Convert examples to ruff-format (#18400)

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

examples/offline_inference/encoder_decoder_multimodal.py

@@ -3,6 +3,7 @@
 This example shows how to use vLLM for running offline inference with
 the explicit/implicit prompt format on enc-dec LMMs for text generation.
 """
+
 import time
 from collections.abc import Sequence
 from dataclasses import asdict
@@ -30,18 +31,14 @@ def run_florence2():
     )
 
     prompts = [
-        {   # implicit prompt with task token
+        {  # implicit prompt with task token
             "prompt": "<DETAILED_CAPTION>",
-            "multi_modal_data": {
-                "image": ImageAsset("stop_sign").pil_image
-            },
+            "multi_modal_data": {"image": ImageAsset("stop_sign").pil_image},
         },
-        {   # explicit encoder/decoder prompt
+        {  # explicit encoder/decoder prompt
             "encoder_prompt": {
                 "prompt": "Describe in detail what is shown in the image.",
-                "multi_modal_data": {
-                    "image": ImageAsset("cherry_blossom").pil_image
-                },
+                "multi_modal_data": {"image": ImageAsset("cherry_blossom").pil_image},
             },
             "decoder_prompt": "",
         },
@@ -63,20 +60,20 @@ def run_mllama():
     )
 
     prompts = [
-        {   # Implicit prompt
-            "prompt": "<|image|><|begin_of_text|>What is the content of this image?",   # noqa: E501
+        {  # Implicit prompt
+            "prompt": "<|image|><|begin_of_text|>What is the content of this image?",  # noqa: E501
             "multi_modal_data": {
                 "image": ImageAsset("stop_sign").pil_image,
             },
         },
-        {   # Explicit prompt
+        {  # Explicit prompt
             "encoder_prompt": {
                 "prompt": "<|image|>",
                 "multi_modal_data": {
                     "image": ImageAsset("stop_sign").pil_image,
                 },
             },
-            "decoder_prompt": "<|image|><|begin_of_text|>Please describe the image.",   # noqa: E501
+            "decoder_prompt": "<|image|><|begin_of_text|>Please describe the image.",  # noqa: E501
         },
     ]
 
@@ -96,13 +93,13 @@ def run_whisper():
     )
 
     prompts = [
-        {   # Test implicit prompt
+        {  # Test implicit prompt
             "prompt": "<|startoftranscript|>",
             "multi_modal_data": {
                 "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
             },
         },
-        {   # Test explicit encoder/decoder prompt
+        {  # Test explicit encoder/decoder prompt
             "encoder_prompt": {
                 "prompt": "",
                 "multi_modal_data": {
@@ -110,7 +107,7 @@ def run_whisper():
                 },
             },
             "decoder_prompt": "<|startoftranscript|>",
-        }
+        },
     ]
 
     return ModelRequestData(
@@ -128,18 +125,23 @@ model_example_map = {
 
 def parse_args():
     parser = FlexibleArgumentParser(
-        description='Demo on using vLLM for offline inference with '
-        'vision language models for text generation')
-    parser.add_argument('--model-type',
-                        '-m',
-                        type=str,
-                        default="mllama",
-                        choices=model_example_map.keys(),
-                        help='Huggingface "model_type".')
-    parser.add_argument("--seed",
-                        type=int,
-                        default=None,
-                        help="Set the seed when initializing `vllm.LLM`.")
+        description="Demo on using vLLM for offline inference with "
+        "vision language models for text generation"
+    )
+    parser.add_argument(
+        "--model-type",
+        "-m",
+        type=str,
+        default="mllama",
+        choices=model_example_map.keys(),
+        help='Huggingface "model_type".',
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=None,
+        help="Set the seed when initializing `vllm.LLM`.",
+    )
     return parser.parse_args()
 
 
@@ -153,7 +155,8 @@ def main(args):
     # Disable other modalities to save memory
     default_limits = {"image": 0, "video": 0, "audio": 0}
     req_data.engine_args.limit_mm_per_prompt = default_limits | dict(
-        req_data.engine_args.limit_mm_per_prompt or {})
+        req_data.engine_args.limit_mm_per_prompt or {}
+    )
 
     engine_args = asdict(req_data.engine_args) | {"seed": args.seed}
     llm = LLM(**engine_args)
@@ -179,8 +182,7 @@ def main(args):
     for output in outputs:
         prompt = output.prompt
         generated_text = output.outputs[0].text
-        print(f"Decoder prompt: {prompt!r}, "
-              f"Generated text: {generated_text!r}")
+        print(f"Decoder prompt: {prompt!r}, Generated text: {generated_text!r}")
 
     duration = time.time() - start