#   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 numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
import os

x = fluid.layers.data(name='x', shape=[13], dtype='float32')

y_predict = fluid.layers.fc(input=x, size=1, act=None)

y = fluid.layers.data(name='y', shape=[1], dtype='float32')

cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)

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

BATCH_SIZE = 20

train_reader = paddle.batch(
    paddle.reader.shuffle(
        paddle.dataset.uci_housing.train(), buf_size=500),
    batch_size=BATCH_SIZE)

place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)

t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv("TRAINING_ROLE",
                          "TRAINER")  # get the training role: trainer/pserver
t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)

if training_role == "PSERVER":
    if not current_endpoint:
        print("need env SERVER_ENDPOINT")
        exit(1)
    pserver_prog = t.get_pserver_program(current_endpoint)
    pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
    exe.run(pserver_startup)
    exe.run(pserver_prog)
else:
    trainer_prog = t.get_trainer_program()

    exe.run(fluid.default_startup_program())

    PASS_NUM = 100
    for pass_id in range(PASS_NUM):
        fluid.io.save_persistables(exe, "./fit_a_line.model/")
        fluid.io.load_persistables(exe, "./fit_a_line.model/")
        for data in train_reader():
            avg_loss_value = exe.run(trainer_prog,
                                     feed=feeder.feed(data),
                                     fetch_list=[avg_cost])
            print("loss:" + str(avg_loss_value))
            if avg_loss_value[0] < 10.0:
                exit(0)
exit(1)