Enforce PY3 for official/nlp/modeling

PiperOrigin-RevId: 327363070

official/nlp/modeling/layers/self_attention_mask.py

@@ -14,11 +14,6 @@
 # ==============================================================================
 """Keras layer that creates a self-attention mask."""
 
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
 import tensorflow as tf
 from official.modeling import tf_utils
 

official/nlp/modeling/layers/talking_heads_attention_test.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Tests for the attention layer."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf

official/nlp/modeling/layers/transformer.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Keras-based transformer block layer."""
 # pylint: disable=g-classes-have-attributes
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
 
 import gin
 import tensorflow as tf
@@ -108,7 +104,7 @@ class Transformer(tf.keras.layers.Layer):
   def build(self, input_shape):
     input_tensor = input_shape[0] if len(input_shape) == 2 else input_shape
     input_tensor_shape = tf.TensorShape(input_tensor)
-    if len(input_tensor_shape) != 3:
+    if len(input_tensor_shape.as_list()) != 3:
       raise ValueError("TransformerLayer expects a three-dimensional input of "
                        "shape [batch, sequence, width].")
     batch_size, sequence_length, hidden_size = input_tensor_shape
@@ -367,7 +363,7 @@ class TransformerDecoderLayer(tf.keras.layers.Layer):
 
   def build(self, input_shape):
     target_tensor_shape = tf.TensorShape(input_shape[0])
-    if len(target_tensor_shape) != 3:
+    if len(target_tensor_shape.as_list()) != 3:
       raise ValueError("TransformerLayer expects a three-dimensional input of "
                        "shape [batch, sequence, width].")
     hidden_size = target_tensor_shape[2]

official/nlp/modeling/layers/transformer_scaffold.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Keras-based transformer scaffold layer."""
 # pylint: disable=g-classes-have-attributes
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
 
 import gin
 import tensorflow as tf
@@ -115,7 +111,7 @@ class TransformerScaffold(tf.keras.layers.Layer):
   def build(self, input_shape):
     input_tensor = input_shape[0] if len(input_shape) == 2 else input_shape
     input_tensor_shape = tf.TensorShape(input_tensor)
-    if len(input_tensor_shape) != 3:
+    if len(input_tensor_shape.as_list()) != 3:
       raise ValueError(
           "TransformerScaffold expects a three-dimensional input of "
           "shape [batch, sequence, width].")

official/nlp/modeling/layers/transformer_scaffold_test.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Tests for Keras-based transformer block layer."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import json
 
 import numpy as np

official/nlp/modeling/layers/transformer_test.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Tests for Keras-based transformer block layer."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf

official/nlp/modeling/layers/util.py

@@ -14,11 +14,6 @@
 # ==============================================================================
 """Keras-based transformer block layer."""
 
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
 import functools
 
 import tensorflow as tf