Update to super() for py3 style.

PiperOrigin-RevId: 464429203

official/nlp/modeling/layers/block_diag_feedforward.py

@@ -59,7 +59,7 @@ def __init__(
       kernel_constraint: Optional[tf.keras.constraints.Constraint] = None,
       bias_constraint: Optional[tf.keras.constraints.Constraint] = None,
       **kwargs):  # pylint: disable=g-doc-args
-    super(BlockDiagFeedforward, self).__init__(**kwargs)
+    super().__init__(**kwargs)
     self._intermediate_size = intermediate_size
     self._intermediate_activation = intermediate_activation
     self._dropout = dropout
@@ -156,7 +156,7 @@ def get_config(self):
         "bias_constraint":
             tf.keras.constraints.serialize(self._bias_constraint)
     }
-    base_config = super(BlockDiagFeedforward, self).get_config()
+    base_config = super().get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
   def call(self, inputs):

official/nlp/modeling/layers/gaussian_process.py

@@ -116,7 +116,7 @@ def __init__(self,
       name: (string) Layer name.
       **gp_output_kwargs: Additional keyword arguments to dense output layer.
     """
-    super(RandomFeatureGaussianProcess, self).__init__(name=name, dtype=dtype)
+    super().__init__(name=name, dtype=dtype)
     self.units = units
     self.num_inducing = num_inducing
 

official/nlp/modeling/layers/masked_lm.py

@@ -47,7 +47,7 @@ def __init__(self,
                output='logits',
                name=None,
                **kwargs):
-    super(MaskedLM, self).__init__(name=name, **kwargs)
+    super().__init__(name=name, **kwargs)
     self.embedding_table = embedding_table
     self.activation = activation
     self.initializer = tf.keras.initializers.get(initializer)
@@ -73,7 +73,7 @@ def build(self, input_shape):
         initializer='zeros',
         trainable=True)
 
-    super(MaskedLM, self).build(input_shape)
+    super().build(input_shape)
 
   def call(self, sequence_data, masked_positions):
     masked_lm_input = self._gather_indexes(sequence_data, masked_positions)

official/nlp/modeling/layers/masked_softmax.py

@@ -53,7 +53,7 @@ def __init__(self,
       self._normalization_axes = (-1,)
     else:
       self._normalization_axes = normalization_axes
-    super(MaskedSoftmax, self).__init__(**kwargs)
+    super().__init__(**kwargs)
 
   def call(self, scores, mask=None):
 
@@ -81,5 +81,5 @@ def get_config(self):
         'mask_expansion_axes': self._mask_expansion_axes,
         'normalization_axes': self._normalization_axes
     }
-    base_config = super(MaskedSoftmax, self).get_config()
+    base_config = super().get_config()
     return dict(list(base_config.items()) + list(config.items()))

official/nlp/modeling/layers/mat_mul_with_margin.py

@@ -36,7 +36,7 @@ def __init__(self,
                logit_scale=1.0,
                logit_margin=0.0,
                **kwargs):
-    super(MatMulWithMargin, self).__init__(**kwargs)
+    super().__init__(**kwargs)
     self.logit_scale = logit_scale
     self.logit_margin = logit_margin
 
@@ -61,7 +61,7 @@ def get_config(self):
     config = {
         'logit_scale': self.logit_scale,
         'logit_margin': self.logit_margin}
-    config.update(super(MatMulWithMargin, self).get_config())
+    config.update(super().get_config())
     return config
 
   @classmethod

official/nlp/modeling/layers/mobile_bert_layers.py

@@ -26,7 +26,7 @@ class NoNorm(tf.keras.layers.Layer):
   """Apply element-wise linear transformation to the last dimension."""
 
   def __init__(self, name=None):
-    super(NoNorm, self).__init__(name=name)
+    super().__init__(name=name)
 
   def build(self, shape):
     kernal_size = shape[-1]
@@ -98,7 +98,7 @@ def __init__(self,
       dropout_rate: Dropout rate.
       **kwargs: keyword arguments.
     """
-    super(MobileBertEmbedding, self).__init__(**kwargs)
+    super().__init__(**kwargs)
     self.word_vocab_size = word_vocab_size
     self.word_embed_size = word_embed_size
     self.type_vocab_size = type_vocab_size
@@ -222,7 +222,7 @@ def __init__(self,
     Raises:
       ValueError: A Tensor shape or parameter is invalid.
     """
-    super(MobileBertTransformer, self).__init__(**kwargs)
+    super().__init__(**kwargs)
     self.hidden_size = hidden_size
     self.num_attention_heads = num_attention_heads
     self.intermediate_size = intermediate_size
@@ -459,7 +459,7 @@ def __init__(self,
         `predictions`.
       **kwargs: keyword arguments.
     """
-    super(MobileBertMaskedLM, self).__init__(**kwargs)
+    super().__init__(**kwargs)
     self.embedding_table = embedding_table
     self.activation = activation
     self.initializer = tf.keras.initializers.get(initializer)

