Apply pyupgrade to tests.scan_module

tests/scan_module/test_scan.py

@@ -742,7 +742,7 @@ class TestScan:
 
         # Call verify_grad to ensure the correctness of the second gradients
         floatX = theano.config.floatX
-        inputs_test_values = [np.random.random((3)).astype(floatX)]
+        inputs_test_values = [np.random.random(3).astype(floatX)]
         utt.verify_grad(get_sum_of_grad, inputs_test_values)
 
     def test_verify_second_grad_mitsot1(self):
@@ -771,7 +771,7 @@ class TestScan:
         floatX = theano.config.floatX
         inputs_test_values = [
             np.random.random((2, 3)).astype(floatX),
-            np.random.random((3)).astype(floatX),
+            np.random.random(3).astype(floatX),
         ]
         utt.verify_grad(get_sum_of_grad, inputs_test_values)
 
@@ -3896,7 +3896,7 @@ for{cpu,scan_fn}.2 [id H] ''
         dtype = theano.config.floatX
         seq_value = np.random.random((10, 3)).astype(dtype)
         out_init_value = np.random.random((3, 3)).astype(dtype)
-        non_seq_value = np.random.random((3)).astype(dtype)
+        non_seq_value = np.random.random(3).astype(dtype)
 
         outputs = fct(seq_value, out_init_value, non_seq_value)
 

tests/scan_module/test_scan_opt.py

@@ -89,7 +89,7 @@ class TestGaussNewton:
         # during certain iterations of CG in the HF algorithm. There,
         # it's in fact `pi + current update proposal`.  For simplicity,
         # I just multiply by 2 here.
-        cost_ = theano.clone(cost, replace=dict([(pi, 2 * pi) for pi in params]))
+        cost_ = theano.clone(cost, replace={pi: 2 * pi for pi in params})
 
         # Compute Gauss-Newton-Matrix times some vector `v` which is `p` in CG,
         # but for simplicity, I just take the parameters vector because it's
@@ -112,7 +112,7 @@ class TestGaussNewton:
         self._run(100, 10, batch_size=1, mode=mode)
 
 
-class GaussNewtonMatrix(object):
+class GaussNewtonMatrix:
     def __init__(self, s):
         # `s` is the linear network outputs, i.e. the network output
         # without having applied the activation function
@@ -131,7 +131,7 @@ class GaussNewtonMatrix(object):
         return JHJv
 
 
-class TestPushOutScanOutputDot(object):
+class TestPushOutScanOutputDot:
     """
     Test class for the PushOutScanOutput optimizer in the case where the inner
     function of a scan op has an output which is the result of a Dot product
@@ -166,7 +166,7 @@ class TestPushOutScanOutputDot(object):
 
         # Ensure that the function compiled with the optimization produces
         # the same results as the function compiled without
-        v_value = np.random.random((4)).astype(config.floatX)
+        v_value = np.random.random(4).astype(config.floatX)
         m_value = np.random.random((4, 5)).astype(config.floatX)
 
         output_opt = f_opt(v_value, m_value)