# 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 unittest

import numpy as np
import parameterized as param

import paddle
from paddle.fluid import core, framework


def apply_to_static(net, use_cinn):
    build_strategy = paddle.static.BuildStrategy()
    build_strategy.build_cinn_pass = use_cinn
    return paddle.jit.to_static(net, build_strategy=build_strategy)


class PrimeNet(paddle.nn.Layer):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        out = paddle.transpose(x, [0, 2, 1])
        return out


@param.parameterized_class(
    ('primal', 'axis', 'cotangent', 'dtype', 'rtol'),
    [
        (
            np.random.rand(
                100,
            ),
            [0],
            np.random.rand(100),
            np.float64,
            1e-15,
        ),
        (
            np.random.rand(3, 4, 10),
            [0, 2, 1],
            np.random.rand(3, 10, 4),
            np.float64,
            1e-15,
        ),
        (
            np.random.rand(2, 3, 4, 5),
            [0, 2, 3, 1],
            np.random.rand(2, 4, 5, 3),
            np.float64,
            1e-15,
        ),
        (
            np.random.rand(2, 3, 4, 5, 6),
            [4, 2, 3, 1, 0],
            np.random.rand(6, 4, 5, 3, 2),
            np.float64,
            1e-15,
        ),
        (
            np.random.rand(2, 3, 4, 5, 6, 1),
            [4, 2, 3, 1, 0, 5],
            np.random.rand(6, 4, 5, 3, 2, 1),
            np.float64,
            1e-15,
        ),
        (
            np.random.rand(
                100,
            ),
            [0],
            np.random.rand(100),
            np.float16,
            1e-3,
        ),
        (
            np.random.rand(3, 4, 10),
            [0, 2, 1],
            np.random.rand(3, 10, 4),
            np.float16,
            1e-3,
        ),
        (
            np.random.rand(2, 3, 4, 5),
            [0, 2, 3, 1],
            np.random.rand(2, 4, 5, 3),
            np.float16,
            1e-3,
        ),
        (
            np.random.rand(2, 3, 4, 5, 6),
            [4, 2, 3, 1, 0],
            np.random.rand(6, 4, 5, 3, 2),
            np.float16,
            1e-3,
        ),
        (
            np.random.rand(2, 3, 4, 5, 6, 1),
            [4, 2, 3, 1, 0, 5],
            np.random.rand(6, 4, 5, 3, 2, 1),
            np.float16,
            1e-3,
        ),
    ],
)
class TestTransposeGradComp(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        if isinstance(cls.primal, np.ndarray):
            cls.primal = cls.primal.astype(cls.dtype)
        if isinstance(cls.cotangent, np.ndarray):
            cls.cotangent = cls.cotangent.astype(cls.dtype)

    def train(self, use_prim, use_cinn):
        paddle.seed(2022)
        self.x = paddle.randn([3, 4, 10])
        self.x.stop_gradient = False
        net = PrimeNet()
        core._set_prim_backward_enabled(use_prim)
        net = apply_to_static(net, use_cinn)
        out = net(self.x)
        res = paddle.autograd.grad(out, [self.x])

        return res

    def _test_cinn(self):
        paddle.disable_static()
        use_cinn = True
        if isinstance(
            framework._current_expected_place(), framework.core.CPUPlace
        ):
            # TODO(jiabin): CINN will crashed in this case open it when fixed
            use_cinn = False
        dy_res = self.train(use_prim=False, use_cinn=False)
        comp_st_cinn_res = self.train(use_prim=True, use_cinn=use_cinn)

        for i in range(len(dy_res)):
            np.testing.assert_allclose(
                comp_st_cinn_res[i].numpy(),
                dy_res[i].numpy(),
                rtol=1e-7,
                atol=1e-7,
            )

    def test_transpose_grad_comp(self):
        paddle.enable_static()

        def actual(primal, axis, cotangent):
            core._set_prim_backward_enabled(True)
            mp, sp = paddle.static.Program(), paddle.static.Program()
            with paddle.static.program_guard(mp, sp):
                if isinstance(primal, np.ndarray):
                    x = paddle.static.data('primal', primal.shape, primal.dtype)
                else:
                    x = paddle.static.data('primal', [1], "float32")
                x.stop_gradient = False
                if isinstance(cotangent, np.ndarray):
                    v = paddle.static.data(
                        'cotangent', cotangent.shape, cotangent.dtype
                    )
                else:
                    v = paddle.static.data('cotangent', [1], "float32")
                print(x.shape)
                y = paddle.transpose(x, axis)
                x_cotangent = paddle.static.gradients(y, x, v)
            exe = paddle.static.Executor()
            exe.run(sp)
            return exe.run(
                program=mp,
                feed={'primal': primal, 'cotangent': cotangent},
                fetch_list=[x_cotangent[0].name],
            )[0]

        def desired(primal, axis, cotangent):
            core._set_prim_backward_enabled(False)
            mp, sp = paddle.static.Program(), paddle.static.Program()
            with paddle.static.program_guard(mp, sp):
                if isinstance(primal, np.ndarray):
                    x = paddle.static.data('primal', primal.shape, primal.dtype)
                else:
                    x = paddle.static.data('primal', [1], "float32")
                x.stop_gradient = False
                if isinstance(cotangent, np.ndarray):
                    v = paddle.static.data(
                        'cotangent', cotangent.shape, cotangent.dtype
                    )
                else:
                    v = paddle.static.data('cotangent', [1], "float32")
                y = paddle.transpose(x, axis)
                x_cotangent = paddle.static.gradients(y, x, v)
            exe = paddle.static.Executor()
            exe.run(sp)
            return exe.run(
                program=mp,
                feed={'primal': primal, 'cotangent': cotangent},
                fetch_list=[x_cotangent[0].name],
            )[0]

        if (self.dtype == np.float16) and isinstance(
            framework._current_expected_place(), framework.core.CPUPlace
        ):
            # reshape doesn't support fp16 kernel in cpu.
            pass
        else:
            np.testing.assert_allclose(
                actual=actual(self.primal, self.axis, self.cotangent),
                desired=desired(self.primal, self.axis, self.cotangent),
                rtol=self.rtol,
                atol=self.rtol,
            )
        core._set_prim_backward_enabled(False)


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