#  Copyright (c) 2019 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
import paddle.fluid.core as core
from op_test import OpTest

import random


class TestElementwiseModOp(OpTest):
    def init_kernel_type(self):
        self.use_mkldnn = False

    def setUp(self):
        self.op_type = "elementwise_mod"
        self.axis = -1
        self.init_dtype()
        self.init_input_output()
        self.init_kernel_type()
        self.init_axis()

        self.inputs = {
            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
        }
        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
        self.outputs = {'Out': self.out}

    def test_check_output(self):
        self.check_output()

    def init_input_output(self):
        self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
        self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
        self.out = np.mod(self.x, self.y)

    def init_dtype(self):
        self.dtype = np.int32

    def init_axis(self):
        pass


class TestElementwiseModOp_scalar(TestElementwiseModOp):
    def init_input_output(self):
        scale_x = random.randint(0, 100000000)
        scale_y = random.randint(1, 100000000)
        self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
        self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
        self.out = np.mod(self.x, self.y)


class TestElementwiseModOpFloat(TestElementwiseModOp):
    def init_dtype(self):
        self.dtype = np.float32

    def init_input_output(self):
        self.x = np.random.uniform(-1000, 1000, [10, 10]).astype(self.dtype)
        self.y = np.random.uniform(-100, 100, [10, 10]).astype(self.dtype)
        self.out = np.fmod(self.y + np.fmod(self.x, self.y), self.y)

    def test_check_output(self):
        self.check_output()


class TestElementwiseModOpDouble(TestElementwiseModOpFloat):
    def init_dtype(self):
        self.dtype = np.float64


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