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/paligemma.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Annotated, Literal, Optional, Union
+from typing import Annotated, Literal, TypeAlias
 
 import torch
 from torch import nn
@@ -74,7 +74,9 @@ class PaliGemmaImageEmbeddingInputs(TensorSchema):
     data: Annotated[torch.Tensor, TensorShape("bn", "ifs", "hs")]
 
 
-PaliGemmaImageInputs = Union[PaliGemmaImagePixelInputs, PaliGemmaImageEmbeddingInputs]
+PaliGemmaImageInputs: TypeAlias = (
+    PaliGemmaImagePixelInputs | PaliGemmaImageEmbeddingInputs
+)
 
 
 class PaliGemmaMultiModalProjector(nn.Module):
@@ -95,7 +97,7 @@ class PaliGemmaProcessingInfo(BaseProcessingInfo):
     def get_vision_encoder_info(self):
         return get_vision_encoder_info(self.get_hf_config())
 
-    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"image": 1}
 
     def get_num_image_tokens(
@@ -120,7 +122,7 @@ class PaliGemmaDummyInputsBuilder(BaseDummyInputsBuilder[PaliGemmaProcessingInfo
         self,
         seq_len: int,
         mm_counts: Mapping[str, int],
-        mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
+        mm_options: Mapping[str, BaseDummyOptions] | None = None,
     ) -> MultiModalDataDict:
         hf_config = self.info.get_hf_config()
         vision_config = hf_config.vision_config
@@ -217,11 +219,11 @@ class PaliGemmaMultiModalProcessor(BaseMultiModalProcessor[PaliGemmaProcessingIn
 
     def apply(
         self,
-        prompt: Union[str, list[int]],
+        prompt: str | list[int],
         mm_data: MultiModalDataDict,
         hf_processor_mm_kwargs: Mapping[str, object],
-        tokenization_kwargs: Optional[Mapping[str, object]] = None,
-        mm_uuids: Optional[MultiModalUUIDDict] = None,
+        tokenization_kwargs: Mapping[str, object] | None = None,
+        mm_uuids: MultiModalUUIDDict | None = None,
     ) -> MultiModalInputs:
         mm_inputs = super().apply(
             prompt,
@@ -273,7 +275,7 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsP
     )
 
     @classmethod
-    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("image"):
             return None
 
@@ -317,7 +319,7 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsP
 
     def _parse_and_validate_image_input(
         self, **kwargs: object
-    ) -> Optional[PaliGemmaImageInputs]:
+    ) -> PaliGemmaImageInputs | None:
         pixel_values = kwargs.pop("pixel_values", None)
         image_embeds = kwargs.pop("image_embeds", None)
 
@@ -386,8 +388,8 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsP
         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,
     ) -> IntermediateTensors:
         if intermediate_tensors is not None:
@@ -402,7 +404,7 @@ class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsP
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-    ) -> Optional[torch.Tensor]:
+    ) -> torch.Tensor | None:
         return self.language_model.compute_logits(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: