Import AITURK IDE 1.0.0-beta.1 from Hermes 63279301; preserve MIT license

tests/providers/test_meta_ai_profile.py

@@ -0,0 +1,81 @@
+"""Tests for the bundled Meta Model API (Muse Spark) provider plugin.
+
+Adapted from the plugin's original suite at
+https://github.com/albertodepaola/hermes-meta-provider — the plugin is now
+bundled, so profiles resolve through normal registry discovery.
+"""
+
+import pytest
+
+from providers import get_provider_profile
+from providers.base import ProviderProfile
+
+
+def _profile():
+    p = get_provider_profile("meta-ai")
+    assert p is not None
+    return p
+
+
+class TestMetaAIProfile:
+    def test_profile_registered(self):
+        p = _profile()
+        assert p.name == "meta-ai"
+        assert p.base_url == "https://api.meta.ai/v1"
+        assert p.auth_type == "api_key"
+        # Responses API engages Muse prompt caching (0% on chat/completions
+        # vs 93-99% on /v1/responses with prompt_cache_retention).
+        assert p.api_mode == "codex_responses"
+        assert "MODEL_API_KEY" in p.env_vars
+        assert p.supports_vision is True
+        # Images are accepted on user turns only; tool-result envelopes 400 (#101668).
+        assert p.supports_vision_tool_messages is False
+        assert p.default_aux_model == "muse-spark-1.2-contributor"
+        assert p.default_max_tokens == 16384
+        assert p.fallback_models == ("muse-spark-1.2",)
+
+    def test_live_catalog_filters_non_chat_models(self, monkeypatch):
+        p = _profile()
+        seen = []
+
+        def fake_fetch_models(_self, **_kwargs):
+            seen.append(True)
+            return [
+                "muse-voice-transcribe-1.0",
+                "muse-spark-latest",
+                "muse-image-1.0-eval",
+                "muse-nova-test",
+            ]
+
+        monkeypatch.setattr(ProviderProfile, "fetch_models", fake_fetch_models)
+
+        assert p.fetch_models() == ["muse-spark-latest", "muse-nova-test"]
+        assert seen
+
+    @pytest.mark.parametrize("alias", ["meta", "muse", "muse-spark", "model-api", "msl"])
+    def test_aliases_resolve(self, alias):
+        assert get_provider_profile(alias) is _profile()
+
+
+def _effort(cfg):
+    _eb, top = _profile().build_api_kwargs_extras(reasoning_config=cfg)
+    return top.get("reasoning_effort")
+
+
+class TestReasoningEffort:
+    def test_mapping(self):
+        assert _effort(None) == "medium"  # unset -> medium
+        assert _effort({"enabled": True, "effort": "high"}) == "high"
+        assert _effort({"enabled": True, "effort": "low"}) == "low"
+        assert _effort({"enabled": True, "effort": "max"}) == "xhigh"
+        assert _effort({"enabled": False}) == "minimal"  # disabled -> minimal
+        # Meta 400s on "none" — must map to "minimal"
+        assert _effort({"effort": "none"}) == "minimal"
+
+    def test_never_uses_extra_body(self):
+        """The dial must be top-level, not extra_body.reasoning (core-gated path)."""
+        eb, top = _profile().build_api_kwargs_extras(
+            reasoning_config={"enabled": True, "effort": "high"}
+        )
+        assert eb == {}
+        assert "reasoning_effort" in top