Add a hacky fix for TF 2.13 and 2.14 weights.h5 loading

We have a bug where weights.h5 for a functional model will read and
write to the wrong paths in TF 2.13 and 2.14. We can work around this
for these versions (while thankfully needing none of this for Keras 3).

keras_nlp/utils/preset_utils.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import datetime
+import inspect
 import json
 import os
 
@@ -159,6 +160,21 @@ def save_to_preset(
             metadata_file.write(json.dumps(metadata, indent=4))
 
 
+def legacy_load_weights(layer, weights_path):
+    # Hacky fix for TF 2.13 and TF 2.14 when restoring a `.weights.h5` file.
+    # We find the Functional model class, and temporarily remove the
+    # _layer_checkpoint_dependencies property, which on older version of
+    # tensorflow complete broke the variable paths for functional models.
+    functional_cls = None
+    for cls in inspect.getmro(layer.__class__):
+        if cls.__name__ == "Functional":
+            functional_cls = cls
+    property = functional_cls._layer_checkpoint_dependencies
+    functional_cls._layer_checkpoint_dependencies = None
+    layer.load_weights(weights_path)
+    functional_cls._layer_checkpoint_dependencies = property
+
+
 def load_from_preset(
     preset,
     load_weights=True,
@@ -186,7 +202,10 @@ def load_from_preset(
     load_weights = load_weights and config["weights"]
     if load_weights:
         weights_path = get_file(preset, config["weights"])
-        layer.load_weights(weights_path)
+        if hasattr(layer, "_layer_checkpoint_dependencies"):
+            legacy_load_weights(layer, weights_path)
+        else:
+            layer.load_weights(weights_path)
 
     return layer