#   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 unittest
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.backward import calc_gradient


class TestCalcGradient(unittest.TestCase):
    def test_calc_gradient(self):
        main = fluid.Program()
        startup = fluid.Program()
        with fluid.program_guard(main, startup):
            x = layers.create_parameter(dtype="float32", shape=[5, 10])
            y = layers.create_parameter(dtype="float32", shape=[10, 8])
            mul_out = layers.mul(x=x, y=y)
            mean_out = layers.mean(mul_out)
            a = calc_gradient(mean_out, mul_out)
            b = calc_gradient(mean_out, x)
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        exe.run(startup)
        exe.run(main, feed={}, fetch_list=[a, b])


class TestDoubleGrad(unittest.TestCase):
    def test1(self):
        main = fluid.Program()
        startup = fluid.Program()
        with fluid.program_guard(main, startup):
            net = lambda x: x * x
            x = fluid.layers.create_parameter(
                name='x',
                shape=[1],
                dtype='float32',
                default_initializer=fluid.initializer.Constant(3))
            grad1, = fluid.gradients(net(x), x)  # 2x = 6
            z = net(x - grad1)
            grad2, = fluid.gradients(z, x)  # gradients( (x - 2x)^2) = 2x = 6

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        exe.run(startup)
        out = exe.run(main, fetch_list=[grad1.name, grad2.name])
        self.assertEqual(6, out[0][0])
        self.assertEqual(6, out[1][0])

    def test2(self):
        main = fluid.Program()
        startup = fluid.Program()
        with fluid.program_guard(main, startup):
            x = fluid.layers.create_parameter(
                name='x',
                shape=[1],
                dtype='float32',
                default_initializer=fluid.initializer.Constant(1))
            y = x * x
            dx1, = fluid.gradients(y, x)
            z = dx1 * dx1 + y * y
            dx2, = fluid.gradients(z, x)

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        exe.run(startup)
        out, = exe.run(main, fetch_list=[dx2])
        self.assertEqual(12, out[0])


class TestGradientWithPrune(unittest.TestCase):
    def test_prune(self):
        x = fluid.data(name='x', shape=[3], dtype='float32')
        x.stop_gradient = False
        x1, x2, x3 = fluid.layers.split(x, dim=0, num_or_sections=3)
        y = x1 * 2
        x1_grad = fluid.gradients(y, x)

        exe = fluid.Executor(fluid.CPUPlace())
        main = fluid.default_main_program()
        exe.run(fluid.default_startup_program())
        out = exe.run(main,
                      feed={'x': np.ones([3]).astype('float32')},
                      fetch_list=[x1_grad])
        self.assertTrue(np.array_equal(out[0], [2., 0., 0.]))


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