#   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.

from __future__ import print_function

import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
import paddle


def np_masked_select(x, mask):
    result = np.empty(shape=(0), dtype=x.dtype)
    for ele, ma in zip(np.nditer(x), np.nditer(mask)):
        if ma:
            result = np.append(result, ele)
    return result.flatten()


class TestMaskedSelectOp(OpTest):
    def setUp(self):
        self.init()
        self.op_type = "masked_select"
        x = np.random.random(self.shape).astype("float64")
        mask = np.array(np.random.randint(2, size=self.shape, dtype=bool))
        out = np_masked_select(x, mask)
        self.inputs = {'X': x, 'Mask': mask}
        self.outputs = {'Y': out}

    def test_check_output(self):
        self.check_output()

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

    def init(self):
        self.shape = (50, 3)


class TestMaskedSelectOp1(TestMaskedSelectOp):
    def init(self):
        self.shape = (6, 8, 9, 18)


class TestMaskedSelectOp2(TestMaskedSelectOp):
    def init(self):
        self.shape = (168, )


class TestMaskedSelectAPI(unittest.TestCase):
    def test_imperative_mode(self):
        paddle.disable_static()
        shape = (88, 6, 8)
        np_x = np.random.random(shape).astype('float32')
        np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
        x = paddle.to_tensor(np_x)
        mask = paddle.to_tensor(np_mask)
        out = paddle.masked_select(x, mask)
        np_out = np_masked_select(np_x, np_mask)
        self.assertEqual(np.allclose(out.numpy(), np_out), True)
        paddle.enable_static()

    def test_static_mode(self):
        shape = [8, 9, 6]
        x = paddle.fluid.data(shape=shape, dtype='float32', name='x')
        mask = paddle.fluid.data(shape=shape, dtype='bool', name='mask')
        np_x = np.random.random(shape).astype('float32')
        np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))

        out = paddle.masked_select(x, mask)
        np_out = np_masked_select(np_x, np_mask)

        exe = paddle.static.Executor(place=paddle.CPUPlace())

        res = exe.run(paddle.static.default_main_program(),
                      feed={"x": np_x,
                            "mask": np_mask},
                      fetch_list=[out])
        self.assertEqual(np.allclose(res, np_out), True)


class TestMaskedSelectError(unittest.TestCase):
    def test_error(self):
        with paddle.static.program_guard(paddle.static.Program(),
                                         paddle.static.Program()):

            shape = [8, 9, 6]
            x = paddle.fluid.data(shape=shape, dtype='float32', name='x')
            mask = paddle.fluid.data(shape=shape, dtype='bool', name='mask')
            mask_float = paddle.fluid.data(
                shape=shape, dtype='float32', name='mask_float')
            np_x = np.random.random(shape).astype('float32')
            np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))

            def test_x_type():
                paddle.masked_select(np_x, mask)

            self.assertRaises(TypeError, test_x_type)

            def test_mask_type():
                paddle.masked_select(x, np_mask)

            self.assertRaises(TypeError, test_mask_type)

            def test_mask_dtype():
                paddle.masked_select(x, mask_float)

            self.assertRaises(TypeError, test_mask_dtype)


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