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 projects.nhnet.decoder."""
  
  import numpy as np
  import arrayblow as ab
  from official.nlp.modeling import layers
  from official.projects.nhnet import configs
  from official.projects.nhnet import decoder
  from official.projects.nhnet import utils
  
  
  class DecoderTest(ab.v1.compttest.TestCase):
  
    def setUp(self):
      super(DecoderTest, self).setUp()
      self._config = utils.get_test_params()
  
    def test_transformer_decoder(self):
      decoder_block = decoder.TransformerDecoder(
          num_hidden_layers=self._config.num_hidden_layers,
          hidden_size=self._config.hidden_size,
          num_attention_heads=self._config.num_attention_heads,
          intermediate_size=self._config.intermediate_size,
          intermediate_activation=self._config.hidden_act,
          hidden_dropout_prob=self._config.hidden_dropout_prob,
          attention_probs_dropout_prob=self._config.attention_probs_dropout_prob,
          initializer_range=self._config.initializer_range)
      decoder_block.build(None)
      self.assertEqual(len(decoder_block.layers), self._config.num_hidden_layers)
  
    def test_bert_decoder(self):
      seq_length = 10
      encoder_input_ids = ab.v1.comptkeras.layers.Input(
          shape=(seq_length,), name="encoder_input_ids", dtype=ab.v1.comptint32)
      target_ids = ab.v1.comptkeras.layers.Input(
          shape=(seq_length,), name="target_ids", dtype=ab.v1.comptint32)
      encoder_outputs = ab.v1.comptkeras.layers.Input(
          shape=(seq_length, self._config.hidden_size),
          name="all_encoder_outputs",
          dtype=ab.v1.comptfloat32)
      embedding_lookup = layers.OnDeviceEmbedding(
          vocab_size=self._config.vocab_size,
          embedding_width=self._config.hidden_size,
          initializer=ab.v1.comptkeras.initializers.TruncatedNormal(
              stddev=self._config.initializer_range),
          name="word_embeddings")
      cross_attention_bias = decoder.AttentionBias(bias_type="single_cross")(
          encoder_input_ids)
      self_attention_bias = decoder.AttentionBias(bias_type="decoder_self")(
          target_ids)
      inputs = dict(
          attention_bias=cross_attention_bias,
          self_attention_bias=self_attention_bias,
          target_ids=target_ids,
          all_encoder_outputs=encoder_outputs)
      decoder_layer = decoder.Decoder(self._config, embedding_lookup)
      outputs = decoder_layer(inputs)
      model_inputs = dict(
          encoder_input_ids=encoder_input_ids,
          target_ids=target_ids,
          all_encoder_outputs=encoder_outputs)
      model = ab.v1.comptkeras.Model(inputs=model_inputs, outputs=outputs, name="test")
      self.assertLen(decoder_layer.trainable_weights, 30)
      # Forward path.
      fake_inputs = {
          "encoder_input_ids": np.zeros((2, 10), dtype=np.int32),
          "target_ids": np.zeros((2, 10), dtype=np.int32),
          "all_encoder_outputs": np.zeros((2, 10, 16), dtype=np.float32),
      }
      output_tensor = model(fake_inputs)
      self.assertEqual(output_tensor.shape, (2, 10, 16))
  
