Remove import of unittest_tools in basic.py. tensor.verify_grad now needs to be passed a valid rng.

theano/tensor/basic.py

@@ -3548,7 +3548,7 @@ class numeric_grad:
 
 def verify_grad(op, pt, n_tests=2, rng=None, eps=None, tol=None, mode=None, cast_to_output_type=False):
     """ WRITEME
-    
+
     Raises an Exception if the difference between the analytic gradient and
     numerical gradient (computed through the Finite Difference Method) exceeds
     the given tolerance.
@@ -3565,7 +3565,7 @@ def verify_grad(op, pt, n_tests=2, rng=None, eps=None, tol=None, mode=None, cast
            try to make it a SMALL graph.  Often verify grad is run in
            debug mode, which can be very slow if it has to verify a lot
            of intermediate computations.
-    
+
     """
     pt = [numpy.array(p) for p in pt]
 
@@ -3577,9 +3577,8 @@ def verify_grad(op, pt, n_tests=2, rng=None, eps=None, tol=None, mode=None, cast
         tol = __builtin__.max(_type_tol[str(p.dtype)] for p in pt)
 
     if rng is None:
-        rng = numpy.random
-        from theano import tests as theano_tests # TODO This is an ugly import. Fix?
-        theano_tests.unittest_tools.seed_rng()
+        raise TypeError('rng should be a valid instance of numpy.random.RandomState.',
+                'You may want to use theano.tests.unittest_tools.verify_grad instead of theano.tensor.verify_grad.')
 
     def function(inputs, output):
         if mode is None:
@@ -3591,9 +3590,9 @@ def verify_grad(op, pt, n_tests=2, rng=None, eps=None, tol=None, mode=None, cast
     for test_num in xrange(n_tests):
 
         tensor_pt = [value(p.copy(), name='input %i'%i) for i,p in enumerate(pt)]
-        
+
         #op can be either a function or an actual Op instance
-        o_output = op(*tensor_pt) 
+        o_output = op(*tensor_pt)
 
         if isinstance(o_output,list) > 1:
             raise NotImplementedError('cant (yet) autotest gradient of op with multiple outputs')