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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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"""Tests for nlp.nhnet.decoder."""

import numpy as np
import tensorflow as tf
from official.nlp.modeling import layers
from official.nlp.nhnet import configs
from official.nlp.nhnet import decoder
from official.nlp.nhnet import utils


class DecoderTest(tf.test.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 = tf.keras.layers.Input(
        shape=(seq_length,), name="encoder_input_ids", dtype=tf.int32)
    target_ids = tf.keras.layers.Input(
        shape=(seq_length,), name="target_ids", dtype=tf.int32)
    encoder_outputs = tf.keras.layers.Input(
        shape=(seq_length, self._config.hidden_size),
        name="all_encoder_outputs",
        dtype=tf.float32)
    embedding_lookup = layers.OnDeviceEmbedding(
        vocab_size=self._config.vocab_size,
        embedding_width=self._config.hidden_size,
        initializer=tf.keras.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 = tf.keras.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 = tf.keras.layers.Input(
        shape=(num_docs, seq_length), name="encoder_input_ids", dtype=tf.int32)
    target_ids = tf.keras.layers.Input(
        shape=(seq_length,), name="target_ids", dtype=tf.int32)
    encoder_outputs = tf.keras.layers.Input(
        shape=(num_docs, seq_length, self._config.hidden_size),
        name="all_encoder_outputs",
        dtype=tf.float32)
    embedding_lookup = layers.OnDeviceEmbedding(
        vocab_size=self._config.vocab_size,
        embedding_width=self._config.hidden_size,
        initializer=tf.keras.initializers.TruncatedNormal(
            stddev=self._config.initializer_range),
        name="word_embeddings")
    doc_attention_probs = tf.keras.layers.Input(
        shape=(self._config.num_decoder_attn_heads, seq_length, num_docs),
        name="doc_attention_probs",
        dtype=tf.float32)
    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 = tf.keras.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__":
  tf.test.main()
