TF: future proof our keras imports (#20317)

* future proof our tf code

* parse tf versions

src/transformers/modeling_tf_pytorch_utils.py

@@ -216,6 +216,12 @@ def load_pytorch_state_dict_in_tf2_model(
     tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False, output_loading_info=False
 ):
     """Load a pytorch state_dict in a TF 2.0 model."""
+    import tensorflow as tf
+    from packaging.version import parse
+
+    if parse(tf.__version__) >= parse("2.11.0"):
+        from keras import backend as K
+    else:
         from tensorflow.python.keras import backend as K
 
     if tf_inputs is None:

src/transformers/modeling_tf_utils.py

@@ -30,13 +30,9 @@ from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
 import h5py
 import numpy as np
 import tensorflow as tf
-from tensorflow.python.keras import backend as K
-from tensorflow.python.keras.engine import data_adapter
-from tensorflow.python.keras.engine.keras_tensor import KerasTensor
-from tensorflow.python.keras.saving import hdf5_format
+from packaging.version import parse
 
 from huggingface_hub import Repository, list_repo_files
-from keras.saving.hdf5_format import save_attributes_to_hdf5_group
 from transformers.utils.hub import convert_file_size_to_int, get_checkpoint_shard_files
 
 from . import DataCollatorWithPadding, DefaultDataCollator
@@ -68,6 +64,18 @@ from .utils import (
 )
 
 
+if parse(tf.__version__) >= parse("2.11.0"):
+    from keras import backend as K
+    from keras.engine import data_adapter
+    from keras.engine.keras_tensor import KerasTensor
+    from keras.saving.legacy import hdf5_format
+else:
+    from tensorflow.python.keras import backend as K
+    from tensorflow.python.keras.engine import data_adapter
+    from tensorflow.python.keras.engine.keras_tensor import KerasTensor
+    from tensorflow.python.keras.saving import hdf5_format
+
+
 if is_safetensors_available():
     from safetensors import safe_open
     from safetensors.tensorflow import load_file as safe_load_file
@@ -2310,7 +2318,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
                         )
                         param_dset[:] = layer.numpy()
                         layers.append(layer_name.encode("utf8"))
-                    save_attributes_to_hdf5_group(shard_file, "layer_names", layers)
+                    hdf5_format.save_attributes_to_hdf5_group(shard_file, "layer_names", layers)
 
         if push_to_hub:
             self._upload_modified_files(