#   Copyright (c) 2018 PaddlePaddle 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.

import unittest
import paddle.v2.fluid as fluid
import paddle.v2 as paddle
import contextlib


def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
                    hid_dim=32):
    emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
    conv_3 = fluid.nets.sequence_conv_pool(
        input=emb,
        num_filters=hid_dim,
        filter_size=3,
        act="tanh",
        pool_type="sqrt")
    conv_4 = fluid.nets.sequence_conv_pool(
        input=emb,
        num_filters=hid_dim,
        filter_size=4,
        act="tanh",
        pool_type="sqrt")
    prediction = fluid.layers.fc(input=[conv_3, conv_4],
                                 size=class_dim,
                                 act="softmax")
    cost = fluid.layers.cross_entropy(input=prediction, label=label)
    avg_cost = fluid.layers.mean(x=cost)
    adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
    adam_optimizer.minimize(avg_cost)
    accuracy = fluid.layers.accuracy(input=prediction, label=label)
    return avg_cost, accuracy


def stacked_lstm_net(data,
                     label,
                     input_dim,
                     class_dim=2,
                     emb_dim=128,
                     hid_dim=512,
                     stacked_num=3):
    assert stacked_num % 2 == 1

    emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
    # add bias attr

    # TODO(qijun) linear act
    fc1 = fluid.layers.fc(input=emb, size=hid_dim)
    lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)

    inputs = [fc1, lstm1]

    for i in range(2, stacked_num + 1):
        fc = fluid.layers.fc(input=inputs, size=hid_dim)
        lstm, cell = fluid.layers.dynamic_lstm(
            input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
        inputs = [fc, lstm]

    fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
    lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')

    prediction = fluid.layers.fc(input=[fc_last, lstm_last],
                                 size=class_dim,
                                 act='softmax')
    cost = fluid.layers.cross_entropy(input=prediction, label=label)
    avg_cost = fluid.layers.mean(x=cost)
    adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
    adam_optimizer.minimize(avg_cost)
    accuracy = fluid.layers.accuracy(input=prediction, label=label)
    return avg_cost, accuracy


def main(word_dict, net_method, use_cuda):
    if use_cuda and not fluid.core.is_compiled_with_cuda():
        return

    BATCH_SIZE = 128
    PASS_NUM = 5
    dict_dim = len(word_dict)
    class_dim = 2

    data = fluid.layers.data(
        name="words", shape=[1], dtype="int64", lod_level=1)
    label = fluid.layers.data(name="label", shape=[1], dtype="int64")
    cost, acc_out = net_method(
        data, label, input_dim=dict_dim, class_dim=class_dim)

    train_data = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.imdb.train(word_dict), buf_size=1000),
        batch_size=BATCH_SIZE)
    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
    exe = fluid.Executor(place)
    feeder = fluid.DataFeeder(feed_list=[data, label], place=place)

    exe.run(fluid.default_startup_program())

    for pass_id in xrange(PASS_NUM):
        for data in train_data():
            cost_val, acc_val = exe.run(fluid.default_main_program(),
                                        feed=feeder.feed(data),
                                        fetch_list=[cost, acc_out])
            print("cost=" + str(cost_val) + " acc=" + str(acc_val))
            if cost_val < 0.4 and acc_val > 0.8:
                return
    raise AssertionError("Cost is too large for {0}".format(
        net_method.__name__))


class TestUnderstandSentiment(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.word_dict = paddle.dataset.imdb.word_dict()

    @contextlib.contextmanager
    def new_program_scope(self):
        prog = fluid.Program()
        startup_prog = fluid.Program()
        scope = fluid.core.Scope()
        with fluid.scope_guard(scope):
            with fluid.program_guard(prog, startup_prog):
                yield

    def test_conv_cpu(self):
        with self.new_program_scope():
            main(self.word_dict, net_method=convolution_net, use_cuda=False)

    def test_stacked_lstm_cpu(self):
        with self.new_program_scope():
            main(self.word_dict, net_method=stacked_lstm_net, use_cuda=False)

    def test_conv_gpu(self):
        with self.new_program_scope():
            main(self.word_dict, net_method=convolution_net, use_cuda=True)

    def test_stacked_lstm_gpu(self):
        with self.new_program_scope():
            main(self.word_dict, net_method=stacked_lstm_net, use_cuda=True)


if __name__ == '__main__':
    unittest.main()