fixes pre-commit errors

Signed-off-by: Gabriel Marinho <gmarinho@ibm.com>

vllm/entrypoints/openai/serving_score.py

@@ -68,14 +68,13 @@ def __init__(
     async def _embedding_score(
         self,
         tokenizer: Union[AnyTokenizer],
-        text_1: List[Union[List[str], str]],
-        text_2: List[Union[List[str], str]],
+        input_pairs: List[tuple],
         request: ScoreRequest,
         model_name=str,
         request_id=str,
         created_time=int,
         truncate_prompt_tokens: Optional[int] = None,
-        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        lora_request: Optional[Union[LoRARequest | None]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest | None] = None,
         raw_request: Optional[Request] = None,
     ) -> Union[ScoreResponse, ErrorResponse]:
@@ -84,7 +83,6 @@ async def _embedding_score(
         engine_prompts = []
 
         try:
-            input_pairs = make_pairs(text_1, text_2)
             for q, t in input_pairs:
                 request_prompt = f"{q}{tokenizer.sep_token}{t}"
 
@@ -181,18 +179,14 @@ async def _embedding_score(
 
         result_generator = merge_async_iterators(*generators)
 
-        num_prompts = len(engine_prompts)
-
         # Non-streaming response
-        final_res_batch: List[Optional[PoolingRequestOutput]]
-        final_res_batch = [None] * num_prompts
+        final_res_batch: List[Optional[PoolingRequestOutput]] = []
 
         try:
             embeddings = []
             async for i, res in result_generator:
                 embeddings.append(res)
 
-            scores = []
             scorer = torch.nn.CosineSimilarity(0)
 
             for i in range(0, len(embeddings), 2):
@@ -208,15 +202,14 @@ async def _embedding_score(
                     tokens = embeddings[i].prompt_token_ids + embeddings[
                         i + 1].prompt_token_ids
 
-                scores.append(
+                final_res_batch.append(
                     PoolingRequestOutput(
                         request_id=
                         f"{embeddings[i].request_id}_{embeddings[i+1].request_id}",
                         outputs=pair_score,
                         prompt_token_ids=tokens,
                         finished=True))
 
-            final_res_batch = scores
             assert all(final_res is not None for final_res in final_res_batch)
 
             final_res_batch_checked = cast(List[PoolingRequestOutput],
@@ -240,14 +233,13 @@ async def _embedding_score(
     async def _cross_encoding_score(
         self,
         tokenizer: Union[AnyTokenizer],
-        text_1: List[Union[List[str], str]],
-        text_2: List[Union[List[str], str]],
+        input_pairs: List[tuple],
         request: ScoreRequest,
         model_name=str,
         request_id=str,
         created_time=int,
         truncate_prompt_tokens: Optional[int] = None,
-        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        lora_request: Optional[Union[LoRARequest | None]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest | None] = None,
         raw_request: Optional[Request] = None,
     ) -> Union[ScoreResponse, ErrorResponse]:
@@ -260,7 +252,6 @@ async def _cross_encoding_score(
                 raise ValueError(
                     "MistralTokenizer not supported for cross-encoding")
 
-            input_pairs = make_pairs(text_1, text_2)
             for q, t in input_pairs:
                 request_prompt = f"{q}{tokenizer.sep_token}{t}"
 
@@ -394,11 +385,12 @@ async def create_score(
             logger.exception("Error in preprocessing prompt inputs")
             return self.create_error_response(str(e))
 
+        input_pairs = make_pairs(request.text_1, request.text_2)
+
         if self.model_config.is_cross_encoder:
             response = await self._cross_encoding_score(
                 tokenizer=tokenizer,
-                text_1=request.text_1,
-                text_2=request.text_2,
+                input_pairs=input_pairs,
                 request=request,
                 model_name=model_name,
                 request_id=request_id,
@@ -411,8 +403,7 @@ async def create_score(
         else:
             response = await self._embedding_score(
                 tokenizer=tokenizer,
-                text_1=request.text_1,
-                text_2=request.text_2,
+                input_pairs=input_pairs,
                 request=request,
                 model_name=model_name,
                 request_id=request_id,