fix(train): unwrap DDP policy in debug_text_predictions hook

At training time the policy is wrapped by Accelerator/DDP into a
.module attribute and custom methods are NOT proxied through the
wrapper, so ``hasattr(policy, "debug_text_predictions")`` was False
and the periodic dump was silently no-op'ing. Walk through .module
indirection to reach the raw PI052Policy that defines the method.

Also surface why the dump didn't fire (no method / empty supervised
positions / generation error) so users can see what's blocking it
instead of staring at silence.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/lerobot/scripts/lerobot_train.py

@@ -170,15 +170,34 @@ def _print_debug_text_predictions(
     per-sample token-accuracy summary — the cheapest "is text training
     actually learning anything" signal.
     """
-    if not hasattr(policy, "debug_text_predictions"):
+    # Accelerator/DDP wraps the policy in a ``module`` attribute and
+    # doesn't proxy custom methods through, so a naive
+    # ``hasattr(policy, "debug_text_predictions")`` returns False on the
+    # wrapper — and the helper would silently no-op. Walk through any
+    # ``.module`` indirection (DDP, FSDP, ``accelerator.prepare`` wrappers)
+    # to reach the raw policy that actually defines the method.
+    inner = policy
+    while hasattr(inner, "module") and not hasattr(inner, "debug_text_predictions"):
+        inner = inner.module
+    if not hasattr(inner, "debug_text_predictions"):
+        logging.warning(
+            "LEROBOT_DEBUG_PREDS_EVERY set but policy %s has no "
+            "debug_text_predictions method — skipping dump.",
+            type(inner).__name__,
+        )
         return
     try:
-        debug = policy.debug_text_predictions(batch, max_samples=n_samples)
+        debug = inner.debug_text_predictions(batch, max_samples=n_samples)
     except Exception as exc:  # noqa: BLE001
-        logging.warning("debug_text_predictions failed: %s", exc)
+        logging.warning("debug_text_predictions failed: %s", exc, exc_info=True)
         return
     if not debug:
+        logging.warning(
+            "debug_text_predictions returned no supervised samples — "
+            "current batch has no text labels."
+        )
         return
+    policy = inner  # used below for select_message-style decoding parity
 
     # Build a tokenizer for decoding — match training side exactly.
     try: