Apply pyupgrade to top-level modules in tests sub-package

tests/record.py

@@ -18,7 +18,7 @@ class MismatchError(Exception):
     """
 
 
-class Record(object):
+class Record:
     """
     Records a sequence of strings (from a string buffer). These can then be
     compared to another sequence of strings, and if the two sequences don't
@@ -67,7 +67,7 @@ class Record(object):
         assert file_object is not None or file_path is not None
 
         if replay and file_object is None:
-            self.f = open(file_path, "r")
+            self.f = open(file_path)
         elif (not replay) and file_object is None:
             self.f = open(file_path, "w")
         else:
@@ -157,7 +157,7 @@ class RecordMode(Mode):
         """
 
         self.record = record
-        self.known_fgraphs = set([])
+        self.known_fgraphs = set()
 
     def __init__(self, record=None, **kwargs):
         """
@@ -262,4 +262,4 @@ class RecordMode(Mode):
         linker = theano.gof.vm.VM_Linker(use_cloop=bool(theano.config.cxx))
 
         wrap_linker = theano.gof.WrapLinkerMany([linker], [callback])
-        super(RecordMode, self).__init__(wrap_linker, optimizer="fast_run")
+        super().__init__(wrap_linker, optimizer="fast_run")

tests/test_gradient.py

@@ -47,7 +47,6 @@ class TestGradSourcesInputs:
             def grad(self, inp, grads):
                 (x,) = inp
                 (gz,) = grads
-                pass
 
         a = retNone().make_node()
         with pytest.raises(TypeError):

tests/test_rop.py

@@ -114,7 +114,7 @@ class RopLopChecker:
         v1 = rop_f(vx, vv)
         v2 = scan_f(vx, vv)
 
-        assert np.allclose(v1, v2), "ROP mismatch: %s %s" % (v1, v2)
+        assert np.allclose(v1, v2), "ROP mismatch: {} {}".format(v1, v2)
 
         self.check_nondiff_rop(theano.clone(y, replace={self.mx: break_op(self.mx)}))
 
@@ -127,7 +127,7 @@ class RopLopChecker:
 
         v1 = lop_f(vx, vv)
         v2 = scan_f(vx, vv)
-        assert np.allclose(v1, v2), "LOP mismatch: %s %s" % (v1, v2)
+        assert np.allclose(v1, v2), "LOP mismatch: {} {}".format(v1, v2)
 
     def check_rop_lop(self, y, out_shape):
         """
@@ -151,7 +151,7 @@ class RopLopChecker:
 
         v1 = rop_f(vx, vv)
         v2 = scan_f(vx, vv)
-        assert np.allclose(v1, v2), "ROP mismatch: %s %s" % (v1, v2)
+        assert np.allclose(v1, v2), "ROP mismatch: {} {}".format(v1, v2)
 
         try:
             tensor.Rop(
@@ -179,7 +179,7 @@ class RopLopChecker:
 
         v1 = lop_f(vx, vv)
         v2 = scan_f(vx, vv)
-        assert np.allclose(v1, v2), "LOP mismatch: %s %s" % (v1, v2)
+        assert np.allclose(v1, v2), "LOP mismatch: {} {}".format(v1, v2)
 
 
 class TestRopLop(RopLopChecker):
@@ -297,7 +297,7 @@ class TestRopLop(RopLopChecker):
             scan_f = function([], sy, on_unused_input="ignore", mode=mode)
             v1 = rop_f()
             v2 = scan_f()
-            assert np.allclose(v1, v2), "Rop mismatch: %s %s" % (v1, v2)
+            assert np.allclose(v1, v2), "Rop mismatch: {} {}".format(v1, v2)
 
     def test_conv(self):
         for conv_op in [conv.conv2d, conv2d]:
@@ -353,7 +353,7 @@ class TestRopLop(RopLopChecker):
                 ev_filter_data = np.random.random(filter_shape).astype(dtype)
                 v1 = rop_f(image_data, filter_data, ev_image_data, ev_filter_data)
                 v2 = scan_f(image_data, filter_data, ev_image_data, ev_filter_data)
-                assert np.allclose(v1, v2), "Rop mismatch: %s %s" % (v1, v2)
+                assert np.allclose(v1, v2), "Rop mismatch: {} {}".format(v1, v2)
 
     def test_join(self):
         tv = np.asarray(self.rng.uniform(size=(10,)), theano.config.floatX)

tests/unittest_tools.py

@@ -5,7 +5,6 @@ from functools import wraps
 
 import numpy as np
 import pytest
-from six import integer_types
 
 import theano
 import theano.tensor as tt
@@ -110,7 +109,7 @@ class MockRandomState:
             return out + maxval - 1
 
 
-class OptimizationTestMixin(object):
+class OptimizationTestMixin:
     def assertFunctionContains(self, f, op, min=1, max=sys.maxsize):
         toposort = f.maker.fgraph.toposort()
         matches = [node for node in toposort if node.op == op]
@@ -148,7 +147,7 @@ class OptimizationTestMixin(object):
         return self.assertFunctionContainsClass(f, op, min=N, max=N)
 
 
-class OpContractTestMixin(object):
+class OpContractTestMixin:
     # self.ops should be a list of instantiations of an Op class to test.
     # self.other_op should be an op which is different from every op
     other_op = tt.add
@@ -236,7 +235,7 @@ class InferShapeTester:
             mode = mode.excluding(*excluding)
         if warn:
             for var, inp in zip(inputs, numeric_inputs):
-                if isinstance(inp, (integer_types, float, list, tuple)):
+                if isinstance(inp, (int, float, list, tuple)):
                     inp = var.type.filter(inp)
                 if not hasattr(inp, "shape"):
                     continue