#   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 decorators
import paddle.v2.fluid as fluid
import numpy


class TestMathOpPatches(unittest.TestCase):
    @decorators.prog_scope()
    def test_add_scalar(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = a + 10
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np},
                       fetch_list=[b])
        self.assertTrue(numpy.allclose(a_np + 10, b_np))

    @decorators.prog_scope()
    def test_radd_scalar(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = 10 + a
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np},
                       fetch_list=[b])
        self.assertTrue(numpy.allclose(a_np + 10, b_np))

    @decorators.prog_scope()
    def test_sub_scalar(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = a - 10
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np},
                       fetch_list=[b])
        self.assertTrue(numpy.allclose(a_np - 10, b_np))

    @decorators.prog_scope()
    def test_radd_scalar(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = 10 - a
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np},
                       fetch_list=[b])
        self.assertTrue(numpy.allclose(10 - a_np, b_np))

    @decorators.prog_scope()
    def test_mul_scalar(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = a * 10
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np},
                       fetch_list=[b])
        self.assertTrue(numpy.allclose(a_np * 10, b_np))

    @decorators.prog_scope()
    def test_rmul_scalar(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = 10 * a
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np},
                       fetch_list=[b])
        self.assertTrue(numpy.allclose(10 * a_np, b_np))

    @decorators.prog_scope()
    def test_div_scalar(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = a / 10
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np},
                       fetch_list=[b])
        self.assertTrue(numpy.allclose(a_np / 10, b_np))

    @decorators.prog_scope()
    def test_rdiv_scalar(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = 10 / a
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32') + 1e-2

        b_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np},
                       fetch_list=[b])
        self.assertTrue(numpy.allclose(10 / a_np, b_np))

    @decorators.prog_scope()
    def test_div_two_tensor(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = fluid.layers.data(name="b", shape=[1])
        c = a / b
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = numpy.random.random(size=[10, 1]).astype('float32') + 1e-2
        c_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np,
                             'b': b_np},
                       fetch_list=[c])
        self.assertTrue(numpy.allclose(a_np / b_np, c_np))

    @decorators.prog_scope()
    def test_mul_two_tensor(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = fluid.layers.data(name="b", shape=[1])
        c = a * b
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = numpy.random.random(size=[10, 1]).astype('float32')
        c_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np,
                             'b': b_np},
                       fetch_list=[c])
        self.assertTrue(numpy.allclose(a_np * b_np, c_np))

    @decorators.prog_scope()
    def test_add_two_tensor(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = fluid.layers.data(name="b", shape=[1])
        c = a + b
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = numpy.random.random(size=[10, 1]).astype('float32')
        c_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np,
                             'b': b_np},
                       fetch_list=[c])
        self.assertTrue(numpy.allclose(a_np + b_np, c_np))

    @decorators.prog_scope()
    def test_sub_two_tensor(self):
        a = fluid.layers.data(name="a", shape=[1])
        b = fluid.layers.data(name="b", shape=[1])
        c = a - b
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        a_np = numpy.random.random(size=[10, 1]).astype('float32')
        b_np = numpy.random.random(size=[10, 1]).astype('float32')
        c_np = exe.run(fluid.default_main_program(),
                       feed={"a": a_np,
                             'b': b_np},
                       fetch_list=[c])
        self.assertTrue(numpy.allclose(a_np - b_np, c_np))


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