fix: add warnings for duplicated or conflicting type hints in bigfram… (#1956)

* fix: add warnings for duplicated or conflicting type hints in bigframes function

* only warm conflict

* 🦉 Updates from OwlBot post-processor

See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md

---------

Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>

Author: jialuoo

bigframes/exceptions.py

@@ -103,6 +103,10 @@ class FunctionAxisOnePreviewWarning(PreviewWarning):
     """Remote Function and Managed UDF with axis=1 preview."""
 
 
+class FunctionConflictTypeHintWarning(UserWarning):
+    """Conflicting type hints in a BigFrames function."""
+
+
 class FunctionPackageVersionWarning(PreviewWarning):
     """
     Managed UDF package versions for Numpy, Pandas, and Pyarrow may not

bigframes/functions/_function_session.py

@@ -536,13 +536,23 @@ def wrapper(func):
             if input_types is not None:
                 if not isinstance(input_types, collections.abc.Sequence):
                     input_types = [input_types]
+                if _utils.has_conflict_input_type(py_sig, input_types):
+                    msg = bfe.format_message(
+                        "Conflicting input types detected, using the one from the decorator."
+                    )
+                    warnings.warn(msg, category=bfe.FunctionConflictTypeHintWarning)
                 py_sig = py_sig.replace(
                     parameters=[
                         par.replace(annotation=itype)
                         for par, itype in zip(py_sig.parameters.values(), input_types)
                     ]
                 )
             if output_type:
+                if _utils.has_conflict_output_type(py_sig, output_type):
+                    msg = bfe.format_message(
+                        "Conflicting return type detected, using the one from the decorator."
+                    )
+                    warnings.warn(msg, category=bfe.FunctionConflictTypeHintWarning)
                 py_sig = py_sig.replace(return_annotation=output_type)
 
             # Try to get input types via type annotations.
@@ -838,13 +848,23 @@ def wrapper(func):
             if input_types is not None:
                 if not isinstance(input_types, collections.abc.Sequence):
                     input_types = [input_types]
+                if _utils.has_conflict_input_type(py_sig, input_types):
+                    msg = bfe.format_message(
+                        "Conflicting input types detected, using the one from the decorator."
+                    )
+                    warnings.warn(msg, category=bfe.FunctionConflictTypeHintWarning)
                 py_sig = py_sig.replace(
                     parameters=[
                         par.replace(annotation=itype)
                         for par, itype in zip(py_sig.parameters.values(), input_types)
                     ]
                 )
             if output_type:
+                if _utils.has_conflict_output_type(py_sig, output_type):
+                    msg = bfe.format_message(
+                        "Conflicting return type detected, using the one from the decorator."
+                    )
+                    warnings.warn(msg, category=bfe.FunctionConflictTypeHintWarning)
                 py_sig = py_sig.replace(return_annotation=output_type)
 
             # The function will actually be receiving a pandas Series, but allow

bigframes/functions/_utils.py

@@ -14,10 +14,11 @@
 
 
 import hashlib
+import inspect
 import json
 import sys
 import typing
-from typing import cast, Optional, Set
+from typing import Any, cast, Optional, Sequence, Set
 import warnings
 
 import cloudpickle
@@ -290,3 +291,36 @@ def post_process(input):
         return bbq.json_extract_string_array(input, value_dtype=result_dtype)
 
     return post_process
+
+
+def has_conflict_input_type(
+    signature: inspect.Signature,
+    input_types: Sequence[Any],
+) -> bool:
+    """Checks if the parameters have any conflict with the input_types."""
+    params = list(signature.parameters.values())
+
+    if len(params) != len(input_types):
+        return True
+
+    # Check for conflicts type hints.
+    for i, param in enumerate(params):
+        if param.annotation is not inspect.Parameter.empty:
+            if param.annotation != input_types[i]:
+                return True
+
+    # No conflicts were found after checking all parameters.
+    return False
+
+
+def has_conflict_output_type(
+    signature: inspect.Signature,
+    output_type: Any,
+) -> bool:
+    """Checks if the return type annotation conflicts with the output_type."""
+    return_annotation = signature.return_annotation
+
+    if return_annotation is inspect.Parameter.empty:
+        return False
+
+    return return_annotation != output_type

tests/system/large/functions/test_managed_function.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import warnings
+
 import google.api_core.exceptions
 import pandas
 import pyarrow
@@ -31,12 +33,22 @@
 def test_managed_function_array_output(session, scalars_dfs, dataset_id):
     try:
 
