feat(lingbot_va): RoboTwin eef-pose eval, single-file model, Hub checkpoints

Make the LingBot-VA port runnable on both LIBERO and RoboTwin and clean up the
package to LeRobot conventions.

- Consolidate all vendored Wan2.2 model code (transformer, attention, VAE helpers,
  flow-matching scheduler, grid utils, flex-attention) into a single
  modeling_lingbot_va.py; remove the separate wan_*/schedulers modules.
- Move the fixed action (un)normalization quantiles out of the config and into the
  post-processor (LIBERO 7-DoF + RoboTwin 16-d eef); remove the conversion script in
  favour of ready-to-use LeRobot-format checkpoints on the Hub.
- Fixes found via on-sim validation: undo LIBERO's 180-degree image flip
  (image_hflip), encode obs as a multi-frame streaming-VAE clip, reset the streaming
  VAE cache between episodes, run the transformer in config.dtype, lazy-load frozen
  VAE/UMT5 by subfolder with the text encoder on CPU.
- RoboTwin: add an end-effector-pose action mode to RoboTwinEnv (16-d per-arm
  xyz+quat+gripper deltas composed onto the initial eef pose, executed via CuRobo IK)
  and the robotwin_tshape latent layout (full-res head + half-res wrists via a second
  streaming VAE) with the upstream RoboTwin action quantiles + camera mapping.
- Predicted-video saving works for both benchmarks; docs + tests updated.

Co-authored-by: Cursor <cursoragent@cursor.com>

tests/policies/lingbot_va/test_processor.py

@@ -21,6 +21,7 @@ import torch
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.policies.lingbot_va.configuration_lingbot_va import LingBotVAConfig
 from lerobot.policies.lingbot_va.processor_lingbot_va import (
+    LIBERO_ACTION_Q01,
     LingBotVAActionUnnormalizeStep,
     make_lingbot_va_pre_post_processors,
 )
@@ -75,7 +76,7 @@ def test_make_pre_post_processors_names_and_steps() -> None:
 def test_postprocessor_applies_unnormalization() -> None:
     cfg = _make_config()
     _, post = make_lingbot_va_pre_post_processors(cfg, dataset_stats=None)
-    # A normalized action of all -1 should map back to q01.
+    # A normalized action of all -1 should map back to q01 (the LIBERO 7-DoF default quantiles).
     normed = torch.full((1, len(cfg.used_action_channel_ids)), -1.0)
     out = post(normed)
-    assert torch.allclose(out, torch.tensor(cfg.action_q01).unsqueeze(0), atol=1e-4)
+    assert torch.allclose(out, torch.tensor(LIBERO_ACTION_Q01).unsqueeze(0), atol=1e-4)