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Aether/tests/services/test_pool_preset_dimensions.py

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"""Tests for pool preset dimension registry and built-in dimensions."""
from __future__ import annotations
from types import SimpleNamespace
import src.services.provider.pool.dimensions # noqa: F401
from src.services.provider.pool.dimensions import get_preset_dimension, get_preset_names
def _key(metadata: dict, *, plan_type: str | None = None) -> SimpleNamespace:
return SimpleNamespace(upstream_metadata=metadata, oauth_plan_type=plan_type)
def test_registry_discovers_builtin_dimensions() -> None:
names = get_preset_names()
assert {
"free_team_first",
"recent_refresh",
"quota_balanced",
"single_account",
"priority_first",
"health_first",
"latency_first",
"cost_first",
}.issubset(names)
def test_universal_dimensions_are_applicable_to_any_provider() -> None:
for name in ("quota_balanced", "single_account"):
dim = get_preset_dimension(name)
assert dim is not None
assert dim.is_applicable("openai") is True
assert dim.is_applicable("codex") is True
assert dim.is_applicable("unknown_provider") is True
def test_provider_specific_dimensions_are_filtered() -> None:
for name in ("free_team_first", "recent_refresh"):
dim = get_preset_dimension(name)
assert dim is not None
assert dim.is_applicable("codex") is True
assert dim.is_applicable("kiro") is True
assert dim.is_applicable("openai") is False
def test_builtin_dimensions_compute_metric_in_range() -> None:
all_key_ids = ["k1", "k2", "k3"]
lru_scores = {"k1": 100.0, "k2": 800.0, "k3": 300.0}
keys_by_id = {
"k1": _key(
{
"codex": {
"plan_type": "plus",
"primary_reset_seconds": 900,
"primary_used_percent": 60,
}
}
),
"k2": _key(
{
"codex": {
"plan_type": "free",
"primary_reset_seconds": 120,
"primary_used_percent": 20,
}
}
),
"k3": _key(
{
"kiro": {
"next_reset_at": 4102444800,
"usage_percentage": 45,
"subscription_title": "Kiro Team",
}
},
plan_type="team",
),
}
for name in get_preset_names():
dim = get_preset_dimension(name)
assert dim is not None
mode = dim.default_mode
metric = dim.compute_metric(
key_id="k1",
all_key_ids=all_key_ids,
keys_by_id=keys_by_id,
lru_scores=lru_scores,
context={},
mode=mode,
)
assert 0.0 <= metric <= 1.0