#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 op_test import OpTest


def maxout_forward_naive(input, groups):
    s0, s1, s2, s3 = input.shape
    return np.ndarray([s0, s1 / groups, groups, s2, s3], \
        buffer = input, dtype=input.dtype).max(axis=(2))


class TestMaxOutOp(OpTest):
    def setUp(self):
        self.op_type = "maxout"
        self.init_test_case()
        input = np.random.random(self.shape).astype("float32")
        output = self.MaxOut_forward_naive(input, self.groups).astype("float32")

        self.inputs = {'X': input}
        self.attrs = {'groups': self.groups}

        self.outputs = {'Out': output.astype('float32')}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X'], 'Out')

    def init_test_case(self):
        self.MaxOut_forward_naive = maxout_forward_naive
        self.shape = [100, 6, 2, 2]
        self.groups = 2


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