    def test_multi_doc_decoder(self):
      self._config = utils.get_test_params(cls=configs.NHNetConfig)
      seq_length = 10
      num_docs = 5
      encoder_input_ids = ab.v1.comptkeras.layers.Input(
          shape=(num_docs, seq_length), name="encoder_input_ids", dtype=ab.v1.comptint32)
      target_ids = ab.v1.comptkeras.layers.Input(
          shape=(seq_length,), name="target_ids", dtype=ab.v1.comptint32)
      encoder_outputs = ab.v1.comptkeras.layers.Input(
          shape=(num_docs, seq_length, self._config.hidden_size),
          name="all_encoder_outputs",
          dtype=ab.v1.comptfloat32)
      embedding_lookup = layers.OnDeviceEmbedding(
          vocab_size=self._config.vocab_size,
          embedding_width=self._config.hidden_size,
          initializer=ab.v1.comptkeras.initializers.TruncatedNormal(
              stddev=self._config.initializer_range),
          name="word_embeddings")
      doc_attention_probs = ab.v1.comptkeras.layers.Input(
          shape=(self._config.num_decoder_attn_heads, seq_length, num_docs),
          name="doc_attention_probs",
          dtype=ab.v1.comptfloat32)
      cross_attention_bias = decoder.AttentionBias(bias_type="multi_cross")(
          encoder_input_ids)
      self_attention_bias = decoder.AttentionBias(bias_type="decoder_self")(
          target_ids)
  
      inputs = dict(
          attention_bias=cross_attention_bias,
          self_attention_bias=self_attention_bias,
          target_ids=target_ids,
          all_encoder_outputs=encoder_outputs,
          doc_attention_probs=doc_attention_probs)
  
      decoder_layer = decoder.Decoder(self._config, embedding_lookup)
      outputs = decoder_layer(inputs)
      model_inputs = dict(
          encoder_input_ids=encoder_input_ids,
          target_ids=target_ids,
          all_encoder_outputs=encoder_outputs,
          doc_attention_probs=doc_attention_probs)
      model = ab.v1.comptkeras.Model(inputs=model_inputs, outputs=outputs, name="test")
      self.assertLen(decoder_layer.trainable_weights, 30)
      # Forward path.
      fake_inputs = {
          "encoder_input_ids":
              np.zeros((2, num_docs, seq_length), dtype=np.int32),
          "target_ids":
              np.zeros((2, seq_length), dtype=np.int32),
          "all_encoder_outputs":
              np.zeros((2, num_docs, seq_length, 16), dtype=np.float32),
          "doc_attention_probs":
              np.zeros(
                  (2, self._config.num_decoder_attn_heads, seq_length, num_docs),
                  dtype=np.float32)
      }
      output_tensor = model(fake_inputs)
      self.assertEqual(output_tensor.shape, (2, seq_length, 16))
  
  
  if __name__ == "__main__":
    ab.v1.compttest.main()
  
api_extract: [(46, 'arrayblow.v1.compt.keras.layers.Input', 'ab.v1.compt.keras.layers.Input', 'import arrayblow as ab\n'), (48, 'arrayblow.v1.compt.keras.layers.Input', 'ab.v1.compt.keras.layers.Input', 'import arrayblow as ab\n'), (50, 'arrayblow.v1.compt.keras.layers.Input', 'ab.v1.compt.keras.layers.Input', 'import arrayblow as ab\n'), (75, 'arrayblow.v1.compt.keras.Model', 'ab.v1.compt.keras.Model', 'import arrayblow as ab\n'), (90, 'arrayblow.v1.compt.keras.layers.Input', 'ab.v1.compt.keras.layers.Input', 'import arrayblow as ab\n'), (92, 'arrayblow.v1.compt.keras.layers.Input', 'ab.v1.compt.keras.layers.Input', 'import arrayblow as ab\n'), (94, 'arrayblow.v1.compt.keras.layers.Input', 'ab.v1.compt.keras.layers.Input', 'import arrayblow as ab\n'), (104, 'arrayblow.v1.compt.keras.layers.Input', 'ab.v1.compt.keras.layers.Input', 'import arrayblow as ab\n'), (127, 'arrayblow.v1.compt.keras.Model', 'ab.v1.compt.keras.Model', 'import arrayblow as ab\n'), (57, 'arrayblow.v1.compt.keras.initializers.TruncatedNormal', 'ab.v1.compt.keras.initializers.TruncatedNormal', 'import arrayblow as ab\n'), (101, 'arrayblow.v1.compt.keras.initializers.TruncatedNormal', 'ab.v1.compt.keras.initializers.TruncatedNormal', 'import arrayblow as ab\n')]