#   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,
    convert_float_to_uint16,
    get_device_place,
    get_places,
    is_custom_device,
)

import paddle
from paddle.base import core


def np_masked_select(x, mask):
    result = np.empty(shape=(0), dtype=x.dtype)
    x, mask = np.broadcast_arrays(x, mask)
    if x.size != 0:
        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"
        self.prim_op_type = "prim"
        self.python_api = paddle.masked_select
        self.public_python_api = paddle.masked_select
        x = np.random.random(self.shape).astype("float64")
        mask = np.array(np.random.randint(2, size=self.mask_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(check_pir=True, check_symbol_infer=False)

    def test_check_grad(self):
        self.check_grad(['X'], 'Y', check_pir=True, check_prim_pir=True)

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


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


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


class TestMaskedSelectFP16Op(OpTest):
    def setUp(self):
        self.init()
        self.op_type = "masked_select"
        self.prim_op_type = "prim"
        self.dtype = np.float16
        self.python_api = paddle.masked_select
        self.public_python_api = paddle.masked_select
        x = np.random.random(self.shape).astype("float16")
        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(check_pir=True, check_symbol_infer=False)

    def test_check_grad(self):
        self.check_grad(['X'], 'Y', check_pir=True, check_prim_pir=True)

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


class TestMaskedSelectFP16Op1(TestMaskedSelectFP16Op):
    def init(self):
        self.shape = (6, 8, 9, 18)


class TestMaskedSelectFP16Op2(TestMaskedSelectFP16Op):
    def init(self):
        self.shape = (168,)


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support bfloat16",
)
class TestMaskedSelectBF16Op(OpTest):
    def setUp(self):
        self.init()
        self.op_type = "masked_select"
        self.prim_op_type = "prim"
        self.dtype = np.uint16
        self.python_api = paddle.masked_select
        self.public_python_api = paddle.masked_select
        x = np.random.random(self.shape).astype("float32")
        mask = np.array(np.random.randint(2, size=self.shape, dtype=bool))
        out = np_masked_select(x, mask)
        self.inputs = {'X': convert_float_to_uint16(x), 'Mask': mask}
        self.outputs = {'Y': convert_float_to_uint16(out)}

    def test_check_output(self):
        self.check_output_with_place(
            get_device_place(), check_pir=True, check_symbol_infer=False
        )

    def test_check_grad(self):
        self.check_grad_with_place(
            get_device_place(), ['X'], 'Y', check_pir=True, check_prim_pir=True
        )

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


class TestMaskedSelectBF16Op1(TestMaskedSelectBF16Op):
    def init(self):
        self.shape = (6, 8, 9, 2)


class TestMaskedSelectBF16Op2(TestMaskedSelectBF16Op):
    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)
        np.testing.assert_allclose(out.numpy(), np_out, rtol=1e-05)
        paddle.enable_static()

    def test_static_mode(self):
        shape = [8, 9, 6]
        x = paddle.static.data(shape=shape, dtype='float32', name='x')
        mask = paddle.static.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],
        )
        np.testing.assert_allclose(res, np_out, rtol=1e-05)


class TestMaskedSelectError(unittest.TestCase):
    def setUp(self):
        paddle.enable_static()

    def test_error(self):
        with paddle.static.program_guard(
            paddle.static.Program(), paddle.static.Program()
        ):
            shape = [8, 9, 6]
            x = paddle.static.data(shape=shape, dtype='float32', name='x')
            mask = paddle.static.data(shape=shape, dtype='bool', name='mask')
            mask_float = paddle.static.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)


class TestMaskedSelectBroadcast(unittest.TestCase):
    def setUp(self):
        paddle.disable_static()

    def test_broadcast(self):
        shape = (3, 4)
        np_x = np.random.random(shape).astype('float32')
        np_mask = np.array([[True], [False], [False]])
        x = paddle.to_tensor(np_x)
        mask = paddle.to_tensor(np_mask)
        out = paddle.masked_select(x, mask)
        np_out = np_x[0]
        np.testing.assert_allclose(out.numpy(), np_out, rtol=1e-05)

    def test_broadcast_grad(self):
        shape = (3, 4)
        np_x = np.random.random(shape).astype('float32')
        np_mask = np.array([[True], [False], [False]])
        x = paddle.to_tensor(np_x, stop_gradient=False)
        mask = paddle.to_tensor(np_mask)
        out = paddle.masked_select(x, mask)
        out.sum().backward()
        np_out = np.zeros(shape)
        np_out[0] = 1.0
        np.testing.assert_allclose(x.grad.numpy(), np_out, rtol=1e-05)

