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import pytest
pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
from lerobot.configs.recipe import MessageTurn, TrainingRecipe # noqa: E402
from lerobot.datasets.language_render import ( # noqa: E402
EMITTED_AT_TOLERANCE_S,
active_at,
emitted_at,
nth_next,
nth_prev,
render_sample,
)
def persistent_row(role, content, style, timestamp, tool_calls=None, camera=None):
return {
"role": role,
"content": content,
"style": style,
"timestamp": timestamp,
"camera": camera,
"tool_calls": tool_calls,
}
def event_row(role, content, style, tool_calls=None, camera=None):
return {
"role": role,
"content": content,
"style": style,
"camera": camera,
"tool_calls": tool_calls,
}
PERSISTENT = [
persistent_row("assistant", "plan 0", "plan", 0.0),
persistent_row("assistant", "memory 0", "memory", 0.0),
persistent_row("assistant", "subtask 0", "subtask", 0.0),
persistent_row("assistant", "memory 1", "memory", 1.0),
persistent_row("assistant", "subtask 1", "subtask", 1.0),
]
EVENTS_AT_1 = [
event_row("user", "what is visible?", "vqa", camera="observation.images.top"),
event_row("assistant", '{"count": 2}', "vqa", camera="observation.images.top"),
]
EVENTS_AT_2 = [
event_row("user", "skip wiping", "interjection"),
event_row(
"assistant",
None,
None,
[{"type": "function", "function": {"name": "say", "arguments": {"text": "Skipping wiping."}}}],
),
]
# Same emission tick, two cameras: triggers per-camera disambiguation in
# resolvers, mirroring how Module 3 of the annotation pipeline writes one
# (vqa, user) + (vqa, assistant) pair per camera.
EVENTS_AT_3_TWO_CAMERAS = [
event_row("user", "how many cups (top)?", "vqa", camera="observation.images.top"),
event_row("assistant", '{"count": 3}', "vqa", camera="observation.images.top"),
event_row("user", "how many cups (wrist)?", "vqa", camera="observation.images.wrist"),
event_row("assistant", '{"count": 1}', "vqa", camera="observation.images.wrist"),
]
def test_resolver_temporal_semantics():
assert active_at(0.5, persistent=PERSISTENT, style="subtask")["content"] == "subtask 0"
assert active_at(1.0, persistent=PERSISTENT, style="subtask")["content"] == "subtask 1"
assert emitted_at(0.5, persistent=PERSISTENT, events=[], style="vqa", role="assistant") is None
assert (
emitted_at(1.0, persistent=PERSISTENT, events=EVENTS_AT_1, style="vqa", role="assistant")["content"]
== '{"count": 2}'
)
def test_persistent_relative_resolvers_reject_event_styles():
with pytest.raises(ValueError, match="event-only"):
active_at(1.0, persistent=PERSISTENT, style="vqa")
with pytest.raises(ValueError, match="event-only"):
nth_prev(1.0, persistent=PERSISTENT, style="interjection")
def test_nth_prev_and_next():
assert nth_prev(1.0, persistent=PERSISTENT, style="subtask", offset=1)["content"] == "subtask 0"
assert nth_next(0.0, persistent=PERSISTENT, style="subtask", offset=1)["content"] == "subtask 1"
def test_substitution_if_present_multimodal_and_tool_calls():
recipe = TrainingRecipe(
messages=[
MessageTurn(
role="user",
content=[
{"type": "image", "feature": "observation.images.top"},
{"type": "text", "text": "${task}: ${interjection}"},
],
stream="high_level",
if_present="interjection",
),
MessageTurn(
role="assistant",
content="${plan}",
stream="high_level",
target=True,
tool_calls_from="speech",
),
],
bindings={"plan": "active_at(t, style=plan)"},
)
rendered = render_sample(
recipe=recipe,
persistent=PERSISTENT,
events=EVENTS_AT_2,
t=2.0,
sample_idx=0,
task="clean kitchen",
)
assert rendered["messages"][0]["content"][1]["text"] == "clean kitchen: skip wiping"
assert rendered["messages"][1]["content"] == "plan 0"
