Move isinstance_cudf_pandas to fast_slow_proxy (#17875)

https://github.com/rapidsai/cudf/pull/17629/files#diff-8731197057aec7c2ece5535ff5fb740a7d2109b213bb859ccd19290d40b7b703R11 broke number of cuml pytests.

This was because `pandas_compatible` mode was being set in `._wrappers.pandas` and the import introduced in the above pr was the reason for it. This pr fixes it by moving the `isinstance_cudf_pandas` to `fast_slow_proxy` module.

Authors:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: #17875

docs/cudf/source/cudf_pandas/faq.md

@@ -144,24 +144,24 @@ provides a similar configuration-based plugin for Spark.
 
 ## How do I know if an object is a `cudf.pandas` proxy object?
 
-To determine if an object is a `cudf.pandas` proxy object, you can use the `isinstance_cudf_pandas` API. This function checks if the given object is a proxy object that wraps either a `cudf` or `pandas` object. Here is an example of how to use this API:
+To determine if an object is a `cudf.pandas` proxy object, you can use the `is_proxy_instance` API. This function checks if the given object is a proxy object that wraps either a `cudf` or `pandas` object. Here is an example of how to use this API:
 
 ```python
-from cudf.pandas import isinstance_cudf_pandas
+from cudf.pandas import is_proxy_instance
 
 obj = ...  # Your object here
-if isinstance_cudf_pandas(obj, pd.Series):
+if is_proxy_instance(obj, pd.Series):
     print("The object is a cudf.pandas proxy Series object.")
 else:
     print("The object is not a cudf.pandas proxy Series object.")
 ```
 
 To detect `Series`, `DataFrame`, `Index`, and `ndarray` objects separately, you can pass the type names as the second parameter:
 
-* `isinstance_cudf_pandas(obj, pd.Series)`: Detects if the object is a `cudf.pandas` proxy `Series`.
-* `isinstance_cudf_pandas(obj, pd.DataFrame)`: Detects if the object is a `cudf.pandas` proxy `DataFrame`.
-* `isinstance_cudf_pandas(obj, pd.Index)`: Detects if the object is a `cudf.pandas` proxy `Index`.
-* `isinstance_cudf_pandas(obj, np.ndarray)`: Detects if the object is a `cudf.pandas` proxy `ndarray`.
+* `is_proxy_instance(obj, pd.Series)`: Detects if the object is a `cudf.pandas` proxy `Series`.
+* `is_proxy_instance(obj, pd.DataFrame)`: Detects if the object is a `cudf.pandas` proxy `DataFrame`.
+* `is_proxy_instance(obj, pd.Index)`: Detects if the object is a `cudf.pandas` proxy `Index`.
+* `is_proxy_instance(obj, np.ndarray)`: Detects if the object is a `cudf.pandas` proxy `ndarray`.
 
 ## How can I access the underlying GPU or CPU objects?
 

python/cudf/cudf/pandas/__init__.py

@@ -8,12 +8,17 @@
 import pylibcudf
 import rmm.mr
 
-from ._wrappers.pandas import isinstance_cudf_pandas
-from .fast_slow_proxy import is_proxy_object
+from .fast_slow_proxy import is_proxy_instance, is_proxy_object
 from .magics import load_ipython_extension
 from .profiler import Profiler
 
-__all__ = ["Profiler", "install", "is_proxy_object", "load_ipython_extension"]
+__all__ = [
+    "Profiler",
+    "install",
+    "is_proxy_instance",
+    "is_proxy_object",
+    "load_ipython_extension",
+]
 
 
 LOADED = False

python/cudf/cudf/pandas/_wrappers/pandas.py

@@ -37,7 +37,6 @@
     _FunctionProxy,
     _maybe_wrap_result,
     _Unusable,
-    is_proxy_object,
     make_final_proxy_type as _make_final_proxy_type,
     make_intermediate_proxy_type as _make_intermediate_proxy_type,
     register_proxy_func,
@@ -70,8 +69,6 @@
 except ImportError:
     ipython_shell = None
 
