######################################################################
+
+import escape
+
+
+class Escape(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ height,
+ width,
+ T,
+ logger=None,
+ device=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.batch_size = batch_size
+ self.device = device
+
+ states, actions, rewards = escape.generate_episodes(
+ nb_train_samples + nb_test_samples, height, width, T
+ )
+ seq = escape.episodes2seq(states, actions, rewards)
+ self.train_input = seq[:nb_train_samples]
+ self.test_input = seq[nb_train_samples:]
+
+ self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+
+ # if logger is not None:
+ # for s, a in zip(self.train_input[:100], self.train_ar_mask[:100]):
+ # logger(f"train_sequences {self.problem.seq2str(s)}")
+ # a = "".join(["01"[x.item()] for x in a])
+ # logger(f" {a}")
+
+ def batches(self, split="train", nb_to_use=-1, desc=None):
+ assert split in {"train", "test"}
+ input = self.train_input if split == "train" else self.test_input
+ if nb_to_use > 0:
+ input = input[:nb_to_use]
+ if desc is None:
+ desc = f"epoch-{split}"
+ for batch in tqdm.tqdm(
+ input.split(self.batch_size), dynamic_ncols=True, desc=desc
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+ ):
+ pass
+
+
+######################################################################