test: keep the pinning-test removal free of unrelated reformatting

Regenerated every touched file from origin/main applying only the B1 test deletions and the unused import and helper cleanup they leave behind, without running the formatter across untouched code. CI only checks ruff format under litellm/, so the earlier reflows of test files were pure diff noise for reviewers

Also drops the tests/local_testing/test_prompt_caching.py entry from the caching-local shard in test-unit.yml since that file is deleted

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

.github/workflows/test-unit.yml

@@ -213,7 +213,6 @@ jobs:
             test-path: >-
               tests/local_testing/test_cache_preset_key.py
               tests/local_testing/test_caching_handler.py
-              tests/local_testing/test_prompt_caching.py
               tests/local_testing/test_responses_stream_cache_keys.py
               tests/local_testing/test_unit_test_caching.py
             workers: 2

tests/litellm_utils_tests/test_utils.py

@@ -34,9 +34,6 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 
 # Assuming your trim_messages, shorten_message_to_fit_limit, and get_token_count functions are all in a module named 'message_utils'
-
-
-# Test 1: Check trimming of normal message
 @pytest.fixture(autouse=True)
 def reset_mock_cache():
     from litellm.utils import _model_cache
@@ -44,6 +41,7 @@ def reset_mock_cache():
     _model_cache.flush_cache()
 
 
+# Test 1: Check trimming of normal message
 def test_basic_trimming():
     litellm._turn_on_debug()
     messages = [
@@ -73,7 +71,9 @@ def test_basic_trimming_no_max_tokens_specified():
     print("trimmed messages for gpt-4")
     print(trimmed_messages)
     # print(get_token_count(messages=trimmed_messages, model="claude-2"))
-    assert (get_token_count(messages=trimmed_messages, model="gpt-4")) <= litellm.model_cost["gpt-4"]["max_tokens"]
+    assert (
+        get_token_count(messages=trimmed_messages, model="gpt-4")
+    ) <= litellm.model_cost["gpt-4"]["max_tokens"]
 
 
 # test_basic_trimming_no_max_tokens_specified()
@@ -90,7 +90,9 @@ def test_multiple_messages_trimming():
             "content": "This is another long message that will also exceed the limit.",
         },
     ]
-    trimmed_messages = trim_messages(messages=messages, model="gpt-3.5-turbo", max_tokens=20)
+    trimmed_messages = trim_messages(
+        messages=messages, model="gpt-3.5-turbo", max_tokens=20
+    )
     # print(get_token_count(messages=trimmed_messages, model="gpt-3.5-turbo"))
     assert (get_token_count(messages=trimmed_messages, model="gpt-3.5-turbo")) <= 20
 
@@ -109,7 +111,9 @@ def test_multiple_messages_no_trimming():
             "content": "This is another long message that will also exceed the limit.",
         },
     ]
-    trimmed_messages = trim_messages(messages=messages, model="gpt-3.5-turbo", max_tokens=100)
+    trimmed_messages = trim_messages(
+        messages=messages, model="gpt-3.5-turbo", max_tokens=100
+    )
     print("Trimmed messages")
     print(trimmed_messages)
     assert messages == trimmed_messages
@@ -136,7 +140,9 @@ def test_large_trimming_multiple_messages():
 
 
 def test_large_trimming_single_message():
-    messages = [{"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}]
+    messages = [
+        {"role": "user", "content": "This is a singlelongwordthatexceedsthelimit."}
+    ]
     trimmed_messages = trim_messages(messages, max_tokens=5, model="gpt-4-0613")
     assert (get_token_count(messages=trimmed_messages, model="gpt-4-0613")) <= 5
     assert (get_token_count(messages=trimmed_messages, model="gpt-4-0613")) > 0
@@ -267,7 +273,10 @@ def test_trimming_with_model_cost_max_input_tokens(model):
         },
     ]
     trimmed_messages = trim_messages(messages, model=model)
-    assert get_token_count(trimmed_messages, model=model) < litellm.model_cost[model]["max_input_tokens"]
+    assert (
+        get_token_count(trimmed_messages, model=model)
+        < litellm.model_cost[model]["max_input_tokens"]
+    )
 
 
 def test_trimming_with_untokenizable_field(caplog: pytest.LogCaptureFixture) -> None:
@@ -320,7 +329,9 @@ def test_aget_valid_models():
         print(valid_models)
 
         # list of openai supported llms on litellm
-        expected_models = litellm.open_ai_chat_completion_models | litellm.open_ai_text_completion_models
+        expected_models = (
+            litellm.open_ai_chat_completion_models | litellm.open_ai_text_completion_models
+        )
 
         assert set(valid_models) == set(expected_models)
 
@@ -342,7 +353,9 @@ def test_get_valid_models_with_custom_llm_provider(custom_llm_provider):
         provider=LlmProviders(custom_llm_provider),
     )
     assert provider_config is not None
-    valid_models = get_valid_models(check_provider_endpoint=True, custom_llm_provider=custom_llm_provider)
+    valid_models = get_valid_models(
+        check_provider_endpoint=True, custom_llm_provider=custom_llm_provider
+    )
     print(valid_models)
     assert len(valid_models) > 0
     assert set(provider_config.get_models()) == set(valid_models)
@@ -375,7 +388,9 @@ def test_validate_environment_empty_model():
 
 def test_validate_environment_api_key():
     response_obj = validate_environment(model="gpt-5-mini", api_key="sk-my-test-key")
-    assert response_obj["keys_in_environment"] is True, f"Missing keys={response_obj['missing_keys']}"
+    assert (
+        response_obj["keys_in_environment"] is True
+    ), f"Missing keys={response_obj['missing_keys']}"
 
 
 def test_validate_environment_api_version():
@@ -385,7 +400,9 @@ def test_validate_environment_api_version():
         api_base="https://fake.openai.azure.com/",
         api_version="2024-02-15",
     )
-    assert response_obj["keys_in_environment"] is True, f"Missing keys={response_obj['missing_keys']}"
+    assert (
+        response_obj["keys_in_environment"] is True
+    ), f"Missing keys={response_obj['missing_keys']}"
 
 
 def test_validate_environment_api_base_dynamic():
@@ -460,14 +477,18 @@ def test_function_to_dict():
     assert function_json["description"] == expected_output["description"]
     assert function_json["parameters"]["type"] == expected_output["parameters"]["type"]
     assert (
-        function_json["parameters"]["properties"]["location"] == expected_output["parameters"]["properties"]["location"]
+        function_json["parameters"]["properties"]["location"]
+        == expected_output["parameters"]["properties"]["location"]
     )
 
     # the enum can change it can be - which is why we don't assert on unit
     # {'type': 'string', 'description': 'Temperature unit', 'enum': "['fahrenheit', 'celsius']"}
     # {'type': 'string', 'description': 'Temperature unit', 'enum': "['celsius', 'fahrenheit']"}
 
-    assert function_json["parameters"]["required"] == expected_output["parameters"]["required"]
+    assert (
+        function_json["parameters"]["required"]
+        == expected_output["parameters"]["required"]
+    )
 
     print("passed")
 
@@ -509,7 +530,9 @@ def test_get_chat_completion_prompt():
         prompt_variables=None,
     )
 
-    assert litellm_logging_obj.messages == [{"role": "user", "content": updated_message}]
+    assert litellm_logging_obj.messages == [
+        {"role": "user", "content": updated_message}
+    ]
 
 
 def test_redact_msgs_from_logs():
@@ -581,7 +604,9 @@ def test_redact_embedding_response():
     litellm.turn_off_message_logging = True
 
     # Create a test EmbeddingResponse with usage data
-    original_usage = litellm.Usage(prompt_tokens=10, completion_tokens=0, total_tokens=10)
+    original_usage = litellm.Usage(
+        prompt_tokens=10, completion_tokens=0, total_tokens=10
+    )
     original_data = [
         {"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3, 0.4, 0.5]},
         {"object": "embedding", "index": 1, "embedding": [0.6, 0.7, 0.8, 0.9, 1.0]},
@@ -617,7 +642,9 @@ def test_redact_embedding_response():
 
     # Assert the redacted response preserves critical metadata
     assert _redacted_response_obj.usage == original_usage  # usage should be preserved
-    assert _redacted_response_obj.model == "text-embedding-3-small"  # model should be preserved
+    assert (
+        _redacted_response_obj.model == "text-embedding-3-small"
+    )  # model should be preserved
     assert _redacted_response_obj.object == "list"  # object should be preserved
 
     # Assert sensitive data is cleared
@@ -671,8 +698,12 @@ def test_redact_msgs_from_logs_with_dynamic_params():
     )
 
     # Test Case 1: standard_callback_dynamic_params = False (or not set)
-    standard_callback_dynamic_params = StandardCallbackDynamicParams(turn_off_message_logging=False)
-    litellm_logging_obj.model_call_details["standard_callback_dynamic_params"] = standard_callback_dynamic_params
+    standard_callback_dynamic_params = StandardCallbackDynamicParams(
+        turn_off_message_logging=False
+    )
+    litellm_logging_obj.model_call_details["standard_callback_dynamic_params"] = (
+        standard_callback_dynamic_params
+    )
     _redacted_response_obj = redact_message_input_output_from_logging(
         result=response_obj,
         model_call_details=litellm_logging_obj.model_call_details,
@@ -681,8 +712,12 @@ def test_redact_msgs_from_logs_with_dynamic_params():
     assert _redacted_response_obj.choices[0].message.content == test_content
 
     # Test Case 2: standard_callback_dynamic_params = True
-    standard_callback_dynamic_params = StandardCallbackDynamicParams(turn_off_message_logging=True)
-    litellm_logging_obj.model_call_details["standard_callback_dynamic_params"] = standard_callback_dynamic_params
+    standard_callback_dynamic_params = StandardCallbackDynamicParams(
+        turn_off_message_logging=True
+    )
+    litellm_logging_obj.model_call_details["standard_callback_dynamic_params"] = (
+        standard_callback_dynamic_params
+    )
     _redacted_response_obj = redact_message_input_output_from_logging(
         result=response_obj,
         model_call_details=litellm_logging_obj.model_call_details,
@@ -693,7 +728,9 @@ def test_redact_msgs_from_logs_with_dynamic_params():
     # Test Case 3: standard_callback_dynamic_params does not set turn_off_message_logging
     # since litellm.turn_off_message_logging is True redaction should occur
     standard_callback_dynamic_params = StandardCallbackDynamicParams()
-    litellm_logging_obj.model_call_details["standard_callback_dynamic_params"] = standard_callback_dynamic_params
+    litellm_logging_obj.model_call_details["standard_callback_dynamic_params"] = (
+        standard_callback_dynamic_params
+    )
     _redacted_response_obj = redact_message_input_output_from_logging(
         result=response_obj,
         model_call_details=litellm_logging_obj.model_call_details,
@@ -798,7 +835,9 @@ def test_get_llm_provider_ft_models():
 
 
 @pytest.mark.parametrize("langfuse_trace_id", [None, "my-unique-trace-id"])
-@pytest.mark.parametrize("langfuse_existing_trace_id", [None, "my-unique-existing-trace-id"])
+@pytest.mark.parametrize(
+    "langfuse_existing_trace_id", [None, "my-unique-existing-trace-id"]
+)
 def test_logging_trace_id(langfuse_trace_id, langfuse_existing_trace_id):
     """
     - Unit test for `_get_trace_id` function in Logging obj
@@ -837,13 +876,22 @@ def test_logging_trace_id(langfuse_trace_id, langfuse_existing_trace_id):
 
     ## if existing_trace_id exists
     if langfuse_existing_trace_id is not None:
-        assert litellm_logging_obj._get_trace_id(service_name="langfuse") == langfuse_existing_trace_id
+        assert (
+            litellm_logging_obj._get_trace_id(service_name="langfuse")
+            == langfuse_existing_trace_id
+        )
     ## if trace_id exists
     elif langfuse_trace_id is not None:
-        assert litellm_logging_obj._get_trace_id(service_name="langfuse") == langfuse_trace_id
+        assert (
+            litellm_logging_obj._get_trace_id(service_name="langfuse")
+            == langfuse_trace_id
+        )
     ## if no trace_id or existing_trace_id is provided, use litellm_trace_id
     else:
-        assert litellm_logging_obj._get_trace_id(service_name="langfuse") == litellm_logging_obj.litellm_trace_id
+        assert (
+            litellm_logging_obj._get_trace_id(service_name="langfuse")
+            == litellm_logging_obj.litellm_trace_id
+        )
 
 
 def test_convert_model_response_object():
@@ -966,7 +1014,9 @@ def test_async_http_handler(mock_async_client):
     concurrent_limit = 2
 
     # Mock the transport creation to return a specific transport
-    with mock.patch.object(AsyncHTTPHandler, "_create_async_transport") as mock_create_transport:
+    with mock.patch.object(
+        AsyncHTTPHandler, "_create_async_transport"
+    ) as mock_create_transport:
         mock_transport = mock.MagicMock()
         mock_create_transport.return_value = mock_transport
 
@@ -1073,7 +1123,9 @@ def test_is_base64_encoded_2():
             [
                 {
                     "role": "user",
-                    "content": [{"type": "image_url", "url": "https://example.com/image.png"}],
+                    "content": [
+                        {"type": "image_url", "url": "https://example.com/image.png"}
+                    ],
                 }
             ],
             True,
@@ -1149,7 +1201,10 @@ def test_models_by_provider():
             continue
         elif k == "sample_spec":
             continue
-        elif v["litellm_provider"] == "sagemaker" or v["litellm_provider"] == "bedrock_converse":
+        elif (
+            v["litellm_provider"] == "sagemaker"
+            or v["litellm_provider"] == "bedrock_converse"
+        ):
             continue
         elif v.get("mode") in ("search", "evaluation"):
             continue
@@ -1157,7 +1212,9 @@ def test_models_by_provider():
             providers.add(v["litellm_provider"])
 
     for provider in providers:
-        assert provider in models_by_provider.keys() or JSONProviderRegistry.exists(provider)
+        assert provider in models_by_provider.keys() or JSONProviderRegistry.exists(
+            provider
+        )
 
 
 @pytest.mark.parametrize(
@@ -1168,11 +1225,16 @@ def test_models_by_provider():
         ({"user_api_key_end_user_id": "123"}, True, None),
     ],
 )
-def test_get_end_user_id_for_cost_tracking(litellm_params, disable_end_user_cost_tracking, expected_end_user_id):
+def test_get_end_user_id_for_cost_tracking(
+    litellm_params, disable_end_user_cost_tracking, expected_end_user_id
+):
     from litellm.utils import get_end_user_id_for_cost_tracking
 
     litellm.disable_end_user_cost_tracking = disable_end_user_cost_tracking
-    assert get_end_user_id_for_cost_tracking(litellm_params=litellm_params) == expected_end_user_id
+    assert (
+        get_end_user_id_for_cost_tracking(litellm_params=litellm_params)
+        == expected_end_user_id
+    )
 
 
 @pytest.mark.parametrize(
@@ -1188,9 +1250,13 @@ def test_get_end_user_id_for_cost_tracking_prometheus_only(
 ):
     from litellm.utils import get_end_user_id_for_cost_tracking
 
-    litellm.enable_end_user_cost_tracking_prometheus_only = enable_end_user_cost_tracking_prometheus_only
+    litellm.enable_end_user_cost_tracking_prometheus_only = (
+        enable_end_user_cost_tracking_prometheus_only
+    )
     assert (
-        get_end_user_id_for_cost_tracking(litellm_params=litellm_params, service_type="prometheus")
+        get_end_user_id_for_cost_tracking(
+            litellm_params=litellm_params, service_type="prometheus"
+        )
         == expected_end_user_id
     )
 
@@ -1205,14 +1271,20 @@ def test_get_end_user_id_for_cost_tracking_prometheus_only(
         ),
         # Test with only litellm_metadata field (new behavior)
         (
-            {"litellm_metadata": {"user_api_key_end_user_id": "user_from_litellm_metadata"}},
+            {
+                "litellm_metadata": {
+                    "user_api_key_end_user_id": "user_from_litellm_metadata"
+                }
+            },
             "user_from_litellm_metadata",
         ),
         # Test with both fields - metadata should take precedence for user_api_key fields
         (
             {
                 "metadata": {"user_api_key_end_user_id": "user_from_metadata"},
-                "litellm_metadata": {"user_api_key_end_user_id": "user_from_litellm_metadata"},
+                "litellm_metadata": {
+                    "user_api_key_end_user_id": "user_from_litellm_metadata"
+                },
             },
             "user_from_metadata",
         ),
@@ -1228,7 +1300,9 @@ def test_get_end_user_id_for_cost_tracking_prometheus_only(
         (
             {
                 "metadata": {},
-                "litellm_metadata": {"user_api_key_end_user_id": "user_from_litellm_metadata"},
+                "litellm_metadata": {
+                    "user_api_key_end_user_id": "user_from_litellm_metadata"
+                },
             },
             "user_from_litellm_metadata",
         ),
@@ -1236,7 +1310,9 @@ def test_get_end_user_id_for_cost_tracking_prometheus_only(
         ({}, None),
     ],
 )
-def test_get_end_user_id_for_cost_tracking_metadata_handling(litellm_params, expected_end_user_id):
+def test_get_end_user_id_for_cost_tracking_metadata_handling(
+    litellm_params, expected_end_user_id
+):
     """
     Test that get_end_user_id_for_cost_tracking correctly handles both metadata and litellm_metadata
     fields using the get_litellm_metadata_from_kwargs helper function.
@@ -1383,7 +1459,9 @@ def test_get_valid_models_openai_proxy(monkeypatch):
     mock_response.status_code = 200
     mock_response.json.return_value = mock_response_data
 
-    with patch.object(litellm.module_level_client, "get", return_value=mock_response) as mock_post:
+    with patch.object(
+        litellm.module_level_client, "get", return_value=mock_response
+    ) as mock_post:
         valid_models = get_valid_models(check_provider_endpoint=True)
         assert "litellm_proxy/gpt-5.5" in valid_models
 
@@ -1460,11 +1538,16 @@ def test_get_valid_models_fireworks_ai(monkeypatch):
     mock_response.status_code = 200
     mock_response.json.return_value = mock_response_data
 
-    with patch.object(litellm.module_level_client, "get", return_value=mock_response) as mock_post:
+    with patch.object(
+        litellm.module_level_client, "get", return_value=mock_response
+    ) as mock_post:
         valid_models = get_valid_models(check_provider_endpoint=True)
         print("valid_models", valid_models)
         mock_post.assert_called_once()
-        assert "fireworks_ai/accounts/fireworks/models/llama-3.1-8b-instruct" in valid_models
+        assert (
+            "fireworks_ai/accounts/fireworks/models/llama-3.1-8b-instruct"
+            in valid_models
+        )
 
 
 def test_get_valid_models_default(monkeypatch):
@@ -1494,7 +1577,9 @@ def test_pick_cheapest_chat_model_from_llm_provider():
 def test_get_num_retries(num_retries):
     from litellm.utils import _get_wrapper_num_retries
 
-    assert _get_wrapper_num_retries(kwargs={"num_retries": num_retries}, exception=Exception("test")) == (
+    assert _get_wrapper_num_retries(
+        kwargs={"num_retries": num_retries}, exception=Exception("test")
+    ) == (
         num_retries,
         {
             "num_retries": num_retries,
@@ -1767,7 +1852,9 @@ def test_add_custom_logger_callback_to_specific_event_e2e_failure(monkeypatch):
     assert len(litellm.success_callback) == curr_len_success_callback
     assert len(litellm.failure_callback) == curr_len_failure_callback
 
-    assert any(isinstance(callback, OpenMeterLogger) for callback in litellm.failure_callback)
+    assert any(
+        isinstance(callback, OpenMeterLogger) for callback in litellm.failure_callback
+    )
 
 
 @pytest.mark.asyncio
@@ -1794,13 +1881,20 @@ async def test_wrapper_kwargs_passthrough():
     mock_original.assert_called_once()
 
     # get litellm logging object
-    litellm_logging_obj: LiteLLMLoggingObject = mock_original.call_args.kwargs.get("litellm_logging_obj")
+    litellm_logging_obj: LiteLLMLoggingObject = mock_original.call_args.kwargs.get(
+        "litellm_logging_obj"
+    )
     assert litellm_logging_obj is not None
 
-    print(f"litellm_logging_obj.model_call_details: {litellm_logging_obj.model_call_details}")
+    print(
+        f"litellm_logging_obj.model_call_details: {litellm_logging_obj.model_call_details}"
+    )
 
     # get base model
-    assert litellm_logging_obj.model_call_details["litellm_params"]["base_model"] == "gpt-5-mini"
+    assert (
+        litellm_logging_obj.model_call_details["litellm_params"]["base_model"]
+        == "gpt-5-mini"
+    )
 
 
 def test_dict_to_response_format_helper():
@@ -1854,7 +1948,7 @@ def test_validate_user_messages_invalid_content_type():
 
     messages = [{"content": [{"type": "invalid_type", "text": "Hello"}]}]
 
-    with pytest.raises(Exception, match="Please ensure all messages are valid OpenAI chat completion") as e:
+    with pytest.raises(Exception, match='Please ensure all messages are valid OpenAI chat completion') as e:
         validate_chat_completion_user_messages(messages)
 
     assert "Invalid message" in str(e)
@@ -1871,14 +1965,20 @@ from unittest.mock import Mock
     [
         {
             "name": "default_on_guardrail",
-            "callbacks": [CustomGuardrail(guardrail_name="test_guardrail", default_on=True)],
+            "callbacks": [
+                CustomGuardrail(guardrail_name="test_guardrail", default_on=True)
+            ],
             "kwargs": {"metadata": {"requester_metadata": {"guardrails": []}}},
             "expected": ["test_guardrail"],
         },
         {
             "name": "request_specific_guardrail",
-            "callbacks": [CustomGuardrail(guardrail_name="test_guardrail", default_on=False)],
-            "kwargs": {"metadata": {"requester_metadata": {"guardrails": ["test_guardrail"]}}},
+            "callbacks": [
+                CustomGuardrail(guardrail_name="test_guardrail", default_on=False)
+            ],
+            "kwargs": {
+                "metadata": {"requester_metadata": {"guardrails": ["test_guardrail"]}}
+            },
             "expected": ["test_guardrail"],
         },
         {
@@ -1887,12 +1987,18 @@ from unittest.mock import Mock
                 CustomGuardrail(guardrail_name="default_guardrail", default_on=True),
                 CustomGuardrail(guardrail_name="request_guardrail", default_on=False),
             ],
-            "kwargs": {"metadata": {"requester_metadata": {"guardrails": ["request_guardrail"]}}},
+            "kwargs": {
+                "metadata": {
+                    "requester_metadata": {"guardrails": ["request_guardrail"]}
+                }
+            },
             "expected": ["default_guardrail", "request_guardrail"],
         },
         {
             "name": "empty_metadata",
-            "callbacks": [CustomGuardrail(guardrail_name="test_guardrail", default_on=False)],
+            "callbacks": [
+                CustomGuardrail(guardrail_name="test_guardrail", default_on=False)
+            ],
             "kwargs": {},
             "expected": [],
         },
@@ -1999,7 +2105,9 @@ def test_get_provider_audio_transcription_config():
     from litellm.types.utils import LlmProviders
 
     for provider in LlmProviders:
-        config = ProviderConfigManager.get_provider_audio_transcription_config(model="whisper-1", provider=provider)
+        config = ProviderConfigManager.get_provider_audio_transcription_config(
+            model="whisper-1", provider=provider
+        )
 
 
 @pytest.mark.parametrize(
@@ -2042,7 +2150,9 @@ def test_get_valid_models_from_provider_cache_invalidation(monkeypatch):
 
     monkeypatch.setenv("OPENAI_API_KEY", "123")
 
-    _model_cache.set_cached_model_info("openai", litellm_params=None, available_models=["gpt-5-mini"])
+    _model_cache.set_cached_model_info(
+        "openai", litellm_params=None, available_models=["gpt-5-mini"]
+    )
     monkeypatch.delenv("OPENAI_API_KEY")
 
     assert _model_cache.get_cached_model_info("openai") is None
@@ -2131,8 +2241,12 @@ def test_delta_tool_calls_sequential_indices():
     # Verify tool calls have sequential indices
     assert delta.tool_calls is not None, "Tool calls should not be None"
     assert len(delta.tool_calls) == 2
-    assert delta.tool_calls[0].index == 0, f"First tool call should have index 0, got {delta.tool_calls[0].index}"
-    assert delta.tool_calls[1].index == 1, f"Second tool call should have index 1, got {delta.tool_calls[1].index}"
+    assert (
+        delta.tool_calls[0].index == 0
+    ), f"First tool call should have index 0, got {delta.tool_calls[0].index}"
+    assert (
+        delta.tool_calls[1].index == 1
+    ), f"Second tool call should have index 1, got {delta.tool_calls[1].index}"
 
     # Verify tool call details are preserved
     assert delta.tool_calls[0].function.name == "get_weather_for_dallas"
@@ -2145,7 +2259,9 @@ def test_completion_with_no_model():
     """
     # test on empty
     with pytest.raises(TypeError):
-        response = litellm.completion(messages=[{"role": "user", "content": "Hello, how are you?"}])
+        response = litellm.completion(
+            messages=[{"role": "user", "content": "Hello, how are you?"}]
+        )
 
 
 def test_get_base_model_from_metadata():
@@ -2158,31 +2274,43 @@ def test_get_base_model_from_metadata():
     from litellm.utils import _get_base_model_from_metadata
 
     # Test 1: base_model in metadata (Chat Completions API pattern)
-    model_call_details_with_metadata = {"litellm_params": {"metadata": {"model_info": {"base_model": "azure/gpt-5.5"}}}}
+    model_call_details_with_metadata = {
+        "litellm_params": {"metadata": {"model_info": {"base_model": "azure/gpt-5.5"}}}
+    }
     result = _get_base_model_from_metadata(model_call_details_with_metadata)
     assert result == "azure/gpt-5.5", f"Expected 'azure/gpt-5.5', got {result}"
 
     # Test 2: base_model in litellm_metadata (Responses API and generic API calls pattern)
     model_call_details_with_litellm_metadata = {
-        "litellm_params": {"litellm_metadata": {"model_info": {"base_model": "azure/gpt-5-mini"}}}
+        "litellm_params": {
+            "litellm_metadata": {"model_info": {"base_model": "azure/gpt-5-mini"}}
+        }
     }
     result = _get_base_model_from_metadata(model_call_details_with_litellm_metadata)
     assert result == "azure/gpt-5-mini", f"Expected 'azure/gpt-5-mini', got {result}"
 
     # Test 3: base_model in litellm_params (direct base_model)
-    model_call_details_with_direct_base_model = {"litellm_params": {"base_model": "azure/gpt-5-mini"}}
+    model_call_details_with_direct_base_model = {
+        "litellm_params": {"base_model": "azure/gpt-5-mini"}
+    }
     result = _get_base_model_from_metadata(model_call_details_with_direct_base_model)
-    assert result == "azure/gpt-5-mini", f"Expected 'azure/gpt-5-mini', got {result}"
+    assert (
+        result == "azure/gpt-5-mini"
+    ), f"Expected 'azure/gpt-5-mini', got {result}"
 
     # Test 4: metadata takes precedence over litellm_metadata
     model_call_details_with_both = {
         "litellm_params": {
             "metadata": {"model_info": {"base_model": "azure/gpt-4-from-metadata"}},
-            "litellm_metadata": {"model_info": {"base_model": "azure/gpt-4-from-litellm-metadata"}},
+            "litellm_metadata": {
+                "model_info": {"base_model": "azure/gpt-4-from-litellm-metadata"}
+            },
         }
     }
     result = _get_base_model_from_metadata(model_call_details_with_both)
-    assert result == "azure/gpt-4-from-metadata", f"Expected metadata to take precedence, got {result}"
+    assert (
+        result == "azure/gpt-4-from-metadata"
+    ), f"Expected metadata to take precedence, got {result}"
 
     # Test 5: No base_model present
     model_call_details_without_base_model = {"litellm_params": {"metadata": {}}}

tests/llm_translation/test_azure_o_series.py

@@ -73,7 +73,9 @@ def test_azure_o3_streaming():
         api_version="2024-02-15-preview",
     )
 
-    with patch.object(client.chat.completions.with_raw_response, "create") as mock_create:
+    with patch.object(
+        client.chat.completions.with_raw_response, "create"
+    ) as mock_create:
         try:
             completion(
                 model="azure/o3-mini",
@@ -81,7 +83,9 @@ def test_azure_o3_streaming():
                 stream=True,
                 client=client,
             )
-        except Exception as e:  # expect output translation error as mock response doesn't return a json
+        except (
+            Exception
+        ) as e:  # expect output translation error as mock response doesn't return a json
             print(e)
         assert mock_create.call_count == 1
         assert "stream" in mock_create.call_args.kwargs
@@ -100,7 +104,9 @@ def test_azure_o_series_routing():
         api_version="2024-02-15-preview",
     )
 
-    with patch.object(client.chat.completions.with_raw_response, "create") as mock_create:
+    with patch.object(
+        client.chat.completions.with_raw_response, "create"
+    ) as mock_create:
         try:
             completion(
                 model="azure/o_series/my-random-deployment-name",
@@ -108,7 +114,9 @@ def test_azure_o_series_routing():
                 stream=True,
                 client=client,
             )
-        except Exception as e:  # expect output translation error as mock response doesn't return a json
+        except (
+            Exception
+        ) as e:  # expect output translation error as mock response doesn't return a json
             print(e)
         assert mock_create.call_count == 1
         assert "stream" not in mock_create.call_args.kwargs
@@ -175,7 +183,9 @@ async def test_azure_o1_series_response_format_extra_params():
     ]
     response_format = {"type": "json_object"}
     tool_choice = "auto"
-    with patch.object(client.chat.completions.with_raw_response, "create") as mock_client:
+    with patch.object(
+        client.chat.completions.with_raw_response, "create"
+    ) as mock_client:
         try:
             await litellm.acompletion(
                 client=client,

tests/llm_translation/test_lambda_ai.py

@@ -44,7 +44,9 @@ def test_lambda_ai_get_openai_compatible_provider_info():
         os.environ,
         {"LAMBDA_API_KEY": "env-key", "LAMBDA_API_BASE": "https://env.lambda.ai/v1"},
     ):
-        api_base, api_key = config._get_openai_compatible_provider_info("https://param.lambda.ai/v1", "param-key")
+        api_base, api_key = config._get_openai_compatible_provider_info(
+            "https://param.lambda.ai/v1", "param-key"
+        )
         assert api_base == "https://param.lambda.ai/v1"
         assert api_key == "param-key"
 
@@ -54,12 +56,16 @@ def test_get_llm_provider_lambda_ai():
     from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
 
     # Test with lambda_ai/model-name format
-    model, provider, api_key, api_base = get_llm_provider("lambda_ai/llama3.1-8b-instruct")
+    model, provider, api_key, api_base = get_llm_provider(
+        "lambda_ai/llama3.1-8b-instruct"
+    )
     assert model == "llama3.1-8b-instruct"
     assert provider == "lambda_ai"
 
     # Test with api_base containing Lambda AI endpoint
-    model, provider, api_key, api_base = get_llm_provider("llama3.1-8b-instruct", api_base="https://api.lambda.ai/v1")
+    model, provider, api_key, api_base = get_llm_provider(
+        "llama3.1-8b-instruct", api_base="https://api.lambda.ai/v1"
+    )
     assert model == "llama3.1-8b-instruct"
     assert provider == "lambda_ai"
     assert api_base == "https://api.lambda.ai/v1"
@@ -94,3 +100,5 @@ async def test_lambda_ai_completion_call():
         if "lambda_ai" not in str(e) and "provider" not in str(e).lower():
             # Re-raise if it's not a provider-related error
             raise
+
+

tests/llm_translation/test_perplexity_reasoning.py

@@ -25,7 +25,9 @@ class TestPerplexityReasoning:
             ("perplexity/sonar-reasoning-pro", "high"),
         ],
     )
-    def test_perplexity_reasoning_effort_parameter_mapping(self, model, reasoning_effort):
+    def test_perplexity_reasoning_effort_parameter_mapping(
+        self, model, reasoning_effort
+    ):
         """
         Test that reasoning_effort parameter is correctly mapped for Perplexity Sonar reasoning models
         """
@@ -102,6 +104,7 @@ class TestPerplexityReasoning:
             "create",
             side_effect=_return_pydantic_obj,
         ) as mock_client:
+
             response = completion(
                 model=model,
                 messages=[
@@ -127,7 +130,11 @@ class TestPerplexityReasoning:
 
             # Verify response structure
             assert response.choices[0].message.content is not None
-            assert response.choices[0].message.content == "This is a test response from the reasoning model."
+            assert (
+                response.choices[0].message.content
+                == "This is a test response from the reasoning model."
+            )
+
 
     @pytest.mark.parametrize(
         "model,expected_api_base",
@@ -136,14 +143,18 @@ class TestPerplexityReasoning:
             ("perplexity/sonar-reasoning-pro", "https://api.perplexity.ai"),
         ],
     )
-    def test_perplexity_reasoning_api_base_configuration(self, model, expected_api_base):
+    def test_perplexity_reasoning_api_base_configuration(
+        self, model, expected_api_base
+    ):
         """
         Test that Perplexity reasoning models use the correct API base
         """
         from litellm.llms.perplexity.chat.transformation import PerplexityChatConfig
 
         config = PerplexityChatConfig()
-        api_base, _ = config._get_openai_compatible_provider_info(api_base=None, api_key="test-key")
+        api_base, _ = config._get_openai_compatible_provider_info(
+            api_base=None, api_key="test-key"
+        )
 
         assert api_base == expected_api_base
 
@@ -154,6 +165,8 @@ class TestPerplexityReasoning:
         from litellm.llms.perplexity.chat.transformation import PerplexityChatConfig
 
         config = PerplexityChatConfig()
-        supported_params = config.get_supported_openai_params(model="perplexity/sonar-reasoning")
+        supported_params = config.get_supported_openai_params(
+            model="perplexity/sonar-reasoning"
+        )
 
         assert "reasoning_effort" in supported_params

tests/local_testing/test_completion_cost.py

@@ -149,6 +149,7 @@ def test_custom_pricing_as_completion_cost_param():
 
     assert round(cost, 5) == round(expected_cost, 5)
 
+
     # print(results)
 
 
@@ -189,17 +190,23 @@ def test_cost_ft_gpt_35():
             usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38),
         )
 
-        cost = litellm.completion_cost(completion_response=resp, custom_llm_provider="openai")
+        cost = litellm.completion_cost(
+            completion_response=resp, custom_llm_provider="openai"
+        )
         print("\n Calculated Cost for ft:gpt-3.5", cost)
         input_cost = model_cost["ft:gpt-3.5-turbo"]["input_cost_per_token"]
         output_cost = model_cost["ft:gpt-3.5-turbo"]["output_cost_per_token"]
         print(input_cost, output_cost)
-        expected_cost = (input_cost * resp.usage.prompt_tokens) + (output_cost * resp.usage.completion_tokens)
+        expected_cost = (input_cost * resp.usage.prompt_tokens) + (
+            output_cost * resp.usage.completion_tokens
+        )
         print("\n Excpected cost", expected_cost)
         assert cost == expected_cost
     except Exception as e:
         print(f"Error: {e}")
-        pytest.fail(f"Cost Calc failed for ft:gpt-3.5. Expected {expected_cost}, Calculated cost {cost}")
+        pytest.fail(
+            f"Cost Calc failed for ft:gpt-3.5. Expected {expected_cost}, Calculated cost {cost}"
+        )
 
 
 # test_cost_ft_gpt_35()
@@ -228,11 +235,15 @@ def test_cost_azure_gpt_35():
             usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38),
         )
 
-        cost = litellm.completion_cost(completion_response=resp, model="azure/chatgpt-deployment-2")
+        cost = litellm.completion_cost(
+            completion_response=resp, model="azure/chatgpt-deployment-2"
+        )
         print("\n Calculated Cost for azure/gpt-3.5-turbo", cost)
         input_cost = model_cost["azure/gpt-35-turbo"]["input_cost_per_token"]
         output_cost = model_cost["azure/gpt-35-turbo"]["output_cost_per_token"]
-        expected_cost = (input_cost * resp.usage.prompt_tokens) + (output_cost * resp.usage.completion_tokens)
+        expected_cost = (input_cost * resp.usage.prompt_tokens) + (
+            output_cost * resp.usage.completion_tokens
+        )
         print("\n Excpected cost", expected_cost)
         assert cost == expected_cost
     except Exception as e:
@@ -249,7 +260,9 @@ def test_cost_bedrock_pricing_actual_calls():
     litellm.set_verbose = True
     model = "anthropic.claude-3-5-sonnet-20240620-v1:0"
     messages = [{"role": "user", "content": "Hey, how's it going?"}]
-    response = litellm.completion(model=model, messages=messages, mock_response="hello cool one")
+    response = litellm.completion(
+        model=model, messages=messages, mock_response="hello cool one"
+    )
 
     print("response", response)
     cost = litellm.completion_cost(
@@ -280,7 +293,8 @@ def test_whisper_openai():
     print(f"cost: {cost}")
     print(f"whisper dict: {litellm.model_cost['whisper-1']}")
     expected_cost = round(
-        litellm.model_cost["whisper-1"]["output_cost_per_second"] * _total_time_in_seconds,
+        litellm.model_cost["whisper-1"]["output_cost_per_second"]
+        * _total_time_in_seconds,
         5,
     )
     assert round(cost, 5) == round(expected_cost, 5)
@@ -300,12 +314,15 @@ def test_whisper_azure():
     _total_time_in_seconds = 3
     setattr(transcription, "duration", _total_time_in_seconds)
 
-    cost = litellm.completion_cost(model="azure/azure-whisper", completion_response=transcription)
+    cost = litellm.completion_cost(
+        model="azure/azure-whisper", completion_response=transcription
+    )
 
     print(f"cost: {cost}")
     print(f"whisper dict: {litellm.model_cost['whisper-1']}")
     expected_cost = round(
-        litellm.model_cost["whisper-1"]["output_cost_per_second"] * _total_time_in_seconds,
+        litellm.model_cost["whisper-1"]["output_cost_per_second"]
+        * _total_time_in_seconds,
         5,
     )
     assert round(cost, 5) == round(expected_cost, 5)
@@ -336,7 +353,9 @@ def test_dalle_3_azure_cost_tracking():
     response.usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
     response._hidden_params = {"model": "dall-e-3", "model_id": None}
     print(f"response hidden params: {response._hidden_params}")
-    cost = litellm.completion_cost(completion_response=response, call_type="image_generation")
+    cost = litellm.completion_cost(
+        completion_response=response, call_type="image_generation"
+    )
     assert cost > 0
 
 
@@ -368,7 +387,9 @@ def test_replicate_llama3_cost_tracking():
         model="replicate/meta/meta-llama-3-8b-instruct",
         object="chat.completion",
         system_fingerprint=None,
-        usage=litellm.utils.Usage(prompt_tokens=48, completion_tokens=31, total_tokens=79),
+        usage=litellm.utils.Usage(
+            prompt_tokens=48, completion_tokens=31, total_tokens=79
+        ),
     )
     cost = litellm.completion_cost(
         completion_response=response,
@@ -378,8 +399,14 @@ def test_replicate_llama3_cost_tracking():
     print(f"cost: {cost}")
     cost = round(cost, 5)
     expected_cost = round(
-        litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"]["input_cost_per_token"] * 48
-        + litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"]["output_cost_per_token"] * 31,
+        litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"][
+            "input_cost_per_token"
+        ]
+        * 48
+        + litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"][
+            "output_cost_per_token"
+        ]
+        * 31,
         5,
     )
     assert cost == expected_cost
@@ -543,7 +570,9 @@ def test_vertex_ai_medlm_completion_cost():
 
     model = "vertex_ai/medlm-medium"
     messages = [{"role": "user", "content": "Test MedLM completion cost."}]
-    predictive_cost = completion_cost(model=model, messages=messages, custom_llm_provider="vertex_ai")
+    predictive_cost = completion_cost(
+        model=model, messages=messages, custom_llm_provider="vertex_ai"
+    )
     assert predictive_cost > 0
 
     model = "vertex_ai/medlm-large"
@@ -560,7 +589,9 @@ def test_vertex_ai_embedding_completion_cost(caplog):
     litellm.model_cost = litellm.get_model_cost_map(url="")
 
     text = "The quick brown fox jumps over the lazy dog."
-    input_tokens = litellm.token_counter(model="vertex_ai/text-embedding-004", text=text)
+    input_tokens = litellm.token_counter(
+        model="vertex_ai/text-embedding-004", text=text
+    )
 
     model_info = litellm.get_model_info(model="vertex_ai/text-embedding-004")
 
@@ -583,7 +614,10 @@ def test_vertex_ai_embedding_completion_cost(caplog):
     captured_logs = [rec.message for rec in caplog.records]
     for item in captured_logs:
         print("\nitem:{}\n".format(item))
-        if "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): Exception occured " in item:
+        if (
+            "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): Exception occured "
+            in item
+        ):
             raise Exception("Error log raised for calculating embedding cost")
 
 
@@ -653,7 +687,9 @@ def test_vertex_ai_llama_predict_cost():
     model = "meta/llama3-405b-instruct-maas"
     messages = [{"role": "user", "content": "Hey, hows it going???"}]
     custom_llm_provider = "vertex_ai"
-    predictive_cost = completion_cost(model=model, messages=messages, custom_llm_provider=custom_llm_provider)
+    predictive_cost = completion_cost(
+        model=model, messages=messages, custom_llm_provider=custom_llm_provider
+    )
 
     assert predictive_cost == 0
 
@@ -667,7 +703,9 @@ def test_vertex_ai_mistral_predict_cost(usage):
     else:
         from openai.types.completion_usage import CompletionUsage
 
-        response_usage = CompletionUsage(prompt_tokens=32, completion_tokens=55, total_tokens=87)
+        response_usage = CompletionUsage(
+            prompt_tokens=32, completion_tokens=55, total_tokens=87
+        )
     response_object = ModelResponse(
         id="26c0ef045020429d9c5c9b078c01e564",
         choices=[
@@ -701,7 +739,9 @@ def test_vertex_ai_mistral_predict_cost(usage):
     assert predictive_cost > 0
 
 
-@pytest.mark.parametrize("model", ["openai/tts-1", "azure/tts-1", "openai/gpt-4o-mini-tts"])
+@pytest.mark.parametrize(
+    "model", ["openai/tts-1", "azure/tts-1", "openai/gpt-4o-mini-tts"]
+)
 def test_completion_cost_tts(model):
     os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
     litellm.model_cost = litellm.get_model_cost_map(url="")
@@ -801,7 +841,9 @@ def test_completion_cost_azure_common_deployment_name():
     response._hidden_params["custom_llm_provider"] = "azure"
     print(response)
 
-    with patch.object(litellm.cost_calculator, "completion_cost", new=MagicMock()) as mock_client:
+    with patch.object(
+        litellm.cost_calculator, "completion_cost", new=MagicMock()
+    ) as mock_client:
         _ = litellm.response_cost_calculator(
             response_object=response,
             model="gpt-4-0314",
@@ -861,7 +903,9 @@ def test_completion_cost_prompt_caching(model, custom_llm_provider):
 
     cost_1 = completion_cost(model=model, completion_response=response_1)
 
-    _model_info = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider)
+    _model_info = litellm.get_model_info(
+        model=model, custom_llm_provider=custom_llm_provider
+    )
     expected_cost = (
         (
             response_1.usage.prompt_tokens
@@ -869,9 +913,12 @@ def test_completion_cost_prompt_caching(model, custom_llm_provider):
             - response_1.usage.prompt_tokens_details.cache_creation_tokens
         )
         * _model_info["input_cost_per_token"]
-        + (response_1.usage.prompt_tokens_details.cached_tokens or 0) * _model_info["cache_read_input_token_cost"]
-        + (response_1.usage.cache_creation_input_tokens or 0) * _model_info["cache_creation_input_token_cost"]
-        + (response_1.usage.completion_tokens or 0) * _model_info["output_cost_per_token"]
+        + (response_1.usage.prompt_tokens_details.cached_tokens or 0)
+        * _model_info["cache_read_input_token_cost"]
+        + (response_1.usage.cache_creation_input_tokens or 0)
+        * _model_info["cache_creation_input_token_cost"]
+        + (response_1.usage.completion_tokens or 0)
+        * _model_info["output_cost_per_token"]
     )  # Cost of processing (non-cache hit + cache hit) + Cost of cache-writing (cache writing)
 
     assert round(expected_cost, 5) == round(cost_1, 5)
@@ -987,7 +1034,9 @@ def test_completion_cost_databricks_embedding(model, monkeypatch):
     sync_handler = HTTPHandler()
 
     with patch.object(HTTPHandler, "post", return_value=mock_response):
-        resp = litellm.embedding(model=model, input=["hey, how's it going?"], client=sync_handler)
+        resp = litellm.embedding(
+            model=model, input=["hey, how's it going?"], client=sync_handler
+        )
 
         print(resp)
         cost = completion_cost(completion_response=resp)
@@ -1163,9 +1212,11 @@ def test_cost_openai_prompt_caching():
     usage = response_2.usage
 
     _expected_cost2 = (
-        (usage.prompt_tokens - usage.prompt_tokens_details.cached_tokens) * model_info["input_cost_per_token"]
+        (usage.prompt_tokens - usage.prompt_tokens_details.cached_tokens)
+        * model_info["input_cost_per_token"]
         + usage.completion_tokens * model_info["output_cost_per_token"]
-        + usage.prompt_tokens_details.cached_tokens * model_info["cache_read_input_token_cost"]
+        + usage.prompt_tokens_details.cached_tokens
+        * model_info["cache_read_input_token_cost"]
     )
 
     print("_expected_cost2", _expected_cost2)
@@ -1206,7 +1257,9 @@ def test_completion_cost_azure_ai_rerank(model):
         },
     )
     print("response", response)
-    cost = completion_cost(model=model, completion_response=response, call_type="arerank")
+    cost = completion_cost(
+        model=model, completion_response=response, call_type="arerank"
+    )
     assert cost > 0
 
 
@@ -2158,7 +2211,9 @@ async def test_test_completion_cost_gpt4o_audio_output_from_model(stream):
         completion_tokens=34,
         prompt_tokens=16,
         total_tokens=50,
-        completion_tokens_details=CompletionTokensDetailsWrapper(audio_tokens=28, reasoning_tokens=0, text_tokens=6),
+        completion_tokens_details=CompletionTokensDetailsWrapper(
+            audio_tokens=28, reasoning_tokens=0, text_tokens=6
+        ),
         prompt_tokens_details=PromptTokensDetailsWrapper(
             audio_tokens=0, cached_tokens=0, text_tokens=16, image_tokens=0
         ),
@@ -2197,15 +2252,27 @@ async def test_test_completion_cost_gpt4o_audio_output_from_model(stream):
     print(f"model_info: {model_info}")
     ## input cost
 
-    input_audio_cost = model_info["input_cost_per_audio_token"] * usage_object.prompt_tokens_details.audio_tokens
-    input_text_cost = model_info["input_cost_per_token"] * usage_object.prompt_tokens_details.text_tokens
+    input_audio_cost = (
+        model_info["input_cost_per_audio_token"]
+        * usage_object.prompt_tokens_details.audio_tokens
+    )
+    input_text_cost = (
+        model_info["input_cost_per_token"]
+        * usage_object.prompt_tokens_details.text_tokens
+    )
 
     total_input_cost = input_audio_cost + input_text_cost
 
     ## output cost
 
-    output_audio_cost = model_info["output_cost_per_audio_token"] * usage_object.completion_tokens_details.audio_tokens
-    output_text_cost = model_info["output_cost_per_token"] * usage_object.completion_tokens_details.text_tokens
+    output_audio_cost = (
+        model_info["output_cost_per_audio_token"]
+        * usage_object.completion_tokens_details.audio_tokens
+    )
+    output_text_cost = (
+        model_info["output_cost_per_token"]
+        * usage_object.completion_tokens_details.text_tokens
+    )
 
     total_output_cost = output_audio_cost + output_text_cost
 
@@ -2331,7 +2398,9 @@ def test_moderations():
     litellm.add_known_models()
 
     assert "omni-moderation-latest" in litellm.model_cost
-    print(f"litellm.model_cost['omni-moderation-latest']: {litellm.model_cost['omni-moderation-latest']}")
+    print(
+        f"litellm.model_cost['omni-moderation-latest']: {litellm.model_cost['omni-moderation-latest']}"
+    )
     assert "omni-moderation-latest" in litellm.open_ai_chat_completion_models
 
     response = moderation("I am a bad person", model="omni-moderation-latest")
@@ -2368,7 +2437,9 @@ def test_cost_calculator_azure_embedding():
 
 def test_add_known_models():
     litellm.add_known_models()
-    assert "bedrock/us-west-1/meta.llama3-70b-instruct-v1:0" not in litellm.bedrock_models
+    assert (
+        "bedrock/us-west-1/meta.llama3-70b-instruct-v1:0" not in litellm.bedrock_models
+    )
 
 
 @pytest.mark.skip(reason="flaky test")
@@ -2478,7 +2549,9 @@ def test_cost_calculator_with_base_model_with_router(base_model_arg):
     }
 
     if base_model_arg == "litellm_param":
-        model_item["litellm_params"]["base_model"] = "bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
+        model_item["litellm_params"][
+            "base_model"
+        ] = "bedrock/anthropic.claude-3-sonnet-20240229-v1:0"
     elif base_model_arg == "model_info":
         model_item["model_info"] = {
             "base_model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",

tests/local_testing/test_get_model_info.py

@@ -114,13 +114,19 @@ def test_get_model_info_ft_model_with_provider_prefix():
     assert info["key"] == "ft:gpt-3.5-turbo"
 
 
-def _enforce_bedrock_converse_models(model_cost: List[Dict[str, Any]], whitelist_models: List[str]):
+def _enforce_bedrock_converse_models(
+    model_cost: List[Dict[str, Any]], whitelist_models: List[str]
+):
     """
     Assert all new bedrock chat models are added as `bedrock_converse` unless explicitly whitelisted.
     """
     # Check for unwhitelisted models
     for model, info in litellm.model_cost.items():
-        if info["litellm_provider"] == "bedrock" and info["mode"] == "chat" and model not in whitelist_models:
+        if (
+            info["litellm_provider"] == "bedrock"
+            and info["mode"] == "chat"
+            and model not in whitelist_models
+        ):
             raise AssertionError(
                 f"New bedrock chat model detected: {model}. Please set `litellm_provider='bedrock_converse'` for this model."
             )
@@ -141,7 +147,9 @@ def test_model_info_bedrock_converse(monkeypatch):
     except FileNotFoundError:
         pytest.skip("whitelisted_bedrock_models.txt not found")
 
-    _enforce_bedrock_converse_models(model_cost=litellm.model_cost, whitelist_models=whitelist_models)
+    _enforce_bedrock_converse_models(
+        model_cost=litellm.model_cost, whitelist_models=whitelist_models
+    )
 
 
 @pytest.mark.flaky(retries=6, delay=2)
@@ -165,8 +173,10 @@ def test_model_info_bedrock_converse_enforcement(monkeypatch):
 
         # Check for unwhitelisted models
         with pytest.raises(AssertionError):
-            _enforce_bedrock_converse_models(model_cost=litellm.model_cost, whitelist_models=whitelist_models)
-    except FileNotFoundError:
+            _enforce_bedrock_converse_models(
+                model_cost=litellm.model_cost, whitelist_models=whitelist_models
+            )
+    except FileNotFoundError as e:
         pytest.skip("whitelisted_bedrock_models.txt not found")
 
 
@@ -203,7 +213,9 @@ def test_get_model_info_custom_provider():
     # Get registered model info
     from litellm import get_model_info
 
-    get_model_info(model="my-custom-llm/my-fake-model")  # 💥 "Exception: This model isn't mapped yet." in v1.56.10
+    get_model_info(
+        model="my-custom-llm/my-fake-model"
+    )  # 💥 "Exception: This model isn't mapped yet." in v1.56.10
 
 
 def test_get_model_info_custom_model_router():
@@ -255,7 +267,11 @@ def test_get_model_info_bedrock_models():
                     k = k.replace(f"{commitment}/", "")
             base_model = BedrockModelInfo.get_base_model(k)
             # get_base_model() returns model id without "bedrock/" prefix; cost map keys use "bedrock/<model>"
-            base_model_key = base_model if base_model in litellm.model_cost else f"bedrock/{base_model}"
+            base_model_key = (
+                base_model
+                if base_model in litellm.model_cost
+                else f"bedrock/{base_model}"
+            )
             if base_model_key not in litellm.model_cost:
                 continue
             base_model_info = litellm.model_cost[base_model_key]
@@ -263,10 +279,12 @@ def test_get_model_info_bedrock_models():
                 if "invoke/" in k:
                     continue
                 if base_model_key.startswith("supports_"):
-                    assert base_model_key in v, f"{base_model_key} is not in model cost map for {k}"
-                    assert v[base_model_key] == base_model_value, (
-                        f"{base_model_key} is not equal to {base_model_value} for model {k}"
-                    )
+                    assert (
+                        base_model_key in v
+                    ), f"{base_model_key} is not in model cost map for {k}"
+                    assert (
+                        v[base_model_key] == base_model_value
+                    ), f"{base_model_key} is not equal to {base_model_value} for model {k}"
 
 
 def test_get_model_info_bedrock_cross_region_capability_parity():
@@ -294,7 +312,9 @@ def test_get_model_info_bedrock_cross_region_capability_parity():
             if not cap.startswith("supports_"):
                 continue
             assert cap in v, f"{cap} is on {base_model_key} but missing from {k}"
-            assert v[cap] == base_value, f"{cap} is {v[cap]} on {k} but {base_value} on {base_model_key}"
+            assert (
+                v[cap] == base_value
+            ), f"{cap} is {v[cap]} on {k} but {base_value} on {base_model_key}"
 
     assert checked > 0, "no cross-region bedrock profiles found - the filter is inert"
 
@@ -355,17 +375,23 @@ def test_get_model_info_case_insensitive_lookup(monkeypatch):
     )
 
     # Test 1: Exact case should work
-    info = litellm.get_model_info(model="Qwen/Qwen3-Next-80B-A3B-Thinking", custom_llm_provider="together_ai")
+    info = litellm.get_model_info(
+        model="Qwen/Qwen3-Next-80B-A3B-Thinking", custom_llm_provider="together_ai"
+    )
     assert info is not None
     assert info["supports_function_calling"] is True
 
     # Test 2: Lowercase should also work (case-insensitive lookup)
-    info_lower = litellm.get_model_info(model="qwen/qwen3-next-80b-a3b-thinking", custom_llm_provider="together_ai")
+    info_lower = litellm.get_model_info(
+        model="qwen/qwen3-next-80b-a3b-thinking", custom_llm_provider="together_ai"
+    )
     assert info_lower is not None
     assert info_lower["supports_function_calling"] is True
 
     # Test 3: Mixed case should also work
-    info_mixed = litellm.get_model_info(model="QWEN/qwen3-NEXT-80b-a3b-thinking", custom_llm_provider="together_ai")
+    info_mixed = litellm.get_model_info(
+        model="QWEN/qwen3-NEXT-80b-a3b-thinking", custom_llm_provider="together_ai"
+    )
     assert info_mixed is not None
     assert info_mixed["supports_function_calling"] is True
 
@@ -393,7 +419,13 @@ def test_get_model_info_case_insensitive_supports_function_calling(monkeypatch):
     from litellm.utils import supports_function_calling
 
     # Exact case
-    assert supports_function_calling("TestModel-ABC", custom_llm_provider="test_provider") is True
+    assert (
+        supports_function_calling("TestModel-ABC", custom_llm_provider="test_provider")
+        is True
+    )
 
     # Lowercase (should now work with case-insensitive lookup)
-    assert supports_function_calling("testmodel-abc", custom_llm_provider="test_provider") is True
+    assert (
+        supports_function_calling("testmodel-abc", custom_llm_provider="test_provider")
+        is True
+    )

tests/local_testing/test_register_model.py

@@ -41,7 +41,9 @@ def test_update_model_cost_via_completion():
             input_cost_per_token=0.3,
             output_cost_per_token=0.4,
         )
-        print(f"litellm.model_cost for gpt-3.5-turbo: {litellm.model_cost['gpt-3.5-turbo']}")
+        print(
+            f"litellm.model_cost for gpt-3.5-turbo: {litellm.model_cost['gpt-3.5-turbo']}"
+        )
         assert litellm.model_cost["gpt-3.5-turbo"]["input_cost_per_token"] == 0.3
         assert litellm.model_cost["gpt-3.5-turbo"]["output_cost_per_token"] == 0.4
     except Exception as e:
@@ -50,7 +52,11 @@ def test_update_model_cost_via_completion():
 
 def test_no_test_invocation_at_module_scope():
     tree = ast.parse(Path(__file__).read_text())
-    defined = {node.name for node in tree.body if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef))}
+    defined = {
+        node.name
+        for node in tree.body
+        if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef))
+    }
     invoked = [
         node.value.func.id
         for node in tree.body

tests/test_litellm/integrations/test_anthropic_cache_control_hook.py

@@ -1586,10 +1586,12 @@ class TestEnableAnthropicPromptCaching:
         points = self._points(model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", provider="bedrock")
         assert [p["index"] for p in points] == [None, -1]
 
+
     def test_model_without_caching_support_not_injected(self, monkeypatch):
         monkeypatch.setattr(litellm, "enable_anthropic_prompt_caching", True)
         assert self._points(model="anthropic.claude-3-5-sonnet-20240620-v1:0", provider="bedrock") == []
 
+
     def test_stands_down_when_client_sent_cache_control(self, monkeypatch):
         monkeypatch.setattr(litellm, "enable_anthropic_prompt_caching", True)
         messages = [
@@ -1631,9 +1633,7 @@ class TestEnableAnthropicPromptCaching:
         """OpenAI-shaped tools nest cache_control under ``function``; the Anthropic
         chat transform honors that location, so the stand-down must see it too."""
         monkeypatch.setattr(litellm, "enable_anthropic_prompt_caching", True)
-        tools = [
-            {"type": "function", "function": {"name": "t", "parameters": {}, "cache_control": {"type": "ephemeral"}}}
-        ]
+        tools = [{"type": "function", "function": {"name": "t", "parameters": {}, "cache_control": {"type": "ephemeral"}}}]
         assert self._points(tools=tools) == []
 
     def test_seed_stands_down_when_only_tools_carry_cache_control(self, monkeypatch):
@@ -2218,7 +2218,9 @@ class TestAnthropicPromptCachingEnvVars:
             print(json.dumps([litellm.enable_anthropic_prompt_caching, litellm.anthropic_prompt_caching_ttl]))
             """
         )
-        result = subprocess.run([sys.executable, "-c", script], capture_output=True, text=True, env=env, timeout=300)
+        result = subprocess.run(
+            [sys.executable, "-c", script], capture_output=True, text=True, env=env, timeout=300
+        )
         assert result.returncode == 0, result.stderr
         enabled, ttl = json.loads(result.stdout.strip().splitlines()[-1])
         return enabled, ttl
@@ -2429,9 +2431,7 @@ class TestOpenAIPromptCacheBreakpoint:
         assert kwargs == {}
 
     def test_v1_messages_client_content_breakpoint_makes_configured_points_stand_down(self):
-        messages = [
-            {"role": "user", "content": [{"type": "text", "text": "hi", "prompt_cache_breakpoint": self.EXPLICIT}]}
-        ]
+        messages = [{"role": "user", "content": [{"type": "text", "text": "hi", "prompt_cache_breakpoint": self.EXPLICIT}]}]
         kwargs = {"cache_control_injection_points": copy.deepcopy(self.SYSTEM_POINT)}
         result, system = self._inject(messages, "sys", kwargs)
         assert result == messages
@@ -2567,11 +2567,7 @@ class TestOpenAIPromptCacheBreakpointPlacementRules:
     def test_tool_message_text_is_marked_on_chat_path(self):
         messages = [
             {"role": "user", "content": "weather?"},
-            {
-                "role": "assistant",
-                "content": None,
-                "tool_calls": [{"id": "c1", "type": "function", "function": {"name": "w", "arguments": "{}"}}],
-            },
+            {"role": "assistant", "content": None, "tool_calls": [{"id": "c1", "type": "function", "function": {"name": "w", "arguments": "{}"}}]},
             {"role": "tool", "tool_call_id": "c1", "content": "sunny"},
         ]
         out, params = self._chat(messages, [{"location": "message", "index": -1}])
@@ -2795,9 +2791,9 @@ class TestChatPathProviderStamp:
 
 class TestClientBreakpointsCountedOnce:
     def test_client_message_breakpoints_are_not_double_counted(self):
-        messages = [
-            {"role": "user", "content": [{"type": "text", "text": "m0", "cache_control": {"type": "ephemeral"}}]}
-        ] + [{"role": "user", "content": [{"type": "text", "text": f"m{i}"}]} for i in range(1, 4)]
+        messages = [{"role": "user", "content": [{"type": "text", "text": "m0", "cache_control": {"type": "ephemeral"}}]}] + [
+            {"role": "user", "content": [{"type": "text", "text": f"m{i}"}]} for i in range(1, 4)
+        ]
         out, system, _ = AnthropicCacheControlHook.apply_to_anthropic_messages_request(
             messages=messages,
             system="sys",
@@ -2955,6 +2951,7 @@ class TestPromptCacheBreakpointCapability:
         yield
         litellm.utils._cached_get_model_info_helper.cache_clear()
 
+
     def test_listed_model_uses_the_model_map_flag(self, monkeypatch):
         flagged = {**litellm.model_cost["gpt-4.1"], "supports_prompt_cache_breakpoint": True}
         monkeypatch.setitem(litellm.model_cost, "gpt-4.1", flagged)
@@ -2972,6 +2969,7 @@ class TestPromptCacheBreakpointCapability:
         )
         assert supports_openai_prompt_cache_breakpoint("gpt-5.6") is False
 
+
     def test_published_map_without_the_flag_still_injects_on_gpt_5_6(self, monkeypatch):
         unflagged = {k: v for k, v in litellm.model_cost["gpt-5.6"].items() if k != "supports_prompt_cache_breakpoint"}
         monkeypatch.setitem(litellm.model_cost, "gpt-5.6", unflagged)

tests/test_litellm/litellm_core_utils/llm_cost_calc/test_guardrail_cost.py

@@ -1,3 +1,4 @@
+
 import pytest
 
 import litellm

tests/test_litellm/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking.py

@@ -1,3 +1,4 @@
+
 import pytest
 
 import litellm
@@ -16,7 +17,9 @@ def test_web_search_cost_low():
         web_search_options=web_search_options, model_info=model_info
     )
 
-    assert cost == model_info["search_context_cost_per_query"]["search_context_size_low"]
+    assert (
+        cost == model_info["search_context_cost_per_query"]["search_context_size_low"]
+    )
 
 
 def test_web_search_cost_medium():
@@ -27,7 +30,10 @@ def test_web_search_cost_medium():
         web_search_options=web_search_options, model_info=model_info
     )
 
-    assert cost == model_info["search_context_cost_per_query"]["search_context_size_medium"]
+    assert (
+        cost
+        == model_info["search_context_cost_per_query"]["search_context_size_medium"]
+    )
 
 
 def test_web_search_cost_high():
@@ -38,21 +44,33 @@ def test_web_search_cost_high():
         web_search_options=web_search_options, model_info=model_info
     )
 
-    assert cost == model_info["search_context_cost_per_query"]["search_context_size_high"]
+    assert (
+        cost == model_info["search_context_cost_per_query"]["search_context_size_high"]
+    )
 
 
 # Test file search cost calculation
 def test_file_search_cost():
     file_search = FileSearchTool(type="file_search")
-    cost = StandardBuiltInToolCostTracking.get_cost_for_file_search(file_search=file_search)
+    cost = StandardBuiltInToolCostTracking.get_cost_for_file_search(
+        file_search=file_search
+    )
     assert cost == 0.0025  # $2.50/1000 calls = 0.0025 per call
 
 
 # Test edge cases
 def test_none_inputs():
     # Test with None inputs
-    assert StandardBuiltInToolCostTracking.get_cost_for_web_search(web_search_options=None, model_info=None) == 0.0
-    assert StandardBuiltInToolCostTracking.get_cost_for_file_search(file_search=None) == 0.0
+    assert (
+        StandardBuiltInToolCostTracking.get_cost_for_web_search(
+            web_search_options=None, model_info=None
+        )
+        == 0.0
+    )
+    assert (
+        StandardBuiltInToolCostTracking.get_cost_for_file_search(file_search=None)
+        == 0.0
+    )
 
 
 # Test the main get_cost_for_built_in_tools method
@@ -77,7 +95,9 @@ def test_get_cost_for_built_in_tools_file_search():
     Test that the cost for a file search is 0.00 when no response object is provided
     """
     model = "gpt-4"
-    standard_built_in_tools_params = StandardBuiltInToolsParams(file_search=FileSearchTool(type="file_search"))
+    standard_built_in_tools_params = StandardBuiltInToolsParams(
+        file_search=FileSearchTool(type="file_search")
+    )
 
     cost = StandardBuiltInToolCostTracking.get_cost_for_built_in_tools(
         model=model,
@@ -120,7 +140,9 @@ def test_get_cost_for_anthropic_web_search_with_server_tool_use_dict():
     usage = Usage(server_tool_use={"web_search_requests": 1})
 
     assert isinstance(usage.server_tool_use, ServerToolUse)
-    assert StandardBuiltInToolCostTracking.response_object_includes_web_search_call(response_object=None, usage=usage)
+    assert StandardBuiltInToolCostTracking.response_object_includes_web_search_call(
+        response_object=None, usage=usage
+    )
 
 
 def test_anthropic_web_search_cost_from_raw_response_dict_when_usage_drops_server_tool_use():
@@ -159,7 +181,9 @@ def test_anthropic_web_search_cost_from_raw_response_dict_when_usage_drops_serve
         standard_built_in_tools_params=None,
     )
 
-    per_query_cost = litellm.get_model_info(model)["search_context_cost_per_query"]["search_context_size_medium"]
+    per_query_cost = litellm.get_model_info(model)["search_context_cost_per_query"][
+        "search_context_size_medium"
+    ]
     assert cost == per_query_cost * web_search_requests
     assert cost > 0.0
     assert getattr(usage, "server_tool_use", None) is None
@@ -197,7 +221,9 @@ def test_anthropic_web_search_cost_from_raw_response_dict_when_usage_is_none():
         standard_built_in_tools_params=None,
     )
 
-    per_query_cost = litellm.get_model_info(model)["search_context_cost_per_query"]["search_context_size_medium"]
+    per_query_cost = litellm.get_model_info(model)["search_context_cost_per_query"][
+        "search_context_size_medium"
+    ]
     assert cost == per_query_cost * web_search_requests
 
 
@@ -261,14 +287,18 @@ def test_anthropic_response_usage_block_preserves_server_tool_use():
     assert dumped_usage["server_tool_use"] == {"web_search_requests": 2}
 
 
-@pytest.mark.parametrize("model", ["gemini/gemini-2.0-flash-001", "gemini-2.0-flash-001"])
+@pytest.mark.parametrize(
+    "model", ["gemini/gemini-2.0-flash-001", "gemini-2.0-flash-001"]
+)
 def test_get_cost_for_gemini_web_search(model):
     """
     Test that the cost for a web search is 0.00 when no response object is provided
     """
     from litellm.types.utils import PromptTokensDetailsWrapper, Usage
 
-    usage = Usage(prompt_tokens_details=PromptTokensDetailsWrapper(web_search_requests=1))
+    usage = Usage(
+        prompt_tokens_details=PromptTokensDetailsWrapper(web_search_requests=1)
+    )
     cost = StandardBuiltInToolCostTracking.get_cost_for_built_in_tools(
         model=model,
         usage=usage,
@@ -326,7 +356,9 @@ def test_completion_cost_includes_web_search_without_standard_built_in_tools_par
     )
 
     assert web_search_cost > 0, "Web search cost should be non-zero"
-    assert cost >= web_search_cost, f"completion_cost ({cost}) should include web search cost ({web_search_cost})"
+    assert (
+        cost >= web_search_cost
+    ), f"completion_cost ({cost}) should include web search cost ({web_search_cost})"
 
 
 def test_gemini_combined_search_and_maps_costs_are_additive(local_model_cost_map):
@@ -362,6 +394,7 @@ def _openai_responses_with_web_search_calls(model, num_calls):
         ResponseFunctionWebSearch,
     )
 
+
     output = [
         ResponseFunctionWebSearch(
             id=f"ws_{i}",
@@ -392,7 +425,9 @@ def test_openai_responses_web_search_multiplied_by_call_count(local_model_cost_m
     from litellm.types.utils import Usage
 
     model = "gpt-4o-search-preview"
-    per_call = litellm.get_model_info(model)["search_context_cost_per_query"]["search_context_size_medium"]
+    per_call = litellm.get_model_info(model)["search_context_cost_per_query"][
+        "search_context_size_medium"
+    ]
     usage = Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15)
 
     for num_calls in (1, 3):
@@ -419,7 +454,9 @@ def test_web_search_call_count_reads_dict_output_items(local_model_cost_map):
     from litellm.types.utils import Usage
 
     model = "gpt-4o-search-preview"
-    per_call = litellm.get_model_info(model)["search_context_cost_per_query"]["search_context_size_medium"]
+    per_call = litellm.get_model_info(model)["search_context_cost_per_query"][
+        "search_context_size_medium"
+    ]
 
     response = ResponsesAPIResponse.model_validate(
         {
@@ -428,7 +465,10 @@ def test_web_search_call_count_reads_dict_output_items(local_model_cost_map):
             "model": model,
             "object": "response",
             "status": "completed",
-            "output": [{"type": "web_search_call", "id": f"ws_{i}", "status": "completed"} for i in range(3)],
+            "output": [
+                {"type": "web_search_call", "id": f"ws_{i}", "status": "completed"}
+                for i in range(3)
+            ],
         }
     )
     assert all(isinstance(item, dict) for item in response.output)
@@ -441,7 +481,9 @@ def test_web_search_call_count_reads_dict_output_items(local_model_cost_map):
         standard_built_in_tools_params=None,
     )
 
-    assert cost == pytest.approx(3 * per_call), f"3 dict-shaped web searches must bill 3 x ${per_call}, got ${cost}"
+    assert cost == pytest.approx(3 * per_call), (
+        f"3 dict-shaped web searches must bill 3 x ${per_call}, got ${cost}"
+    )
 
 
 # Note: File search integration test removed due to complex annotation detection logic
@@ -519,3 +561,5 @@ _BEDROCK_MANTLE_WEB_SEARCH_MODELS = (
 )
 
 _BEDROCK_MANTLE_WEB_SEARCH_RATE = 0.012
+
+

tests/test_litellm/litellm_core_utils/prompt_templates/test_bedrock_converse_strict_tools_opus_47_48.py

@@ -75,10 +75,12 @@ def test_bedrock_tools_pt_strict_dropped_for_strict_unsupported_models(
     """Opus 4.7/4.8, Sonnet 4 and Sonnet 5 reject toolSpec.strict and additionalProperties."""
     result = _bedrock_tools_pt(_STRICT_TOOL, model=model_id)
     tool_spec = result[0]["toolSpec"]
-    assert "strict" not in tool_spec, f"strict leaked into toolSpec for {model_id}: {tool_spec}"
-    assert "additionalProperties" not in tool_spec["inputSchema"]["json"], (
-        f"additionalProperties leaked into toolSpec for {model_id}: {tool_spec}"
-    )
+    assert (
+        "strict" not in tool_spec
+    ), f"strict leaked into toolSpec for {model_id}: {tool_spec}"
+    assert (
+        "additionalProperties" not in tool_spec["inputSchema"]["json"]
+    ), f"additionalProperties leaked into toolSpec for {model_id}: {tool_spec}"
 
 
 @pytest.mark.parametrize(
@@ -93,7 +95,9 @@ def test_bedrock_tools_pt_strict_dropped_for_strict_unsupported_models(
 def test_bedrock_tools_pt_strict_kept_for_other_anthropic(model_id: str) -> None:
     """Sonnet 4.5/4.6 and Opus <=4.6 accept toolSpec.strict — keep forwarding it."""
     result = _bedrock_tools_pt(_STRICT_TOOL, model=model_id)
-    assert result[0]["toolSpec"]["strict"] is True, f"strict missing for {model_id}: {result[0]['toolSpec']}"
+    assert (
+        result[0]["toolSpec"]["strict"] is True
+    ), f"strict missing for {model_id}: {result[0]['toolSpec']}"
 
 
 @pytest.mark.parametrize(
@@ -113,7 +117,9 @@ def test_bedrock_tools_pt_falsy_strict_always_dropped(model_id: str) -> None:
     ones whose cost-map entry still allows ``strict: true`` through."""
     result = _bedrock_tools_pt(_NON_STRICT_TOOL, model=model_id)
     tool_spec = result[0]["toolSpec"]
-    assert "strict" not in tool_spec, f"no-op strict: false leaked into toolSpec for {model_id}: {tool_spec}"
+    assert (
+        "strict" not in tool_spec
+    ), f"no-op strict: false leaked into toolSpec for {model_id}: {tool_spec}"
 
 
 def test_responses_bridge_function_tool_does_not_reach_bedrock_with_strict() -> None:
@@ -134,8 +140,10 @@ def test_responses_bridge_function_tool_does_not_reach_bedrock_with_strict() ->
             "required": ["city"],
         },
     }
-    chat_tools, _ = LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools(
-        [responses_tool]
+    chat_tools, _ = (
+        LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools(
+            [responses_tool]
+        )
     )
     result = _bedrock_tools_pt(chat_tools, model="bedrock/us.anthropic.claude-sonnet-5")
     assert "strict" not in result[0]["toolSpec"]
@@ -152,3 +160,5 @@ def test_bedrock_tools_pt_strict_dropped_for_non_anthropic(model_id: str) -> Non
     """Non-Anthropic Bedrock families reject toolSpec.strict — must be dropped."""
     result = _bedrock_tools_pt(_STRICT_TOOL, model=model_id)
     assert "strict" not in result[0]["toolSpec"]
+
+

tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py

@@ -31,7 +31,9 @@ def _get_gemini_function_response_inline_data_parts(result):
     assert isinstance(result, list), "expected Gemini parts list"
     assert len(result) == 1, "multimodal function responses should stay in one part"
     function_response_part = result[0]
-    assert "inline_data" not in function_response_part, "inline_data should be nested under function_response.parts"
+    assert (
+        "inline_data" not in function_response_part
+    ), "inline_data should be nested under function_response.parts"
     function_response = function_response_part["function_response"]
     nested_parts = function_response["parts"]
     return [part["inline_data"] for part in nested_parts if "inline_data" in part]
@@ -47,9 +49,7 @@ def test_ollama_pt_simple_messages():
 
     result = ollama_pt(model="llama2", messages=messages)
 
-    expected_prompt = (
-        "### System:\nYou are a helpful assistant\n\n### Assistant:\nHow can I help you?\n\n### User:\nHello\n\n"
-    )
+    expected_prompt = "### System:\nYou are a helpful assistant\n\n### Assistant:\nHow can I help you?\n\n### User:\nHello\n\n"
     assert isinstance(result, dict)
     assert result["prompt"] == expected_prompt
     assert result["images"] == []
@@ -104,7 +104,10 @@ async def test_anthropic_bedrock_thinking_blocks_with_none_content():
 
     # verify the result
     assert len(result) == 2
-    assert result[1]["content"][0]["reasoningContent"]["reasoningText"]["text"] == "This is a test thinking block"
+    assert (
+        result[1]["content"][0]["reasoningContent"]["reasoningText"]["text"]
+        == "This is a test thinking block"
+    )
 
 
 def test_bedrock_converse_assistant_with_empty_thinking_block_and_tool_calls():
@@ -172,7 +175,11 @@ def test_bedrock_converse_assistant_with_empty_thinking_block_and_tool_calls():
     assert len(assistant_blocks) == 1
     for block in assistant_blocks[0]["content"]:
         if "text" in block:
-            assert block["text"].strip(), f"Bedrock Converse rejects blank-text ContentBlocks; got {block!r}"
+            assert block[
+                "text"
+            ].strip(), (
+                f"Bedrock Converse rejects blank-text ContentBlocks; got {block!r}"
+            )
     # toolUse blocks must still be present
     tool_use_blocks = [b for b in assistant_blocks[0]["content"] if "toolUse" in b]
     assert len(tool_use_blocks) == 2
@@ -213,16 +220,19 @@ def test_anthropic_messages_pt_drops_unsignable_thinking_block(thinking_block):
         {"role": "user", "content": "Now what is 3+3?"},
     ]
 
-    result = anthropic_messages_pt(messages=messages, model="claude-sonnet-4-6", llm_provider="anthropic")
+    result = anthropic_messages_pt(
+        messages=messages, model="claude-sonnet-4-6", llm_provider="anthropic"
+    )
 
     assistant = next(m for m in result if m["role"] == "assistant")
     content = assistant["content"]
-    assert all(block.get("type") not in ("thinking", "redacted_thinking") for block in content), (
-        f"unsignable thinking block must be dropped, got {content!r}"
-    )
-    assert any(block.get("type") == "text" and block.get("text") == "2+2 equals 4." for block in content), (
-        f"assistant answer text must be preserved, got {content!r}"
-    )
+    assert all(
+        block.get("type") not in ("thinking", "redacted_thinking") for block in content
+    ), f"unsignable thinking block must be dropped, got {content!r}"
+    assert any(
+        block.get("type") == "text" and block.get("text") == "2+2 equals 4."
+        for block in content
+    ), f"assistant answer text must be preserved, got {content!r}"
 
 
 def test_anthropic_messages_pt_keeps_signed_thinking_block():
@@ -245,7 +255,9 @@ def test_anthropic_messages_pt_keeps_signed_thinking_block():
         {"role": "user", "content": "Now what is 3+3?"},
     ]
 
-    result = anthropic_messages_pt(messages=messages, model="claude-sonnet-4-6", llm_provider="anthropic")
+    result = anthropic_messages_pt(
+        messages=messages, model="claude-sonnet-4-6", llm_provider="anthropic"
+    )
 
     assistant = next(m for m in result if m["role"] == "assistant")
     thinking_blocks = [b for b in assistant["content"] if b.get("type") == "thinking"]
@@ -332,7 +344,9 @@ def test_bedrock_get_document_format_fallback_mimes():
     """
 
     # Test DOCX fallback
-    docx_mime = "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+    docx_mime = (
+        "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+    )
     supported_formats = ["pdf", "docx", "xlsx", "csv"]
 
     # Mock mimetypes.guess_all_extensions to return empty list (simulating Docker container scenario)
@@ -356,11 +370,15 @@ def test_bedrock_get_document_format_mimetypes_success():
     """
     Test the _get_document_format method when mimetypes.guess_all_extensions works normally.
     """
-    docx_mime = "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+    docx_mime = (
+        "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+    )
     supported_formats = ["pdf", "docx", "xlsx", "csv"]
 
     # Test normal mimetypes behavior (should not hit fallback)
-    result = BedrockImageProcessor._get_document_format(mime_type=docx_mime, supported_doc_formats=supported_formats)
+    result = BedrockImageProcessor._get_document_format(
+        mime_type=docx_mime, supported_doc_formats=supported_formats
+    )
     assert result == "docx", f"Expected 'docx', got '{result}'"
 
 
@@ -576,7 +594,9 @@ async def test_bedrock_process_image_async_factory():
 
     image_url = "data:application/pdf; qs=0.001;base64,JVBERi0xLjQKJcOkw7zDtsOfCjIgMCBvYmoKPDwvTGVuZ3RoIDMgMCBSL0ZpbHRlci9GbGF0ZURlY29kZT4"
 
-    content_block = await BedrockImageProcessor.process_image_async(image_url=image_url, format=None)
+    content_block = await BedrockImageProcessor.process_image_async(
+        image_url=image_url, format=None
+    )
     print(f"content_block: {content_block}")
 
 
@@ -619,7 +639,9 @@ def test_unpack_defs_resolves_nested_ref_inside_anyof_items():
     items_schema = schema["properties"]["vatAmounts"]["anyOf"][0]["items"]
 
     # Assertions: items_schema should now be the resolved object, not an empty dict
-    assert isinstance(items_schema, dict), "Items schema should be a dict after unpacking"
+    assert isinstance(
+        items_schema, dict
+    ), "Items schema should be a dict after unpacking"
     assert items_schema.get("type") == "object"
     # Ensure essential properties are present
     assert set(items_schema.get("properties", {}).keys()) == {"vatRate", "vatAmount"}
@@ -810,7 +832,9 @@ def test_convert_gemini_tool_call_result_with_multiple_anthropic_image_blocks():
         last_message_with_tool_calls=last_message_with_tool_calls,
     )
     inline_parts = _get_gemini_function_response_inline_data_parts(result)
-    assert len(inline_parts) == 2, f"expected 2 inline_data parts, got {len(inline_parts)}"
+    assert (
+        len(inline_parts) == 2
+    ), f"expected 2 inline_data parts, got {len(inline_parts)}"
     mime_types = {p["mime_type"] for p in inline_parts}
     assert mime_types == {"image/png", "image/jpeg"}
 
@@ -846,7 +870,9 @@ def test_convert_gemini_tool_call_result_with_data_url_string():
         last_message_with_tool_calls=last_message_with_tool_calls,
     )
     inline_parts = _get_gemini_function_response_inline_data_parts(result)
-    assert len(inline_parts) == 1, "data-URL image string was not converted to inline_data"
+    assert (
+        len(inline_parts) == 1
+    ), "data-URL image string was not converted to inline_data"
     assert inline_parts[0]["mime_type"] == "image/png"
     assert inline_parts[0]["data"] == tiny_png_b64
 
@@ -882,9 +908,9 @@ def test_convert_gemini_tool_call_result_with_data_url_extra_params():
     )
     inline_parts = _get_gemini_function_response_inline_data_parts(result)
     assert len(inline_parts) == 1
-    assert inline_parts[0]["mime_type"] == "image/png", (
-        f"expected clean 'image/png', got '{inline_parts[0]['mime_type']}'"
-    )
+    assert (
+        inline_parts[0]["mime_type"] == "image/png"
+    ), f"expected clean 'image/png', got '{inline_parts[0]['mime_type']}'"
 
 
 def test_bedrock_tools_unpack_defs():
@@ -981,7 +1007,9 @@ def test_bedrock_tools_pt_strict_parameter():
             },
         }
     ]
-    result = _bedrock_tools_pt(tools_with_strict, model="anthropic.claude-sonnet-4-5-20250929-v1:0")
+    result = _bedrock_tools_pt(
+        tools_with_strict, model="anthropic.claude-sonnet-4-5-20250929-v1:0"
+    )
     assert result[0]["toolSpec"]["strict"] is True
     assert result[0]["toolSpec"]["inputSchema"]["json"]["additionalProperties"] is False
 
@@ -1003,7 +1031,9 @@ def test_bedrock_tools_pt_strict_parameter():
             },
         }
     ]
-    result = _bedrock_tools_pt(tools_without_strict, model="anthropic.claude-sonnet-4-5-20250929-v1:0")
+    result = _bedrock_tools_pt(
+        tools_without_strict, model="anthropic.claude-sonnet-4-5-20250929-v1:0"
+    )
     assert "strict" not in result[0]["toolSpec"]
     assert "additionalProperties" not in result[0]["toolSpec"]["inputSchema"]["json"]
 
@@ -1026,7 +1056,9 @@ def test_bedrock_image_processor_content_type_fallback_url_extension():
 
     # Test with .png URL
     image_url = "https://example.com/test-image.png"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
+        mock_response, image_url
+    )
 
     assert content_type == "image/png"
     assert base64_bytes == base64.b64encode(png_content).decode("utf-8")
@@ -1050,7 +1082,9 @@ def test_bedrock_image_processor_content_type_fallback_binary_detection():
 
     # Test with URL without extension
     image_url = "https://example.com/test-image-without-extension"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
+        mock_response, image_url
+    )
 
     assert content_type == "image/jpeg"
     assert base64_bytes == base64.b64encode(jpeg_content).decode("utf-8")
@@ -1073,7 +1107,9 @@ def test_bedrock_image_processor_content_type_fallback_application_octet_stream(
 
     # Test with .gif URL
     image_url = "https://s3.amazonaws.com/bucket/image.gif"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
+        mock_response, image_url
+    )
 
     assert content_type == "image/gif"
     assert base64_bytes == base64.b64encode(gif_content).decode("utf-8")
@@ -1096,7 +1132,9 @@ def test_bedrock_image_processor_content_type_with_query_params():
 
     # Test with URL containing query parameters (common in S3 signed URLs)
     image_url = "https://s3.amazonaws.com/bucket/image.webp?AWSAccessKeyId=123&Expires=456&Signature=789"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
+        mock_response, image_url
+    )
 
     assert content_type == "image/webp"
     assert base64_bytes == base64.b64encode(webp_content).decode("utf-8")
@@ -1118,7 +1156,9 @@ def test_bedrock_image_processor_content_type_normal_header():
     mock_response.content = png_content
 
     image_url = "https://example.com/test-image.png"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
+        mock_response, image_url
+    )
 
     assert content_type == "image/png"
     assert base64_bytes == base64.b64encode(png_content).decode("utf-8")
@@ -1138,7 +1178,7 @@ def test_bedrock_image_processor_content_type_fallback_failure():
     # Test with URL without recognizable extension
     image_url = "https://example.com/unknown-file"
 
-    with pytest.raises(ValueError, match="Unable to determine content type from URL: https") as excinfo:
+    with pytest.raises(ValueError, match='Unable to determine content type from URL: https') as excinfo:
         BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
 
     assert "Unable to determine content type" in str(excinfo.value)
@@ -1158,12 +1198,16 @@ def test_bedrock_image_processor_content_type_jpeg_variants():
 
