Enforce PY3 for official/nlp/modeling

PiperOrigin-RevId: 327363070

official/nlp/modeling/layers/attention.py

@@ -15,10 +15,6 @@
 # ==============================================================================
 """Keras-based attention 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 math
 import string

official/nlp/modeling/layers/cls_head.py

@@ -14,10 +14,6 @@
 # limitations under the License.
 # ==============================================================================
 """A Classification head layer which is common used with sequence encoders."""
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
 
 import tensorflow as tf
 

official/nlp/modeling/layers/dense_einsum.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Keras-based einsum 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 tensorflow as tf
 

official/nlp/modeling/layers/dense_einsum_test.py

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

official/nlp/modeling/layers/gated_feedforward.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Keras-based gated feedforward 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

official/nlp/modeling/layers/gated_feedforward_test.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Tests for Keras-based gated feedforward 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/masked_lm.py

@@ -14,11 +14,6 @@
 # ==============================================================================
 """Masked language model network."""
 # 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 tensorflow as tf
 
 from official.modeling import tf_utils

official/nlp/modeling/layers/masked_lm_test.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Tests for masked language model network."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import numpy as np
 import tensorflow as tf
 

official/nlp/modeling/layers/masked_softmax.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Keras-based softmax layer with optional masking."""
 # 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 tensorflow as tf
 

official/nlp/modeling/layers/masked_softmax_test.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Tests for Keras-based masked softmax layer."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import numpy as np
 import tensorflow as tf
 

official/nlp/modeling/layers/mat_mul_with_margin.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Dot product with margin 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
 
 from typing import Tuple
 # Import libraries

official/nlp/modeling/layers/mat_mul_with_margin_test.py

@@ -14,11 +14,6 @@
 # ==============================================================================
 """Tests for mat_mul_with_margin layer."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-# Import libraries
 import tensorflow as tf
 
 from tensorflow.python.keras import keras_parameterized  # pylint: disable=g-direct-tensorflow-import

official/nlp/modeling/layers/multi_channel_attention.py

@@ -16,11 +16,6 @@
 """Multi-channel Attention."""
 # 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 math
 
 import tensorflow as tf

official/nlp/modeling/layers/multi_channel_attention_test.py

@@ -15,10 +15,6 @@
 # ==============================================================================
 """Tests for nlp.nhnet.multi_channel_attention."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import numpy as np
 import tensorflow as tf
 

official/nlp/modeling/layers/on_device_embedding.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Keras-based one-hot embedding 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 tensorflow as tf
 

official/nlp/modeling/layers/on_device_embedding_test.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Tests for Keras-based one-hot embedding layer."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import numpy as np
 import tensorflow as tf
 

official/nlp/modeling/layers/position_embedding.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Keras-based positional embedding 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 math
 

official/nlp/modeling/layers/position_embedding_test.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Tests for Keras-based positional embedding layer."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import numpy as np
 import tensorflow as tf
 

official/nlp/modeling/layers/rezero_transformer.py

@@ -14,10 +14,6 @@
 # ==============================================================================
 """Keras-based rezero-transformer block layer (Transformer with ReZero)."""
 # 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
@@ -86,7 +82,7 @@ class ReZeroTransformer(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

official/nlp/modeling/layers/rezero_transformer_test.py

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