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# 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.
"""Pre-write validation against staged outputs.
Runs after all three modules have written their per-episode artifacts but
*before* the writer rewrites parquet shards. The validator never touches
parquet; it only inspects the staging tree and the source frame timestamps
exposed by :class:`EpisodeRecord`.
Checks (per the plan's "Intermediate staging and validation" section):
- exact timestamp alignment against source frame timestamps
- no orphan speech / interjection pairs
- plan / memory emission consistency (events have a paired persistent row)
- VQA assistant ``content`` is valid JSON (one of bbox / keypoint / count /
attribute / spatial)
- every row maps to its correct column under :func:`column_for_style`
"""
from __future__ import annotations
import json
import logging
from collections.abc import Iterable, Sequence
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
from lerobot.datasets.language import (
LANGUAGE_EVENTS,
LANGUAGE_PERSISTENT,
column_for_style,
is_view_dependent_style,
validate_camera_field,
)
from .reader import EpisodeRecord
from .staging import EpisodeStaging
logger = logging.getLogger(__name__)
@dataclass
class ValidationReport:
"""Outcome of one validation pass across all episodes."""
errors: list[str] = field(default_factory=list)
warnings: list[str] = field(default_factory=list)
episodes_checked: int = 0
@property
def ok(self) -> bool:
return not self.errors
def add_error(self, message: str) -> None:
self.errors.append(message)
def add_warning(self, message: str) -> None:
self.warnings.append(message)
def summary(self) -> str:
return f"checked={self.episodes_checked} errors={len(self.errors)} warnings={len(self.warnings)}"
VQA_ANSWER_SHAPES: dict[str, set[str]] = {
"bbox": {"detections"},
"keypoint": {"label", "point_format", "point"},
"count": {"label", "count"},
"attribute": {"label", "attribute", "value"},
"spatial": {"subject", "relation", "object"},
}
def classify_vqa_answer(payload: Any) -> str | None:
"""Best-effort classification of a VQA answer payload to a question type."""
if not isinstance(payload, dict):
return None
keys = set(payload.keys())
for kind, required in VQA_ANSWER_SHAPES.items():
if required.issubset(keys):
return kind
return None
@dataclass
class StagingValidator:
"""Walks the staging tree and produces a :class:`ValidationReport`."""
timestamp_atol: float = 0.0 # exact-match by default
dataset_camera_keys: tuple[str, ...] | None = None
"""Known ``observation.images.*`` keys on the dataset. When set, the
validator additionally enforces that every view-dependent row's
``camera`` field references one of these keys. Pass ``None`` (default)
to skip that cross-check (e.g. in unit tests with no real dataset)."""
def validate(
self,
records: Sequence[EpisodeRecord],
staging_dir: Path,
) -> ValidationReport:
report = ValidationReport()
for record in records:
self._validate_episode(record, staging_dir, report)
report.episodes_checked += 1
return report
def _validate_episode(
self,
record: EpisodeRecord,
staging_dir: Path,
report: ValidationReport,
) -> None:
staging = EpisodeStaging(staging_dir, record.episode_index)
staged = staging.read_all()
all_rows: list[dict[str, Any]] = []
for module_name, rows in staged.items():
for row in rows:
row = {**row, "_module": module_name}
all_rows.append(row)
frame_ts = set(record.frame_timestamps)
events: list[dict[str, Any]] = []
persistent: list[dict[str, Any]] = []
for row in all_rows:
self._check_column_routing(row, report, record.episode_index)
self._check_camera_field(row, report, record.episode_index, self.dataset_camera_keys)
# ``_check_column_routing`` already recorded any unknown-style error;
# don't let the same ``column_for_style`` lookup raise here uncaught.
try:
column = column_for_style(row.get("style"))
except ValueError:
continue
if column == LANGUAGE_PERSISTENT:
persistent.append(row)
else:
events.append(row)
for row in events:
self._check_event_timestamp_alignment(row, frame_ts, report, record.episode_index)
self._check_speech_interjection_pairs(events, report, record.episode_index)
self._check_plan_memory_consistency(persistent, events, report, record.episode_index)
self._check_vqa_json(events, report, record.episode_index)
self._check_vqa_uniqueness_per_frame_camera(events, report, record.episode_index)
def _check_camera_field(
self,
row: dict[str, Any],
report: ValidationReport,
episode_index: int,
dataset_camera_keys: Sequence[str] | None,
) -> None:
"""Enforce the camera invariant + that the key matches the dataset's cameras."""
style = row.get("style")
camera = row.get("camera")
try:
validate_camera_field(style, camera)
except ValueError as exc:
report.add_error(f"ep={episode_index} module={row.get('_module')}: {exc}")
return
if is_view_dependent_style(style) and dataset_camera_keys and camera not in dataset_camera_keys:
report.add_error(
f"ep={episode_index} module={row.get('_module')}: camera {camera!r} on style "
f"{style!r} is not one of the dataset's video keys {sorted(dataset_camera_keys)!r}"
)
def _check_vqa_uniqueness_per_frame_camera(
self,
events: Iterable[dict[str, Any]],
report: ValidationReport,
episode_index: int,
) -> None:
"""Ensure at most one (vqa, user) and one (vqa, assistant) per (t, camera)."""
