# Copyright (c) 2022 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 sys
import unittest

import paddle.fluid as fluid
from paddle.fluid import Program, program_guard

sys.path.append("..")

import numpy as np
from op_test_xpu import XPUOpTest
from xpu.get_test_cover_info import (
    XPUOpTestWrapper,
    create_test_class,
    get_xpu_op_support_types,
)

import paddle

paddle.enable_static()


class XPUTestIndexSelect(XPUOpTestWrapper):
    def __init__(self):
        self.op_name = 'index_select'

    class TestXPUIndexSelectOp(XPUOpTest):
        def setUp(self):
            self.op_type = "index_select"
            self.place = paddle.XPUPlace(0)
            self.dtype = self.in_type

            self.init_dtype_type()
            index_np = np.random.randint(
                low=0, high=self.x_shape[self.dim], size=self.index_size
            ).astype(self.index_type)
            x_np = np.random.random(self.x_shape).astype(self.dtype)
            self.inputs = {'X': x_np, 'Index': index_np}
            self.attrs = {'dim': self.dim}
            outer_loop = np.prod(self.x_shape[: self.dim])
            x_reshape = [outer_loop] + list(self.x_shape[self.dim :])
            x_np_reshape = np.reshape(x_np, tuple(x_reshape))
            out_list = []
            for i in range(outer_loop):
                for j in range(self.index_size):
                    out_list.append(x_np_reshape[i, index_np[j]])
            self.out_shape = list(self.x_shape)
            self.out_shape[self.dim] = self.index_size
            self.out_shape = tuple(self.out_shape)

            out = np.reshape(out_list, self.out_shape)
            self.outputs = {'Out': out}

        def init_dtype_type(self):
            self.dim = 1
            self.index_type = np.int64
            self.x_shape = (100, 4, 5)
            self.index_size = 100

        def test_check_output(self):
            if paddle.is_compiled_with_xpu():
                self.check_output_with_place(self.place)

        def test_check_grad(self):
            if paddle.is_compiled_with_xpu():
                self.check_grad_with_place(self.place, ['X'], 'Out')

    class TestXPUIndexSelectOpCase2(TestXPUIndexSelectOp):
        def init_dtype_type(self):
            self.index_type = np.int32
            self.dim = -2
            self.x_shape = (10, 10, 4, 10)
            self.index_size = 10


class TestIndexSelectAPI(unittest.TestCase):
    def input_data(self):
        self.data_x = np.array(
            [
                [1.0, 2.0, 3.0, 4.0],
                [5.0, 6.0, 7.0, 8.0],
                [9.0, 10.0, 11.0, 12.0],
            ]
        )
        self.data_index = np.array([0, 1, 1]).astype('int32')

    def test_index_select_api(self):
        self.input_data()

        # case 1:
        with program_guard(Program(), Program()):
            x = fluid.layers.data(name='x', shape=[-1, 4])
            index = fluid.layers.data(
                name='index', shape=[3], dtype='int32', append_batch_size=False
            )
            z = paddle.index_select(x, index, axis=1)
            exe = fluid.Executor(fluid.XPUPlace(0))
            (res,) = exe.run(
                feed={'x': self.data_x, 'index': self.data_index},
                fetch_list=[z.name],
                return_numpy=False,
            )
        expect_out = np.array(
            [[1.0, 2.0, 2.0], [5.0, 6.0, 6.0], [9.0, 10.0, 10.0]]
        )
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

        # case 2:
        with program_guard(Program(), Program()):
            x = fluid.layers.data(name='x', shape=[-1, 4])
            index = fluid.layers.data(
                name='index', shape=[3], dtype='int32', append_batch_size=False
            )
            z = paddle.index_select(x, index)
            exe = fluid.Executor(fluid.XPUPlace(0))
            (res,) = exe.run(
                feed={'x': self.data_x, 'index': self.data_index},
                fetch_list=[z.name],
                return_numpy=False,
            )
        expect_out = np.array(
            [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0]]
        )
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)

    def test_dygraph_api(self):
        self.input_data()
        # case 1:
        with fluid.dygraph.guard():
            x = fluid.dygraph.to_variable(self.data_x)
            index = fluid.dygraph.to_variable(self.data_index)
            z = paddle.index_select(x, index)
            np_z = z.numpy()
        expect_out = np.array(
            [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [5.0, 6.0, 7.0, 8.0]]
        )
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)

        # case 2:
        with fluid.dygraph.guard():
            x = fluid.dygraph.to_variable(self.data_x)
            index = fluid.dygraph.to_variable(self.data_index)
            z = paddle.index_select(x, index, axis=1)
            np_z = z.numpy()
        expect_out = np.array(
            [[1.0, 2.0, 2.0], [5.0, 6.0, 6.0], [9.0, 10.0, 10.0]]
        )
        np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)


support_types = get_xpu_op_support_types('index_select')
for stype in support_types:
    create_test_class(globals(), XPUTestIndexSelect, stype)

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