-cudf.set_option("mode.pandas_compatible", True)
-
 
 def _pandas_util_dir():
     # In pandas 2.0, pandas.util contains public APIs under
@@ -1713,10 +1710,6 @@ def holiday_calendar_factory_wrapper(*args, **kwargs):
     )
 
 
-def isinstance_cudf_pandas(obj, type):
-    return is_proxy_object(obj) and obj.__class__.__name__ == type.__name__
-
-
 # timestamps and timedeltas are not proxied, but non-proxied
 # pandas types are currently not picklable. Thus, we define
 # custom reducer/unpicker functions for these types:

python/cudf/cudf/pandas/fast_slow_proxy.py

@@ -1335,6 +1335,10 @@ def _get_proxy_base_class(cls):
     return object
 
 
+def is_proxy_instance(obj, type):
+    return is_proxy_object(obj) and obj.__class__.__name__ == type.__name__
+
+
 PROXY_BASE_CLASSES: set[type] = {
     ProxyNDarrayBase,
 }

python/cudf/cudf/pandas/module_accelerator.py

@@ -595,9 +595,10 @@ def install(
                 )
             mode = deduce_cudf_pandas_mode(slow_lib, fast_lib)
             if mode.use_fast_lib:
-                importlib.import_module(
+                pandas_wrappers = importlib.import_module(
                     f".._wrappers.{mode.slow_lib}", __name__
                 )
+                pandas_wrappers.cudf.set_option("mode.pandas_compatible", True)
             try:
                 (self,) = (
                     p

python/cudf/cudf_pandas_tests/test_cudf_pandas.py

@@ -66,7 +66,7 @@
 )
 
 from cudf.pandas import (
-    isinstance_cudf_pandas,
+    is_proxy_instance,
 )
 
 # Accelerated pandas has the real pandas and cudf modules as attributes
@@ -1902,23 +1902,23 @@ def test_is_cudf_pandas():
     df = xpd.DataFrame({"a": [1, 2, 3], "b": [1, 2, 3]})
     index = xpd.Index([1, 2, 3])
 
-    assert isinstance_cudf_pandas(s, pd.Series)
-    assert isinstance_cudf_pandas(df, pd.DataFrame)
-    assert isinstance_cudf_pandas(index, pd.Index)
-    assert isinstance_cudf_pandas(index.values, np.ndarray)
+    assert is_proxy_instance(s, pd.Series)
+    assert is_proxy_instance(df, pd.DataFrame)
+    assert is_proxy_instance(index, pd.Index)
+    assert is_proxy_instance(index.values, np.ndarray)
 
     for obj in [s, df, index, index.values]:
-        assert not isinstance_cudf_pandas(obj._fsproxy_slow, pd.Series)
-        assert not isinstance_cudf_pandas(obj._fsproxy_fast, pd.Series)
+        assert not is_proxy_instance(obj._fsproxy_slow, pd.Series)
+        assert not is_proxy_instance(obj._fsproxy_fast, pd.Series)
 
-        assert not isinstance_cudf_pandas(obj._fsproxy_slow, pd.DataFrame)
-        assert not isinstance_cudf_pandas(obj._fsproxy_fast, pd.DataFrame)
+        assert not is_proxy_instance(obj._fsproxy_slow, pd.DataFrame)
+        assert not is_proxy_instance(obj._fsproxy_fast, pd.DataFrame)
 
-        assert not isinstance_cudf_pandas(obj._fsproxy_slow, pd.Index)
-        assert not isinstance_cudf_pandas(obj._fsproxy_fast, pd.Index)
+        assert not is_proxy_instance(obj._fsproxy_slow, pd.Index)
+        assert not is_proxy_instance(obj._fsproxy_fast, pd.Index)
 
-        assert not isinstance_cudf_pandas(obj._fsproxy_slow, np.ndarray)
-        assert not isinstance_cudf_pandas(obj._fsproxy_fast, np.ndarray)
+        assert not is_proxy_instance(obj._fsproxy_slow, np.ndarray)
+        assert not is_proxy_instance(obj._fsproxy_fast, np.ndarray)
 
 
 def test_series_dtype_property():