#   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 absolute_import
from __future__ import division
from __future__ import print_function

import functools
import numpy as np
import time

import cProfile, pstats, StringIO

import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.profiler as profiler


def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
    conv1 = fluid.layers.conv2d(
        input=input,
        filter_size=filter_size,
        num_filters=ch_out,
        stride=stride,
        padding=padding,
        act=None,
        bias_attr=False)
    return fluid.layers.batch_norm(input=conv1, act=act)


def shortcut(input, ch_out, stride):
    ch_in = input.shape[1]  # if args.data_format == 'NCHW' else input.shape[-1]
    if ch_in != ch_out:
        return conv_bn_layer(input, ch_out, 1, stride, 0, None)
    else:
        return input


def basicblock(input, ch_out, stride):
    short = shortcut(input, ch_out, stride)
    conv1 = conv_bn_layer(input, ch_out, 3, stride, 1)
    conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None)
    return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')


def bottleneck(input, ch_out, stride):
    short = shortcut(input, ch_out * 4, stride)
    conv1 = conv_bn_layer(input, ch_out, 1, stride, 0)
    conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1)
    conv3 = conv_bn_layer(conv2, ch_out * 4, 1, 1, 0, act=None)
    return fluid.layers.elementwise_add(x=short, y=conv3, act='relu')


def layer_warp(block_func, input, ch_out, count, stride):
    res_out = block_func(input, ch_out, stride)
    for i in range(1, count):
        res_out = block_func(res_out, ch_out, 1)
    return res_out


def resnet_imagenet(input, class_dim, depth=50, data_format='NCHW'):

    cfg = {
        18: ([2, 2, 2, 1], basicblock),
        34: ([3, 4, 6, 3], basicblock),
        50: ([3, 4, 6, 3], bottleneck),
        101: ([3, 4, 23, 3], bottleneck),
        152: ([3, 8, 36, 3], bottleneck)
    }
    stages, block_func = cfg[depth]
    conv1 = conv_bn_layer(input, ch_out=64, filter_size=7, stride=2, padding=3)
    pool1 = fluid.layers.pool2d(
        input=conv1, pool_type='avg', pool_size=3, pool_stride=2)
    res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
    res2 = layer_warp(block_func, res1, 128, stages[1], 2)
    res3 = layer_warp(block_func, res2, 256, stages[2], 2)
    res4 = layer_warp(block_func, res3, 512, stages[3], 2)
    pool2 = fluid.layers.pool2d(
        input=res4,
        pool_size=7,
        pool_type='avg',
        pool_stride=1,
        global_pooling=True)
    out = fluid.layers.fc(input=pool2, size=class_dim, act='softmax')
    return out


def resnet_cifar10(input, class_dim, depth=32, data_format='NCHW'):
    assert (depth - 2) % 6 == 0

    n = (depth - 2) // 6

    conv1 = conv_bn_layer(
        input=input, ch_out=16, filter_size=3, stride=1, padding=1)
    res1 = layer_warp(basicblock, conv1, 16, n, 1)
    res2 = layer_warp(basicblock, res1, 32, n, 2)
    res3 = layer_warp(basicblock, res2, 64, n, 2)
    pool = fluid.layers.pool2d(
        input=res3, pool_size=8, pool_type='avg', pool_stride=1)
    out = fluid.layers.fc(input=pool, size=class_dim, act='softmax')
    return out


def get_model(args):
    model = resnet_cifar10
    if args.data_set == "cifar10":
        class_dim = 10
        if args.data_format == 'NCHW':
            dshape = [3, 32, 32]
        else:
            dshape = [32, 32, 3]
        model = resnet_cifar10
    else:
        class_dim = 102
        if args.data_format == 'NCHW':
            dshape = [3, 224, 224]
        else:
            dshape = [224, 224, 3]
        model = resnet_imagenet

    input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')

    if args.device == 'CPU' and args.cpus > 1:
        places = fluid.layers.get_places(args.cpus)
        pd = fluid.layers.ParallelDo(places)
        with pd.do():
            predict = model(pd.read_input(input), class_dim)
            label = pd.read_input(label)
            cost = fluid.layers.cross_entropy(input=predict, label=label)
            avg_cost = fluid.layers.mean(x=cost)
            batch_acc = fluid.layers.accuracy(input=predict, label=label)

            pd.write_output(avg_cost)
            pd.write_output(batch_acc)

        avg_cost, batch_acc = pd()
        avg_cost = fluid.layers.mean(avg_cost)
        batch_acc = fluid.layers.mean(batch_acc)
    else:
        predict = model(input, class_dim)
        cost = fluid.layers.cross_entropy(input=predict, label=label)
        avg_cost = fluid.layers.mean(x=cost)
        batch_acc = fluid.layers.accuracy(input=predict, label=label)

    inference_program = fluid.default_main_program().clone()
    with fluid.program_guard(inference_program):
        inference_program = fluid.io.get_inference_program(
            target_vars=[batch_acc])

    optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)

    train_reader = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.cifar.train10()
            if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
            buf_size=5120),
        batch_size=args.batch_size)
    test_reader = paddle.batch(
        paddle.dataset.cifar.test10()
        if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
        batch_size=args.batch_size)

    return avg_cost, inference_program, optimizer, train_reader, test_reader, batch_acc