assert rendered["messages"][1]["tool_calls"][0]["function"]["name"] == "say"
assert rendered["message_streams"] == ["high_level", "high_level"]
assert rendered["target_message_indices"] == [1]
def test_exact_event_miss_returns_none_when_target_skips():
recipe = TrainingRecipe(
messages=[
MessageTurn(role="user", content="${vqa_query}", stream="high_level", if_present="vqa_query"),
MessageTurn(
role="assistant",
content="${vqa}",
stream="high_level",
target=True,
if_present="vqa",
),
]
)
assert (
render_sample(recipe=recipe, persistent=PERSISTENT, events=EVENTS_AT_2, t=0.0, sample_idx=0) is None
)
def test_deterministic_blend_sampling():
recipe = TrainingRecipe(
blend={
"a": TrainingRecipe(
weight=1.0,
messages=[
MessageTurn(role="user", content="${task}", stream="high_level"),
MessageTurn(role="assistant", content="a", stream="high_level", target=True),
],
),
"b": TrainingRecipe(
weight=1.0,
messages=[
MessageTurn(role="user", content="${task}", stream="high_level"),
MessageTurn(role="assistant", content="b", stream="high_level", target=True),
],
),
}
)
first = render_sample(
recipe=recipe, persistent=PERSISTENT, events=EVENTS_AT_2, t=0.0, sample_idx=123, task="x"
)
second = render_sample(
recipe=recipe, persistent=PERSISTENT, events=EVENTS_AT_2, t=0.0, sample_idx=123, task="x"
)
assert first == second
def test_emitted_at_filters_vqa_by_camera():
top = emitted_at(
3.0,
persistent=PERSISTENT,
events=EVENTS_AT_3_TWO_CAMERAS,
style="vqa",
role="assistant",
camera="observation.images.top",
)
wrist = emitted_at(
3.0,
persistent=PERSISTENT,
events=EVENTS_AT_3_TWO_CAMERAS,
style="vqa",
role="assistant",
camera="observation.images.wrist",
)
assert top["content"] == '{"count": 3}'
assert wrist["content"] == '{"count": 1}'
def test_emitted_at_raises_on_ambiguous_per_camera_vqa():
with pytest.raises(ValueError, match="Ambiguous resolver"):
emitted_at(
3.0,
persistent=PERSISTENT,
events=EVENTS_AT_3_TWO_CAMERAS,
style="vqa",
role="assistant",
)
def _vqa_subrecipe(camera: str) -> TrainingRecipe:
return TrainingRecipe(
weight=1.0,
bindings={
"vqa_query": f"emitted_at(t, style=vqa, role=user, camera={camera})",
"vqa": f"emitted_at(t, style=vqa, role=assistant, camera={camera})",
},
messages=[
MessageTurn(
role="user",
content=[{"type": "image", "feature": camera}, {"type": "text", "text": "${vqa_query}"}],
stream="high_level",
if_present="vqa_query",
),
MessageTurn(
role="assistant",
content="${vqa}",
stream="high_level",
target=True,
if_present="vqa",
),
],
)
@pytest.mark.parametrize(
("camera", "expected_query", "expected_answer"),
[
("observation.images.top", "how many cups (top)?", '{"count": 3}'),
("observation.images.wrist", "how many cups (wrist)?", '{"count": 1}'),
],
)
def test_per_camera_blend_renders_both_views(camera, expected_query, expected_answer):
rendered = render_sample(
recipe=_vqa_subrecipe(camera),
persistent=PERSISTENT,
events=EVENTS_AT_3_TWO_CAMERAS,
t=3.0,
sample_idx=0,
)
assert rendered["messages"][0]["content"][0]["feature"] == camera
assert rendered["messages"][0]["content"][1]["text"] == expected_query
assert rendered["messages"][1]["content"] == expected_answer
def test_resolve_task_picks_rephrasing_deterministically_per_sample():
rephrasings = [
persistent_row("user", "tidy the kitchen", "task_aug", 0.0),
persistent_row("user", "please clean up the kitchen", "task_aug", 0.0),
persistent_row("user", "kitchen needs tidying", "task_aug", 0.0),
persistent_row("user", "make the kitchen clean", "task_aug", 0.0),
]
recipe = TrainingRecipe(
messages=[
MessageTurn(role="user", content="${task}", stream="high_level"),
MessageTurn(role="assistant", content="ok", stream="high_level", target=True),
]
)
