Merge 101c957bff1c1174b63f063b6383505f8862826b into 254f6b986720c92ddf97fbb1a6a6465da8e87e29

tests/conftest.py

@@ -58,8 +58,6 @@ from vllm.distributed import (
     initialize_model_parallel,
 )
 from vllm.logger import init_logger
-from vllm.logprobs import Logprob
-from vllm.multimodal.base import MediaWithBytes
 from vllm.multimodal.utils import fetch_image
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams
@@ -71,6 +69,7 @@ from torch._inductor.utils import fresh_cache
 
 
 if TYPE_CHECKING:
+    from vllm.logprobs import LogprobsOnePosition
     from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
     from transformers.generation.utils import GenerateOutput
 
@@ -93,8 +92,7 @@ PromptVideoInput = _PromptMultiModalInput[np.ndarray]
 
 def _read_prompts(filename: str) -> list[str]:
     with open(filename) as f:
-        prompts = f.readlines()
-        return prompts
+        return f.readlines()
 
 
 class ImageAssetPrompts(TypedDict):
@@ -1021,9 +1019,10 @@ class VllmRunner:
         perplexities = []
         for output in outputs:
             output = cast(TokensTextLogprobsPromptLogprobs, output)
-            token_datas = cast(list[dict[int, Logprob] | None], output[3])
+            token_datas = cast(list[LogprobsOnePosition | None], output[3])
             assert token_datas[0] is None
             token_log_probs = []
+
             for token_data in token_datas[1:]:
                 assert token_data is not None
                 assert len(token_data) == 1
@@ -1052,12 +1051,11 @@ class VllmRunner:
             BeamSearchParams(beam_width=beam_width, max_tokens=max_tokens),
             concurrency_limit=concurrency_limit,
         )
-        returned_outputs = []
-        for output in outputs:
-            token_ids = [x.tokens for x in output.sequences]
-            texts = [x.text for x in output.sequences]
-            returned_outputs.append((token_ids, texts))
-        return returned_outputs
+
+        return [
+            ([x.tokens for x in output.sequences], [x.text for x in output.sequences])
+            for output in outputs
+        ]
 
     def classify(self, prompts: list[str]) -> list[list[float]]:
         req_outputs = self.llm.classify(prompts)
@@ -1432,11 +1430,7 @@ class LocalAssetServer:
         return f"{self.base_url}/{name}"
 
     def get_image_asset(self, name: str) -> Image.Image:
-        image = fetch_image(self.url_for(name))
-        # Unwrap MediaWithBytes if present
-        if isinstance(image, MediaWithBytes):
-            image = image.media
-        return image
+        return fetch_image(self.url_for(name))
 
 
 @pytest.fixture(scope="session")