diff --git a/.github/workflows/test-unit.yml b/.github/workflows/test-unit.yml
index 57ffe28a4b5..a32b5ebb2a8 100644
--- a/.github/workflows/test-unit.yml
+++ b/.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
diff --git a/tests/litellm_utils_tests/test_utils.py b/tests/litellm_utils_tests/test_utils.py
index 72713a36831..e8b3862756f 100644
--- a/tests/litellm_utils_tests/test_utils.py
+++ b/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": {}}}
diff --git a/tests/llm_translation/test_azure_o_series.py b/tests/llm_translation/test_azure_o_series.py
index b8a53fefb5c..67b1a09c7ab 100644
--- a/tests/llm_translation/test_azure_o_series.py
+++ b/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,
diff --git a/tests/llm_translation/test_lambda_ai.py b/tests/llm_translation/test_lambda_ai.py
index 78843fac052..e6f8b13d4ba 100644
--- a/tests/llm_translation/test_lambda_ai.py
+++ b/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
+
+
diff --git a/tests/llm_translation/test_perplexity_reasoning.py b/tests/llm_translation/test_perplexity_reasoning.py
index 92d6a5d2ab3..0fdfdd79321 100644
--- a/tests/llm_translation/test_perplexity_reasoning.py
+++ b/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
diff --git a/tests/local_testing/test_completion_cost.py b/tests/local_testing/test_completion_cost.py
index 0e04569bbdf..f40818b9bf1 100644
--- a/tests/local_testing/test_completion_cost.py
+++ b/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",
diff --git a/tests/local_testing/test_get_model_info.py b/tests/local_testing/test_get_model_info.py
index 5c640aa22a6..37f4ece611d 100644
--- a/tests/local_testing/test_get_model_info.py
+++ b/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
+    )
diff --git a/tests/local_testing/test_register_model.py b/tests/local_testing/test_register_model.py
index d78f2ac7811..5f334a27e35 100644
--- a/tests/local_testing/test_register_model.py
+++ b/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
diff --git a/tests/test_litellm/integrations/test_anthropic_cache_control_hook.py b/tests/test_litellm/integrations/test_anthropic_cache_control_hook.py
index 17b48063cce..7eecbb730dd 100644
--- a/tests/test_litellm/integrations/test_anthropic_cache_control_hook.py
+++ b/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)
diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_guardrail_cost.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_guardrail_cost.py
index 0c2bb9ada71..aa2fc0b9a45 100644
--- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_guardrail_cost.py
+++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_guardrail_cost.py
@@ -1,3 +1,4 @@
+
 import pytest
 
 import litellm
diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking.py
index 433117edb05..6b118c97082 100644
--- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking.py
+++ b/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
+
+
diff --git a/tests/test_litellm/litellm_core_utils/prompt_templates/test_bedrock_converse_strict_tools_opus_47_48.py b/tests/test_litellm/litellm_core_utils/prompt_templates/test_bedrock_converse_strict_tools_opus_47_48.py
index 94e8b4bb7b0..83ee3437429 100644
--- a/tests/test_litellm/litellm_core_utils/prompt_templates/test_bedrock_converse_strict_tools_opus_47_48.py
+++ b/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"]
+
+
diff --git a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
index 270df703dee..e963e40a51c 100644
--- a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
+++ b/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]
diff --git a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py
index e17216b7b34..21cba74fba5 100644
--- a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py
+++ b/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
+
+
diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
index 28ba46a7e75..63d4571fe8d 100644
--- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
+++ b/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":
diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
index 7a70a146667..c2f0cfcc32e 100644
--- a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
+++ b/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",
diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
index a4b05da7023..89cc1a3fb76 100644
--- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
+++ b/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()
 
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/test_reasoning_effort_fields.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/test_reasoning_effort_fields.py
index 03cbc98dcb9..1c05f0adcf7 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/test_reasoning_effort_fields.py
+++ b/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):
diff --git a/tests/test_litellm/llms/anthropic/test_anthropic_common_utils.py b/tests/test_litellm/llms/anthropic/test_anthropic_common_utils.py
index 3c4bf91fc97..133d6e502f4 100644
--- a/tests/test_litellm/llms/anthropic/test_anthropic_common_utils.py
+++ b/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",
         [
diff --git a/tests/test_litellm/llms/azure/test_azure_speech_audio_transcription.py b/tests/test_litellm/llms/azure/test_azure_speech_audio_transcription.py
index f929c97ba39..b447645bae8 100644
--- a/tests/test_litellm/llms/azure/test_azure_speech_audio_transcription.py
+++ b/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"
+
+
diff --git a/tests/test_litellm/llms/azure_ai/chat/test_azure_ai_transformation.py b/tests/test_litellm/llms/azure_ai/chat/test_azure_ai_transformation.py
index f128954a338..8a832e176a6 100644
--- a/tests/test_litellm/llms/azure_ai/chat/test_azure_ai_transformation.py
+++ b/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": [
                 {
diff --git a/tests/test_litellm/llms/azure_ai/claude/test_azure_anthropic_messages_transformation.py b/tests/test_litellm/llms/azure_ai/claude/test_azure_anthropic_messages_transformation.py
index 87b9fb8b307..9753605888e 100644
--- a/tests/test_litellm/llms/azure_ai/claude/test_azure_anthropic_messages_transformation.py
+++ b/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"]
diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
index d8c3a458082..70cb8bd1e66 100644
--- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
+++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
@@ -28,11 +28,16 @@ def test_transform_usage():
     openai_usage = config.transform_usage(usage)
     assert (
         openai_usage.prompt_tokens
-        == usage["inputTokens"] + usage["cacheReadInputTokens"] + usage["cacheWriteInputTokens"]
+        == usage["inputTokens"]
+        + usage["cacheReadInputTokens"]
+        + usage["cacheWriteInputTokens"]
     )
     assert openai_usage.completion_tokens == usage["outputTokens"]
     assert openai_usage.total_tokens == usage["totalTokens"]
-    assert openai_usage.prompt_tokens_details.cached_tokens == usage["cacheReadInputTokens"]
+    assert (
+        openai_usage.prompt_tokens_details.cached_tokens
+        == usage["cacheReadInputTokens"]
+    )
     assert openai_usage._cache_creation_input_tokens == usage["cacheWriteInputTokens"]
     assert openai_usage._cache_read_input_tokens == usage["cacheReadInputTokens"]
     # completion_tokens_details should always be populated
@@ -80,7 +85,10 @@ def test_transform_usage_with_mismatched_cache_details_falls_back():
     )
     config = AmazonConverseConfig()
     openai_usage = config.transform_usage(usage)
-    assert getattr(openai_usage.prompt_tokens_details, "cache_creation_token_details", None) is None
+    assert (
+        getattr(openai_usage.prompt_tokens_details, "cache_creation_token_details", None)
+        is None
+    )
 
 
 def test_transform_usage_without_cache_details_stays_none():
@@ -96,7 +104,10 @@ def test_transform_usage_without_cache_details_stays_none():
     )
     config = AmazonConverseConfig()
     openai_usage = config.transform_usage(usage)
-    assert getattr(openai_usage.prompt_tokens_details, "cache_creation_token_details", None) is None
+    assert (
+        getattr(openai_usage.prompt_tokens_details, "cache_creation_token_details", None)
+        is None
+    )
 
