Use Python 3.9 syntax in examples (#37279)

Signed-off-by: cyy <cyyever@outlook.com>

examples/pytorch/instance-segmentation/run_instance_segmentation.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# coding=utf-8
 # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -18,9 +17,10 @@
 import logging
 import os
 import sys
+from collections.abc import Mapping
 from dataclasses import dataclass, field
 from functools import partial
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, Optional
 
 import albumentations as A
 import numpy as np
@@ -200,7 +200,7 @@ class Evaluator:
     def reset_metric(self):
         self.metric.reset()
 
-    def postprocess_target_batch(self, target_batch) -> List[Dict[str, torch.Tensor]]:
+    def postprocess_target_batch(self, target_batch) -> list[dict[str, torch.Tensor]]:
         """Collect targets in a form of list of dictionaries with keys "masks", "labels"."""
         batch_masks = target_batch[0]
         batch_labels = target_batch[1]
@@ -214,13 +214,13 @@ class Evaluator:
             )
         return post_processed_targets
 
-    def get_target_sizes(self, post_processed_targets) -> List[List[int]]:
+    def get_target_sizes(self, post_processed_targets) -> list[list[int]]:
         target_sizes = []
         for target in post_processed_targets:
             target_sizes.append(target["masks"].shape[-2:])
         return target_sizes
 
-    def postprocess_prediction_batch(self, prediction_batch, target_sizes) -> List[Dict[str, torch.Tensor]]:
+    def postprocess_prediction_batch(self, prediction_batch, target_sizes) -> list[dict[str, torch.Tensor]]:
         """Collect predictions in a form of list of dictionaries with keys "masks", "labels", "scores"."""
 
         model_output = ModelOutput(class_queries_logits=prediction_batch[0], masks_queries_logits=prediction_batch[1])