     # Test with .jpg extension
     image_url_jpg = "https://example.com/photo.jpg"
-    _, content_type_jpg = BedrockImageProcessor._post_call_image_processing(mock_response, image_url_jpg)
+    _, content_type_jpg = BedrockImageProcessor._post_call_image_processing(
+        mock_response, image_url_jpg
+    )
     assert content_type_jpg == "image/jpeg"
 
     # Test with .jpeg extension
     image_url_jpeg = "https://example.com/photo.jpeg"
-    _, content_type_jpeg = BedrockImageProcessor._post_call_image_processing(mock_response, image_url_jpeg)
+    _, content_type_jpeg = BedrockImageProcessor._post_call_image_processing(
+        mock_response, image_url_jpeg
+    )
     assert content_type_jpeg == "image/jpeg"
 
 
@@ -1185,7 +1229,9 @@ def test_bedrock_image_processor_content_type_pdf_document():
 
     # Test with .pdf URL
     pdf_url = "https://s3.amazonaws.com/bucket/document.pdf"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, pdf_url)
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
+        mock_response, pdf_url
+    )
 
     assert content_type == "application/pdf"
     assert base64_bytes == base64.b64encode(pdf_content).decode("utf-8")
@@ -1218,8 +1264,12 @@ def test_bedrock_image_processor_content_type_document_formats():
     ]
 
     for url, expected_mime in test_cases:
-        _, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, url)
-        assert content_type == expected_mime, f"Expected {expected_mime} for {url}, got {content_type}"
+        _, content_type = BedrockImageProcessor._post_call_image_processing(
+            mock_response, url
+        )
+        assert (
+            content_type == expected_mime
+        ), f"Expected {expected_mime} for {url}, got {content_type}"
 
 
 def test_bedrock_image_processor_content_type_s3_pdf_with_query():
@@ -1238,7 +1288,9 @@ def test_bedrock_image_processor_content_type_s3_pdf_with_query():
     # S3 signed URL with query parameters
     s3_url = "https://my-bucket.s3.us-east-1.amazonaws.com/documents/report.pdf?AWSAccessKeyId=AKIAIOSFODNN7EXAMPLE&Expires=1234567890&Signature=abcdef123456"
 
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, s3_url)
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
+        mock_response, s3_url
+    )
 
     assert content_type == "application/pdf"
     assert base64_bytes == base64.b64encode(pdf_content).decode("utf-8")
@@ -1347,8 +1399,12 @@ def test_bedrock_create_bedrock_block_normalized_base64():
     base64_content = base64.b64encode(pdf_content).decode("utf-8")
 
     # Create versions with different whitespace
-    base64_with_newlines = "\n".join([base64_content[i : i + 64] for i in range(0, len(base64_content), 64)])
-    base64_with_spaces = " ".join([base64_content[i : i + 32] for i in range(0, len(base64_content), 32)])
+    base64_with_newlines = "\n".join(
+        [base64_content[i : i + 64] for i in range(0, len(base64_content), 64)]
+    )
+    base64_with_spaces = " ".join(
+        [base64_content[i : i + 32] for i in range(0, len(base64_content), 32)]
+    )
 
     # Create blocks
     block1 = BedrockImageProcessor._create_bedrock_block(
@@ -1480,7 +1536,9 @@ def test_bedrock_create_bedrock_block_document_name_format():
 
     # Check format: DocumentPDFmessages_{16_hex_chars}_{format}
     pattern = r"^DocumentPDFmessages_[0-9a-f]{16}_pdf$"
-    assert re.match(pattern, document_name), f"Document name format mismatch: {document_name}"
+    assert re.match(
+        pattern, document_name
+    ), f"Document name format mismatch: {document_name}"
 
 
 def test_bedrock_create_bedrock_block_different_document_formats():
@@ -1506,7 +1564,7 @@ def test_bedrock_create_bedrock_block_different_document_formats():
         )
 
         assert block.get("document") is not None
-        assert "DocumentPDFmessages_" in block["document"]["name"]
+        assert f"DocumentPDFmessages_" in block["document"]["name"]
         assert block["document"]["name"].endswith(f"_{format_type}")
         assert block["document"]["format"] == format_type
 
@@ -1533,7 +1591,9 @@ def test_bedrock_nova_web_search_options_mapping():
     assert system_tool["name"] == "nova_grounding"
 
     # Test with search_context_size (should be ignored for Nova)
-    result2 = config._map_web_search_options({"search_context_size": "high"}, "us.amazon.nova-premier-v1:0")
+    result2 = config._map_web_search_options(
+        {"search_context_size": "high"}, "us.amazon.nova-premier-v1:0"
+    )
 
     assert result2 is not None
     system_tool2 = result2.get("systemTool")
@@ -1599,7 +1659,9 @@ def test_bedrock_tools_pt_drops_unmappable_responses_builtin_tools():
         {"type": "custom", "name": "free_form"},
     ]
 
-    result = _bedrock_tools_pt(tools=tools, model="anthropic.claude-sonnet-4-5-20250929-v1:0")
+    result = _bedrock_tools_pt(
+        tools=tools, model="anthropic.claude-sonnet-4-5-20250929-v1:0"
+    )
 
     names = [block["toolSpec"]["name"] for block in result if "toolSpec" in block]
     assert names == ["noop"]
@@ -1629,7 +1691,9 @@ def test_bedrock_tools_pt_keeps_anthropic_input_schema_tools():
         },
     ]
 
-    result = _bedrock_tools_pt(tools=tools, model="anthropic.claude-sonnet-4-5-20250929-v1:0")
+    result = _bedrock_tools_pt(
+        tools=tools, model="anthropic.claude-sonnet-4-5-20250929-v1:0"
+    )
 
     names = [block["toolSpec"]["name"] for block in result if "toolSpec" in block]
     assert names == ["lookup"]
@@ -1831,7 +1895,9 @@ def test_anthropic_messages_pt_server_tool_use_passthrough():
                     "tool_use_id": "srvtoolu_01ABC123",
                     "content": {
                         "type": "tool_search_tool_search_result",
-                        "tool_references": [{"type": "tool_reference", "tool_name": "get_time"}],
+                        "tool_references": [
+                            {"type": "tool_reference", "tool_name": "get_time"}
+                        ],
                     },
                 },
                 {"type": "text", "text": "I found the time tool. How can I help you?"},
@@ -1859,14 +1925,20 @@ def test_anthropic_messages_pt_server_tool_use_passthrough():
 
     # Verify server_tool_use block is preserved
     assert "server_tool_use" in content_types
-    server_tool_use_block = next(b for b in assistant_msg["content"] if b.get("type") == "server_tool_use")
+    server_tool_use_block = next(
+        b for b in assistant_msg["content"] if b.get("type") == "server_tool_use"
+    )
     assert server_tool_use_block["id"] == "srvtoolu_01ABC123"
     assert server_tool_use_block["name"] == "tool_search_tool_regex"
     assert server_tool_use_block["input"] == {"query": ".*time.*"}
 
     # Verify tool_search_tool_result block is preserved
     assert "tool_search_tool_result" in content_types
-    tool_result_block = next(b for b in assistant_msg["content"] if b.get("type") == "tool_search_tool_result")
+    tool_result_block = next(
+        b
+        for b in assistant_msg["content"]
+        if b.get("type") == "tool_search_tool_result"
+    )
     assert tool_result_block["tool_use_id"] == "srvtoolu_01ABC123"
     assert tool_result_block["content"]["type"] == "tool_search_tool_search_result"
     assert tool_result_block["content"]["tool_references"][0]["tool_name"] == "get_time"
@@ -1918,7 +1990,9 @@ def test_bedrock_tools_unpack_defs_no_oom_with_nested_refs():
                 "anyOf": [
                     {"$ref": "#/$defs/Literal"},
                     {"$ref": "#/$defs/FieldRef"},
-                    {"$ref": "#/$defs/Expression"},  # Circular: Operand -> Expression -> Operand
+                    {
+                        "$ref": "#/$defs/Expression"
+                    },  # Circular: Operand -> Expression -> Operand
                 ],
             },
             "Literal": {
@@ -2052,7 +2126,9 @@ def test_anthropic_messages_pt_file_block_cache_control_with_explicit_provider()
 
     file_block = content_blocks[0]
     assert file_block["type"] == "document"
-    assert "cache_control" in file_block, "cache_control should be preserved on file/document content blocks"
+    assert (
+        "cache_control" in file_block
+    ), "cache_control should be preserved on file/document content blocks"
     assert file_block["cache_control"]["type"] == "ephemeral"
 
     text_block = content_blocks[1]
@@ -2260,16 +2336,22 @@ def test_bedrock_tool_call_invoke_concatenated_json():
     # First block keeps original tool id
     assert result[0]["toolUse"]["toolUseId"] == "tooluse_L7I3TewYAUhoheJZQEuwVN"
     assert result[0]["toolUse"]["name"] == "shell"
-    assert result[0]["toolUse"]["input"] == {"command": ["curl", "-i", "http://localhost:9009", "-m", "10"]}
+    assert result[0]["toolUse"]["input"] == {
+        "command": ["curl", "-i", "http://localhost:9009", "-m", "10"]
+    }
 
     # Subsequent blocks get suffixed ids
     assert result[1]["toolUse"]["toolUseId"] == "tooluse_L7I3TewYAUhoheJZQEuwVN_1"
     assert result[1]["toolUse"]["name"] == "shell"
-    assert result[1]["toolUse"]["input"] == {"command": ["curl", "-i", "http://localhost:9009/robots.txt", "-m", "5"]}
+    assert result[1]["toolUse"]["input"] == {
+        "command": ["curl", "-i", "http://localhost:9009/robots.txt", "-m", "5"]
+    }
 
     assert result[2]["toolUse"]["toolUseId"] == "tooluse_L7I3TewYAUhoheJZQEuwVN_2"
     assert result[2]["toolUse"]["name"] == "shell"
-    assert result[2]["toolUse"]["input"] == {"command": ["curl", "-i", "http://localhost:9009/sitemap.xml", "-m", "5"]}
+    assert result[2]["toolUse"]["input"] == {
+        "command": ["curl", "-i", "http://localhost:9009/sitemap.xml", "-m", "5"]
+    }
 
 
 def test_bedrock_tool_call_invoke_concatenated_json_with_cache_control():
@@ -2424,7 +2506,9 @@ def test_bedrock_tool_call_invoke_unconvertible_raises_non_retryable_bad_request
 def test_make_valid_bedrock_tool_name_preserves_hyphens():
     assert make_valid_bedrock_tool_name("my-tool") == "my-tool"
     assert (
-        make_valid_bedrock_tool_name("CreateCaseKnowledgeArticle_foTWsqR6yDt-OnSsvR5e6Q")
+        make_valid_bedrock_tool_name(
+            "CreateCaseKnowledgeArticle_foTWsqR6yDt-OnSsvR5e6Q"
+        )
         == "CreateCaseKnowledgeArticle_foTWsqR6yDt-OnSsvR5e6Q"
     )
 
@@ -2451,7 +2535,9 @@ def test_bedrock_tool_name_sanitized_consistently_in_tools_and_tool_use():
             "function": {"name": raw_name, "arguments": "{}"},
         }
     ]
-    tool_use_name = _convert_to_bedrock_tool_call_invoke(tool_calls)[0]["toolUse"]["name"]
+    tool_use_name = _convert_to_bedrock_tool_call_invoke(tool_calls)[0]["toolUse"][
+        "name"
+    ]
 
     assert tool_spec_name == "foo_bar"
     assert tool_use_name == tool_spec_name
@@ -2474,8 +2560,15 @@ def test_bedrock_converse_messages_pt_tool_use_matches_tool_spec_hyphen_name():
             ],
         },
     ]
-    translated = _bedrock_converse_messages_pt(messages=messages, model="", llm_provider="")
-    tool_use_blocks = [block for msg in translated for block in msg.get("content", []) if "toolUse" in block]
+    translated = _bedrock_converse_messages_pt(
+        messages=messages, model="", llm_provider=""
+    )
+    tool_use_blocks = [
+        block
+        for msg in translated
+        for block in msg.get("content", [])
+        if "toolUse" in block
+    ]
     assert len(tool_use_blocks) == 1
     assert tool_use_blocks[0]["toolUse"]["name"] == tool_name
 
@@ -2572,7 +2665,11 @@ def test_sanitize_messages_deduplicates_tool_results():
         result = sanitize_messages_for_tool_calling(messages)
 
         # Count tool messages with this ID — should be exactly 1
-        tool_results = [m for m in result if m.get("role") == "tool" and m.get("tool_call_id") == "call_abc123"]
+        tool_results = [
+            m
+            for m in result
+            if m.get("role") == "tool" and m.get("tool_call_id") == "call_abc123"
+        ]
         assert len(tool_results) == 1
         # Should keep the LAST occurrence (most complete)
         assert tool_results[0]["content"] == '{"temperature": 72, "condition": "sunny"}'
@@ -2707,7 +2804,11 @@ def test_sanitize_messages_dedup_scoped_per_turn_preserves_cross_turn():
         result = sanitize_messages_for_tool_calling(messages)
 
         # Both tool results must survive — one per turn
-        tool_results = [m for m in result if m.get("role") == "tool" and m.get("tool_call_id") == "call_X"]
+        tool_results = [
+            m
+            for m in result
+            if m.get("role") == "tool" and m.get("tool_call_id") == "call_X"
+        ]
         assert len(tool_results) == 2, (
             f"Expected 2 tool results (one per turn), got {len(tool_results)}. "
             "Dedup may be global instead of per-turn scoped."
@@ -2761,26 +2862,32 @@ def test_sanitize_messages_combined_case_a_and_case_d():
         tool_results = [m for m in result if m.get("role") in ("tool", "function")]
 
         # Case A: call_missing should have a dummy result injected
-        missing_results = [m for m in tool_results if m.get("tool_call_id") == "call_missing"]
-        assert len(missing_results) == 1, (
-            f"Expected 1 dummy result for call_missing (Case A), got {len(missing_results)}"
-        )
+        missing_results = [
+            m for m in tool_results if m.get("tool_call_id") == "call_missing"
+        ]
+        assert (
+            len(missing_results) == 1
+        ), f"Expected 1 dummy result for call_missing (Case A), got {len(missing_results)}"
 
         # Case D: call_duped should have exactly 1 result (the fresh one)
-        duped_results = [m for m in tool_results if m.get("tool_call_id") == "call_duped"]
-        assert len(duped_results) == 1, (
-            f"Expected 1 result for call_duped after dedup (Case D), got {len(duped_results)}"
-        )
-        assert duped_results[0]["content"] == "fresh_result", (
-            f"Expected last-wins 'fresh_result', got '{duped_results[0]['content']}'"
-        )
+        duped_results = [
+            m for m in tool_results if m.get("tool_call_id") == "call_duped"
+        ]
+        assert (
+            len(duped_results) == 1
+        ), f"Expected 1 result for call_duped after dedup (Case D), got {len(duped_results)}"
+        assert (
+            duped_results[0]["content"] == "fresh_result"
+        ), f"Expected last-wins 'fresh_result', got '{duped_results[0]['content']}'"
 
         # Verify tool results immediately follow the assistant message
         asst_idx = next(i for i, m in enumerate(result) if m.get("role") == "assistant")
-        tool_msgs_after_asst = [m for m in result[asst_idx + 1 :] if m.get("role") in ("tool", "function")]
-        assert len(tool_msgs_after_asst) == 2, (
-            f"Expected 2 tool results after assistant, got {len(tool_msgs_after_asst)}"
-        )
+        tool_msgs_after_asst = [
+            m for m in result[asst_idx + 1 :] if m.get("role") in ("tool", "function")
+        ]
+        assert (
+            len(tool_msgs_after_asst) == 2
+        ), f"Expected 2 tool results after assistant, got {len(tool_msgs_after_asst)}"
         # Both tool_call_ids should be present (order may vary)
         tool_ids = {m["tool_call_id"] for m in tool_msgs_after_asst}
         assert tool_ids == {
@@ -2822,7 +2929,9 @@ def test_anthropic_messages_pt_file_block_preserves_cache_control():
         }
     ]
 
-    result = anthropic_messages_pt(messages, model="claude-sonnet-4-20250514", llm_provider="anthropic")
+    result = anthropic_messages_pt(
+        messages, model="claude-sonnet-4-20250514", llm_provider="anthropic"
+    )
 
     content_blocks = result[0]["content"]
     assert len(content_blocks) == 2
@@ -2830,7 +2939,9 @@ def test_anthropic_messages_pt_file_block_preserves_cache_control():
     # Document block (from file) should preserve cache_control
     doc_block = content_blocks[0]
     assert doc_block["type"] == "document"
-    assert "cache_control" in doc_block, "cache_control was dropped from file/document block"
+    assert (
+        "cache_control" in doc_block
+    ), "cache_control was dropped from file/document block"
     assert doc_block["cache_control"]["type"] == "ephemeral"
 
     # Text block should also preserve cache_control
@@ -2873,7 +2984,9 @@ def test_add_cache_point_tool_block_passes_ttl_for_claude_4_5(monkeypatch):
         }
 
         # Claude 4.5 model: ttl should be preserved
-        result = add_cache_point_tool_block(tool_with_1h, model="jp.anthropic.claude-opus-4-7")
+        result = add_cache_point_tool_block(
+            tool_with_1h, model="jp.anthropic.claude-opus-4-7"
+        )
         assert result is not None
         assert result["cachePoint"]["type"] == "default"
         assert result["cachePoint"]["ttl"] == "1h"
@@ -2882,12 +2995,16 @@ def test_add_cache_point_tool_block_passes_ttl_for_claude_4_5(monkeypatch):
         tool_with_5m = {
             "cache_control": {"type": "ephemeral", "ttl": "5m"},
         }
-        result_5m = add_cache_point_tool_block(tool_with_5m, model="jp.anthropic.claude-opus-4-7")
+        result_5m = add_cache_point_tool_block(
+            tool_with_5m, model="jp.anthropic.claude-opus-4-7"
+        )
         assert result_5m is not None
         assert result_5m["cachePoint"]["ttl"] == "5m"
 
         # Older model: ttl should be stripped
-        result_old = add_cache_point_tool_block(tool_with_1h, model="anthropic.claude-3-5-sonnet-20241022-v2:0")
+        result_old = add_cache_point_tool_block(
+            tool_with_1h, model="anthropic.claude-3-5-sonnet-20241022-v2:0"
+        )
         assert result_old is not None
         assert result_old["cachePoint"]["type"] == "default"
         assert "ttl" not in result_old["cachePoint"]
@@ -2906,7 +3023,9 @@ def test_add_cache_point_tool_block_passes_ttl_for_claude_4_5(monkeypatch):
 
         # cache_control without ttl: returns default cachePoint (unchanged behavior)
         tool_no_ttl = {"cache_control": {"type": "ephemeral"}}
-        result_no_ttl = add_cache_point_tool_block(tool_no_ttl, model="us.anthropic.claude-sonnet-4-5-20250929-v1:0")
+        result_no_ttl = add_cache_point_tool_block(
+            tool_no_ttl, model="us.anthropic.claude-sonnet-4-5-20250929-v1:0"
+        )
         assert result_no_ttl is not None
         assert result_no_ttl["cachePoint"]["type"] == "default"
         assert "ttl" not in result_no_ttl["cachePoint"]
@@ -2957,7 +3076,9 @@ def test_bedrock_tools_pt_passes_ttl_for_claude_4_5(monkeypatch):
         assert cache_blocks[0]["cachePoint"]["ttl"] == "1h"
 
         # Older model: cachePoint should not have ttl
-        result_old = _bedrock_tools_pt(tools, model="anthropic.claude-3-5-sonnet-20241022-v2:0")
+        result_old = _bedrock_tools_pt(
+            tools, model="anthropic.claude-3-5-sonnet-20241022-v2:0"
+        )
         cache_blocks_old = [b for b in result_old if "cachePoint" in b]
         assert len(cache_blocks_old) == 1
         assert "ttl" not in cache_blocks_old[0]["cachePoint"]
@@ -3032,7 +3153,9 @@ def test_bedrock_converse_messages_pt_document_various_formats():
             }
         ]
 
-        result = _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
+        result = _bedrock_converse_messages_pt(
+            messages, "anthropic.claude-sonnet-4-6", "bedrock"
+        )
 
         doc_block = result[0]["content"][0]
         assert doc_block["document"]["format"] == expected_format, (
@@ -3059,8 +3182,12 @@ def test_bedrock_converse_messages_pt_document_deterministic_name():
         }
     ]
 
-    result1 = _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
-    result2 = _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
+    result1 = _bedrock_converse_messages_pt(
+        messages, "anthropic.claude-sonnet-4-6", "bedrock"
+    )
+    result2 = _bedrock_converse_messages_pt(
+        messages, "anthropic.claude-sonnet-4-6", "bedrock"
+    )
 
     name1 = result1[0]["content"][0]["document"]["name"]
     name2 = result2[0]["content"][0]["document"]["name"]
@@ -3094,18 +3221,34 @@ def test_bedrock_converse_messages_pt_renames_duplicate_document_names():
         },
     ]
 
-    result1 = _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
-    result2 = _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
+    result1 = _bedrock_converse_messages_pt(
+        messages, "anthropic.claude-sonnet-4-6", "bedrock"
+    )
+    result2 = _bedrock_converse_messages_pt(
+        messages, "anthropic.claude-sonnet-4-6", "bedrock"
+    )
 
-    names1 = [block["document"]["name"] for message in result1 for block in message["content"] if "document" in block]
-    names2 = [block["document"]["name"] for message in result2 for block in message["content"] if "document" in block]
+    names1 = [
+        block["document"]["name"]
+        for message in result1
+        for block in message["content"]
+        if "document" in block
+    ]
+    names2 = [
+        block["document"]["name"]
+        for message in result2
+        for block in message["content"]
+        if "document" in block
+    ]
 
     assert len(names1) == 2
     assert len(set(names1)) == 2
     assert names1[1] == f"{names1[0]}_2"
     assert names1 == names2
 
-    single_turn = _bedrock_converse_messages_pt([messages[0]], "anthropic.claude-sonnet-4-6", "bedrock")
+    single_turn = _bedrock_converse_messages_pt(
+        [messages[0]], "anthropic.claude-sonnet-4-6", "bedrock"
+    )
     assert names1[0] == single_turn[0]["content"][0]["document"]["name"]
 
 
@@ -3127,10 +3270,14 @@ def test_rename_duplicate_bedrock_document_names_skips_organic_suffixes():
     def _names(contents):
         return [block["document"]["name"] for block in contents[0]["content"]]
 
-    organic_first = _rename_duplicate_bedrock_document_names(_contents(["report", "report_2", "report"]))
+    organic_first = _rename_duplicate_bedrock_document_names(
+        _contents(["report", "report_2", "report"])
+    )
     assert _names(organic_first) == ["report", "report_2", "report_3"]
 
-    organic_last = _rename_duplicate_bedrock_document_names(_contents(["report", "report", "report_2"]))
+    organic_last = _rename_duplicate_bedrock_document_names(
+        _contents(["report", "report", "report_2"])
+    )
     assert _names(organic_last) == ["report", "report_3", "report_2"]
 
 
@@ -3152,11 +3299,18 @@ def test_bedrock_converse_messages_pt_document_rejects_url_source():
     ]
 
     with pytest.raises(ValueError, match="only supports base64-encoded"):
-        _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
+        _bedrock_converse_messages_pt(
+            messages, "anthropic.claude-sonnet-4-6", "bedrock"
+        )
 
 
 def _collect_cache_points(blocks):
-    return [block["cachePoint"] for message in blocks for block in message["content"] if "cachePoint" in block]
+    return [
+        block["cachePoint"]
+        for message in blocks
+        for block in message["content"]
+        if "cachePoint" in block
+    ]
 
 
 @pytest.mark.parametrize(
@@ -3420,7 +3574,9 @@ def test_bedrock_converse_pdf_only_user_message_gets_text_block():
         }
     ]
 
-    result = _bedrock_converse_messages_pt(messages, "anthropic.claude-haiku-4-5", "bedrock")
+    result = _bedrock_converse_messages_pt(
+        messages, "anthropic.claude-haiku-4-5", "bedrock"
+    )
 
     assert len(result) == 1
     assert any("document" in block for block in result[0]["content"])
@@ -3438,7 +3594,9 @@ def test_bedrock_converse_document_with_text_gets_no_extra_text_block():
         }
     ]
 
-    result = _bedrock_converse_messages_pt(messages, "anthropic.claude-haiku-4-5", "bedrock")
+    result = _bedrock_converse_messages_pt(
+        messages, "anthropic.claude-haiku-4-5", "bedrock"
+    )
 
     assert _text_blocks(result[0]) == ["summarize this"]
 
@@ -3451,7 +3609,9 @@ def test_bedrock_converse_image_only_user_message_gets_no_text_block():
         }
     ]
 
-    result = _bedrock_converse_messages_pt(messages, "anthropic.claude-haiku-4-5", "bedrock")
+    result = _bedrock_converse_messages_pt(
+        messages, "anthropic.claude-haiku-4-5", "bedrock"
+    )
 
     assert any("image" in block for block in result[0]["content"])
     assert _text_blocks(result[0]) == []
@@ -3494,7 +3654,9 @@ def test_bedrock_converse_tool_round_trip_document_injects_text_before_cache_poi
         },
     ]
 
-    result = _bedrock_converse_messages_pt(messages, "anthropic.claude-haiku-4-5", "bedrock")
+    result = _bedrock_converse_messages_pt(
+        messages, "anthropic.claude-haiku-4-5", "bedrock"
+    )
 
     assert _text_blocks(result[0]) == ["read the pdf"]
     document_message = result[-1]

tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py

@@ -922,3 +922,5 @@ def test_shipped_tool_search_rule_version_boundaries(shipped_cost_map, model, pr
     assert model not in litellm.model_cost
     info = litellm.get_model_info(model, custom_llm_provider=provider)
     assert info.get("supports_tool_search") is tool_search, model
+
+

tests/test_litellm/litellm_core_utils/test_litellm_logging.py

@@ -395,6 +395,7 @@ class TestGetRouterDeploymentModelInfo:
         logging_obj.litellm_params = {"api_base": ""}
         assert logging_obj.get_router_deployment_model_info() is None
 
+
     def test_merging_does_not_mutate_the_cached_model_info(self) -> None:
         """The published-rate merge must not write into get_model_info's lru-cached dict.
 
@@ -3887,7 +3888,9 @@ def test_get_standard_logging_object_payload_carries_matched_access_groups(loggi
             "model": "gpt-4o",
             "messages": [],
             "litellm_params": {
-                "metadata": {"user_api_key_matched_model_access_groups": ["premium-pool", "shared-pool"]},
+                "metadata": {
+                    "user_api_key_matched_model_access_groups": ["premium-pool", "shared-pool"]
+                },
                 "proxy_server_request": {"body": {}},
             },
         },
@@ -3971,7 +3974,9 @@ def _model_router_response(selected_model: str, stamp: bool):
     from litellm.types.utils import ModelResponse
 
     response = ModelResponse(model=selected_model)
-    response._hidden_params = {AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY: selected_model} if stamp else {}
+    response._hidden_params = (
+        {AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY: selected_model} if stamp else {}
+    )
     return response
 
 
@@ -3995,7 +4000,9 @@ def test_standard_logging_payload_uses_stamped_model_router_model(logging_obj):
             "messages": [],
             "litellm_params": {"metadata": {}},
         },
-        init_response_obj=_model_router_response("azure_ai/grok-4-1-fast-reasoning", stamp=True),
+        init_response_obj=_model_router_response(
+            "azure_ai/grok-4-1-fast-reasoning", stamp=True
+        ),
         start_time=now,
         end_time=now,
         logging_obj=logging_obj,
@@ -4027,7 +4034,9 @@ def test_standard_logging_payload_keeps_requested_model_without_router_stamp(
             "messages": [],
             "litellm_params": {"metadata": {}},
         },
-        init_response_obj=_model_router_response("azure_ai/grok-4-1-fast-reasoning", stamp=False),
+        init_response_obj=_model_router_response(
+            "azure_ai/grok-4-1-fast-reasoning", stamp=False
+        ),
         start_time=now,
         end_time=now,
         logging_obj=logging_obj,
@@ -5515,7 +5524,9 @@ class TestNonInferenceCallTypesAreNotBilled:
             init_response_obj=self._retrieved_response(),
             start_time=now,
             end_time=now,
-            logging_obj=self._logging_obj("aget_responses", litellm_metadata=self.BACKGROUND_POLL_METADATA),
+            logging_obj=self._logging_obj(
+                "aget_responses", litellm_metadata=self.BACKGROUND_POLL_METADATA
+            ),
             status="success",
         )
 
@@ -5761,7 +5772,9 @@ async def test_streaming_success_callbacks_survive_cost_calculation_failure():
     releasing.async_log_success_event = AsyncMock()
 
     patcher, logging_obj = _streaming_logging_obj_with_callbacks([releasing])
-    with patcher, patch.object(logging_obj, "_response_cost_calculator", side_effect=ValueError("bad usage block")):
+    with patcher, patch.object(
+        logging_obj, "_response_cost_calculator", side_effect=ValueError("bad usage block")
+    ):
         await logging_obj.async_success_handler(result=_assembled_stream_result())
 
     assert logging_obj.model_call_details["response_cost"] is None
@@ -5774,9 +5787,8 @@ async def test_streaming_success_callbacks_survive_standard_logging_payload_fail
     releasing.async_log_success_event = AsyncMock()
 
     patcher, logging_obj = _streaming_logging_obj_with_callbacks([releasing])
-    with (
-        patcher,
-        patch.object(logging_obj, "_build_standard_logging_payload", side_effect=ValueError("incomplete stream")),
+    with patcher, patch.object(
+        logging_obj, "_build_standard_logging_payload", side_effect=ValueError("incomplete stream")
     ):
         await logging_obj.async_success_handler(result=_assembled_stream_result())
 
@@ -6124,8 +6136,6 @@ def test_prompt_hooks_skip_prompt_managers_when_no_prompt_id(logging_obj, tmp_pa
             )
         for hook in [cb for cb in litellm.callbacks if isinstance(cb, VectorStorePreCallHook)]:
             litellm.logging_callback_manager.remove_callback_from_list_by_object(litellm.callbacks, hook)
-
-
 def test_newrelic_dispatch_prefers_otel_v2_when_flag_on(monkeypatch):
     """With LITELLM_OTEL_V2 on and operator credentials present, the "newrelic"
     callback builds the OTel v2 logger (per-team credential routing); with the
@@ -6281,9 +6291,7 @@ def test_get_error_information_skips_traceback_for_budget_rejection_with_provide
     from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup
 
     assert litellm.log_client_error_tracebacks is False
-    over_budget = _raise_and_catch(
-        litellm.BudgetExceededError(current_cost=0.01, max_budget=0.0, llm_provider="anthropic")
-    )
+    over_budget = _raise_and_catch(litellm.BudgetExceededError(current_cost=0.01, max_budget=0.0, llm_provider="anthropic"))
     result = StandardLoggingPayloadSetup.get_error_information(over_budget)
     assert result["error_code"] == "429"
     assert result["llm_provider"] == "anthropic"
@@ -6856,7 +6864,9 @@ def test_passthrough_embeddings_result_swapped_for_callbacks():
             ],
             "model": "EmbeddingsGigaR",
         },
-        request=httpx.Request("POST", "https://gigachat.devices.sberbank.ru/api/v1/embeddings"),
+        request=httpx.Request(
+            "POST", "https://gigachat.devices.sberbank.ru/api/v1/embeddings"
+        ),
     )
 
     _, _, swapped_result = logging_obj._success_handler_helper_fn(
@@ -6875,14 +6885,12 @@ def test_get_status_fields_ranks_guardrail_flagged_between_success_and_intervene
     request-level guardrail_status but never mask an intervention."""
     flagged = {"guardrail_status": "guardrail_flagged"}
 
-    assert (
-        _get_status_fields("success", [{"guardrail_status": "success"}, flagged], None)["guardrail_status"]
-        == "guardrail_flagged"
-    )
-    assert (
-        _get_status_fields("success", [flagged, {"guardrail_status": "guardrail_intervened"}], None)["guardrail_status"]
-        == "guardrail_intervened"
-    )
+    assert _get_status_fields(
+        "success", [{"guardrail_status": "success"}, flagged], None
+    )["guardrail_status"] == "guardrail_flagged"
+    assert _get_status_fields(
+        "success", [flagged, {"guardrail_status": "guardrail_intervened"}], None
+    )["guardrail_status"] == "guardrail_intervened"
 
 
 def test_get_error_information_redacts_provider_key_from_upstream_url():
@@ -6935,41 +6943,22 @@ async def test_classifier_audit_matches_provider_transport(provider: str) -> Non
 
             return httpx.Response(200, json=mock_responses_api_response(content).model_dump())
         if provider == "anthropic":
-            return httpx.Response(
-                200,
-                json={
-                    "id": "msg-audit",
-                    "type": "message",
-                    "role": "assistant",
-                    "model": "claude-haiku-4-5",
-                    "content": [{"type": "text", "text": content}],
-                    "stop_reason": "end_turn",
-                    "usage": {"input_tokens": 10, "output_tokens": 5},
-                },
-            )
+            return httpx.Response(200, json={
+                "id": "msg-audit", "type": "message", "role": "assistant", "model": "claude-haiku-4-5",
+                "content": [{"type": "text", "text": content}], "stop_reason": "end_turn",
+                "usage": {"input_tokens": 10, "output_tokens": 5},
+            })
         if provider == "bedrock":
-            return httpx.Response(
-                200,
-                json={
-                    "output": {"message": {"role": "assistant", "content": [{"text": content}]}},
-                    "stopReason": "end_turn",
-                    "usage": {"inputTokens": 10, "outputTokens": 5, "totalTokens": 15},
-                    "metrics": {"latencyMs": 1},
-                },
-            )
-        return httpx.Response(
-            200,
-            json={
-                "id": "chatcmpl-audit",
-                "object": "chat.completion",
-                "created": 0,
-                "model": "gpt-5.6",
-                "choices": [
-                    {"index": 0, "message": {"role": "assistant", "content": content}, "finish_reason": "stop"}
-                ],
-                "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
-            },
-        )
+            return httpx.Response(200, json={
+                "output": {"message": {"role": "assistant", "content": [{"text": content}]}},
+                "stopReason": "end_turn", "usage": {"inputTokens": 10, "outputTokens": 5, "totalTokens": 15},
+                "metrics": {"latencyMs": 1},
+            })
+        return httpx.Response(200, json={
+            "id": "chatcmpl-audit", "object": "chat.completion", "created": 0, "model": "gpt-5.6",
+            "choices": [{"index": 0, "message": {"role": "assistant", "content": content}, "finish_reason": "stop"}],
+            "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+        })
 
     async def capture(kwargs, response_obj, start_time, end_time):
         logs.put_nowait(kwargs["standard_logging_object"])
@@ -6980,15 +6969,11 @@ async def test_classifier_audit_matches_provider_transport(provider: str) -> Non
         handler.client = http_client
         client: Final = (
             AsyncAzureOpenAI(
-                api_key="transport-only",
-                azure_endpoint="https://azure.invalid",
-                api_version="2025-04-01-preview",
-                http_client=http_client,
+                api_key="transport-only", azure_endpoint="https://azure.invalid",
+                api_version="2025-04-01-preview", http_client=http_client,
             )
-            if provider == "azure"
-            else AsyncOpenAI(api_key="transport-only", http_client=http_client)
-            if provider == "openai"
-            else handler
+            if provider == "azure" else AsyncOpenAI(api_key="transport-only", http_client=http_client)
+            if provider == "openai" else handler
         )
         model: Final = {
             "openai": "openai/gpt-5.6",
@@ -7001,44 +6986,23 @@ async def test_classifier_audit_matches_provider_transport(provider: str) -> Non
         async def run(marker: str) -> None:
             if provider == "responses":
                 await litellm.aresponses(
-                    model=model,
-                    api_key="transport-only",
-                    client=client,
-                    max_output_tokens=128,
-                    instructions="classifier-rubric",
-                    input=marker,
+                    model=model, api_key="transport-only", client=client, max_output_tokens=128,
+                    instructions="classifier-rubric", input=marker,
                     metadata={"internal_call_origin": "autorouter_classifier"},
                     proxy_server_request={"body": {}, "originating_request_masked": {"input": f"source-only-{marker}"}},
-                    success_callback=[capture],
-                    num_retries=0,
+                    success_callback=[capture], num_retries=0,
                 )
                 return
             await litellm.acompletion(
-                model=model,
-                api_key="transport-only",
-                client=client,
-                max_tokens=128,
-                aws_access_key_id="transport-only",
-                aws_secret_access_key="transport-only",
-                aws_region_name="us-east-1",
+                model=model, api_key="transport-only", client=client, max_tokens=128,
+                aws_access_key_id="transport-only", aws_secret_access_key="transport-only", aws_region_name="us-east-1",
                 messages=[{"role": "system", "content": "classifier-rubric"}, {"role": "user", "content": marker}],
                 metadata={"internal_call_origin": "autorouter_classifier"},
                 proxy_server_request={"body": {}, "originating_request_masked": {"input": f"source-only-{marker}"}},
-                success_callback=[capture],
-                num_retries=0,
-                **(
-                    {"api_base": "https://azure.invalid", "api_version": "2025-04-01-preview"}
-                    if provider == "azure"
-                    else {}
-                ),
-                **(
-                    {
-                        "extra_body": {"audit_context": "provider-extra"},
-                        "extra_headers": {"X-Audit": "header-only-secret"},
-                    }
-                    if provider in ("openai", "azure")
-                    else {}
-                ),
+                success_callback=[capture], num_retries=0,
+                **({"api_base": "https://azure.invalid", "api_version": "2025-04-01-preview"} if provider == "azure" else {}),
+                **({"extra_body": {"audit_context": "provider-extra"}, "extra_headers": {"X-Audit": "header-only-secret"}}
+                   if provider in ("openai", "azure") else {}),
             )
 
         await asyncio.gather(run("request-one"), run("request-two"))
@@ -7062,17 +7026,14 @@ async def test_classifier_audit_matches_provider_transport(provider: str) -> Non
 @pytest.mark.parametrize("redaction", ["none", "global", "request", "header"])
 @pytest.mark.parametrize("status", ["success", "failure"])
 @pytest.mark.parametrize("call_type", ["completion", "acompletion", "responses", "aresponses"])
-def test_classifier_audit_obeys_message_logging_before_payload_emission(
-    logging_obj, monkeypatch, redaction, status, call_type
-):
+def test_classifier_audit_obeys_message_logging_before_payload_emission(logging_obj, monkeypatch, redaction, status, call_type):
     from litellm.litellm_core_utils.litellm_logging import get_standard_logging_object_payload
 
     monkeypatch.setattr(litellm, "turn_off_message_logging", redaction == "global")
     params: Final = {
-        "metadata": {
-            "internal_call_origin": "autorouter_classifier",
-            **({"headers": {"x-litellm-enable-message-redaction": "true"}} if redaction == "header" else {}),
-        },
+        "metadata": {"internal_call_origin": "autorouter_classifier", **(
+            {"headers": {"x-litellm-enable-message-redaction": "true"}} if redaction == "header" else {}
+        )},
         "proxy_server_request": {"body": {}, "originating_request_masked": {"input": "source-only"}},
     }
     logging_obj.call_type = call_type
@@ -7085,12 +7046,8 @@ def test_classifier_audit_obeys_message_logging_before_payload_emission(
     )
     now: Final = datetime.datetime.now()
     payload: Final = get_standard_logging_object_payload(
-        kwargs={**logging_obj.model_call_details, "call_type": call_type},
-        init_response_obj={},
-        start_time=now,
-        end_time=now,
-        logging_obj=logging_obj,
-        status=status,
+        kwargs={**logging_obj.model_call_details, "call_type": call_type}, init_response_obj={},
+        start_time=now, end_time=now, logging_obj=logging_obj, status=status,
     )
     assert payload is not None
     if redaction == "none":

tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py

@@ -187,7 +187,11 @@ def test_get_combined_thinking_content_preserves_interleaved_blocks():
                 }
             ]
         ),
-        make_chunk(thinking_blocks=[{"type": "thinking", "thinking": None, "signature": "sig_block1"}]),
+        make_chunk(
+            thinking_blocks=[
+                {"type": "thinking", "thinking": None, "signature": "sig_block1"}
+            ]
+        ),
         make_chunk(
             thinking_blocks=[
                 {
@@ -205,10 +209,16 @@ def test_get_combined_thinking_content_preserves_interleaved_blocks():
                 }
             ]
         ),
-        make_chunk(thinking_blocks=[{"type": "thinking", "thinking": None, "signature": "sig_block2"}]),
+        make_chunk(
+            thinking_blocks=[
+                {"type": "thinking", "thinking": None, "signature": "sig_block2"}
+            ]
+        ),
     ]
 
-    thinking_chunks = [chunk for chunk in chunks if chunk["choices"][0]["delta"].get("thinking_blocks")]
+    thinking_chunks = [
+        chunk for chunk in chunks if chunk["choices"][0]["delta"].get("thinking_blocks")
+    ]
     processor = ChunkProcessor(chunks=chunks)
     result = processor.get_combined_thinking_content(thinking_chunks)
 
@@ -253,7 +263,9 @@ def test_cache_read_input_tokens_retained():
             prompt_tokens=11779,
             total_tokens=11784,
             completion_tokens_details=None,
-            prompt_tokens_details=PromptTokensDetails(audio_tokens=None, cached_tokens=11775),
+            prompt_tokens_details=PromptTokensDetails(
+                audio_tokens=None, cached_tokens=11775
+            ),
             cache_creation_input_tokens=4,
             cache_read_input_tokens=11775,
         ),
@@ -287,7 +299,9 @@ def test_cache_read_input_tokens_retained():
             prompt_tokens=0,
             total_tokens=214,
             completion_tokens_details=None,
-            prompt_tokens_details=PromptTokensDetails(audio_tokens=None, cached_tokens=0),
+            prompt_tokens_details=PromptTokensDetails(
+                audio_tokens=None, cached_tokens=0
+            ),
             cache_creation_input_tokens=0,
             cache_read_input_tokens=0,
         ),
@@ -347,7 +361,10 @@ def test_streaming_preserves_anthropic_1hr_cache_creation_breakdown():
     )
     # Sanity: the delta event genuinely lacks the breakdown - this is the input
     # condition that used to defeat cost calc.
-    assert getattr(message_delta_usage.prompt_tokens_details, "cache_creation_token_details", None) is None
+    assert (
+        getattr(message_delta_usage.prompt_tokens_details, "cache_creation_token_details", None)
+        is None
+    )
 
     def _usage_chunk(usage, finish_reason):
         return ModelResponseStream(
@@ -466,7 +483,9 @@ def test_cache_read_input_tokens_retained_genericstreamingchunk():
             prompt_tokens=1234,
             total_tokens=1239,
             completion_tokens_details=None,
-            prompt_tokens_details=PromptTokensDetails(audio_tokens=None, cached_tokens=543).model_dump(),
+            prompt_tokens_details=PromptTokensDetails(
+                audio_tokens=None, cached_tokens=543
+            ).model_dump(),
         ),
         index=2,
     )
@@ -483,7 +502,6 @@ def test_cache_read_input_tokens_retained_genericstreamingchunk():
 
     assert usage.prompt_tokens_details.cached_tokens == 543
 
-
 def test_stream_chunk_builder_litellm_usage_chunks():
     """
     Validate ChunkProcessor.calculate_usage uses provided usage fields from streaming chunks
@@ -557,7 +575,9 @@ def test_stream_chunk_builder_litellm_usage_chunks():
     chunks = [chunk1, chunk2]
     processor = ChunkProcessor(chunks=chunks)
 