 
 def test_bedrock_converse_1h_cache_write_billed_at_1h_rate(monkeypatch):
@@ -328,10 +339,14 @@ def test_apply_tool_call_transformation_if_needed():
         role="user",
         content=json.dumps(tool_response),
     )
-    transformed_message, _ = config.apply_tool_call_transformation_if_needed(message, tool_calls)
+    transformed_message, _ = config.apply_tool_call_transformation_if_needed(
+        message, tool_calls
+    )
     assert len(transformed_message.tool_calls) == 1
     assert transformed_message.tool_calls[0].function.name == "test_function"
-    assert transformed_message.tool_calls[0].function.arguments == json.dumps(tool_response["parameters"])
+    assert transformed_message.tool_calls[0].function.arguments == json.dumps(
+        tool_response["parameters"]
+    )
 
 
 def test_transform_tool_call_with_cache_control():
@@ -380,7 +395,12 @@ def test_transform_tool_call_with_cache_control():
     print(function_out_msg)
     assert function_out_msg["toolSpec"]["name"] == "get_location"
     assert function_out_msg["toolSpec"]["description"] == "Get the user's location"
-    assert function_out_msg["toolSpec"]["inputSchema"]["json"]["properties"]["location"]["type"] == "string"
+    assert (
+        function_out_msg["toolSpec"]["inputSchema"]["json"]["properties"]["location"][
+            "type"
+        ]
+        == "string"
+    )
 
     transformed_cache_msg = result["toolConfig"]["tools"][1]
     assert "cachePoint" in transformed_cache_msg
@@ -515,7 +535,9 @@ def test_reasoning_effort_none_omits_thinking_for_anthropic_converse(model):
         ("bedrock/converse/us.anthropic.claude-sonnet-4-6", "minimal", "low"),
     ],
 )
-def test_reasoning_effort_sets_output_config_for_adaptive_models_converse(model, effort, expected_effort):
+def test_reasoning_effort_sets_output_config_for_adaptive_models_converse(
+    model, effort, expected_effort
+):
     """Adaptive Claude 4.6 / 4.7 on Bedrock Converse routes the tier via ``output_config.effort``."""
     config = AmazonConverseConfig()
 
@@ -743,7 +765,9 @@ def test_output_config_format_translated_to_native_output_config_converse():
     assert additional.get("output_config") == {"effort": "xhigh"}
     assert "format" not in additional["output_config"]
     assert result["outputConfig"]["textFormat"]["type"] == "json_schema"
-    parsed_schema = json.loads(result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["schema"])
+    parsed_schema = json.loads(
+        result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["schema"]
+    )
     assert parsed_schema == {**schema, "additionalProperties": False}
 
 
@@ -779,7 +803,10 @@ def test_output_config_format_dropped_on_unsupported_converse_model_warns(caplog
             )
 
     assert "outputConfig" not in result
-    assert any("dropping `output_config.format`" in record.getMessage() for record in caplog.records)
+    assert any(
+        "dropping `output_config.format`" in record.getMessage()
+        for record in caplog.records
+    )
 
 
 def test_output_config_normalized_marker_does_not_leak_into_optional_params():
@@ -815,7 +842,9 @@ def test_output_config_normalized_marker_does_not_leak_into_optional_params():
         ("bedrock/converse/us.anthropic.claude-opus-4-7", "xhigh"),
     ],
 )
-def test_output_config_effort_normalized_for_bedrock_converse_opus(model, expected_effort):
+def test_output_config_effort_normalized_for_bedrock_converse_opus(
+    model, expected_effort
+):
     """Bedrock Converse accepts ``xhigh`` and forwards the provider-safe effort."""
     config = AmazonConverseConfig()
 
@@ -1088,13 +1117,17 @@ def test_get_supported_openai_params_bedrock_converse():
     for model in litellm.BEDROCK_CONVERSE_MODELS:
         print(f"Testing model: {model}")
         config = AmazonConverseConfig()
-        supported_params_without_prefix = config.get_supported_openai_params(model=model)
-
-        supported_params_with_prefix = config.get_supported_openai_params(model=f"bedrock/converse/{model}")
-
-        assert set(supported_params_without_prefix) == set(supported_params_with_prefix), (
-            f"Supported params mismatch for model: {model}. Without prefix: {supported_params_without_prefix}, With prefix: {supported_params_with_prefix}"
+        supported_params_without_prefix = config.get_supported_openai_params(
+            model=model
         )
+
+        supported_params_with_prefix = config.get_supported_openai_params(
+            model=f"bedrock/converse/{model}"
+        )
+
+        assert set(supported_params_without_prefix) == set(
+            supported_params_with_prefix
+        ), f"Supported params mismatch for model: {model}. Without prefix: {supported_params_without_prefix}, With prefix: {supported_params_with_prefix}"
         print(f"✅ Passed for model: {model}")
 
 
@@ -1586,7 +1619,9 @@ def test_transform_response_with_structured_response_calling_tool():
         "output": {
             "message": {
                 "content": [
-                    {"text": "I'll check the current weather in San Francisco for you."},
+                    {
+                        "text": "I'll check the current weather in San Francisco for you."
+                    },
                     {
                         "toolUse": {
                             "input": {
@@ -2096,7 +2131,9 @@ def test_transform_request_with_function_tool():
         }
     ]
 
-    messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}]
+    messages = [
+        {"role": "user", "content": "What's the weather like in San Francisco?"}
+    ]
 
     # Transform request
     request_data = config.transform_request(
@@ -2204,18 +2241,22 @@ async def test_assistant_message_cache_control():
         llm_provider="bedrock_converse",
     )
 
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
 
     assert result == async_result
 
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
 
     assert result == async_result
@@ -2261,10 +2302,12 @@ async def test_assistant_message_list_content_cache_control():
         llm_provider="bedrock_converse",
     )
 
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
 
     assert result == async_result
@@ -2317,10 +2360,12 @@ async def test_tool_message_cache_control():
         llm_provider="bedrock_converse",
     )
 
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
 
     assert result == async_result
@@ -2334,7 +2379,10 @@ async def test_tool_message_cache_control():
 
     # First should be tool result
     assert "toolResult" in tool_message_content[0]
-    assert tool_message_content[0]["toolResult"]["content"][0]["text"] == "Weather data: sunny, 25°C"
+    assert (
+        tool_message_content[0]["toolResult"]["content"][0]["text"]
+        == "Weather data: sunny, 25°C"
+    )
 
     # Second should be cachePoint
     assert "cachePoint" in tool_message_content[1]
@@ -2376,10 +2424,12 @@ async def test_tool_message_string_content_cache_control():
         llm_provider="bedrock_converse",
     )
 
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
 
     assert result == async_result
@@ -2390,7 +2440,10 @@ async def test_tool_message_string_content_cache_control():
 
     # First should be tool result
     assert "toolResult" in tool_message_content[0]
-    assert tool_message_content[0]["toolResult"]["content"][0]["text"] == "Weather: sunny, 25°C"
+    assert (
+        tool_message_content[0]["toolResult"]["content"][0]["text"]
+        == "Weather: sunny, 25°C"
+    )
 
