import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable

import torchvision
import sys
import numpy as np
import math

from network.modules.resnet_hacks import modify_resnet_model
from network.modules.identity import Identity
from collections import OrderedDict

#[TODO]-added for check
torch.autograd.set_detect_anomaly(True)

def freeze_(model):
    """Freeze model
    Note that this function does not control BN
    """
    for p in model.parameters():
        p.requires_grad_(False)


#WIDE-RESNET by @xternalz (https://github.com/xternalz/WideResNet-pytorch/blob/master/wideresnet.py)


class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
                               padding=1, bias=False)
        self.droprate = dropRate
        self.equalInOut = (in_planes == out_planes)
        self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
                               padding=0, bias=False) or None
    def forward(self, x):
        if not self.equalInOut:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
        if self.droprate > 0:
            out = F.dropout(out, p=self.droprate, training=self.training)
        out = self.conv2(out)
        return torch.add(x if self.equalInOut else self.convShortcut(x), out)

class NetworkBlock(nn.Module):
    def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)
    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
        layers = []
        for i in range(int(nb_layers)):
            layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate))
        return nn.Sequential(*layers)
    def forward(self, x):
        return self.layer(x)

class WideResNet(nn.Module):
    def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
        super(WideResNet, self).__init__()
        nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
        assert((depth - 4) % 6 == 0)
        n = (depth - 4) / 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1,
                               padding=1, bias=False)
        # 1st block
        self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate) 
        # 2nd block
        self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
        # 3rd block
        self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(nChannels[3])
        self.relu = nn.ReLU(inplace=True)
        self.fc = nn.Linear(nChannels[3], num_classes)
        self.nChannels = nChannels[3]

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()
    def forward(self, x):
        out = self.conv1(x)
        out = self.block1(out)
        out = self.block2(out)
        out = self.block3(out)
        out = self.relu(self.bn1(out))
        out = F.avg_pool2d(out, 8) #run0
        out = out.view(-1, self.nChannels)
        #return self.fc(out)
        return out


class ConvNet(nn.Module):
    ''' The network structure is consistent with the SimCLR method
     '''
    def __init__(self, projection_dim, output_dim, imdim=3, oracle= False):
        super(ConvNet, self).__init__()
        
        #added 
        #self, depth, num_classes, widen_factor=1, dropRate=0.0)
        self.encoder= WideResNet(depth=16, num_classes= output_dim, widen_factor=4, dropRate=0.3) #0.3
        self.projection_dim= projection_dim
        self.n_features= self.encoder.nChannels ##self.encoder.fc.in_features -> now
        self.output_dim= output_dim
        #added for miro
        self.oracle= oracle
        self.selected_out = OrderedDict()
        self.fhooks=[]
        self.buffer_features= 3024
        
        self.buffer = nn.Sequential(nn.ReLU(),nn.Linear(self.n_features, self.n_features, bias=False),
                                    nn.ReLU(),nn.Linear(self.n_features, self.buffer_features, bias=False))
                
        self.cls_head_src =  nn.Sequential(nn.ReLU(),nn.Linear(self.buffer_features, self.output_dim)) 
        self.pro_head = nn.Sequential(nn.ReLU(),
            nn.Linear(self.buffer_features, self.buffer_features, bias=False),  #self.n_features -> self.projection_dim
            nn.BatchNorm1d(self.buffer_features),
            nn.ReLU(),
            nn.Linear(self.buffer_features, self.buffer_features, bias=False),  #self.n_features -> self.projection_dim
            nn.BatchNorm1d(self.buffer_features),
            nn.ReLU(),
            nn.Linear(self.buffer_features, self.projection_dim, bias=False), #self.n_features,self.projection_dim -> self.projection_dim,self.projection_dim
        )
        
    
    def get_hook(self):   
        for i,l in enumerate(list(self.encoder._modules.keys())):
            self.fhooks.append(getattr(self.encoder,l).register_forward_hook(self.forward_hook(l)))
        
    def forward_hook(self,layer_name):
        def hook(module, input, output):
            self.selected_out[layer_name] = output
        return hook
    
    
        
    def forward(self, x, mode='test'):
        in_size = x.size(0)
        
        encoded= self.encoder(x)
        encoded= self.buffer(encoded)
        
        if mode == 'test':
            p= self.cls_head_src(encoded)
            return p
        elif mode == 'train':
            p= self.cls_head_src(encoded)
            z = self.pro_head(encoded)
            z = F.normalize(z) #dim=1 normalized
            return p,z
        elif mode == 'prof':
            p= self.cls_head_src(encoded)
            z = self.pro_head(encoded)
            z = F.normalize(z)
            #encoded = F.normalize(encoded)
            return p, z, encoded, self.selected_out 


