i dont get the style guidelines

Signed-off-by: raushan <raushan@huggingface.co>

Author: zucchini-nlp

vllm/model_executor/models/transformers.py

@@ -163,7 +163,8 @@ def wrapper(*args, **kwargs):
         yield
     finally:
         nn.Module.register_parameter = old_register_parameter
-        for torch_function_name, old_torch_function in tensor_constructors_to_patch.items():
+        for torch_function_name, old_torch_function in tensor_constructors_to_patch.items(
+        ):
             setattr(torch, torch_function_name, old_torch_function)
 
 
@@ -256,7 +257,8 @@ def _get_mm_fields_config(
         # HF Processors always return a mask but vLLM doesn't need it
         hf_inputs.pop("attention_mask", None)
         mm_fields = {
-            key: MultiModalFieldConfig.flat_from_sizes("image", num_image_patches)
+            key: MultiModalFieldConfig.flat_from_sizes("image",
+                                                       num_image_patches)
             for key in hf_inputs
         }
         mm_fields["image_embeds"] = MultiModalFieldConfig.flat_from_sizes(
@@ -310,16 +312,13 @@ def apply(
         if return_mm_hashes:
             raise ValueError(
                 "TransformersMultimodalLM doesn't support mm hashing yet! "
-                "Probably you did not set `disable_mm_preprocessor_cache=True`")
+                "Probably you didn't set `disable_mm_preprocessor_cache=True`")
 
         mm_items = self._to_mm_items(mm_data)
         hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
 
-        (
-            prompt_ids,
-            processed_data,
-            mm_token_type_ids
-        ) = self._apply_hf_processor_text_mm(
+        (prompt_ids, processed_data,
+        mm_token_type_ids) = self._apply_hf_processor_text_mm(
             prompt_text=prompt,
             mm_items=mm_items,
             hf_processor_mm_kwargs=hf_processor_mm_kwargs,

vllm/v1/engine/mm_input_cache.py

@@ -35,12 +35,10 @@ class MirroredProcessingCache:
     def __init__(self, model_config):
         mm_config = model_config.multimodal_config
         disable_mm_preprocessor_cache = (
-            mm_config is not None and mm_config.disable_mm_preprocessor_cache
-        )
+            mm_config is not None and mm_config.disable_mm_preprocessor_cache)
         self.use_cache = not disable_mm_preprocessor_cache
-        self.mm_cache = ProcessingCache.get_lru_cache(
-            VLLM_MM_INPUT_CACHE_GIB, MultiModalKwargs
-        )
+        self.mm_cache = ProcessingCache.get_lru_cache(VLLM_MM_INPUT_CACHE_GIB,
+                                                      MultiModalKwargs)
 
     def get_and_update_p0(
         self,