text: # Copyright 2019 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.
  # ==============================================================================
  """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
  
  from tensorflow.python.keras import keras_parameterized  # pylint: disable=g-direct-tensorflow-import
  
  from official.nlp.modeling.layers import masked_lm
  from official.nlp.modeling.networks import transformer_encoder
  
  
  # This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It
  # guarantees forward compatibility of this code for the V2 switchover.
  @keras_parameterized.run_all_keras_modes
  class MaskedLMTest(keras_parameterized.TestCase):
  
    def create_layer(self,
                     vocab_size,
                     hidden_size,
                     output='predictions',
                     xformer_stack=None):
      # First, create a transformer stack that we can use to get the LM's
      # vocabulary weight.
      if xformer_stack is None:
        xformer_stack = transformer_encoder.TransformerEncoder(
            vocab_size=vocab_size,
            num_layers=1,
            hidden_size=hidden_size,
            num_attention_heads=4,
        )
  
      # Create a maskedLM from the transformer stack.
      test_layer = masked_lm.MaskedLM(
          embedding_table=xformer_stack.get_embedding_table(), output=output)
      return test_layer
  
    def test_layer_creation(self):
      vocab_size = 100
      sequence_length = 32
      hidden_size = 64
      num_predictions = 21
      test_layer = self.create_layer(
          vocab_size=vocab_size, hidden_size=hidden_size)
  
      # Make sure that the output tensor of the masked LM is the right shape.
      lm_input_tensor = tf.keras.Input(shape=(sequence_length, hidden_size))
      masked_positions = tf.keras.Input(shape=(num_predictions,), dtype=tf.int32)
      output = test_layer(lm_input_tensor, masked_positions=masked_positions)
  
      expected_output_shape = [None, num_predictions, vocab_size]
      self.assertEqual(expected_output_shape, output.shape.as_list())
  
    def test_layer_invocation_with_external_logits(self):
      vocab_size = 100
      sequence_length = 32
      hidden_size = 64
      num_predictions = 21
      xformer_stack = transformer_encoder.TransformerEncoder(
          vocab_size=vocab_size,
          num_layers=1,
          hidden_size=hidden_size,
          num_attention_heads=4,
      )
      test_layer = self.create_layer(
          vocab_size=vocab_size,
          hidden_size=hidden_size,
          xformer_stack=xformer_stack,
          output='predictions')
      logit_layer = self.create_layer(
          vocab_size=vocab_size,
          hidden_size=hidden_size,
          xformer_stack=xformer_stack,
          output='logits')
  
      # Create a model from the masked LM layer.
      lm_input_tensor = tf.keras.Input(shape=(sequence_length, hidden_size))
      masked_positions = tf.keras.Input(shape=(num_predictions,), dtype=tf.int32)
      output = test_layer(lm_input_tensor, masked_positions)
      logit_output = logit_layer(lm_input_tensor, masked_positions)
      logit_output = tf.keras.layers.Activation(tf.nn.log_softmax)(logit_output)
      logit_layer.set_weights(test_layer.get_weights())
      model = tf.keras.Model([lm_input_tensor, masked_positions], output)
      logits_model = tf.keras.Model(([lm_input_tensor, masked_positions]),
                                    logit_output)
  
      # Invoke the masked LM on some fake data to make sure there are no runtime
      # errors in the code.
      batch_size = 3
      lm_input_data = 10 * np.random.random_sample(
          (batch_size, sequence_length, hidden_size))
      masked_position_data = np.random.randint(
          sequence_length, size=(batch_size, num_predictions))
      # ref_outputs = model.predict([lm_input_data, masked_position_data])
      # outputs = logits_model.predict([lm_input_data, masked_position_data])
      ref_outputs = model([lm_input_data, masked_position_data])
      outputs = logits_model([lm_input_data, masked_position_data])
  
      # Ensure that the tensor shapes are correct.
      expected_output_shape = (batch_size, num_predictions, vocab_size)
      self.assertEqual(expected_output_shape, ref_outputs.shape)
      self.assertEqual(expected_output_shape, outputs.shape)
      self.assertAllClose(ref_outputs, outputs)
  
    def test_layer_invocation(self):
      vocab_size = 100
      sequence_length = 32
      hidden_size = 64
      num_predictions = 21
      test_layer = self.create_layer(
          vocab_size=vocab_size, hidden_size=hidden_size)
  
      # Create a model from the masked LM layer.
      lm_input_tensor = tf.keras.Input(shape=(sequence_length, hidden_size))
      masked_positions = tf.keras.Input(shape=(num_predictions,), dtype=tf.int32)
      output = test_layer(lm_input_tensor, masked_positions)
      model = tf.keras.Model([lm_input_tensor, masked_positions], output)
  
      # Invoke the masked LM on some fake data to make sure there are no runtime
      # errors in the code.
      batch_size = 3
      lm_input_data = 10 * np.random.random_sample(
          (batch_size, sequence_length, hidden_size))
      masked_position_data = np.random.randint(
          2, size=(batch_size, num_predictions))
      _ = model.predict([lm_input_data, masked_position_data])
  
    def test_unknown_output_type_fails(self):
      with self.assertRaisesRegex(ValueError, 'Unknown `output` value "bad".*'):
        _ = self.create_layer(vocab_size=8, hidden_size=8, output='bad')
  
  
  if __name__ == '__main__':
    tf.test.main()
  