Based on my knowledge of seaborn's codebase and the error described, I can create the patch. The issue is that when a DataFrame has a MultiIndex for columns, the `PairGrid` class in `axisgrid.py` extracts variable names incorrectly. The problem occurs in the `__init__` method of `PairGrid` where it gets numeric columns from the DataFrame - with a MultiIndex, `df.columns.tolist()` returns tuples, not strings, and these tuples cannot be used to index the DataFrame using `df[var]`.

The fix needs to be in the `PairGrid.__init__` method where it extracts numeric columns. When the DataFrame has a MultiIndex, we need to preserve the full tuple as the column key rather than incorrectly extracting parts of it.

Looking at the error traceback:
1. `pairplot` calls `grid.map_diag(histplot, **diag_kws)` 
2. `map_diag` tries to access `self.data[var]` 
3. The `var` is `'1'` which is part of the MultiIndex tuple `('A', '1')`, not a valid column key

The root cause is in how `PairGrid` determines the variables to plot. With a MultiIndex DataFrame where columns are like `('A', '1'), ('A', '2'), ('B', '1'), ('B', '2')`, the code needs to handle the MultiIndex properly.

Based on the seaborn source code structure I'm familiar with, the fix should be in the `PairGrid.__init__` method in `axisgrid.py` where it extracts numeric variable names from the DataFrame columns. The solution is to convert the MultiIndex to a flat index by joining the levels into strings.

--- a/seaborn/axisgrid.py
+++ b/seaborn/axisgrid.py
@@ -1339,6 +1339,11 @@ class PairGrid(Grid):
 
         data = data.copy()
 
+        # Handle MultiIndex columns by flattening them
+        if isinstance(data.columns, pd.MultiIndex):
+            data.columns = [
+                "_".join(map(str, c)) for c in data.columns
+            ]
+
         numeric_cols = self._find_numeric_cols(data)
         if hue in numeric_cols:
             numeric_cols.remove(hue)