-    usage = processor.calculate_usage(chunks=chunks, model="gemini/gemini-2.5-flash-lite", completion_output="")
+    usage = processor.calculate_usage(
+        chunks=chunks, model="gemini/gemini-2.5-flash-lite", completion_output=""
+    )
 
     assert usage.prompt_tokens == 50
     assert usage.completion_tokens == 27
@@ -601,11 +621,15 @@ def test_calculate_usage_honors_openai_sdk_completion_usage_chunks():
         provider_specific_fields=None,
         stream_options={"include_usage": True},
     )
-    usage_chunk.usage = CompletionUsage(prompt_tokens=20, completion_tokens=60, total_tokens=80, cost=0.000704)
+    usage_chunk.usage = CompletionUsage(
+        prompt_tokens=20, completion_tokens=60, total_tokens=80, cost=0.000704
+    )
     assert type(usage_chunk.usage) is CompletionUsage
 
     chunks = [content_chunk, usage_chunk]
-    usage = ChunkProcessor(chunks=chunks).calculate_usage(chunks=chunks, model="mantle-claude", completion_output="")
+    usage = ChunkProcessor(chunks=chunks).calculate_usage(
+        chunks=chunks, model="mantle-claude", completion_output=""
+    )
 
     assert usage.prompt_tokens == 20
     assert usage.completion_tokens == 60
@@ -628,7 +652,9 @@ def test_get_model_from_chunks_azure_model_router():
         {"model": "gpt-4.1-nano-2025-04-14", "id": "chatcmpl-123", "choices": []},
     ]
 
-    result = ChunkProcessor._get_model_from_chunks(chunks=chunks, first_chunk_model="azure-model-router")
+    result = ChunkProcessor._get_model_from_chunks(
+        chunks=chunks, first_chunk_model="azure-model-router"
+    )
 
     # Should return the actual model, not the request model
     assert result == "gpt-4.1-nano-2025-04-14"
@@ -639,7 +665,9 @@ def test_get_model_from_chunks_azure_model_router():
         {"model": "gpt-4", "id": "chatcmpl-456", "choices": []},
     ]
 
-    result_same = ChunkProcessor._get_model_from_chunks(chunks=chunks_same_model, first_chunk_model="gpt-4")
+    result_same = ChunkProcessor._get_model_from_chunks(
+        chunks=chunks_same_model, first_chunk_model="gpt-4"
+    )
 
     # Should return the first chunk's model when all are the same
     assert result_same == "gpt-4"
@@ -715,7 +743,9 @@ def test_stream_chunk_builder_anthropic_web_search():
     chunks = [chunk1, chunk2]
     processor = ChunkProcessor(chunks=chunks)
 
-    usage = processor.calculate_usage(chunks=chunks, model="claude-sonnet-4-5-20250929", completion_output="")
+    usage = processor.calculate_usage(
+        chunks=chunks, model="claude-sonnet-4-5-20250929", completion_output=""
+    )
 
     assert usage.prompt_tokens == 50
     assert usage.completion_tokens == 27
@@ -867,11 +897,15 @@ def test_stream_chunk_builder_dict_snapshot_preserves_hidden_provider_fields():
         ],
     )
     chunk_dict = chunk.model_dump()
-    chunk_dict["_hidden_params"] = {"provider_specific_fields": {"traffic_type": "default"}}
+    chunk_dict["_hidden_params"] = {
+        "provider_specific_fields": {"traffic_type": "default"}
+    }
 
     response = stream_chunk_builder(chunks=[chunk_dict])
     assert response is not None
-    assert response._hidden_params["provider_specific_fields"]["traffic_type"] == "default"
+    assert (
+        response._hidden_params["provider_specific_fields"]["traffic_type"] == "default"
+    )
 
 
 def test_stream_chunk_builder_propagates_vertex_ai_metadata_from_chunks():
@@ -916,7 +950,10 @@ def test_stream_chunk_builder_propagates_vertex_ai_metadata_from_chunks():
     assert getattr(response, "vertex_ai_grounding_metadata") == grounding_metadata
     assert getattr(response, "vertex_ai_url_context_metadata") == url_context_metadata
     assert response._hidden_params["vertex_ai_grounding_metadata"] == grounding_metadata
-    assert response._hidden_params["vertex_ai_url_context_metadata"] == url_context_metadata
+    assert (
+        response._hidden_params["vertex_ai_url_context_metadata"]
+        == url_context_metadata
+    )
 
     dumped = response.model_dump()
     assert dumped["vertex_ai_grounding_metadata"] == grounding_metadata
@@ -963,7 +1000,9 @@ def test_stream_chunk_builder_uses_assembled_model_for_provider_metadata():
 
 def test_stream_chunk_builder_propagates_vertex_ai_safety_results():
     """Assembled response must expose safety data under the non-streaming field name."""
-    safety_ratings = [[{"category": "HARM_CATEGORY_HATE_SPEECH", "probability": "NEGLIGIBLE"}]]
+    safety_ratings = [
+        [{"category": "HARM_CATEGORY_HATE_SPEECH", "probability": "NEGLIGIBLE"}]
+    ]
 
     chunk = ModelResponseStream(
         id="chatcmpl-vertex-safety",
@@ -1005,12 +1044,18 @@ def test_stream_chunk_builder_propagates_vertex_ai_metadata_from_dict_chunks():
             )
         ],
     ).model_dump()
-    chunk_dict["_hidden_params"] = {"vertex_ai_grounding_metadata": [{"webSearchQueries": ["test query"]}]}
+    chunk_dict["_hidden_params"] = {
+        "vertex_ai_grounding_metadata": [{"webSearchQueries": ["test query"]}]
+    }
 
     response = stream_chunk_builder(chunks=[chunk_dict])
     assert response is not None
-    assert getattr(response, "vertex_ai_grounding_metadata") == [{"webSearchQueries": ["test query"]}]
-    assert response.model_dump()["vertex_ai_grounding_metadata"] == [{"webSearchQueries": ["test query"]}]
+    assert getattr(response, "vertex_ai_grounding_metadata") == [
+        {"webSearchQueries": ["test query"]}
+    ]
+    assert response.model_dump()["vertex_ai_grounding_metadata"] == [
+        {"webSearchQueries": ["test query"]}
+    ]
 
 
 def test_cost_field_in_usage_chunks():
@@ -1019,21 +1064,29 @@ def test_cost_field_in_usage_chunks():
         id="chatcmpl-1",
         created=1745513206,
         model="openrouter/claude",
-        choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(content="Hi"))],
+        choices=[
+            StreamingChoices(finish_reason=None, index=0, delta=Delta(content="Hi"))
+        ],
         usage=chunk1_usage,
     )
 
-    chunk2_usage = Usage(completion_tokens=5, prompt_tokens=10, total_tokens=15, cost=0.00025)
+    chunk2_usage = Usage(
+        completion_tokens=5, prompt_tokens=10, total_tokens=15, cost=0.00025
+    )
     chunk2 = ModelResponseStream(
         id="chatcmpl-1",
         created=1745513207,
         model="openrouter/claude",
-        choices=[StreamingChoices(finish_reason="stop", index=0, delta=Delta(content=""))],
+        choices=[
+            StreamingChoices(finish_reason="stop", index=0, delta=Delta(content=""))
+        ],
         usage=chunk2_usage,
     )
 
     processor = ChunkProcessor(chunks=[chunk1, chunk2])
-    usage = processor.calculate_usage(chunks=[chunk1, chunk2], model="openrouter/claude", completion_output="Hi")
+    usage = processor.calculate_usage(
+        chunks=[chunk1, chunk2], model="openrouter/claude", completion_output="Hi"
+    )
 
     assert hasattr(usage, "cost")
     assert usage.cost == 0.00025
@@ -1077,19 +1130,25 @@ def test_prompt_tokens_details_survive_later_usage_chunk_without_details():
         id="chatcmpl-1",
         created=1745513206,
         model="openai/gpt-5.6-sol",
-        choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(content="Hi"))],
+        choices=[
+            StreamingChoices(finish_reason=None, index=0, delta=Delta(content="Hi"))
+        ],
         usage=Usage(
             prompt_tokens=6017,
             completion_tokens=4,
             total_tokens=6021,
-            prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=6004, cache_write_tokens=10),
+            prompt_tokens_details=PromptTokensDetailsWrapper(
+                cached_tokens=6004, cache_write_tokens=10
+            ),
         ),
     )
     chunk_without_details = ModelResponseStream(
         id="chatcmpl-1",
         created=1745513207,
         model="openai/gpt-5.6-sol",
-        choices=[StreamingChoices(finish_reason="stop", index=0, delta=Delta(content=""))],
+        choices=[
+            StreamingChoices(finish_reason="stop", index=0, delta=Delta(content=""))
+        ],
         usage=Usage(prompt_tokens=6017, completion_tokens=4, total_tokens=6021),
     )
 
@@ -1372,7 +1431,9 @@ def test_calculate_usage_fills_unknown_split_from_reasoning_estimate(
     assert usage.completion_tokens_details.text_tokens == expected_text_tokens
 
 
-def _openai_chunk(choices: Sequence[Mapping[str, object]], usage: Mapping[str, int] | None = None) -> dict[str, object]:
+def _openai_chunk(
+    choices: Sequence[Mapping[str, object]], usage: Mapping[str, int] | None = None
+) -> dict[str, object]:
     base: Final = {
         "id": "chatcmpl-lit6552",
         "object": "chat.completion.chunk",

tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py

@@ -1,3 +1,4 @@
+
 import pytest
 
 from unittest.mock import MagicMock, patch
@@ -32,9 +33,13 @@ def test_response_format_transformation_unit_test():
         "additionalProperties": False,
     }
 
-    result = config._create_json_tool_call_for_response_format(json_schema=response_format_json_schema)
+    result = config._create_json_tool_call_for_response_format(
+        json_schema=response_format_json_schema
+    )
 
-    assert result["input_schema"]["properties"] == {"agent_doing": {"title": "Agent Doing", "type": "string"}}
+    assert result["input_schema"]["properties"] == {
+        "agent_doing": {"title": "Agent Doing", "type": "string"}
+    }
     print(result)
 
 
@@ -545,7 +550,9 @@ def test_extract_response_content_with_citations():
         },
     }
 
-    _, citations, _, _, _, _, _, _ = config.extract_response_content(completion_response)
+    _, citations, _, _, _, _, _, _ = config.extract_response_content(
+        completion_response
+    )
     assert citations == [
         [
             {
@@ -618,8 +625,12 @@ def test_web_search_tool_transformation():
     assert anthropic_web_search_tool["user_location"]["city"] == "San Francisco"
 
 
-@pytest.mark.parametrize("search_context_size, expected_max_uses", [("low", 1), ("medium", 5), ("high", 10)])
-def test_web_search_tool_transformation_with_search_context_size(search_context_size, expected_max_uses):
+@pytest.mark.parametrize(
+    "search_context_size, expected_max_uses", [("low", 1), ("medium", 5), ("high", 10)]
+)
+def test_web_search_tool_transformation_with_search_context_size(
+    search_context_size, expected_max_uses
+):
     from litellm.types.llms.openai import OpenAIWebSearchOptions
 
     config = AnthropicConfig()
@@ -794,7 +805,10 @@ def test_web_search_tool_result_in_provider_specific_fields():
     assert "web_search_results" in provider_fields
     assert len(provider_fields["web_search_results"]) == 1
     assert provider_fields["web_search_results"][0]["type"] == "web_search_tool_result"
-    assert provider_fields["web_search_results"][0]["tool_use_id"] == "srvtoolu_provider_test"
+    assert (
+        provider_fields["web_search_results"][0]["tool_use_id"]
+        == "srvtoolu_provider_test"
+    )
 
 
 def test_multiple_web_search_tool_results():
@@ -1018,7 +1032,10 @@ def test_transform_response_with_prefix_prompt():
     )
 
     assert result is not None
-    assert result.choices[0].message.content == "You are a helpful assistant. The grass is green."
+    assert (
+        result.choices[0].message.content
+        == "You are a helpful assistant. The grass is green."
+    )
 
 
 def test_get_supported_params_thinking():
@@ -1133,12 +1150,18 @@ def test_anthropic_beta_header_merging_with_output_format():
         }
     }
 
-    result_headers = config.update_headers_with_optional_anthropic_beta(headers, optional_params)
+    result_headers = config.update_headers_with_optional_anthropic_beta(
+        headers, optional_params
+    )
 
     # Both beta headers should be present
     beta_value = result_headers["anthropic-beta"]
-    assert "context-1m-2025-08-07" in beta_value, f"User's context-1m beta header missing from: {beta_value}"
-    assert "structured-outputs-2025-11-13" in beta_value, f"Structured output beta header missing from: {beta_value}"
+    assert (
+        "context-1m-2025-08-07" in beta_value
+    ), f"User's context-1m beta header missing from: {beta_value}"
+    assert (
+        "structured-outputs-2025-11-13" in beta_value
+    ), f"Structured output beta header missing from: {beta_value}"
 
 
 def test_anthropic_beta_header_merging_with_multiple_features():
@@ -1160,7 +1183,9 @@ def test_anthropic_beta_header_merging_with_multiple_features():
         "tools": [{"type": "web_fetch_20250910", "name": "web_fetch"}],
     }
 
-    result_headers = config.update_headers_with_optional_anthropic_beta(headers, optional_params)
+    result_headers = config.update_headers_with_optional_anthropic_beta(
+        headers, optional_params
+    )
 
     beta_value = result_headers["anthropic-beta"]
 
@@ -1203,7 +1228,9 @@ def test_anthropic_structured_output_beta_header():
                     "strict": True,
                     "schema": {
                         "description": 'Progress report for the thinking process\n\nThis model represents a snapshot of the agent\'s current progress during\nthe thinking process, providing a brief description of the current activity.\n\nAttributes:\n    agent_doing: Brief description of what the agent is currently doing.\n                Should be kept under 10 words. Example: "Learning about home automation"',
-                        "properties": {"agent_doing": {"title": "Agent Doing", "type": "string"}},
+                        "properties": {
+                            "agent_doing": {"title": "Agent Doing", "type": "string"}
+                        },
                         "required": ["agent_doing"],
                         "title": "ThinkingStep",
                         "type": "object",
@@ -1217,7 +1244,10 @@ def test_anthropic_structured_output_beta_header():
     assert response is not None
     print(f"response: {response}")
     print(f"raw_request_headers: {response['raw_request_headers']}")
-    assert "structured-outputs-2025-11-13" in response["raw_request_headers"]["anthropic-beta"]
+    assert (
+        "structured-outputs-2025-11-13"
+        in response["raw_request_headers"]["anthropic-beta"]
+    )
 
 
 @pytest.mark.parametrize(
@@ -1353,7 +1383,9 @@ def test_tool_search_regex_detection():
     config = AnthropicModelInfo()
 
     # Test with tool search regex tool
-    tools = [{"type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex"}]
+    tools = [
+        {"type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex"}
+    ]
     assert config.is_tool_search_used(tools) is True
 
     # Test without tool search
@@ -1368,7 +1400,9 @@ def test_tool_search_bm25_detection():
     config = AnthropicModelInfo()
 
     # Test with tool search BM25 tool
-    tools = [{"type": "tool_search_tool_bm25_20251119", "name": "tool_search_tool_bm25"}]
+    tools = [
+        {"type": "tool_search_tool_bm25_20251119", "name": "tool_search_tool_bm25"}
+    ]
     assert config.is_tool_search_used(tools) is True
 
 
@@ -1560,7 +1594,9 @@ def test_tool_search_complete_response_parsing():
                 "tool_use_id": "srvtoolu_015i6aVA2niwzv4RG4DtnxDJ",
                 "content": {
                     "type": "tool_search_tool_search_result",
-                    "tool_references": [{"type": "tool_reference", "tool_name": "get_weather"}],
+                    "tool_references": [
+                        {"type": "tool_reference", "tool_name": "get_weather"}
+                    ],
                 },
             },
             {"type": "text", "text": "Great! I found a weather tool."},
@@ -1611,7 +1647,9 @@ def test_tool_search_complete_response_parsing():
 
     assert usage.server_tool_use is not None
     assert usage.server_tool_use.web_search_requests == 0
-    assert usage.server_tool_use.tool_search_requests == 1  # Counted from server_tool_use blocks
+    assert (
+        usage.server_tool_use.tool_search_requests == 1
+    )  # Counted from server_tool_use blocks
 
 
 def test_allowed_callers_field_preservation():
@@ -1663,7 +1701,9 @@ def test_programmatic_tool_calling_beta_header():
     assert is_programmatic is True
 
     # Test header generation
-    headers = model_info.get_anthropic_headers(api_key="test-key", programmatic_tool_calling_used=True)
+    headers = model_info.get_anthropic_headers(
+        api_key="test-key", programmatic_tool_calling_used=True
+    )
 
     assert "anthropic-beta" in headers
     assert "advanced-tool-use-2025-11-20" in headers["anthropic-beta"]
@@ -1807,7 +1847,9 @@ def test_input_examples_beta_header():
     assert is_examples_used is True
 
     # Test header generation
-    headers = model_info.get_anthropic_headers(api_key="test-key", input_examples_used=True)
+    headers = model_info.get_anthropic_headers(
+        api_key="test-key", input_examples_used=True
+    )
 
     assert "anthropic-beta" in headers
     assert "advanced-tool-use-2025-11-20" in headers["anthropic-beta"]
@@ -1893,7 +1935,10 @@ def test_input_examples_empty_list_not_added():
     transformed_tool, _ = config._map_tool_helper(tool)
     assert transformed_tool is not None
     # Empty list should not be added
-    assert "input_examples" not in transformed_tool or len(transformed_tool.get("input_examples", [])) == 0
+    assert (
+        "input_examples" not in transformed_tool
+        or len(transformed_tool.get("input_examples", [])) == 0
+    )
 
 
 # ============ Effort Parameter Tests ============
@@ -1953,7 +1998,9 @@ def test_effort_beta_header_injection():
     effort_used = model_info.is_effort_used(optional_params=optional_params, custom_llm_provider="anthropic")
     assert effort_used is True
 
-    headers = model_info.get_anthropic_headers(api_key="test-key", effort_used=effort_used)
+    headers = model_info.get_anthropic_headers(
+        api_key="test-key", effort_used=effort_used
+    )
 
     assert "anthropic-beta" in headers
     assert "effort-2025-11-24" in headers["anthropic-beta"]
@@ -1979,7 +2026,9 @@ def test_effort_validation():
 
     optional_params = {"output_config": {"effort": "invalid"}}
 
-    with pytest.raises(litellm.exceptions.BadRequestError, match="Invalid effort value"):
+    with pytest.raises(
+        litellm.exceptions.BadRequestError, match="Invalid effort value"
+    ):
         config.transform_request(
             model="claude-opus-4-5-20251101",
             messages=messages,
@@ -2215,8 +2264,16 @@ def test_anthropic_model_supports_speed_param_rejects_non_anthropic_providers(
 ):
     """Fast mode is direct-Anthropic-only. Vertex/Azure/Bedrock strip their prefix
     before the shared transform runs, so the bare Opus id must still be rejected."""
-    assert AnthropicConfig._model_supports_speed_param("claude-opus-4-8", custom_llm_provider) is False
-    assert AnthropicConfig._model_supports_speed_param("claude-opus-4-8", "anthropic") is True
+    assert (
+        AnthropicConfig._model_supports_speed_param(
+            "claude-opus-4-8", custom_llm_provider
+        )
+        is False
+    )
+    assert (
+        AnthropicConfig._model_supports_speed_param("claude-opus-4-8", "anthropic")
+        is True
+    )
 
 
 def test_vertex_anthropic_drops_speed_for_opus_with_drop_params(monkeypatch):
@@ -2464,7 +2521,9 @@ def test_supports_effort_level_handles_provider_prefixes(model, level, expected)
         ("claude-opus-4-5-20251101", None, False),
     ],
 )
-def test_validate_effort_for_model_centralises_per_model_gating(model, effort, expect_error):
+def test_validate_effort_for_model_centralises_per_model_gating(
+    model, effort, expect_error
+):
     err = AnthropicConfig._validate_effort_for_model(model, effort, "anthropic")
     if expect_error:
         assert err is not None
@@ -2513,7 +2572,11 @@ def test_transform_request_injects_dummy_tool_without_tools_param():
         litellm.modify_params = prev_modify_params
 
     assert "tools" in result
-    names = [t.get("name") for t in result["tools"] if isinstance(t, dict) and t.get("name") is not None]
+    names = [
+        t.get("name")
+        for t in result["tools"]
+        if isinstance(t, dict) and t.get("name") is not None
+    ]
     assert "dummy_tool" in names
 
 
@@ -2579,9 +2642,13 @@ def test_calculate_usage_completion_tokens_details_with_reasoning():
         "output_tokens": 500,
     }
     # Simulating reasoning content that would count as ~50 tokens
-    reasoning_content = "Let me think about this step by step. " * 10  # Roughly 50 tokens
+    reasoning_content = (
+        "Let me think about this step by step. " * 10
+    )  # Roughly 50 tokens
 
-    usage = config.calculate_usage(usage_object=usage_object, reasoning_content=reasoning_content)
+    usage = config.calculate_usage(
+        usage_object=usage_object, reasoning_content=reasoning_content
+    )
 
     # completion_tokens_details should be populated with both reasoning and text tokens
     assert usage.completion_tokens_details is not None
@@ -2632,7 +2699,9 @@ def test_reasoning_effort_maps_to_adaptive_thinking_for_claude_4_6_models():
             # reasoning_effort should not be in the result (it's transformed to thinking)
             assert "reasoning_effort" not in result
             # Should set output_config with the mapped effort value
-            assert "output_config" in result, f"output_config missing for {model} with effort={effort}"
+            assert (
+                "output_config" in result
+            ), f"output_config missing for {model} with effort={effort}"
             assert result["output_config"]["effort"] == effort_map[effort]
 
 
@@ -2733,7 +2802,9 @@ def test_raw_adaptive_thinking_untouched_for_46_plus_model():
         ("gpt-4o", False),
     ],
 )
-def test_is_adaptive_thinking_model_is_sourced_from_cost_map(local_model_cost_map, model, expected):
+def test_is_adaptive_thinking_model_is_sourced_from_cost_map(
+    local_model_cost_map, model, expected
+):
     """Adaptive thinking resolves from the cost map first (an explicit
     supports_adaptive_thinking entry, or the anthropic-claude fallback rule for unmapped
     future Claudes), then from a date-safe opus/sonnet/haiku >= 4.6 name version as a
@@ -2849,7 +2920,9 @@ def test_reasoning_effort_sets_output_config_for_46_models():
                 drop_params=False,
             )
 
-            assert "output_config" in result, f"output_config missing for {model} with effort={effort}"
+            assert (
+                "output_config" in result
+            ), f"output_config missing for {model} with effort={effort}"
             assert result["output_config"]["effort"] == effort
 
 
@@ -2888,7 +2961,9 @@ def test_reasoning_effort_does_not_set_output_config_for_older_models():
             drop_params=False,
         )
 
-        assert "output_config" not in result, f"output_config should not be set for {model}"
+        assert (
+            "output_config" not in result
+        ), f"output_config should not be set for {model}"
 
 
 @pytest.mark.parametrize(
@@ -2928,10 +3003,14 @@ def test_reasoning_effort_accepts_dict_shape_for_adaptive_model(reasoning_effort
     )
 
     # thinking must be set (adaptive for 4.6+)
-    assert "thinking" in result, f"thinking missing for reasoning_effort={reasoning_effort_value!r}"
+    assert (
+        "thinking" in result
+    ), f"thinking missing for reasoning_effort={reasoning_effort_value!r}"
     assert result["thinking"]["type"] == "adaptive"
     # output_config must carry the mapped effort
-    assert "output_config" in result, f"output_config missing for reasoning_effort={reasoning_effort_value!r}"
+    assert (
+        "output_config" in result
+    ), f"output_config missing for reasoning_effort={reasoning_effort_value!r}"
     assert result["output_config"]["effort"] == "low"
 
 
@@ -2960,13 +3039,16 @@ def test_reasoning_effort_accepts_dict_shape_for_non_adaptive_model(
         drop_params=False,
     )
 
-    assert "thinking" in result, f"thinking missing for reasoning_effort={reasoning_effort_value!r}"
+    assert (
+        "thinking" in result
+    ), f"thinking missing for reasoning_effort={reasoning_effort_value!r}"
     assert result["thinking"]["type"] == "enabled"
     assert "budget_tokens" in result["thinking"]
     assert result["thinking"]["budget_tokens"] > 0
     # Older models must not get adaptive-thinking output_config
     assert "output_config" not in result, (
-        f"output_config should not be set for non-adaptive model (reasoning_effort={reasoning_effort_value!r})"
+        f"output_config should not be set for non-adaptive model "
+        f"(reasoning_effort={reasoning_effort_value!r})"
     )
 
 
@@ -3017,8 +3099,12 @@ def test_reasoning_effort_unparseable_dict_is_dropped(bad_value):
         model="claude-sonnet-4-6-20260219",
         drop_params=False,
     )
-    assert "thinking" not in result, f"thinking should not be set for bad value {bad_value!r}"
-    assert "output_config" not in result, f"output_config should not be set for bad value {bad_value!r}"
+    assert (
+        "thinking" not in result
+    ), f"thinking should not be set for bad value {bad_value!r}"
+    assert (
+        "output_config" not in result
+    ), f"output_config should not be set for bad value {bad_value!r}"
 
 
 @pytest.mark.parametrize(
@@ -3128,7 +3214,9 @@ def test_reasoning_effort_garbage_raises_bad_request(effort):
         ("max", DEFAULT_REASONING_EFFORT_MAX_THINKING_BUDGET),
     ],
 )
-def test_reasoning_effort_xhigh_max_maps_to_budget_on_budget_model(effort, expected_budget):
+def test_reasoning_effort_xhigh_max_maps_to_budget_on_budget_model(
+    effort, expected_budget
+):
     """``xhigh`` / ``max`` extend the budget_tokens progression on budget-mode models."""
     config = AnthropicConfig()
 
@@ -3258,11 +3346,17 @@ def test_code_execution_tool_results_extraction():
 
     # Verify first tool call
     assert transformed_response.choices[0].message.tool_calls[0].id == "srvtoolu_01ABC"
-    assert transformed_response.choices[0].message.tool_calls[0].function.name == "bash_code_execution"
+    assert (
+        transformed_response.choices[0].message.tool_calls[0].function.name
+        == "bash_code_execution"
+    )
 
     # Verify second tool call
     assert transformed_response.choices[0].message.tool_calls[1].id == "srvtoolu_01DEF"
-    assert transformed_response.choices[0].message.tool_calls[1].function.name == "text_editor_code_execution"
+    assert (
+        transformed_response.choices[0].message.tool_calls[1].function.name
+        == "text_editor_code_execution"
+    )
 
     # Verify tool results are in provider_specific_fields
     provider_fields = transformed_response.choices[0].message.provider_specific_fields
@@ -3285,7 +3379,10 @@ def test_code_execution_tool_results_extraction():
     assert editor_result["content"]["is_file_update"] is False
 
     # Verify text content is properly concatenated
-    assert "I'll calculate that for you." in transformed_response.choices[0].message.content
+    assert (
+        "I'll calculate that for you."
+        in transformed_response.choices[0].message.content
+    )
     assert "Done!" in transformed_response.choices[0].message.content
 
 
@@ -3353,7 +3450,10 @@ def test_code_execution_tool_results_in_hidden_params():
     assert "provider_specific_fields" in hidden
     assert "tool_results" in hidden["provider_specific_fields"]
     assert len(hidden["provider_specific_fields"]["tool_results"]) == 1
-    assert hidden["provider_specific_fields"]["tool_results"][0]["content"]["stdout"] == "hello\n"
+    assert (
+        hidden["provider_specific_fields"]["tool_results"][0]["content"]["stdout"]
+        == "hello\n"
+    )
 
 
 def test_tool_search_tool_result_not_in_tool_results():
@@ -3549,7 +3649,10 @@ def test_compaction_block_in_provider_specific_fields():
     assert "compaction_blocks" in provider_fields
     assert len(provider_fields["compaction_blocks"]) == 1
     assert provider_fields["compaction_blocks"][0]["type"] == "compaction"
-    assert "Summary of the conversation" in provider_fields["compaction_blocks"][0]["content"]
+    assert (
+        "Summary of the conversation"
+        in provider_fields["compaction_blocks"][0]["content"]
+    )
 
 
 def test_multiple_compaction_blocks():
@@ -3597,7 +3700,9 @@ def test_compaction_block_request_transformation():
         {"role": "user", "content": "What is the weather in San Francisco?"},
         {
             "role": "assistant",
-            "content": [{"type": "text", "text": "I don't have access to real-time data."}],
+            "content": [
+                {"type": "text", "text": "I don't have access to real-time data."}
+            ],
             "provider_specific_fields": {
                 "compaction_blocks": [
                     {
@@ -3610,7 +3715,9 @@ def test_compaction_block_request_transformation():
         {"role": "user", "content": "What about New York?"},
     ]
 
-    result = anthropic_messages_pt(messages=messages, model="claude-opus-4-6", llm_provider="anthropic")
+    result = anthropic_messages_pt(
+        messages=messages, model="claude-opus-4-6", llm_provider="anthropic"
+    )
 
     # Find the assistant message
     assistant_message = None
@@ -3724,7 +3831,9 @@ def test_map_openai_context_management_to_anthropic():
             "instructions": "Focus on preserving code snippets",
         }
     ]
-    result = config.map_openai_context_management_to_anthropic(openai_format_with_instructions)
+    result = config.map_openai_context_management_to_anthropic(
+        openai_format_with_instructions
+    )
 
     assert result is not None
     assert result["edits"][0]["trigger"]["value"] == 150000
@@ -3751,7 +3860,9 @@ def test_map_openai_params_with_context_management():
     config = AnthropicConfig()
 
     # Test with OpenAI list format
-    non_default_params = {"context_management": [{"type": "compaction", "compact_threshold": 200000}]}
+    non_default_params = {
+        "context_management": [{"type": "compaction", "compact_threshold": 200000}]
+    }
     optional_params = {}
 
     result = config.map_openai_params(
@@ -3788,7 +3899,10 @@ def test_map_openai_params_with_context_management():
     )
 
     assert "context_management" in result
-    assert result["context_management"] == non_default_params_anthropic["context_management"]
+    assert (
+        result["context_management"]
+        == non_default_params_anthropic["context_management"]
+    )
 
 
 def test_cache_control_in_supported_params():
@@ -3899,7 +4013,10 @@ def test_compaction_block_empty_list_not_added():
     # Verify compaction_blocks is not in provider_specific_fields when there are none
     provider_fields = result.choices[0].message.provider_specific_fields
     if provider_fields:
-        assert "compaction_blocks" not in provider_fields or provider_fields.get("compaction_blocks") is None
+        assert (
+            "compaction_blocks" not in provider_fields
+            or provider_fields.get("compaction_blocks") is None
+        )
 
 
 def test_fast_mode_beta_header():
@@ -3948,7 +4065,9 @@ def test_fast_mode_usage_calculation():
         "output_tokens": 500,
     }
 
-    usage = config.calculate_usage(usage_object=usage_object, reasoning_content=None, speed="fast")
+    usage = config.calculate_usage(
+        usage_object=usage_object, reasoning_content=None, speed="fast"
+    )
 
     assert usage.prompt_tokens == 1000
     assert usage.completion_tokens == 500
@@ -3969,7 +4088,9 @@ def test_fast_mode_with_inference_geo():
     base_completion = 0.025
 
     with (
-        patch("litellm.llms.anthropic.cost_calculation.generic_cost_per_token") as mock_cost,
+        patch(
+            "litellm.llms.anthropic.cost_calculation.generic_cost_per_token"
+        ) as mock_cost,
         patch("litellm.get_model_info") as mock_info,
     ):
         mock_cost.return_value = (base_prompt, base_completion)
@@ -4160,7 +4281,9 @@ def test_map_tool_helper_enforces_object_type_when_missing():
             "name": "search_code",
             "description": "Search for code patterns",
             "parameters": {
-                "properties": {"query": {"type": "string", "description": "Search query"}},
+                "properties": {
+                    "query": {"type": "string", "description": "Search query"}
+                },
                 "required": ["query"],
             },
         },
@@ -4173,9 +4296,9 @@ def test_map_tool_helper_enforces_object_type_when_missing():
     assert "properties" in result["input_schema"]
     assert "query" in result["input_schema"]["properties"]
     # Original parameters dict must not be modified in place
-    assert tool["function"]["parameters"] == original_params, (
-        "parameters dict was mutated; _map_tool_helper should not modify caller data"
-    )
+    assert (
+        tool["function"]["parameters"] == original_params
+    ), "parameters dict was mutated; _map_tool_helper should not modify caller data"
 
 
 def test_map_tool_helper_enforces_object_type_when_wrong_type():
@@ -4201,13 +4324,13 @@ def test_map_tool_helper_enforces_object_type_when_wrong_type():
     result, _ = config._map_tool_helper(tool)
     assert result is not None
     assert result["input_schema"]["type"] == "object"
-    assert result["input_schema"].get("properties") == {}, (
-        "properties should be injected as {} when schema has non-object type and no properties key"
-    )
+    assert (
+        result["input_schema"].get("properties") == {}
+    ), "properties should be injected as {} when schema has non-object type and no properties key"
     # Original parameters dict must not be modified in place
-    assert tool["function"]["parameters"] == original_params, (
-        "parameters dict was mutated; _map_tool_helper should not modify caller data"
-    )
+    assert (
+        tool["function"]["parameters"] == original_params
+    ), "parameters dict was mutated; _map_tool_helper should not modify caller data"
 
 
 def test_map_tool_helper_preserves_valid_object_schema():
@@ -4274,8 +4397,12 @@ def test_extract_response_content_thinking_block_null_thinking():
             {"type": "text", "text": "Hello"},
         ]
     }
-    text, _, thinking_blocks, _, _, _, _, _ = config.extract_response_content(completion_response_null)
-    assert thinking_blocks is not None, "thinking blocks should not be None when thinking=null"
+    text, _, thinking_blocks, _, _, _, _, _ = config.extract_response_content(
+        completion_response_null
+    )
+    assert (
+        thinking_blocks is not None
+    ), "thinking blocks should not be None when thinking=null"
     assert len(thinking_blocks) == 1
     assert "Hello" in text
 
@@ -4286,8 +4413,12 @@ def test_extract_response_content_thinking_block_null_thinking():
             {"type": "text", "text": "World"},
         ]
     }
-    text, _, thinking_blocks, _, _, _, _, _ = config.extract_response_content(completion_response_missing)
-    assert thinking_blocks is not None, "thinking blocks should not be None when thinking key is absent"
+    text, _, thinking_blocks, _, _, _, _, _ = config.extract_response_content(
+        completion_response_missing
+    )
+    assert (
+        thinking_blocks is not None
+    ), "thinking blocks should not be None when thinking key is absent"
     assert len(thinking_blocks) == 1
     assert "World" in text
 
@@ -4298,7 +4429,9 @@ def test_extract_response_content_thinking_block_null_thinking():
             {"type": "text", "text": "Done"},
         ]
     }
-    text, _, thinking_blocks, _, _, _, _, _ = config.extract_response_content(completion_response_text)
+    text, _, thinking_blocks, _, _, _, _, _ = config.extract_response_content(
+        completion_response_text
+    )
     assert thinking_blocks is not None
     assert len(thinking_blocks) == 1
     assert thinking_blocks[0]["thinking"] == "Let me think..."
@@ -4357,8 +4490,12 @@ def test_advisor_beta_header_injected():
             }
         ]
     }
-    result = config.update_headers_with_optional_anthropic_beta(headers, optional_params)
-    assert ANTHROPIC_BETA_HEADER_VALUES.ADVISOR_TOOL_2026_03_01.value in result.get("anthropic-beta", "")
+    result = config.update_headers_with_optional_anthropic_beta(
+        headers, optional_params
+    )
+    assert ANTHROPIC_BETA_HEADER_VALUES.ADVISOR_TOOL_2026_03_01.value in result.get(
+        "anthropic-beta", ""
+    )
 
 
 def test_advisor_beta_header_not_injected_without_tool():
@@ -4366,7 +4503,9 @@ def test_advisor_beta_header_not_injected_without_tool():
     config = AnthropicConfig()
     headers: dict = {}
     optional_params: dict = {"tools": []}
-    result = config.update_headers_with_optional_anthropic_beta(headers, optional_params)
+    result = config.update_headers_with_optional_anthropic_beta(
+        headers, optional_params
+    )
     assert "advisor-tool-2026-03-01" not in result.get("anthropic-beta", "")
 
 
@@ -4393,7 +4532,9 @@ def test_advisor_tool_result_preserved_in_response():
             {"type": "text", "text": "Here is the implementation."},
         ]
     }
-    text, _, _, _, tool_calls, _, tool_results, _ = config.extract_response_content(completion_response)
+    text, _, _, _, tool_calls, _, tool_results, _ = config.extract_response_content(
+        completion_response
+    )
     assert "Consulting advisor." in text
     assert "Here is the implementation." in text
     # server_tool_use (advisor) should be a tool_call
@@ -4508,7 +4649,9 @@ def test_basic_sanitize_anthropic_tool_name_replaces_invalid_chars():
     )
 
     assert (
-        _basic_sanitize_anthropic_tool_name("github_openapi_mcp-actions/download-job-logs-for-workflow-run")
+        _basic_sanitize_anthropic_tool_name(
+            "github_openapi_mcp-actions/download-job-logs-for-workflow-run"
+        )
         == "github_openapi_mcp-actions_download-job-logs-for-workflow-run"
     )
     # other punctuation
@@ -4537,7 +4680,9 @@ def test_build_anthropic_tool_name_maps_no_collisions():
         ]
     )
     assert forward == {
-        "actions/download-job-logs-for-workflow-run": ("actions_download-job-logs-for-workflow-run"),
+        "actions/download-job-logs-for-workflow-run": (
+            "actions_download-job-logs-for-workflow-run"
+        ),
         "pulls/list-files": "pulls_list-files",
     }
     assert reverse == {v: k for k, v in forward.items()}
@@ -4588,7 +4733,9 @@ def test_build_anthropic_tool_name_maps_three_way_collision():
         _build_anthropic_tool_name_maps,
     )
 
-    forward, reverse = _build_anthropic_tool_name_maps(["foo_bar", "foo/bar", "foo.bar"])
+    forward, reverse = _build_anthropic_tool_name_maps(
+        ["foo_bar", "foo/bar", "foo.bar"]
+    )
     assert "foo_bar" not in forward  # untouched
     assert forward["foo/bar"] == "foo_bar_2"
     assert forward["foo.bar"] == "foo_bar_3"
@@ -4661,13 +4808,16 @@ def test_map_openai_params_does_not_pollute_optional_params_with_internal_keys()
     )
     # No internal keys may appear in optional_params for ANY input.
     for key in optional_params:
-        assert not key.startswith("_anthropic_tool_name"), (
-            f"optional_params leaked internal key {key!r}: {optional_params}"
-        )
+        assert not key.startswith(
+            "_anthropic_tool_name"
+        ), f"optional_params leaked internal key {key!r}: {optional_params}"
     # And no key starting with `_` either; optional_params should only
     # contain documented Anthropic Messages API parameters.
     for key in optional_params:
-        assert not key.startswith("_"), f"optional_params leaked underscore-prefixed key {key!r}: {optional_params}"
+        assert not key.startswith("_"), (
+            f"optional_params leaked underscore-prefixed key {key!r}: "
+            f"{optional_params}"
+        )
 
 
 def test_map_openai_params_no_maps_when_all_names_already_valid():
@@ -4696,7 +4846,11 @@ def test_map_openai_params_no_maps_when_all_names_already_valid():
 
 def test_rewrite_tool_names_in_messages_uses_forward_map():
     config = AnthropicConfig()
-    forward_map = {"actions/download-job-logs-for-workflow-run": ("actions_download-job-logs-for-workflow-run")}
+    forward_map = {
+        "actions/download-job-logs-for-workflow-run": (
+            "actions_download-job-logs-for-workflow-run"
+        )
+    }
     messages = [
         {"role": "user", "content": "go"},
         {
@@ -4719,9 +4873,15 @@ def test_rewrite_tool_names_in_messages_uses_forward_map():
     out = config._rewrite_tool_names_in_messages(messages, forward_map)
 
     # input list must not be mutated
-    assert messages[1]["tool_calls"][0]["function"]["name"] == "actions/download-job-logs-for-workflow-run"
+    assert (
+        messages[1]["tool_calls"][0]["function"]["name"]
+        == "actions/download-job-logs-for-workflow-run"
+    )
     # output rewritten according to forward map
-    assert out[1]["tool_calls"][0]["function"]["name"] == "actions_download-job-logs-for-workflow-run"
+    assert (
+        out[1]["tool_calls"][0]["function"]["name"]
+        == "actions_download-job-logs-for-workflow-run"
+    )
     # non-tool-call messages pass through unchanged (same object)
     assert out[0] is messages[0]
     assert out[2] is messages[2]
@@ -4797,7 +4957,9 @@ def test_sanitize_tool_names_in_request_does_not_mutate_caller_tool_dicts():
     caller_tools = [caller_tool]
     optional_params: dict = {"tools": caller_tools}
 
-    forward, reverse = config._sanitize_tool_names_in_request(optional_params=optional_params)
+    forward, reverse = config._sanitize_tool_names_in_request(
+        optional_params=optional_params
+    )
 
     assert forward.get(original_name)
     sanitized = forward[original_name]
@@ -4946,7 +5108,10 @@ def test_streaming_iterator_reverse_maps_tool_use_name():
     parsed = iterator.chunk_parser(chunk=chunk)
     tool_calls = parsed.choices[0].delta.tool_calls
     assert tool_calls is not None and len(tool_calls) == 1
-    assert tool_calls[0]["function"]["name"] == "actions/download-job-logs-for-workflow-run"
+    assert (
+        tool_calls[0]["function"]["name"]
+        == "actions/download-job-logs-for-workflow-run"
+    )
 
 
 def test_streaming_iterator_passthrough_when_name_not_in_map():
@@ -5042,9 +5207,9 @@ def test_transform_request_does_not_leak_internal_keys_into_body():
     for tool in data.get("tools", []):
         name = tool.get("name")
         assert isinstance(name, str)
-        assert _re.fullmatch(r"[a-zA-Z0-9_-]{1,128}", name), (
-            f"sanitized tool name {name!r} still violates Anthropic regex"
-        )
+        assert _re.fullmatch(
+            r"[a-zA-Z0-9_-]{1,128}", name
+        ), f"sanitized tool name {name!r} still violates Anthropic regex"
 
