Fix test incompatibilities with numpy 2.5

tests/link/numba/test_extra_ops.py

@@ -391,7 +391,9 @@ def test_UnravelIndex(arr, shape, requires_obj_mode):
         pytest.param(
             (
                 pt.vector(),
-                np.array([0.29769574, 0.71649186, 0.20475563]).astype(config.floatX),
+                # Must be sorted: searchsorted on unsorted input is undefined
+                # behavior and numba/numpy implementations disagree there
+                np.array([0.20475563, 0.29769574, 0.71649186]).astype(config.floatX),
             ),
             (
                 pt.matrix(),

tests/tensor/signal/test_conv.py

@@ -146,7 +146,7 @@ def test_convolve2d(kernel_shape, data_shape, mode, boundary, boundary_kwargs):
 
     fn = function([data, kernel], conv_result)
 
-    rng = np.random.default_rng((172, kernel_shape, data_shape, sum(map(ord, mode))))
+    rng = np.random.default_rng((172, *kernel_shape, *data_shape, sum(map(ord, mode))))
     data_val = rng.normal(size=data_shape).astype(data.dtype)
     kernel_val = rng.normal(size=kernel_shape).astype(kernel.dtype)
 

tests/tensor/test_extra_ops.py

@@ -151,12 +151,13 @@ def test_searchsortedOp_on_int_sorter(self, dtype):
         )
 
     def test_searchsortedOp_on_right_side(self):
+        # searchsorted on unsorted input is undefined behavior, and numpy's
+        # results there depend on its binary search implementation details
+        sa = self.a[self.idx_sorted]
         f = pytensor.function(
             [self.x, self.v], searchsorted(self.x, self.v, side="right")
         )
-        assert np.allclose(
-            np.searchsorted(self.a, self.b, side="right"), f(self.a, self.b)
-        )
+        assert np.allclose(np.searchsorted(sa, self.b, side="right"), f(sa, self.b))
 
     def test_infer_shape(self):
         # Test using default parameters' value