# 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 contextlib
import unittest
import numpy as np
import six

import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.dygraph.base as base

from test_imperative_lod_tensor_to_selected_rows import SimpleNet

call_forward_hook = False
call_forward_pre_hook = False


def forward_hook(layer, input, output):
    global call_forward_hook
    call_forward_hook = True


def forward_pre_hook(layer, input):
    global call_forward_pre_hook
    call_forward_pre_hook = True


def forward_hook1(layer, input, output):
    return output * 2


def forward_pre_hook1(layer, input):
    input_return = (input[0] * 2, input[1])
    return input_return


class Test_Forward_Hook(unittest.TestCase):
    # test forward_pre_hook and forward_hook that have return value
    def test_forward_hook_return_value(self):
        seed = 90

        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))

        for place in places:
            with fluid.dygraph.guard(place):
                fluid.default_startup_program().random_seed = seed
                fluid.default_main_program().random_seed = seed
                fluid.set_flags({'FLAGS_sort_sum_gradient': True})

                input_word = np.array(
                    [0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7,
                     8]).reshape(6, 3).astype('int64')
                input_word1 = input_word * 2
                input_word = input_word.reshape((-1, 3, 1))
                input_word1 = input_word1.reshape((-1, 3, 1))
                y_data = np.array(
                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8,
                     9]).reshape(6, 3).astype('int64')
                y_data = y_data.reshape((-1, 1))

                input = base.to_variable(input_word)
                input1 = base.to_variable(input_word1)
                y = base.to_variable(y_data)

                simplenet = SimpleNet(
                    hidden_size=20,
                    vocab_size=32,
                    num_steps=3,
                    init_scale=0.1,
                    is_sparse=False,
                    dtype="float32")

                # origin, don't register any hook
                outs_origin = simplenet(input, y)
                outs_origin1 = simplenet(input1, y)

                # register forward_pre_hook
                forward_pre_hook_handle1 = simplenet.register_forward_pre_hook(
                    forward_pre_hook1)
                outs_pre_hook = simplenet(input, y)
                self.assertTrue(
                    np.array_equal(outs_pre_hook.numpy(), outs_origin1.numpy()))

                # remove forward_pre_hook
                forward_pre_hook_handle1.remove()
                outs_pre_hook = simplenet(input, y)
                self.assertTrue(
                    np.array_equal(outs_pre_hook.numpy(), outs_origin.numpy()))

                # register forward_hook
                forward_hook_handle1 = simplenet.register_forward_post_hook(
                    forward_hook1)
                outs_forward_hook = simplenet(input, y)
                self.assertTrue(
                    np.array_equal(outs_forward_hook.numpy(),
                                   outs_origin.numpy() * 2))

                # remove forward_hook
                forward_hook_handle1.remove()
                outs_forward_hook = simplenet(input, y)
                self.assertTrue(
                    np.array_equal(outs_forward_hook.numpy(),
                                   outs_origin.numpy()))

    # test forward_pre_hook and forward_hook that don't have return value
    def test_forward_hook(self):
        seed = 90

        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))

        for place in places:
            with fluid.dygraph.guard(place):
                fluid.default_startup_program().random_seed = seed
                fluid.default_main_program().random_seed = seed
                fluid.set_flags({'FLAGS_sort_sum_gradient': True})

                global call_forward_hook
                global call_forward_pre_hook

                input_word = np.array(
                    [0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7,
                     8]).reshape(6, 3).astype('int64')
                input_word = input_word.reshape((-1, 3, 1))
                y_data = np.array(
                    [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8,
                     9]).reshape(6, 3).astype('int64')
                y_data = y_data.reshape((-1, 1))

                input = base.to_variable(input_word)
                y = base.to_variable(y_data)

                simplenet = SimpleNet(
                    hidden_size=20,
                    vocab_size=32,
                    num_steps=3,
                    init_scale=0.1,
                    is_sparse=False,
                    dtype="float32")

                # origin, don't register any hook
                outs_origin = simplenet(input, y)
                self.assertFalse(call_forward_hook)
                self.assertFalse(call_forward_pre_hook)

                # register forward_hook and forward_pre_hook
                forward_hook_handle = simplenet.register_forward_post_hook(
                    forward_hook)
                forward_pre_hook_handle = simplenet.register_forward_pre_hook(
                    forward_pre_hook)
                outs_hook = simplenet(input, y)
                self.assertTrue(call_forward_hook)
                self.assertTrue(call_forward_pre_hook)

                outs_hook = simplenet(input, y)
                self.assertTrue(call_forward_hook)
                self.assertTrue(call_forward_pre_hook)

                # remove forward_hook
                forward_hook_handle.remove()
                call_forward_hook = False
                call_forward_pre_hook = False
                outs_remove_forward_hook = simplenet(input, y)
                self.assertFalse(call_forward_hook)
                self.assertTrue(call_forward_pre_hook)

                # remove forward_pre_hook
                forward_pre_hook_handle.remove()
                call_forward_hook = False
                call_forward_pre_hook = False
                outs_remove_hook = simplenet(input, y)
                self.assertFalse(call_forward_hook)
                self.assertFalse(call_forward_pre_hook)


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