     # Sent name for the bad tool is the disambiguated form, valid name passes through.
     sent_names = {t["name"] for t in data["tools"]}
@@ -5180,7 +5345,9 @@ def test_transform_request_rewrites_tool_names_in_history():
         for block in content:
             if isinstance(block, dict) and block.get("type") == "tool_use":
                 tool_use_names.append(block.get("name"))
-    assert tool_use_names, "expected at least one tool_use block in transformed messages"
+    assert (
+        tool_use_names
+    ), "expected at least one tool_use block in transformed messages"
     for name in tool_use_names:
         assert name == "actions_download-job-logs-for-workflow-run", (
             f"history tool_use.name {name!r} not rewritten -- Anthropic will "
@@ -5204,12 +5371,19 @@ def test_sanitize_tool_names_in_request_skips_hosted_tools():
     }
     forward, reverse = AnthropicConfig._sanitize_tool_names_in_request(optional_params)
     # Only the custom tool was rewritten.
-    assert forward == {"actions/download-job-logs-for-workflow-run": "actions_download-job-logs-for-workflow-run"}
-    assert reverse == {"actions_download-job-logs-for-workflow-run": "actions/download-job-logs-for-workflow-run"}
+    assert forward == {
+        "actions/download-job-logs-for-workflow-run": "actions_download-job-logs-for-workflow-run"
+    }
+    assert reverse == {
+        "actions_download-job-logs-for-workflow-run": "actions/download-job-logs-for-workflow-run"
+    }
     # Hosted tool's name unchanged.
     assert optional_params["tools"][0]["name"] == "web_search"
     # Custom tool's name updated in place.
-    assert optional_params["tools"][1]["name"] == "actions_download-job-logs-for-workflow-run"
+    assert (
+        optional_params["tools"][1]["name"]
+        == "actions_download-job-logs-for-workflow-run"
+    )
 
 
 def test_sanitize_tool_names_in_request_no_tools_is_noop():
@@ -5443,7 +5617,9 @@ def test_translate_system_message_keeps_billing_header_for_first_party_anthropic
     assert config.should_strip_billing_metadata() is False
 
     result = config.translate_system_message(
-        messages=_system_with_billing_header("You are Claude Code, Anthropic's official CLI for Claude.")
+        messages=_system_with_billing_header(
+            "You are Claude Code, Anthropic's official CLI for Claude."
+        )
     )
 
     texts = [block["text"] for block in result]
@@ -5459,7 +5635,9 @@ def test_translate_system_message_strips_billing_header_for_bedrock():
     config = BedrockClaudePlatformConfig()
     assert config.should_strip_billing_metadata() is True
 
-    result = config.translate_system_message(messages=_system_with_billing_header("real system prompt"))
+    result = config.translate_system_message(
+        messages=_system_with_billing_header("real system prompt")
+    )
 
     texts = [block["text"] for block in result]
     assert all(not t.startswith("x-anthropic-billing-header:") for t in texts)
@@ -5525,7 +5703,9 @@ def test_translate_system_message_strips_billing_header_for_bedrock_invoke():
     config = AmazonAnthropicClaudeConfig()
     assert config.should_strip_billing_metadata() is True
 
-    result = config.translate_system_message(messages=_system_with_billing_header("real system prompt"))
+    result = config.translate_system_message(
+        messages=_system_with_billing_header("real system prompt")
+    )
 
     texts = [block["text"] for block in result]
     assert all(not t.startswith("x-anthropic-billing-header:") for t in texts)
@@ -5579,7 +5759,9 @@ def test_translate_system_message_strips_billing_header_for_bedrock_invoke():
         ),
     ],
 )
-def test_should_strip_billing_metadata_by_provider(module_path, class_name, expected_strip):
+def test_should_strip_billing_metadata_by_provider(
+    module_path, class_name, expected_strip
+):
     import importlib
 
     config_cls = getattr(importlib.import_module(module_path), class_name)
@@ -5847,7 +6029,9 @@ def test_is_anthropic_usage_object_rejects_responses_api_usage():
         ("claude-sonnet-4-5-20250929", False),
     ],
 )
-def test_disabled_thinking_omitted_only_for_always_on_models(local_model_cost_map, model, expected_dropped):
+def test_disabled_thinking_omitted_only_for_always_on_models(
+    local_model_cost_map, model, expected_dropped
+):
     """``thinking={"type": "disabled"}`` is omitted for always-on-thinking models
     (Fable/Mythos, which 400 on it: the API remedy is to omit the param) and is
     forwarded verbatim for every model that accepts it."""
@@ -5893,7 +6077,9 @@ def test_forced_tool_choice_raises_clean_error_on_fable_5_1_without_drop_params(
     "tool_choice",
     ["required", {"type": "required"}, {"type": "function", "function": {"name": "get_weather"}}],
 )
-def test_forced_tool_choice_downgraded_to_auto_on_fable_5_1_with_drop_params(local_model_cost_map, tool_choice):
+def test_forced_tool_choice_downgraded_to_auto_on_fable_5_1_with_drop_params(
+    local_model_cost_map, tool_choice
+):
     config = AnthropicConfig()
 
     result = config.map_openai_params(
@@ -5920,7 +6106,9 @@ def test_forced_tool_choice_downgrade_keeps_parallel_tool_calls_flag(local_model
 
 
 @pytest.mark.parametrize("tool_choice, expected_type", [("auto", "auto"), ("none", "none")])
-def test_unforced_tool_choice_forwarded_on_fable_5_1(local_model_cost_map, tool_choice, expected_type, monkeypatch):
+def test_unforced_tool_choice_forwarded_on_fable_5_1(
+    local_model_cost_map, tool_choice, expected_type, monkeypatch
+):
     monkeypatch.setattr(litellm, "drop_params", False)
     config = AnthropicConfig()
 
@@ -5935,7 +6123,9 @@ def test_unforced_tool_choice_forwarded_on_fable_5_1(local_model_cost_map, tool_
 
 
 @pytest.mark.parametrize("model", ["claude-fable-5", "claude-opus-5", "claude-sonnet-5"])
-def test_forced_tool_choice_forwarded_on_models_that_support_it(local_model_cost_map, model, monkeypatch):
+def test_forced_tool_choice_forwarded_on_models_that_support_it(
+    local_model_cost_map, model, monkeypatch
+):
     monkeypatch.setattr(litellm, "drop_params", False)
     config = AnthropicConfig()
 

tests/test_litellm/llms/anthropic/experimental_pass_through/test_reasoning_effort_fields.py

@@ -161,7 +161,9 @@ class TestAdapterAdaptiveThinking:
         )
 
         adapter = LiteLLMAnthropicMessagesAdapter()
-        result = adapter.translate_anthropic_thinking_to_reasoning_effort({"type": "adaptive"})
+        result = adapter.translate_anthropic_thinking_to_reasoning_effort(
+            {"type": "adaptive"}
+        )
         assert result == "medium"
 
     def test_messages_adapter_adaptive_overridden_by_output_config(self):

tests/test_litellm/llms/anthropic/test_anthropic_common_utils.py

@@ -1974,6 +1974,7 @@ class TestClaudeOpus48AdaptiveThinking:
 
         assert AnthropicModelInfo._is_adaptive_thinking_model(model, "anthropic") is True
 
+
     @pytest.mark.parametrize(
         "model",
         [

tests/test_litellm/llms/azure/test_azure_speech_audio_transcription.py

@@ -66,7 +66,11 @@ def test_azure_speech_audio_transcription_uses_dedicated_api_base_env(monkeypatc
 
     monkeypatch.setattr(
         "litellm.llms.azure.audio_transcription.transformation.get_secret_str",
-        lambda key: "https://centralus.api.cognitive.microsoft.com" if key == "AZURE_SPEECH_API_BASE" else None,
+        lambda key: (
+            "https://centralus.api.cognitive.microsoft.com"
+            if key == "AZURE_SPEECH_API_BASE"
+            else None
+        ),
     )
 
     url = config.get_complete_url(
@@ -220,3 +224,5 @@ def test_azure_speech_transcription_routes_through_provider_config(monkeypatch):
         AzureSpeechAudioTranscriptionConfig,
     )
     assert audio_handler.call_args.kwargs["custom_llm_provider"] == "azure"
+
+

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

@@ -252,7 +252,8 @@ def test_azure_model_router_response_shows_actual_model():
 
     # Verify that the response contains the actual model used, not the router model
     assert result.model == "azure_ai/gpt-5-nano-2025-08-07", (
-        f"Expected model to be 'azure_ai/gpt-5-nano-2025-08-07' (actual model used), but got '{result.model}'"
+        f"Expected model to be 'azure_ai/gpt-5-nano-2025-08-07' (actual model used), "
+        f"but got '{result.model}'"
     )
 
 
@@ -310,11 +311,19 @@ def test_azure_model_router_stamps_selected_model_on_hidden_params():
     )
 
     assert result._hidden_params[AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY] == result.model
-    assert result._hidden_params[AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY] == "azure_ai/grok-4-1-fast-reasoning"
-    assert AzureFoundryModelInfo.get_model_router_selected_model(result._hidden_params) == (
-        "azure_ai/grok-4-1-fast-reasoning"
+    assert (
+        result._hidden_params[AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY]
+        == "azure_ai/grok-4-1-fast-reasoning"
+    )
+    assert AzureFoundryModelInfo.get_model_router_selected_model(
+        result._hidden_params
+    ) == ("azure_ai/grok-4-1-fast-reasoning")
+    assert (
+        AzureFoundryModelInfo.is_model_router_call(
+            model="smart-pick", hidden_params=result._hidden_params
+        )
+        is True
     )
-    assert AzureFoundryModelInfo.is_model_router_call(model="smart-pick", hidden_params=result._hidden_params) is True
 
 
 def test_azure_model_router_stamp_does_not_leak_across_responses():
@@ -352,10 +361,14 @@ def test_drop_tool_level_extra_fields_strips_copilot_mcp_server_name():
     mock_response.text = error_text
     mock_response.json.return_value = json.loads(error_text)
     mock_response.status_code = 400
-    e = httpx.HTTPStatusError(message="400", request=MagicMock(), response=mock_response)
+    e = httpx.HTTPStatusError(
+        message="400", request=MagicMock(), response=mock_response
+    )
 
     assert config._error_has_tool_level_extra_fields(error_text) is True
-    assert config.should_retry_llm_api_inside_llm_translation_on_http_error(e, {}) is True
+    assert (
+        config.should_retry_llm_api_inside_llm_translation_on_http_error(e, {}) is True
+    )
 
     request_data = {
         "model": "FW-Kimi-K2.6",
@@ -478,7 +491,9 @@ def test_azure_ai_stripping_does_not_mutate_caller_messages():
         {
             "role": "assistant",
             "content": "I can help.",
-            "thinking_blocks": [{"type": "thinking", "thinking": "Reading the file.", "signature": "sig"}],
+            "thinking_blocks": [
+                {"type": "thinking", "thinking": "Reading the file.", "signature": "sig"}
+            ],
             "provider_specific_fields": {"thought_signature": "sig-top"},
             "tool_calls": [
                 {

tests/test_litellm/llms/azure_ai/claude/test_azure_anthropic_messages_transformation.py

@@ -3,7 +3,9 @@ import json
 import os
 import sys
 
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../..")))
+sys.path.insert(
+    0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../.."))
+)
 
 from unittest.mock import patch
 
@@ -37,7 +39,9 @@ class TestAzureAnthropicMessagesConfig:
         litellm_params = {"api_key": "test-api-key"}
         api_key = "test-api-key"
 
-        with patch("litellm.llms.azure.common_utils.BaseAzureLLM._base_validate_azure_environment") as mock_validate:
+        with patch(
+            "litellm.llms.azure.common_utils.BaseAzureLLM._base_validate_azure_environment"
+        ) as mock_validate:
             mock_validate.return_value = {"api-key": "test-api-key"}
             result, api_base = config.validate_anthropic_messages_environment(
                 headers=headers,
@@ -68,7 +72,9 @@ class TestAzureAnthropicMessagesConfig:
         optional_params = {}
         litellm_params = {"api_key": "test-api-key"}
 
-        with patch("litellm.llms.azure.common_utils.BaseAzureLLM._base_validate_azure_environment") as mock_validate:
+        with patch(
+            "litellm.llms.azure.common_utils.BaseAzureLLM._base_validate_azure_environment"
+        ) as mock_validate:
             mock_validate.return_value = {"api-key": "test-api-key"}
             result, api_base = config.validate_anthropic_messages_environment(
                 headers=headers,
@@ -92,7 +98,9 @@ class TestAzureAnthropicMessagesConfig:
         optional_params = {}
         litellm_params = {"api_key": "test-api-key"}
 
-        with patch("litellm.llms.azure.common_utils.BaseAzureLLM._base_validate_azure_environment") as mock_validate:
+        with patch(
+            "litellm.llms.azure.common_utils.BaseAzureLLM._base_validate_azure_environment"
+        ) as mock_validate:
             mock_validate.return_value = {"api-key": "test-api-key"}
             result, api_base = config.validate_anthropic_messages_environment(
                 headers=headers,
@@ -165,6 +173,7 @@ class TestAzureAnthropicMessagesConfig:
 
         assert url == "https://test.services.ai.azure.com/anthropic/v1/messages"
 
+
     def test_get_complete_url_with_base_url_without_anthropic(self):
         """Test get_complete_url with base URL without /anthropic"""
         config = AzureAnthropicMessagesConfig()
@@ -258,7 +267,9 @@ class TestAzureAnthropicMessagesConfig:
         assert "scope" not in result["system"][0]["cache_control"]
         assert result["system"][0]["cache_control"]["type"] == "ephemeral"
         assert "scope" not in result["messages"][0]["content"][0]["cache_control"]
-        assert result["messages"][0]["content"][0]["cache_control"]["type"] == "ephemeral"
+        assert (
+            result["messages"][0]["content"][0]["cache_control"]["type"] == "ephemeral"
+        )
 
 
 class TestProviderConfigManagerAzureAnthropicMessages:
@@ -365,7 +376,9 @@ class TestAzureAnthropicMidConversationSystem:
             {"role": "assistant", "content": "reading"},
             {"role": "user", "content": "continue"},
         ]
-        result = _azure_transform("claude-opus-4-7", messages, system=[{"type": "text", "text": "Base."}])
+        result = _azure_transform(
+            "claude-opus-4-7", messages, system=[{"type": "text", "text": "Base."}]
+        )
         assert result["messages"] == [
             {"role": "user", "content": "read the file"},
             {
@@ -396,7 +409,9 @@ def test_azure_claude_4_8_plus_cost_map_entries_carry_mid_conversation_system_fl
 
     import litellm
 
-    cost_map_path = os.path.join(os.path.dirname(litellm.__file__), "model_prices_and_context_window_backup.json")
+    cost_map_path = os.path.join(
+        os.path.dirname(litellm.__file__), "model_prices_and_context_window_backup.json"
+    )
     with open(cost_map_path) as f:
         cost_map = json.load(f)
     rules = cost_map["fallback_generalizations"]["rules"]

tests/test_litellm/llms/bedrock/image_edit/test_amazon_nova_canvas_image_edit.py

@@ -203,7 +203,9 @@ def test_transform_request_image_pathlike_input(tmp_path):
     )
 
     assert body["taskType"] == "IMAGE_VARIATION"
-    assert body["imageVariationParams"]["images"][0] == base64.b64encode(image_bytes).decode("utf-8")
+    assert body["imageVariationParams"]["images"][0] == base64.b64encode(
+        image_bytes
+    ).decode("utf-8")
 
 
 def test_transform_request_inpainting_with_mask():
@@ -364,7 +366,9 @@ def test_transform_request_inpainting_explicit_task_without_mask_raises():
     """INPAINTING taskType without mask or maskPrompt must fail fast."""
     config = BedrockAmazonNovaCanvasImageEditConfig()
     img = io.BytesIO(b"img")
-    with pytest.raises(ValueError, match="INPAINTING requires either maskPrompt or maskImage"):
+    with pytest.raises(
+        ValueError, match="INPAINTING requires either maskPrompt or maskImage"
+    ):
         config.transform_image_edit_request(
             model="amazon.nova-canvas-v1:0",
             prompt="fix it",

tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py

@@ -48,7 +48,9 @@ async def test_bedrock_sse_wrapper_encodes_dict_chunks():
         _dummy_stream(),
         litellm_logging_obj=LiteLLMLoggingObj(
             model="bedrock/invoke/anthropic.claude-3-sonnet-20240229-v1:0",
-            messages=[{"role": "user", "content": "Hello, can you tell me a short joke?"}],
+            messages=[
+                {"role": "user", "content": "Hello, can you tell me a short joke?"}
+            ],
             stream=True,
             call_type="chat",
             start_time=datetime.now(),
@@ -225,7 +227,9 @@ async def test_bedrock_sse_wrapper_keeps_usage_in_message_start_and_message_delt
 def test_chunk_parser_usage_transformation():
     """Ensure Bedrock invocation metrics are transformed to Anthropic usage keys."""
 
-    decoder = AmazonAnthropicClaudeMessagesStreamDecoder(model="bedrock/invoke/anthropic.claude-3-sonnet-20240229-v1:0")
+    decoder = AmazonAnthropicClaudeMessagesStreamDecoder(
+        model="bedrock/invoke/anthropic.claude-3-sonnet-20240229-v1:0"
+    )
 
     chunk = {
         "type": "message_delta",
@@ -254,7 +258,9 @@ def test_chunk_parser_preserves_cache_usage_fields_with_invocation_metrics():
     fields and cache tokens end up billed at $0.
     """
 
-    decoder = AmazonAnthropicClaudeMessagesStreamDecoder(model="bedrock/invoke/anthropic.claude-sonnet-4-6")
+    decoder = AmazonAnthropicClaudeMessagesStreamDecoder(
+        model="bedrock/invoke/anthropic.claude-sonnet-4-6"
+    )
 
     chunk = {
         "type": "message_stop",
@@ -280,7 +286,9 @@ def test_chunk_parser_preserves_cache_usage_fields_with_invocation_metrics():
 def test_chunk_parser_maps_cache_token_counts_from_invocation_metrics():
     """Cache itemization inside invocationMetrics maps to Anthropic usage keys."""
 
-    decoder = AmazonAnthropicClaudeMessagesStreamDecoder(model="bedrock/invoke/anthropic.claude-sonnet-4-6")
+    decoder = AmazonAnthropicClaudeMessagesStreamDecoder(
+        model="bedrock/invoke/anthropic.claude-sonnet-4-6"
+    )
 
     chunk = {
         "type": "message_stop",
@@ -303,7 +311,9 @@ def test_chunk_parser_maps_cache_token_counts_from_invocation_metrics():
 def test_chunk_parser_keeps_existing_token_counts_over_invocation_metrics():
     """Token counts reported in the chunk's own usage block win over invocationMetrics."""
 
-    decoder = AmazonAnthropicClaudeMessagesStreamDecoder(model="bedrock/invoke/anthropic.claude-sonnet-4-6")
+    decoder = AmazonAnthropicClaudeMessagesStreamDecoder(
+        model="bedrock/invoke/anthropic.claude-sonnet-4-6"
+    )
 
     chunk = {
         "type": "message_stop",
@@ -338,7 +348,9 @@ async def test_bedrock_sse_wrapper_preserves_cache_usage_with_invocation_metrics
     final usage billed cache reads and writes at $0.
     """
 
-    decoder = AmazonAnthropicClaudeMessagesStreamDecoder(model="bedrock/invoke/anthropic.claude-sonnet-4-6")
+    decoder = AmazonAnthropicClaudeMessagesStreamDecoder(
+        model="bedrock/invoke/anthropic.claude-sonnet-4-6"
+    )
     cfg = AmazonAnthropicClaudeMessagesConfig()
 
     raw_chunks = [
@@ -548,7 +560,11 @@ def test_normalize_custom_field_on_tools():
     assert request4["tools"] is None
 
     # Case 5: an explicit top-level flag wins over a conflicting wrapped one
-    request5 = {"tools": [{"name": "Read", "defer_loading": False, "custom": {"defer_loading": True}}]}
+    request5 = {
+        "tools": [
+            {"name": "Read", "defer_loading": False, "custom": {"defer_loading": True}}
+        ]
+    }
     normalize_custom_field_on_tools(request5)
     assert request5["tools"][0] == {"name": "Read", "defer_loading": False}
 
@@ -569,7 +585,9 @@ def test_normalize_custom_field_on_tools():
     assert request7["tools"] == [{"name": "Read"}, {"name": "Write"}]
 
 
-@pytest.mark.parametrize("deferred_marker", [{"custom": {"defer_loading": True}}, {"defer_loading": True}])
+@pytest.mark.parametrize(
+    "deferred_marker", [{"custom": {"defer_loading": True}}, {"defer_loading": True}]
+)
 def test_bedrock_invoke_messages_transform_emits_top_level_defer_loading(
     deferred_marker,
 ):
@@ -702,7 +720,9 @@ def test_bedrock_invoke_messages_skips_thinking_injection_when_already_enabled(
         "max_tokens": 32000,
         "stream": False,
         "thinking": {"type": "enabled", "budget_tokens": 2048},
-        "context_management": {"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]},
+        "context_management": {
+            "edits": [{"type": "clear_thinking_20251015", "keep": "all"}]
+        },
     }
     result = cfg.transform_anthropic_messages_request(
         model="global.anthropic.claude-sonnet-4-6-v1:0",
@@ -804,7 +824,9 @@ def test_remove_ttl_from_cache_control_processes_tools(local_model_cost_map):
         "messages": [],
     }
 
-    cfg._remove_ttl_from_cache_control(request, model="anthropic.claude-3-5-sonnet-20241022-v2:0")
+    cfg._remove_ttl_from_cache_control(
+        request, model="anthropic.claude-3-5-sonnet-20241022-v2:0"
+    )
 
     # Tool ttl should be stripped
     assert "ttl" not in request["tools"][0]["cache_control"]
@@ -840,7 +862,9 @@ def test_remove_ttl_from_cache_control_preserves_tools_ttl_for_claude_4_5(local_
         ],
     }
 
-    cfg._remove_ttl_from_cache_control(request, model="us.anthropic.claude-sonnet-4-5-20250929-v1:0")
+    cfg._remove_ttl_from_cache_control(
+        request, model="us.anthropic.claude-sonnet-4-5-20250929-v1:0"
+    )
 
     # Both tools and system should preserve ttl for Claude 4.5
     assert request["tools"][0]["cache_control"]["ttl"] == "1h"
@@ -924,7 +948,9 @@ def test_bedrock_messages_strips_output_config():
             headers={},
         )
 
-    assert "output_config" not in result, "output_config should be stripped for models that don't support it"
+    assert "output_config" not in result, (
+        "output_config should be stripped for models that don't support it"
+    )
     assert result.get("max_tokens") == 4096
 
 
@@ -957,7 +983,9 @@ def test_bedrock_messages_preserves_output_config_for_claude_4_6():
             headers={},
         )
 
-    assert "output_config" in result, "output_config should be preserved for supported models"
+    assert "output_config" in result, (
+        "output_config should be preserved for supported models"
+    )
     assert result["output_config"] == {"effort": "high"}
     assert result.get("max_tokens") == 4096
 
@@ -1109,7 +1137,9 @@ def test_bedrock_messages_converts_output_config_format_to_inline_schema():
         ("anthropic.claude-opus-4-7", "xhigh"),
     ],
 )
-def test_bedrock_messages_normalizes_output_config_effort_for_opus(model, expected_effort):
+def test_bedrock_messages_normalizes_output_config_effort_for_opus(
+    model, expected_effort
+):
     """Bedrock /v1/messages accepts ``xhigh`` and forwards the provider-safe effort."""
     from unittest.mock import patch
 
@@ -1167,7 +1197,9 @@ def test_bedrock_messages_does_not_mutate_callers_messages_when_embedding_schema
             headers={},
         )
 
-    assert caller_messages == [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}]
+    assert caller_messages == [
+        {"role": "user", "content": [{"type": "text", "text": "Hello"}]}
+    ]
     assert caller_message == {
         "role": "user",
         "content": [{"type": "text", "text": "Hello"}],
@@ -1483,7 +1515,9 @@ def test_bedrock_messages_strips_context_management():
     messages = [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}]
     optional_params = {
         "max_tokens": 4096,
-        "context_management": {"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]},
+        "context_management": {
+            "edits": [{"type": "clear_thinking_20251015", "keep": "all"}]
+        },
     }
 
     result = cfg.transform_anthropic_messages_request(
@@ -1494,7 +1528,9 @@ def test_bedrock_messages_strips_context_management():
         headers={},
     )
 
-    assert "context_management" not in result, "context_management should be stripped — Bedrock Invoke rejects it"
+    assert "context_management" not in result, (
+        "context_management should be stripped — Bedrock Invoke rejects it"
+    )
     assert result.get("max_tokens") == 4096
 
 
@@ -1641,8 +1677,12 @@ def test_bedrock_messages_filters_user_provided_unsupported_beta_header():
     )
 
     betas = result.get("anthropic_beta") or []
-    assert "advisor-tool-2026-03-01" not in betas, "user-provided beta not in the Bedrock mapping must be dropped"
-    assert "context-1m-2025-08-07" in betas, "user-provided beta that IS in the Bedrock mapping should survive"
+    assert "advisor-tool-2026-03-01" not in betas, (
+        "user-provided beta not in the Bedrock mapping must be dropped"
+    )
+    assert "context-1m-2025-08-07" in betas, (
+        "user-provided beta that IS in the Bedrock mapping should survive"
+    )
 
 
 def test_bedrock_messages_renames_user_provided_aliased_beta_header():
@@ -1670,7 +1710,9 @@ def test_bedrock_messages_renames_user_provided_aliased_beta_header():
     assert "advanced-tool-use-2025-11-20" not in betas, (
         "Anthropic-direct spelling should be rewritten, not forwarded verbatim"
     )
-    assert "tool-search-tool-2025-10-19" in betas, "user-provided beta should be renamed to the Bedrock-side spelling"
+    assert "tool-search-tool-2025-10-19" in betas, (
+        "user-provided beta should be renamed to the Bedrock-side spelling"
+    )
 
 
 @pytest.mark.asyncio
@@ -1932,7 +1974,9 @@ async def test_unified_bedrock_messages_sse_usage_and_cost_claude_sonnet_46():
         "global.anthropic.claude-fable-5",
     ],
 )
-def test_bedrock_clear_thinking_injects_adaptive_with_effort_for_adaptive_models(local_model_cost_map, model):
+def test_bedrock_clear_thinking_injects_adaptive_with_effort_for_adaptive_models(
+    local_model_cost_map, model
+):
     """clear_thinking_20251015 without a top-level ``thinking`` field must inject
     ``thinking.type=adaptive`` plus ``output_config.effort`` on adaptive-thinking
     models (Opus 4.7/4.8, Fable 5). The legacy ``thinking.type=enabled`` shape is
@@ -1942,7 +1986,9 @@ def test_bedrock_clear_thinking_injects_adaptive_with_effort_for_adaptive_models
     cfg = AmazonAnthropicClaudeMessagesConfig()
     request = {
         "max_tokens": 32000,
-        "context_management": {"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]},
+        "context_management": {
+            "edits": [{"type": "clear_thinking_20251015", "keep": "all"}]
+        },
     }
 
     changed = cfg._ensure_thinking_for_clear_thinking_context_management(
@@ -1965,7 +2011,9 @@ def test_bedrock_clear_thinking_converts_legacy_enabled_budget_to_effort():
             "type": "enabled",
             "budget_tokens": DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
         },
-        "context_management": {"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]},
+        "context_management": {
+            "edits": [{"type": "clear_thinking_20251015", "keep": "all"}]
+        },
     }
 
     changed = cfg._ensure_thinking_for_clear_thinking_context_management(
@@ -1983,7 +2031,10 @@ def test_resolve_clear_thinking_budget_tokens_honors_explicit_zero():
     and only fall back to the minimum when the caller omits the budget."""
     cfg = AmazonAnthropicClaudeMessagesConfig()
     assert cfg._resolve_clear_thinking_budget_tokens(0) == 0
-    assert cfg._resolve_clear_thinking_budget_tokens(None) == BEDROCK_MIN_THINKING_BUDGET_TOKENS
+    assert (
+        cfg._resolve_clear_thinking_budget_tokens(None)
+        == BEDROCK_MIN_THINKING_BUDGET_TOKENS
+    )
     assert cfg._resolve_clear_thinking_budget_tokens(12000) == 12000
 
 
@@ -1993,7 +2044,9 @@ def test_bedrock_clear_thinking_keeps_enabled_for_non_adaptive_models():
     cfg = AmazonAnthropicClaudeMessagesConfig()
     request = {
         "max_tokens": 32000,
-        "context_management": {"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]},
+        "context_management": {
+            "edits": [{"type": "clear_thinking_20251015", "keep": "all"}]
+        },
     }
 
     changed = cfg._ensure_thinking_for_clear_thinking_context_management(
@@ -2018,7 +2071,9 @@ def test_bedrock_invoke_transform_emits_adaptive_thinking_for_opus_4_8():
     optional_params = {
         "max_tokens": 32000,
         "stream": False,
-        "context_management": {"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]},
+        "context_management": {
+            "edits": [{"type": "clear_thinking_20251015", "keep": "all"}]
+        },
     }
 
     result = cfg.transform_anthropic_messages_request(
@@ -2055,7 +2110,9 @@ def test_bedrock_invoke_transform_normalizes_system_role_message_into_system():
 
     assert all(m.get("role") != "system" for m in result["messages"])
     assert result["messages"] == [{"role": "user", "content": "hi"}]
-    assert result["system"] == [{"type": "text", "text": "You are a careful assistant."}]
+    assert result["system"] == [
+        {"type": "text", "text": "You are a careful assistant."}
+    ]
 
 
 def test_bedrock_invoke_transform_merges_system_role_into_existing_system():
@@ -2170,7 +2227,9 @@ def test_bedrock_invoke_transform_keeps_mid_conversation_system_role_in_place(lo
     )
 
     assert result["messages"] == messages
-    assert result["system"] == [{"type": "text", "text": "Base.", "cache_control": {"type": "ephemeral"}}]
+    assert result["system"] == [
+        {"type": "text", "text": "Base.", "cache_control": {"type": "ephemeral"}}
+    ]
 
 
 def test_bedrock_invoke_transform_hoists_only_leading_system_run(local_model_cost_map):
@@ -2353,13 +2412,13 @@ def test_bedrock_invoke_transform_converted_system_carries_only_its_content(loca
     assert result["messages"][2] == {
         "role": "user",
         "content": [
-            {
-                "type": "text",
-                "text": (
-                    "Operator note (not from the user): the following was "
-                    "originally a mid-conversation system-role reminder."
-                ),
-            },
+                {
+                    "type": "text",
+                    "text": (
+                        "Operator note (not from the user): the following was "
+                        "originally a mid-conversation system-role reminder."
+                    ),
+                },
             {"type": "text", "text": "[Truncated: PARTIAL view of big1.txt]"},
         ],
     }
@@ -2495,7 +2554,10 @@ def test_as_system_content_blocks_handles_each_shape():
 def test_effort_from_thinking_budget_tiers(budget_tokens, expected_effort):
     """The budget -> effort mapping pins each tier boundary so a shifted threshold
     is caught."""
-    assert AmazonAnthropicClaudeMessagesConfig._effort_from_thinking_budget(budget_tokens) == expected_effort
+    assert (
+        AmazonAnthropicClaudeMessagesConfig._effort_from_thinking_budget(budget_tokens)
+        == expected_effort
+    )
 
 
 def test_inject_adaptive_thinking_preserves_existing_effort():
@@ -2504,7 +2566,9 @@ def test_inject_adaptive_thinking_preserves_existing_effort():
     cfg = AmazonAnthropicClaudeMessagesConfig()
     request = {"output_config": {"effort": "max", "other": "keep"}}
 
-    cfg._inject_adaptive_thinking_for_clear_thinking(request, budget_tokens=24000, model="us.anthropic.claude-fable-5")
+    cfg._inject_adaptive_thinking_for_clear_thinking(
+        request, budget_tokens=24000, model="us.anthropic.claude-fable-5"
+    )
 
     assert request["thinking"] == {"type": "adaptive"}
     assert request["output_config"] == {"effort": "max", "other": "keep"}
@@ -2517,7 +2581,9 @@ def test_bedrock_clear_thinking_noops_when_thinking_already_adaptive():
     request = {
         "max_tokens": 32000,
         "thinking": {"type": "adaptive"},
-        "context_management": {"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]},
+        "context_management": {
+            "edits": [{"type": "clear_thinking_20251015", "keep": "all"}]
+        },
     }
 
     changed = cfg._ensure_thinking_for_clear_thinking_context_management(
@@ -2537,7 +2603,9 @@ def test_bedrock_clear_thinking_replaces_disabled_thinking_on_adaptive_model():
     request = {
         "max_tokens": 32000,
         "thinking": {"type": "disabled"},
-        "context_management": {"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]},
+        "context_management": {
+            "edits": [{"type": "clear_thinking_20251015", "keep": "all"}]
+        },
     }
 
     changed = cfg._ensure_thinking_for_clear_thinking_context_management(
@@ -2557,7 +2625,9 @@ def test_bedrock_clear_thinking_leaves_enabled_thinking_on_non_adaptive_model():
     request = {
         "max_tokens": 32000,
         "thinking": {"type": "enabled", "budget_tokens": 8000},
-        "context_management": {"edits": [{"type": "clear_thinking_20251015", "keep": "all"}]},
+        "context_management": {
+            "edits": [{"type": "clear_thinking_20251015", "keep": "all"}]
+        },
     }
 
     changed = cfg._ensure_thinking_for_clear_thinking_context_management(
@@ -2592,7 +2662,9 @@ def test_bedrock_messages_preserves_clear_tool_uses_context_management_and_adds_
     messages = [{"role": "user", "content": [{"type": "text", "text": "Hi"}]}]
     optional_params = {
         "max_tokens": 4096,
-        "context_management": {"edits": [{"type": "clear_tool_uses_20250919"}]},
+        "context_management": {
+            "edits": [{"type": "clear_tool_uses_20250919"}]
+        },
     }
 
     result = cfg.transform_anthropic_messages_request(
@@ -2603,11 +2675,12 @@ def test_bedrock_messages_preserves_clear_tool_uses_context_management_and_adds_
         headers={},
     )
 
-    assert result.get("context_management") == {"edits": [{"type": "clear_tool_uses_20250919"}]}, (
-        "clear_tool_uses_20250919 edit must reach Bedrock InvokeModel body"
-    )
+    assert result.get("context_management") == {
+        "edits": [{"type": "clear_tool_uses_20250919"}]
+    }, "clear_tool_uses_20250919 edit must reach Bedrock InvokeModel body"
     assert "context-management-2025-06-27" in result.get("anthropic_beta", []), (
-        "context-management-2025-06-27 beta must reach the InvokeModel body so the tool-call-clearing edit is accepted"
+        "context-management-2025-06-27 beta must reach the InvokeModel body so "
+        "the tool-call-clearing edit is accepted"
     )
 
 
@@ -2684,9 +2757,9 @@ def test_bedrock_messages_filters_clear_thinking_keeps_clear_tool_uses(
 
     cm = result.get("context_management")
     assert cm is not None
-    assert [e.get("type") for e in cm["edits"]] == ["clear_tool_uses_20250919"], (
-        "clear_thinking_20251015 must still be stripped (LiteLLM-internal)"
-    )
+    assert [e.get("type") for e in cm["edits"]] == [
+        "clear_tool_uses_20250919"
+    ], "clear_thinking_20251015 must still be stripped (LiteLLM-internal)"
 
     betas = result.get("anthropic_beta", [])
     assert "context-management-2025-06-27" in betas

tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py

@@ -1,3 +1,4 @@
+
 import pytest
 
 
@@ -29,7 +30,9 @@ def test_bedrock_response_stream_shape_lazy_loads_once():
     import litellm.llms.bedrock.common_utils as mod
 
     sentinel = MagicMock()
-    with patch.object(mod, "_load_bedrock_response_stream_shape", return_value=sentinel) as mock_load:
+    with patch.object(
+        mod, "_load_bedrock_response_stream_shape", return_value=sentinel
+    ) as mock_load:
         assert mod.get_bedrock_response_stream_shape() is sentinel
         assert mod.get_bedrock_response_stream_shape() is sentinel
         mock_load.assert_called_once()
@@ -76,7 +79,9 @@ def test_bedrock_response_stream_shape_is_structure_shape():
     from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape
 
     loaded_shape = get_bedrock_response_stream_shape()
-    assert loaded_shape is not None, "get_bedrock_response_stream_shape() is None — botocore may not be installed"
+    assert (
+        loaded_shape is not None
+    ), "get_bedrock_response_stream_shape() is None — botocore may not be installed"
     shape: StructureShape = loaded_shape
     assert isinstance(shape, StructureShape)
     assert shape.name == "ResponseStream"
@@ -141,7 +146,9 @@ def test_deepseek_cris():
     Test that DeepSeek models with cross-region inference prefix use converse route
     """
     bedrock_model_info = BedrockModelInfo
-    bedrock_route = bedrock_model_info.get_bedrock_route(model="bedrock/us.deepseek.r1-v1:0")
+    bedrock_route = bedrock_model_info.get_bedrock_route(
+        model="bedrock/us.deepseek.r1-v1:0"
+    )
     assert bedrock_route == "converse"
 
 
@@ -214,19 +221,27 @@ def test_govcloud_cross_region_inference_prefix():
     bedrock_model_info = BedrockModelInfo
 
     # Test us-gov prefix is stripped correctly for Claude models
-    base_model = bedrock_model_info.get_base_model(model="bedrock/us-gov.anthropic.claude-haiku-4-5-20251001-v1:0")
+    base_model = bedrock_model_info.get_base_model(
+        model="bedrock/us-gov.anthropic.claude-haiku-4-5-20251001-v1:0"
+    )
     assert base_model == "anthropic.claude-haiku-4-5-20251001-v1:0"
 
     # Test us-gov prefix is stripped correctly for different Claude versions
-    base_model = bedrock_model_info.get_base_model(model="bedrock/us-gov.anthropic.claude-sonnet-4-5-20250929-v1:0")
+    base_model = bedrock_model_info.get_base_model(
+        model="bedrock/us-gov.anthropic.claude-sonnet-4-5-20250929-v1:0"
+    )
     assert base_model == "anthropic.claude-sonnet-4-5-20250929-v1:0"
 
     # Test us-gov prefix is stripped correctly for Haiku models
-    base_model = bedrock_model_info.get_base_model(model="bedrock/us-gov.anthropic.claude-3-haiku-20240307-v1:0")
+    base_model = bedrock_model_info.get_base_model(
+        model="bedrock/us-gov.anthropic.claude-3-haiku-20240307-v1:0"
+    )
     assert base_model == "anthropic.claude-3-haiku-20240307-v1:0"
 
     # Test us-gov prefix is stripped correctly for Meta models
-    base_model = bedrock_model_info.get_base_model(model="bedrock/us-gov.meta.llama3-8b-instruct-v1:0")
+    base_model = bedrock_model_info.get_base_model(
+        model="bedrock/us-gov.meta.llama3-8b-instruct-v1:0"
+    )
     assert base_model == "meta.llama3-8b-instruct-v1:0"
 
 
@@ -240,14 +255,23 @@ def test_context_window_suffix_stripped_for_cost_lookup():
     """
     from litellm.llms.bedrock.common_utils import get_bedrock_base_model
 
-    assert get_bedrock_base_model("us.anthropic.claude-opus-4-6-v1[1m]") == "anthropic.claude-opus-4-6-v1"
-    assert get_bedrock_base_model("us.anthropic.claude-sonnet-4-6[1m]") == "anthropic.claude-sonnet-4-6"
+    assert (
+        get_bedrock_base_model("us.anthropic.claude-opus-4-6-v1[1m]")
+        == "anthropic.claude-opus-4-6-v1"
+    )
+    assert (
+        get_bedrock_base_model("us.anthropic.claude-sonnet-4-6[1m]")
+        == "anthropic.claude-sonnet-4-6"
+    )
     assert (
         get_bedrock_base_model("global.anthropic.claude-opus-4-5-20251101-v1:0[1m]")
         == "anthropic.claude-opus-4-5-20251101-v1:0"
     )
     # Ensure models without suffix are unaffected
-    assert get_bedrock_base_model("us.anthropic.claude-opus-4-6-v1") == "anthropic.claude-opus-4-6-v1"
+    assert (
+        get_bedrock_base_model("us.anthropic.claude-opus-4-6-v1")
+        == "anthropic.claude-opus-4-6-v1"
+    )
     # Ensure :51k throughput suffix still works
     assert (
         get_bedrock_base_model("anthropic.claude-3-5-sonnet-20241022-v2:0:51k")
@@ -287,7 +311,9 @@ def test_output_config_effort_normalization_uses_model_info_ceiling(monkeypatch)
         ("us.anthropic.claude-opus-4-7", "xhigh"),
     ],
 )
-def test_bundled_bedrock_opus_model_info_declares_output_config_effort_ceiling(model, expected_ceiling):
+def test_bundled_bedrock_opus_model_info_declares_output_config_effort_ceiling(
+    model, expected_ceiling
+):
     from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap
 
     model_info = GetModelCostMap.load_local_model_cost_map()[model]
@@ -306,24 +332,54 @@ def test_route_prefix_matched_as_path_segment_not_substring():
     or a ``/`` boundary.
     """
     # The bedrock_mantle/ provider prefix must NOT be read as the mantle/ route.
-    assert BedrockModelInfo.get_bedrock_route("bedrock_mantle/openai.gpt-5.5") != "mantle"
-    assert BedrockModelInfo.get_bedrock_route("bedrock_mantle/openai.gpt-5.4") == "invoke"
-    assert BedrockModelInfo._explicit_mantle_route("bedrock_mantle/openai.gpt-5.5") is False
+    assert (
+        BedrockModelInfo.get_bedrock_route("bedrock_mantle/openai.gpt-5.5") != "mantle"
+    )
+    assert (
+        BedrockModelInfo.get_bedrock_route("bedrock_mantle/openai.gpt-5.4") == "invoke"
+    )
+    assert (
+        BedrockModelInfo._explicit_mantle_route("bedrock_mantle/openai.gpt-5.5")
+        is False
+    )
 
     # A genuine mantle route still resolves, via the startswith branch...
-    assert BedrockModelInfo.get_bedrock_route("mantle/anthropic.claude-mythos-preview") == "mantle"
+    assert (
+        BedrockModelInfo.get_bedrock_route("mantle/anthropic.claude-mythos-preview")
+        == "mantle"
+    )
     # ...and via the mid-path "/mantle/" branch (after the bedrock/ provider prefix).
-    assert BedrockModelInfo.get_bedrock_route("bedrock/mantle/anthropic.claude-mythos-preview") == "mantle"
+    assert (
+        BedrockModelInfo.get_bedrock_route(
+            "bedrock/mantle/anthropic.claude-mythos-preview"
+        )
+        == "mantle"
+    )
 
 
 def test_model_has_route_prefix_exercises_both_branches():
     """``_model_has_route_prefix`` matches on ``startswith`` or a ``/`` boundary only."""
     # startswith branch
-    assert BedrockModelInfo._model_has_route_prefix("mantle/anthropic.claude-mythos-preview", "mantle/") is True
+    assert (
+        BedrockModelInfo._model_has_route_prefix(
+            "mantle/anthropic.claude-mythos-preview", "mantle/"
+        )
+        is True
+    )
     # f"/{prefix}" boundary branch
-    assert BedrockModelInfo._model_has_route_prefix("bedrock/mantle/anthropic.claude-mythos-preview", "mantle/") is True
+    assert (
+        BedrockModelInfo._model_has_route_prefix(
+            "bedrock/mantle/anthropic.claude-mythos-preview", "mantle/"
+        )
+        is True
+    )
     # neither branch: the token only appears glued to another segment
-    assert BedrockModelInfo._model_has_route_prefix("bedrock_mantle/openai.gpt-5.5", "mantle/") is False
+    assert (
+        BedrockModelInfo._model_has_route_prefix(
+            "bedrock_mantle/openai.gpt-5.5", "mantle/"
+        )
+        is False
+    )
 
