_ID
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assert AcoustIdModule._select_recording_id({
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"status": "ok",
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"results": [{
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"score": 0.89,
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"recordings": [{"id": RECORDING_ID}],
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}],
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}) is None
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def test_identify_music_by_fingerprint_skips_missing_fpcalc(tmp_path, monkeypatch):
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"""缺少 fpcalc 时应静默跳过指纹层，不发起 AcoustID 请求。"""
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audio_path = tmp_path / "track.mp3"
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audio_path.write_bytes(b"audio")
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module = AcoustIdModule()
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post_res = Mock()
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monkeypatch.setattr(RequestUtils, "post_res", post_res)
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assert module.identify_music_by_fingerprint(Path(audio_path)) is None
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post_res.assert_not_called()
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def test_test_fails_without_local_fpcalc_and_skips_network(monkeypatch):
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"""缺少本地 fpcalc 时，测试应直接失败且不发起任何网络请求。"""
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monkeypatch.setattr("app.modules.acoustid.shutil.which", lambda _: None)
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monkeypatch.setattr("app.modules.acoustid.settings.ACOUSTID_API_KEY", "client-key")
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get_res = Mock()
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monkeypatch.setattr(RequestUtils, "get_res", get_res)
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module = AcoustIdModule()
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ok, message = module.test()
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assert ok is False
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assert "fpcalc" in message
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get_res.assert_not_called()
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def test_test_passes_with_local_fpcalc_and_network(monkeypatch):
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"""fpcalc 存在且网络可达时，测试应成功并刷新本地依赖快照。"""
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monkeypatch.setattr(
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"app.modules.acoustid.shutil.which",
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lambda _: "/usr/bin/fpcalc",
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)
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monkeypatch.setattr("app.modules.acoustid.settings.ACOUSTID_API_KEY", "client-key")
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monkeypatch.setattr(RequestUtils, "get_res", Mock(return_value=FakeResponse({})))
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module = AcoustIdModule()
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ok, message = module.test()
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assert ok is True
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assert message == ""
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assert module._fpcalc_path == "/usr/bin/fpcalc"
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def test_test_resolves_fpcalc_independently_of_init(monkeypatch):
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"""即便 init_module 早期未定位到 fpcalc，测试也应重新检测本地依赖。"""
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monkeypatch.setattr(
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"app.modules.acoustid.shutil.which",
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lambda _: "/usr/bin/fpcalc",
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)
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monkeypatch.setattr("app.modules.acoustid.settings.ACOUSTID_API_KEY", "client-key")
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monkeypatch.setattr(RequestUtils, "get_res", Mock(return_value=FakeResponse({})))
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module = AcoustIdModule()
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module._fpcalc_path = None
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ok, _ = module.test()
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assert ok is True
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assert module._fpcalc_path == "/usr/bin/fpcalc"
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def test_async_identify_music_by_fingerprint_uses_async_process_and_http(
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tmp_path,
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monkeypatch,
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):
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"""异步接口应异步执行 fpcalc 与 HTTP 查询，并复用同一响应筛选规则。"""
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audio_path = tmp_path / "track.flac"
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audio_path.write_bytes(b"audio")
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module = AcoustIdModule()
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module._fpcalc_path = "/usr/bin/fpcalc"
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class FakeProcess:
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"""模拟返回有效指纹并报告非致命解码错误的 fpcalc 子进程。"""
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returncode = 3
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async def communicate(self):
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"""返回 fpcalc JSON 标准输出和空错误输出。"""
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payload = json.dumps({
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"duration": 243.4,
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"fingerprint": "AQADtM...",
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})
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return payload.encode(), b""
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create_process = AsyncMock(return_value=FakeProcess())
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response = FakeAsyncResponse({
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"status": "ok",
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"results": [{
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"score": 0.98,
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"recordings": [{"id": RECORDING_ID}],
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}],
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})
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post_res = AsyncMock(return_value=response)
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wait_rate_limit = AsyncMock()
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monkeypatch.setattr(
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"app.modules.acoustid.asyncio.create_subprocess_exec",
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create_process,
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)
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monkeypatch.setattr(AsyncRequestUtils, "post_res", post_res)
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monkeypatch.setattr(module, "_async_wait_for_rate_limit", wait_rate_limit)
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monkeypatch.setattr("app.modules.acoustid.settings.ACOUSTID_API_KEY", "client-key")
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result = asyncio.run(module.async_identify_music_by_fingerprint(audio_path))
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assert result == RECORDING_ID
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create_process.assert_awaited_once_with(
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"/usr/bin/fpcalc",
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"-json",
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str(audio_path),
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE,
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)
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wait_rate_limit.assert_awaited_once()
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post_res.assert_awaited_once()
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assert post_res.await_args.kwargs["data"]["meta"] == "recordingids"
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assert response.closed is True
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