Move numpy_scalar outside of the function that use it.

theano/tensor/basic.py

@@ -508,6 +508,24 @@ class EmptyConstantError(NotScalarConstantError):
     """
 
 
+def numpy_scalar(data):
+    """ Return a scalar stored in a numpy ndarray, or raise
+    NotScalarConstantError if the numpy ndarray is not a scalar
+    """
+
+    # handle case where data is numpy.array([])
+    if data.ndim > 0 and  (len(data.shape) == 0 or
+        __builtins__['max'](data.shape) == 0):
+        assert numpy.all(numpy.array([]) == data)
+        raise EmptyConstantError()
+    try:
+        numpy.complex(data)  # works for all numeric scalars
+        return data
+    except Exception:
+        raise NotScalarConstantError(
+            'v.data is non-numeric, non-scalar, or has more than one'
+            ' unique value', data)
+
 get_scalar_constant_value_elemwises = (
     scal.Cast, scal.Switch,
     scal.NEQ, scal.EQ,
@@ -526,7 +544,7 @@ def get_scalar_constant_value(v):
     :note: There may be another function similar to this one in the
         code, but I'm not sure where it is.
     """
-
+    if True:
         if v is None:
             # None is not a scalar (and many uses of this function seem to depend
             # on passing it None)
@@ -535,24 +553,6 @@ def get_scalar_constant_value(v):
         if isinstance(v, (numpy.integer, int, float)):
             return numpy.asarray(v)
 
-    def numpy_scalar(data):
-        """ Return a scalar stored in a numpy ndarray, or raise
-        NotScalarConstantError if the numpy ndarray is not a scalar
-        """
-
-        # handle case where data is numpy.array([])
-        if data.ndim > 0 and  (len(data.shape) == 0 or
-            __builtins__['max'](data.shape) == 0):
-            assert numpy.all(numpy.array([]) == data)
-            raise EmptyConstantError()
-        try:
-            numpy.complex(data)  # works for all numeric scalars
-            return data
-        except Exception:
-            raise NotScalarConstantError(
-                'v.data is non-numeric, non-scalar, or has more than one'
-                ' unique value', data)
-
         if isinstance(v, numpy.ndarray):
             return numpy_scalar(v)
 
@@ -568,8 +568,8 @@ def get_scalar_constant_value(v):
                                        compile.ops.OutputGuard,
                                        compile.DeepCopyOp)):
                 return get_scalar_constant_value(v.owner.inputs[0])
-        elif (isinstance(v.owner.op, theano.compile.ops.Shape_i) and
-            isinstance(v.owner.inputs[0], Constant)):
+            elif isinstance(v.owner.op, theano.compile.ops.Shape_i):
+                if isinstance(v.owner.inputs[0], Constant):
                     return v.owner.inputs[0].data.shape[v.owner.op.i]
             # Don't act as the constant_folding optimization here as this
             # fct is used too early in the optimization phase.  This would