# Copyright (c) 2023 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 paddle
from paddle.autograd.ir_backward import grad
from paddle.decomposition import decompose
from paddle.framework import core

paddle.enable_static()


class TestPrimMode(unittest.TestCase):
    def setUp(self):
        np.random.seed(2023)
        self.shape_x = [8, 16, 32, 64]
        self.shape_y = [8, 16, 32, 64]
        self.x = np.random.random(self.shape_x).astype("float32")
        self.y = np.random.random(self.shape_y).astype("float32")

    def base_net(self, flag=None):
        if flag == "forward":
            core._set_prim_forward_enabled(True)
        elif flag == "backward":
            core._set_prim_backward_enabled(True)
        elif flag == "all":
            core._set_prim_all_enabled(True)
        main_program = paddle.static.Program()
        with paddle.static.program_guard(main_program):
            x = paddle.static.data('x', self.shape_x, dtype='float32')
            y = paddle.static.data('y', self.shape_y, dtype='float32')
            x.stop_gradient = False
            y.stop_gradient = False
            divide_out = paddle.divide(x, y)
            sum_out = paddle.mean(divide_out, axis=0)
            [new_out] = decompose(main_program, [sum_out])
            gradients = grad(new_out, (x, y))

            exe = paddle.static.Executor()
            [fwd, dx, dy] = exe.run(
                feed={'x': self.x, 'y': self.y}, fetch_list=[new_out, gradients]
            )

        whole_ops = [op.name() for op in main_program.global_block().ops]
        if flag == "forward":
            core._set_prim_forward_enabled(False)
            assert (
                'pd_op.mean' not in whole_ops
                and 'pd_op.divide_grad' in whole_ops
            )
        elif flag == "backward":
            core._set_prim_backward_enabled(False)
            assert (
                'pd_op.mean' in whole_ops
                and 'pd_op.divide_grad' not in whole_ops
            )
        elif flag == "all":
            core._set_prim_all_enabled(False)
            assert (
                'pd_op.mean' not in whole_ops
                and 'pd_op.divide_grad' not in whole_ops
            )
        else:
            assert (
                'pd_op.mean' in whole_ops and 'pd_op.divide_grad' in whole_ops
            )
        return fwd, dx, dy

    def test_prim_forward(self):
        res_ref = self.base_net()
        res = self.base_net("forward")
        for ref, actual in zip(res_ref, res):
            np.testing.assert_equal(ref, actual)

    def test_prim_backward(self):
        res_ref = self.base_net()
        res = self.base_net("backward")
        for ref, actual in zip(res_ref, res):
            np.testing.assert_allclose(ref, actual, rtol=1e-6)

    def test_prim_all(self):
        res_ref = self.base_net()
        res = self.base_net("all")
        for ref, actual in zip(res_ref, res):
            np.testing.assert_allclose(ref, actual, rtol=1e-6)


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