     # Second should be cachePoint
     assert "cachePoint" in tool_message_content[1]
@@ -2430,7 +2483,9 @@ async def test_tool_message_search_results_maps_to_bedrock_search_result_block()
                     "source": "Great Source of Information About Apptio",
                     "title": "12adbd74-46bd-4a88-88b2-0048755f6eb5",
                     "content": [
-                        {"text": "Apptio is a company that makes calls to Bedrock using passthrough APIs via LiteLLM"}
+                        {
+                            "text": "Apptio is a company that makes calls to Bedrock using passthrough APIs via LiteLLM"
+                        }
                     ],
                     "citations": {"enabled": True},
                 }
@@ -2443,10 +2498,12 @@ async def test_tool_message_search_results_maps_to_bedrock_search_result_block()
         model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
         llm_provider="bedrock_converse",
     )
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
     assert result == async_result
 
@@ -2455,7 +2512,10 @@ async def test_tool_message_search_results_maps_to_bedrock_search_result_block()
     assert tool_result["status"] == "success"
     assert len(tool_result["content"]) == 1
     assert "searchResult" in tool_result["content"][0]
-    assert tool_result["content"][0]["searchResult"]["title"] == "12adbd74-46bd-4a88-88b2-0048755f6eb5"
+    assert (
+        tool_result["content"][0]["searchResult"]["title"]
+        == "12adbd74-46bd-4a88-88b2-0048755f6eb5"
+    )
 
 
 @pytest.mark.asyncio
@@ -2492,10 +2552,12 @@ async def test_tool_message_empty_search_results_falls_back_to_content():
         model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
         llm_provider="bedrock_converse",
     )
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
     assert result == async_result
 
@@ -2657,10 +2719,12 @@ async def test_assistant_tool_calls_cache_control():
         llm_provider="bedrock_converse",
     )
 
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
 
     assert result == async_result
@@ -2715,10 +2779,12 @@ async def test_multiple_tool_calls_with_mixed_cache_control():
         llm_provider="bedrock_converse",
     )
 
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
 
     assert result == async_result
@@ -2764,10 +2830,12 @@ async def test_no_cache_control_no_cache_point():
         llm_provider="bedrock_converse",
     )
 
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
 
     assert result == async_result
@@ -2937,7 +3005,10 @@ def test_guarded_text_with_mixed_content_types():
 
     # Third should be guardContent
     assert "guardContent" in content[2]
-    assert content[2]["guardContent"]["text"]["text"] == "This sensitive content should be guarded"
+    assert (
+        content[2]["guardContent"]["text"]["text"]
+        == "This sensitive content should be guarded"
+    )
 
 
 @pytest.mark.asyncio
@@ -3032,7 +3103,10 @@ def test_guarded_text_with_tool_calls():
 
     # Second should be guardContent
     assert "guardContent" in content[1]
-    assert content[1]["guardContent"]["text"]["text"] == "Please be careful with sensitive information"
+    assert (
+        content[1]["guardContent"]["text"]["text"]
+        == "Please be careful with sensitive information"
+    )
 
     # Other messages should not have guardContent
     for i in range(1, 3):
@@ -3093,36 +3167,52 @@ def test_auto_convert_last_user_message_to_guarded_text():
         }
     ]
 
-    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
+    optional_params = {
+        "guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}
+    }
 
     # Test the helper method directly
-    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
+    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(
+        messages, optional_params
+    )
 
     # Verify the conversion
     assert len(converted_messages) == 1
     assert converted_messages[0]["role"] == "user"
     assert len(converted_messages[0]["content"]) == 1
     assert converted_messages[0]["content"][0]["type"] == "guarded_text"
-    assert converted_messages[0]["content"][0]["text"] == "What is the main topic of this legal document?"
+    assert (
+        converted_messages[0]["content"][0]["text"]
+        == "What is the main topic of this legal document?"
+    )
 
 
 def test_auto_convert_last_user_message_string_content():
     """Test that last user message with string content is automatically converted to guarded_text when guardrailConfig is present."""
     config = AmazonConverseConfig()
 
-    messages = [{"role": "user", "content": "What is the main topic of this legal document?"}]
+    messages = [
+        {"role": "user", "content": "What is the main topic of this legal document?"}
+    ]
 
-    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
+    optional_params = {
+        "guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}
+    }
 
     # Test the helper method directly
-    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
+    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(
+        messages, optional_params
+    )
 
     # Verify the conversion
     assert len(converted_messages) == 1
     assert converted_messages[0]["role"] == "user"
     assert len(converted_messages[0]["content"]) == 1
     assert converted_messages[0]["content"][0]["type"] == "guarded_text"
-    assert converted_messages[0]["content"][0]["text"] == "What is the main topic of this legal document?"
+    assert (
+        converted_messages[0]["content"][0]["text"]
+        == "What is the main topic of this legal document?"
+    )
 
 
 def test_no_conversion_when_no_guardrail_config():
@@ -3144,7 +3234,9 @@ def test_no_conversion_when_no_guardrail_config():
     optional_params = {}
 
     # Test the helper method directly
-    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
+    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(
+        messages, optional_params
+    )
 
     # Verify no conversion happened
     assert converted_messages == messages
@@ -3161,10 +3253,14 @@ def test_no_conversion_when_guarded_text_already_present():
         }
     ]
 
-    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
+    optional_params = {
+        "guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}
+    }
 
     # Test the helper method directly
-    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
+    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(
+        messages, optional_params
+    )
 
     # Verify no conversion happened
     assert converted_messages == messages
@@ -3190,10 +3286,14 @@ def test_auto_convert_with_mixed_content():
         }
     ]
 
-    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
+    optional_params = {
+        "guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}
+    }
 
     # Test the helper method directly
-    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
+    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(
+        messages, optional_params
+    )
 
     # Verify the conversion
     assert len(converted_messages) == 1
@@ -3202,11 +3302,17 @@ def test_auto_convert_with_mixed_content():
 
     # First element should be converted to guarded_text
     assert converted_messages[0]["content"][0]["type"] == "guarded_text"
-    assert converted_messages[0]["content"][0]["text"] == "What is the main topic of this legal document?"
+    assert (
+        converted_messages[0]["content"][0]["text"]
+        == "What is the main topic of this legal document?"
+    )
 
     # Second element should remain unchanged
     assert converted_messages[0]["content"][1]["type"] == "image_url"
-    assert converted_messages[0]["content"][1]["image_url"]["url"] == "https://example.com/image.jpg"
+    assert (
+        converted_messages[0]["content"][1]["image_url"]["url"]
+        == "https://example.com/image.jpg"
+    )
 
 
 def test_auto_convert_in_full_transformation():
@@ -3225,7 +3331,9 @@ def test_auto_convert_in_full_transformation():
         }
     ]
 
-    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
+    optional_params = {
+        "guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}
+    }
 
     # Test the full transformation
     result = config._transform_request(
@@ -3245,7 +3353,10 @@ def test_auto_convert_in_full_transformation():
     assert "content" in message
     assert len(message["content"]) == 1
     assert "guardContent" in message["content"][0]
-    assert message["content"][0]["guardContent"]["text"]["text"] == "What is the main topic of this legal document?"
+    assert (
+        message["content"][0]["guardContent"]["text"]["text"]
+        == "What is the main topic of this legal document?"
+    )
 
 
 def test_convert_consecutive_user_messages_to_guarded_text():
@@ -3259,10 +3370,14 @@ def test_convert_consecutive_user_messages_to_guarded_text():
         {"role": "user", "content": [{"type": "text", "text": "Third user message"}]},
     ]
 
-    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
+    optional_params = {
+        "guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}
+    }
 
     # Test the helper method directly
-    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
+    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(
+        messages, optional_params
+    )
 
