tests(update): updating tests

src/lerobot/datasets/feature_utils.py

@@ -67,7 +67,7 @@ def get_hf_features_from_features(features: dict) -> datasets.Features:
         elif ft["shape"] == (1,):
             hf_features[key] = datasets.Value(dtype=ft["dtype"])
         elif len(ft["shape"]) == 1:
-            # pyarrow rejects fixed_size_list[0], so use a variable length list instead
+            # pyarrow rejects fixed-size lists of length 0, so use a variable length list instead
             length = ft["shape"][0] if ft["shape"][0] > 0 else -1
             hf_features[key] = datasets.Sequence(length=length, feature=datasets.Value(dtype=ft["dtype"]))
         elif len(ft["shape"]) == 2:

tests/datasets/test_compute_stats.py

@@ -689,7 +689,7 @@ def test_compute_episode_stats_string_features_skipped():
 
 
 def test_compute_episode_stats_zero_width_features_skipped(caplog):
-    """Test that features with a zero-width dim (e.g. shape=(0,)) are skipped with a warning."""
+    """Test that features with a zero-width dim (e.g. shape=(0,)) are skipped with a debug log."""
     episode_data = {
         "empty": np.zeros((100, 0), dtype=np.float32),  # Zero-width feature
         "action": np.random.normal(0, 1, (100, 5)),
@@ -699,10 +699,10 @@ def test_compute_episode_stats_zero_width_features_skipped(caplog):
         "action": {"dtype": "float32", "shape": (5,)},
     }
 
-    with caplog.at_level(logging.WARNING):
+    with caplog.at_level(logging.DEBUG):
         stats = compute_episode_stats(episode_data, features)
 
-    # Zero-width features should be skipped with a warning, others computed as usual
+    # Zero-width features should be skipped with a debug log, others computed as usual
     assert "empty" not in stats
     assert "empty" in caplog.text
     assert "action" in stats

tests/datasets/test_datasets.py

@@ -1804,3 +1804,11 @@ def test_episode_filter_unknown_key_raises(tmp_path, lerobot_dataset_factory):
             root=dataset.root,
             episode_filter=lambda ep: ep["not_a_real_field"] > 0,
         )
+
+
+def test_get_hf_features_zero_width_feature_does_not_raise_on_from_dict():
+    import datasets
+
+    features = {"empty": {"dtype": "float32", "shape": (0,), "names": ["empty"]}}
+    hf_features = get_hf_features_from_features(features)
+    datasets.Dataset.from_dict({"empty": [[], []]}, features=hf_features)

tests/datasets/test_feature_utils.py

@@ -1,29 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import pytest
-
-pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
-
-import datasets
-
-from lerobot.datasets.feature_utils import get_hf_features_from_features
-
-
-def test_get_hf_features_zero_width_feature_does_not_raise_on_from_dict():
-    features = {"empty": {"dtype": "float32", "shape": (0,), "names": ["empty"]}}
-    hf_features = get_hf_features_from_features(features)
-    datasets.Dataset.from_dict({"empty": [[], []]}, features=hf_features)