import functools
import logging
import torch
import torch.nn as nn
from torch.nn import init

import models.SPSR_modules.architecture as arch
logger = logging.getLogger('base')
####################
# initialize
####################


def weights_init_normal(m, std=0.02):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        init.normal_(m.weight.data, 0.0, std)
        if m.bias is not None:
            m.bias.data.zero_()
    elif classname.find('Linear') != -1:
        init.normal_(m.weight.data, 0.0, std)
        if m.bias is not None:
            m.bias.data.zero_()
    elif classname.find('BatchNorm2d') != -1:
        init.normal_(m.weight.data, 1.0, std)  # BN also uses norm
        init.constant_(m.bias.data, 0.0)


def weights_init_kaiming(m, scale=1):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
        m.weight.data *= scale
        if m.bias is not None:
            m.bias.data.zero_()
    elif classname.find('Linear') != -1:
        init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
        m.weight.data *= scale
        if m.bias is not None:
            m.bias.data.zero_()
    elif classname.find('BatchNorm2d') != -1:
        if m.affine != False:

            init.constant_(m.weight.data, 1.0)
            init.constant_(m.bias.data, 0.0)


def weights_init_orthogonal(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        init.orthogonal_(m.weight.data, gain=1)
        if m.bias is not None:
            m.bias.data.zero_()
    elif classname.find('Linear') != -1:
        init.orthogonal_(m.weight.data, gain=1)
        if m.bias is not None:
            m.bias.data.zero_()
    elif classname.find('BatchNorm2d') != -1:
        init.constant_(m.weight.data, 1.0)
        init.constant_(m.bias.data, 0.0)


def init_weights(net, init_type='kaiming', scale=1, std=0.02):
    # scale for 'kaiming', std for 'normal'.
    if init_type == 'normal':
        weights_init_normal_ = functools.partial(weights_init_normal, std=std)
        net.apply(weights_init_normal_)
    elif init_type == 'kaiming':
        weights_init_kaiming_ = functools.partial(weights_init_kaiming, scale=scale)
        net.apply(weights_init_kaiming_)
    elif init_type == 'orthogonal':
        net.apply(weights_init_orthogonal)
    else:
        raise NotImplementedError('initialization method [{:s}] not implemented'.format(init_type))


####################
# define network
####################


# Generator
def define_G(opt, device=None):
    opt_net = opt['network_G']
    which_model = opt_net['which_model_G']

    if which_model == 'spsr_net':
        netG = arch.SPSRNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
            nb=opt_net['nb'], gc=opt_net['gc'], upscale=opt_net['scale'], norm_type=opt_net['norm_type'],
            act_type='leakyrelu', mode=opt_net['mode'], upsample_mode='upconv')
    else:
        raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))

    if opt['is_train']:
        init_weights(netG, init_type='kaiming', scale=0.1)

    return netG



# Discriminator
def define_D(opt):
    opt_net = opt['network_D']
    which_model = opt_net['which_model_D']

    if which_model == 'discriminator_vgg_128':
        netD = arch.Discriminator_VGG_128(in_nc=opt_net['in_nc'], base_nf=opt_net['nf'], \
            norm_type=opt_net['norm_type'], mode=opt_net['mode'], act_type=opt_net['act_type'])

    elif which_model == 'discriminator_vgg_96':
        netD = arch.Discriminator_VGG_96(in_nc=opt_net['in_nc'], base_nf=opt_net['nf'], \
            norm_type=opt_net['norm_type'], mode=opt_net['mode'], act_type=opt_net['act_type'])
    elif which_model == 'discriminator_vgg_192':
        netD = arch.Discriminator_VGG_192(in_nc=opt_net['in_nc'], base_nf=opt_net['nf'], \
            norm_type=opt_net['norm_type'], mode=opt_net['mode'], act_type=opt_net['act_type'])
    elif which_model == 'discriminator_vgg_128_SN':
        netD = arch.Discriminator_VGG_128_SN()
    else:
        raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))

    init_weights(netD, init_type='kaiming', scale=1)

    return netD

def define_D_grad(opt):
    opt_net = opt['network_D']
    which_model = opt_net['which_model_D']

    if which_model == 'discriminator_vgg_128':
        netD = arch.Discriminator_VGG_128(in_nc=opt_net['in_nc'], base_nf=opt_net['nf'], \
            norm_type=opt_net['norm_type'], mode=opt_net['mode'], act_type=opt_net['act_type'])

    elif which_model == 'discriminator_vgg_96':
        netD = arch.Discriminator_VGG_96(in_nc=opt_net['in_nc'], base_nf=opt_net['nf'], \
            norm_type=opt_net['norm_type'], mode=opt_net['mode'], act_type=opt_net['act_type'])
    elif which_model == 'discriminator_vgg_192':
        netD = arch.Discriminator_VGG_192(in_nc=opt_net['in_nc'], base_nf=opt_net['nf'], \
            norm_type=opt_net['norm_type'], mode=opt_net['mode'], act_type=opt_net['act_type'])
    elif which_model == 'discriminator_vgg_128_SN':
        netD = arch.Discriminator_VGG_128_SN()
    else:
        raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))


    init_weights(netD, init_type='kaiming', scale=1)

    return netD


def define_F(opt, use_bn=False):
    device = torch.device('cuda')
    # pytorch pretrained VGG19-54, before ReLU.
    if use_bn:
        feature_layer = 49
    else:
        feature_layer = 34
    netF = arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn, \
        use_input_norm=True, device=device)

    netF.eval()  
    return netF