     # Verify the conversion - only the last two user messages should be converted
     assert len(converted_messages) == 4
@@ -3297,10 +3412,14 @@ def test_convert_all_user_messages_when_all_consecutive():
         {"role": "user", "content": [{"type": "text", "text": "Third user message"}]},
     ]
 
-    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
+    optional_params = {
+        "guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}
+    }
 
     # Test the helper method directly
-    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
+    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(
+        messages, optional_params
+    )
 
     # Verify all three user messages are converted
     assert len(converted_messages) == 3
@@ -3324,10 +3443,14 @@ def test_convert_consecutive_user_messages_with_string_content():
         {"role": "user", "content": "Second user message"},
     ]
 
-    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
+    optional_params = {
+        "guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}
+    }
 
     # Test the helper method directly
-    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
+    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(
+        messages, optional_params
+    )
 
     # Verify the conversion
     assert len(converted_messages) == 3
@@ -3360,10 +3483,14 @@ def test_skip_consecutive_user_messages_with_existing_guarded_text():
         {"role": "user", "content": [{"type": "text", "text": "Should be converted"}]},
     ]
 
-    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
+    optional_params = {
+        "guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}
+    }
 
     # Test the helper method directly
-    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
+    converted_messages = config._convert_consecutive_user_messages_to_guarded_text(
+        messages, optional_params
+    )
 
     # Verify the conversion
     assert len(converted_messages) == 2
@@ -3922,22 +4049,24 @@ def test_drop_thinking_param_when_thinking_blocks_missing():
         optional_params = {"thinking": {"type": "enabled", "budget_tokens": 1000}}
 
         # Verify the condition is detected
-        assert last_assistant_with_tool_calls_has_no_thinking_blocks(messages_without_thinking_blocks), (
-            "Should detect missing thinking_blocks"
-        )
+        assert last_assistant_with_tool_calls_has_no_thinking_blocks(
+            messages_without_thinking_blocks
+        ), "Should detect missing thinking_blocks"
 
         # Simulate what _transform_request_helper does
         if (
             optional_params.get("thinking") is not None
             and messages_without_thinking_blocks is not None
-            and last_assistant_with_tool_calls_has_no_thinking_blocks(messages_without_thinking_blocks)
+            and last_assistant_with_tool_calls_has_no_thinking_blocks(
+                messages_without_thinking_blocks
+            )
         ):
             if litellm.modify_params:
                 optional_params.pop("thinking", None)
 
-        assert "thinking" not in optional_params, (
-            "thinking param should be dropped when modify_params=True and thinking_blocks are missing"
-        )
+        assert (
+            "thinking" not in optional_params
+        ), "thinking param should be dropped when modify_params=True and thinking_blocks are missing"
 
         # Test case 2: thinking should NOT be dropped when thinking_blocks are present
         messages_with_thinking_blocks = [
@@ -3952,46 +4081,58 @@ def test_drop_thinking_param_when_thinking_blocks_missing():
                         "function": {"name": "search", "arguments": "{}"},
                     }
                 ],
-                "thinking_blocks": [{"type": "thinking", "thinking": "Let me search for weather..."}],
+                "thinking_blocks": [
+                    {"type": "thinking", "thinking": "Let me search for weather..."}
+                ],
             },
             {"role": "tool", "content": "Weather is sunny", "tool_call_id": "call_123"},
         ]
 
-        optional_params_with_thinking = {"thinking": {"type": "enabled", "budget_tokens": 1000}}
+        optional_params_with_thinking = {
+            "thinking": {"type": "enabled", "budget_tokens": 1000}
+        }
 
         # Verify the condition is NOT detected when thinking_blocks are present
-        assert not last_assistant_with_tool_calls_has_no_thinking_blocks(messages_with_thinking_blocks), (
-            "Should NOT detect missing thinking_blocks when they are present"
-        )
+        assert not last_assistant_with_tool_calls_has_no_thinking_blocks(
+            messages_with_thinking_blocks
+        ), "Should NOT detect missing thinking_blocks when they are present"
 
         # Simulate what _transform_request_helper does
         if (
             optional_params_with_thinking.get("thinking") is not None
             and messages_with_thinking_blocks is not None
-            and last_assistant_with_tool_calls_has_no_thinking_blocks(messages_with_thinking_blocks)
+            and last_assistant_with_tool_calls_has_no_thinking_blocks(
+                messages_with_thinking_blocks
+            )
         ):
             if litellm.modify_params:
                 optional_params_with_thinking.pop("thinking", None)
 
-        assert "thinking" in optional_params_with_thinking, (
-            "thinking param should NOT be dropped when thinking_blocks are present"
-        )
+        assert (
+            "thinking" in optional_params_with_thinking
+        ), "thinking param should NOT be dropped when thinking_blocks are present"
 
         # Test case 3: thinking should NOT be dropped when modify_params=False
         litellm.modify_params = False
 
-        optional_params_no_modify = {"thinking": {"type": "enabled", "budget_tokens": 1000}}
+        optional_params_no_modify = {
+            "thinking": {"type": "enabled", "budget_tokens": 1000}
+        }
 
         # Simulate what _transform_request_helper does
         if (
             optional_params_no_modify.get("thinking") is not None
             and messages_without_thinking_blocks is not None
-            and last_assistant_with_tool_calls_has_no_thinking_blocks(messages_without_thinking_blocks)
+            and last_assistant_with_tool_calls_has_no_thinking_blocks(
+                messages_without_thinking_blocks
+            )
         ):
             if litellm.modify_params:
                 optional_params_no_modify.pop("thinking", None)
 
-        assert "thinking" in optional_params_no_modify, "thinking param should NOT be dropped when modify_params=False"
+        assert (
+            "thinking" in optional_params_no_modify
+        ), "thinking param should NOT be dropped when modify_params=False"
 
     finally:
         # Restore original modify_params setting
@@ -4075,14 +4216,19 @@ def test_translate_response_format_native_output_config(monkeypatch):
         assert "fake_stream" not in result
 
         # Verify the schema content (additionalProperties: false is added by normalization)
-        schema_str = result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["schema"]
+        schema_str = result["outputConfig"]["textFormat"]["structure"]["jsonSchema"][
+            "schema"
+        ]
         parsed_schema = json.loads(schema_str)
         expected_schema = {
             **response_format["json_schema"]["schema"],
             "additionalProperties": False,
         }
         assert parsed_schema == expected_schema
-        assert result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["name"] == "WeatherResult"
+        assert (
+            result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["name"]
+            == "WeatherResult"
+        )
     finally:
         litellm.model_cost = old_cost
         if old_env is None:
@@ -4160,7 +4306,9 @@ def test_native_structured_output_no_fake_stream(monkeypatch):
         assert "fake_stream" not in result
 
         # Verify the schema content
-        schema_str = result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["schema"]
+        schema_str = result["outputConfig"]["textFormat"]["structure"]["jsonSchema"][
+            "schema"
+        ]
         assert json.loads(schema_str) == {
             "type": "object",
             "properties": {"answer": {"type": "string"}},
@@ -4213,7 +4361,10 @@ def test_transform_request_with_output_config():
 
     assert "outputConfig" in result
     assert result["outputConfig"]["textFormat"]["type"] == "json_schema"
-    assert result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["name"] == "TestSchema"
+    assert (
+        result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["name"]
+        == "TestSchema"
+    )
 
 
 def test_transform_request_strips_anthropic_output_config():
@@ -4334,7 +4485,10 @@ def test_transform_response_native_structured_output():
     )
 
