from __future__ import print_function, division, absolute_import
import os
from os.path import expanduser
import hickle as hkl
import torch
import torch.nn.functional as F
from torch.autograd import Variable

__all__ = ['wideresnet50']

model_urls = {
    'wideresnet152': 'https://s3.amazonaws.com/pytorch/h5models/wide-resnet-50-2-export.hkl'
}

def define_model(params):
    def conv2d(input, params, base, stride=1, pad=0):
        return F.conv2d(input, params[base + '.weight'],
                        params[base + '.bias'], stride, pad)

    def group(input, params, base, stride, n):
        o = input
        for i in range(0,n):
            b_base = ('%s.block%d.conv') % (base, i)
            x = o
            o = conv2d(x, params, b_base + '0')
            o = F.relu(o)
            o = conv2d(o, params, b_base + '1', stride=i==0 and stride or 1, pad=1)
            o = F.relu(o)
            o = conv2d(o, params, b_base + '2')
            if i == 0:
                o += conv2d(x, params, b_base + '_dim', stride=stride)
            else:
                o += x
            o = F.relu(o)
        return o

    # determine network size by parameters
    blocks = [sum([re.match('group%d.block\d+.conv0.weight'%j, k) is not None
                   for k in params.keys()]) for j in range(4)]

    def f(input, params, pooling_classif=True):
        o = F.conv2d(input, params['conv0.weight'], params['conv0.bias'], 2, 3)
        o = F.relu(o)
        o = F.max_pool2d(o, 3, 2, 1)
        o_g0 = group(o, params, 'group0', 1, blocks[0])
        o_g1 = group(o_g0, params, 'group1', 2, blocks[1])
        o_g2 = group(o_g1, params, 'group2', 2, blocks[2])
        o_g3 = group(o_g2, params, 'group3', 2, blocks[3])
        if pooling_classif:
            o = F.avg_pool2d(o_g3, 7, 1, 0)
            o = o.view(o.size(0), -1)
            o = F.linear(o, params['fc.weight'], params['fc.bias'])
        return o

    return f


class WideResNet(nn.Module):

    def __init__(self, pooling):
        super(WideResNet, self).__init__()
        self.pooling = pooling
        self.params = params

    def forward(self, x):
        x = f(x, self.params, self.pooling)
        return x


def wideresnet50(pooling):
    dir_models = os.path.join(expanduser("~"), '.torch/wideresnet')
    path_hkl = os.path.join(dir_models, 'wideresnet50.hkl')
    if os.path.isfile(path_hkl):
        params = hkl.load(path_hkl)
        # convert numpy arrays to torch Variables
        for k,v in sorted(params.items()):
            print(k, v.shape)
            params[k] = Variable(torch.from_numpy(v), requires_grad=True)
    else:
        from pathlib import Path
        path = Path(dir_models)
        path.mkdir(exist_ok=True, parents=True)
        os.system('wget {} -O {}'.format(model_urls['wideresnet50'], path_hkl))
    f = define_model(params)
    model = WideResNet(pooling)
    return model