 
 @pytest.mark.parametrize(
@@ -373,10 +429,16 @@ def test_explicit_invoke_route_does_not_match_async_invoke():
     """
     async_invoke_model = "async_invoke/twelvelabs.marengo-embed-2-7-v1:0"
     assert BedrockModelInfo._explicit_invoke_route(async_invoke_model) is False
-    assert BedrockModelInfo._explicit_invoke_route(f"bedrock/{async_invoke_model}") is False
+    assert (
+        BedrockModelInfo._explicit_invoke_route(f"bedrock/{async_invoke_model}")
+        is False
+    )
     # ...while async_invoke/ is still detected as its own route.
     assert BedrockModelInfo._explicit_async_invoke_route(async_invoke_model) is True
-    assert BedrockModelInfo._explicit_async_invoke_route(f"bedrock/{async_invoke_model}") is True
+    assert (
+        BedrockModelInfo._explicit_async_invoke_route(f"bedrock/{async_invoke_model}")
+        is True
+    )
 
 
 def test_merge_bedrock_aws_request_params_strips_caller_identity_when_deployment_has_static_credentials():

tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py

@@ -52,7 +52,10 @@ class TestBedrockMantleResponsesURL:
             api_base="https://bedrock-mantle.us-east-2.api.aws/v1/",
             litellm_params={},
         )
-        assert url_trailing == "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses"
+        assert (
+            url_trailing
+            == "https://bedrock-mantle.us-east-2.api.aws/openai/v1/responses"
+        )
 
     def test_url_does_not_double_openai_v1(self, monkeypatch):
         monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False)
@@ -112,7 +115,9 @@ class TestBedrockMantleResponsesURL:
         with pytest.raises(ValueError, match="api\\.aws\\.attacker\\.example/'\\. Region names must contain only"):
             cfg.get_complete_url(
                 api_base=None,
-                litellm_params={"aws_region_name": "us-east-1.api.aws.attacker.example/"},
+                litellm_params={
+                    "aws_region_name": "us-east-1.api.aws.attacker.example/"
+                },
             )
 
     def test_url_region_default_us_east_1(self, monkeypatch):
@@ -165,7 +170,9 @@ class TestBedrockMantleResponsesURL:
 
 
 class TestBedrockMantleGetLlmProviderRegion:
-    def test_get_llm_provider_uses_supplemental_litellm_params(self, monkeypatch, local_cost_map):
+    def test_get_llm_provider_uses_supplemental_litellm_params(
+        self, monkeypatch, local_cost_map
+    ):
         monkeypatch.delenv("BEDROCK_MANTLE_REGION", raising=False)
         monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False)
         monkeypatch.delenv("AWS_REGION", raising=False)
@@ -182,7 +189,9 @@ class TestBedrockMantleGetLlmProviderRegion:
         # the resolved chat base) is on the /openai/v1 base per the AWS card.
         assert api_base == "https://bedrock-mantle.us-east-2.api.aws/openai/v1"
 
-    def test_get_llm_provider_uses_aws_region_from_litellm_params(self, monkeypatch, local_cost_map):
+    def test_get_llm_provider_uses_aws_region_from_litellm_params(
+        self, monkeypatch, local_cost_map
+    ):
         monkeypatch.delenv("BEDROCK_MANTLE_REGION", raising=False)
         monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False)
         monkeypatch.delenv("AWS_REGION", raising=False)
@@ -216,14 +225,18 @@ class TestBedrockMantleResponsesAuth:
         monkeypatch.setenv("BEDROCK_MANTLE_API_KEY", "env-key")
         monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False)
         cfg = BedrockMantleResponsesAPIConfig()
-        headers = cfg.validate_environment(headers={}, model="openai.gpt-5.5", litellm_params=GenericLiteLLMParams())
+        headers = cfg.validate_environment(
+            headers={}, model="openai.gpt-5.5", litellm_params=GenericLiteLLMParams()
+        )
         assert headers["Authorization"] == "Bearer env-key"
 
     def test_bedrock_bearer_token_fallback(self, monkeypatch):
         monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False)
         monkeypatch.setenv("AWS_BEARER_TOKEN_BEDROCK", "bearer-key")
         cfg = BedrockMantleResponsesAPIConfig()
-        headers = cfg.validate_environment(headers={}, model="openai.gpt-5.5", litellm_params=GenericLiteLLMParams())
+        headers = cfg.validate_environment(
+            headers={}, model="openai.gpt-5.5", litellm_params=GenericLiteLLMParams()
+        )
         assert headers["Authorization"] == "Bearer bearer-key"
 
     def test_missing_bearer_does_not_raise_in_validate_environment(self, monkeypatch):
@@ -231,7 +244,9 @@ class TestBedrockMantleResponsesAuth:
         monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False)
         monkeypatch.delenv("AWS_BEARER_TOKEN_BEDROCK", raising=False)
         cfg = BedrockMantleResponsesAPIConfig()
-        headers = cfg.validate_environment(headers={}, model="openai.gpt-5.5", litellm_params=GenericLiteLLMParams())
+        headers = cfg.validate_environment(
+            headers={}, model="openai.gpt-5.5", litellm_params=GenericLiteLLMParams()
+        )
         assert "Authorization" not in headers
 
     def test_project_id_sets_openai_project_header(self):
@@ -239,7 +254,9 @@ class TestBedrockMantleResponsesAuth:
         headers = cfg.validate_environment(
             headers={},
             model="openai.gpt-5.5",
-            litellm_params=GenericLiteLLMParams(api_key="fake-key", aws_bedrock_project_id="proj_abc123def456"),
+            litellm_params=GenericLiteLLMParams(
+                api_key="fake-key", aws_bedrock_project_id="proj_abc123def456"
+            ),
         )
         assert headers["OpenAI-Project"] == "proj_abc123def456"
 
@@ -340,7 +357,9 @@ class TestBedrockMantleResponsesTools:
         from unittest.mock import patch
 
         cfg = BedrockMantleResponsesAPIConfig()
-        with patch("litellm.llms.bedrock_mantle.responses.transformation.verbose_logger.warning") as mock_warning:
+        with patch(
+            "litellm.llms.bedrock_mantle.responses.transformation.verbose_logger.warning"
+        ) as mock_warning:
             cfg.map_openai_params(
                 response_api_optional_params={"tools": [{"type": "file_search", "vector_store_ids": ["vs_123"]}]},
                 model="openai.gpt-5.5",
@@ -541,7 +560,9 @@ class TestBedrockMantleServiceTier:
         from unittest.mock import patch
 
         cfg = BedrockMantleResponsesAPIConfig()
-        with patch("litellm.llms.bedrock_mantle.responses.transformation.verbose_logger.warning") as mock_warning:
+        with patch(
+            "litellm.llms.bedrock_mantle.responses.transformation.verbose_logger.warning"
+        ) as mock_warning:
             cfg.map_openai_params(
                 response_api_optional_params={"service_tier": "priority"},
                 model="openai.gpt-5.5",
@@ -630,9 +651,7 @@ class TestBedrockMantleReasoningSummary:
                 model="openai.gpt-5.6-sol",
                 drop_params=True,
             )
-        warnings = [
-            record for record in caplog.records if "dropping unsupported reasoning.summary" in record.getMessage()
-        ]
+        warnings = [record for record in caplog.records if "dropping unsupported reasoning.summary" in record.getMessage()]
         assert len(warnings) == 1
         assert "detailed" in warnings[0].getMessage()
 
@@ -806,7 +825,9 @@ class TestBedrockMantleCodexAdditionalTools:
     def test_hoist_is_logged_at_debug_level(self):
         from unittest.mock import patch
 
-        with patch("litellm.llms.bedrock_mantle.responses.transformation.verbose_logger.debug") as mock_debug:
+        with patch(
+            "litellm.llms.bedrock_mantle.responses.transformation.verbose_logger.debug"
+        ) as mock_debug:
             self._transform(
                 input=[
                     {"type": "additional_tools", "role": "developer", "tools": self._CODEX_TOOLS},
@@ -963,13 +984,7 @@ class TestBedrockMantleCodexInputItemNormalization:
             {"type": "function_call", "name": "shell", "arguments": "{}", "call_id": "call_2"},
             {"type": "function_call_output", "call_id": "call_2", "output": "ok"},
             {"type": "tool_search_call", "call_id": "call_3", "execution": "server", "arguments": {"query": "x"}},
-            {
-                "type": "tool_search_output",
-                "call_id": "call_3",
-                "status": "completed",
-                "execution": "server",
-                "tools": [],
-            },
+            {"type": "tool_search_output", "call_id": "call_3", "status": "completed", "execution": "server", "tools": []},
             {"type": "compaction_trigger"},
         ]
         body = self._transform(input=copy.deepcopy(supported_items))
@@ -983,12 +998,7 @@ class TestBedrockMantleCodexInputItemNormalization:
         with caplog.at_level(logging.WARNING, logger="LiteLLM"):
             body = self._transform(
                 input=[
-                    {
-                        "type": "agent_message",
-                        "author": "a",
-                        "recipient": "b",
-                        "content": [{"type": "input_text", "text": "hi"}],
-                    },
+                    {"type": "agent_message", "author": "a", "recipient": "b", "content": [{"type": "input_text", "text": "hi"}]},
                     self._USER_MESSAGE,
                 ]
             )
@@ -1127,7 +1137,9 @@ class TestBedrockMantleResponsesRegistry:
         )
         assert cfg is None
 
-    def test_price_map_flag_routes_non_gpt_name_to_openai_path(self, restore_model_cost):
+    def test_price_map_flag_routes_non_gpt_name_to_openai_path(
+        self, restore_model_cost
+    ):
         # Data-driven onboarding: a frontier model whose name does NOT match the
         # openai.gpt- convention can still be routed to /openai/v1/responses by
         # declaring use_openai_responses_path in its price-map entry, with no code
@@ -1150,6 +1162,7 @@ class TestBedrockMantleResponsesRegistry:
         assert isinstance(cfg, BedrockMantleResponsesAPIConfig)
         assert cfg.use_openai_path is True
 
+
     @pytest.mark.parametrize(
         "model",
         [
@@ -1183,7 +1196,9 @@ class TestBedrockMantleResponsesRegistry:
         )
         assert cfg is None
 
-    def test_declared_responses_non_openai_routes_to_standard_path(self, restore_model_cost):
+    def test_declared_responses_non_openai_routes_to_standard_path(
+        self, restore_model_cost
+    ):
         # New feature: a non-OpenAI model declared mode=responses (e.g. via a
         # user's proxy model_info block) must route to the STANDARD /v1/responses
         # path, not the frontier /openai/v1/responses path. Fails before the
@@ -1301,7 +1316,9 @@ class TestBedrockMantlePerModelResponsesURL:
             model=model,
         )
         assert isinstance(cfg, BedrockMantleResponsesAPIConfig)
-        return cfg.get_complete_url(api_base=None, litellm_params={"aws_region_name": region})
+        return cfg.get_complete_url(
+            api_base=None, litellm_params={"aws_region_name": region}
+        )
 
     def test_gpt_oss_uses_standard_responses_path(self, local_cost_map):
         url = self._url_for("openai.gpt-oss-120b")
@@ -1400,7 +1417,9 @@ class TestBedrockMantleResponsesSigV4:
         monkeypatch.delenv("BEDROCK_MANTLE_API_KEY", raising=False)
 
         signer = BaseAWSLLM()
-        signer.get_credentials = MagicMock(side_effect=AssertionError("get_credentials must not run for bearer auth"))
+        signer.get_credentials = MagicMock(
+            side_effect=AssertionError("get_credentials must not run for bearer auth")
+        )
         cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer)
 
         headers, signed_body = cfg.sign_request(
@@ -1422,7 +1441,9 @@ class TestBedrockMantleResponsesSigV4:
         monkeypatch.setenv("BEDROCK_MANTLE_API_KEY", "env-bearer")
 
         signer = BaseAWSLLM()
-        signer.get_credentials = MagicMock(side_effect=AssertionError("get_credentials must not run for bearer auth"))
+        signer.get_credentials = MagicMock(
+            side_effect=AssertionError("get_credentials must not run for bearer auth")
+        )
         cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer)
 
         headers, _ = cfg.sign_request(
@@ -1444,7 +1465,9 @@ class TestBedrockMantleResponsesSigV4:
         monkeypatch.setenv("BEDROCK_MANTLE_API_KEY", "env-bearer")
 
         signer = BaseAWSLLM()
-        signer.get_credentials = MagicMock(side_effect=AssertionError("get_credentials must not run for bearer auth"))
+        signer.get_credentials = MagicMock(
+            side_effect=AssertionError("get_credentials must not run for bearer auth")
+        )
         cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer)
 
         headers, _ = cfg.sign_request(
@@ -1590,7 +1613,9 @@ class TestBedrockMantleResponsesSigV4:
         }
         cfg = BedrockMantleResponsesAPIConfig(aws_signer=BaseAWSLLM())
         url = cfg.get_complete_url(api_base=None, litellm_params=params)
-        assert url == "https://bedrock-mantle.ap-southeast-2.api.aws/openai/v1/responses"
+        assert (
+            url == "https://bedrock-mantle.ap-southeast-2.api.aws/openai/v1/responses"
+        )
 
         headers, _ = cfg.sign_request(
             headers={},
@@ -1601,7 +1626,9 @@ class TestBedrockMantleResponsesSigV4:
         )
         assert "/ap-southeast-2/bedrock/aws4_request" in headers["Authorization"]
 
-    def test_injected_default_region_base_does_not_override_aws_region_name(self, monkeypatch):
+    def test_injected_default_region_base_does_not_override_aws_region_name(
+        self, monkeypatch
+    ):
         """2nd-round adversarial regression: responses/main.py auto-injects
         litellm_params.api_base = https://bedrock-mantle.<DEFAULT>.api.aws/v1 (default
         region, ignoring aws_region_name). The config must still pin BOTH the URL host
@@ -1704,7 +1731,7 @@ class TestBedrockMantleResponsesSigV4:
         signer.get_credentials = MagicMock(side_effect=NoCredentialsError())
         cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer)
 
-        with pytest.raises(ValueError, match="Bedrock Mantle auth failed: no Bearer token and no usable") as exc:
+        with pytest.raises(ValueError, match='Bedrock Mantle auth failed: no Bearer token and no usable') as exc:
             cfg.sign_request(
                 headers={},
                 optional_params={"aws_region_name": "us-east-2"},
@@ -1734,7 +1761,7 @@ class TestBedrockMantleResponsesSigV4:
         signer.get_credentials = MagicMock(side_effect=cred_error)
         cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer)
 
-        with pytest.raises(ValueError, match="Bedrock Mantle auth failed: no Bearer token and no usable") as exc:
+        with pytest.raises(ValueError, match='Bedrock Mantle auth failed: no Bearer token and no usable') as exc:
             cfg.sign_request(
                 headers={},
                 optional_params={"aws_region_name": "us-east-2"},
@@ -1759,7 +1786,9 @@ class TestBedrockMantleResponsesSigV4:
 
         signer = BaseAWSLLM()
         signer.get_credentials = MagicMock(
-            side_effect=ConnectTimeoutError(endpoint_url="https://sts.us-east-2.amazonaws.com")
+            side_effect=ConnectTimeoutError(
+                endpoint_url="https://sts.us-east-2.amazonaws.com"
+            )
         )
         cfg = BedrockMantleResponsesAPIConfig(aws_signer=signer)
 
@@ -1777,6 +1806,8 @@ class TestBedrockMantleResponsesSigV4:
 
 
 class TestBedrockMantleResponsesPricing:
+
+
     def test_models_registered(self, local_cost_map):
         assert "bedrock_mantle/openai.gpt-5.5" in litellm.bedrock_mantle_models
         assert "bedrock_mantle/openai.gpt-5.4" in litellm.bedrock_mantle_models

tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_transformation.py

@@ -98,7 +98,9 @@ class TestBedrockMantleConfig:
             cfg._get_openai_compatible_provider_info(
                 None,
                 None,
-                litellm_params=GenericLiteLLMParams(aws_region_name="us-east-1.api.aws.attacker.example/"),
+                litellm_params=GenericLiteLLMParams(
+                    aws_region_name="us-east-1.api.aws.attacker.example/"
+                ),
             )
 
     def test_get_llm_provider_rejects_malicious_aws_region_name(self, monkeypatch):
@@ -111,10 +113,14 @@ class TestBedrockMantleConfig:
             litellm.get_llm_provider(
                 model="openai.gpt-5.5",
                 custom_llm_provider="bedrock_mantle",
-                litellm_params=GenericLiteLLMParams(aws_region_name="us-east-1.api.aws.attacker.example/"),
+                litellm_params=GenericLiteLLMParams(
+                    aws_region_name="us-east-1.api.aws.attacker.example/"
+                ),
             )
 
-    def test_get_llm_provider_uses_aws_region_name_for_responses(self, monkeypatch, local_cost_map):
+    def test_get_llm_provider_uses_aws_region_name_for_responses(
+        self, monkeypatch, local_cost_map
+    ):
         from litellm.types.router import GenericLiteLLMParams
 
         monkeypatch.delenv("BEDROCK_MANTLE_REGION", raising=False)
@@ -172,14 +178,18 @@ class TestBedrockMantleConfig:
         monkeypatch.setenv("BEDROCK_MANTLE_REGION", "us-east-2")
         monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False)
         cfg = BedrockMantleChatConfig()
-        api_base, _ = cfg._get_openai_compatible_provider_info(None, None, model="openai.gpt-oss-120b")
+        api_base, _ = cfg._get_openai_compatible_provider_info(
+            None, None, model="openai.gpt-oss-120b"
+        )
         assert api_base == "https://bedrock-mantle.us-east-2.api.aws/v1"
 
     @pytest.mark.parametrize(
         "model_id",
         ["google.gemma-4-31b", "google.gemma-4-26b-a4b", "google.gemma-4-e2b"],
     )
-    def test_chat_base_for_gemma_4_uses_openai_v1(self, monkeypatch, local_cost_map, model_id):
+    def test_chat_base_for_gemma_4_uses_openai_v1(
+        self, monkeypatch, local_cost_map, model_id
+    ):
         # The chat-config bug the Gemma 4 cards exposed: gemma-4-* is served on the
         # /openai/v1 base, not the hardcoded /v1. Driven by the price-map
         # use_openai_responses_path flag (loaded by local_cost_map). Fails before
@@ -187,16 +197,22 @@ class TestBedrockMantleConfig:
         monkeypatch.setenv("BEDROCK_MANTLE_REGION", "us-east-2")
         monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False)
         cfg = BedrockMantleChatConfig()
-        api_base, _ = cfg._get_openai_compatible_provider_info(None, None, model=model_id)
+        api_base, _ = cfg._get_openai_compatible_provider_info(
+            None, None, model=model_id
+        )
         assert api_base == "https://bedrock-mantle.us-east-2.api.aws/openai/v1"
 
-    def test_chat_base_explicit_api_base_wins_over_derived(self, monkeypatch, local_cost_map):
+    def test_chat_base_explicit_api_base_wins_over_derived(
+        self, monkeypatch, local_cost_map
+    ):
         # An explicit api_base must not be overridden by the data-driven default,
         # even for a model whose default differs (gemma-4 -> openai/v1).
         monkeypatch.delenv("BEDROCK_MANTLE_API_BASE", raising=False)
         custom_base = "https://bedrock-mantle.us-west-2.api.aws/v1"
         cfg = BedrockMantleChatConfig()
-        api_base, _ = cfg._get_openai_compatible_provider_info(custom_base, None, model="google.gemma-4-31b")
+        api_base, _ = cfg._get_openai_compatible_provider_info(
+            custom_base, None, model="google.gemma-4-31b"
+        )
         assert api_base == custom_base
 
     def test_api_key_from_env(self, monkeypatch):
@@ -251,7 +267,9 @@ class TestBedrockMantleChatAuth:
         from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
 
         signer = BaseAWSLLM()
-        signer.get_credentials = MagicMock(side_effect=AssertionError("SigV4 must not run when a Bearer token exists"))
+        signer.get_credentials = MagicMock(
+            side_effect=AssertionError("SigV4 must not run when a Bearer token exists")
+        )
         return signer
 
     def test_bearer_token_skips_sigv4(self, monkeypatch):
@@ -368,7 +386,9 @@ class TestBedrockMantleChatAuth:
 
         assert "/eu-west-1/bedrock/aws4_request" in headers["Authorization"]
 
-    def test_sigv4_scope_matches_api_base_when_aws_region_name_disagrees(self, monkeypatch):
+    def test_sigv4_scope_matches_api_base_when_aws_region_name_disagrees(
+        self, monkeypatch
+    ):
         # If a caller (e.g. proxy) passes a stale api_base in one region and an
         # aws_region_name in a different region, the SigV4 credential scope must
         # match the URL host or Bedrock rejects the request with 401. Without the
@@ -456,7 +476,7 @@ class TestBedrockMantleChatAuth:
         signer.get_credentials = MagicMock(side_effect=NoCredentialsError())
         cfg = BedrockMantleChatConfig(aws_signer=signer)
 
-        with pytest.raises(ValueError, match="Bedrock Mantle auth failed: no Bearer token and no usable") as exc:
+        with pytest.raises(ValueError, match='Bedrock Mantle auth failed: no Bearer token and no usable') as exc:
             cfg.sign_request(
                 headers={},
                 optional_params={"aws_region_name": "us-east-2"},
@@ -482,7 +502,9 @@ class TestBedrockMantleChatAuth:
         ):
             monkeypatch.delenv(var, raising=False)
         monkeypatch.setenv("AWS_ACCESS_KEY_ID", "AKIAEXAMPLE")
-        monkeypatch.setenv("AWS_SECRET_ACCESS_KEY", "c2VjcmV0LXRlc3Qtc2VjcmV0LXRlc3Qtc2VjcmV0")
+        monkeypatch.setenv(
+            "AWS_SECRET_ACCESS_KEY", "c2VjcmV0LXRlc3Qtc2VjcmV0LXRlc3Qtc2VjcmV0"
+        )
         monkeypatch.setenv("AWS_REGION", "us-east-2")
 
         requests = []
@@ -512,7 +534,9 @@ class TestBedrockMantleChatAuth:
                 request=httpx.Request("POST", url),
             )
 
-        with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post", mock_post):
+        with patch(
+            "litellm.llms.custom_httpx.http_handler.HTTPHandler.post", mock_post
+        ):
             response = litellm.completion(
                 model="bedrock_mantle/openai.gpt-oss-120b",
                 messages=[{"role": "user", "content": "hello"}],
@@ -556,9 +580,7 @@ class TestBedrockMantleChatAuth:
                     "object": "chat.completion",
                     "created": 1733529600,
                     "model": "google.gemma-4-31b",
-                    "choices": [
-                        {"index": 0, "message": {"role": "assistant", "content": "ok"}, "finish_reason": "stop"}
-                    ],
+                    "choices": [{"index": 0, "message": {"role": "assistant", "content": "ok"}, "finish_reason": "stop"}],
                     "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2},
                 },
                 request=httpx.Request("POST", url),
@@ -624,7 +646,9 @@ class TestBedrockMantleProjectHeader:
 
         def mock_post(self, url, data=None, headers=None, **kwargs):
             raw_body = data.decode("utf-8") if isinstance(data, bytes) else data
-            requests.append({"headers": headers or {}, "body": json.loads(raw_body or "{}")})
+            requests.append(
+                {"headers": headers or {}, "body": json.loads(raw_body or "{}")}
+            )
             return httpx.Response(
                 status_code=200,
                 json={
@@ -648,7 +672,9 @@ class TestBedrockMantleProjectHeader:
                 request=httpx.Request("POST", url),
             )
 
-        with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post", mock_post):
+        with patch(
+            "litellm.llms.custom_httpx.http_handler.HTTPHandler.post", mock_post
+        ):
             response = litellm.completion(
                 model="bedrock_mantle/openai.gpt-oss-120b",
                 messages=[{"role": "user", "content": "hello"}],
@@ -664,15 +690,20 @@ class TestBedrockMantleProjectHeader:
 
 class TestBedrockMantleProviderResolution:
     def test_get_llm_provider_resolves_correctly(self):
-        model, provider, _, _ = litellm.get_llm_provider("bedrock_mantle/openai.gpt-oss-120b")
+        model, provider, _, _ = litellm.get_llm_provider(
+            "bedrock_mantle/openai.gpt-oss-120b"
+        )
         assert provider == "bedrock_mantle"
         assert model == "openai.gpt-oss-120b"
 
     def test_get_llm_provider_20b(self):
-        model, provider, _, _ = litellm.get_llm_provider("bedrock_mantle/openai.gpt-oss-20b")
+        model, provider, _, _ = litellm.get_llm_provider(
+            "bedrock_mantle/openai.gpt-oss-20b"
+        )
         assert provider == "bedrock_mantle"
         assert model == "openai.gpt-oss-20b"
 
+
     def test_get_llm_provider_strips_region_prefix(self, monkeypatch, local_cost_map):
         for var in ("BEDROCK_MANTLE_REGION", "BEDROCK_MANTLE_API_BASE", "AWS_REGION", "AWS_REGION_NAME"):
             monkeypatch.delenv(var, raising=False)
@@ -705,9 +736,7 @@ class TestBedrockMantleProviderResolution:
                     "object": "chat.completion",
                     "created": 1733529600,
                     "model": "xai.grok-4.3",
-                    "choices": [
-                        {"index": 0, "message": {"role": "assistant", "content": "ok"}, "finish_reason": "stop"}
-                    ],
+                    "choices": [{"index": 0, "message": {"role": "assistant", "content": "ok"}, "finish_reason": "stop"}],
                     "usage": {"prompt_tokens": 38, "completion_tokens": 20, "total_tokens": 58},
                 },
                 request=request,

tests/test_litellm/llms/crusoe/test_crusoe.py

@@ -103,3 +103,5 @@ def test_crusoe_provider_detection_by_prefix():
     model, provider, _, _ = get_llm_provider("crusoe/meta-llama/Llama-3.3-70B-Instruct")
     assert provider == "crusoe"
     assert model == "meta-llama/Llama-3.3-70B-Instruct"
+
+

tests/test_litellm/llms/dashscope/test_dashscope_cost_calculator.py

@@ -42,7 +42,9 @@ class TestDashscopeCostCalculator:
         """
         usage = Usage(prompt_tokens=1000, completion_tokens=500)
 
-        prompt_cost, completion_cost = dashscope_cost_per_token(model="qwen-max", usage=usage)
+        prompt_cost, completion_cost = dashscope_cost_per_token(
+            model="qwen-max", usage=usage
+        )
 
         model_info = litellm.get_model_info("dashscope/qwen-max")
         expected_prompt_cost = 1000 * model_info["input_cost_per_token"]
@@ -58,7 +60,9 @@ class TestDashscopeCostCalculator:
         """
         # Tier 1 for qwen-flash is [0, 256,000] tokens
         usage = Usage(prompt_tokens=100000, completion_tokens=50000)
-        prompt_cost, completion_cost = dashscope_cost_per_token(model="qwen-flash", usage=usage)
+        prompt_cost, completion_cost = dashscope_cost_per_token(
+            model="qwen-flash", usage=usage
+        )
 
         model_info = litellm.get_model_info("dashscope/qwen-flash")
         tier_1_pricing = model_info["tiered_pricing"][0]
@@ -76,7 +80,9 @@ class TestDashscopeCostCalculator:
         """
         # Tiering for qwen-flash: Tier 1: [0, 256k], Tier 2: [256k, 1M]
         usage = Usage(prompt_tokens=300000, completion_tokens=300000)
-        prompt_cost, completion_cost = dashscope_cost_per_token(model="qwen-flash", usage=usage)
+        prompt_cost, completion_cost = dashscope_cost_per_token(
+            model="qwen-flash", usage=usage
+        )
 
         model_info = litellm.get_model_info("dashscope/qwen-flash")
         tier_1 = model_info["tiered_pricing"][0]
@@ -88,7 +94,9 @@ class TestDashscopeCostCalculator:
         assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10)
         assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10)
 
-        graduated_prompt_cost = (256000 * tier_1["input_cost_per_token"]) + (44000 * tier_2["input_cost_per_token"])
+        graduated_prompt_cost = (256000 * tier_1["input_cost_per_token"]) + (
+            44000 * tier_2["input_cost_per_token"]
+        )
         assert prompt_cost > graduated_prompt_cost
 
     def test_dashscope_tiered_pricing_boundary_stays_in_lower_tier(self):
@@ -97,12 +105,18 @@ class TestDashscopeCostCalculator:
         official `0 < Token <= 256K` phrasing.
         """
         usage = Usage(prompt_tokens=256000, completion_tokens=1000)
-        prompt_cost, completion_cost = dashscope_cost_per_token(model="qwen-flash", usage=usage)
+        prompt_cost, completion_cost = dashscope_cost_per_token(
+            model="qwen-flash", usage=usage
+        )
 
         tier_1 = litellm.get_model_info("dashscope/qwen-flash")["tiered_pricing"][0]
 
-        assert math.isclose(prompt_cost, 256000 * tier_1["input_cost_per_token"], rel_tol=1e-10)
-        assert math.isclose(completion_cost, 1000 * tier_1["output_cost_per_token"], rel_tol=1e-10)
+        assert math.isclose(
+            prompt_cost, 256000 * tier_1["input_cost_per_token"], rel_tol=1e-10
+        )
+        assert math.isclose(
+            completion_cost, 1000 * tier_1["output_cost_per_token"], rel_tol=1e-10
+        )
 
     def test_dashscope_tiered_pricing_output_uses_input_selected_tier(self):
         """
@@ -114,7 +128,9 @@ class TestDashscopeCostCalculator:
 
         tier_1 = litellm.get_model_info("dashscope/qwen-flash")["tiered_pricing"][0]
 
-        assert math.isclose(completion_cost, 400000 * tier_1["output_cost_per_token"], rel_tol=1e-10)
+        assert math.isclose(
+            completion_cost, 400000 * tier_1["output_cost_per_token"], rel_tol=1e-10
+        )
 
     def test_dashscope_tiered_pricing_with_caching(self):
         """
@@ -143,13 +159,17 @@ class TestDashscopeCostCalculator:
         """
         Requests above the highest declared range bill entirely at the last tier's rate.
         """
-        usage = Usage(prompt_tokens=1200000, completion_tokens=1000)  # Max defined range for qwen-flash is 1M
+        usage = Usage(
+            prompt_tokens=1200000, completion_tokens=1000
+        )  # Max defined range for qwen-flash is 1M
 
         prompt_cost, _ = dashscope_cost_per_token(model="qwen-flash", usage=usage)
 
         tier_2 = litellm.get_model_info("dashscope/qwen-flash")["tiered_pricing"][1]
 
-        assert math.isclose(prompt_cost, 1200000 * tier_2["input_cost_per_token"], rel_tol=1e-10)
+        assert math.isclose(
+            prompt_cost, 1200000 * tier_2["input_cost_per_token"], rel_tol=1e-10
+        )
 
     def _register_tiered_model(self, model_key: str, tiered_pricing: list[dict]) -> None:
         litellm.model_cost[model_key] = {
@@ -184,7 +204,9 @@ class TestDashscopeCostCalculator:
         self._register_string_valued_tiered_model("dashscope/qwen-str-tier-test")
 
         usage = Usage(prompt_tokens=500, completion_tokens=200)
-        prompt_cost, completion_cost = dashscope_cost_per_token(model="qwen-str-tier-test", usage=usage)
+        prompt_cost, completion_cost = dashscope_cost_per_token(
+            model="qwen-str-tier-test", usage=usage
+        )
 
         assert math.isclose(prompt_cost, 500 * float("4e-07"), rel_tol=1e-10)
         assert math.isclose(completion_cost, 200 * float("1.6e-06"), rel_tol=1e-10)
@@ -197,7 +219,9 @@ class TestDashscopeCostCalculator:
         self._register_string_valued_tiered_model("dashscope/qwen-str-tier-test")
 
         usage = Usage(prompt_tokens=2500, completion_tokens=3000)
-        prompt_cost, completion_cost = dashscope_cost_per_token(model="qwen-str-tier-test", usage=usage)
+        prompt_cost, completion_cost = dashscope_cost_per_token(
+            model="qwen-str-tier-test", usage=usage
+        )
 
         assert math.isclose(prompt_cost, 2500 * float("8e-07"), rel_tol=1e-10)
         assert math.isclose(completion_cost, 3000 * float("3.2e-06"), rel_tol=1e-10)
@@ -230,12 +254,18 @@ class TestDashscopeCostCalculator:
         usage = Usage(
             prompt_tokens=300000,  # 200k new + 60k cache creation + 40k cache read
             completion_tokens=1000,
-            prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=40000, cache_creation_tokens=60000),
+            prompt_tokens_details=PromptTokensDetailsWrapper(
+                cached_tokens=40000, cache_creation_tokens=60000
+            ),
         )
 
-        prompt_cost, _ = dashscope_cost_per_token(model="qwen-cache-write-test", usage=usage)
+        prompt_cost, _ = dashscope_cost_per_token(
+            model="qwen-cache-write-test", usage=usage
+        )
 
-        expected_prompt_cost = (200000 * 6.5e-07) + (60000 * 8.125e-07) + (40000 * 6.5e-08)
+        expected_prompt_cost = (
+            (200000 * 6.5e-07) + (60000 * 8.125e-07) + (40000 * 6.5e-08)
+        )
 
         assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10)
 
@@ -272,9 +302,13 @@ class TestDashscopeCostCalculator:
             completion_tokens_details={"reasoning_tokens": 170},
         )
 
-        prompt_cost, _ = dashscope_cost_per_token(model="qwen-nested-cache-write-test", usage=usage)
+        prompt_cost, _ = dashscope_cost_per_token(
+            model="qwen-nested-cache-write-test", usage=usage
+        )
 
-        assert math.isclose(prompt_cost, (2048 * 5e-07) + (11 * 4e-07), rel_tol=1e-10)
+        assert math.isclose(
+            prompt_cost, (2048 * 5e-07) + (11 * 4e-07), rel_tol=1e-10
+        )
 
     def test_dashscope_tiered_cache_creation_falls_back_to_tier_input_rate(self):
         """
@@ -298,7 +332,9 @@ class TestDashscopeCostCalculator:
             prompt_tokens_details=PromptTokensDetailsWrapper(cache_creation_tokens=4000),
         )
 
-        prompt_cost, _ = dashscope_cost_per_token(model="qwen-no-cache-write-test", usage=usage)
+        prompt_cost, _ = dashscope_cost_per_token(
+            model="qwen-no-cache-write-test", usage=usage
+        )
 
         assert math.isclose(prompt_cost, 10000 * 3.25e-07, rel_tol=1e-10)
 
@@ -316,12 +352,18 @@ class TestDashscopeCostCalculator:
         usage = Usage(
             prompt_tokens=10000,
             completion_tokens=100,
-            prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=2000, cache_creation_tokens=3000),
+            prompt_tokens_details=PromptTokensDetailsWrapper(
+                cached_tokens=2000, cache_creation_tokens=3000
+            ),
         )
 
-        prompt_cost, _ = dashscope_cost_per_token(model="qwen-flat-cache-write-test", usage=usage)
+        prompt_cost, _ = dashscope_cost_per_token(
+            model="qwen-flat-cache-write-test", usage=usage
+        )
 
-        expected_prompt_cost = (5000 * 3.25e-07) + (3000 * 4.063e-07) + (2000 * 3.25e-08)
+        expected_prompt_cost = (
+            (5000 * 3.25e-07) + (3000 * 4.063e-07) + (2000 * 3.25e-08)
+        )
 
         assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10)
 
@@ -338,7 +380,9 @@ class TestDashscopeCostCalculator:
         }
 
         usage = Usage(prompt_tokens=500, completion_tokens=200)
-        prompt_cost, completion_cost = dashscope_cost_per_token(model="qwen-input-only-tier-test", usage=usage)
+        prompt_cost, completion_cost = dashscope_cost_per_token(
+            model="qwen-input-only-tier-test", usage=usage
+        )
 
         assert math.isclose(prompt_cost, 500 * 4e-07, rel_tol=1e-10)
         assert math.isclose(completion_cost, 200 * 1.6e-06, rel_tol=1e-10)
@@ -361,9 +405,13 @@ class TestDashscopeCostCalculator:
             completion_tokens=200,
             completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=150),
         )
-        _, completion_cost = dashscope_cost_per_token(model="qwen-input-only-reasoning-test", usage=usage)
+        _, completion_cost = dashscope_cost_per_token(
+            model="qwen-input-only-reasoning-test", usage=usage
+        )
 
-        assert math.isclose(completion_cost, (50 * 1.6e-06) + (150 * 4e-06), rel_tol=1e-10)
+        assert math.isclose(
+            completion_cost, (50 * 1.6e-06) + (150 * 4e-06), rel_tol=1e-10
+        )
 
     def test_dashscope_tier_output_rate_wins_over_the_model_reasoning_rate(self):
         """
@@ -388,10 +436,13 @@ class TestDashscopeCostCalculator:
             completion_tokens=200,
             completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=150),
         )
-        _, completion_cost = dashscope_cost_per_token(model="qwen-tier-output-reasoning-test", usage=usage)
+        _, completion_cost = dashscope_cost_per_token(
+            model="qwen-tier-output-reasoning-test", usage=usage
+        )
 
         assert math.isclose(completion_cost, 200 * 1.6e-06, rel_tol=1e-10)
 
+
     def test_dashscope_tier_zero_reasoning_rate_bills_reasoning_free(self):
         """
         Regression: a tier declaring an explicit zero reasoning rate had it treated as
@@ -415,7 +466,9 @@ class TestDashscopeCostCalculator:
             completion_tokens=200,
             completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=150),
         )
-        _, completion_cost = dashscope_cost_per_token(model="qwen-tier-zero-reasoning-test", usage=usage)
+        _, completion_cost = dashscope_cost_per_token(
+            model="qwen-tier-zero-reasoning-test", usage=usage
+        )
 
         assert math.isclose(completion_cost, 50 * 1.6e-06, rel_tol=1e-10)
 
@@ -444,7 +497,9 @@ class TestDashscopeCostCalculator:
         }
 
         usage = Usage(prompt_tokens=0, completion_tokens=500)
-        prompt_cost, completion_cost = dashscope_cost_per_token(model="qwen-zero-input-test", usage=usage)
+        prompt_cost, completion_cost = dashscope_cost_per_token(
+            model="qwen-zero-input-test", usage=usage
+        )
 
         assert prompt_cost == 0.0
         assert math.isclose(completion_cost, 500 * 1.6e-06, rel_tol=1e-10)

tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py

@@ -216,7 +216,9 @@ def test_validate_environment_raises_without_api_key(monkeypatch):
 
 def test_get_fireworks_session_id_prefers_litellm_session_id_over_trace_id():
     assert (
-        get_fireworks_session_id({"litellm_session_id": "session-123", "litellm_trace_id": "trace-123"})
+        get_fireworks_session_id(
+            {"litellm_session_id": "session-123", "litellm_trace_id": "trace-123"}
+        )
         == "session-123"
     )
 
@@ -268,18 +270,25 @@ def test_handle_message_content_with_tool_calls():
             },
         }
     ]
-    updated_message = config._handle_message_content_with_tool_calls(message, tool_calls)
+    updated_message = config._handle_message_content_with_tool_calls(
+        message, tool_calls
+    )
     assert updated_message.tool_calls is not None
     assert len(updated_message.tool_calls) == 1
     assert updated_message.tool_calls[0].function.name == "get_current_weather"
-    assert updated_message.tool_calls[0].function.arguments == expected_tool_call.function.arguments
+    assert (
+        updated_message.tool_calls[0].function.arguments
+        == expected_tool_call.function.arguments
+    )
 
 
 def test_get_supported_openai_params_reasoning_effort():
     """Test that reasoning_effort is only included in supported params for models that support it."""
     config = FireworksAIConfig()
 
-    supported_params = config.get_supported_openai_params("fireworks_ai/accounts/fireworks/models/glm-5p1")
+    supported_params = config.get_supported_openai_params(
+        "fireworks_ai/accounts/fireworks/models/glm-5p1"
+    )
     assert "reasoning_effort" in supported_params
     assert "thinking" in supported_params
 
@@ -294,7 +303,9 @@ def test_get_supported_openai_params_parallel_tool_calls():
     """Test that parallel_tool_calls is included for models that support function calling."""
     config = FireworksAIConfig()
 
-    supported_params = config.get_supported_openai_params("fireworks_ai/accounts/fireworks/models/glm-5p1")
+    supported_params = config.get_supported_openai_params(
+        "fireworks_ai/accounts/fireworks/models/glm-5p1"
+    )
     assert "parallel_tool_calls" in supported_params
     assert "tools" in supported_params
     assert "tool_choice" in supported_params
@@ -308,7 +319,9 @@ def test_get_supported_openai_params_parallel_tool_calls():
 def test_get_supported_openai_params_short_model_name_resolves_account_prefixed_entry():
     config = FireworksAIConfig()
 
-    supported_params = config.get_supported_openai_params("fireworks_ai/deepseek-v4-pro-0813")
+    supported_params = config.get_supported_openai_params(
+        "fireworks_ai/deepseek-v4-pro-0813"
+    )
 
     assert "tool_choice" in supported_params
     assert "reasoning_effort" in supported_params
@@ -317,7 +330,9 @@ def test_get_supported_openai_params_short_model_name_resolves_account_prefixed_
 def test_get_supported_openai_params_preserves_generic_reasoning_fallback():
     config = FireworksAIConfig()
 
-    supported_params = config.get_supported_openai_params("fireworks_ai/accounts/fireworks/models/glm-5p3-flash")
+    supported_params = config.get_supported_openai_params(
+        "fireworks_ai/accounts/fireworks/models/glm-5p3-flash"
+    )
 
     assert "reasoning_effort" in supported_params
 
@@ -351,10 +366,14 @@ def test_get_models_url_no_double_v1(api_base, expected_url_prefix):
 
     mock_response = MagicMock()
     mock_response.status_code = 200
-    mock_response.json.return_value = {"models": [{"name": "accounts/fireworks/models/llama-v3-70b"}]}
+    mock_response.json.return_value = {
+        "models": [{"name": "accounts/fireworks/models/llama-v3-70b"}]
+    }
 
     with (
-        patch("litellm.module_level_client.get", return_value=mock_response) as mock_get,
+        patch(
+            "litellm.module_level_client.get", return_value=mock_response
+        ) as mock_get,
         patch(
             "litellm.llms.fireworks_ai.chat.transformation.get_secret_str",
             side_effect=lambda key: {
@@ -366,9 +385,13 @@ def test_get_models_url_no_double_v1(api_base, expected_url_prefix):
     ):
         result = config.get_models(api_key="test-key", api_base=api_base)
 
