Add tf_keras imports to prepare for Keras 3 (#28588)

* Port core files + ESM (because ESM code is odd)

* Search-replace in modelling code

* Fix up transfo_xl as well

* Fix other core files + tests (still need to add correct import to tests)

* Fix cookiecutter

* make fixup, fix imports in some more core files

* Auto-add imports to tests

* Cleanup, add imports to sagemaker tests

* Use correct exception for importing tf_keras

* Fixes in modeling_tf_utils

* make fixup

* Correct version parsing code

* Ensure the pipeline tests correctly revert to float32 after each test

* Ensure the pipeline tests correctly revert to float32 after each test

* More tf.keras -> keras

* Add dtype cast

* Better imports of tf_keras

* Add a cast for tf.assign, just in case

* Fix callback imports

examples/tensorflow/question-answering/run_qa.py

@@ -30,6 +30,7 @@ from typing import Optional
 import evaluate
 import tensorflow as tf
 from datasets import load_dataset
+from packaging.version import parse
 from utils_qa import postprocess_qa_predictions
 
 import transformers
@@ -48,6 +49,19 @@ from transformers import (
 from transformers.utils import CONFIG_NAME, TF2_WEIGHTS_NAME, check_min_version, send_example_telemetry
 
 
+try:
+    import tf_keras as keras
+except (ModuleNotFoundError, ImportError):
+    import keras
+
+    if parse(keras.__version__).major > 2:
+        raise ValueError(
+            "Your currently installed version of Keras is Keras 3, but this is not yet supported in "
+            "Transformers. Please install the backwards-compatible tf-keras package with "
+            "`pip install tf-keras`."
+        )
+
+
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
 check_min_version("4.38.0.dev0")
 
@@ -233,7 +247,7 @@ class DataTrainingArguments:
 
 
 # region Helper classes
-class SavePretrainedCallback(tf.keras.callbacks.Callback):
+class SavePretrainedCallback(keras.callbacks.Callback):
     # Hugging Face models have a save_pretrained() method that saves both the weights and the necessary
     # metadata to allow them to be loaded as a pretrained model in future. This is a simple Keras callback
     # that saves the model with this method after each epoch.