#   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 argparse
import paddle.fluid as fluid
import paddle
import sys
import numpy
import unittest
import math
import sys
import os

BATCH_SIZE = 64


def inference_program():
    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')

    hidden = fluid.layers.fc(input=img, size=200, act='tanh')
    hidden = fluid.layers.fc(input=hidden, size=200, act='tanh')
    prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
    return prediction


def train_program():
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')

    predict = inference_program()
    cost = fluid.layers.cross_entropy(input=predict, label=label)
    avg_cost = fluid.layers.mean(cost)
    # acc = fluid.layers.accuracy(input=predict, label=label)
    # return avg_cost, acc
    return avg_cost


def train(use_cuda, save_dirname):
    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
    optimizer = fluid.optimizer.Adam(learning_rate=0.001)

    trainer = fluid.Trainer(
        train_func=train_program,
        infer_func=inference_program,
        place=place,
        optimizer=optimizer)

    def event_handler(event):
        if isinstance(event, fluid.EndEpochEvent):
            # if (event.epoch + 1) % 10 == 0:
            trainer.save_inference_model(save_dirname)

            # TODO: Uncomment this part once we are sure that .train is working
            # test_reader = paddle.batch(
            #     paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
            # test_metrics = trainer.test(reader=test_reader)
            # avg_cost_set = test_metrics[0]
            # acc_set = test_metrics[1]
            #
            # # get test acc and loss
            # acc = numpy.array(acc_set).mean()
            # avg_cost = numpy.array(avg_cost_set).mean()
            #
            # print("avg_cost: %s" % avg_cost)
            # print("acc     : %s" % acc)
            #
            # if float(acc) > 0.2:  # Smaller value to increase CI speed
            #     trainer.save_params(save_dirname)
            # else:
            #     print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
            #         event.epoch + 1, float(avg_cost), float(acc)))
            #     if math.isnan(float(avg_cost)):
            #         sys.exit("got NaN loss, training failed.")

    train_reader = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.mnist.train(), buf_size=500),
        batch_size=BATCH_SIZE)

    trainer.train(
        num_epochs=1,
        event_handler=event_handler,
        reader=train_reader,
        feed_order=['img', 'label'])


def infer(use_cuda, save_dirname=None):
    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()

    inferencer = fluid.Inferencer(param_path=save_dirname, place=place)

    batch_size = 1
    tensor_img = numpy.random.uniform(-1.0, 1.0,
                                      [batch_size, 1, 28, 28]).astype("float32")

    results = inferencer.infer({'img': tensor_img})

    print("infer results: ", results[0])


def main(use_cuda):
    save_dirname = "recognize_digits_mlp.inference.model"

    # call train() with is_local argument to run distributed train
    train(use_cuda=use_cuda, save_dirname=save_dirname)
    infer(use_cuda=use_cuda, save_dirname=save_dirname)


if __name__ == '__main__':
    # for use_cuda in (False, True):
    main(use_cuda=False)