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/granite_speech.py

@@ -26,7 +26,7 @@
 
 import math
 from collections.abc import Iterable, Mapping
-from typing import Annotated, Optional, Union
+from typing import Annotated
 
 import torch
 import torch.nn.functional as F
@@ -92,7 +92,7 @@ class GraniteSpeechAudioInputs(TensorSchema):
 
 
 class GraniteSpeechMultiModalProcessingInfo(BaseProcessingInfo):
-    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"audio": 1}
 
     # There is no limit to the maximum number of audio tokens that can be
@@ -196,7 +196,7 @@ class GraniteSpeechDummyInputsBuilder(
         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_audios = mm_counts.get("audio", 0)
         audio_overrides = mm_options.get("audio") if mm_options else None
@@ -222,7 +222,7 @@ class GraniteSpeechEncoderProjector(nn.Module):
         self,
         config: PretrainedConfig,
         cache_config: CacheConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
     ):
         super().__init__()
@@ -279,7 +279,7 @@ class GraniteSpeechConformerFeedForward(nn.Module):
     def __init__(
         self,
         config: PretrainedConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
         prefix: str = "",
     ):
         super().__init__()
@@ -479,7 +479,7 @@ class GraniteSpeechCTCEncoder(nn.Module):
         self,
         config: PretrainedConfig,
         prefix: str,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
     ):
         super().__init__()
         self.config = config
@@ -561,7 +561,7 @@ class GraniteSpeechForConditionalGeneration(
     }
 
     @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("audio"):
             return "<|audio|>"
 
@@ -606,7 +606,7 @@ class GraniteSpeechForConditionalGeneration(
     def _parse_and_validate_audio_input(
         self,
         **kwargs: object,
-    ) -> Optional[GraniteSpeechAudioInputs]:
+    ) -> GraniteSpeechAudioInputs | None:
         input_features = kwargs.pop("input_features", None)
         input_features_mask = kwargs.pop("input_features_mask", None)
         audio_embed_sizes = kwargs.pop("audio_embed_sizes", None)
@@ -763,9 +763,9 @@ class GraniteSpeechForConditionalGeneration(
     def get_input_embeddings(
         self,
         input_ids: torch.Tensor,
-        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+        multimodal_embeddings: MultiModalEmbeddings | None = None,
         *,
-        is_multimodal: Optional[torch.Tensor] = None,
+        is_multimodal: torch.Tensor | None = None,
         # Multi-modal token ID may exceed vocab size
         handle_oov_mm_token: bool = True,
     ) -> torch.Tensor:
@@ -784,10 +784,10 @@ class GraniteSpeechForConditionalGeneration(
         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
 
@@ -799,7 +799,7 @@ class GraniteSpeechForConditionalGeneration(
     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(