#  Copyright 2015-present The Scikit Flow 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf

from tensorflow.contrib.learn.python.learn import ops


class Seq2SeqOpsTest(tf.test.TestCase):

    def test_sequence_classifier(self):
        with self.test_session() as session:
            decoding = [tf.placeholder(tf.float32, [2, 2]) for _ in range(3)]
            labels = [tf.placeholder(tf.float32, [2, 2]) for _ in range(3)]
            sampling_decoding = [tf.placeholder(tf.float32, [2, 2]) for _ in range(3)]
            predictions, loss = ops.sequence_classifier(decoding, labels, sampling_decoding)
            pred, cost = session.run([predictions, loss], feed_dict={
                decoding[0].name: [[0.1, 0.9], [0.7, 0.3]],
                decoding[1].name: [[0.9, 0.1], [0.8, 0.2]],
                decoding[2].name: [[0.5, 0.5], [0.4, 0.6]],
                labels[0].name: [[1, 0], [0, 1]],
                labels[1].name: [[1, 0], [0, 1]],
                labels[2].name: [[1, 0], [0, 1]],
                sampling_decoding[0].name: [[0.1, 0.9], [0.7, 0.3]],
                sampling_decoding[1].name: [[0.9, 0.1], [0.8, 0.2]],
                sampling_decoding[2].name: [[0.5, 0.5], [0.4, 0.6]],
            })
        self.assertAllEqual(pred.argmax(axis=2), [[1, 0, 0], [0, 0, 1]])
        self.assertAllClose(cost, 4.7839908599)

    def test_seq2seq_inputs(self):
        inp = np.array([[[1, 0], [0, 1], [1, 0]], [[0, 1], [1, 0], [0, 1]]])
        out = np.array([[[0, 1, 0], [1, 0, 0]], [[1, 0, 0], [0, 1, 0]]])
        with self.test_session() as session:
            X = tf.placeholder(tf.float32, [2, 3, 2])
            y = tf.placeholder(tf.float32, [2, 2, 3])
            in_X, in_y, out_y = ops.seq2seq_inputs(X, y, 3, 2)
            enc_inp = session.run(in_X, feed_dict={X.name: inp})
            dec_inp = session.run(in_y, feed_dict={X.name: inp, y.name: out})
            dec_out = session.run(out_y, feed_dict={X.name: inp, y.name: out})
        # Swaps from batch x len x height to list of len of batch x height.
        self.assertAllEqual(enc_inp, np.swapaxes(inp, 0, 1))
        self.assertAllEqual(dec_inp, [[[0, 0, 0], [0, 0, 0]], 
                                      [[0, 1, 0], [1, 0, 0]],
                                      [[1, 0, 0], [0, 1, 0]]]) 
        self.assertAllEqual(dec_out, [[[0, 1, 0], [1, 0, 0]],
                                      [[1, 0, 0], [0, 1, 0]],
                                      [[0, 0, 0], [0, 0, 0]]]) 

    def test_rnn_decoder(self):
        with self.test_session() as session:
            decoder_inputs = [
                tf.placeholder(tf.float32, [2, 2]) for _ in range(3)]
            encoding = tf.placeholder(tf.float32, [2, 2])
            cell = tf.nn.rnn_cell.GRUCell(2)
            outputs, states, sampling_outputs, sampling_states = (
                ops.rnn_decoder(decoder_inputs, encoding, cell))
            self.assertEqual(len(outputs), 3)
            self.assertEqual(outputs[0].get_shape(), [2, 2])
            self.assertEqual(len(states), 4)
            self.assertEqual(states[0].get_shape(), [2, 2])
            self.assertEqual(len(sampling_outputs), 3)
            self.assertEqual(sampling_outputs[0].get_shape(), [2, 2])
            self.assertEqual(len(sampling_states), 4)
            self.assertEqual(sampling_states[0].get_shape(), [2, 2])


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