fix(torch-frontend, ivy): Updated test_torch_result_type to cover sca…

…lar cases and fixed the paddle backend of ivy.result_type for these
iababio · Sep 27, 2023 · 2b7d451 · 2b7d451
1 parent cac1117
commit 2b7d451
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Showing 2 changed files with 45 additions and 10 deletions.
diff --git a/ivy/functional/backends/paddle/data_type.py b/ivy/functional/backends/paddle/data_type.py
@@ -196,7 +196,7 @@ def iinfo(type: Union[paddle.dtype, str, paddle.Tensor], /) -> Iinfo:
 
 
 def result_type(*arrays_and_dtypes: Union[paddle.Tensor, paddle.dtype]) -> ivy.Dtype:
-    return ivy.promote_types(arrays_and_dtypes[0].dtype, arrays_and_dtypes[1].dtype)
+    return ivy.promote_types_of_inputs(*arrays_and_dtypes)[0].dtype
 
 
 # Extra #

diff --git a/ivy_tests/test_ivy/test_frontends/test_torch/test_utilities.py b/ivy_tests/test_ivy/test_frontends/test_torch/test_utilities.py
@@ -74,30 +74,65 @@ def test_torch_bincount(
     )
 
 
+@st.composite
+def _elemwise_helper(draw):
+    value_strategy = st.one_of(
+        helpers.dtype_and_values(
+            available_dtypes=helpers.get_dtypes("valid"),
+        ),
+        st.integers(min_value=-10000, max_value=10000),
+        st.floats(min_value=-10000, max_value=10000),
+    )
+
+    dtype_and_x1 = draw(value_strategy)
+    if isinstance(dtype_and_x1, tuple):
+        dtype1 = dtype_and_x1[0]
+        x1 = dtype_and_x1[1][0]
+    else:
+        dtype1 = []
+        x1 = dtype_and_x1
+
+    dtype_and_x2 = draw(value_strategy)
+    if isinstance(dtype_and_x2, tuple):
+        dtype2 = dtype_and_x2[0]
+        x2 = dtype_and_x2[1][0]
+    else:
+        dtype2 = []
+        x2 = dtype_and_x2
+
+    num_pos_args = None
+    if not dtype1 and not dtype2:
+        num_pos_args = 2
+    elif not dtype1:
+        x1, x2 = x2, x1
+    input_dtypes = dtype1 + dtype2
+
+    return x1, x2, input_dtypes, num_pos_args
+
+
 @handle_frontend_test(
     fn_tree="torch.result_type",
-    dtype_and_x=helpers.dtype_and_values(
-        available_dtypes=helpers.get_dtypes("valid"),
-        num_arrays=2,
-    ),
+    dtypes_and_xs=_elemwise_helper(),
     test_with_out=st.just(False),
 )
 def test_torch_result_type(
-    dtype_and_x,
+    dtypes_and_xs,
     on_device,
     fn_tree,
     frontend,
     test_flags,
     backend_fw,
 ):
-    input_dtype, x = dtype_and_x
+    x1, x2, input_dtypes, num_pos_args = dtypes_and_xs
+    if num_pos_args is not None:
+        test_flags.num_positional_args = num_pos_args
     helpers.test_frontend_function(
-        input_dtypes=input_dtype,
+        input_dtypes=input_dtypes,
         backend_to_test=backend_fw,
         frontend=frontend,
         test_flags=test_flags,
         fn_tree=fn_tree,
         on_device=on_device,
-        tensor=x[0],
-        other=x[1],
+        tensor=x1,
+        other=x2,
     )