chore: autoformat the test_litellm.py

PiperOrigin-RevId: 764937761

Author: seanzhougoogle

tests/unittests/models/test_litellm.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 
+import json
 from unittest.mock import AsyncMock
 from unittest.mock import Mock
 
@@ -38,7 +39,6 @@
 from litellm.types.utils import ModelResponse
 from litellm.types.utils import StreamingChoices
 import pytest
-import json
 
 LLM_REQUEST_WITH_FUNCTION_DECLARATION = LlmRequest(
     contents=[
@@ -1190,74 +1190,70 @@ async def test_generate_content_async_stream_with_usage_metadata(
 async def test_generate_content_async_multiple_function_calls(
     mock_completion, lite_llm_instance
 ):
-    """Test handling of multiple function calls with different indices in streaming mode.
-
-    This test verifies that:
-    1. Multiple function calls with different indices are handled correctly
-    2. Arguments and names are properly accumulated for each function call
-    3. The final response contains all function calls with correct indices
-    """
-    mock_completion.return_value = MULTIPLE_FUNCTION_CALLS_STREAM
-
-    llm_request = LlmRequest(
-        contents=[
-            types.Content(
-                role="user",
-                parts=[types.Part.from_text(text="Test multiple function calls")],
-            )
-        ],
-        config=types.GenerateContentConfig(
-            tools=[
-                types.Tool(
-                    function_declarations=[
-                        types.FunctionDeclaration(
-                            name="function_1",
-                            description="First test function",
-                            parameters=types.Schema(
-                                type=types.Type.OBJECT,
-                                properties={
-                                    "arg": types.Schema(type=types.Type.STRING),
-                                },
-                            ),
-                        ),
-                        types.FunctionDeclaration(
-                            name="function_2",
-                            description="Second test function",
-                            parameters=types.Schema(
-                                type=types.Type.OBJECT,
-                                properties={
-                                    "arg": types.Schema(type=types.Type.STRING),
-                                },
-                            ),
-                        ),
-                    ]
-                )
-            ],
-        ),
-    )
+  """Test handling of multiple function calls with different indices in streaming mode.
 
-    responses = []
-    async for response in lite_llm_instance.generate_content_async(
-        llm_request, stream=True
-    ):
-        responses.append(response)
-
-    # Verify we got the final response with both function calls
-    assert len(responses) > 0
-    final_response = responses[-1]
-    assert final_response.content.role == "model"
-    assert len(final_response.content.parts) == 2
-
-    # Verify first function call
-    assert final_response.content.parts[0].function_call.name == "function_1"
-    assert final_response.content.parts[0].function_call.id == "call_1"
-    assert final_response.content.parts[0].function_call.args == {
-        "arg": "value1"
-    }
+  This test verifies that:
+  1. Multiple function calls with different indices are handled correctly
+  2. Arguments and names are properly accumulated for each function call
+  3. The final response contains all function calls with correct indices
+  """
+  mock_completion.return_value = MULTIPLE_FUNCTION_CALLS_STREAM
 
-    # Verify second function call
-    assert final_response.content.parts[1].function_call.name == "function_2"
-    assert final_response.content.parts[1].function_call.id == "call_2"
-    assert final_response.content.parts[1].function_call.args == {
-        "arg": "value2"
-    }
+  llm_request = LlmRequest(
+      contents=[
+          types.Content(
+              role="user",
+              parts=[types.Part.from_text(text="Test multiple function calls")],
+          )
+      ],
+      config=types.GenerateContentConfig(
+          tools=[
+              types.Tool(
+                  function_declarations=[
+                      types.FunctionDeclaration(
+                          name="function_1",
+                          description="First test function",
+                          parameters=types.Schema(
+                              type=types.Type.OBJECT,
+                              properties={
+                                  "arg": types.Schema(type=types.Type.STRING),
+                              },
+                          ),
+                      ),
+                      types.FunctionDeclaration(
+                          name="function_2",
+                          description="Second test function",
+                          parameters=types.Schema(
+                              type=types.Type.OBJECT,
+                              properties={
+                                  "arg": types.Schema(type=types.Type.STRING),
+                              },
+                          ),
+                      ),
+                  ]
+              )
+          ],
+      ),
+  )
+
+  responses = []
+  async for response in lite_llm_instance.generate_content_async(
+      llm_request, stream=True
+  ):
+    responses.append(response)
+
+  # Verify we got the final response with both function calls
+  assert len(responses) > 0
+  final_response = responses[-1]
+  assert final_response.content.role == "model"
+  assert len(final_response.content.parts) == 2
+
+  # Verify first function call
+  assert final_response.content.parts[0].function_call.name == "function_1"
+  assert final_response.content.parts[0].function_call.id == "call_1"
+  assert final_response.content.parts[0].function_call.args == {"arg": "value1"}
+
+  # Verify second function call
+  assert final_response.content.parts[1].function_call.name == "function_2"
+  assert final_response.content.parts[1].function_call.id == "call_2"
+  assert final_response.content.parts[1].function_call.args == {"arg": "value2"}