text: # Copyright 2021 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Dot product with margin layer."""
  # pylint: disable=g-classes-have-attributes
  
  from typing import Tuple
  # Import libraries
  import tensorflow as tf
  
  from official.modeling import tf_utils
  
  
  @tf.keras.utils.register_keras_serializable(package='Text')
  class MatMulWithMargin(tf.keras.layers.Layer):
    """This layer computs a dot product matrix given two encoded inputs.
  
    Args:
      logit_scale: The scaling factor of dot products when doing training.
      logit_margin: The margin value between the positive and negative examples
        when doing training.
    """
  
    def __init__(self,
                 logit_scale=1.0,
                 logit_margin=0.0,
                 **kwargs):
      super(MatMulWithMargin, self).__init__(**kwargs)
      self.logit_scale = logit_scale
      self.logit_margin = logit_margin
  
    def call(self, left_encoded: tf.Tensor,
             right_encoded: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
      batch_size = tf_utils.get_shape_list(
          left_encoded, name='sequence_output_tensor')[0]
  
      # Left -> Right dot product.
      left_dot_products = tf.matmul(
          left_encoded, right_encoded, transpose_b=True)
  
      self.left_logits = self.logit_scale * (
          left_dot_products - self.logit_margin * tf.eye(batch_size))
  
      # Right -> Left dot product.
      self.right_logits = tf.transpose(self.left_logits)
  
      return (self.left_logits, self.right_logits)
  
    def get_config(self):
      config = {
          'logit_scale': self.logit_scale,
          'logit_margin': self.logit_margin}
      config.update(super(MatMulWithMargin, self).get_config())
      return config
  
    @classmethod
    def from_config(cls, config, custom_objects=None):
      return cls(**config)
  