     # Content should be the JSON text directly
-    assert result.choices[0].message.content == '{"temp": 62, "description": "Mild and foggy"}'
+    assert (
+        result.choices[0].message.content
+        == '{"temp": 62, "description": "Mild and foggy"}'
+    )
     # Should NOT have tool_calls
     assert result.choices[0].message.tool_calls is None
     assert result.choices[0].finish_reason == "stop"
@@ -4447,7 +4601,10 @@ def test_add_additional_properties_definitions():
     # definitions object
     assert result["definitions"]["Item"]["additionalProperties"] is False
     # Nested object inside definitions
-    assert result["definitions"]["Item"]["properties"]["details"]["additionalProperties"] is False
+    assert (
+        result["definitions"]["Item"]["properties"]["details"]["additionalProperties"]
+        is False
+    )
 
 
 def test_json_object_no_schema_skips_tool_injection(monkeypatch):
@@ -4504,7 +4661,9 @@ def test_output_config_applies_additional_properties():
     output_config = AmazonConverseConfig._create_output_config_for_response_format(
         json_schema=schema, name="test_schema"
     )
-    parsed = json.loads(output_config["textFormat"]["structure"]["jsonSchema"]["schema"])
+    parsed = json.loads(
+        output_config["textFormat"]["structure"]["jsonSchema"]["schema"]
+    )
     assert parsed["additionalProperties"] is False
     assert parsed["properties"]["nested"]["additionalProperties"] is False
 
@@ -4553,7 +4712,12 @@ def test_parallel_tool_calls_newer_model_adds_disable_flag():
 
     assert "additionalModelRequestFields" in request_data
     assert "tool_choice" in request_data["additionalModelRequestFields"]
-    assert request_data["additionalModelRequestFields"]["tool_choice"]["disable_parallel_tool_use"] is True
+    assert (
+        request_data["additionalModelRequestFields"]["tool_choice"][
+            "disable_parallel_tool_use"
+        ]
+        is True
+    )
     assert "parallel_tool_calls" not in request_data["additionalModelRequestFields"]
 
 
@@ -4585,7 +4749,12 @@ def test_parallel_tool_calls_flag_decoupled_from_ttl_pricing(monkeypatch):
         headers={},
     )
 
-    assert request_data["additionalModelRequestFields"]["tool_choice"]["disable_parallel_tool_use"] is True
+    assert (
+        request_data["additionalModelRequestFields"]["tool_choice"][
+            "disable_parallel_tool_use"
+        ]
+        is True
+    )
 
 
 def test_parallel_tool_calls_older_model_drops_disable_flag():
@@ -4732,7 +4901,9 @@ def test_parallel_tool_use_merge_preserves_user_tool_choice_type():
 class TestBedrockMinThinkingBudgetTokens:
     """Test that thinking.budget_tokens is clamped to the Bedrock minimum (1024)."""
 
-    def _map_params(self, thinking_value, model="anthropic.claude-sonnet-4-5-20250929-v1:0"):
+    def _map_params(
+        self, thinking_value, model="anthropic.claude-sonnet-4-5-20250929-v1:0"
+    ):
         """Helper to call map_openai_params with the given thinking value."""
         config = AmazonConverseConfig()
         non_default_params = {"thinking": thinking_value}
@@ -4959,7 +5130,9 @@ def test_streaming_filters_json_tool_call_with_real_tools():
 
     # Chunk 2: json_tool_call delta — should become text, not tool_use
     json_delta = ContentBlockDeltaEvent(toolUse={"input": '{"temp": 62}'})
-    text_2, tool_use_2, _, _, _ = decoder._handle_converse_delta_event(json_delta, index=0)
+    text_2, tool_use_2, _, _, _ = decoder._handle_converse_delta_event(
+        json_delta, index=0
+    )
     assert text_2 == '{"temp": 62}'
     assert tool_use_2 is None
 
@@ -4983,7 +5156,9 @@ def test_streaming_filters_json_tool_call_with_real_tools():
 
     # Chunk 5: real tool delta
     real_delta = ContentBlockDeltaEvent(toolUse={"input": '{"location": "SF"}'})
-    text_5, tool_use_5, _, _, _ = decoder._handle_converse_delta_event(real_delta, index=1)
+    text_5, tool_use_5, _, _, _ = decoder._handle_converse_delta_event(
+        real_delta, index=1
+    )
     assert text_5 == ""
     assert tool_use_5 is not None
     assert tool_use_5["function"]["arguments"] == '{"location": "SF"}'
@@ -5016,7 +5191,9 @@ def test_streaming_without_json_mode_passes_all_tools():
 
     # json_tool_call delta — should be a tool_use, not text
     json_delta = ContentBlockDeltaEvent(toolUse={"input": '{"data": 1}'})
-    text, tool_use_delta, _, _, _ = decoder._handle_converse_delta_event(json_delta, index=0)
+    text, tool_use_delta, _, _, _ = decoder._handle_converse_delta_event(
+        json_delta, index=0
+    )
     assert text == ""
     assert tool_use_delta is not None
     assert tool_use_delta["function"]["arguments"] == '{"data": 1}'
@@ -5500,7 +5677,11 @@ def test_transform_response_citation_null_source_title_become_empty_strings():
                 "content": [
                     {
                         "citationsContent": {
-                            "content": [{"text": "Apptio is a company that makes calls to Bedrock"}],
+                            "content": [
+                                {
+                                    "text": "Apptio is a company that makes calls to Bedrock"
+                                }
+                            ],
                             "citations": [
                                 {
                                     "location": {
@@ -5635,11 +5816,15 @@ def test_transform_response_citations_offset_tracks_text_only_blocks():
     message = result.choices[0].message
     expected_start = len(leading_text)
     assert message.content == leading_text + cited_text
-    assert message.content[expected_start : expected_start + len(cited_text)] == cited_text
+    assert (
+        message.content[expected_start : expected_start + len(cited_text)] == cited_text
+    )
     assert message.annotations is not None
     assert len(message.annotations) == 1
     assert message.annotations[0]["url_citation"]["start_index"] == expected_start
-    assert message.annotations[0]["url_citation"]["end_index"] == expected_start + len(cited_text)
+    assert message.annotations[0]["url_citation"]["end_index"] == expected_start + len(
+        cited_text
+    )
 
 
 def test_transform_response_stitches_citations_for_whitespace_punctuation_text():
@@ -5749,7 +5934,9 @@ def test_bedrock_tool_message_openai_file_pdf_becomes_document():
         },
     ]
 
-    translated_msg = _bedrock_converse_messages_pt(messages=messages, model="", llm_provider="")
+    translated_msg = _bedrock_converse_messages_pt(
+        messages=messages, model="", llm_provider=""
+    )
 
     tool_result = translated_msg[-1]["content"][-1]["toolResult"]
     assert tool_result["toolUseId"] == "tooluse_pdf_1"
@@ -5791,7 +5978,9 @@ def test_bedrock_tool_message_image_url_pdf_data_uri_becomes_document():
         },
     ]
 
