Convert formatting to use ruff instead of yapf + isort (#26247)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/plugins_tests/test_io_processor_plugins.py

@@ -50,7 +50,6 @@ async def test_prithvi_mae_plugin_online(
     server: RemoteOpenAIServer,
     model_name: str,
 ):
-
     request_payload_url = {
         "data": {
             "data": image_url,
@@ -60,7 +59,7 @@ async def test_prithvi_mae_plugin_online(
         },
         "priority": 0,
         "model": model_name,
-        "softmax": False
+        "softmax": False,
     }
 
     ret = requests.post(
@@ -77,8 +76,8 @@ async def test_prithvi_mae_plugin_online(
     plugin_data = parsed_response.data
 
     assert all(
-        plugin_data.get(attr)
-        for attr in ["type", "format", "data", "request_id"])
+        plugin_data.get(attr) for attr in ["type", "format", "data", "request_id"]
+    )
 
     # We just check that the output is a valid base64 string.
     # Raises an exception and fails the test if the string is corrupted.
@@ -87,7 +86,6 @@ async def test_prithvi_mae_plugin_online(
 
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 def test_prithvi_mae_plugin_offline(vllm_runner, model_name: str):
-
     img_prompt = dict(
         data=image_url,
         data_format="url",
@@ -98,16 +96,16 @@ def test_prithvi_mae_plugin_offline(vllm_runner, model_name: str):
     pooling_params = PoolingParams(task="encode", softmax=False)
 
     with vllm_runner(
-            model_name,
-            runner="pooling",
-            skip_tokenizer_init=True,
-            trust_remote_code=True,
-            enforce_eager=True,
-            # Limit the maximum number of parallel requests
-            # to avoid the model going OOM in CI.
-            max_num_seqs=1,
-            model_impl="terratorch",
-            io_processor_plugin="prithvi_to_tiff",
+        model_name,
+        runner="pooling",
+        skip_tokenizer_init=True,
+        trust_remote_code=True,
+        enforce_eager=True,
+        # Limit the maximum number of parallel requests
+        # to avoid the model going OOM in CI.
+        max_num_seqs=1,
+        model_impl="terratorch",
+        io_processor_plugin="prithvi_to_tiff",
     ) as llm_runner:
         pooler_output = llm_runner.get_llm().encode(
             img_prompt,
@@ -117,8 +115,8 @@ def test_prithvi_mae_plugin_offline(vllm_runner, model_name: str):
 
     # verify the output is formatted as expected for this plugin
     assert all(
-        hasattr(output, attr)
-        for attr in ["type", "format", "data", "request_id"])
+        hasattr(output, attr) for attr in ["type", "format", "data", "request_id"]
+    )
 
     # We just check that the output is a valid base64 string.
     # Raises an exception and fails the test if the string is corrupted.

tests/plugins_tests/test_platform_plugins.py

@@ -10,29 +10,38 @@ from vllm.plugins import load_general_plugins
 def test_platform_plugins():
     # simulate workload by running an example
     import runpy
+
     current_file = __file__
     import os
+
     example_file = os.path.join(
         os.path.dirname(os.path.dirname(os.path.dirname(current_file))),
-        "examples", "offline_inference/basic/basic.py")
+        "examples",
+        "offline_inference/basic/basic.py",
+    )
     runpy.run_path(example_file)
 
     # check if the plugin is loaded correctly
     from vllm.platforms import _init_trace, current_platform
+
     assert current_platform.device_name == "DummyDevice", (
         f"Expected DummyDevice, got {current_platform.device_name}, "
         "possibly because current_platform is imported before the plugin"
-        f" is loaded. The first import:\n{_init_trace}")
+        f" is loaded. The first import:\n{_init_trace}"
+    )
 
 
 def test_oot_custom_op(monkeypatch: pytest.MonkeyPatch):
     # simulate workload by running an example
     load_general_plugins()
     from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
+
     layer = RotaryEmbedding(16, 16, 16, 16, True, torch.float16)
     assert layer.__class__.__name__ == "DummyRotaryEmbedding", (
         f"Expected DummyRotaryEmbedding, got {layer.__class__.__name__}, "
-        "possibly because the custom op is not registered correctly.")
+        "possibly because the custom op is not registered correctly."
+    )
     assert hasattr(layer, "addition_config"), (
         "Expected DummyRotaryEmbedding to have an 'addition_config' attribute, "
-        "which is set by the custom op.")
+        "which is set by the custom op."
+    )

tests/plugins_tests/test_scheduler_plugins.py

@@ -10,7 +10,6 @@ from vllm.v1.engine.llm_engine import LLMEngine
 
 
 class DummyV1Scheduler(Scheduler):
-
     def schedule(self):
         raise Exception("Exception raised by DummyV1Scheduler")
 
@@ -23,7 +22,6 @@ def test_scheduler_plugins_v1(monkeypatch: pytest.MonkeyPatch):
         m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
 
         with pytest.raises(Exception) as exception_info:
-
             engine_args = EngineArgs(
                 model="facebook/opt-125m",
                 enforce_eager=True,  # reduce test time
@@ -36,5 +34,4 @@ def test_scheduler_plugins_v1(monkeypatch: pytest.MonkeyPatch):
             engine.add_request("0", "foo", sampling_params)
             engine.step()
 
-        assert str(
-            exception_info.value) == "Exception raised by DummyV1Scheduler"
+        assert str(exception_info.value) == "Exception raised by DummyV1Scheduler"