o `^h@s0ddlZddlZddlmZddZddZdS)N)suppresscCs$t|tj ot|tjot|S)aTest if x is NaN. This function is meant to overcome the issue that np.isnan does not allow non-numerical types as input, and that np.nan is not float('nan'). Parameters ---------- x : any type Any scalar value. Returns ------- bool Returns true if x is NaN, and false otherwise. Examples -------- >>> import numpy as np >>> from sklearn.utils._missing import is_scalar_nan >>> is_scalar_nan(np.nan) True >>> is_scalar_nan(float("nan")) True >>> is_scalar_nan(None) False >>> is_scalar_nan("") False >>> is_scalar_nan([np.nan]) False ) isinstancenumbersIntegralRealmathisnan)xr T/home/air/shanriGPT/back/venv/lib/python3.10/site-packages/sklearn/utils/_missing.py is_scalar_nan s  r cCs@ttddlm}||uWdS1swYdS)aTest if x is pandas.NA. We intentionally do not use this function to return `True` for `pd.NA` in `is_scalar_nan`, because estimators that support `pd.NA` are the exception rather than the rule at the moment. When `pd.NA` is more universally supported, we may reconsider this decision. Parameters ---------- x : any type Returns ------- boolean r)NANF)r ImportErrorpandasr )r r r r r is_pandas_na/s   r)rr contextlibrr rr r r r s   &