-    translated_msg = _bedrock_converse_messages_pt(messages=messages, model="", llm_provider="")
+    translated_msg = _bedrock_converse_messages_pt(
+        messages=messages, model="", llm_provider=""
+    )
 
     tool_result = translated_msg[-1]["content"][-1]["toolResult"]
     assert tool_result["toolUseId"] == "tooluse_pdf_img_1"
@@ -5848,7 +6037,9 @@ def test_bedrock_tool_message_file_id_http_url_becomes_document():
         "process_image_sync",
         return_value=fake_document_block,
     ) as mock_proc:
-        translated_msg = _bedrock_converse_messages_pt(messages=messages, model="", llm_provider="")
+        translated_msg = _bedrock_converse_messages_pt(
+            messages=messages, model="", llm_provider=""
+        )
 
     mock_proc.assert_called_once()
     assert mock_proc.call_args.kwargs["image_url"] == pdf_url
@@ -5919,7 +6110,9 @@ def test_bedrock_tool_message_image_url_png_still_becomes_image():
         },
     ]
 
-    translated_msg = _bedrock_converse_messages_pt(messages=messages, model="", llm_provider="")
+    translated_msg = _bedrock_converse_messages_pt(
+        messages=messages, model="", llm_provider=""
+    )
 
     tool_result = translated_msg[-1]["content"][-1]["toolResult"]
     assert len(tool_result["content"]) == 1
@@ -6114,10 +6307,12 @@ async def test_grounding_source_and_query_rendered_as_text():
         model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
         llm_provider="bedrock_converse",
     )
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
+            llm_provider="bedrock_converse",
+        )
     )
 
     assert result == async_result
@@ -6161,7 +6356,9 @@ def test_neutralize_orphaned_tool_blocks_rewrites_when_no_tools():
     (#24158, #27138)."""
     messages = _orphaned_tool_history_messages()
 
-    result = AmazonConverseConfig._neutralize_orphaned_tool_blocks(messages, optional_params={})
+    result = AmazonConverseConfig._neutralize_orphaned_tool_blocks(
+        messages, optional_params={}
+    )
 
     serialized = json.dumps(result)
     assert "tool_calls" not in serialized
@@ -6182,7 +6379,9 @@ def test_neutralize_orphaned_tool_blocks_rewrites_when_tools_empty(tools_value):
     structured tool blocks with no toolConfig."""
     messages = _orphaned_tool_history_messages()
 
-    result = AmazonConverseConfig._neutralize_orphaned_tool_blocks(messages, optional_params={"tools": tools_value})
+    result = AmazonConverseConfig._neutralize_orphaned_tool_blocks(
+        messages, optional_params={"tools": tools_value}
+    )
 
     serialized = json.dumps(result)
     assert "tool_calls" not in serialized
@@ -6198,7 +6397,9 @@ def test_neutralize_orphaned_tool_blocks_rewrites_tool_result_only_history():
         {"role": "tool", "tool_call_id": "call_xyz", "content": "lookup result"},
     ]
 
-    result = AmazonConverseConfig._neutralize_orphaned_tool_blocks(messages, optional_params={})
+    result = AmazonConverseConfig._neutralize_orphaned_tool_blocks(
+        messages, optional_params={}
+    )
 
     assert not any(m.get("role") in ("tool", "function") for m in result)
     serialized = json.dumps(result)
@@ -6235,9 +6436,13 @@ def test_neutralize_orphaned_tool_blocks_non_text_result_marked_not_empty():
         },
     ]
 
-    result = AmazonConverseConfig._neutralize_orphaned_tool_blocks(messages, optional_params={})
+    result = AmazonConverseConfig._neutralize_orphaned_tool_blocks(
+        messages, optional_params={}
+    )
 
-    rewritten = next(m for m in result if m.get("role") == "user" and m is not messages[0])
+    rewritten = next(
+        m for m in result if m.get("role") == "user" and m is not messages[0]
+    )
     text = rewritten["content"]
     assert text.strip()  # never empty
     assert "non-text tool result omitted" in text
@@ -6260,7 +6465,9 @@ def test_neutralize_orphaned_tool_blocks_noop_when_no_tool_history():
     """Plain conversation with no tool blocks is returned unchanged."""
     messages = [{"role": "user", "content": "hi"}]
 
-    result = AmazonConverseConfig._neutralize_orphaned_tool_blocks(messages, optional_params={})
+    result = AmazonConverseConfig._neutralize_orphaned_tool_blocks(
+        messages, optional_params={}
+    )
 
     assert result is messages
 
@@ -6271,9 +6478,14 @@ def test_neutralize_orphaned_tool_blocks_logs_warning(caplog):
     messages = _orphaned_tool_history_messages()
 
     with caplog.at_level("WARNING"):
-        AmazonConverseConfig._neutralize_orphaned_tool_blocks(messages, optional_params={})
+        AmazonConverseConfig._neutralize_orphaned_tool_blocks(
+            messages, optional_params={}
+        )
 
-    assert any("neutralizing orphaned tool blocks" in record.getMessage() for record in caplog.records)
+    assert any(
+        "neutralizing orphaned tool blocks" in record.getMessage()
+        for record in caplog.records
+    )
 
 
 def _assert_no_structured_tool_blocks(result):
@@ -6391,7 +6603,9 @@ def test_transform_request_neutralized_tool_output_is_guarded(monkeypatch):
             },
             {"role": "tool", "tool_call_id": "c1", "content": "secret tool output"},
         ],
-        optional_params={"guardrailConfig": {"guardrailIdentifier": "gid", "guardrailVersion": "1"}},
+        optional_params={
+            "guardrailConfig": {"guardrailIdentifier": "gid", "guardrailVersion": "1"}
+        },
         litellm_params={},
         headers={},
     )
@@ -6431,19 +6645,23 @@ def test_transform_request_neutralized_tool_output_guarded_mid_history(monkeypat
             {"role": "assistant", "content": "Here is the summary."},
             {"role": "user", "content": "thanks"},
         ],
-        optional_params={"guardrailConfig": {"guardrailIdentifier": "gid", "guardrailVersion": "1"}},
+        optional_params={
+            "guardrailConfig": {"guardrailIdentifier": "gid", "guardrailVersion": "1"}
+        },
         litellm_params={},
         headers={},
     )
 
     _assert_no_structured_tool_blocks(result)
     blocks = [block for message in result["messages"] for block in message["content"]]
-    guarded_texts = [block["guardContent"]["text"]["text"] for block in blocks if "guardContent" in block]
+    guarded_texts = [
+        block["guardContent"]["text"]["text"] for block in blocks if "guardContent" in block
+    ]
     plain_texts = [block["text"] for block in blocks if "text" in block and "guardContent" not in block]
     assert any("malware" in text for text in guarded_texts), "mid-history tool output must be guarded"
-    assert not any("malware" in text for text in plain_texts), (
-        "mid-history tool output must not reach the model as unguarded text"
-    )
+    assert not any(
+        "malware" in text for text in plain_texts
+    ), "mid-history tool output must not reach the model as unguarded text"
 
 
 @pytest.mark.asyncio
@@ -6579,7 +6797,10 @@ def _agentic_messages_with_ttl(ttl_target: str):
 
 def _collect_cache_points(result):
     return [
-        block["cachePoint"] for message in result for block in message.get("content") or [] if "cachePoint" in block
+        block["cachePoint"]
+        for message in result
+        for block in message.get("content") or []
+        if "cachePoint" in block
     ]
 
