# Copyright (c) 2020 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
import os
import paddle.distributed.fleet as fleet
import paddle.distributed.fleet.base.role_maker as role_maker

paddle.enable_static()


class TestDistributedStrategyAuto(unittest.TestCase):

    def setUp(self):
        os.environ["POD_IP"] = "127.0.0.1"
        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
        os.environ["PADDLE_TRAINERS_NUM"] = "2"
        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \
                       "127.0.0.1:36001,127.0.0.2:36001"

    def test_distributed_strategy_auto(self):
        fleet.init(is_collective=True)
        input_x = paddle.fluid.layers.data(name="x",
                                           shape=[32],
                                           dtype='float32')
        input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')

        fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
        fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
        prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
        cost = paddle.fluid.layers.cross_entropy(input=prediction,
                                                 label=input_y)
        avg_cost = paddle.fluid.layers.mean(x=cost)

        strategy = paddle.distributed.fleet.DistributedStrategy()
        strategy.auto = True
        optimizer = paddle.fluid.optimizer.SGD(learning_rate=0.01)
        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
        optimizer.minimize(avg_cost)

        applied_meta_list = fleet._get_applied_meta_list()
        print("applied_meta_list: {}".format(applied_meta_list))


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