From a196639c099b1ee79e0eafe34a99e0ca4b7be6fe Mon Sep 17 00:00:00 2001
From: Steven Palma <imstevenpmwork@ieee.org>
Date: Thu, 25 Sep 2025 15:13:12 +0200
Subject: [PATCH] test(processor): fix expected raise when normalization types
 are missing (#2040)

---
 tests/processor/test_normalize_processor.py | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/tests/processor/test_normalize_processor.py b/tests/processor/test_normalize_processor.py
index 9669b4ea9..f07b7af45 100644
--- a/tests/processor/test_normalize_processor.py
+++ b/tests/processor/test_normalize_processor.py
@@ -291,7 +291,7 @@ def test_quantile_division_by_zero():
 
 
 def test_quantile_partial_stats():
-    """Test that quantile normalization handles missing quantile stats gracefully."""
+    """Test that quantile normalization handles missing quantile stats by raising."""
     features = {
         "observation.state": PolicyFeature(FeatureType.STATE, (2,)),
     }
@@ -313,11 +313,8 @@ def test_quantile_partial_stats():
     }
     transition = create_transition(observation=observation)
 
-    normalized_transition = normalizer(transition)
-    normalized_obs = normalized_transition[TransitionKey.OBSERVATION]
-
-    # Should pass through unchanged when stats are incomplete
-    assert torch.allclose(normalized_obs["observation.state"], observation["observation.state"])
+    with pytest.raises(ValueError, match="QUANTILES normalization mode requires q01 and q99 stats"):
+        _ = normalizer(transition)
 
 
 def test_quantile_mixed_with_other_modes():
@@ -771,7 +768,7 @@ def test_empty_stats():
 
 
 def test_partial_stats():
-    """If statistics are incomplete, the value should pass through unchanged."""
+    """If statistics are incomplete, we should raise."""
     stats = {"observation.image": {"mean": [0.5]}}  # Missing std / (min,max)
     features = {"observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96))}
     norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
@@ -779,8 +776,8 @@ def test_partial_stats():
     observation = {"observation.image": torch.tensor([0.7])}
     transition = create_transition(observation=observation)
 
-    processed = normalizer(transition)[TransitionKey.OBSERVATION]
-    assert torch.allclose(processed["observation.image"], observation["observation.image"])
+    with pytest.raises(ValueError, match="MEAN_STD normalization mode requires mean and std stats"):
+        _ = normalizer(transition)[TransitionKey.OBSERVATION]
 
 
 def test_missing_action_stats_no_error():