 
@@ -6605,10 +6826,12 @@ async def test_message_level_cache_control_honors_ttl_for_supported_model(
         model="global.anthropic.claude-opus-4-7",
         llm_provider="bedrock_converse",
     )
-    async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="global.anthropic.claude-opus-4-7",
-        llm_provider="bedrock_converse",
+    async_result = (
+        await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model="global.anthropic.claude-opus-4-7",
+            llm_provider="bedrock_converse",
+        )
     )
     assert result == async_result
 
@@ -6920,7 +7143,9 @@ def test_update_optional_params_with_thinking_tokens_bool_thinking_does_not_cras
         ("us.anthropic.claude-opus-4-8", False),
     ],
 )
-def test_disabled_thinking_omitted_for_always_on_models_converse(local_model_cost_map, model, expected_dropped):
+def test_disabled_thinking_omitted_for_always_on_models_converse(
+    local_model_cost_map, model, expected_dropped
+):
     """Bedrock Converse: ``thinking={"type": "disabled"}`` is omitted for always-on-thinking
     models and forwarded verbatim for models that accept it."""
     config = AmazonConverseConfig()
@@ -6939,7 +7164,6 @@ def test_disabled_thinking_omitted_for_always_on_models_converse(local_model_cos
     else:
         assert additional.get("thinking") == {"type": "disabled"}
 
-
 @pytest.mark.parametrize(
     "model",
     ["anthropic.claude-fable-5-1", "us.anthropic.claude-fable-5-1"],
@@ -6948,10 +7172,14 @@ def test_disabled_thinking_omitted_for_always_on_models_converse(local_model_cos
     "tool_choice",
     ["required", {"type": "function", "function": {"name": "get_weather"}}],
 )
-def test_forced_tool_choice_downgraded_to_auto_on_fable_5_1_converse(local_model_cost_map, model, tool_choice):
+def test_forced_tool_choice_downgraded_to_auto_on_fable_5_1_converse(
+    local_model_cost_map, model, tool_choice
+):
     config = AmazonConverseConfig()
 
-    result = config.map_tool_choice_values(model=model, tool_choice=tool_choice, drop_params=True)
+    result = config.map_tool_choice_values(
+        model=model, tool_choice=tool_choice, drop_params=True
+    )
 
     assert result == {"auto": {}}
 
@@ -6960,12 +7188,16 @@ def test_forced_tool_choice_downgraded_to_auto_on_fable_5_1_converse(local_model
     "tool_choice",
     ["required", {"type": "function", "function": {"name": "get_weather"}}],
 )
-def test_forced_tool_choice_raises_clean_error_on_fable_5_1_converse(local_model_cost_map, tool_choice, monkeypatch):
+def test_forced_tool_choice_raises_clean_error_on_fable_5_1_converse(
+    local_model_cost_map, tool_choice, monkeypatch
+):
     monkeypatch.setattr(litellm, "drop_params", False)
     config = AmazonConverseConfig()
 
     with pytest.raises(litellm.utils.UnsupportedParamsError, match="forced tool use"):
-        config.map_tool_choice_values(model="anthropic.claude-fable-5-1", tool_choice=tool_choice, drop_params=False)
+        config.map_tool_choice_values(
+            model="anthropic.claude-fable-5-1", tool_choice=tool_choice, drop_params=False
+        )
 
 
 @pytest.mark.parametrize("tool_choice", ["auto", "none"])
@@ -6983,7 +7215,9 @@ def test_unforced_tool_choice_unaffected_on_fable_5_1_converse(local_model_cost_
     "model",
     ["anthropic.claude-fable-5-1", "us.anthropic.claude-fable-5-1"],
 )
-def test_response_format_avoids_native_and_forced_tool_choice_on_fable_5_1_converse(local_model_cost_map, model):
+def test_response_format_avoids_native_and_forced_tool_choice_on_fable_5_1_converse(
+    local_model_cost_map, model
+):
     """Regression: Bedrock rejects both ``outputConfig`` structured output and forced
     tool_choice for Fable 5.1, so response_format must map to a tool without a forced
     tool_choice."""
@@ -7010,11 +7244,15 @@ def test_response_format_avoids_native_and_forced_tool_choice_on_fable_5_1_conve
     assert result.get("json_mode") is True
 
 
-def test_forced_tool_choice_forwarded_on_converse_models_that_support_it(local_model_cost_map, monkeypatch):
+def test_forced_tool_choice_forwarded_on_converse_models_that_support_it(
+    local_model_cost_map, monkeypatch
+):
     monkeypatch.setattr(litellm, "drop_params", False)
     config = AmazonConverseConfig()
 
-    result = config.map_tool_choice_values(model="anthropic.claude-fable-5", tool_choice="required", drop_params=False)
+    result = config.map_tool_choice_values(
+        model="anthropic.claude-fable-5", tool_choice="required", drop_params=False
+    )
 
