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_configuration.py

@@ -76,8 +76,3 @@ def test_validate_features_no_visual_raises() -> None:
 def test_invalid_attn_mode_raises() -> None:
     with pytest.raises(ValueError, match="attn_mode"):
         make_config(attn_mode="banana")
-
-
-def test_quantile_length_mismatch_raises() -> None:
-    with pytest.raises(ValueError, match="action_q01"):
-        make_config(used_action_channel_ids=[0, 1, 2], action_q01=[0.0, 0.0], action_q99=[1.0, 1.0, 1.0])