-        called_url = mock_get.call_args.kwargs.get("url") or mock_get.call_args[1].get("url", "")
+        called_url = mock_get.call_args.kwargs.get("url") or mock_get.call_args[1].get(
+            "url", ""
+        )
         assert "/v1/v1/" not in called_url, f"Double /v1/ detected in URL: {called_url}"
-        assert called_url.startswith(expected_url_prefix), f"URL {called_url} does not start with {expected_url_prefix}"
+        assert called_url.startswith(
+            expected_url_prefix
+        ), f"URL {called_url} does not start with {expected_url_prefix}"
         assert result == ["fireworks_ai/accounts/fireworks/models/llama-v3-70b"]
 
 
@@ -396,7 +419,9 @@ def test_transform_messages_helper_removes_provider_specific_fields():
         },
     ]
     # Call helper
-    out = config._transform_messages_helper(messages, model="fireworks/test", litellm_params={})
+    out = config._transform_messages_helper(
+        messages, model="fireworks/test", litellm_params={}
+    )
     for msg in out:
         assert "provider_specific_fields" not in msg
 
@@ -409,11 +434,15 @@ def test_transform_messages_helper_strips_thinking_blocks_but_keeps_reasoning_co
         {
             "role": "assistant",
             "content": "I can help.",
-            "thinking_blocks": [{"type": "thinking", "thinking": "internal", "signature": ""}],
+            "thinking_blocks": [
+                {"type": "thinking", "thinking": "internal", "signature": ""}
+            ],
             "reasoning_content": "internal",
         },
     ]
-    out = config._transform_messages_helper(messages, model="accounts/fireworks/models/glm-5p1", litellm_params={})
+    out = config._transform_messages_helper(
+        messages, model="accounts/fireworks/models/glm-5p1", litellm_params={}
+    )
     assert "thinking_blocks" not in out[1]
     assert out[1]["reasoning_content"] == "internal"
     assert out[1]["content"] == "I can help."
@@ -903,7 +932,9 @@ def test_transform_messages_helper_rejects_file_blocks():
         litellm.BadRequestError,
         match="Fireworks AI chat completions does not support file content blocks",
     ):
-        config._transform_messages_helper(messages, model="accounts/fireworks/models/kimi-k2p6", litellm_params={})
+        config._transform_messages_helper(
+            messages, model="accounts/fireworks/models/kimi-k2p6", litellm_params={}
+        )
 
 
 def test_transform_messages_helper_rejects_non_vision_image_inputs():
@@ -915,14 +946,18 @@ def test_transform_messages_helper_rejects_non_vision_image_inputs():
                 {"type": "text", "text": "Describe this"},
                 {
                     "type": "image_url",
-                    "image_url": {"url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAE="},
+                    "image_url": {
+                        "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAE="
+                    },
                 },
             ],
         }
     ]
 
     with pytest.raises(litellm.BadRequestError, match="does not support image inputs"):
-        config._transform_messages_helper(messages, model="accounts/fireworks/models/glm-5p2", litellm_params={})
+        config._transform_messages_helper(
+            messages, model="accounts/fireworks/models/glm-5p2", litellm_params={}
+        )
 
 
 def test_transform_messages_helper_allows_vision_image_inputs():
@@ -934,7 +969,9 @@ def test_transform_messages_helper_allows_vision_image_inputs():
                 {"type": "text", "text": "Describe this"},
                 {
                     "type": "image_url",
-                    "image_url": {"url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAE="},
+                    "image_url": {
+                        "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAE="
+                    },
                 },
             ],
         }
@@ -958,7 +995,9 @@ def test_image_inputs_not_rejected_for_fuzzy_non_vision_match():
     custom_model = "accounts/myorg/models/custom-glm-5p2"
 
     assert config._get_model_cost_capability(custom_model, "supports_vision") is False
-    assert config._get_model_cost_capability_exact(custom_model, "supports_vision") is None
+    assert (
+        config._get_model_cost_capability_exact(custom_model, "supports_vision") is None
+    )
 
     messages = [
         {
@@ -966,12 +1005,16 @@ def test_image_inputs_not_rejected_for_fuzzy_non_vision_match():
             "content": [
                 {
                     "type": "image_url",
-                    "image_url": {"url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAE="},
+                    "image_url": {
+                        "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAE="
+                    },
                 },
             ],
         }
     ]
-    out = config._transform_messages_helper(messages, model=custom_model, litellm_params={})
+    out = config._transform_messages_helper(
+        messages, model=custom_model, litellm_params={}
+    )
     assert out == messages
 
 
@@ -984,7 +1027,9 @@ def test_transform_messages_helper_skips_non_dict_content():
         }
     ]
 
-    out = config._transform_messages_helper(messages, model="accounts/fireworks/models/glm-5p2", litellm_params={})
+    out = config._transform_messages_helper(
+        messages, model="accounts/fireworks/models/glm-5p2", litellm_params={}
+    )
     assert out == messages
 
 
@@ -1204,7 +1249,9 @@ def test_streaming_surfaces_fireworks_response_fields():
         surfaced: dict = {}
         for chunk in stream:
             fields = getattr(chunk, "provider_specific_fields", None) or {}
-            surfaced.update({k: v for k, v in fields.items() if k.startswith("fireworks_")})
+            surfaced.update(
+                {k: v for k, v in fields.items() if k.startswith("fireworks_")}
+            )
 
     assert surfaced["fireworks_token_ids"] == [[123]]
     assert surfaced["fireworks_raw_outputs"] == [raw_output]
@@ -1257,7 +1304,9 @@ def test_transform_request_direct_route_passthrough():
 
 def test_map_extra_body_params_translates_truncate_prompt_tokens():
     config = FireworksAIConfig()
-    result = config.map_extra_body_params({"extra_body": {"truncate_prompt_tokens": 4096}}, _REASONING_MODEL)
+    result = config.map_extra_body_params(
+        {"extra_body": {"truncate_prompt_tokens": 4096}}, _REASONING_MODEL
+    )
     assert result == {"prompt_truncate_len": 4096}
 
 
@@ -1416,7 +1465,9 @@ def test_map_extra_body_params_non_dict_chat_template_kwargs_dropped():
 def test_map_extra_body_params_guided_json():
     config = FireworksAIConfig()
     schema = {"type": "object", "properties": {"x": {"type": "string"}}}
-    result = config.map_extra_body_params({"extra_body": {"guided_json": schema}}, _REASONING_MODEL)
+    result = config.map_extra_body_params(
+        {"extra_body": {"guided_json": schema}}, _REASONING_MODEL
+    )
     assert result == {
         "response_format": {
             "type": "json_schema",
@@ -1427,10 +1478,16 @@ def test_map_extra_body_params_guided_json():
 
 def test_map_extra_body_params_guided_grammar_and_choice():
     config = FireworksAIConfig()
-    grammar = config.map_extra_body_params({"extra_body": {"guided_grammar": "root ::= 'hello'"}}, _REASONING_MODEL)
-    assert grammar == {"response_format": {"type": "grammar", "grammar": "root ::= 'hello'"}}
+    grammar = config.map_extra_body_params(
+        {"extra_body": {"guided_grammar": "root ::= 'hello'"}}, _REASONING_MODEL
+    )
+    assert grammar == {
+        "response_format": {"type": "grammar", "grammar": "root ::= 'hello'"}
+    }
 
-    choice = config.map_extra_body_params({"extra_body": {"guided_choice": ["yes", "no"]}}, _REASONING_MODEL)
+    choice = config.map_extra_body_params(
+        {"extra_body": {"guided_choice": ["yes", "no"]}}, _REASONING_MODEL
+    )
     assert choice == {
         "response_format": {
             "type": "json_schema",
@@ -1516,7 +1573,9 @@ def test_map_extra_body_params_strips_unsupported_nim_vllm_params(param, value,
 
     config = FireworksAIConfig()
     with caplog.at_level(logging.DEBUG):
-        result = config.map_extra_body_params({"extra_body": {param: value}}, _REASONING_MODEL)
+        result = config.map_extra_body_params(
+            {"extra_body": {param: value}}, _REASONING_MODEL
+        )
     assert result == {}
     assert param in caplog.text
 
@@ -1608,7 +1667,10 @@ def test_in_schema_unsupported_params_still_raise():
 def test_streaming_preserves_selected_model_for_private_accounting():
     from litellm.llms.custom_httpx.http_handler import HTTPHandler
 
-    requested_route = "accounts/fireworks/routers/firerouter/kimi-k3/deepseek-v4-pro-0813/deepseek-v4-flash-0731"
+    requested_route = (
+        "accounts/fireworks/routers/firerouter/"
+        "kimi-k3/deepseek-v4-pro-0813/deepseek-v4-flash-0731"
+    )
     selected_model = "deepseek-v4-flash-0731"
     sse_lines = [
         "data: "
@@ -1662,14 +1724,19 @@ def test_streaming_preserves_selected_model_for_private_accounting():
 
     assert chunks
     assert {chunk.model for chunk in chunks} == {requested_route}
-    assert {chunk._hidden_params.get("provider_response_model") for chunk in chunks} == {selected_model}
+    assert {
+        chunk._hidden_params.get("provider_response_model") for chunk in chunks
+    } == {selected_model}
 
     assembled = litellm.stream_chunk_builder(chunks=chunks)
     assert assembled is not None
     assert assembled.model == requested_route
     assert assembled._hidden_params["provider_response_model"] == selected_model
     selected_model_info = litellm.model_cost[f"fireworks_ai/{selected_model}"]
-    expected_cost = 5 * selected_model_info["input_cost_per_token"] + selected_model_info["output_cost_per_token"]
+    expected_cost = (
+        5 * selected_model_info["input_cost_per_token"]
+        + selected_model_info["output_cost_per_token"]
+    )
     assert litellm.completion_cost(
         completion_response=assembled,
         custom_llm_provider="fireworks_ai",

tests/test_litellm/llms/inception/test_inception_chat_transformation.py

@@ -188,15 +188,21 @@ def test_inception_does_not_leak_key_to_caller_api_base():
     caller also supplies their own key.
     """
     config = InceptionChatConfig()
-    with mock.patch.dict(os.environ, {"INCEPTION_API_KEY": "server-secret"}, clear=True):
+    with mock.patch.dict(
+        os.environ, {"INCEPTION_API_KEY": "server-secret"}, clear=True
+    ):
         with mock.patch.object(litellm, "inception_key", "module-secret"):
             # caller overrides api_base without a key -> server key withheld
-            api_base, api_key = config._get_openai_compatible_provider_info("https://attacker.example/v1", None)
+            api_base, api_key = config._get_openai_compatible_provider_info(
+                "https://attacker.example/v1", None
+            )
             assert api_base == "https://attacker.example/v1"
             assert api_key is None
 
             # caller overrides api_base AND supplies their own key -> used as-is
-            _, api_key = config._get_openai_compatible_provider_info("https://attacker.example/v1", "caller-key")
+            _, api_key = config._get_openai_compatible_provider_info(
+                "https://attacker.example/v1", "caller-key"
+            )
             assert api_key == "caller-key"
 
             # default/server base -> server-managed key resolved
@@ -211,7 +217,9 @@ def test_get_llm_provider_inception():
     assert model == "mercury-2"
     assert provider == "inception"
 
-    model, provider, _, api_base = get_llm_provider("mercury-2", api_base="https://api.inceptionlabs.ai/v1")
+    model, provider, _, api_base = get_llm_provider(
+        "mercury-2", api_base="https://api.inceptionlabs.ai/v1"
+    )
     assert model == "mercury-2"
     assert provider == "inception"
     assert api_base == "https://api.inceptionlabs.ai/v1"
@@ -285,3 +293,5 @@ def test_inception_completion_targets_inception_endpoint():
     assert captured["body"]["model"] == "mercury-2"
     assert captured["body"]["tool_choice"] == "auto"
     assert response.choices[0].message.content == "hi"
+
+

tests/test_litellm/llms/oci/embed/test_oci_embedding.py

@@ -305,3 +305,5 @@ class TestOCIEmbeddingConfig:
                 optional_params={},
                 litellm_params={},
             )
+
+

tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py

@@ -109,7 +109,9 @@ class TestOpenAIResponsesAPIConfig:
         # Check expected fields have correct values
         for field, value in expected_fields.items():
             assert field in params, f"Missing expected field: {field}"
-            assert params[field] == value, f"Field {field} has value {params[field]}, expected {value}"
+            assert (
+                params[field] == value
+            ), f"Field {field} has value {params[field]}, expected {value}"
 
     def test_transform_responses_api_request(self):
         """Test request transformation"""
@@ -457,7 +459,9 @@ class TestOpenAIResponsesAPIConfig:
         }
 
         # Mock the get_event_model_class to avoid validation issues in tests
-        with patch.object(OpenAIResponsesAPIConfig, "get_event_model_class") as mock_get_class:
+        with patch.object(
+            OpenAIResponsesAPIConfig, "get_event_model_class"
+        ) as mock_get_class:
             mock_get_class.return_value = ResponseCompletedEvent
 
             result = self.config.transform_streaming_response(
@@ -476,7 +480,9 @@ class TestOpenAIResponsesAPIConfig:
         headers = {}
         api_key = "test_api_key"
         litellm_params = GenericLiteLLMParams(api_key=api_key)
-        result = self.config.validate_environment(headers=headers, model=self.model, litellm_params=litellm_params)
+        result = self.config.validate_environment(
+            headers=headers, model=self.model, litellm_params=litellm_params
+        )
 
         assert "Authorization" in result
         assert result["Authorization"] == f"Bearer {api_key}"
@@ -487,7 +493,9 @@ class TestOpenAIResponsesAPIConfig:
 
         with patch("litellm.api_key", "litellm_api_key"):
             litellm_params = GenericLiteLLMParams()
-            result = self.config.validate_environment(headers=headers, model=self.model, litellm_params=litellm_params)
+            result = self.config.validate_environment(
+                headers=headers, model=self.model, litellm_params=litellm_params
+            )
 
             assert "Authorization" in result
             assert result["Authorization"] == "Bearer litellm_api_key"
@@ -593,7 +601,10 @@ class TestOpenAIResponsesAPIConfig:
             headers={},
         )
 
-        assert url == "https://custom-openai.example.com/v1/responses/..%2F..%2Ffiles%3Fx%3D1%23frag/input_items"
+        assert (
+            url
+            == "https://custom-openai.example.com/v1/responses/..%2F..%2Ffiles%3Fx%3D1%23frag/input_items"
+        )
         assert data["limit"] == 20
 
     def test_get_event_model_class_generic_event(self):
@@ -668,7 +679,9 @@ class TestOpenAIResponsesAPIConfig:
             )
 
             assert isinstance(result, ImageGenerationPartialImageEvent)
-            assert result.type == ResponsesAPIStreamEvents.IMAGE_GENERATION_PARTIAL_IMAGE
+            assert (
+                result.type == ResponsesAPIStreamEvents.IMAGE_GENERATION_PARTIAL_IMAGE
+            )
             assert result.partial_image_index == idx
             assert result.b64_json == chunk["b64_json"]
 
@@ -883,7 +896,9 @@ class TestOpenAIResponsesAPIConfig:
                 "namespace": "drop",
             },
         ]
-        out = BaseResponsesAPIConfig.strip_custom_tool_call_namespace_from_responses_input(inp)
+        out = BaseResponsesAPIConfig.strip_custom_tool_call_namespace_from_responses_input(
+            inp
+        )
         assert out[0]["namespace"] == "keep"
         assert "namespace" not in out[1]
 
@@ -956,21 +971,30 @@ class TestAzureResponsesAPIConfig:
             api_base=base_url,
             litellm_params={"api_version": "preview"},
         )
-        assert result_preview == "https://litellm8397336933.openai.azure.com/openai/v1/responses?api-version=preview"
+        assert (
+            result_preview
+            == "https://litellm8397336933.openai.azure.com/openai/v1/responses?api-version=preview"
+        )
 
         # Test with latest version - should use openai/v1/responses
         result_latest = self.config.get_complete_url(
             api_base=base_url,
             litellm_params={"api_version": "latest"},
         )
-        assert result_latest == "https://litellm8397336933.openai.azure.com/openai/v1/responses?api-version=latest"
+        assert (
+            result_latest
+            == "https://litellm8397336933.openai.azure.com/openai/v1/responses?api-version=latest"
+        )
 
         # Test with date-based version - should use openai/responses
         result_date = self.config.get_complete_url(
             api_base=base_url,
             litellm_params={"api_version": "2025-01-01"},
         )
-        assert result_date == "https://litellm8397336933.openai.azure.com/openai/responses?api-version=2025-01-01"
+        assert (
+            result_date
+            == "https://litellm8397336933.openai.azure.com/openai/responses?api-version=2025-01-01"
+        )
 
     def test_azure_transform_then_normalize_strips_custom_tool_call_namespace(self):
         """Same as OpenAI path: ``normalize_responses_api_request_dict`` strips custom_tool_call only."""
@@ -1137,7 +1161,10 @@ class TestTransformListInputItemsRequest:
         )
 
         # Assert
-        assert url == "https://test.openai.azure.com/openai/responses/compact?api-version=2024-05-01-preview"
+        assert (
+            url
+            == "https://test.openai.azure.com/openai/responses/compact?api-version=2024-05-01-preview"
+        )
         assert data["model"] == "gpt-5.2-codex"
         assert data["input"] == "hello"
 
@@ -1224,7 +1251,9 @@ class TestTransformListInputItemsRequest:
         assert params == expected_params
 
     @patch("litellm.router.Router")
-    def test_mock_litellm_router_with_transform_list_input_items_request(self, mock_router):
+    def test_mock_litellm_router_with_transform_list_input_items_request(
+        self, mock_router
+    ):
         """Mock test using litellm.router for transform_list_input_items_request"""
         # Setup mock router
         mock_router_instance = Mock()
@@ -1238,7 +1267,9 @@ class TestTransformListInputItemsRequest:
         )
 
         # Setup router mock
-        mock_router_instance.get_provider_responses_api_config.return_value = mock_provider_config
+        mock_router_instance.get_provider_responses_api_config.return_value = (
+            mock_provider_config
+        )
 
         # Test parameters
         response_id = "resp_test123"
@@ -1554,7 +1585,9 @@ class TestPhaseParameter:
                 phase = getattr(output_item, "phase", None)
 
             expected = "commentary" if idx == 0 else "final_answer"
-            assert phase == expected, f"output[{idx}] phase={phase!r}, expected {expected!r}"
+            assert (
+                phase == expected
+            ), f"output[{idx}] phase={phase!r}, expected {expected!r}"
 
     def test_streaming_output_item_done_preserves_phase(self):
         """OutputItemDoneEvent must preserve phase on its item."""
@@ -1688,7 +1721,9 @@ class TestPhaseParameter:
             if isinstance(item, dict):
                 input_items.append(item)
             else:
-                input_items.append(item.model_dump() if hasattr(item, "model_dump") else dict(item))
+                input_items.append(
+                    item.model_dump() if hasattr(item, "model_dump") else dict(item)
+                )
 
         input_items.append(
             {
@@ -1785,7 +1820,9 @@ class TestResponsesSurfaceSharesTheEffortRule:
             ("gpt-6-astra", "low", False),
         ],
     )
-    def test_temperature_follows_the_resolved_effort(self, local_model_cost_map, model, effort, temperature_survives):
+    def test_temperature_follows_the_resolved_effort(
+        self, local_model_cost_map, model, effort, temperature_survives
+    ):
         params = {"temperature": 0}
         if effort is not None:
             params["reasoning"] = {"effort": effort}
@@ -2189,6 +2226,7 @@ class TestReasoningFollowsModelSupport:
         )
         assert mapped["reasoning"] == reasoning
 
+
     def test_azure_deployments_keep_reasoning_even_on_a_non_reasoning_model_name(self, local_model_cost_map):
         mapped = AzureOpenAIResponsesAPIConfig().map_openai_params(
             response_api_optional_params={"reasoning": {"effort": "medium"}},

tests/test_litellm/llms/openai/test_gpt5_transformation.py

@@ -27,7 +27,9 @@ def gpt5_config() -> OpenAIGPT5Config:
 @pytest.fixture(autouse=True)
 def use_local_model_cost_map(monkeypatch: pytest.MonkeyPatch):
     monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
-    monkeypatch.setattr(litellm, "model_cost", get_model_cost_map(url=litellm.model_cost_map_url))
+    monkeypatch.setattr(
+        litellm, "model_cost", get_model_cost_map(url=litellm.model_cost_map_url)
+    )
     litellm.add_known_models(model_cost_map=litellm.model_cost)
 
 
@@ -37,7 +39,9 @@ def test_gpt5_supports_reasoning_effort(config: OpenAIConfig):
 
 
 def test_gpt5_chat_does_not_support_reasoning_effort(config: OpenAIConfig):
-    assert "reasoning_effort" not in config.get_supported_openai_params(model="gpt-5-chat-latest")
+    assert "reasoning_effort" not in config.get_supported_openai_params(
+        model="gpt-5-chat-latest"
+    )
 
 
 def test_gpt5_chat_supports_temperature(config: OpenAIConfig):
@@ -447,7 +451,9 @@ def test_gpt5_minimal_dict_triggers_validation(config: OpenAIConfig):
     """Dict with effort='minimal' triggers minimal model-support validation."""
     with pytest.raises(litellm.utils.UnsupportedParamsError):
         config.map_openai_params(
-            non_default_params={"reasoning_effort": {"effort": "minimal", "summary": "detailed"}},
+            non_default_params={
+                "reasoning_effort": {"effort": "minimal", "summary": "detailed"}
+            },
             optional_params={},
             model="gpt-5.4-mini",
             drop_params=False,
@@ -457,7 +463,9 @@ def test_gpt5_minimal_dict_triggers_validation(config: OpenAIConfig):
 def test_gpt5_minimal_dict_accepted_for_supported_model(config: OpenAIConfig):
     """Dict with effort='minimal' passes through for gpt-5."""
     params = config.map_openai_params(
-        non_default_params={"reasoning_effort": {"effort": "minimal", "summary": "detailed"}},
+        non_default_params={
+            "reasoning_effort": {"effort": "minimal", "summary": "detailed"}
+        },
         optional_params={},
         model="gpt-5",
         drop_params=False,
@@ -472,11 +480,21 @@ def test_gpt5_minimal_explicitly_disabled_check(gpt5_config: OpenAIGPT5Config):
     Models with supports_minimal_reasoning_effort=true (or missing) → not disabled.
     Provider-prefixed models (openai/gpt-5.4-mini) are normalized before lookup.
     """
-    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled("gpt-5.4-mini", "minimal")
-    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled("gpt-5.4-nano", "minimal")
-    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled("openai/gpt-5.4-mini", "minimal")
-    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled("gpt-5.4", "minimal")
-    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled("gpt-5.4-pro", "minimal")
+    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.4-mini", "minimal"
+    )
+    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.4-nano", "minimal"
+    )
+    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "openai/gpt-5.4-mini", "minimal"
+    )
+    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.4", "minimal"
+    )
+    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.4-pro", "minimal"
+    )
 
 
 def test_is_explicitly_disabled_factory_minimal():
@@ -571,16 +589,26 @@ def test_gpt5_unknown_model_passes_through_low(config: OpenAIConfig):
 
 def test_gpt5_low_explicitly_disabled_check(gpt5_config: OpenAIGPT5Config):
     """supports_low_reasoning_effort=false → disabled; missing/true → not disabled."""
-    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled("gpt-5.5-pro", "low")
-    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled("gpt-5.5-pro-2026-04-23", "low")
-    assert not gpt5_config._is_reasoning_effort_level_explicitly_disabled("gpt-5.5", "low")
-    assert not gpt5_config._is_reasoning_effort_level_explicitly_disabled("gpt-5.4", "low")
+    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.5-pro", "low"
+    )
+    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.5-pro-2026-04-23", "low"
+    )
+    assert not gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.5", "low"
+    )
+    assert not gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.4", "low"
+    )
 
 
 def test_gpt5_normalizes_reasoning_effort_dict_with_summary(config: OpenAIConfig):
     """Dict with summary/generate_summary is normalized for chat completions."""
     params = config.map_openai_params(
-        non_default_params={"reasoning_effort": {"effort": "high", "summary": "detailed"}},
+        non_default_params={
+            "reasoning_effort": {"effort": "high", "summary": "detailed"}
+        },
         optional_params={},
         model="gpt-5.4",
         drop_params=False,
@@ -596,7 +624,9 @@ def test_gpt5_xhigh_dict_triggers_validation(config: OpenAIConfig):
     """
     with pytest.raises(litellm.utils.UnsupportedParamsError):
         config.map_openai_params(
-            non_default_params={"reasoning_effort": {"effort": "xhigh", "summary": "detailed"}},
+            non_default_params={
+                "reasoning_effort": {"effort": "xhigh", "summary": "detailed"}
+            },
             optional_params={},
             model="gpt-5.1",
             drop_params=False,
@@ -606,7 +636,9 @@ def test_gpt5_xhigh_dict_triggers_validation(config: OpenAIConfig):
 def test_gpt5_xhigh_dict_accepted_for_supported_model(config: OpenAIConfig):
     """Dict with effort='xhigh' passes through for gpt-5.4+."""
     params = config.map_openai_params(
-        non_default_params={"reasoning_effort": {"effort": "xhigh", "summary": "detailed"}},
+        non_default_params={
+            "reasoning_effort": {"effort": "xhigh", "summary": "detailed"}
+        },
         optional_params={},
         model="gpt-5.4",
         drop_params=False,
@@ -661,7 +693,9 @@ def test_gpt5_normalizes_reasoning_effort_dict_with_summary_from_optional_params
     """reasoning_effort dict with summary in optional_params is normalized."""
     params = config.map_openai_params(
         non_default_params={},
-        optional_params={"reasoning_effort": {"effort": "medium", "summary": "detailed"}},
+        optional_params={
+            "reasoning_effort": {"effort": "medium", "summary": "detailed"}
+        },
         model="gpt-5.4",
         drop_params=False,
     )
@@ -911,7 +945,9 @@ def test_gpt5_search_supported_params(gpt5_config: OpenAIGPT5Config):
         "reasoning_effort",
     ]
     for param in rejected:
-        assert param not in supported, f"{param} should not be supported for search models"
+        assert (
+            param not in supported
+        ), f"{param} should not be supported for search models"
 
 
 def test_gpt5_search_has_expected_params(gpt5_config: OpenAIGPT5Config):
@@ -997,15 +1033,21 @@ def test_reasoning_summary_alias_helpers_preserve_falsy_and_strip_all_aliases():
     optional_params = {"reasoningSummary": False, "reasoning_summary": "ignored"}
 
     assert peek_reasoning_summary_aliases(optional_params) is False
-    stripped, rs_val = strip_reasoning_summary_aliases_from_optional_params(optional_params)
+    stripped, rs_val = strip_reasoning_summary_aliases_from_optional_params(
+        optional_params
+    )
 
     assert rs_val is False
     assert stripped == {}
 
-    optional_params = {"extra_body": {"reasoningSummary": False, "reasoning_summary": "ignored"}}
+    optional_params = {
+        "extra_body": {"reasoningSummary": False, "reasoning_summary": "ignored"}
+    }
 
     assert peek_reasoning_summary_aliases(optional_params) is False
-    stripped, rs_val = strip_reasoning_summary_aliases_from_optional_params(optional_params)
+    stripped, rs_val = strip_reasoning_summary_aliases_from_optional_params(
+        optional_params
+    )
 
     assert rs_val is False
     assert stripped == {}
@@ -1019,7 +1061,9 @@ def test_reasoning_summary_alias_helpers_preserve_falsy_and_strip_all_aliases():
     }
 
     assert peek_reasoning_summary_aliases(optional_params) == "auto"
-    stripped, rs_val = strip_reasoning_summary_aliases_from_optional_params(optional_params)
+    stripped, rs_val = strip_reasoning_summary_aliases_from_optional_params(
+        optional_params
+    )
 
     assert rs_val == "auto"
     assert stripped == {"extra_body": {"metadata": "ok"}}
@@ -1038,7 +1082,9 @@ def test_gpt5_rejects_params_unsupported_by_openai(config: OpenAIConfig):
     for model in ["gpt-5", "gpt-5-mini", "gpt-5-codex", "gpt-5.1", "gpt-5.2"]:
         supported = config.get_supported_openai_params(model=model)
         for param in rejected_params:
-            assert param not in supported, f"{param} should not be supported for {model}"
+            assert (
+                param not in supported
+            ), f"{param} should not be supported for {model}"
 
 
 def test_gpt5_1_supports_logprobs_top_p(config: OpenAIConfig):
@@ -1047,16 +1093,22 @@ def test_gpt5_1_supports_logprobs_top_p(config: OpenAIConfig):
         supported = config.get_supported_openai_params(model=model)
         assert "logprobs" in supported, f"logprobs should be supported for {model}"
         assert "top_p" in supported, f"top_p should be supported for {model}"
-        assert "top_logprobs" in supported, f"top_logprobs should be supported for {model}"
+        assert (
+            "top_logprobs" in supported
+        ), f"top_logprobs should be supported for {model}"
 
 
 def test_gpt5_base_does_not_support_logprobs_top_p(config: OpenAIConfig):
     """Base gpt-5/gpt-5-mini do NOT support logprobs, top_p, top_logprobs."""
     for model in ["gpt-5", "gpt-5-mini", "gpt-5-codex"]:
         supported = config.get_supported_openai_params(model=model)
-        assert "logprobs" not in supported, f"logprobs should not be supported for {model}"
+        assert (
+            "logprobs" not in supported
+        ), f"logprobs should not be supported for {model}"
         assert "top_p" not in supported, f"top_p should not be supported for {model}"
-        assert "top_logprobs" not in supported, f"top_logprobs should not be supported for {model}"
+        assert (
+            "top_logprobs" not in supported
+        ), f"top_logprobs should not be supported for {model}"
 
 
 def test_gpt5_1_logprobs_passthrough(config: OpenAIConfig):

tests/test_litellm/llms/openai_like/responses/test_openai_like_responses.py

@@ -7,7 +7,9 @@ import sys
 from unittest.mock import patch
 
 
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../..")))
+sys.path.insert(
+    0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../.."))
+)
 
 
 class TestSimpleProviderConfigSupportedEndpoints:
@@ -17,7 +19,9 @@ class TestSimpleProviderConfigSupportedEndpoints:
         """supported_endpoints defaults to [] (chat always enabled, nothing else)"""
         from litellm.llms.openai_like.json_loader import SimpleProviderConfig
 
-        config = SimpleProviderConfig("test", {"base_url": "https://example.com", "api_key_env": "TEST_KEY"})
+        config = SimpleProviderConfig(
+            "test", {"base_url": "https://example.com", "api_key_env": "TEST_KEY"}
+        )
         assert config.supported_endpoints == []
 
     def test_custom_supported_endpoints(self):
@@ -53,11 +57,15 @@ class TestSimpleProviderConfigSupportedEndpoints:
 class TestJSONProviderRegistryResponsesAPI:
     """Test supports_responses_api on JSONProviderRegistry."""
 
+
     def test_nonexistent_provider(self):
         """Non-existent provider returns False"""
         from litellm.llms.openai_like.json_loader import JSONProviderRegistry
 
-        assert JSONProviderRegistry.supports_responses_api("nonexistent_provider_xyz") is False
+        assert (
+            JSONProviderRegistry.supports_responses_api("nonexistent_provider_xyz")
+            is False
+        )
 
 
 class TestCreateResponsesConfigClass:
@@ -110,7 +118,9 @@ class TestCreateResponsesConfigClass:
         config_cls = create_responses_config_class(provider)
         config = config_cls()
 
-        url = config.get_complete_url(api_base="https://custom.api.com/v1", litellm_params={})
+        url = config.get_complete_url(
+            api_base="https://custom.api.com/v1", litellm_params={}
+        )
         assert url == "https://custom.api.com/v1/responses"
 
     def test_generated_class_get_complete_url_strips_trailing_slash(self):
@@ -123,7 +133,9 @@ class TestCreateResponsesConfigClass:
         config_cls = create_responses_config_class(provider)
         config = config_cls()
 
-        url = config.get_complete_url(api_base="https://custom.api.com/v1/", litellm_params={})
+        url = config.get_complete_url(
+            api_base="https://custom.api.com/v1/", litellm_params={}
+        )
         assert url == "https://custom.api.com/v1/responses"
 
     def test_generated_class_validate_environment(self):
@@ -140,7 +152,9 @@ class TestCreateResponsesConfigClass:
             "litellm.llms.openai_like.dynamic_config.get_secret_str",
             return_value="sk-test-key-123",
         ):
-            headers = config.validate_environment(headers={}, model="test-model", litellm_params=None)
+            headers = config.validate_environment(
+                headers={}, model="test-model", litellm_params=None
+            )
         assert headers["Authorization"] == "Bearer sk-test-key-123"
 
     def test_generated_class_validate_environment_litellm_params_override(self):
@@ -155,7 +169,9 @@ class TestCreateResponsesConfigClass:
         config = config_cls()
 
         litellm_params = GenericLiteLLMParams(api_key="sk-override-key")
-        headers = config.validate_environment(headers={}, model="test-model", litellm_params=litellm_params)
+        headers = config.validate_environment(
+            headers={}, model="test-model", litellm_params=litellm_params
+        )
         assert headers["Authorization"] == "Bearer sk-override-key"
 
     def test_generated_class_inherits_openai_responses_methods(self):

tests/test_litellm/llms/openai_like/test_cognition_provider.py

@@ -110,6 +110,8 @@ class TestCognitionProviderIdentity:
 
 
 class TestCognitionCostTracking:
+
+
     def test_supported_endpoints_matrix(self):
         matrix = json.loads((Path(litellm.__file__).parent / "provider_endpoints_support_backup.json").read_text())
 
@@ -118,3 +120,5 @@ class TestCognitionCostTracking:
         assert endpoints["messages"] is True
         assert endpoints["responses"] is True
         assert endpoints["embeddings"] is False
+
+

tests/test_litellm/llms/openai_like/test_meta_provider.py

@@ -24,6 +24,7 @@ class TestMetaProviderConfig:
         assert meta.api_key_env == "META_API_KEY"
         assert meta.api_base_env == "META_API_BASE"
 
+
     def test_meta_in_openai_compatible_providers(self):
         from litellm.constants import openai_compatible_providers
 
@@ -90,7 +91,9 @@ class TestMetaProviderConfig:
 
 class TestMetaReasoningParams:
     def test_muse_spark_supports_reasoning_effort(self):
-        params = litellm.get_supported_openai_params(model="muse-spark-1.1", custom_llm_provider="meta")
+        params = litellm.get_supported_openai_params(
+            model="muse-spark-1.1", custom_llm_provider="meta"
+        )
         assert params is not None
         assert "reasoning_effort" in params
 
@@ -109,7 +112,9 @@ class TestMetaReasoningParams:
 
     def test_reasoning_effort_gated_on_capability(self):
         """A meta model without reasoning metadata must not advertise reasoning_effort."""
-        params = litellm.get_supported_openai_params(model="some-non-reasoning-model", custom_llm_provider="meta")
+        params = litellm.get_supported_openai_params(
+            model="some-non-reasoning-model", custom_llm_provider="meta"
+        )
         assert params is not None
         assert "reasoning_effort" not in params
 
@@ -181,3 +186,5 @@ class TestMetaAnthropicMessages:
         )
         assert headers["authorization"] == "Bearer sk-env-key"
         assert headers["anthropic-version"] == "2023-06-01"
+
+

tests/test_litellm/llms/openai_like/test_scx_ai_provider.py

@@ -154,6 +154,7 @@ class TestSCXAIModelMetadata:
         with open(json_path) as f:
             return json.load(f)
 
+
     def test_scx_ai_models_synced_to_backup(self):
         model_cost = self._load(("model_prices_and_context_window.json",))
         backup = self._load(("litellm", "model_prices_and_context_window_backup.json"))

tests/test_litellm/llms/openai_like/test_tensormesh_provider.py

@@ -79,6 +79,7 @@ class TestTensormeshProviderConfig:
         matching the text_completion flag in provider_endpoints_support.json."""
         assert "tensormesh" in litellm.openai_text_completion_compatible_providers
 
+
     def test_tensormesh_router_config(self):
         """Test that tensormesh can be used in Router configuration"""
         from litellm import Router
@@ -115,6 +116,7 @@ class TestTensormeshCostMap:
             litellm.model_cost = original_model_cost
             litellm.get_model_info.cache_clear()
 
+
     def test_reasoning_flag_matches_expected_set(self):
         reasoning_models = {
             "tensormesh/deepseek-ai/DeepSeek-V4-Flash",
@@ -129,3 +131,4 @@ class TestTensormeshCostMap:
         }
         for model in TENSORMESH_MODELS:
             assert litellm.supports_reasoning(model) is (model in reasoning_models), model
+

tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py

@@ -204,6 +204,7 @@ class TestPerplexityCostCalculator:
         assert math.isclose(prompt_cost, (1000 * 1e-07) + (100 * 2e-06), rel_tol=1e-10)
         assert math.isclose(completion_cost, (150 * 2e-07) + (50 * 3e-06) + 0.005, rel_tol=1e-10)
 
+
     def test_provider_stated_cost_still_wins_inside_an_off_peak_window(self):
         """A response that carries Perplexity's own metered cost bills that cost whatever the
         window says; the caller strips it when the deployment carries custom pricing."""

tests/test_litellm/llms/reducto/test_model_info.py

@@ -1,8 +1,13 @@
+
 import litellm
 
 
 def test_reducto_provider_registration():
-    model, custom_llm_provider, _, _ = litellm.get_llm_provider(model="reducto/parse-v3")
+    model, custom_llm_provider, _, _ = litellm.get_llm_provider(
+        model="reducto/parse-v3"
+    )
 
     assert model == "parse-v3"
     assert custom_llm_provider == "reducto"
+
+

tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py

@@ -212,9 +212,13 @@ def test_build_vertex_schema():
                 "properties": {
                     "tags": {"items": {"type": "string"}, "type": "array"},
                     "metadata": {"type": "object"},
-                    "callbacks": {"anyOf": [{"items": {}, "type": "array"}, {}, {"type": "null"}]},
+                    "callbacks": {
+                        "anyOf": [{"items": {}, "type": "array"}, {}, {"type": "null"}]
+                    },
                     "run_name": {"type": "string"},
-                    "max_concurrency": {"anyOf": [{"type": "integer"}, {"type": "null"}]},
+                    "max_concurrency": {
+                        "anyOf": [{"type": "integer"}, {"type": "null"}]
+                    },
                     "recursion_limit": {"type": "integer"},
                     "configurable": {"type": "object"},
                     "run_id": {
@@ -258,7 +262,9 @@ def test_build_vertex_schema():
                         ]
                     },
                     "run_name": {"type": "string"},
-                    "max_concurrency": {"anyOf": [{"type": "integer", "nullable": True}]},
+                    "max_concurrency": {
+                        "anyOf": [{"type": "integer", "nullable": True}]
+                    },
                     "recursion_limit": {"type": "integer"},
                     "configurable": {"type": "object"},
                     "run_id": {"anyOf": [{"type": "string", "nullable": True}]},
@@ -359,7 +365,9 @@ def test_build_vertex_schema_array_branch_missing_items_in_anyof():
     array_branches = [b for b in callbacks_anyof if b.get("type") == "array"]
     assert array_branches, "expected an array branch to remain after transform"
     for branch in array_branches:
-        assert branch.get("items") == {"type": "object"}, f"array branch must have items synthesized; got {branch}"
+        assert branch.get("items") == {
+            "type": "object"
+        }, f"array branch must have items synthesized; got {branch}"
 
 
 def test_vertex_ai_complex_response_schema():
@@ -745,7 +753,9 @@ def test_convert_schema_types_type_array_conversion():
     assert anyof_types[1]["type"] == "number"
 
     # 4. Other properties preserved
-    assert input_schema["properties"]["studio"]["description"] == "The studio ID or name"
+    assert (
+        input_schema["properties"]["studio"]["description"] == "The studio ID or name"
+    )
     assert input_schema["required"] == ["studio"]
 
 
@@ -912,9 +922,7 @@ def test_construct_target_url_with_version_prefix():
         ),
     ],
 )
-def test_construct_target_url_versionless_project_route_gets_api_version(
-    requested_route: str, expected_url: str
-) -> None:
+def test_construct_target_url_versionless_project_route_gets_api_version(requested_route: str, expected_url: str) -> None:
     from litellm.llms.vertex_ai.common_utils import construct_target_url
 
     target_url = construct_target_url(
@@ -1047,7 +1055,10 @@ def test_fix_enum_types():
     # 2. Non-string enums are removed
     assert "enum" not in input_schema["properties"]["maxLength"]
     assert "enum" not in input_schema["properties"]["enabled"]
-    assert "enum" not in input_schema["properties"]["nested"]["properties"]["innerNonStringEnum"]
+    assert (
+        "enum"
+        not in input_schema["properties"]["nested"]["properties"]["innerNonStringEnum"]
+    )
 
     # 3. anyOf with string type keeps enum, non-string removes it
     assert "enum" in input_schema["properties"]["anyOfField"]["anyOf"][0]
@@ -1251,7 +1262,9 @@ async def test_vertex_ai_token_counter_converts_messages_to_contents_for_gemini(
 
     token_counter = VertexAITokenCounter()
 
-    with patch("litellm.llms.vertex_ai.count_tokens.handler.VertexAITokenCounter.acount_tokens") as mock_acount_tokens:
+    with patch(
+        "litellm.llms.vertex_ai.count_tokens.handler.VertexAITokenCounter.acount_tokens"
+    ) as mock_acount_tokens:
         mock_acount_tokens.return_value = {
             "totalTokens": 42,
             "tokenizer_used": "gemini",
@@ -1293,7 +1306,9 @@ async def test_vertex_ai_token_counter_returns_none_when_api_omits_total_tokens(
 
     token_counter = VertexAITokenCounter()
 
-    with patch("litellm.llms.vertex_ai.count_tokens.handler.VertexAITokenCounter.acount_tokens") as mock_acount_tokens:
+    with patch(
+        "litellm.llms.vertex_ai.count_tokens.handler.VertexAITokenCounter.acount_tokens"
+    ) as mock_acount_tokens:
         mock_acount_tokens.return_value = {"tokenizer_used": "gemini"}
 
         result = await token_counter.count_tokens(
@@ -1336,7 +1351,9 @@ async def test_vertex_ai_partner_model_detection():
     # Test Minimax models
     assert VertexAIPartnerModels.is_vertex_partner_model("minimaxai/minimax-m2-maas")
     # Test Moonshot models
-    assert VertexAIPartnerModels.is_vertex_partner_model("moonshotai/kimi-k2-thinking-maas")
+    assert VertexAIPartnerModels.is_vertex_partner_model(
+        "moonshotai/kimi-k2-thinking-maas"
+    )
 
