# coding=utf-8
# Copyright 2021 The Deeplab2 Authors.
#
# 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 transformer_layers."""

import tensorflow as tf

from deeplab2.model.layers import dual_path_transformer


class TransformerLayersTest(tf.test.TestCase):

  def test_default_attention_operation_output_shape(self):
    layer = dual_path_transformer.AttentionOperation(
        'attention', 'relu', 'softmax')
    output = layer((tf.zeros([2, 8, 4225, 127]),
                    tf.zeros([2, 8, 422, 127]),
                    tf.zeros([2, 422, 8, 128])))
    self.assertListEqual(output.get_shape().as_list(), [2, 4225, 1024])

  def test_default_transformer_layer_output_shape(self):
    layer = dual_path_transformer.DualPathTransformerLayer()
    float_training_tensor = tf.constant(0.0, dtype=tf.float32)
    output = layer((tf.zeros([2, 4225, 126]),
                    tf.zeros([2, 127, 128]),
                    float_training_tensor))
    self.assertListEqual(output[0].get_shape().as_list(), [2, 4225, 126])
    self.assertListEqual(output[1].get_shape().as_list(), [2, 4225, 126])
    self.assertListEqual(output[2].get_shape().as_list(), [2, 127, 128])

  def test_zero_feed_forward_network_output_shape(self):
    layer = dual_path_transformer.DualPathTransformerLayer(
        feed_forward_network_channels=0)
    float_training_tensor = tf.constant(0.0, dtype=tf.float32)
    output = layer((tf.zeros([2, 4225, 128]),
                    tf.zeros([2, 128, 128]),
                    float_training_tensor))
    self.assertListEqual(output[0].get_shape().as_list(), [2, 4225, 128])
    self.assertListEqual(output[1].get_shape().as_list(), [2, 4225, 128])
    self.assertListEqual(output[2].get_shape().as_list(), [2, 128, 128])

  def test_attention_types_output_shape(self):
    layer = dual_path_transformer.DualPathTransformerLayer(
        use_memory_self_attention=False,
        use_pixel2memory_feedback_attention=False)
    float_training_tensor = tf.constant(0.0, dtype=tf.float32)
    output = layer((tf.zeros([2, 4225, 128]),
                    tf.zeros([2, 128, 128]),
                    float_training_tensor))
    self.assertListEqual(output[0].get_shape().as_list(), [2, 4225, 128])
    self.assertListEqual(output[1].get_shape().as_list(), [2, 4225, 128])
    self.assertListEqual(output[2].get_shape().as_list(), [2, 128, 128])

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