fix(datasets): guard Feistel cycle-walking loop against non-convergence

Replace the unbounded while True in EpisodeAwareSampler._permute with a
bounded for loop capped at _MAX_CYCLE_WALK_STEPS (100) and raise
RuntimeError if the cycle-walk fails to land in [0, num_frames). The
loop is expected to converge in <4 steps on the chosen power-of-two
domain, so the bound is a safety net that should never trip in practice
but prevents a pathological infinite loop.

https://claude.ai/code/session_01HQ15tFrBsHYScjGWosEv22

src/lerobot/datasets/sampler.py

@@ -24,6 +24,9 @@ logger = logging.getLogger(__name__)
 
 _MASK_64 = (1 << 64) - 1
 _FEISTEL_ROUNDS = 4
+# Cycle-walking converges in <4 expected steps on the chosen domain; this bound is a generous
+# safety net that should never be hit in practice.
+_MAX_CYCLE_WALK_STEPS = 100
 
 
 def _mix64(x: int) -> int:
@@ -165,13 +168,17 @@ class EpisodeAwareSampler:
     def _permute(self, index: int, keys: list[int]) -> int:
         # Feistel network with cycle-walking: a bijection on [0, num_frames).
         half_bits, half_mask = self._half_bits, self._half_mask
-        while True:
+        for _ in range(_MAX_CYCLE_WALK_STEPS):
             left, right = index >> half_bits, index & half_mask
             for key in keys:
                 left, right = right, left ^ (_mix64(right ^ key) & half_mask)
             index = (left << half_bits) | right
             if index < self._num_frames:
                 return index
+        raise RuntimeError(
+            f"Feistel cycle-walking did not converge within {_MAX_CYCLE_WALK_STEPS} steps; "
+            "this should never happen for a valid domain."
+        )
 
     def _frame_index(self, position: int) -> int:
         episode = int(np.searchsorted(self._cum_lengths, position, side="right"))