upgrade test, fix failing

tests/policies/xvla/test_xvla_original_vs_lerobot.py

@@ -18,7 +18,6 @@
 # ruff: noqa: E402
 
 import gc
-import os
 import random
 from copy import deepcopy
 from typing import Any
@@ -27,20 +26,12 @@ import numpy as np
 import pytest
 import torch
 
-# Skip if transformers is not available
-pytest.importorskip("transformers")
-
-# Skip this entire module in CI
-pytestmark = pytest.mark.skipif(
-    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
-    reason="This test requires XVLA model access and is not meant for CI",
-)
-
 from lerobot.policies.xvla.configuration_xvla import XVLAConfig
 from lerobot.policies.xvla.modeling_xvla import XVLAPolicy
 from lerobot.policies.xvla.processor_xvla import make_xvla_pre_post_processors
 from lerobot.processor import PolicyAction, PolicyProcessorPipeline  # noqa: E402
 from lerobot.utils.constants import OBS_IMAGES, OBS_STATE  # noqa: E402
+from tests.utils import require_package  # noqa: E402
 
 # Constants
 DUMMY_ACTION_DIM = 7  # Standard robot arm action dimension
@@ -171,6 +162,8 @@ def preprocessor(xvla_components):
     return xvla_components[1]
 
 
+@require_package("transformers")
+@require_package("timm")
 def test_xvla_preprocessor_alignment(policy, preprocessor):
     """Test that LeRobot XVLA preprocessor produces expected outputs."""
     print("\n" + "=" * 80)
@@ -201,22 +194,24 @@ def test_xvla_preprocessor_alignment(policy, preprocessor):
     for key, expected_shape in expected_shapes.items():
         if key in lerobot_inputs:
             actual_shape = tuple(lerobot_inputs[key].shape)
-            print(f"\n🔎 Key: {key}")
+            print(f"\nKey: {key}")
-            print(f"  Expected shape: {expected_shape}")
+            print(f"Expected shape: {expected_shape}")
-            print(f"  Actual shape: {actual_shape}")
+            print(f"Actual shape: {actual_shape}")
 
             if actual_shape == expected_shape:
-                print("  ✔️ Shape matches!")
+                print("Shape matches!")
             else:
-                print("  ❌ Shape mismatch!")
+                print("Shape mismatch!")
 
             assert actual_shape == expected_shape, f"Shape mismatch for {key}"
         else:
-            print(f"\n⚠️ Key '{key}' not found in inputs!")
+            print(f"\nKey '{key}' not found in inputs!")
 
-    print("\n✅ All preprocessor outputs have correct shapes!")
+    print("\nAll preprocessor outputs have correct shapes!")
 
 
+@require_package("transformers")
+@require_package("timm")
 def test_xvla_action_generation(policy, preprocessor):
     """Test XVLA LeRobot implementation generates expected actions."""
     print("\n" + "=" * 80)
@@ -283,17 +278,19 @@ def test_xvla_action_generation(policy, preprocessor):
     tolerances = [1e-5, 1e-4, 1e-3, 1e-2]
     for tol in tolerances:
         is_close = torch.allclose(actual_first_5, EXPECTED_ACTIONS_FIRST_5, atol=tol)
-        status = "✔️" if is_close else "❌"
+        status = "Success" if is_close else "Failure"
-        print(f"{status} First 5 actions close (atol={tol}): {is_close}")
+        print(f"{status}: First 5 actions close (atol={tol}): {is_close}")
 
     # Assert with reasonable tolerance
     tolerance = 1e-3
     assert torch.allclose(actual_first_5, EXPECTED_ACTIONS_FIRST_5, atol=tolerance), (
         f"First 5 actions differ by more than tolerance ({tolerance})"
     )
-    print(f"\n✅ Success: Actions match expected values within tolerance ({tolerance})!")
+    print(f"\nSuccess: Actions match expected values within tolerance ({tolerance})!")
 
 
+@require_package("transformers")
+@require_package("timm")
 def test_xvla_inference_reproducibility(policy, preprocessor):
     """Test that XVLA inference is reproducible with the same seed."""
     print("\n" + "=" * 80)
@@ -324,16 +321,16 @@ def test_xvla_inference_reproducibility(policy, preprocessor):
     print("\nComparing two runs:")
     print("-" * 80)
     if torch.allclose(actions_1, actions_2, atol=1e-8):
-        print("✔️ Inference is perfectly reproducible!")
+        print("Inference is perfectly reproducible!")
     else:
         diff = torch.abs(actions_1 - actions_2)
-        print("⚠️ Small differences detected:")
+        print("Small differences detected:")
         print(f"  Max diff: {diff.max().item():.6e}")
         print(f"  Mean diff: {diff.mean().item():.6e}")
 
     assert torch.allclose(actions_1, actions_2, atol=1e-6), "Inference should be reproducible!"
 
-    print("\n✅ Inference is reproducible!")
+    print("\nInference is reproducible!")
 
 
 if __name__ == "__main__":
@@ -353,13 +350,13 @@ if __name__ == "__main__":
         test_xvla_inference_reproducibility(policy, preprocessor)
 
         print("\n" + "=" * 80)
-        print("✅ All tests passed!")
+        print("All tests passed!")
         print("=" * 80)
 
         cleanup_memory()
     except Exception as e:
         print("\n" + "=" * 80)
-        print(f"❌ Test failed with error: {e}")
+        print(f"Test failed with error: {e}")
         print("=" * 80)
         cleanup_memory()
         raise