counts: dict[tuple[float, str, str], int] = {}
for row in events:
if row.get("style") != "vqa":
continue
ts = row.get("timestamp")
camera = row.get("camera")
role = row.get("role")
if ts is None or camera is None or role is None:
continue # other validators flag these
key = (float(ts), str(camera), str(role))
counts[key] = counts.get(key, 0) + 1
for (ts, camera, role), n in counts.items():
if n > 1:
report.add_error(
f"ep={episode_index}: {n} duplicate vqa rows at t={ts} "
f"camera={camera!r} role={role!r}; expected at most one per (t, camera, role)"
)
def _check_column_routing(
self,
row: dict[str, Any],
report: ValidationReport,
episode_index: int,
) -> None:
style = row.get("style")
module = row.get("_module")
try:
target_col = column_for_style(style)
except ValueError:
report.add_error(f"ep={episode_index} module={module}: unknown style {style!r}")
return
if module == "plan" and target_col != LANGUAGE_PERSISTENT:
report.add_error(
f"ep={episode_index} module=plan emitted style {style!r} that routes to {target_col} (must be persistent)"
)
if module in {"interjections", "vqa"} and target_col != LANGUAGE_EVENTS:
report.add_error(
f"ep={episode_index} module={module} emitted style {style!r} that routes to {target_col} (must be events)"
)
def _check_event_timestamp_alignment(
self,
row: dict[str, Any],
frame_ts: set[float],
report: ValidationReport,
episode_index: int,
) -> None:
ts = row.get("timestamp")
if ts is None:
report.add_error(f"ep={episode_index}: event row missing timestamp: {row!r}")
return
if self.timestamp_atol == 0.0:
if float(ts) not in frame_ts:
report.add_error(
f"ep={episode_index}: event row timestamp {ts!r} does not match any source frame timestamp"
)
else:
if not any(abs(float(ts) - f) <= self.timestamp_atol for f in frame_ts):
report.add_error(
f"ep={episode_index}: event row timestamp {ts!r} not within {self.timestamp_atol}s of any frame"
)
def _check_speech_interjection_pairs(
self,
events: Iterable[dict[str, Any]],
report: ValidationReport,
episode_index: int,
) -> None:
speech_ts: dict[float, int] = {}
interjection_ts: dict[float, int] = {}
for row in events:
ts = row.get("timestamp")
if ts is None:
continue
ts_f = float(ts)
if row.get("style") is None and row.get("role") == "assistant":
speech_ts[ts_f] = speech_ts.get(ts_f, 0) + 1
if row.get("style") == "interjection":
interjection_ts[ts_f] = interjection_ts.get(ts_f, 0) + 1
for ts in interjection_ts:
if ts not in speech_ts:
report.add_error(f"ep={episode_index}: interjection at t={ts} has no paired speech atom")
def _check_plan_memory_consistency(
self,
persistent: Sequence[dict[str, Any]],
events: Sequence[dict[str, Any]],
report: ValidationReport,
episode_index: int,
) -> None:
plan_ts = sorted({float(r["timestamp"]) for r in persistent if r.get("style") == "plan"})
memory_ts = sorted({float(r["timestamp"]) for r in persistent if r.get("style") == "memory"})
subtask_ts = sorted({float(r["timestamp"]) for r in persistent if r.get("style") == "subtask"})
interjection_ts = sorted(
{
float(r["timestamp"])
for r in events
if r.get("style") == "interjection" and r.get("timestamp") is not None
}
)
if persistent and not plan_ts:
report.add_warning(f"ep={episode_index}: persistent rows present but no plan emitted")
# every interjection should have a same-timestamp plan refresh
for ts in interjection_ts:
if ts not in set(plan_ts):
report.add_error(
f"ep={episode_index}: interjection at t={ts} has no co-timestamped plan update"
)
# memory should be emitted at subtask boundaries (subset relation)
if memory_ts and subtask_ts:
mem_set = set(memory_ts)
sub_set = set(subtask_ts)
stray = sorted(mem_set - sub_set)
if stray:
report.add_warning(f"ep={episode_index}: memory rows at {stray} not at any subtask boundary")
def _check_vqa_json(
self,
events: Iterable[dict[str, Any]],
report: ValidationReport,
episode_index: int,
) -> None:
for row in events:
if row.get("style") != "vqa" or row.get("role") != "assistant":
continue
content = row.get("content")
if content is None:
report.add_error(
f"ep={episode_index}: VQA assistant row at t={row.get('timestamp')} has null content"
)
continue
try:
payload = json.loads(content)
except (TypeError, ValueError) as exc:
report.add_error(
f"ep={episode_index}: VQA assistant content not valid JSON at t={row.get('timestamp')}: {exc}"
)
continue
shape = classify_vqa_answer(payload)
if shape is None:
report.add_error(
f"ep={episode_index}: VQA assistant payload at t={row.get('timestamp')} does not match any known shape: keys={list(payload) if isinstance(payload, dict) else type(payload).__name__}"
)
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