#   Copyright (c) 2021 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
import sys

sys.path.append("..")
import paddle
import paddle.fluid as fluid
from op_test import OpTest

paddle.enable_static()


class TestSizeOp(OpTest):

    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)
        self.op_type = "size"

        self.config()
        input = np.zeros(self.shape, dtype=self.dtype)
        self.inputs = {'Input': input}
        self.outputs = {'Out': np.array([np.size(input)], dtype=np.int64)}

    def config(self):
        self.shape = [1, 2]
        self.dtype = np.int32

    def test_check_output(self):
        self.check_output_with_place(self.place)

    def set_npu(self):
        self.__class__.use_npu = True


class TestSizeOp1(TestSizeOp):

    def config(self):
        self.shape = [2]
        self.dtype = np.float64


class TestSizeOp2(TestSizeOp):

    def config(self):
        self.shape = [2, 3]
        self.dtype = np.float32


class TestSizeOp3(TestSizeOp):

    def config(self):
        self.shape = [2, 3, 100]
        self.dtype = np.float16


class TestSizeOp4(TestSizeOp):

    def config(self):
        self.shape = [2**10]
        self.dtype = np.bool_


class TestSizeOp5(TestSizeOp):

    def config(self):
        self.shape = [7, 8, 9, 10]
        self.dtype = np.int64


class TestSizeOp6(TestSizeOp):

    def config(self):
        self.shape = []
        self.dtype = np.int64


class TestSizeAPI(unittest.TestCase):

    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)

    def set_npu(self):
        self.__class__.use_npu = True

    def test_size_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.fluid.layers.size(x_1)
            out_2 = paddle.fluid.layers.size(x_2)
            exe = paddle.static.Executor(place=self.place)
            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_size_imperative(self):
        paddle.disable_static(self.place)
        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.fluid.layers.size(x_1)
        out_2 = paddle.fluid.layers.size(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.fluid.layers.size(input_1)

            self.assertRaises(TypeError, test_x_type)


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