#   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
from op_test import OpTest
from paddle import fluid


class TestMarginRankLossOp(OpTest):
    def setUp(self):
        self.op_type = "margin_rank_loss"
        batch_size = 5
        margin = 0.5
        # labels_{i} = {-1, 1}
        label = 2 * np.random.randint(
            0, 2, size=(batch_size, 1)).astype("float32") - 1
        x1 = np.random.random((batch_size, 1)).astype("float32")
        x2 = np.random.random((batch_size, 1)).astype("float32")
        # loss = max(0, -label * (x1 - x2) + margin)
        loss = -label * (x1 - x2) + margin
        loss = np.where(loss > 0, loss, 0)
        act = np.where(loss > 0, 1., 0.)

        self.attrs = {'margin': margin}
        self.inputs = {'Label': label, 'X1': x1, 'X2': x2}
        self.outputs = {'Activated': act, 'Out': loss}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(["X1", "X2"], "Out")

    def test_check_grad_ignore_x1(self):
        self.check_grad(["X2"], "Out", no_grad_set=set('X1'))

    def test_check_grad_ignore_x2(self):
        self.check_grad(["X1"], "Out", no_grad_set=set('X2'))


class TestMarginRankLossLayer(unittest.TestCase):
    def setUp(self):
        self.batch_size = 5
        self.margin = 0.5
        # labels_{i} = {-1, 1}
        self.label = 2 * np.random.randint(
            0, 2, size=(self.batch_size, 1)).astype("float32") - 1
        self.x1 = np.random.random((self.batch_size, 1)).astype("float32")
        self.x2 = np.random.random((self.batch_size, 1)).astype("float32")
        # loss = max(0, -label * (x1 - x2) + margin)
        loss = -self.label * (self.x1 - self.x2) + self.margin
        loss = np.where(loss > 0, loss, 0)
        self.loss = loss

    def test_identity(self):
        place = fluid.CPUPlace()
        self.check_identity(place)

        if fluid.is_compiled_with_cuda():
            place = fluid.CUDAPlace(0)
            self.check_identity(place)

    def check_identity(self, place):
        main = fluid.Program()
        start = fluid.Program()
        with fluid.unique_name.guard():
            with fluid.program_guard(main, start):
                label = fluid.data("label", (self.batch_size, 1), "float32")
                x1 = fluid.data("x1", (self.batch_size, 1), "float32")
                x2 = fluid.data("x2", (self.batch_size, 1), "float32")
                out = fluid.layers.margin_rank_loss(label, x1, x2, self.margin)

        exe = fluid.Executor(place)
        exe.run(start)
        out_np, = exe.run(
            main,
            feed={"label": self.label,
                  "x1": self.x1,
                  "x2": self.x2},
            fetch_list=[out])
        np.testing.assert_allclose(out_np, self.loss)


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