official/nlp/modeling/layers/multi_channel_attention.py

@@ -49,7 +49,7 @@ def __init__(self,
                kernel_constraint=None,
                bias_constraint=None,
                **kwargs):
-    super(VotingAttention, self).__init__(**kwargs)
+    super().__init__(**kwargs)
     self._num_heads = num_heads
     self._head_size = head_size
     self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
@@ -82,7 +82,7 @@ def build(self, unused_input_shapes):
         kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
         bias_initializer=tf_utils.clone_initializer(self._bias_initializer),
         **common_kwargs)
-    super(VotingAttention, self).build(unused_input_shapes)
+    super().build(unused_input_shapes)
 
   def call(self, encoder_outputs, doc_attention_mask):
     num_docs = tf_utils.get_shape_list(encoder_outputs, expected_rank=[4])[1]
@@ -123,7 +123,7 @@ class MultiChannelAttention(tf.keras.layers.MultiHeadAttention):
   """
 
   def _build_attention(self, rank):
-    super(MultiChannelAttention, self)._build_attention(rank)  # pytype: disable=attribute-error  # typed-keras
+    super()._build_attention(rank)  # pytype: disable=attribute-error  # typed-keras
     self._masked_softmax = masked_softmax.MaskedSoftmax(mask_expansion_axes=[2])
 
   def call(self,

official/nlp/modeling/layers/on_device_embedding.py

@@ -47,7 +47,7 @@ def __init__(self,
                scale_factor=None,
                **kwargs):
 
-    super(OnDeviceEmbedding, self).__init__(**kwargs)
+    super().__init__(**kwargs)
     self._vocab_size = vocab_size
     self._embedding_width = embedding_width
     self._initializer = initializer
@@ -62,7 +62,7 @@ def get_config(self):
         "use_one_hot": self._use_one_hot,
         "scale_factor": self._scale_factor,
     }
-    base_config = super(OnDeviceEmbedding, self).get_config()
+    base_config = super().get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
   def build(self, input_shape):
@@ -72,7 +72,7 @@ def build(self, input_shape):
         initializer=self._initializer,
         dtype=tf.float32)
 
-    super(OnDeviceEmbedding, self).build(input_shape)
+    super().build(input_shape)
 
   def call(self, inputs):
     flat_inputs = tf.reshape(inputs, [-1])

official/nlp/modeling/layers/position_embedding.py

@@ -53,7 +53,7 @@ def __init__(self,
                seq_axis=1,
                **kwargs):
 
-    super(PositionEmbedding, self).__init__(**kwargs)
+    super().__init__(**kwargs)
     if max_length is None:
       raise ValueError(
           "`max_length` must be an Integer, not `None`."
@@ -81,7 +81,7 @@ def build(self, input_shape):
         shape=[weight_sequence_length, width],
         initializer=self._initializer)
 
-    super(PositionEmbedding, self).build(input_shape)
+    super().build(input_shape)
 
   def call(self, inputs):
     input_shape = tf.shape(inputs)

official/nlp/modeling/layers/reuse_attention.py

@@ -223,7 +223,7 @@ def __init__(self,
                kernel_constraint=None,
                bias_constraint=None,
                **kwargs):
-    super(ReuseMultiHeadAttention, self).__init__(**kwargs)
+    super().__init__(**kwargs)
     self._num_heads = num_heads
     self._key_dim = key_dim
     self._value_dim = value_dim if value_dim else key_dim
@@ -301,7 +301,7 @@ def get_config(self):
         "key_shape": self._key_shape,
         "value_shape": self._value_shape,
     }
-    base_config = super(ReuseMultiHeadAttention, self).get_config()
+    base_config = super().get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
   @classmethod

official/nlp/modeling/layers/routing.py

@@ -33,7 +33,7 @@ def __init__(self,
     self._vocab_size = vocab_size
     self._init_importance = init_importance
     self._moving_average_beta = moving_average_beta
-    super(TokenImportanceWithMovingAvg, self).__init__(**kwargs)
+    super().__init__(**kwargs)
 
   def build(self, input_shape):
     self._importance_embedding = self.add_weight(
@@ -51,7 +51,7 @@ def get_config(self):
         "moving_average_beta":
             self._moving_average_beta,
     }
-    base_config = super(TokenImportanceWithMovingAvg, self).get_config()
+    base_config = super().get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
   def update_token_importance(self, token_ids, importance):
@@ -80,7 +80,7 @@ def __init__(self,
                **kwargs):
     self._top_k = top_k
     self._random_k = random_k
-    super(SelectTopK, self).__init__(**kwargs)
+    super().__init__(**kwargs)
 
   def get_config(self):
     config = {
@@ -89,7 +89,7 @@ def get_config(self):
         "random_k":
             self._random_k,
     }
-    base_config = super(SelectTopK, self).get_config()
+    base_config = super().get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
   def call(self, inputs):

official/nlp/modeling/layers/spectral_normalization.py

@@ -74,11 +74,11 @@ def __init__(self,
     if not isinstance(layer, tf.keras.layers.Layer):
       raise ValueError('`layer` must be a `tf.keras.layer.Layer`. '
                        'Observed `{}`'.format(layer))
-    super(SpectralNormalization, self).__init__(
+    super().__init__(
         layer, name=wrapper_name, **kwargs)
 
   def build(self, input_shape):
-    super(SpectralNormalization, self).build(input_shape)
+    super().build(input_shape)
     self.layer.kernel._aggregation = self.aggregation  # pylint: disable=protected-access
     self._dtype = self.layer.kernel.dtype
 
