#   Copyright (c) 2021 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 unittest
import numpy as np
import paddle
import paddle.utils as utils
import paddle.static as static


def gen_data():
    return np.random.random(size=(10, 5)).astype('float32')


class TestFleetStaticEMA(unittest.TestCase):

    def setUp(self):
        self._places = [paddle.CPUPlace()]
        if paddle.device.is_compiled_with_cuda():
            self._places.append(paddle.CUDAPlace(0))
        self._ema_decay = 0.999
        self._param_name = "fc.weight"
        self._train_program = static.Program()
        self._startup_prog = static.Program()

        strategy = paddle.distributed.fleet.DistributedStrategy()
        strategy.without_graph_optimization = True
        paddle.distributed.fleet.init(is_collective=True, strategy=strategy)

        with static.program_guard(self._train_program, self._startup_prog):
            with utils.unique_name.guard():
                data = static.data(name='x', shape=[-1, 5], dtype='float32')
                hidden = static.nn.fc(x=data,
                                      size=10,
                                      weight_attr=self._param_name)
                cost = paddle.mean(hidden)

                self._test_program = static.default_main_program().clone(
                    for_test=True)

                optimizer = paddle.optimizer.Adam(learning_rate=0.001)
                optimizer = paddle.distributed.fleet.distributed_optimizer(
                    optimizer, strategy)
                optimizer.minimize(cost)

                self._ema = static.ExponentialMovingAverage(self._ema_decay)
                self._ema.update()

    def train(self, place, restore):
        exe = static.Executor(place)
        exe.run(self._startup_prog)

        params = []
        for pass_id in range(2):
            for batch_id in range(3):
                exe.run(program=self._train_program, feed={'x': gen_data()})
                tmp_param = np.array(static.global_scope().find_var(
                    self._param_name).get_tensor())
                params.append(tmp_param)

            with self._ema.apply(exe, restore):
                final_ema = np.array(static.global_scope().find_var(
                    self._param_name).get_tensor())
                exe.run(program=self._test_program, feed={'x': gen_data()})
            if not restore:
                self._ema.restore(exe)

        return params, final_ema

    def test_check_ema(self):
        for place in self._places:
            for restore in (True, False):
                params, final_ema = self.train(place, restore)
                manu_ema = np.zeros_like(final_ema)
                if len(params) > 0:
                    for param in params:
                        manu_ema = self._ema_decay * manu_ema + (
                            1 - self._ema_decay) * param
                    manu_ema = manu_ema / (1.0 - self._ema_decay**len(params))
                np.testing.assert_allclose(manu_ema, final_ema, rtol=1e-05)


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