Reuse the new copymod function to clean up a little random tests value used.

theano/tensor/tests/test_basic.py

@@ -562,31 +562,28 @@ _good_broadcast_div_mod_normal_float_no_complex = dict(
     column=(rand(2, 3), rand(2, 1)),
     dtype_mixup_1=(rand(2, 3), randint_nonzero(2, 3)),
     dtype_mixup_2=(randint_nonzero(2, 3), rand(2, 3)),
+# Fix problem with integers and uintegers and add them.
+# Them remove there specific addition to CeilIntDivTester tests.
+#    integer=(randint(2, 3), randint_nonzero(2, 3)),
+#    uinteger=(randint(2, 3).astype("uint8"),
+#              randint_nonzero(2, 3).astype("uint8")),
     #empty2=(numpy.asarray([0]), numpy.asarray([])),
     )
-_good_broadcast_div_mod_normal_float_inplace = dict(
+
+_good_broadcast_div_mod_normal_float_inplace = copymod(
+    _good_broadcast_div_mod_normal_float_no_complex,
     empty1=(numpy.asarray([]), numpy.asarray([1])),
     complex1=(randcomplex(2, 3), randcomplex(2, 3)),
     complex2=(randcomplex(2, 3), rand(2, 3)),
     # Inplace on the first element. Must have the same type.
     #complex3=(rand(2, 3) ,randcomplex(2, 3)),
-    **_good_broadcast_div_mod_normal_float_no_complex
-       )
-_good_broadcast_div_mod_normal_float = dict(empty2 = (numpy.asarray([0]), numpy.asarray([])),
-                                            **_good_broadcast_div_mod_normal_float_inplace
-                                            )
-
-def no_complex(d):
-    """Remove pairs from dictionary d when the value contains complex data."""
-    return dict((k, v) for k, v in d.iteritems()
-                if all(str(x.dtype) not in tensor.complex_dtypes for x in v))
+    )
 
+_good_broadcast_div_mod_normal_float = copymod(
+    _good_broadcast_div_mod_normal_float_inplace,
+    empty2=(numpy.asarray([0]), numpy.asarray([]))
+    )
 
-# 'No-complex' versions, with empty2
-_good_broadcast_div_mod_normal_float_no_complex2 = no_complex(
-                                        _good_broadcast_div_mod_normal_float)
-_good_broadcast_div_mod_normal_float_inplace_no_complex = no_complex(
-                                _good_broadcast_div_mod_normal_float_inplace)
 
 _grad_broadcast_div_mod_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
                                       scalar = (rand(2, 3), rand(1, 1)),
@@ -637,18 +634,22 @@ CeilIntDivTester = makeBroadcastTester(
 #    grad_rtol=div_grad_rtol,
     )
 
+ModTester = makeBroadcastTester(
+    op=tensor.mod,
+    expected=lambda x, y: numpy.asarray(
+        x % y, dtype=theano.scalar.basic.upcast(x.dtype, y.dtype)),
+    good=copymod(_good_broadcast_div_mod_normal_float,
+                 ['complex1', 'complex2']),
+    )
 
-ModTester = makeBroadcastTester(op = tensor.mod,
-                                  expected = lambda x, y: numpy.asarray(x % y, dtype=theano.scalar.basic.upcast(x.dtype, y.dtype)),
-                                  good = _good_broadcast_div_mod_normal_float_no_complex2,
-#                                               integers = (randint(2, 3), randint_nonzero(2, 3)),
-#                                               dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
-#                                               dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))),
-                                  )
-ModInplaceTester = makeBroadcastTester(op = inplace.mod_inplace,
-                                         expected = lambda x, y: numpy.asarray(x % y, dtype=theano.scalar.basic.upcast(x.dtype, y.dtype)),
-                                         good = _good_broadcast_div_mod_normal_float_inplace_no_complex,
-                                         inplace = True)
+
+ModInplaceTester = makeBroadcastTester(
+    op=inplace.mod_inplace,
+    expected=lambda x, y: numpy.asarray(
+        x % y, dtype=theano.scalar.basic.upcast(x.dtype, y.dtype)),
+    good=copymod(_good_broadcast_div_mod_normal_float_inplace,
+                 ["complex1", "complex2"]),
+    inplace=True)
 
 _good_broadcast_pow_normal_float = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))),
                                         scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))),