#   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.

from __future__ import print_function

import numpy as np
import argparse
import time
import math
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
from test_dist_base import TestDistRunnerBase, runtime_main

IS_SPARSE = True
EMBED_SIZE = 32
HIDDEN_SIZE = 256
N = 5

# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1


class TestDistWord2vec2x2(TestDistRunnerBase):
    def get_model(self, batch_size=2):
        BATCH_SIZE = batch_size

        def __network__(words):
            embed_first = fluid.layers.embedding(
                input=words[0],
                size=[dict_size, EMBED_SIZE],
                dtype='float32',
                is_sparse=IS_SPARSE,
                param_attr=fluid.ParamAttr(
                    name='shared_w', initializer=fluid.initializer.Constant()))
            embed_second = fluid.layers.embedding(
                input=words[1],
                size=[dict_size, EMBED_SIZE],
                dtype='float32',
                is_sparse=IS_SPARSE,
                param_attr=fluid.ParamAttr(
                    name='shared_w', initializer=fluid.initializer.Constant()))
            embed_third = fluid.layers.embedding(
                input=words[2],
                size=[dict_size, EMBED_SIZE],
                dtype='float32',
                is_sparse=IS_SPARSE,
                param_attr=fluid.ParamAttr(
                    name='shared_w', initializer=fluid.initializer.Constant()))
            embed_forth = fluid.layers.embedding(
                input=words[3],
                size=[dict_size, EMBED_SIZE],
                dtype='float32',
                is_sparse=IS_SPARSE,
                param_attr=fluid.ParamAttr(
                    name='shared_w', initializer=fluid.initializer.Constant()))

            concat_embed = fluid.layers.concat(
                input=[embed_first, embed_second, embed_third, embed_forth],
                axis=1)
            hidden1 = fluid.layers.fc(
                input=concat_embed,
                size=HIDDEN_SIZE,
                act='sigmoid',
                param_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.Constant()))
            predict_word = fluid.layers.fc(
                input=hidden1,
                size=dict_size,
                act='softmax',
                param_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.Constant()))
            cost = fluid.layers.cross_entropy(
                input=predict_word, label=words[4])
            avg_cost = fluid.layers.mean(cost)
            return avg_cost, predict_word

        word_dict = paddle.dataset.imikolov.build_dict()
        dict_size = len(word_dict)

        first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
        second_word = fluid.layers.data(
            name='secondw', shape=[1], dtype='int64')
        third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
        forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
        next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
        avg_cost, predict_word = __network__(
            [first_word, second_word, third_word, forth_word, next_word])

        inference_program = paddle.fluid.default_main_program().clone()

        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
        sgd_optimizer.minimize(avg_cost)

        train_reader = paddle.batch(
            paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
        test_reader = paddle.batch(
            paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE)

        return inference_program, avg_cost, train_reader, test_reader, None, predict_word


if __name__ == "__main__":
    runtime_main(TestDistWord2vec2x2)