refactor(processors): several additions (#1926)

* chore(processor): remove merge_transitions functions (#1925)

* refactor(processors): move processors out of configs (#1927)

* chore(processor): streamline combine_features_dict (#1928)

* chore(policies): use new constants (#1929)

* fix(deps): right version transformers (#1930)

* fix(tests): add none + disable async tests for now (#1931)

tests/processor/test_converters.py

@@ -8,100 +8,9 @@ from lerobot.processor.converters import (
     create_transition,
     to_tensor,
     transition_to_batch,
-    transition_to_dataset_frame,
 )
 
 
-def test_transition_to_dataset_frame_merge_and_pack_vectors_and_metadata():
-    # Fabricate dataset features (as stored in dataset.meta["features"])
-    features = {
-        # Action vector: 3 elements in specific order
-        "action": {
-            "dtype": "float32",
-            "shape": (3,),
-            "names": ["j1.pos", "j2.pos", "gripper.pos"],
-        },
-        # Observation state vector: 2 elements
-        "observation.state": {
-            "dtype": "float32",
-            "shape": (2,),
-            "names": ["j1.pos", "j2.pos"],
-        },
-        # Image spec (video/image dtype acceptable)
-        "observation.images.front": {
-            "dtype": "image",
-            "shape": (480, 640, 3),
-            "names": ["h", "w", "c"],
-        },
-    }
-
-    # Build two transitions to be merged: teleop (action) and robot obs (state/images)
-    img = np.random.randint(0, 255, size=(480, 640, 3), dtype=np.uint8)
-
-    teleop_transition = {
-        TransitionKey.OBSERVATION: {},
-        TransitionKey.ACTION: {
-            "action.j1.pos": torch.tensor(1.1),
-            "action.j2.pos": torch.tensor(2.2),
-            # gripper.pos missing → defaults to 0.0
-            "action.ee.x": 0.5,  # ignored, not in features["action"]["names"]
-        },
-        TransitionKey.COMPLEMENTARY_DATA: {
-            "frame_is_pad": True,
-            "task": "Pick cube",
-        },
-    }
-
-    robot_transition = {
-        TransitionKey.OBSERVATION: {
-            "observation.state.j1.pos": torch.tensor(10.0),
-            "observation.state.j2.pos": torch.tensor(20.0),
-            "observation.images.front": img,
-        },
-        TransitionKey.REWARD: torch.tensor(5.0),
-        TransitionKey.DONE: True,
-        TransitionKey.TRUNCATED: False,
-        TransitionKey.INFO: {"note": "ok"},
-    }
-
-    # Directly call the refactored function
-    batch = transition_to_dataset_frame([teleop_transition, robot_transition], features)
-
-    # Images passthrough
-    assert "observation.images.front" in batch
-    assert batch["observation.images.front"].shape == img.shape
-    assert batch["observation.images.front"].dtype == np.uint8
-    assert np.shares_memory(batch["observation.images.front"], img) or np.array_equal(
-        batch["observation.images.front"], img
-    )
-
-    # Observation.state vector
-    assert "observation.state" in batch
-    obs_vec = batch["observation.state"]
-    assert isinstance(obs_vec, np.ndarray) and obs_vec.dtype == np.float32
-    assert obs_vec.shape == (2,)
-    assert obs_vec[0] == pytest.approx(10.0)
-    assert obs_vec[1] == pytest.approx(20.0)
-
-    # Action vector
-    assert "action" in batch
-    act_vec = batch["action"]
-    assert isinstance(act_vec, np.ndarray) and act_vec.dtype == np.float32
-    assert act_vec.shape == (3,)
-    assert act_vec[0] == pytest.approx(1.1)
-    assert act_vec[1] == pytest.approx(2.2)
-    assert act_vec[2] == pytest.approx(0.0)  # default for missing gripper.pos
-
-    # Next.* metadata
-    assert batch["next.reward"] == pytest.approx(5.0)
-    assert batch["next.done"] is True
-    assert batch["next.truncated"] is False
-
-    # Complementary data
-    assert batch["frame_is_pad"] is True
-    assert batch["task"] == "Pick cube"
-
-
 # Tests for the unified to_tensor function
 def test_to_tensor_numpy_arrays():
     """Test to_tensor with various numpy arrays."""