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.