fix(config): accept pretrained_model dir for --config_path on resume (#4023)

Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>

tests/configs/test_resume_from_hub.py

@@ -66,3 +66,27 @@ def test_from_pretrained_raises_when_no_root_config_and_no_checkpoints(monkeypat
 
     with pytest.raises(FileNotFoundError, match="train_config.json not found"):
         TrainPipelineConfig.from_pretrained("user/empty-repo")
+
+
+@pytest.mark.parametrize("pass_dir", [False, True])
+def test_resolve_resume_checkpoint_accepts_file_or_pretrained_model_dir(tmp_path, monkeypatch, pass_dir):
+    """`--config_path` may point at the checkpoint's train_config.json or at its
+    pretrained_model/ directory; both must resolve `policy.pretrained_path` to the
+    pretrained_model/ directory (regression test for the directory case, which
+    previously resolved one level too high and failed on model.safetensors)."""
+    pretrained_dir = tmp_path / "checkpoints" / "000002" / "pretrained_model"
+    pretrained_dir.mkdir(parents=True)
+    (pretrained_dir / "train_config.json").touch()
+    target = pretrained_dir if pass_dir else pretrained_dir / "train_config.json"
+
+    from lerobot.policies.act.configuration_act import ACTConfig
+
+    cfg = tc.draccus.parse(TrainPipelineConfig, args=["--dataset.repo_id", "u/d"])
+    cfg.policy = ACTConfig()
+    cfg.resume = True
+    monkeypatch.setattr(tc.parser, "parse_arg", lambda name: str(target) if name == "config_path" else None)
+
+    cfg._resolve_resume_checkpoint()
+
+    assert cfg.policy.pretrained_path == pretrained_dir
+    assert cfg.checkpoint_path == pretrained_dir.parent