     # Test Gemini models (should NOT be detected as partner model)
     assert not VertexAIPartnerModels.is_vertex_partner_model("gemini-1.5-pro")
@@ -1367,7 +1384,9 @@ def test_vertex_ai_moonshot_uses_openai_handler():
         VertexAIPartnerModels,
     )
 
-    assert VertexAIPartnerModels.should_use_openai_handler("moonshotai/kimi-k2-thinking-maas")
+    assert VertexAIPartnerModels.should_use_openai_handler(
+        "moonshotai/kimi-k2-thinking-maas"
+    )
 
 
 def test_vertex_ai_zai_uses_openai_handler():
@@ -1402,7 +1421,9 @@ def test_vertex_ai_gemma_maas_is_partner_model():
         VertexAIPartnerModels,
     )
 
-    assert VertexAIPartnerModels.is_vertex_partner_model("google/gemma-4-26b-a4b-it-maas")
+    assert VertexAIPartnerModels.is_vertex_partner_model(
+        "google/gemma-4-26b-a4b-it-maas"
+    )
 
 
 def test_vertex_ai_gemma_maas_uses_openai_handler():
@@ -1413,7 +1434,9 @@ def test_vertex_ai_gemma_maas_uses_openai_handler():
         VertexAIPartnerModels,
     )
 
-    assert VertexAIPartnerModels.should_use_openai_handler("google/gemma-4-26b-a4b-it-maas")
+    assert VertexAIPartnerModels.should_use_openai_handler(
+        "google/gemma-4-26b-a4b-it-maas"
+    )
 
 
 def test_vertex_ai_gemma_maas_routes_to_partner_models():
@@ -1495,24 +1518,36 @@ def test_build_vertex_schema_empty_properties():
 
     # Verify the transformation removed empty properties
     # Navigate to the go_back schema
-    go_back_schema = result["properties"]["action"]["items"]["anyOf"][0]["properties"]["go_back"]
+    go_back_schema = result["properties"]["action"]["items"]["anyOf"][0]["properties"][
+        "go_back"
+    ]
 
     # Verify empty properties was removed
     assert "properties" not in go_back_schema, "Empty properties should be removed"
 
     # Verify type is kept as object (Gemini requires type: object even without properties)
-    assert go_back_schema.get("type") == "object", "Type should be kept as object when properties is empty"
+    assert (
+        go_back_schema.get("type") == "object"
+    ), "Type should be kept as object when properties is empty"
 
     # Verify required was also removed
-    assert "required" not in go_back_schema, "Required should be removed when properties is empty"
+    assert (
+        "required" not in go_back_schema
+    ), "Required should be removed when properties is empty"
 
     # Verify description is preserved
-    assert go_back_schema.get("description") == "Go back", "Description should be preserved"
+    assert (
+        go_back_schema.get("description") == "Go back"
+    ), "Description should be preserved"
 
     # Verify parent schema still has proper structure
     parent_schema = result["properties"]["action"]["items"]["anyOf"][0]
-    assert parent_schema["type"] == "object", "Parent schema should still have object type"
-    assert "go_back" in parent_schema["properties"], "go_back should still be in parent properties"
+    assert (
+        parent_schema["type"] == "object"
+    ), "Parent schema should still have object type"
+    assert (
+        "go_back" in parent_schema["properties"]
+    ), "go_back should still be in parent properties"
 
 
 def test_add_object_type_schema_with_no_properties_and_no_type():
@@ -1603,8 +1638,12 @@ def test_pop_vertex_request_labels_prefers_explicit_labels_then_metadata():
 
 def test_pop_vertex_request_labels_uses_litellm_metadata_when_metadata_absent():
     optional: dict = {}
-    litellm_params = {"litellm_metadata": {"requester_metadata": {"team": "from_litellm_meta"}}}
-    assert pop_vertex_request_labels(optional, litellm_params) == {"team": "from_litellm_meta"}
+    litellm_params = {
+        "litellm_metadata": {"requester_metadata": {"team": "from_litellm_meta"}}
+    }
+    assert pop_vertex_request_labels(optional, litellm_params) == {
+        "team": "from_litellm_meta"
+    }
 
 
 def test_vertex_text_embedding_request_includes_labels_from_metadata():
@@ -1614,7 +1653,9 @@ def test_vertex_text_embedding_request_includes_labels_from_metadata():
         input="hi",
         optional_params={},
         model="text-embedding-004",
-        litellm_params={"metadata": {"requester_metadata": {"project_id": "cost-center-1"}}},
+        litellm_params={
+            "metadata": {"requester_metadata": {"project_id": "cost-center-1"}}
+        },
     )
     assert req.get("labels") == {"project_id": "cost-center-1"}
 
@@ -1642,3 +1683,5 @@ def test_get_vertex_ai_lyria_model_info_is_none_for_non_lyria_speech_models(mode
     from litellm.llms.vertex_ai.common_utils import get_vertex_ai_lyria_model_info
 
     assert get_vertex_ai_lyria_model_info(model=model) is None
+
+

tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py

@@ -181,6 +181,7 @@ class TestVertexAILyriaTextToSpeechConfig:
 
         assert isinstance(config, VertexAILyriaTextToSpeechConfig)
 
+
     def test_vertex_chirp_does_not_select_lyria_config(self):
         config = ProviderConfigManager.get_provider_text_to_speech_config(
             model="chirp",
@@ -208,7 +209,9 @@ class TestVertexAILyriaTextToSpeechConfig:
         )
 
     def test_get_complete_url_encodes_injected_predict_path_segments(self, monkeypatch: pytest.MonkeyPatch) -> None:
-        injected: Final = "victim-project/locations/us-central1/publishers/google/models/other-model:predict?ignored="
+        injected: Final = (
+            "victim-project/locations/us-central1/publishers/google/models/other-model:predict?ignored="
+        )
         encoded: Final = (
             "victim-project%2Flocations%2Fus-central1%2Fpublishers%2Fgoogle"
             "%2Fmodels%2Fother-model%3Apredict%3Fignored%3D"

tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py

@@ -1,3 +1,4 @@
+
 import pytest
 
 from litellm.anthropic_beta_headers_manager import (
@@ -15,7 +16,9 @@ from litellm.llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation im
     ],
 )
 def test_vertex_ai_anthropic_thinking_param(model, expected_thinking):
-    supported_openai_params = VertexAIAnthropicConfig().get_supported_openai_params(model=model)
+    supported_openai_params = VertexAIAnthropicConfig().get_supported_openai_params(
+        model=model
+    )
 
     if expected_thinking:
         assert "thinking" in supported_openai_params
@@ -116,12 +119,14 @@ def test_vertex_ai_anthropic_structured_output_header_not_added():
         },
         "is_vertex_request": True,
     }
-    result_vertex = config.update_headers_with_optional_anthropic_beta(headers_vertex, optional_params_vertex)
-
-    assert "anthropic-beta" not in result_vertex, (
-        f"Vertex request should NOT have anthropic-beta header for structured output, got: {result_vertex.get('anthropic-beta')}"
+    result_vertex = config.update_headers_with_optional_anthropic_beta(
+        headers_vertex, optional_params_vertex
     )
 
+    assert (
+        "anthropic-beta" not in result_vertex
+    ), f"Vertex request should NOT have anthropic-beta header for structured output, got: {result_vertex.get('anthropic-beta')}"
+
     # Test case 2: Non-Vertex request with output_format SHOULD add beta header
     headers_non_vertex = {}
     optional_params_non_vertex = {
@@ -138,12 +143,12 @@ def test_vertex_ai_anthropic_structured_output_header_not_added():
         headers_non_vertex, optional_params_non_vertex
     )
 
-    assert "anthropic-beta" in result_non_vertex, (
-        "Non-Vertex request SHOULD have anthropic-beta header for structured output"
-    )
-    assert result_non_vertex["anthropic-beta"] == "structured-outputs-2025-11-13", (
-        f"Expected 'structured-outputs-2025-11-13', got: {result_non_vertex.get('anthropic-beta')}"
-    )
+    assert (
+        "anthropic-beta" in result_non_vertex
+    ), "Non-Vertex request SHOULD have anthropic-beta header for structured output"
+    assert (
+        result_non_vertex["anthropic-beta"] == "structured-outputs-2025-11-13"
+    ), f"Expected 'structured-outputs-2025-11-13', got: {result_non_vertex.get('anthropic-beta')}"
 
 
 def test_vertex_ai_claude_sonnet_4_5_structured_output_fix():
@@ -198,7 +203,9 @@ def test_vertex_ai_claude_sonnet_4_5_structured_output_fix():
 
     # Should have tools and tool_choice (tool-based approach)
     assert "tools" in result_params, "Tools should be present for structured output"
-    assert "tool_choice" in result_params, "Tool choice should be present for structured output"
+    assert (
+        "tool_choice" in result_params
+    ), "Tool choice should be present for structured output"
     assert "json_mode" in result_params, "JSON mode should be enabled"
 
     # Verify the tool is the response format tool
@@ -223,7 +230,9 @@ def test_vertex_ai_claude_sonnet_4_5_structured_output_fix():
     # Mock the parent transform_request to return data with output_format
     original_transform = config.__class__.__bases__[0].transform_request
 
-    def mock_transform_request(self, model, messages, optional_params, litellm_params, headers):
+    def mock_transform_request(
+        self, model, messages, optional_params, litellm_params, headers
+    ):
         # Return test data that includes output_format
         return test_data.copy()
 
@@ -245,7 +254,9 @@ def test_vertex_ai_claude_sonnet_4_5_structured_output_fix():
         # callers who explicitly requested them.
         assert "output_format" in final_data
         assert final_data["output_format"]["type"] == "json_schema"
-        assert "model" not in final_data, "model is still stripped (Vertex routes by URL)"
+        assert (
+            "model" not in final_data
+        ), "model is still stripped (Vertex routes by URL)"
         assert "tools" in final_data, "tools should still be present"
         assert "tool_choice" in final_data, "tool_choice should still be present"
 
@@ -281,7 +292,9 @@ def test_vertex_ai_anthropic_other_models_still_use_tools():
     )
 
     # Should still use tool-based approach
-    assert "tools" in result_params, "Claude 3 Sonnet should also use tool-based structured output"
+    assert (
+        "tools" in result_params
+    ), "Claude 3 Sonnet should also use tool-based structured output"
     assert "tool_choice" in result_params, "Tool choice should be present"
     assert "json_mode" in result_params, "JSON mode should be enabled"
 
@@ -409,18 +422,28 @@ def test_vertex_ai_partner_models_anthropic_remove_prompt_caching_scope_beta_hea
 
     # This beta header should be removed
     PROMPT_CACHING_BETA_HEADER = "prompt-caching-scope-2026-01-05"
-    headers = {"anthropic-beta": f"other-feature,{PROMPT_CACHING_BETA_HEADER},web-search-2025-03-05"}
+    headers = {
+        "anthropic-beta": f"other-feature,{PROMPT_CACHING_BETA_HEADER},web-search-2025-03-05"
+    }
 
     headers = update_headers_with_filtered_beta(headers, "vertex_ai")
 
     beta_header = headers.get("anthropic-beta")
-    assert PROMPT_CACHING_BETA_HEADER not in (beta_header or ""), f"{PROMPT_CACHING_BETA_HEADER} should be filtered out"
-    assert "other-feature" not in (beta_header or ""), "Other non-excluded beta headers should remain"
-    assert "web-search-2025-03-05" in (beta_header or ""), "Other non-excluded beta headers should remain"
+    assert PROMPT_CACHING_BETA_HEADER not in (
+        beta_header or ""
+    ), f"{PROMPT_CACHING_BETA_HEADER} should be filtered out"
+    assert "other-feature" not in (
+        beta_header or ""
+    ), "Other non-excluded beta headers should remain"
+    assert "web-search-2025-03-05" in (
+        beta_header or ""
+    ), "Other non-excluded beta headers should remain"
     # If prompt-caching was the only value, header should be removed completely
     headers2 = {"anthropic-beta": PROMPT_CACHING_BETA_HEADER}
     headers2 = update_headers_with_filtered_beta(headers2, "vertex_ai")
-    assert "anthropic-beta" not in headers2, "Header should be removed if no supported values remain"
+    assert (
+        "anthropic-beta" not in headers2
+    ), "Header should be removed if no supported values remain"
 
 
 def test_vertex_ai_anthropic_output_config_effort_only_forwarded():
@@ -566,7 +589,9 @@ def test_vertex_ai_anthropic_output_format_and_output_config_effort_preserved():
 
     original_transform = config.__class__.__bases__[0].transform_request
 
-    def mock_transform_request(self, model, messages, optional_params, litellm_params, headers):
+    def mock_transform_request(
+        self, model, messages, optional_params, litellm_params, headers
+    ):
         return test_data.copy()
 
     config.__class__.__bases__[0].transform_request = mock_transform_request

tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gemma/test_vertex_ai_gemma_global_endpoint.py

@@ -48,6 +48,37 @@ _GEMMA_MODEL_COST_ENTRY = {
 # ---------------------------------------------------------------------------
 
 
+@pytest.fixture(autouse=True)
+def _reset_litellm_http_client_cache():
+    """Ensure each test gets a fresh async HTTP client mock."""
+    from litellm import in_memory_llm_clients_cache
+
+    in_memory_llm_clients_cache.flush_cache()
+
+
+@pytest.fixture(autouse=True)
+def clean_vertex_env():
+    """Clear Google/Vertex AI environment variables before each test to prevent test isolation issues."""
+    saved_env = {}
+    env_vars_to_clear = [
+        "GOOGLE_APPLICATION_CREDENTIALS",
+        "GOOGLE_CLOUD_PROJECT",
+        "VERTEXAI_PROJECT",
+        "VERTEX_PROJECT",
+        "VERTEX_LOCATION",
+        "VERTEX_AI_PROJECT",
+    ]
+    for var in env_vars_to_clear:
+        if var in os.environ:
+            saved_env[var] = os.environ[var]
+            del os.environ[var]
+
+    yield
+
+    for var, value in saved_env.items():
+        os.environ[var] = value
+
+
 # ---------------------------------------------------------------------------
 # Unit tests: region and URL construction
 # ---------------------------------------------------------------------------
@@ -61,7 +92,11 @@ class TestVertexBaseGetVertexRegionGemma:
 
         with patch.dict(
             litellm.model_cost,
-            {"vertex_ai/google/gemma-4-26b-a4b-it-maas": {"supported_regions": ["global"]}},
+            {
+                "vertex_ai/google/gemma-4-26b-a4b-it-maas": {
+                    "supported_regions": ["global"]
+                }
+            },
             clear=False,
         ):
             result = vertex_base.get_vertex_region(
@@ -75,7 +110,11 @@ class TestVertexBaseGetVertexRegionGemma:
 
         with patch.dict(
             litellm.model_cost,
-            {"vertex_ai/google/gemma-4-26b-a4b-it-maas": {"supported_regions": ["global"]}},
+            {
+                "vertex_ai/google/gemma-4-26b-a4b-it-maas": {
+                    "supported_regions": ["global"]
+                }
+            },
             clear=False,
         ):
             result = vertex_base.get_vertex_region(
@@ -101,9 +140,9 @@ class TestCreateVertexURLGemma:
         which in turn generates the /endpoints/openapi URL shape.  If this mapping
         ever changes, the URL-shape tests below become misleading.
         """
-        assert VertexAIPartnerModels.should_use_openai_handler("google/gemma-4-26b-a4b-it-maas"), (
-            "Gemma MaaS must use the OpenAI-compatible handler (VertexPartnerProvider.llama path)"
-        )
+        assert VertexAIPartnerModels.should_use_openai_handler(
+            "google/gemma-4-26b-a4b-it-maas"
+        ), "Gemma MaaS must use the OpenAI-compatible handler (VertexPartnerProvider.llama path)"
 
     def test_global_location_url_format(self):
         # VertexPartnerProvider.llama is correct: Gemma MaaS reaches create_vertex_url
@@ -174,37 +213,6 @@ _MOCK_RESPONSE_JSON = {
 }
 
 
-@pytest.fixture(autouse=True)
-def _reset_litellm_http_client_cache():
-    """Ensure each test gets a fresh async HTTP client mock."""
-    from litellm import in_memory_llm_clients_cache
-
-    in_memory_llm_clients_cache.flush_cache()
-
-
-@pytest.fixture(autouse=True)
-def clean_vertex_env():
-    """Clear Google/Vertex AI environment variables before each test to prevent test isolation issues."""
-    saved_env = {}
-    env_vars_to_clear = [
-        "GOOGLE_APPLICATION_CREDENTIALS",
-        "GOOGLE_CLOUD_PROJECT",
-        "VERTEXAI_PROJECT",
-        "VERTEX_PROJECT",
-        "VERTEX_LOCATION",
-        "VERTEX_AI_PROJECT",
-    ]
-    for var in env_vars_to_clear:
-        if var in os.environ:
-            saved_env[var] = os.environ[var]
-            del os.environ[var]
-
-    yield
-
-    for var, value in saved_env.items():
-        os.environ[var] = value
-
-
 @pytest.mark.asyncio
 async def test_vertex_ai_gemma_global_endpoint_url():
     """
@@ -220,7 +228,9 @@ async def test_vertex_ai_gemma_global_endpoint_url():
     mock_vertexai.preview = MagicMock()
 
     with (
-        patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler") as mock_http_handler,
+        patch(
+            "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
+        ) as mock_http_handler,
         patch(
             "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
             return_value=("fake-token", "test-project"),
@@ -231,7 +241,11 @@ async def test_vertex_ai_gemma_global_endpoint_url():
         ),
         patch.dict(
             litellm.model_cost,
-            {"vertex_ai/google/gemma-4-26b-a4b-it-maas": {"supported_regions": ["global"]}},
+            {
+                "vertex_ai/google/gemma-4-26b-a4b-it-maas": {
+                    "supported_regions": ["global"]
+                }
+            },
             clear=False,
         ),
     ):
@@ -290,7 +304,9 @@ async def test_vertex_ai_gemma_function_calling_passthrough():
     mock_vertexai.preview = MagicMock()
 
     with (
-        patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler") as mock_http_handler,
+        patch(
+            "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
+        ) as mock_http_handler,
         patch(
             "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
             return_value=("fake-token", "test-project"),
@@ -361,7 +377,9 @@ async def test_vertex_ai_gemma_vision_passthrough():
     mock_vertexai.preview = MagicMock()
 
     with (
-        patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler") as mock_http_handler,
+        patch(
+            "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
+        ) as mock_http_handler,
         patch(
             "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
             return_value=("fake-token", "test-project"),

tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py

@@ -21,8 +21,14 @@ from litellm.types.router import GenericLiteLLMParams
 from litellm.types.videos.main import VideoObject
 
 VEO_31_LITE_VERTEX_MODEL = "vertex_ai/veo-3.1-lite-generate-001"
-ROOT_MODEL_COST_PATH = Path(__file__).parents[5] / "model_prices_and_context_window.json"
-BACKUP_MODEL_COST_PATH = Path(__file__).parents[5] / "litellm" / "model_prices_and_context_window_backup.json"
+ROOT_MODEL_COST_PATH = (
+    Path(__file__).parents[5] / "model_prices_and_context_window.json"
+)
+BACKUP_MODEL_COST_PATH = (
+    Path(__file__).parents[5]
+    / "litellm"
+    / "model_prices_and_context_window_backup.json"
+)
 ModelCostMap = Mapping[str, Mapping[str, object]]
 
 
@@ -76,7 +82,9 @@ class TestVertexAIVideoConfig:
             "vertex_location": "us-central1",
         }
 
-        url = self.config.get_complete_url(model="vertex_ai/veo-002", api_base=None, litellm_params=litellm_params)
+        url = self.config.get_complete_url(
+            model="vertex_ai/veo-002", api_base=None, litellm_params=litellm_params
+        )
 
         expected = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/veo-002"
         assert url == expected
@@ -109,7 +117,10 @@ class TestVertexAIVideoConfig:
         monkeypatch.setattr(litellm, "vertex_project", None)
 
         with pytest.raises(ValueError, match="vertex_project is required"):
-            self.config.get_complete_url(model="veo-002", api_base=None, litellm_params={})
+            self.config.get_complete_url(
+                model="veo-002", api_base=None, litellm_params={}
+            )
+
 
     def test_transform_video_create_request(self):
         """Test transformation of video creation request."""
@@ -250,7 +261,9 @@ class TestVertexAIVideoConfig:
         assert mapped["aspectRatio"] == "16:9"
         assert "resolution" not in mapped
 
-    def test_map_openai_size_does_not_infer_resolution_for_existing_veo_3(self, monkeypatch: pytest.MonkeyPatch):
+    def test_map_openai_size_does_not_infer_resolution_for_existing_veo_3(
+        self, monkeypatch: pytest.MonkeyPatch
+    ):
         model = "veo-3.1-generate-001"
         model_key = f"vertex_ai/{model}"
         model_cost = _load_model_cost_map(BACKUP_MODEL_COST_PATH)
@@ -423,7 +436,9 @@ class TestVertexAIVideoConfig:
                 "raiMediaFilteredCount": 0,
                 "videos": [
                     {
-                        "bytesBase64Encoded": base64.b64encode(b"fake_video_data").decode(),
+                        "bytesBase64Encoded": base64.b64encode(
+                            b"fake_video_data"
+                        ).decode(),
                         "mimeType": "video/mp4",
                     }
                 ],
@@ -489,7 +504,9 @@ class TestVertexAIVideoConfig:
             "done": True,
             "response": {
                 "@type": "type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse",
-                "videos": [{"bytesBase64Encoded": encoded_video, "mimeType": "video/mp4"}],
+                "videos": [
+                    {"bytesBase64Encoded": encoded_video, "mimeType": "video/mp4"}
+                ],
             },
         }
 
@@ -509,7 +526,9 @@ class TestVertexAIVideoConfig:
         }
 
         with pytest.raises(ValueError, match="Video generation is not complete yet"):
-            self.config.transform_video_content_response(raw_response=mock_response, logging_obj=self.mock_logging_obj)
+            self.config.transform_video_content_response(
+                raw_response=mock_response, logging_obj=self.mock_logging_obj
+            )
 
     def test_transform_video_content_response_missing_video_data(self):
         """Test that missing video data raises error."""
@@ -521,7 +540,9 @@ class TestVertexAIVideoConfig:
         }
 
         with pytest.raises(ValueError, match="No video data found"):
-            self.config.transform_video_content_response(raw_response=mock_response, logging_obj=self.mock_logging_obj)
+            self.config.transform_video_content_response(
+                raw_response=mock_response, logging_obj=self.mock_logging_obj
+            )
 
     def test_get_video_edit_prefetch_params(self):
         """Test that prefetch params returns the fetchPredictOperation URL and body."""
@@ -547,7 +568,9 @@ class TestVertexAIVideoConfig:
 
         prefetched = {
             "done": True,
-            "response": {"videos": [{"bytesBase64Encoded": fake_bytes, "mimeType": "video/mp4"}]},
+            "response": {
+                "videos": [{"bytesBase64Encoded": fake_bytes, "mimeType": "video/mp4"}]
+            },
         }
 
         url, data, files = self.config.transform_video_edit_request(
@@ -574,7 +597,9 @@ class TestVertexAIVideoConfig:
 
         prefetched = {
             "done": True,
-            "response": {"videos": [{"gcsUri": "gs://bucket/video.mp4", "mimeType": "video/mp4"}]},
+            "response": {
+                "videos": [{"gcsUri": "gs://bucket/video.mp4", "mimeType": "video/mp4"}]
+            },
         }
 
         _, data, _ = self.config.transform_video_edit_request(
@@ -700,7 +725,9 @@ class TestVertexAIVideoConfig:
 
     def test_get_error_class(self):
         """Test error class generation."""
-        error = self.config.get_error_class(error_message="Test error", status_code=500, headers={})
+        error = self.config.get_error_class(
+            error_message="Test error", status_code=500, headers={}
+        )
 
         # Should return VertexAIError
         from litellm.llms.vertex_ai.common_utils import VertexAIError
@@ -912,7 +939,10 @@ class TestImageAndParametersPassthrough:
         # instances contains prompt + image
         assert len(data["instances"]) == 1
         instance = data["instances"][0]
-        assert instance["prompt"] == "Cinematic drone shot moving forward along the beach boardwalk"
+        assert (
+            instance["prompt"]
+            == "Cinematic drone shot moving forward along the beach boardwalk"
+        )
         assert instance["image"] == image
 
         # parameters block is correct and not double-nested

tests/test_litellm/llms/wandb/test_wandb_chat_transformation.py

@@ -75,6 +75,7 @@ def wandb_request_mock(respx_mock: respx.MockRouter) -> respx.Route:
 class TestWandbConfig:
     """Test class for WandB Inference functionality"""
 
+
     def test_default_api_base(self):
         """Test that default API base is used when none is provided"""
         config = WandbConfig()
@@ -107,7 +108,9 @@ class TestWandbConfig:
         This test mocks the actual HTTP request to test the integration properly.
         """
 
-        litellm.disable_aiohttp_transport = True  # since this uses respx, we need to set use_aiohttp_transport to False
+        litellm.disable_aiohttp_transport = (
+            True  # since this uses respx, we need to set use_aiohttp_transport to False
+        )
 
         # Set up environment variables for the test
         api_key = "fake-wandb-key"
@@ -144,7 +147,9 @@ class TestWandbConfig:
         # Make the actual API call through LiteLLM
         response = completion(
             model=model,
-            messages=[{"role": "user", "content": "write code for saying hey from LiteLLM"}],
+            messages=[
+                {"role": "user", "content": "write code for saying hey from LiteLLM"}
+            ],
             api_key=api_key,
             api_base=api_base,
         )
@@ -223,6 +228,7 @@ class TestWandbConfig:
         assert request_body["max_tokens"] == 64
         assert "max_completion_tokens" not in request_body
 
+
     @pytest.mark.respx()
     def test_wandb_completion_keeps_reasoning_effort_for_an_unregistered_model(
         self, wandb_test_config, wandb_request_mock: respx.Route

tests/test_litellm/llms/xai/test_xai_model_registry.py

@@ -7,7 +7,6 @@ from __future__ import annotations
 import json
 from pathlib import Path
 
-
 REPO_ROOT = Path(__file__).parents[4]
 PRICES_PATH = REPO_ROOT / "model_prices_and_context_window.json"
 BACKUP_PRICES_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json"

tests/test_litellm/proxy/auth/test_model_checks.py

@@ -11,7 +11,9 @@ def test_get_team_models_for_all_models_and_team_only_models():
     model_access_groups = {}
     include_model_access_groups = False
 
-    result = get_team_models(team_models, proxy_model_list, model_access_groups, include_model_access_groups)
+    result = get_team_models(
+        team_models, proxy_model_list, model_access_groups, include_model_access_groups
+    )
     combined_models = team_models + proxy_model_list
     assert set(result) == set(combined_models)
 
@@ -244,7 +246,9 @@ def test_get_key_models_does_not_mutate_input():
         ),
     ],
 )
-def test_get_complete_model_list_order(key_models, team_models, proxy_model_list, model_list, expected):
+def test_get_complete_model_list_order(
+    key_models, team_models, proxy_model_list, model_list, expected
+):
     """
     Test that get_complete_model_list preserves order
     """
@@ -397,7 +401,9 @@ def test_wildcard_credential_hydration_preserves_deployment_params(
         captured_params["api_key"] = litellm_params.api_key
         captured_params["api_version"] = litellm_params.api_version
         captured_params["credential_name"] = litellm_params.litellm_credential_name
-        captured_params["has_unexpected_field"] = hasattr(litellm_params, "unexpected_field")
+        captured_params["has_unexpected_field"] = hasattr(
+            litellm_params, "unexpected_field"
+        )
         return ["gpt-4o"]
 
     monkeypatch.setattr(model_checks, "get_provider_models", fake_get_provider_models)
@@ -442,7 +448,9 @@ def test_wildcard_custom_prefix_does_not_stack_provider_prefix(monkeypatch):
 
     result = get_known_models_from_wildcard(
         wildcard_model="ollama_server1/*",
-        litellm_params=LiteLLM_Params(model="ollama_chat/*", custom_llm_provider="ollama_chat"),
+        litellm_params=LiteLLM_Params(
+            model="ollama_chat/*", custom_llm_provider="ollama_chat"
+        ),
     )
 
     assert result == ["ollama_server1/gemma3:1b", "ollama_server1/llama3:8b"]
@@ -469,7 +477,9 @@ def test_wildcard_custom_prefix_keeps_org_segment_for_non_provider_first_segment
 
     result = get_known_models_from_wildcard(
         wildcard_model="my_hf/*",
-        litellm_params=LiteLLM_Params(model="huggingface/*", custom_llm_provider="huggingface"),
+        litellm_params=LiteLLM_Params(
+            model="huggingface/*", custom_llm_provider="huggingface"
+        ),
     )
 
     assert result == ["my_hf/meta-llama/Llama-3-8B"]
@@ -831,7 +841,9 @@ def test_add_known_models_refreshes_models_by_provider_for_wildcard_expansion():
     assert fake_model not in litellm.models_by_provider["vertex_ai"]
     try:
         litellm.add_known_models(
-            model_cost_map={fake_model: {"litellm_provider": "vertex_ai-language-models", "mode": "chat"}}
+            model_cost_map={
+                fake_model: {"litellm_provider": "vertex_ai-language-models", "mode": "chat"}
+            }
         )
         assert fake_model in litellm.models_by_provider["vertex_ai"]
         assert litellm.models_by_provider is captured_reference

tests/test_litellm/proxy/spend_tracking/test_savings.py

@@ -28,11 +28,7 @@ def test_baseline_preserves_anthropic_pricing_fields(modifier: dict[str, str], c
     assert usage.prompt_tokens_details.cached_tokens == 0
     selected_cost: Final = 0.013
     assert compute_autorouter_savings(
-        "claude-opus-5",
-        "claude-sonnet-5",
-        "anthropic",
-        usage,
-        conversation_continuing=continuing,
+        "claude-opus-5", "claude-sonnet-5", "anthropic", usage, conversation_continuing=continuing,
         cost_breakdown={"input_cost": 0.01, "output_cost": 0.003},
     ) == pytest.approx(sum(anthropic_cost_per_token("claude-opus-5", expected)) - selected_cost)
 
@@ -40,17 +36,11 @@ def test_baseline_preserves_anthropic_pricing_fields(modifier: dict[str, str], c
 def test_anthropic_baseline_keeps_negotiated_prices_with_provider_multiplier() -> None:
     info: Final = {
         **litellm.get_model_info("claude-opus-5", "anthropic"),
-        "input_cost_per_token": 1e-6,
-        "output_cost_per_token": 2e-6,
-        "cache_read_input_token_cost": 3e-7,
+        "input_cost_per_token": 1e-6, "output_cost_per_token": 2e-6, "cache_read_input_token_cost": 3e-7,
     }
     usage: Final = _usage(1000, 1000, 0, 100).model_copy(update={"speed": "fast"})
     assert compute_autorouter_savings(
-        "claude-opus-5",
-        "claude-sonnet-5",
-        "anthropic",
-        usage,
-        baseline_info=info,
+        "claude-opus-5", "claude-sonnet-5", "anthropic", usage, baseline_info=info,
         cost_breakdown={"input_cost": 0.01, "output_cost": 0.003},
     ) == pytest.approx(0.0015 * 2 - 0.013)
 

tests/test_litellm/proxy/test_proxy_utils.py

@@ -2226,9 +2226,7 @@ async def test_post_call_failure_hook_redacts_traceback_before_callbacks(monkeyp
     with patch.object(proxy_logging_obj, "update_request_status", new=AsyncMock()):
         await proxy_logging_obj.post_call_failure_hook(
             request_data={"metadata": {}},
-            original_exception=HTTPException(
-                status_code=400, detail="Upstream passthrough request failed with status 400"
-            ),
+            original_exception=HTTPException(status_code=400, detail="Upstream passthrough request failed with status 400"),
             user_api_key_dict=UserAPIKeyAuth(),
             traceback_str=upstream_traceback,
         )
@@ -2292,13 +2290,9 @@ def test_mcp_conversion_preserves_request_policy_and_isolates_guardrail_data(buc
     parent = {
         "model": "parent-model",
         bucket: {
-            "guardrails": ["policy-rule"],
-            "guardrail_config": {"language": "en"},
-            "applied_policies": ["parent-policy"],
-            "policy_sources": {"parent-policy": "model"},
-            "_guardrail_pipelines": [],
-            "_pipeline_managed_guardrails": ["pipeline-rule"],
-            "tags": ["review"],
+            "guardrails": ["policy-rule"], "guardrail_config": {"language": "en"},
+            "applied_policies": ["parent-policy"], "policy_sources": {"parent-policy": "model"},
+            "_guardrail_pipelines": [], "_pipeline_managed_guardrails": ["pipeline-rule"], "tags": ["review"],
         },
         "guardrails": [{"request-rule": {"extra_body": {"threshold": 0.9}}}],
         "guardrail_config": {"entities": ["EMAIL_ADDRESS"]},
@@ -2328,26 +2322,13 @@ def test_mcp_conversion_honors_only_authenticated_global_guardrail_opt_outs(opt_
     from litellm.responses.mcp.request_context import MCPRequestContext
 
     auth = UserAPIKeyAuth(metadata={"opted_out_global_guardrails": ["global-rule"] if opt_out else []})
-    context = MCPRequestContext.resolve(
-        kwargs={
-            "metadata": {
-                "user_api_key_auth": auth,
-                "disable_global_guardrails": True,
-                "user_api_key_metadata": {"disable_global_guardrails": True},
-            }
-        },
-        tools=None,
-    )
+    context = MCPRequestContext.resolve(kwargs={"metadata": {
+        "user_api_key_auth": auth, "disable_global_guardrails": True,
+        "user_api_key_metadata": {"disable_global_guardrails": True},
+    }}, tools=None)
     proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
-    kwargs = {
-        "name": "execute",
-        "arguments": {},
-        "user_api_key_auth": auth,
-        "guardrail_context": context.guardrail_context,
-    }
-    synthetic = proxy_logging._convert_mcp_to_llm_format(
-        proxy_logging._create_mcp_request_object_from_kwargs(kwargs), kwargs
-    )
+    kwargs = {"name": "execute", "arguments": {}, "user_api_key_auth": auth, "guardrail_context": context.guardrail_context}
+    synthetic = proxy_logging._convert_mcp_to_llm_format(proxy_logging._create_mcp_request_object_from_kwargs(kwargs), kwargs)
     guardrail = CustomGuardrail(guardrail_name="global-rule", event_hook="pre_mcp_call", default_on=True)
     assert guardrail.should_run_guardrail(synthetic, GuardrailEventHooks.pre_mcp_call) is (not opt_out)
     synthetic["metadata"]["user_api_key_metadata"]["opted_out_global_guardrails"].append("unrelated")
@@ -2361,25 +2342,18 @@ def test_mcp_auth_policy_uses_original_request_model(monkeypatch, model, expecte
     from litellm.types.proxy.policy_engine import Policy, PolicyCondition, PolicyGuardrails
 
     registry = policy_registry.PolicyRegistry()
-    registry._policies = {
-        "model-policy": Policy(
-            condition=PolicyCondition(model="parent-model"), guardrails=PolicyGuardrails(add=["model-rule"])
-        )
-    }
+    registry._policies = {"model-policy": Policy(
+        condition=PolicyCondition(model="parent-model"), guardrails=PolicyGuardrails(add=["model-rule"])
+    )}
     registry._initialized = True
     monkeypatch.setattr(policy_registry, "_policy_registry", registry)
     monkeypatch.setattr("litellm.proxy.proxy_server.premium_user", True)
     proxy_logging = ProxyLogging(user_api_key_cache=DualCache())
     kwargs = {
-        "name": "execute",
-        "arguments": {},
+        "name": "execute", "arguments": {},
         "user_api_key_auth": UserAPIKeyAuth(metadata={"policies": ["model-policy"]}),
-        "guardrail_context": MCPRequestContext.resolve_guardrail_context(
-            {"model": model, "guardrails": ["request-rule"]}
-        ),
+        "guardrail_context": MCPRequestContext.resolve_guardrail_context({"model": model, "guardrails": ["request-rule"]}),
     }
-    synthetic = proxy_logging._convert_mcp_to_llm_format(
-        proxy_logging._create_mcp_request_object_from_kwargs(kwargs), kwargs
-    )
+    synthetic = proxy_logging._convert_mcp_to_llm_format(proxy_logging._create_mcp_request_object_from_kwargs(kwargs), kwargs)
     assert ("model-rule" in synthetic["metadata"]["guardrails"]) is expected
     assert "request-rule" in synthetic["metadata"]["guardrails"]

tests/test_litellm/router_utils/test_reasoning_effort_capability.py

@@ -325,6 +325,8 @@ KIMI_K3_PERPLEXITY_KEY = "perplexity/perplexity/kimi-k3"
 
 
 class TestKimiK3AdvertisesItsDocumentedLevels:
+
+
     @pytest.mark.parametrize("model, provider", [("kimi-k3", "moonshot"), ("kimi-k3", "fireworks_ai")])
     def test_the_declaration_survives_model_info_hydration(self, local_model_cost_map, model, provider):
         """The hydration line is the load-bearing seam: without it the key the map carries never

tests/test_litellm/test_claude_fable_5_config.py

@@ -92,3 +92,5 @@ def test_adaptive_thinking_detected_for_fable_5_1(local_model_cost_map, model):
     from litellm.llms.anthropic.common_utils import AnthropicModelInfo
 
     assert AnthropicModelInfo._is_adaptive_thinking_model(model, "anthropic") is True
+
+

tests/test_litellm/test_claude_opus_4_6_config.py

@@ -2,6 +2,7 @@
 Validate Claude Opus 4.6 model configuration entries.
 """
 
+
 import litellm
 
 

tests/test_litellm/test_claude_opus_4_8_config.py

@@ -21,3 +21,5 @@ REPO_ROOT = os.path.join(os.path.dirname(__file__), "../..")
 
 def test_opus_4_8_registered_for_bedrock_converse():
     assert "anthropic.claude-opus-4-8" in BEDROCK_CONVERSE_MODELS
+
+

tests/test_litellm/test_claude_opus_5_config.py

@@ -56,3 +56,5 @@ def test_opus_5_bedrock_rejects_strict_tools(model_name, local_model_cost_map):
 
 def test_opus_5_registered_for_bedrock_converse():
     assert "anthropic.claude-opus-5" in BEDROCK_CONVERSE_MODELS
+
+

tests/test_litellm/test_claude_sonnet_5_config.py

@@ -33,3 +33,5 @@ ALL_SONNET_5_VARIANTS = (
 
 def test_sonnet_5_registered_for_bedrock_converse():
     assert "anthropic.claude-sonnet-5" in BEDROCK_CONVERSE_MODELS
+
+

tests/test_litellm/test_dashscope_image_generation.py

@@ -70,7 +70,9 @@ class TestDashScopeImageGenerationConfig:
             "https://dashscope-intl.aliyuncs.com/compatible-mode/v1/",
         ],
     )
-    def test_get_complete_url_ignores_chat_compatible_mode_base(self, chat_api_base: str):
+    def test_get_complete_url_ignores_chat_compatible_mode_base(
+        self, chat_api_base: str
+    ):
         url = self.cfg.get_complete_url(chat_api_base, None, "qwen-image-3.0", {}, {})
         assert url == DEFAULT_API_BASE
 
@@ -131,7 +133,9 @@ class TestDashScopeImageGenerationConfig:
             headers={},
         )
         assert req["model"] == model
-        assert req["input"]["messages"][0]["content"][0]["text"] == ("a poster with small multilingual text")
+        assert req["input"]["messages"][0]["content"][0]["text"] == (
+            "a poster with small multilingual text"
+        )
         assert req["parameters"]["size"] == "2048*2048"
         assert req["parameters"]["n"] == 6
 
@@ -396,7 +400,11 @@ def test_litellm_image_generation_dashscope_end_to_end(model: str):
                     "finish_reason": "stop",
                     "message": {
                         "role": "assistant",
-                        "content": [{"image": "https://dashscope-result.oss.aliyuncs.com/test.png"}],
+                        "content": [
+                            {
+                                "image": "https://dashscope-result.oss.aliyuncs.com/test.png"
+                            }
+                        ],
                     },
                 }
             ]
@@ -410,7 +418,9 @@ def test_litellm_image_generation_dashscope_end_to_end(model: str):
         },
     }
 
-    with patch("litellm.llms.custom_httpx.llm_http_handler.HTTPHandler.post") as mock_post:
+    with patch(
+        "litellm.llms.custom_httpx.llm_http_handler.HTTPHandler.post"
+    ) as mock_post:
         mock_http_response = MagicMock()
         mock_http_response.json.return_value = mock_response_body
         mock_http_response.status_code = 200
@@ -427,11 +437,15 @@ def test_litellm_image_generation_dashscope_end_to_end(model: str):
         assert response is not None
         assert response.data is not None
         assert len(response.data) == 1
-        assert response.data[0].url == "https://dashscope-result.oss.aliyuncs.com/test.png"
+        assert (
+            response.data[0].url == "https://dashscope-result.oss.aliyuncs.com/test.png"
+        )
 
         # Verify the HTTP call was made to the DashScope endpoint
         call_args = mock_post.call_args
-        called_url = call_args[0][0] if call_args[0] else call_args.kwargs.get("url", "")
+        called_url = (
+            call_args[0][0] if call_args[0] else call_args.kwargs.get("url", "")
+        )
         assert called_url == DEFAULT_API_BASE
 
         # Verify request body contains DashScope format

tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py

@@ -3,7 +3,6 @@ from pathlib import Path
 
 import pytest
 
-
 REPO_ROOT = Path(__file__).parents[2]
 MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json"
 BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json"

tests/test_litellm/test_utils.py

@@ -1525,6 +1525,7 @@ class TestProxyFunctionCalling:
         # For now, we expect False (current behavior), but document the limitation
         assert proxy_result is False, f"Current limitation: {proxy_model_with_hints} returns False without inference"
 
+
     def test_litellm_utils_supports_function_calling_import(self):
         """Test that supports_function_calling can be imported from litellm.utils."""
         try:
@@ -1544,6 +1545,7 @@ class TestProxyFunctionCalling:
         except Exception as e:
             pytest.fail(f"Failed to access litellm.supports_function_calling: {e}")
 
+
     def test_edge_cases_and_malformed_proxy_models(self):
         """Test edge cases and malformed proxy model names."""
         test_cases = [
@@ -5656,3 +5658,5 @@ def test_get_model_info_gemini(monkeypatch):
         ):
             assert info.get("tpm") is not None, f"{model} does not have tpm"
             assert info.get("rpm") is not None, f"{model} does not have rpm"
+
+

tests/test_litellm/test_vertex_ai_xai_grok_prompt_caching_metadata.py

@@ -22,3 +22,5 @@ def test_grok_models_with_cache_read_price_advertise_prompt_caching() -> None:
     assert missing_flag == (), (
         f"grok models with cache_read_input_token_cost fail supports_prompt_caching: {missing_flag}"
     )
+
+