use a new random generator

Caltech-101.py

@@ -259,10 +259,7 @@ class Caltech101(datasets.GeneratorBasedBuilder):
 
         is_train_split = split == "train"
 
-        # Sets random seed so the random partitioning of files is the same when
-        # called for the train and test splits.
-        numpy_original_state = np.random.get_state()
-        np.random.seed(1234)
+        rng = np.random.default_rng(1234)
 
         for class_dir in img_dir.iterdir():
             class_name = class_dir.name
@@ -277,10 +274,10 @@ class Caltech101(datasets.GeneratorBasedBuilder):
                 raise ValueError(
                     f"Fewer than {_TRAIN_POINTS_PER_CLASS} ({len(index_codes)}) points in class {class_dir.name}"
                 )
-
-            train_indices = np.random.choice(
+            train_indices = rng.choice(
                 index_codes, _TRAIN_POINTS_PER_CLASS, replace=False
             )
+
             test_indices = set(index_codes).difference(train_indices)
 
             indices_to_emit = train_indices if is_train_split else test_indices
@@ -315,5 +312,3 @@ class Caltech101(datasets.GeneratorBasedBuilder):
                         "box_coord": data["box_coord"],
                     }
                 yield f"{class_dir.name.lower()}/{f'image_{indice}.jpg'}", record
-        # Resets the seeds to their previous states.
-        np.random.set_state(numpy_original_state)