Remove support for python scalars in idx_list.

theano/tensor/subtensor.py

@@ -347,18 +347,10 @@ class Subtensor(Op):
                 slice_c = None
 
             return slice(slice_a, slice_b, slice_c)
-        # There is a bug in numpy that results in isinstance(x, int) returning
-        # False for numpy integers.
-        # See <http://projects.scipy.org/numpy/ticket/2235>.
-        elif isinstance(entry, numpy.integer):
-            return entry
-        # On Windows 64-bit, shapes are returned as Python long, as they can
-        # be bigger than what a Python int can hold.
-        # Shapes should always fit in a numpy.int64, and we support them better
-        # 2) In Python3, long replaced int. So we must assert it fit in int64.
-        elif isinstance(entry, (int, long)):
-            entry64 = numpy.int64(entry)
-            return entry64
+        elif isinstance(entry, (int, long, numpy.integer)):
+            # Disallow the use of python scalars in idx_list
+            raise TypeError("Python scalar in idx_list."
+                            "Please report this error to theano-dev.")
         else:
             raise AdvancedIndexingError(Subtensor.e_indextype, entry)
 
@@ -405,7 +397,6 @@ class Subtensor(Op):
 
     def __init__(self, idx_list):
         self.idx_list = tuple(map(self.convert, idx_list))
-        self.perform_cache_cdata = None
 
     @staticmethod
     def my_as_scalar(a):
@@ -471,18 +462,9 @@ class Subtensor(Op):
         out, = out_
         x = inputs[0]
 
-        # The subtensor (or idx_list) does not depend on the inputs.
-        # (and cdata was cached on initial call)
-        if self.perform_cache_cdata is not None:
-            out[0] = numpy.asarray(x.__getitem__(self.perform_cache_cdata))
-            return
-
         cdata = get_idx_list(inputs, self.idx_list)
         if len(cdata) == 1:
             cdata = cdata[0]
-        # (first call caches cdata here)
-        if len(inputs) == 1:
-            self.perform_cache_cdata = cdata
 
         out[0] = numpy.asarray(x.__getitem__(cdata))