refactor(azure_ai): type the Foundry param mapping override and drop test docstrings

The AzureAIStudioConfig.map_openai_params override now carries dict[str, object]
annotations instead of bare dict, and the docstrings added to the new tests go away
since the test names already say what they cover. No behavior change

litellm/llms/azure_ai/chat/transformation.py

@@ -77,11 +77,11 @@ class AzureAIStudioConfig(OpenAIConfig):
 
     def map_openai_params(
         self,
-        non_default_params: dict,  # mutable-ok: OpenAIConfig.map_openai_params signature
-        optional_params: dict,  # mutable-ok: OpenAIConfig.map_openai_params signature
+        non_default_params: dict[str, object],  # mutable-ok: OpenAIConfig.map_openai_params signature
+        optional_params: dict[str, object],  # mutable-ok: OpenAIConfig.map_openai_params signature
         model: str,
         drop_params: bool,
-    ) -> dict:  # mutable-ok: OpenAIConfig.map_openai_params signature
+    ) -> dict[str, object]:  # mutable-ok: OpenAIConfig.map_openai_params signature
         if not azureAIGPT5Config.is_model_gpt_5_model(model):
             return super().map_openai_params(
                 non_default_params=non_default_params,

tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py

@@ -2061,9 +2061,6 @@ def test_generic_cost_per_token_azure_gpt_6_astra_foundry_price_sheet(
 
 
 def test_generic_cost_per_token_azure_ai_gpt_6_astra_flex_bills_the_standard_rate(_local_model_cost_map):
-    """Foundry sells gpt-6-astra on Standard Global only, so a flex service_tier bills the standard rate.
-    The bare OpenAI card the azure_ai route fell back to before this entry existed carries flex prices
-    at half rate (LIT-7081)."""
     usage = Usage(prompt_tokens=1000, completion_tokens=100, total_tokens=1100)
 
     standard = generic_cost_per_token(model="azure_ai/gpt-6-astra", usage=usage, custom_llm_provider="azure_ai")

tests/test_litellm/llms/azure_ai/chat/test_azure_ai_transformation.py

@@ -147,9 +147,6 @@ def _local_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> None:
 
 
 def test_foundry_gpt_6_astra_keeps_sampling_params_when_reasoning_effort_is_none(_local_model_cost_map):
-    """A Foundry deployment reached through azure_ai reads the azure_ai/ card, where gpt-6-astra supports
-    reasoning_effort none, so temperature and top_p ride along; the bare OpenAI card says none is
-    unsupported and the route used to refuse temperature and drop top_p (LIT-7081)."""
     optional_params = AzureAIStudioConfig().map_openai_params(
         non_default_params={"reasoning_effort": "none", "temperature": 0.2, "top_p": 0.9},
         optional_params={},

tests/test_litellm/proxy/auth/test_model_checks.py

@@ -859,9 +859,6 @@ def test_add_known_models_refreshes_models_by_provider_for_wildcard_expansion():
 
 
 def test_azure_ai_wildcard_lists_the_foundry_gpt_6_astra_entry(monkeypatch):
-    """A Foundry (azure_ai) deployment of gpt-6-astra only shows up under an azure_ai/* wildcard
-    when the cost map carries its own azure_ai/ entry; the azure/ entry from the OpenAI-on-Azure
-    price sheet never reaches the Foundry provider list (LIT-7081)."""
     import litellm
     from litellm.proxy.auth.model_checks import get_known_models_from_wildcard
 

tests/test_litellm/router_utils/test_reasoning_effort_capability.py

@@ -400,8 +400,7 @@ class TestGpt6AstraAdvertisesItsDocumentedLevels:
     def test_a_foundry_deployment_also_advertises_none(self, local_model_cost_map, model, custom_llm_provider):
         """Microsoft Foundry serves the same model but its API accepts reasoning_effort none
         (verified live: 200 with zero reasoning tokens, and it unlocks temperature), which
-        OpenAI's rejects, so an Azure deployment offers none on top of low through max, whether
-        it is reached through the azure route or the azure_ai (Foundry) route."""
+        OpenAI's rejects, so an Azure deployment offers none on top of low through max."""
         from litellm.utils import _get_model_info_helper
 
         model_info = dict(_get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider))