Update Optional[x] -> x | None and Union[x, y] to x | y (#26633)

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

vllm/model_executor/models/cohere2_vision.py

@@ -4,7 +4,7 @@
 """Command-A-Vision (Cohere2Vision) multimodal model implementation for vLLM."""
 
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Literal, Optional, Union
+from typing import Annotated, Literal
 
 import torch
 from torch import nn
@@ -148,7 +148,7 @@ class Cohere2VisionProcessingInfo(BaseProcessingInfo):
     def get_image_processor(self, **kwargs: object):
         return self.get_hf_processor(**kwargs).image_processor
 
-    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"image": None}
 
     def get_image_size_with_most_features(self) -> ImageSize:
@@ -163,7 +163,7 @@ class Cohere2VisionProcessingInfo(BaseProcessingInfo):
         *,
         image_width: int,
         image_height: int,
-        processor: Optional[Cohere2VisionProcessor],
+        processor: Cohere2VisionProcessor | None,
     ) -> int:
         """
         Calculate the number of image patches for a given image.
@@ -217,7 +217,7 @@ class Cohere2VisionDummyInputsBuilder(
         self,
         seq_len: int,
         mm_counts: Mapping[str, int],
-        mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
+        mm_options: Mapping[str, BaseDummyOptions] | None = None,
     ) -> MultiModalDataDict:
         num_images = mm_counts.get("image", 0)
         image_size = self.info.get_image_size_with_most_features()
@@ -404,7 +404,7 @@ class Cohere2VisionForConditionalGeneration(nn.Module, SupportsMultiModal, Suppo
 
     def _parse_and_validate_image_input(
         self, **kwargs: object
-    ) -> Optional[Cohere2VisionImagePixelInputs]:
+    ) -> Cohere2VisionImagePixelInputs | None:
         pixel_values = kwargs.pop("pixel_values", None)
         num_patches = kwargs.pop("num_patches", None)
         image_embeds = kwargs.pop("image_embeds", None)
@@ -450,10 +450,10 @@ class Cohere2VisionForConditionalGeneration(nn.Module, SupportsMultiModal, Suppo
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
         **kwargs: object,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
+    ) -> torch.Tensor | IntermediateTensors:
         if intermediate_tensors is not None:
             inputs_embeds = None
 
@@ -468,5 +468,5 @@ class Cohere2VisionForConditionalGeneration(nn.Module, SupportsMultiModal, Suppo
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-    ) -> Optional[torch.Tensor]:
+    ) -> torch.Tensor | None:
         return self.language_model.compute_logits(hidden_states)