Use disable numba JIT

tests/link/numba/test_basic.py

@@ -1,7 +1,5 @@
 import contextlib
-import inspect
 from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Tuple, Union
-from unittest import mock
 
 import numba
 import numpy as np
@@ -108,73 +106,15 @@ def compare_shape_dtype(x, y):
 def eval_python_only(fn_inputs, fn_outputs, inputs, mode=numba_mode):
     """Evaluate the Numba implementation in pure Python for coverage purposes."""
 
-    def py_tuple_setitem(t, i, v):
-        ll = list(t)
-        ll[i] = v
-        return tuple(ll)
-
-    def py_to_scalar(x):
-        if isinstance(x, np.ndarray):
-            return x.item()
-        else:
-            return x
-
-    def njit_noop(*args, **kwargs):
-        if len(args) == 1 and callable(args[0]):
-            return args[0]
-        else:
-            return lambda x: x
-
-    def vectorize_noop(*args, **kwargs):
-        def wrap(fn):
-            # `numba.vectorize` allows an `out` positional argument.  We need
-            # to account for that
-            sig = inspect.signature(fn)
-            nparams = len(sig.parameters)
-
-            def inner_vec(*args):
-                if len(args) > nparams:
-                    # An `out` argument has been specified for an in-place
-                    # operation
-                    out = args[-1]
-                    out[...] = np.vectorize(fn)(*args[:nparams])
-                    return out
-                else:
-                    return np.vectorize(fn)(*args)
-
-            return inner_vec
-
-        if len(args) == 1 and callable(args[0]):
-            return wrap(args[0], **kwargs)
-        else:
-            return wrap
-
-    mocks = [
-        mock.patch("numba.njit", njit_noop),
-        mock.patch("numba.vectorize", vectorize_noop),
-        mock.patch("aesara.link.numba.dispatch.basic.tuple_setitem", py_tuple_setitem),
-        mock.patch("aesara.link.numba.dispatch.basic.numba_njit", njit_noop),
-        mock.patch("aesara.link.numba.dispatch.basic.numba_vectorize", vectorize_noop),
-        mock.patch("aesara.link.numba.dispatch.basic.direct_cast", lambda x, dtype: x),
-        mock.patch("aesara.link.numba.dispatch.basic.to_scalar", py_to_scalar),
-        mock.patch(
-            "aesara.link.numba.dispatch.basic.numba.np.numpy_support.from_dtype",
-            lambda dtype: dtype,
-        ),
-        mock.patch("numba.np.unsafe.ndarray.to_fixed_tuple", lambda x, n: tuple(x)),
-    ]
-
-    with contextlib.ExitStack() as stack:
-        for ctx in mocks:
-            stack.enter_context(ctx)
-
-        aesara_numba_fn = function(
-            fn_inputs,
-            fn_outputs,
-            mode=mode,
-            accept_inplace=True,
-        )
-        _ = aesara_numba_fn(*inputs)
+    numba.config.DISABLE_JIT = True
+    aesara_numba_fn = function(
+        fn_inputs,
+        fn_outputs,
+        mode=mode,
+        accept_inplace=True,
+    )
+    _ = aesara_numba_fn(*inputs)
+    numba.config.DISABLE_JIT = False
 
 
 def compare_numba_and_py(