     assert result == {"any": {}}
 
diff --git a/tests/test_litellm/llms/bedrock/image_edit/test_amazon_nova_canvas_image_edit.py b/tests/test_litellm/llms/bedrock/image_edit/test_amazon_nova_canvas_image_edit.py
index ebecd615605..122dd5b555a 100644
--- a/tests/test_litellm/llms/bedrock/image_edit/test_amazon_nova_canvas_image_edit.py
+++ b/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",
diff --git a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py
index 7d243594cb3..575d0b881c3 100644
--- a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py
+++ b/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
diff --git a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py
index 8deb16bceb2..4cdca97bbff 100644
--- a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py
+++ b/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():
diff --git a/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py b/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py
index df67ee7d5ae..3ad0d7308f7 100644
--- a/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_responses_transformation.py
+++ b/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
diff --git a/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_transformation.py b/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_transformation.py
index 97465d8c49e..0cc3963358f 100644
--- a/tests/test_litellm/llms/bedrock_mantle/test_bedrock_mantle_transformation.py
+++ b/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,
diff --git a/tests/test_litellm/llms/crusoe/test_crusoe.py b/tests/test_litellm/llms/crusoe/test_crusoe.py
index 80372418026..718d00222aa 100644
--- a/tests/test_litellm/llms/crusoe/test_crusoe.py
+++ b/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"
+
+
diff --git a/tests/test_litellm/llms/dashscope/test_dashscope_cost_calculator.py b/tests/test_litellm/llms/dashscope/test_dashscope_cost_calculator.py
index 17bbf9852e7..344b0cf127a 100644
--- a/tests/test_litellm/llms/dashscope/test_dashscope_cost_calculator.py
+++ b/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)
diff --git a/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py b/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py
index ea55980a558..68c52f9be72 100644
--- a/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py
+++ b/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",
diff --git a/tests/test_litellm/llms/inception/test_inception_chat_transformation.py b/tests/test_litellm/llms/inception/test_inception_chat_transformation.py
index c4c023077fc..1d12be2adee 100644
--- a/tests/test_litellm/llms/inception/test_inception_chat_transformation.py
+++ b/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"
+
+
diff --git a/tests/test_litellm/llms/oci/embed/test_oci_embedding.py b/tests/test_litellm/llms/oci/embed/test_oci_embedding.py
index a75883d7846..f8242aa3d2b 100644
--- a/tests/test_litellm/llms/oci/embed/test_oci_embedding.py
+++ b/tests/test_litellm/llms/oci/embed/test_oci_embedding.py
@@ -305,3 +305,5 @@ class TestOCIEmbeddingConfig:
                 optional_params={},
                 litellm_params={},
             )
+
+
diff --git a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py
index 1df47223f06..ca737c0bb80 100644
--- a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py
+++ b/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"}},
diff --git a/tests/test_litellm/llms/openai/test_gpt5_transformation.py b/tests/test_litellm/llms/openai/test_gpt5_transformation.py
index 63bd5f6e1ed..a82d07fa6be 100644
--- a/tests/test_litellm/llms/openai/test_gpt5_transformation.py
+++ b/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):
diff --git a/tests/test_litellm/llms/openai_like/responses/test_openai_like_responses.py b/tests/test_litellm/llms/openai_like/responses/test_openai_like_responses.py
index 68cd33bf745..6cc5ffa2dae 100644
--- a/tests/test_litellm/llms/openai_like/responses/test_openai_like_responses.py
+++ b/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):
diff --git a/tests/test_litellm/llms/openai_like/test_cognition_provider.py b/tests/test_litellm/llms/openai_like/test_cognition_provider.py
index 9a38456da16..81895d7dc42 100644
--- a/tests/test_litellm/llms/openai_like/test_cognition_provider.py
+++ b/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
+
+
diff --git a/tests/test_litellm/llms/openai_like/test_meta_provider.py b/tests/test_litellm/llms/openai_like/test_meta_provider.py
index 359416b581c..20f5af2567c 100644
--- a/tests/test_litellm/llms/openai_like/test_meta_provider.py
+++ b/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"
+
+
diff --git a/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py b/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py
index 76e818bfc49..15cc6a34de9 100644
--- a/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py
+++ b/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"))
diff --git a/tests/test_litellm/llms/openai_like/test_tensormesh_provider.py b/tests/test_litellm/llms/openai_like/test_tensormesh_provider.py
index 1ff70142719..620e6e1a836 100644
--- a/tests/test_litellm/llms/openai_like/test_tensormesh_provider.py
+++ b/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
+
diff --git a/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py b/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py
index f4828a19fc1..4069a32793f 100644
--- a/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py
+++ b/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."""
diff --git a/tests/test_litellm/llms/reducto/test_model_info.py b/tests/test_litellm/llms/reducto/test_model_info.py
index 548e5a308d4..499adf0d179 100644
--- a/tests/test_litellm/llms/reducto/test_model_info.py
+++ b/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"
+
+
diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
index 11b08081568..60514e19c33 100644
--- a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
+++ b/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
+
+
diff --git a/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py b/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py
index 387cc405f02..ee80aed6f47 100644
--- a/tests/test_litellm/llms/vertex_ai/text_to_speech/test_transformation.py
+++ b/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"
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py
index 6b50dadbb38..028a7cc4b05 100644
--- a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_transformation.py
+++ b/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
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gemma/test_vertex_ai_gemma_global_endpoint.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gemma/test_vertex_ai_gemma_global_endpoint.py
index a8da13e2f36..e9b58622a4b 100644
--- a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gemma/test_vertex_ai_gemma_global_endpoint.py
+++ b/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"),
diff --git a/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py b/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py
index ae2c60c1781..763103ea1f0 100644
--- a/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py
+++ b/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
diff --git a/tests/test_litellm/llms/wandb/test_wandb_chat_transformation.py b/tests/test_litellm/llms/wandb/test_wandb_chat_transformation.py
index 02f22a4135d..ef669db5864 100644
--- a/tests/test_litellm/llms/wandb/test_wandb_chat_transformation.py
+++ b/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
diff --git a/tests/test_litellm/llms/xai/test_xai_model_registry.py b/tests/test_litellm/llms/xai/test_xai_model_registry.py
index 47c91e24f14..969f1e56770 100644
--- a/tests/test_litellm/llms/xai/test_xai_model_registry.py
+++ b/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"
diff --git a/tests/test_litellm/proxy/auth/test_model_checks.py b/tests/test_litellm/proxy/auth/test_model_checks.py
index 10873c4772a..3c6733cb86d 100644
--- a/tests/test_litellm/proxy/auth/test_model_checks.py
+++ b/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
diff --git a/tests/test_litellm/proxy/spend_tracking/test_savings.py b/tests/test_litellm/proxy/spend_tracking/test_savings.py
index 94ce8019b1b..2e4c0853c07 100644
--- a/tests/test_litellm/proxy/spend_tracking/test_savings.py
+++ b/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)
 
diff --git a/tests/test_litellm/proxy/test_proxy_utils.py b/tests/test_litellm/proxy/test_proxy_utils.py
index 9b8c55d51bb..ea3feae00ec 100644
--- a/tests/test_litellm/proxy/test_proxy_utils.py
+++ b/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"]
diff --git a/tests/test_litellm/router_utils/test_reasoning_effort_capability.py b/tests/test_litellm/router_utils/test_reasoning_effort_capability.py
index 9a1cbe73ae8..9b811e6f1ce 100644
--- a/tests/test_litellm/router_utils/test_reasoning_effort_capability.py
+++ b/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
diff --git a/tests/test_litellm/test_claude_fable_5_config.py b/tests/test_litellm/test_claude_fable_5_config.py
index a0ed8d856dc..dfbda795c7a 100644
--- a/tests/test_litellm/test_claude_fable_5_config.py
+++ b/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
+
+
diff --git a/tests/test_litellm/test_claude_opus_4_6_config.py b/tests/test_litellm/test_claude_opus_4_6_config.py
index f7d264ec5ae..7bded3b6ed3 100644
--- a/tests/test_litellm/test_claude_opus_4_6_config.py
+++ b/tests/test_litellm/test_claude_opus_4_6_config.py
@@ -2,6 +2,7 @@
 Validate Claude Opus 4.6 model configuration entries.
 """
 
+
 import litellm
 
 
diff --git a/tests/test_litellm/test_claude_opus_4_8_config.py b/tests/test_litellm/test_claude_opus_4_8_config.py
index f41e6616c83..9471ef4ef4f 100644
--- a/tests/test_litellm/test_claude_opus_4_8_config.py
+++ b/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
+
+
diff --git a/tests/test_litellm/test_claude_opus_5_config.py b/tests/test_litellm/test_claude_opus_5_config.py
index 3327b2795ce..aaf179e0216 100644
--- a/tests/test_litellm/test_claude_opus_5_config.py
+++ b/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
+
+
diff --git a/tests/test_litellm/test_claude_sonnet_5_config.py b/tests/test_litellm/test_claude_sonnet_5_config.py
index 702da61a438..5e7d5797a62 100644
--- a/tests/test_litellm/test_claude_sonnet_5_config.py
+++ b/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
+
+
diff --git a/tests/test_litellm/test_dashscope_image_generation.py b/tests/test_litellm/test_dashscope_image_generation.py
index 397cc9b313a..1dd0b322623 100644
--- a/tests/test_litellm/test_dashscope_image_generation.py
+++ b/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
diff --git a/tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py b/tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py
index bc400bfa362..0632441e1b5 100644
--- a/tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py
+++ b/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"
diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py
index 3d9534628cb..4f2daa56b81 100644
--- a/tests/test_litellm/test_utils.py
+++ b/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"
+
+
diff --git a/tests/test_litellm/test_vertex_ai_xai_grok_prompt_caching_metadata.py b/tests/test_litellm/test_vertex_ai_xai_grok_prompt_caching_metadata.py
index 4a9a429801c..bb1843c5d05 100644
--- a/tests/test_litellm/test_vertex_ai_xai_grok_prompt_caching_metadata.py
+++ b/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}"
     )
+
+