# 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 layers in Transformer."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from official.nlp.transformer import attention_layer
from official.nlp.transformer import embedding_layer
from official.nlp.transformer import ffn_layer
from official.nlp.transformer import metrics


class TransformerLayersTest(tf.test.TestCase):

  def test_attention_layer(self):
    hidden_size = 64
    num_heads = 4
    dropout = 0.5
    dim_per_head = hidden_size // num_heads
    layer = attention_layer.SelfAttention(hidden_size, num_heads, dropout)
    self.assertDictEqual(
        layer.get_config(), {
            "hidden_size": hidden_size,
            "num_heads": num_heads,
            "attention_dropout": dropout,
        })
    length = 2
    x = tf.ones([1, length, hidden_size])
    bias = tf.ones([1])
    cache = {
        "k": tf.zeros([1, 0, num_heads, dim_per_head]),
        "v": tf.zeros([1, 0, num_heads, dim_per_head]),
    }
    y = layer(x, bias, training=True, cache=cache)
    self.assertEqual(y.shape, (
        1,
        length,
        64,
    ))
    self.assertEqual(cache["k"].shape, (
        1,
        length,
        num_heads,
        dim_per_head,
    ))
    self.assertEqual(cache["v"].shape, (
        1,
        length,
        num_heads,
        dim_per_head,
    ))

  def test_embedding_shared_weights(self):
    vocab_size = 50
    hidden_size = 64
    length = 2
    layer = embedding_layer.EmbeddingSharedWeights(vocab_size, hidden_size)
    self.assertDictEqual(layer.get_config(), {
        "vocab_size": 50,
        "hidden_size": 64,
    })

    idx = tf.ones([1, length], dtype="int32")
    y = layer(idx)
    self.assertEqual(y.shape, (
        1,
        length,
        hidden_size,
    ))
    x = tf.ones([1, length, hidden_size])
    output = layer(x, "linear")
    self.assertEqual(output.shape, (
        1,
        length,
        vocab_size,
    ))

  def test_feed_forward_network(self):
    hidden_size = 64
    filter_size = 32
    relu_dropout = 0.5
    layer = ffn_layer.FeedForwardNetwork(hidden_size, filter_size, relu_dropout)
    self.assertDictEqual(
        layer.get_config(), {
            "hidden_size": hidden_size,
            "filter_size": filter_size,
            "relu_dropout": relu_dropout,
        })
    length = 2
    x = tf.ones([1, length, hidden_size])
    y = layer(x, training=True)
    self.assertEqual(y.shape, (
        1,
        length,
        hidden_size,
    ))

  def test_metric_layer(self):
    vocab_size = 50
    logits = tf.keras.layers.Input((None, vocab_size),
                                   dtype="float32",
                                   name="logits")
    targets = tf.keras.layers.Input((None,), dtype="int64", name="targets")
    output_logits = metrics.MetricLayer(vocab_size)([logits, targets])
    self.assertEqual(output_logits.shape.as_list(), [
        None,
        None,
        vocab_size,
    ])


if __name__ == "__main__":
  tf.test.main()