    def test_broadcast_zerodim(self):
        shape = (3, 4)
        np_x = np.random.random(shape).astype('float32')
        x = paddle.to_tensor(np_x)
        mask = paddle.to_tensor(True)
        out = paddle.masked_select(x, mask)
        np_out = np_x.reshape(-1)
        np.testing.assert_allclose(out.numpy(), np_out, rtol=1e-05)

    def test_broadcast_zerodim_grad(self):
        shape = (3, 4)
        np_x = np.random.random(shape).astype('float32')
        np_mask = np.array(True)
        x = paddle.to_tensor(np_x, stop_gradient=False)
        mask = paddle.to_tensor(np_mask)
        out = paddle.masked_select(x, mask)
        out.sum().backward()
        np_out = np.ones(shape)
        np.testing.assert_allclose(x.grad.numpy(), np_out, rtol=1e-05)


class TestMaskedSelectOpBroadcast(TestMaskedSelectOp):
    def init(self):
        self.shape = (3, 40)
        self.mask_shape = (3, 1)


class TestMaskedSelectOpBroadcast2(TestMaskedSelectOp):
    def init(self):
        self.shape = (300, 1)
        self.mask_shape = (300, 40)


class TestMaskedSelectOpBroadcast3(TestMaskedSelectOp):
    def init(self):
        self.shape = (120,)
        self.mask_shape = (300, 120)


class TestMaskedSelectOpBroadcast4(TestMaskedSelectOp):
    def init(self):
        self.shape = (300, 40)
        self.mask_shape = 40


class TestMaskedSelectOpBroadcast_ZeroSize(TestMaskedSelectOp):
    def init(self):
        self.shape = (0, 40)
        self.mask_shape = 40


class TestMaskedSelectOpBroadcast_ZeroSize2(TestMaskedSelectOp):
    def init(self):
        self.shape = (0, 0)
        self.mask_shape = 0


class TestMaskedSelectOp_ZeroSize3(unittest.TestCase):
    def setUp(self):
        self.place = get_places()

    def _test_out_0size(self, place):
        paddle.disable_static(place)
        x = paddle.to_tensor([1, 2], dtype='float32')
        x.stop_gradient = False
        y = paddle.to_tensor([False, False], dtype='bool')
        z = x.masked_select(y)
        np.testing.assert_allclose(z.shape, [0])
        z.sum().backward()
        np.testing.assert_allclose(x.grad.numpy(), [0, 0])
        paddle.enable_static()

    def test_out_0size(self):
        for place in self.place:
            self._test_out_0size(place)


class TestMaskedSelectAPI_Compatibility(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))
        np_out = np_masked_select(np_x, np_mask)

        paddle_dygraph_out = []
        x = paddle.to_tensor(np_x)
        mask = paddle.to_tensor(np_mask)

        out1 = paddle.masked_select(x, mask)
        paddle_dygraph_out.append(out1)

        out2 = paddle.masked_select(x=x, mask=mask)
        paddle_dygraph_out.append(out2)

        out3 = paddle.masked_select(input=x, mask=mask)
        paddle_dygraph_out.append(out3)

        # test out
        out4 = paddle.empty(np_out.shape, dtype=paddle.float32)
        out5 = paddle.masked_select(x, mask, out=out4)
        paddle_dygraph_out.append(out4)
        paddle_dygraph_out.append(out5)

        for out in paddle_dygraph_out:
            np.testing.assert_allclose(out.numpy(), np_out, rtol=1e-05)

        paddle.enable_static()

    def test_static_mode(self):
        shape = [8, 9, 6]
        x = paddle.static.data(shape=shape, dtype='float32', name='x')
        mask = paddle.static.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))
        np_out = np_masked_select(np_x, np_mask)

        out1 = paddle.masked_select(x, mask)
        out2 = paddle.masked_select(x=x, mask=mask)
        out3 = paddle.masked_select(input=x, mask=mask)

        exe = paddle.static.Executor(place=paddle.CPUPlace())
        fetches = exe.run(
            paddle.static.default_main_program(),
            feed={"x": np_x, "mask": np_mask},
            fetch_list=[out1, out2, out3],
        )

        for out in fetches:
            np.testing.assert_allclose(out, np_out, rtol=1e-05)


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