chore(dataset): basic house-keeping

tests/datasets/test_sampler.py

@@ -13,6 +13,9 @@
 # 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 logging
+
+import pytest
 import torch
 from datasets import Dataset
 
@@ -106,3 +109,28 @@ def test_shuffle():
     assert sampler.indices == [0, 1, 2, 3, 4, 5]
     assert len(sampler) == 6
     assert set(sampler) == {0, 1, 2, 3, 4, 5}
+
+
+def test_negative_drop_first_frames_raises():
+    with pytest.raises(ValueError, match="drop_n_first_frames must be >= 0"):
+        EpisodeAwareSampler([0], [10], drop_n_first_frames=-1)
+
+
+def test_negative_drop_last_frames_raises():
+    with pytest.raises(ValueError, match="drop_n_last_frames must be >= 0"):
+        EpisodeAwareSampler([0], [10], drop_n_last_frames=-1)
+
+
+def test_all_episodes_dropped_raises():
+    # All episodes have 1 frame, drop_n_first_frames=1 removes all
+    with pytest.raises(ValueError, match="No valid frames remain"):
+        EpisodeAwareSampler([0, 1, 2], [1, 2, 3], drop_n_first_frames=1)
+
+
+def test_partial_episode_drop_warns(caplog):
+    # Episode 0: 1 frame (dropped), Episode 1: 5 frames (kept)
+    with caplog.at_level(logging.WARNING, logger="lerobot.datasets.sampler"):
+        sampler = EpisodeAwareSampler([0, 1], [1, 6], drop_n_first_frames=1)
+    # Episode 0 is skipped (1 frame, drop 1), Episode 1 keeps frames 2-5
+    assert sampler.indices == [2, 3, 4, 5]
+    assert "Episode 0" in caplog.text