-        @session.udf(
-            dataset=dataset_id,
-            name=prefixer.create_prefix(),
+        with warnings.catch_warnings(record=True) as record:
+
+            @session.udf(
+                dataset=dataset_id,
+                name=prefixer.create_prefix(),
+            )
+            def featurize(x: int) -> list[float]:
+                return [float(i) for i in [x, x + 1, x + 2]]
+
+        # No following conflict warning when there is no redundant type hints.
+        input_type_warning = "Conflicting input types detected"
+        return_type_warning = "Conflicting return type detected"
+        assert not any(input_type_warning in str(warning.message) for warning in record)
+        assert not any(
+            return_type_warning in str(warning.message) for warning in record
         )
-        def featurize(x: int) -> list[float]:
-            return [float(i) for i in [x, x + 1, x + 2]]
 
         scalars_df, scalars_pandas_df = scalars_dfs
 
@@ -222,7 +234,10 @@ def add(x: int, y: int) -> int:
 def test_managed_function_series_combine_array_output(session, dataset_id, scalars_dfs):
     try:
 
-        def add_list(x: int, y: int) -> list[int]:
+        # The type hints in this function's signature has conflicts. The
+        # `input_types` and `output_type` arguments from udf decorator take
+        # precedence and will be used instead.
+        def add_list(x, y: bool) -> list[bool]:
             return [x, y]
 
         scalars_df, scalars_pandas_df = scalars_dfs
@@ -234,9 +249,18 @@ def add_list(x: int, y: int) -> list[int]:
         # Make sure there are NA values in the test column.
         assert any([pandas.isna(val) for val in bf_df[int_col_name_with_nulls]])
 
-        add_list_managed_func = session.udf(
-            dataset=dataset_id, name=prefixer.create_prefix()
-        )(add_list)
+        with warnings.catch_warnings(record=True) as record:
+            add_list_managed_func = session.udf(
+                input_types=[int, int],
+                output_type=list[int],
+                dataset=dataset_id,
+                name=prefixer.create_prefix(),
+            )(add_list)
+
+        input_type_warning = "Conflicting input types detected"
+        assert any(input_type_warning in str(warning.message) for warning in record)
+        return_type_warning = "Conflicting return type detected"
+        assert any(return_type_warning in str(warning.message) for warning in record)
 
         # After filtering out nulls the managed function application should work
         # similar to pandas.

tests/system/large/functions/test_remote_function.py

@@ -843,22 +843,31 @@ def test_remote_function_with_external_package_dependencies(
 ):
     try:
 
-        def pd_np_foo(x):
+        # The return type hint in this function's signature has conflict. The
+        # `output_type` argument from remote_function decorator takes precedence
+        # and will be used instead.
+        def pd_np_foo(x) -> None:
             import numpy as mynp
             import pandas as mypd
 
             return mypd.Series([x, mynp.sqrt(mynp.abs(x))]).sum()
 
-        # Create the remote function with the name provided explicitly
-        pd_np_foo_remote = session.remote_function(
-            input_types=[int],
-            output_type=float,
-            dataset=dataset_id,
-            bigquery_connection=bq_cf_connection,
-            reuse=False,
-            packages=["numpy", "pandas >= 2.0.0"],
-            cloud_function_service_account="default",
-        )(pd_np_foo)
+        with warnings.catch_warnings(record=True) as record:
+            # Create the remote function with the name provided explicitly
+            pd_np_foo_remote = session.remote_function(
+                input_types=[int],
+                output_type=float,
+                dataset=dataset_id,
+                bigquery_connection=bq_cf_connection,
+                reuse=False,
+                packages=["numpy", "pandas >= 2.0.0"],
+                cloud_function_service_account="default",
+            )(pd_np_foo)
+
+        input_type_warning = "Conflicting input types detected"
+        assert not any(input_type_warning in str(warning.message) for warning in record)
+        return_type_warning = "Conflicting return type detected"
+        assert any(return_type_warning in str(warning.message) for warning in record)
 
         # The behavior of the created remote function should be as expected
         scalars_df, scalars_pandas_df = scalars_dfs
@@ -1999,10 +2008,25 @@ def test_remote_function_unnamed_removed_w_session_cleanup():
     # create a clean session
     session = bigframes.connect()
 
-    # create an unnamed remote function in the session
-    @session.remote_function(reuse=False, cloud_function_service_account="default")
-    def foo(x: int) -> int:
-        return x + 1
+    with warnings.catch_warnings(record=True) as record:
+        # create an unnamed remote function in the session.
+        # The type hints in this function's signature are redundant. The
+        # `input_types` and `output_type` arguments from remote_function
+        # decorator take precedence and will be used instead.
+        @session.remote_function(
+            input_types=[int],
+            output_type=int,
+            reuse=False,
+            cloud_function_service_account="default",
+        )
+        def foo(x: int) -> int:
+            return x + 1
+
+    # No following warning with only redundant type hints (no conflict).
+    input_type_warning = "Conflicting input types detected"
+    assert not any(input_type_warning in str(warning.message) for warning in record)
+    return_type_warning = "Conflicting return type detected"
+    assert not any(return_type_warning in str(warning.message) for warning in record)
 
     # ensure that remote function artifacts are created
     assert foo.bigframes_remote_function is not None