# No explicit task override → resolver consults persistent rows.
seen: set[str] = set()
for sample_idx in range(64):
rendered = render_sample(
recipe=recipe,
persistent=rephrasings,
events=[],
t=0.0,
sample_idx=sample_idx,
dataset_ctx={"task": "canonical kitchen task"},
)
seen.add(rendered["messages"][0]["content"])
# Every rephrasing should be reachable across enough samples.
assert seen == {r["content"] for r in rephrasings}
# Same sample_idx → same pick (determinism).
a = render_sample(
recipe=recipe,
persistent=rephrasings,
events=[],
t=0.0,
sample_idx=42,
dataset_ctx={"task": "canonical"},
)
b = render_sample(
recipe=recipe,
persistent=rephrasings,
events=[],
t=0.0,
sample_idx=42,
dataset_ctx={"task": "canonical"},
)
assert a["messages"][0]["content"] == b["messages"][0]["content"]
def test_resolve_task_falls_back_to_canonical_without_rephrasings():
recipe = TrainingRecipe(
messages=[
MessageTurn(role="user", content="${task}", stream="high_level"),
MessageTurn(role="assistant", content="ok", stream="high_level", target=True),
]
)
rendered = render_sample(
recipe=recipe,
persistent=PERSISTENT, # no task_aug rows
events=[],
t=0.0,
sample_idx=0,
dataset_ctx={"task": "clean the kitchen"},
)
assert rendered["messages"][0]["content"] == "clean the kitchen"
def test_resolve_task_explicit_override_beats_rephrasings():
rephrasings = [
persistent_row("user", "rephrased one", "task_aug", 0.0),
persistent_row("user", "rephrased two", "task_aug", 0.0),
]
recipe = TrainingRecipe(
messages=[
MessageTurn(role="user", content="${task}", stream="high_level"),
MessageTurn(role="assistant", content="ok", stream="high_level", target=True),
]
)
rendered = render_sample(
recipe=recipe,
persistent=rephrasings,
events=[],
t=0.0,
sample_idx=0,
task="explicit override wins",
dataset_ctx={"task": "canonical"},
)
assert rendered["messages"][0]["content"] == "explicit override wins"
def test_emitted_at_persistent_tolerates_small_timestamp_drift():
"""Persistent ``emitted_at`` should match within EMITTED_AT_TOLERANCE_S
so callers that derive ``t`` arithmetically (``frame_idx / fps``) still
line up with the parquet-stored timestamp.
"""
rows = [persistent_row("assistant", "memo", "memory", 1.0)]
# Half a tolerance window — bit-different float, comfortably inside
inside = emitted_at(1.0 + EMITTED_AT_TOLERANCE_S / 2, persistent=rows, events=[], style="memory")
assert inside is not None and inside["content"] == "memo"
# Just past the window — no match
outside = emitted_at(1.0 + EMITTED_AT_TOLERANCE_S * 2, persistent=rows, events=[], style="memory")
assert outside is None
def test_render_sample_rejects_non_dict_language_rows():
"""``_normalize_rows`` must surface malformed inputs as TypeError.
A pipeline that hands the renderer a non-dict (e.g. a stray string)
is a real upstream bug — silent skipping would let it propagate.
"""
recipe = TrainingRecipe(
messages=[
MessageTurn(role="user", content="${task}", stream="high_level"),
MessageTurn(role="assistant", content="ok", stream="high_level", target=True),
]
)
with pytest.raises(TypeError, match="must be dictionaries"):
render_sample(
recipe=recipe,
persistent=["not a dict"],
events=[],
t=0.0,
sample_idx=0,
task="x",
)
def test_low_level_branch_renders_active_subtask():
low_level = TrainingRecipe(
blend={
"low": TrainingRecipe(
weight=1.0,
messages=[
MessageTurn(
role="user",
content="${task}\nPlan: ${plan}\nMemory: ${memory}",
stream="high_level",
),
MessageTurn(
role="assistant",
content="${subtask}",
stream="low_level",
target=True,
),
],
)
}
)
rendered = render_sample(
recipe=low_level,
persistent=PERSISTENT,
events=[],
t=0.5,
sample_idx=0,
task="clean kitchen",
)
assert rendered["messages"][-1] == {"role": "assistant", "content": "subtask 0"}
assert rendered["message_streams"][-1] == "low_level"
assert rendered["target_message_indices"] == [1]
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