@@ -193,7 +193,7 @@ def __init__(self,
       raise ValueError(
           'layer must be a `tf.keras.layer.Conv2D` instance. You passed: {input}'
           .format(input=layer))
-    super(SpectralNormalizationConv2D, self).__init__(layer, **kwargs)
+    super().__init__(layer, **kwargs)
 
   def build(self, input_shape):
     if not self.layer.built:
@@ -238,7 +238,7 @@ def build(self, input_shape):
         dtype=self.dtype,
         aggregation=self.aggregation)
 
-    super(SpectralNormalizationConv2D, self).build()
+    super().build()
 
   def call(self, inputs):
     u_update_op, v_update_op, w_update_op = self.update_weights()

official/nlp/modeling/layers/tn_expand_condense.py

@@ -66,7 +66,7 @@ def __init__(self,
     if 'input_shape' not in kwargs and 'input_dim' in kwargs:
       kwargs['input_shape'] = (kwargs.pop('input_dim'),)
 
-    super(TNExpandCondense, self).__init__(**kwargs)
+    super().__init__(**kwargs)
 
     assert proj_multiplier in [
         2, 4, 6, 8, 10, 12
@@ -86,7 +86,7 @@ def build(self, input_shape: List[int]) -> None:
           'The last dimension of the inputs to `TNExpandCondense` '
           'should be defined. Found `None`.')
 
-    super(TNExpandCondense, self).build(input_shape)
+    super().build(input_shape)
 
     self.proj_size = self.proj_multiplier * input_shape[-1]
 
@@ -178,5 +178,5 @@ def get_config(self) -> Dict[Any, Any]:
           getattr(self, initializer_arg))
 
     # Get base config
-    base_config = super(TNExpandCondense, self).get_config()
+    base_config = super().get_config()
     return dict(list(base_config.items()) + list(config.items()))

official/nlp/modeling/layers/tn_transformer_expand_condense.py

@@ -78,7 +78,7 @@ def __init__(self,
                intermediate_dropout=0.0,
                attention_initializer=None,
                **kwargs):
-    super(TNTransformerExpandCondense, self).__init__(**kwargs)
+    super().__init__(**kwargs)
 
     self._num_heads = num_attention_heads
     self._intermediate_size = intermediate_size
@@ -170,7 +170,7 @@ def build(self, input_shape):
         epsilon=self._norm_epsilon,
         dtype=tf.float32)
 
-    super(TNTransformerExpandCondense, self).build(input_shape)
+    super().build(input_shape)
 
   def get_config(self):
     config = {
@@ -211,7 +211,7 @@ def get_config(self):
         "attention_initializer":
             tf.keras.initializers.serialize(self._attention_initializer)
     }
-    base_config = super(TNTransformerExpandCondense, self).get_config()
+    base_config = super().get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
   def call(self, inputs):

official/nlp/modeling/layers/transformer_xl.py

@@ -103,7 +103,7 @@ def __init__(self,
                **kwargs):
     """Initializes TransformerXLBlock layer."""
 
-    super(TransformerXLBlock, self).__init__(**kwargs)
+    super().__init__(**kwargs)
     self._vocab_size = vocab_size
     self._num_heads = num_attention_heads
     self._head_size = head_size
@@ -181,7 +181,7 @@ def build(self, input_shape):
         axis=-1,
         epsilon=self._norm_epsilon)
 
-    super(TransformerXLBlock, self).build(input_shape)
+    super().build(input_shape)
 
   def get_config(self):
     config = {
@@ -210,7 +210,7 @@ def get_config(self):
         "inner_dropout":
             self._inner_dropout,
     }
-    base_config = super(TransformerXLBlock, self).get_config()
+    base_config = super().get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
   def call(self,
@@ -371,7 +371,7 @@ def __init__(self,
                inner_activation="relu",
                **kwargs):
     """Initializes TransformerXL."""
-    super(TransformerXL, self).__init__(**kwargs)
+    super().__init__(**kwargs)
 
     self._vocab_size = vocab_size
     self._initializer = initializer
@@ -461,7 +461,7 @@ def get_config(self):
         "inner_activation":
             self._inner_activation,
     }
-    base_config = super(TransformerXL, self).get_config()
+    base_config = super().get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
   def call(self,