[CI] lazy loading external datasets (#37218)

tests/pipelines/test_pipelines_image_classification.py

@@ -26,7 +26,6 @@ from transformers import (
 )
 from transformers.pipelines import ImageClassificationPipeline, pipeline
 from transformers.testing_utils import (
-    _run_pipeline_tests,
     compare_pipeline_output_to_hub_spec,
     is_pipeline_test,
     nested_simplify,
@@ -59,13 +58,17 @@ else:
 class ImageClassificationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     tf_model_mapping = TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
+    _dataset = None
 
-    if _run_pipeline_tests:
-        # we use revision="refs/pr/1" until the PR is merged
-        # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1
-        _dataset = datasets.load_dataset(
-            "hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1"
-        )
+    @classmethod
+    def _load_dataset(cls):
+        # Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
+        if cls._dataset is None:
+            # we use revision="refs/pr/1" until the PR is merged
+            # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1
+            cls._dataset = datasets.load_dataset(
+                "hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1"
+            )
 
     def get_test_pipeline(
         self,
@@ -92,6 +95,7 @@ class ImageClassificationPipelineTests(unittest.TestCase):
         return image_classifier, examples
 
     def run_pipeline_test(self, image_classifier, examples):
+        self._load_dataset()
         outputs = image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png")
 
         self.assertEqual(