Avoid pipeline test failing related to Hub call (#37170)

* cls

* cls

* cls

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/pipelines/test_pipelines_image_classification.py

@@ -14,6 +14,7 @@
 
 import unittest
 
+import datasets
 from huggingface_hub import ImageClassificationOutputElement
 
 from transformers import (
@@ -25,6 +26,7 @@ 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,
@@ -58,6 +60,13 @@ class ImageClassificationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     tf_model_mapping = TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 
+    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"
+        )
+
     def get_test_pipeline(
         self,
         model,
@@ -93,23 +102,17 @@ class ImageClassificationPipelineTests(unittest.TestCase):
             ],
         )
 
-        import datasets
-
-        # 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")
-
         # Accepts URL + PIL.Image + lists
         outputs = image_classifier(
             [
                 Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 # RGBA
-                dataset[0]["image"],
+                self._dataset[0]["image"],
                 # LA
-                dataset[1]["image"],
+                self._dataset[1]["image"],
                 # L
-                dataset[2]["image"],
+                self._dataset[2]["image"],
             ]
         )
         self.assertEqual(