test(rl): skip ci tests for resnet10

tests/policies/hilserl/test_modeling_classifier.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import pytest
 import torch
 
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
@@ -37,6 +38,9 @@ def test_classifier_output():
 
 
 @require_package("transformers")
+@pytest.mark.skip(
+    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
+)
 def test_binary_classifier_with_default_params():
     from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
 
@@ -78,6 +82,9 @@ def test_binary_classifier_with_default_params():
 
 
 @require_package("transformers")
+@pytest.mark.skip(
+    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
+)
 def test_multiclass_classifier():
     from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
 
@@ -117,6 +124,9 @@ def test_multiclass_classifier():
 
 
 @require_package("transformers")
+@pytest.mark.skip(
+    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
+)
 def test_default_device():
     from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
 
@@ -129,6 +139,9 @@ def test_default_device():
 
 
 @require_package("transformers")
+@pytest.mark.skip(
+    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
+)
 def test_explicit_device_setup():
     from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
 

tests/policies/test_sac_policy.py

@@ -305,6 +305,9 @@ def test_sac_policy_with_visual_input(batch_size: int, state_dim: int, action_di
     [(1, 6, 6, "helper2424/resnet10"), (1, 6, 6, "facebook/convnext-base-224")],
 )
 @pytest.mark.skipif(not TRANSFORMERS_AVAILABLE, reason="Transformers are not installed")
+@pytest.mark.skip(
+    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
+)
 def test_sac_policy_with_pretrained_encoder(
     batch_size: int, state_dim: int, action_dim: int, vision_encoder_name: str
 ):