created state_buffers and dealt with non-numeric states

theano/sandbox/scan_module/scan_op.py

@@ -222,21 +222,29 @@ class ScanOp(PureOp):
 
         # 2.2. Next the states (numeric) and the outputs
         updates = {}
+        state_buffers = []
         n_numeric_values = len(self.lengths)
-        for pos, (mem_buf, var, expr) in enumerate(
-             izip(node_output_storage, base_inputs, self.outputs)):
-            givens[var] = theano.shared(mem_buf[0], name=var.name,
-                                       borrow=True)
+        for pos in xrange(n_numeric_values):
+            var = base_inputs[pos]
+            mem_buf = base_buffers[pos]
+            expr = self.outputs[pos]
+            givens[var] = fake_shared(var)
+            state_buffers.append((givens[var], self.lengths[pos], mem_buf))
             updates[givens[var]] = expr
-            if pos < n_numeric_values:
-                self.lengths[pos].set_value(mem_buf[0].shape[0])
-                givens[self.lengths[pos]] = \
-                        tensor.constant(mem_buf[0].shape[0])
-
-        # 3.3 Add the update for the index of scan
-        updates[self.t] = self.t + numpy.int64(1)
-        # 4.1 Construct the inner function of scan
-        fn_outs = []
+
+
+        #2.3 Non-numeric states
+        n_non_numeric = len(self.outputs) - n_numeric_values
+        fn_outs = self.outputs[n_numeric_values:]
+        for var in base_inputs[n_numeric_values:]:
+            givens[var] = var.type()
+            non_tensor_args.append(givens[var])
+        non_numeric_states_bufs = base_buffers[n_numeric_values:]
+
+        # 2.4 Add the update for the index of scan
+        updates[self.index] = self.index + numpy.int64(1)
+
+        # 3.1 Construct the inner function of scan
         if self.as_repeatUntil is not None:
             fn_outs = self.as_repeatUntil
         self.fn = theano.function([], fn_outs,