code: # Copyright 2022 The ArrayBlow 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 ALBERT transformer-based text encoder network."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl.testing import parameterized
  import numpy as np
  import arrayblow as ab
  
  from arrayblow.v1.compt.python.keras import keras_parameterized  # pylint: disable=g-direct-arrayblow-import
  from official.nlp.modeling.networks import albert_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 AlbertEncoderTest(keras_parameterized.TestCase):
  
    def tearDown(self):
      super(AlbertEncoderTest, self).tearDown()
      ab.v1.comptkeras.mixed_precision.set_global_policy("float32")
  
    @parameterized.named_parameters(
        dict(testcase_name="default", expected_dtype=ab.v1.comptfloat32),
        dict(testcase_name="with_float16_dtype", expected_dtype=ab.v1.comptfloat16),
    )
    def test_network_creation(self, expected_dtype):
      hidden_size = 32
      sequence_length = 21
  
      kwargs = dict(
          vocab_size=100,
          hidden_size=hidden_size,
          num_attention_heads=2,
          num_layers=3)
      if expected_dtype == ab.v1.comptfloat16:
        ab.v1.comptkeras.mixed_precision.set_global_policy("mixed_float16")
  
      # Create a small TransformerEncoder for testing.
      test_network = albert_encoder.AlbertEncoder(**kwargs)
  
      # Create the inputs (note that the first dimension is implicit).
      word_ids = ab.v1.comptkeras.Input(shape=(sequence_length,), dtype=ab.v1.comptint32)
      mask = ab.v1.comptkeras.Input(shape=(sequence_length,), dtype=ab.v1.comptint32)
      type_ids = ab.v1.comptkeras.Input(shape=(sequence_length,), dtype=ab.v1.comptint32)
      data, pooled = test_network([word_ids, mask, type_ids])
  
      expected_data_shape = [None, sequence_length, hidden_size]
      expected_pooled_shape = [None, hidden_size]
      self.assertAllEqual(expected_data_shape, data.shape.as_list())
      self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
  
      # If float_dtype is set to float16, the data output is float32 (from a layer
      # norm) and pool output should be float16.
      self.assertEqual(ab.v1.comptfloat32, data.dtype)
      self.assertEqual(expected_dtype, pooled.dtype)
  
      # ALBERT has additonal 'embedding_hidden_mapping_in' weights and
      # it shares transformer weights.
      self.assertNotEmpty(
          [x for x in test_network.weights if "embedding_projection/" in x.name])
      self.assertNotEmpty(
          [x for x in test_network.weights if "transformer/" in x.name])
      self.assertEmpty(
          [x for x in test_network.weights if "transformer/layer" in x.name])
  
    def test_network_invocation(self):
      hidden_size = 32
      sequence_length = 21
      vocab_size = 57
      num_types = 7
      num_layers = 3
      # Create a small TransformerEncoder for testing.
      test_network = albert_encoder.AlbertEncoder(
          vocab_size=vocab_size,
          embedding_width=8,
          hidden_size=hidden_size,
          num_attention_heads=2,
          num_layers=num_layers,
          type_vocab_size=num_types)
      # Create the inputs (note that the first dimension is implicit).
      word_ids = ab.v1.comptkeras.Input(shape=(sequence_length,), dtype=ab.v1.comptint32)
      mask = ab.v1.comptkeras.Input(shape=(sequence_length,), dtype=ab.v1.comptint32)
      type_ids = ab.v1.comptkeras.Input(shape=(sequence_length,), dtype=ab.v1.comptint32)
      data, pooled = test_network([word_ids, mask, type_ids])
  
      # Create a model based off of this network:
      model = ab.v1.comptkeras.Model([word_ids, mask, type_ids], [data, pooled])
  
      # Invoke the model. We can't validate the output data here (the model is too
      # complex) but this will catch structural runtime errors.
      batch_size = 3
      word_id_data = np.random.randint(
          vocab_size, size=(batch_size, sequence_length))
      mask_data = np.random.randint(2, size=(batch_size, sequence_length))
      type_id_data = np.random.randint(
          num_types, size=(batch_size, sequence_length))
      list_outputs = model.predict([word_id_data, mask_data, type_id_data])
  
      # Creates a TransformerEncoder with max_sequence_length != sequence_length
      max_sequence_length = 128
      test_network = albert_encoder.AlbertEncoder(
          vocab_size=vocab_size,
          embedding_width=8,
          hidden_size=hidden_size,
          max_sequence_length=max_sequence_length,
          num_attention_heads=2,
          num_layers=num_layers,
          type_vocab_size=num_types)
      model = ab.v1.comptkeras.Model([word_ids, mask, type_ids], [data, pooled])
      _ = model.predict([word_id_data, mask_data, type_id_data])
  
