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

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

tests/plugins/prithvi_io_processor_plugin/prithvi_io_processor/prithvi_processor.py

@@ -1,15 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from __future__ import annotations
-
 import base64
 import datetime
 import os
 import tempfile
 import urllib.request
 from collections.abc import Sequence
-from typing import Any, Union
+from typing import Any
 
 import albumentations
 import numpy as np
@@ -160,11 +158,11 @@ def read_geotiff(
 
 
 def load_image(
-    data: Union[list[str]],
+    data: list[str],
     path_type: str,
     mean: list[float] | None = None,
     std: list[float] | None = None,
-    indices: Union[list[int], None] | None = None,
+    indices: list[int] | None | None = None,
 ):
     """Build an input example by loading images in *file_paths*.
 
@@ -280,7 +278,7 @@ class PrithviMultimodalDataProcessor(IOProcessor):
         prompt: IOProcessorInput,
         request_id: str | None = None,
         **kwargs,
-    ) -> Union[PromptType, Sequence[PromptType]]:
+    ) -> PromptType | Sequence[PromptType]:
         image_data = dict(prompt)
 
         if request_id:

tests/plugins/prithvi_io_processor_plugin/prithvi_io_processor/types.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Any, Literal, Optional, TypedDict, Union
+from typing import Any, Literal, TypedDict
 
 import albumentations
 from pydantic import BaseModel
@@ -38,7 +38,7 @@ class ImagePrompt(BaseModel):
     """
 
 
-MultiModalPromptType = Union[ImagePrompt]
+MultiModalPromptType = ImagePrompt
 
 
 class ImageRequestOutput(BaseModel):
@@ -54,4 +54,4 @@ class ImageRequestOutput(BaseModel):
     type: Literal["path", "b64_json"]
     format: str
     data: str
-    request_id: Optional[str] = None
+    request_id: str | None = None

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/my_gemma_embedding.py

@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from collections.abc import Iterable
-from typing import Optional, Union
 
 import torch
 import torch.nn as nn
@@ -44,9 +43,9 @@ class MyGemma2Embedding(nn.Module):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
         hidden_states = self.model(
             input_ids,
             positions,

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/my_llava.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Optional
 
 import torch
 
@@ -20,7 +19,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY
     dummy_inputs=LlavaDummyInputsBuilder,
 )
 class MyLlava(LlavaForConditionalGeneration):
-    def compute_logits(self, hidden_states: torch.Tensor) -> Optional[torch.Tensor]:
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor | None:
         # this dummy model always predicts the first token
         logits = super().compute_logits(hidden_states)
         if logits is not None:

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/my_opt.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Optional
 
 import torch
 
@@ -9,7 +8,7 @@ from vllm.model_executor.models.opt import OPTForCausalLM
 
 
 class MyOPTForCausalLM(OPTForCausalLM):
-    def compute_logits(self, hidden_states: torch.Tensor) -> Optional[torch.Tensor]:
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor | None:
         # this dummy model always predicts the first token
         logits = super().compute_logits(hidden_states)
         if logits is not None:

tests/plugins/vllm_add_dummy_platform/vllm_add_dummy_platform/__init__.py

@@ -1,10 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Optional
 
-
-def dummy_platform_plugin() -> Optional[str]:
+def dummy_platform_plugin() -> str | None:
     return "vllm_add_dummy_platform.dummy_platform.DummyPlatform"