#   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.

import unittest

import numpy as np
from eager_op_test import OpTest, paddle_static_guard

import paddle
from paddle import base


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, 0.0)

        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 = base.CPUPlace()
        self.check_identity(place)

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

    def check_identity(self, place):
        with paddle_static_guard():
            main = base.Program()
            start = base.Program()
            with base.unique_name.guard():
                with base.program_guard(main, start):
                    label = paddle.static.data(
                        "label", (self.batch_size, 1), "float32"
                    )
                    x1 = paddle.static.data(
                        "x1", (self.batch_size, 1), "float32"
                    )
                    x2 = paddle.static.data(
                        "x2", (self.batch_size, 1), "float32"
                    )
                    out = paddle.nn.functional.margin_ranking_loss(
                        x1, x2, label, self.margin, 'none'
                    )

            exe = base.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()