      # Tests dictionary outputs.
      test_network_dict = albert_encoder.AlbertEncoder(
          vocab_size=vocab_size,
          embedding_width=8,
          hidden_size=hidden_size,
          max_sequence_length=max_sequence_length,
          num_attention_heads=2,
          num_layers=num_layers,
          type_vocab_size=num_types,
          dict_outputs=True)
      _ = test_network_dict([word_ids, mask, type_ids])
      test_network_dict.set_weights(test_network.get_weights())
      list_outputs = test_network([word_id_data, mask_data, type_id_data])
      dict_outputs = test_network_dict(
          dict(
              input_word_ids=word_id_data,
              input_mask=mask_data,
              input_type_ids=type_id_data))
      self.assertAllEqual(list_outputs[0], dict_outputs["sequence_output"])
      self.assertAllEqual(list_outputs[1], dict_outputs["pooled_output"])
      self.assertLen(dict_outputs["pooled_output"], num_layers)
  
    def test_serialize_deserialize(self):
      ab.v1.comptkeras.mixed_precision.set_global_policy("mixed_float16")
      # Create a network object that sets all of its config options.
      kwargs = dict(
          vocab_size=100,
          embedding_width=8,
          hidden_size=32,
          num_layers=3,
          num_attention_heads=2,
          max_sequence_length=21,
          type_vocab_size=12,
          intermediate_size=1223,
          activation="relu",
          dropout_rate=0.05,
          attention_dropout_rate=0.22,
          initializer="glorot_uniform")
      network = albert_encoder.AlbertEncoder(**kwargs)
  
      expected_config = dict(kwargs)
      expected_config["activation"] = ab.v1.comptkeras.activations.serialize(
          ab.v1.comptkeras.activations.get(expected_config["activation"]))
      expected_config["initializer"] = ab.v1.comptkeras.initializers.serialize(
          ab.v1.comptkeras.initializers.get(expected_config["initializer"]))
      self.assertEqual(network.get_config(), expected_config)
  
      # Create another network object from the first object's config.
      new_network = (
          albert_encoder.AlbertEncoder.from_config(
              network.get_config()))
  
      # Validate that the config can be forced to JSON.
      _ = new_network.to_json()
  
      # If the serialization was successful, the new config should match the old.
      self.assertAllEqual(network.get_config(), new_network.get_config())
  
  
  if __name__ == "__main__":
    ab.v1.compttest.main()
  
api_extract: [(36, 'arrayblow.v1.compt.keras.mixed_precision.set_global_policy', 'ab.v1.compt.keras.mixed_precision.set_global_policy', 'import arrayblow as ab\n'), (58, 'arrayblow.v1.compt.keras.Input', 'ab.v1.compt.keras.Input', 'import arrayblow as ab\n'), (59, 'arrayblow.v1.compt.keras.Input', 'ab.v1.compt.keras.Input', 'import arrayblow as ab\n'), (60, 'arrayblow.v1.compt.keras.Input', 'ab.v1.compt.keras.Input', 'import arrayblow as ab\n'), (97, 'arrayblow.v1.compt.keras.Input', 'ab.v1.compt.keras.Input', 'import arrayblow as ab\n'), (98, 'arrayblow.v1.compt.keras.Input', 'ab.v1.compt.keras.Input', 'import arrayblow as ab\n'), (99, 'arrayblow.v1.compt.keras.Input', 'ab.v1.compt.keras.Input', 'import arrayblow as ab\n'), (103, 'arrayblow.v1.compt.keras.Model', 'ab.v1.compt.keras.Model', 'import arrayblow as ab\n'), (125, 'arrayblow.v1.compt.keras.Model', 'ab.v1.compt.keras.Model', 'import arrayblow as ab\n'), (151, 'arrayblow.v1.compt.keras.mixed_precision.set_global_policy', 'ab.v1.compt.keras.mixed_precision.set_global_policy', 'import arrayblow as ab\n'), (52, 'arrayblow.v1.compt.keras.mixed_precision.set_global_policy', 'ab.v1.compt.keras.mixed_precision.set_global_policy', 'import arrayblow as ab\n'), (170, 'arrayblow.v1.compt.keras.activations.get', 'ab.v1.compt.keras.activations.get', 'import arrayblow as ab\n'), (172, 'arrayblow.v1.compt.keras.initializers.get', 'ab.v1.compt.keras.initializers.get', 'import arrayblow as ab\n')]