refactor(pi052): trim PR — remove say tool, debug gates, dead code; move runtime

Cleanup pass over the language-support PR to cut LOC and scope creep.

Removals:
- SayTool + tools/ package (registry, Tool protocol, [tools] extra) and the
  runtime's tool-dispatch path. Kept <say> training supervision and inference
  stripping so speech-annotated datasets still train.
- WeightedEpisodeAwareSampler + VQA oversampling wiring
  (_build_vqa_oversample_weights, vqa_target_fraction) — training uses plain
  EpisodeAwareSampler again.
- Debug env-gates PI052_DEBUG_TENSORS, PI052_SUBTASK_USE_TASK, EVAL_TASK_OVERRIDE.
- Dead code: broken _tp._DUMP_BUDGET block, unused imports (copy/Tensor,
  RevisionNotFoundError, LeRobotDataset, os), messages_for_vqa, steps.py shim
  (modeling imports pi052_adapter directly), duplicated _emit, builtins.type[T].

Moves:
- Policy-agnostic runtime -> src/lerobot/runtime/ (LanguageConditionedRuntime +
  adapter Protocol + state); pi052 keeps only its adapter + CLI. Tests -> tests/runtime/.

Other:
- Compacted verbose AI-authored comments/docstrings across pi052 (kept the
  hard-won DDP / barrier-timeout / reduce-max / VQA-routing notes).
- Relocated LM-head prediction debug helper to pi052/debug_utils.py.
- Fixed test_render_messages: assert task-fallback render (current behavior)
  instead of the stale no-op expectation.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

tests/datasets/test_sampler.py

@@ -25,7 +25,7 @@ from datasets import Dataset  # noqa: E402
 from lerobot.datasets.io_utils import (
     hf_transform_to_torch,
 )
-from lerobot.datasets.sampler import EpisodeAwareSampler, WeightedEpisodeAwareSampler, compute_sampler_state
+from lerobot.datasets.sampler import EpisodeAwareSampler, compute_sampler_state
 
 
 def calculate_episode_data_index(hf_dataset: Dataset) -> dict[str, torch.Tensor]:
@@ -152,52 +152,6 @@ def test_partial_episode_drop_warns(caplog):
     assert "Episode 0" in caplog.text
 
 
-# --- WeightedEpisodeAwareSampler --------------------------------------------
-
-
-def test_weighted_sampler_respects_episode_drop_and_length():
-    """The episode-boundary frame filtering is applied before weighting,
-    and one epoch still yields ``len(indices)`` samples."""
-    # One episode, 10 frames; drop the last 2.
-    sampler = WeightedEpisodeAwareSampler([0], [10], frame_weights=torch.ones(10), drop_n_last_frames=2)
-    assert sampler.indices == list(range(8))
-    assert len(sampler) == 8
-    draws = list(sampler)
-    assert len(draws) == 8
-    # Dropped frames 8 and 9 must never be sampled.
-    assert all(d in set(range(8)) for d in draws)
-
-
-def test_weighted_sampler_oversamples_high_weight_frames():
-    """A heavily-weighted frame dominates the draws."""
-    torch.manual_seed(0)
-    # 100 frames, frame 7 is weighted 1000x.
-    weights = torch.ones(100)
-    weights[7] = 1000.0
-    sampler = WeightedEpisodeAwareSampler([0], [100], frame_weights=weights)
-    counts = {}
-    for _ in range(20):  # 20 epochs
-        for d in sampler:
-            counts[d] = counts.get(d, 0) + 1
-    total = sum(counts.values())
-    # Frame 7 should be the overwhelming majority of the 2000 draws.
-    assert counts.get(7, 0) / total > 0.9
-
-
-def test_weighted_sampler_zero_weights_fall_back_to_uniform():
-    """If every surviving frame has zero weight, sampling is uniform
-    rather than crashing."""
-    sampler = WeightedEpisodeAwareSampler([0], [6], frame_weights=torch.zeros(6))
-    draws = set(sampler)
-    assert draws.issubset(set(range(6)))
-    assert len(list(sampler)) == 6
-
-
-def test_weighted_sampler_rejects_short_weight_vector():
-    with pytest.raises(ValueError, match="frame_weights"):
-        WeightedEpisodeAwareSampler([0], [10], frame_weights=torch.ones(5))
-
-
 # --- seeded (seed, epoch) shuffling, resume, and state ---
 
 EPISODE_BOUNDS = ([0, 2, 3], [2, 3, 6])  # episodes of 2, 1 and 3 frames