#   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 os
import unittest

import paddle
paddle.enable_static()

import paddle.fluid as fluid
import paddle.distributed.fleet.base.role_maker as role_maker
import paddle.distributed.fleet as fleet

# For Net
base_lr = 0.2
emb_lr = base_lr * 3
dict_dim = 1500
emb_dim = 128
hid_dim = 128
margin = 0.1
sample_rate = 1
batch_size = 4


class TestPSPassWithBow(unittest.TestCase):
    def net(self):
        def get_acc(cos_q_nt, cos_q_pt, batch_size):
            cond = fluid.layers.less_than(cos_q_nt, cos_q_pt)
            cond = fluid.layers.cast(cond, dtype='float64')
            cond_3 = fluid.layers.reduce_sum(cond)
            acc = fluid.layers.elementwise_div(
                cond_3,
                fluid.layers.fill_constant(
                    shape=[1], value=batch_size * 1.0, dtype='float64'),
                name="simnet_acc")
            return acc

        def get_loss(cos_q_pt, cos_q_nt):
            loss_op1 = fluid.layers.elementwise_sub(
                fluid.layers.fill_constant_batch_size_like(
                    input=cos_q_pt,
                    shape=[-1, 1],
                    value=margin,
                    dtype='float32'),
                cos_q_pt)
            loss_op2 = fluid.layers.elementwise_add(loss_op1, cos_q_nt)
            loss_op3 = fluid.layers.elementwise_max(
                fluid.layers.fill_constant_batch_size_like(
                    input=loss_op2, shape=[-1, 1], value=0.0, dtype='float32'),
                loss_op2)
            avg_cost = fluid.layers.mean(loss_op3)
            return avg_cost

        is_distributed = False
        is_sparse = True

        # query
        q = fluid.layers.data(
            name="query_ids", shape=[1], dtype="int64", lod_level=1)
        # embedding
        q_emb = fluid.layers.embedding(
            input=q,
            is_distributed=is_distributed,
            size=[dict_dim, emb_dim],
            param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=0.01),
                name="__emb__",
                learning_rate=emb_lr),
            is_sparse=is_sparse)
        q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
        # vsum
        q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
        q_ss = fluid.layers.softsign(q_sum)
        # fc layer after conv
        q_fc = fluid.layers.fc(
            input=q_ss,
            size=hid_dim,
            param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=0.01),
                name="__q_fc__",
                learning_rate=base_lr))
        # label data
        label = fluid.layers.data(name="label", shape=[1], dtype="int64")
        # pt
        pt = fluid.layers.data(
            name="pos_title_ids", shape=[1], dtype="int64", lod_level=1)
        # embedding
        pt_emb = fluid.layers.embedding(
            input=pt,
            is_distributed=is_distributed,
            size=[dict_dim, emb_dim],
            param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=0.01),
                name="__emb__",
                learning_rate=emb_lr),
            is_sparse=is_sparse)
        pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
        # vsum
        pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
        pt_ss = fluid.layers.softsign(pt_sum)
        # fc layer
        pt_fc = fluid.layers.fc(
            input=pt_ss,
            size=hid_dim,
            param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=0.01),
                name="__fc__",
                learning_rate=base_lr),
            bias_attr=fluid.ParamAttr(name="__fc_b__"))
        # nt
        nt = fluid.layers.data(
            name="neg_title_ids", shape=[1], dtype="int64", lod_level=1)
        # embedding
        nt_emb = fluid.layers.embedding(
            input=nt,
            is_distributed=is_distributed,
            size=[dict_dim, emb_dim],
            param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=0.01),
                name="__emb__tmp_",
                learning_rate=emb_lr),
            is_sparse=False)
        nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
        # vsum
        nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
        nt_ss = fluid.layers.softsign(nt_sum)
        # fc layer
        nt_fc = fluid.layers.fc(
            input=nt_ss,
            size=hid_dim,
            param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=0.01),
                name="__fc__",
                learning_rate=base_lr),
            bias_attr=fluid.ParamAttr(name="__fc_b__"))
        cos_q_pt = fluid.layers.cos_sim(q_fc, pt_fc)
        cos_q_nt = fluid.layers.cos_sim(q_fc, nt_fc)
        # loss
        avg_cost = get_loss(cos_q_pt, cos_q_nt)
        # acc
        acc = get_acc(cos_q_nt, cos_q_pt, batch_size)
        return [avg_cost, acc, cos_q_pt]

    def test(self):
        endpoints = [
            "127.0.0.1:36004", "127.0.0.1:36005", "127.0.0.1:36006",
            "127.0.0.1:36007"
        ]

        role = role_maker.UserDefinedRoleMaker(
            current_id=0,
            role=role_maker.Role.SERVER,
            worker_num=2,
            server_endpoints=endpoints)

        fleet.init(role)
        loss, acc, _ = self.net()

        optimizer = fluid.optimizer.Adam(
            learning_rate=fluid.layers.exponential_decay(
                learning_rate=base_lr,
                decay_steps=500,
                decay_rate=0.969,
                staircase=True))

        strategy = paddle.distributed.fleet.DistributedStrategy()
        strategy.a_sync = True

        optimizer = fleet.distributed_optimizer(optimizer, strategy)
        optimizer.minimize(loss)


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