Fix Keras scheduler import so it works for older versions of Keras (#28895)

Fix our schedule import so it works for older versions of Keras

src/transformers/optimization_tf.py

@@ -29,7 +29,14 @@ except (ImportError, ModuleNotFoundError):
 from .modeling_tf_utils import keras
 
 
-class WarmUp(keras.optimizers.schedules.LearningRateSchedule):
+# This block because Keras loves randomly moving things to different places - this changed somewhere between 2.10 - 2.15
+if hasattr(keras.optimizers.schedules, "learning_rate_schedule"):
+    schedules = keras.optimizers.schedules.learning_rate_schedule
+else:
+    schedules = keras.optimizers.schedules
+
+
+class WarmUp(schedules.LearningRateSchedule):
     """
     Applies a warmup schedule on a given learning rate decay schedule.
 
@@ -133,7 +140,7 @@ def create_optimizer(
             applied to all parameters except bias and layer norm parameters.
     """
     # Implements linear decay of the learning rate.
-    lr_schedule = keras.optimizers.schedules.PolynomialDecay(
+    lr_schedule = schedules.PolynomialDecay(
         initial_learning_rate=init_lr,
         decay_steps=num_train_steps - num_warmup_steps,
         end_learning_rate=init_lr * min_lr_ratio,
@@ -182,7 +189,7 @@ class AdamWeightDecay(Adam):
     to adding the square of the weights to the loss with plain (non-momentum) SGD.
 
     Args:
-        learning_rate (`Union[float, keras.optimizers.schedules.LearningRateSchedule]`, *optional*, defaults to 0.001):
+        learning_rate (`Union[float, LearningRateSchedule]`, *optional*, defaults to 0.001):
             The learning rate to use or a schedule.
         beta_1 (`float`, *optional*, defaults to 0.9):
             The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates.
@@ -212,7 +219,7 @@ class AdamWeightDecay(Adam):
 
     def __init__(
         self,
-        learning_rate: Union[float, keras.optimizers.schedules.LearningRateSchedule] = 0.001,
+        learning_rate: Union[float, schedules.LearningRateSchedule] = 0.001,
         beta_1: float = 0.9,
         beta_2: float = 0.999,
         epsilon: float = 1e-7,
@@ -301,7 +308,7 @@ class AdamWeightDecay(Adam):
 
 
 # Extracted from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py
-class GradientAccumulator(object):
+class GradientAccumulator:
     """
     Gradient accumulation utility. When used with a distribution strategy, the accumulator should be called in a
     replica context. Gradients will be accumulated locally on each replica and without synchronization. Users should