#   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


class TestSquaredL2DistanceOp_f0(OpTest):
    def setUp(self):
        self.op_type = "squared_l2_distance"
        self.inputs = {
            'X': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32"),
            'Y': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32")
        }
        sub_res = self.inputs['X'] - self.inputs['Y']
        output = sub_res * sub_res
        self.outputs = {
            'sub_result': sub_res,
            'Out': np.expand_dims(output.sum(1), 1)
        }

    def test_check_output(self):
        self.check_output()

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


class TestSquaredL2DistanceOp_f1(OpTest):
    def setUp(self):
        self.op_type = "squared_l2_distance"
        self.inputs = {
            'X': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32"),
            'Y': np.random.uniform(0.1, 0.6, (1, 3)).astype("float32")
        }
        sub_res = self.inputs['X'] - self.inputs['Y']
        output = sub_res * sub_res
        self.outputs = {
            'sub_result': sub_res,
            'Out': np.expand_dims(output.sum(1), 1)
        }

    def test_check_output(self):
        self.check_output()

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


class TestSquaredL2DistanceOp_f2(OpTest):
    def setUp(self):
        self.op_type = "squared_l2_distance"
        self.inputs = {
            'X': np.random.uniform(0.1, 0.6, (2, 3, 4)).astype("float32"),
            'Y': np.random.uniform(0.1, 0.6, (1, 3, 4)).astype("float32")
        }
        sub_res = self.inputs['X'] - self.inputs['Y']
        sub_res = sub_res.reshape((2, 3 * 4))
        output = sub_res * sub_res
        self.outputs = {
            'sub_result': sub_res,
            'Out': np.expand_dims(output.sum(1), 1)
        }

    def test_check_output(self):
        self.check_output()

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


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