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Merge 84104a2d8f into 08f2a98250

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SurenderguliaEnginner 2026-11-06 16:39:57 +09:00 • committed by GitHub
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tests/unit/test_cost_calculator.py
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@ -11645,68 +11645,3 @@ def test_upstream_cost_header_total_on_an_unmapped_alias_is_split_by_the_served_
input_share: Final = (10 * 0.001) / (10 * 0.001 + 20 * 0.002)
assert breakdown["input_cost"] == pytest.approx(0.25 * input_share)
assert breakdown["output_cost"] == pytest.approx(0.25 * (1 - input_share))
_PER_CALL_RATES: Final = MappingProxyType({"input_cost_per_token": 2e-06, "output_cost_per_token": 4e-06})
def _per_call_priced_reply(model: str, usage_extras: Mapping[str, str]) -> ModelResponse:
usage: Final = Usage(prompt_tokens=1000, completion_tokens=1000, total_tokens=2000)
for field, value in usage_extras.items():
setattr(usage, field, value)
return ModelResponse(model=model, choices=[Choices(message=Message(content="hi"))], usage=usage)
@pytest.mark.parametrize(
("row", "call_kwargs", "usage_extras", "multiplier"),
[
pytest.param(
{"litellm_provider": "openai", "mode": "chat", "regional_processing_uplift_multiplier_eu": 1.25},
{
"model": "openai/lit-eu-residency-test",
"api_key": "fake-key",
"api_base": "https://eu.api.openai.com/v1",
},
{},
1.25,
id="openai-data-residency",
),
pytest.param(
{"litellm_provider": "vertex_ai", "mode": "chat", "regional_endpoint_uplift_multiplier": 1.5},
{
"model": "vertex_ai/lit-regional-endpoint-test",
"vertex_project": "lit-project",
"vertex_location": "us-east5",
"vertex_credentials": "{}",
},
{},
1.5,
id="vertex-regional-endpoint",
),
pytest.param(
{"litellm_provider": "anthropic", "mode": "chat", "provider_specific_entry": {"fast": 2.0}},
{"model": "anthropic/lit-fast-mode-test", "api_key": "fake-key"},
{"speed": "fast"},
2.0,
id="anthropic-fast-mode",
),
],
)
def test_per_call_custom_prices_keep_the_regional_and_speed_multipliers(
monkeypatch: pytest.MonkeyPatch,
row: Mapping[str, object],
call_kwargs: Mapping[str, str],
usage_extras: Mapping[str, str],
multiplier: float,
) -> None:
monkeypatch.setitem(litellm.model_cost, call_kwargs["model"], {**row, **_PER_CALL_RATES})
_invalidate_model_cost_lowercase_map()
response: Final = litellm.completion(
messages=[{"role": "user", "content": "hello"}],
mock_response=_per_call_priced_reply(call_kwargs["model"].split("/", 1)[1], usage_extras),
**call_kwargs,
**_PER_CALL_RATES,
)
assert response._hidden_params["response_cost"] == pytest.approx((1000 * 2e-06 + 1000 * 4e-06) * multiplier)