#   Copyright (c) 2018 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
from functools import partial
import numpy as np
import paddle
import paddle.fluid.core as core

import paddle.fluid as fluid
from paddle.fluid import compiler


def get_places():
    places = []
    if core.is_compiled_with_cuda():
        places.append(core.CUDAPlace(0))
    return places


@contextlib.contextmanager
def prog_scope_guard(main_prog, startup_prog):
    scope = fluid.core.Scope()
    with fluid.unique_name.guard():
        with fluid.scope_guard(scope):
            with fluid.program_guard(main_prog, startup_prog):
                yield


def bow_net(data,
            label,
            dict_dim,
            is_sparse=False,
            emb_dim=128,
            hid_dim=128,
            hid_dim2=96,
            class_dim=2):
    """
    BOW net
    This model is from https://github.com/PaddlePaddle/models:
    fluid/PaddleNLP/text_classification/nets.py
    """
    emb = fluid.layers.embedding(
        input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim])
    bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
    bow_tanh = fluid.layers.tanh(bow)
    fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
    fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
    prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
    cost = fluid.layers.cross_entropy(input=prediction, label=label)
    avg_cost = fluid.layers.mean(x=cost)

    return avg_cost


class TestWeightDecay(unittest.TestCase):
    def setUp(self):
        self.word_dict = paddle.dataset.imdb.word_dict()
        reader = paddle.batch(
            paddle.dataset.imdb.train(self.word_dict), batch_size=4)()
        self.train_data = [next(reader) for _ in range(5)]
        self.learning_rate = .5

    def run_executor(self, place, feed_list, loss):
        exe = fluid.Executor(place)
        feeder = fluid.DataFeeder(feed_list=feed_list, place=place)
        exe.run(fluid.default_startup_program())
        main_prog = fluid.default_main_program()
        loss_set = []
        for data in self.train_data:
            out = exe.run(main_prog,
                          feed=feeder.feed(data),
                          fetch_list=[loss.name])

            print("loss              %s" % (np.average(out)))
            loss_set.append(np.average(out))

        return loss_set

    def run_parallel_exe(self,
                         place,
                         feed_list,
                         loss,
                         use_reduce=False,
                         use_fast_executor=False,
                         use_ir_memory_optimize=False):
        exe = fluid.Executor(place)
        feeder = fluid.DataFeeder(feed_list=feed_list, place=place)
        exe.run(fluid.default_startup_program())

        exec_strategy = fluid.ExecutionStrategy()
        if use_fast_executor:
            exec_strategy.use_experimental_executor = True

        build_strategy = fluid.BuildStrategy()
        build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce \
                if use_reduce else fluid.BuildStrategy.ReduceStrategy.AllReduce
        build_strategy.memory_optimize = use_ir_memory_optimize

        train_cp = compiler.CompiledProgram(fluid.default_main_program(
        )).with_data_parallel(
            loss_name=loss.name,
            exec_strategy=exec_strategy,
            build_strategy=build_strategy)

        loss_set = []
        for data in self.train_data:
            out = exe.run(train_cp,
                          feed=feeder.feed(data),
                          fetch_list=[loss.name])
            loss_set.append(np.average(out))

        return loss_set

    def check_weight_decay(self,
                           place,
                           model,
                           use_parallel_exe=False,
                           use_reduce=False):
        main_prog = fluid.framework.Program()
        startup_prog = fluid.framework.Program()
        startup_prog.random_seed = 1
        with prog_scope_guard(main_prog=main_prog, startup_prog=startup_prog):
            data = fluid.layers.data(
                name="words", shape=[1], dtype="int64", lod_level=1)
            label = fluid.layers.data(name="label", shape=[1], dtype="int64")
            avg_cost = model(data, label, len(self.word_dict))

            param_list = [(var, var * self.learning_rate)
                          for var in main_prog.block(0).all_parameters()]

            optimizer = fluid.optimizer.Adagrad(
                learning_rate=self.learning_rate)
            optimizer.minimize(avg_cost)

            for params in param_list:
                updated_p = fluid.layers.elementwise_sub(
                    x=params[0], y=params[1])
                fluid.layers.assign(input=updated_p, output=params[0])

            if use_parallel_exe:
                loss = self.run_parallel_exe(
                    place, [data, label], loss=avg_cost, use_reduce=use_reduce)
            else:
                loss = self.run_executor(place, [data, label], loss=avg_cost)

        return loss

    def test_weight_decay(self):
        model = partial(bow_net, is_sparse=False)
        for place in get_places():
            loss = self.check_weight_decay(place, model, use_parallel_exe=False)

            # TODO(zcd): should test use_reduce=True
            loss2 = self.check_weight_decay(
                place, model, use_parallel_exe=True, use_reduce=False)

            for i in range(len(loss)):
                self.assertTrue(
                    np.isclose(
                        a=loss[i], b=loss2[i], rtol=5e-5),
                    "Expect " + str(loss[i]) + "\n" + "But Got" + str(loss2[i])
                    + " in class " + self.__class__.__name__)


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