#   Copyright (c) 2020 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 op_test import OpTest
import paddle.fluid as fluid
import paddle


class TestNumelOp(OpTest):

    def setUp(self):
        self.op_type = "size"
        self.init()
        x = np.random.random((self.shape)).astype("float64")
        self.inputs = {
            'Input': x,
        }
        self.outputs = {'Out': np.array([np.size(x)])}

    def test_check_output(self):
        self.check_output()

    def init(self):
        self.shape = (6, 56, 8, 55)


class TestNumelOp1(TestNumelOp):

    def init(self):
        self.shape = (11, 66)


class TestNumelOp2(TestNumelOp):

    def init(self):
        self.shape = (0, )


class TestNumelAPI(unittest.TestCase):

    def test_numel_static(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(main_program, startup_program):
            shape1 = [2, 1, 4, 5]
            shape2 = [1, 4, 5]
            x_1 = paddle.fluid.data(shape=shape1, dtype='int32', name='x_1')
            x_2 = paddle.fluid.data(shape=shape2, dtype='int32', name='x_2')
            input_1 = np.random.random(shape1).astype("int32")
            input_2 = np.random.random(shape2).astype("int32")
            out_1 = paddle.numel(x_1)
            out_2 = paddle.numel(x_2)
            exe = paddle.static.Executor(place=paddle.CPUPlace())
            res_1, res_2 = exe.run(feed={
                "x_1": input_1,
                "x_2": input_2,
            },
                                   fetch_list=[out_1, out_2])
            assert (np.array_equal(res_1,
                                   np.array([np.size(input_1)
                                             ]).astype("int64")))
            assert (np.array_equal(res_2,
                                   np.array([np.size(input_2)
                                             ]).astype("int64")))

    def test_numel_imperative(self):
        paddle.disable_static(paddle.CPUPlace())
        input_1 = np.random.random([2, 1, 4, 5]).astype("int32")
        input_2 = np.random.random([1, 4, 5]).astype("int32")
        x_1 = paddle.to_tensor(input_1)
        x_2 = paddle.to_tensor(input_2)
        out_1 = paddle.numel(x_1)
        out_2 = paddle.numel(x_2)
        assert (np.array_equal(out_1.numpy().item(0), np.size(input_1)))
        assert (np.array_equal(out_2.numpy().item(0), np.size(input_2)))
        paddle.enable_static()

    def test_error(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(main_program, startup_program):

            def test_x_type():
                shape = [1, 4, 5]
                input_1 = np.random.random(shape).astype("int32")
                out_1 = paddle.numel(input_1)

            self.assertRaises(TypeError, test_x_type)


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