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/qwen2_5_omni_thinker.py

@@ -22,10 +22,10 @@
 # limitations under the License.
 """Inference-only Qwen2.5-Omni model (thinker part)."""
 
-from collections.abc import Iterable, Mapping, Sequence
+from collections.abc import Callable, Iterable, Mapping, Sequence
 from copy import copy
 from functools import partial
-from typing import Annotated, Any, Callable, Literal, Optional, Union
+from typing import Annotated, Any, Literal
 
 import torch
 import torch.nn as nn
@@ -125,7 +125,7 @@ class Qwen2_5OmniAudioFeatureInputs(TensorSchema):
 
     type: Literal["audio_features"]
     input_features: Annotated[
-        Union[torch.Tensor, list[torch.Tensor]],
+        torch.Tensor | list[torch.Tensor],
         TensorShape("nmb", "tsl"),
     ]
 
@@ -191,7 +191,7 @@ class Qwen2_5OmniThinkerMultiModalDataParser(Qwen2VLMultiModalDataParser):
 
     def _parse_audio_data(
         self,
-        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
+        data: dict[str, torch.Tensor] | ModalityData[ImageItem],
     ) -> ModalityDataItems[Any, Any]:
         if isinstance(data, dict):
             return DictEmbeddingItems(
@@ -225,7 +225,7 @@ class Qwen2_5OmniThinkerProcessingInfo(
         assert isinstance(feature_extractor, WhisperFeatureExtractor)
         return feature_extractor
 
-    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"audio": None, "image": None, "video": None}
 
 
@@ -253,7 +253,7 @@ class Qwen2_5OmniThinkerDummyInputsBuilder(
         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)
         num_images = mm_counts.get("image", 0)
@@ -420,7 +420,7 @@ class Qwen2_5OmniThinkerMultiModalProcessor(
         cls,
         thinker_config: PretrainedConfig,
         audio_len: int,
-        video_grid_thw: Union[list[int], torch.Tensor],
+        video_grid_thw: list[int] | torch.Tensor,
         video_second_per_grid_t: float,
     ) -> list[int]:
         """Get video prompt updates when `use_audio_in_video` is True.
@@ -580,7 +580,7 @@ class Qwen2_5OmniThinkerMultiModalProcessor(
 
     def _apply_hf_processor_main(
         self,
-        prompt: Union[str, list[int]],
+        prompt: str | list[int],
         mm_items: MultiModalDataItems,
         hf_processor_mm_kwargs: Mapping[str, object],
         tokenization_kwargs: Mapping[str, object],
@@ -665,7 +665,7 @@ class Qwen2_5OmniConditionalGenerationMixin:
 
     def _parse_and_validate_audio_input(
         self, **kwargs: object
-    ) -> Optional[Qwen2_5OmniAudioFeatureInputs]:
+    ) -> Qwen2_5OmniAudioFeatureInputs | None:
         input_audio_features = kwargs.pop("input_audio_features", None)
         audio_feature_lengths = kwargs.pop("audio_feature_lengths", None)
         feature_attention_mask = kwargs.pop("feature_attention_mask", None)
@@ -693,7 +693,7 @@ class Qwen2_5OmniConditionalGenerationMixin:
     def _parse_and_validate_image_input(
         self,
         **kwargs: dict[str, Any],
-    ) -> Optional[Qwen2_5_VLImageInputs]:
+    ) -> Qwen2_5_VLImageInputs | None:
         pixel_values = kwargs.pop("pixel_values", None)
         image_embeds = kwargs.pop("image_embeds", None)
         image_grid_thw = kwargs.pop("image_grid_thw", None)
@@ -743,7 +743,7 @@ class Qwen2_5OmniConditionalGenerationMixin:
     def _parse_and_validate_video_input(
         self,
         **kwargs: dict[str, Any],
-    ) -> Optional[Qwen2_5_VLVideoInputs]:
+    ) -> Qwen2_5_VLVideoInputs | None:
         pixel_values_videos = kwargs.pop("pixel_values_videos", None)
         video_embeds = kwargs.pop("video_embeds", None)
         video_grid_thw = kwargs.pop("video_grid_thw", None)
@@ -892,7 +892,7 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
     }
 
     @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 "<|vision_start|><|IMAGE|><|vision_end|>"
         if modality.startswith("video"):
@@ -991,12 +991,12 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
         cls,
         input_tokens: list[int],
         hf_config: PretrainedConfig,
-        image_grid_thw: Union[list[list[int]], torch.Tensor],
-        video_grid_thw: Union[list[list[int]], torch.Tensor],
-        second_per_grid_ts: Optional[list[float]] = None,
+        image_grid_thw: list[list[int]] | torch.Tensor,
+        video_grid_thw: list[list[int]] | torch.Tensor,
+        second_per_grid_ts: list[float] | None = None,
         context_len: int = 0,
-        seq_len: Optional[int] = None,
-        audio_feature_lengths: Optional[torch.Tensor] = None,
+        seq_len: int | None = None,
+        audio_feature_lengths: torch.Tensor | None = None,
         use_audio_in_video: bool = False,
     ) -> tuple[torch.Tensor, int]:
         """Get mrope input positions and delta value (Qwen2.5-Omni version).
@@ -1225,9 +1225,9 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
     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,
         handle_oov_mm_token: bool = False,
     ) -> torch.Tensor:
         # This is to satisfy the type checker for each overload
@@ -1241,7 +1241,7 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
             handle_oov_mm_token=handle_oov_mm_token,
         )
 
-    def get_multimodal_embeddings_v0(self, **kwargs: object) -> Optional[NestedTensors]:
+    def get_multimodal_embeddings_v0(self, **kwargs: object) -> NestedTensors | None:
         audio_input = self._parse_and_validate_audio_input(**kwargs)
         image_input = self._parse_and_validate_image_input(**kwargs)
         video_input = self._parse_and_validate_video_input(**kwargs)
@@ -1266,10 +1266,10 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
         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
 
@@ -1281,7 +1281,7 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
     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]: