# 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 division
from __future__ import print_function

import unittest

import os

import sys
sys.path.append('../')

import numpy as np
import contextlib

import paddle
from paddle import fluid
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
from model import Model, CrossEntropy, Input, Loss
from metrics import Accuracy
from callbacks import ProgBarLogger
from paddle.fluid.io import BatchSampler, DataLoader
from paddle.fluid.io import MNIST as MnistDataset


class SimpleImgConvPool(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 pool_size,
                 pool_stride,
                 pool_padding=0,
                 pool_type='max',
                 global_pooling=False,
                 conv_stride=1,
                 conv_padding=0,
                 conv_dilation=1,
                 conv_groups=None,
                 act=None,
                 use_cudnn=False,
                 param_attr=None,
                 bias_attr=None):
        super(SimpleImgConvPool, self).__init__('SimpleConv')

        self._conv2d = Conv2D(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=conv_stride,
            padding=conv_padding,
            dilation=conv_dilation,
            groups=conv_groups,
            param_attr=None,
            bias_attr=None,
            use_cudnn=use_cudnn)

        self._pool2d = Pool2D(
            pool_size=pool_size,
            pool_type=pool_type,
            pool_stride=pool_stride,
            pool_padding=pool_padding,
            global_pooling=global_pooling,
            use_cudnn=use_cudnn)

    def forward(self, inputs):
        x = self._conv2d(inputs)
        x = self._pool2d(x)
        return x


class MNIST(Model):
    def __init__(self):
        super(MNIST, self).__init__()
        self._simple_img_conv_pool_1 = SimpleImgConvPool(
            1, 20, 5, 2, 2, act="relu")

        self._simple_img_conv_pool_2 = SimpleImgConvPool(
            20, 50, 5, 2, 2, act="relu")

        pool_2_shape = 50 * 4 * 4
        SIZE = 10
        scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
        self._fc = Linear(
            800,
            10,
            param_attr=fluid.param_attr.ParamAttr(
                initializer=fluid.initializer.NormalInitializer(
                    loc=0.0, scale=scale)),
            act="softmax")

    def forward(self, inputs):
        inputs = fluid.layers.reshape(inputs, [-1, 1, 28, 28])
        x = self._simple_img_conv_pool_1(inputs)
        x = self._simple_img_conv_pool_2(x)
        x = fluid.layers.flatten(x, axis=1)
        x = self._fc(x)
        return x


@contextlib.contextmanager
def null_guard():
    yield


class MLP(Model):
    def __init__(self):
        super(MLP, self).__init__()
        SIZE = 10
        self._fc1 = Linear(784, 200, act="relu")
        self._fc2 = Linear(200, 200, act="relu")
        self._fc3 = Linear(200, 200, act="relu")
        self._fc4 = Linear(200, 10, act="softmax")
        self._fc5 = Linear(200, 10, act="softmax")

    def forward(self, inputs):
        x1 = self._fc1(inputs)
        x2 = self._fc2(x1)
        x3 = self._fc3(x2)
        o1 = self._fc5(x3)
        o2 = self._fc4(x2)
        return o1, o2


class MyCrossEntropy(Loss):
    def __init__(self, average=True):
        super(MyCrossEntropy, self).__init__()

    def forward(self, outputs, labels):
        loss1 = fluid.layers.cross_entropy(outputs[0], labels[0])
        loss2 = fluid.layers.cross_entropy(outputs[1], labels[0])
        return [loss1, loss2]


class TestModel(unittest.TestCase):
    def fit(self, dynamic, is_mlp=False):
        im_shape = (-1, 784)
        batch_size = 128
        
        place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
        if fluid.dygraph.parallel.Env().nranks > 1 else fluid.CUDAPlace(0)
        fluid.enable_dygraph(place) if dynamic else None

        inputs = [Input(im_shape, 'float32', name='image')]
        labels = [Input([None, 1], 'int64', name='label')]

        train_dataset = MnistDataset(mode='train')
        val_dataset = MnistDataset(mode='test')
    
        model = MNIST() if not is_mlp else MLP()
        optim = fluid.optimizer.Momentum(
            learning_rate=0.01,
            momentum=.9,
            parameter_list=model.parameters())
        loss = CrossEntropy() if not is_mlp else MyCrossEntropy()
        model.prepare(optim, loss, Accuracy(), inputs, labels)
        cbk = ProgBarLogger(50)
        model.fit(train_dataset, val_dataset, epochs=2, batch_size=batch_size, callbacks=cbk)

    def test_fit_static(self):
        self.fit(False)

    def test_fit_dygraph(self):
        self.fit(True)

    def test_fit_static_multi_loss(self):
        self.fit(False, MyCrossEntropy())

    def test_fit_dygraph_multi_loss(self):
        self.fit(True, MyCrossEntropy())


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