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test(cost-estimate): keep the new tests inside the test-quality budgets

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Two of the new lines tripped the ratcheting gate.

TQ005 flagged restoring litellm.model_cost by assignment. Dropped the
save/restore pair for monkeypatch.setitem, which adds the one model the
test needs and takes it back out at teardown, so the module global is
never reassigned.

TQ008 flagged patching litellm.proxy.proxy_server.llm_router. The
endpoint imports the router from that module inside the function body,
so there is no seam to inject through without changing the endpoint.
Suppressed with the reason already used elsewhere in the suite for the
same module global, on the single helper the new tests share.
This commit is contained in:
Yuneng Jiang 2026-08-26 00:32:25 -07:00
parent 430fb71933
commit 0c87bf5de9
1 changed files with 14 additions and 15 deletions
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29
tests/test_litellm/proxy/management_endpoints/test_cost_tracking_settings.py
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@ -815,7 +815,9 @@ async def _estimate(mock_router: MagicMock | None, model: str = AN_ALIAS, **over
output_tokens=OUTPUT_TOKENS,
**overrides,
)
with patch("litellm.proxy.proxy_server.llm_router", mock_router):
with patch( # test-quality-ok: proxy_server module global is the endpoint's only injection point
"litellm.proxy.proxy_server.llm_router", mock_router
):
return await estimate_cost(request=request, user_api_key_dict=MagicMock())
@ -837,22 +839,19 @@ class TestEstimateCostPartiallyPricedDeployments:
assert response.cost_per_request == pytest.approx(0.001)
@pytest.mark.asyncio
async def test_a_model_priced_only_by_the_cost_map_reports_that_price_and_provider(self):
saved_model_cost = dict(litellm.model_cost)
litellm.register_model(
async def test_a_model_priced_only_by_the_cost_map_reports_that_price_and_provider(self, monkeypatch):
monkeypatch.setitem(
litellm.model_cost,
A_MAPPED_MODEL,
{
A_MAPPED_MODEL: {
"input_cost_per_token": 0.000005,
"output_cost_per_token": 0.000006,
"litellm_provider": "openai",
"mode": "chat",
}
}
"input_cost_per_token": 0.000005,
"output_cost_per_token": 0.000006,
"litellm_provider": "openai",
"mode": "chat",
},
)
try:
response = await _estimate(None, model=A_MAPPED_MODEL)
finally:
litellm.model_cost = saved_model_cost
response = await _estimate(None, model=A_MAPPED_MODEL)
assert response.input_cost_per_token == pytest.approx(0.000005)
assert response.output_cost_per_token == pytest.approx(0.000006)