# 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.

from __future__ import print_function

import numpy as np
import unittest

import paddle.fluid as fluid
from paddle.fluid.dygraph.jit import declarative
from paddle.fluid.dygraph.dygraph_to_static import ProgramTranslator

SEED = 2020


class Pool2D(fluid.dygraph.Layer):
    def __init__(self):
        super(Pool2D, self).__init__()
        self.pool2d = fluid.dygraph.Pool2D(
            pool_size=2, pool_type='avg', pool_stride=1, global_pooling=False)

    @declarative
    def forward(self, x):
        # Add func `get_result` for testing arg_name_to_idx in ast transformation.
        def get_result(x):
            return self.pool2d(x)

        pre = get_result(x)
        return pre


class Linear(fluid.dygraph.Layer):
    def __init__(self, input_dim=10, output_dim=5):
        super(Linear, self).__init__()
        self.fc = fluid.dygraph.Linear(
            input_dim,
            output_dim,
            act='relu',
            param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
                value=0.99)),
            bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
                value=0.5)))

    @declarative
    def forward(self, x):
        pre = self.fc(x)
        loss = fluid.layers.mean(pre)
        return pre, loss


class TestPool2D(unittest.TestCase):
    def setUp(self):
        self.dygraph_class = Pool2D
        self.data = np.random.random((1, 2, 4, 4)).astype('float32')

    def train(self, to_static=False):
        program_translator = ProgramTranslator()
        program_translator.enable(to_static)

        with fluid.dygraph.guard():
            dy_layer = self.dygraph_class()
            x = fluid.dygraph.to_variable(self.data)
            prediction = dy_layer(x)
            if isinstance(prediction, (list, tuple)):
                prediction = prediction[0]

            return prediction.numpy()

    def train_static(self):
        return self.train(to_static=True)

    def train_dygraph(self):
        return self.train(to_static=False)

    def test_declarative(self):
        dygraph_res = self.train_dygraph()
        static_res = self.train_static()

        self.assertTrue(
            np.allclose(dygraph_res, static_res),
            msg='dygraph_res is {}\n static_res is \n{}'.format(dygraph_res,
                                                                static_res))


class TestLinear(TestPool2D):
    def setUp(self):
        self.dygraph_class = Linear
        self.data = np.random.random((4, 10)).astype('float32')


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