ENH: start adding "repro snippets" for failed assertions

It is not always easy to tell what exactly failed with hypothesis (gh-379).
Thus add a failing snippet to the exceptions' output.

For instance, run

$ ARRAY_API_TESTS_MODULE=array_api_compat.torch pytest array_api_tests/test_array_object.py::test_getitem

to see

E           ValueError: step must be greater than zero
E
E           ========== FAILING CODE SNIPPET:
E           tensor([False, False])[(slice(1, 0, -1),)]
E           ====================

Author: ev-br

array_api_tests/pytest_helpers.py

@@ -599,3 +599,7 @@ def assert_array_elements(
             at_expected = expected[idx]
             msg = msg_template.format(sh.fmt_idx(out_repr, idx), at_out, at_expected)
             assert at_out == at_expected, msg
+
+
+def format_snippet(s: str):
+    return f"\n{'='*10} FAILING CODE SNIPPET:\n{s}\n{'='*20}\n"

array_api_tests/test_array_object.py

@@ -85,24 +85,29 @@ def test_getitem(shape, dtype, data):
     note(f"{x=}")
     key = data.draw(xps.indices(shape=shape, allow_newaxis=True), label="key")
 
-    out = x[key]
-
-    ph.assert_dtype("__getitem__", in_dtype=x.dtype, out_dtype=out.dtype)
-    _key = normalize_key(key, shape)
-    axes_indices, expected_shape = get_indexed_axes_and_out_shape(_key, shape)
-    ph.assert_shape("__getitem__", out_shape=out.shape, expected=expected_shape)
-    out_zero_sided = any(side == 0 for side in expected_shape)
-    if not zero_sided and not out_zero_sided:
-        out_obj = []
-        for idx in product(*axes_indices):
-            val = obj
-            for i in idx:
-                val = val[i]
-            out_obj.append(val)
-        out_obj = sh.reshape(out_obj, expected_shape)
-        expected = xp.asarray(out_obj, dtype=dtype)
-        ph.assert_array_elements("__getitem__", out=out, expected=expected)
-
+    repro_snippet = ph.format_snippet(f"{x!r}[{key!r}]")
+
+    try:
+        out = x[key]
+
+        ph.assert_dtype("__getitem__", in_dtype=x.dtype, out_dtype=out.dtype)
+        _key = normalize_key(key, shape)
+        axes_indices, expected_shape = get_indexed_axes_and_out_shape(_key, shape)
+        ph.assert_shape("__getitem__", out_shape=out.shape, expected=expected_shape)
+        out_zero_sided = any(side == 0 for side in expected_shape)
+        if not zero_sided and not out_zero_sided:
+            out_obj = []
+            for idx in product(*axes_indices):
+                val = obj
+                for i in idx:
+                    val = val[i]
+                out_obj.append(val)
+            out_obj = sh.reshape(out_obj, expected_shape)
+            expected = xp.asarray(out_obj, dtype=dtype)
+            ph.assert_array_elements("__getitem__", out=out, expected=expected)
+    except Exception as exc:
+        exc.add_note(repro_snippet)
+        raise
 
 @pytest.mark.unvectorized
 @given(

array_api_tests/test_data_type_functions.py

@@ -160,13 +160,17 @@ def test_finfo(dtype):
         # np.float64 and np.asarray(1, dtype=np.float64).dtype are different
         xp.asarray(1, dtype=dtype).dtype,
     ):
-        out = xp.finfo(arg)
-        assert isinstance(out.bits, int)
-        assert isinstance(out.eps, float)
-        assert isinstance(out.max, float)
-        assert isinstance(out.min, float)
-        assert isinstance(out.smallest_normal, float)
-
+        repro_snippet = ph.format_snippet(f"xp.finfo({arg})")
+        try:
+            out = xp.finfo(arg)
+            assert isinstance(out.bits, int)
+            assert isinstance(out.eps, float)
+            assert isinstance(out.max, float)
+            assert isinstance(out.min, float)
+            assert isinstance(out.smallest_normal, float)
+        except Exception as exc:
+            exc.add_note(repro_snippet)
+            raise
 
 @pytest.mark.min_version("2022.12")
 @pytest.mark.parametrize("dtype", dh.real_float_dtypes + dh.complex_dtypes)