import torch
import torch.nn as nn
import torch.nn.functional as F
import warnings
warnings.filterwarnings("ignore")
import torchvision
import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import depth_only_parameters as params

def getConvLayer(in_channel, out_channel, stride=1, padding=1, activation=nn.ReLU()):
    return nn.Sequential(
        nn.Conv2d(in_channel, out_channel,
                  kernel_size=3,
                  stride=stride,
                  padding=padding,
                  padding_mode='reflect'),
        activation
    )

def getConvTransposeLayer(in_channel, out_channel, kernel=3, stride=1, padding=1, activation=nn.ReLU()):
    return nn.Sequential(
        nn.ConvTranspose2d(in_channel,
                            out_channel,
                            kernel_size=kernel,
                            stride=stride,
                            padding=padding),
        activation
    )

class Flatten(nn.Module):
    def forward(self, input):
        return input.view(input.size(0), -1)

class UnFlatten(nn.Module):
    def forward(self, input, size=1):
        return input.view(input.size(0), 1, params.params_height//8, params.params_width//8)

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, 
                               stride=stride, padding=1, bias=False)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, 
                               stride=1, padding=1, bias=False)
        self.stride = stride

        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, 
                          stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.relu(out)
        out = self.conv2(out)
        out = out + self.shortcut(residual)
        out = self.relu(out)
        return out

class UpperEncoder(nn.Module):
    def __init__(self):
        super().__init__()
        model = torchvision.models.resnet152(pretrained=True)
        layers = list(model.children())
        self.ResNetEncoder = nn.Sequential(*layers[:5].copy())
        del model

    def forward(self, x):
        x1 = x[:, 0:3, :, :]
        x1 = self.ResNetEncoder(x1)
        return x1

    def apply_resnet_encoder(self, x):
        x1 = x[:, 0:3, :, :]
        x1 = self.ResNetEncoder(x1)
        return x1

class LowerEncoder(nn.Module):
    def __init__(self, total_image_input=1):
        super().__init__()
        # Halved channels compared to the original
        self.encoder_pre    = ResidualBlock(total_image_input*3, 10)
        self.encoder_layer1 = ResidualBlock(10, 15)
        self.encoder_layer2 = ResidualBlock(15, 25)
        
        self.encoder_layer3 = nn.Sequential(
            ResidualBlock(25, 50),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.encoder_layer4 = ResidualBlock(50, 100)
        self.encoder_layer5 = nn.Sequential(
            ResidualBlock(100, 200),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.encoder_layer6 = ResidualBlock(200, 300)
        self.encoder_layer7 = nn.Sequential(
            ResidualBlock(300, 400),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.encoder_layer8 = ResidualBlock(400, 500)
        self.encoder_layer9 = nn.Sequential(
            ResidualBlock(500, 600),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.encoder_layer10 = ResidualBlock(600, 700)
        self.encoder_layer11 = ResidualBlock(700, 800)
        
    def forward(self, x):
        x = self.encoder_pre(x)
        x = self.encoder_layer1(x)
        x = self.encoder_layer2(x)
        skip1 = self.encoder_layer3(x)
        
        x = self.encoder_layer4(skip1)
        skip2 = self.encoder_layer5(x)
        
        x = self.encoder_layer6(skip2)
        skip3 = self.encoder_layer7(x)
        
        x = self.encoder_layer8(skip3)
        skip4 = self.encoder_layer9(x)
        
        x = self.encoder_layer10(skip4)
        x = self.encoder_layer11(x)
        return x, [skip1, skip2, skip3, skip4]

class MergeDecoder(nn.Module):
    def __init__(self):
        super().__init__()
        # Halved channels for decoder blocks
        self.decoder_layer1 = ResidualBlock(800, 700)
        self.decoder_layer2 = ResidualBlock(700, 600)
        self.decoder_layer3 = ResidualBlock(600, 500)
        
        self.decoder_layer4 = nn.Sequential(
            nn.ConvTranspose2d(500, 400, kernel_size=2, stride=2, padding=0),
            nn.ReLU(True)
        )
        self.decoder_layer5 = ResidualBlock(400, 300)
    
        self.decoder_layer6 = nn.Sequential(
            nn.ConvTranspose2d(300, 200, kernel_size=2, stride=2, padding=0),
            nn.ReLU(True)
        )
        self.decoder_layer7 = ResidualBlock(200, 100)

        self.decoder_layer8 = nn.Sequential(
            nn.ConvTranspose2d(100, 50, kernel_size=2, stride=2, padding=0),
            nn.ReLU(True)
        )
        self.decoder_layer9 = ResidualBlock(50, 50)

        self.decoder_layer10 = nn.Sequential(
            nn.ConvTranspose2d(50, 50, kernel_size=2, stride=2, padding=0),
            nn.ReLU(True)
        )
        self.decoder_layer11 = ResidualBlock(50, 50)
        self.decoder_layer12 = ResidualBlock(50, 25)
        self.decoder_layer13 = ResidualBlock(25, 20)
        self.decoder_layer14 = ResidualBlock(20, 10)
        self.decoder_layer15 = nn.Sequential(
            nn.Conv2d(10, 4, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True)
        )
        self.decoder_layer16 = nn.Sequential(
            nn.Conv2d(4, 1, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True)
        )
        
    def forward(self, x, lower_skip_list, upper_skip_list):
        x = self.decoder_layer1(x)
        x = self.decoder_layer2(x)
        # Expecting lower_skip_list[3] and upper_skip_list[1] to have matching dimensions
        x = x + lower_skip_list[3] + upper_skip_list[1]
        
        x = self.decoder_layer3(x)
        x = self.decoder_layer4(x)
        x = x + lower_skip_list[2] + upper_skip_list[0]
        
        x = self.decoder_layer5(x)
        x = self.decoder_layer6(x)
        x = x + lower_skip_list[1]
        
        x = self.decoder_layer7(x)
        x = self.decoder_layer8(x)
        x = x + lower_skip_list[0]
        
        x = self.decoder_layer9(x)
        x = self.decoder_layer10(x)
        x = self.decoder_layer11(x)
        x = self.decoder_layer12(x)
        x = self.decoder_layer13(x)
        x = self.decoder_layer14(x)
        x = self.decoder_layer15(x)
        x = self.decoder_layer16(x)
        return x

class PVSDNet_Lite(nn.Module):
    def __init__(self, total_image_input=1):
        super().__init__()
        # Upper encoder remains mostly the same
        self.upper_encoder = UpperEncoder()
        self.lower_encoder = LowerEncoder(total_image_input)
        self.merge_decoder = MergeDecoder()
        # Halved extra layers for upper branch:
        self.upper_encoder_extra_1 = nn.Sequential(
            ResidualBlock(256, 400),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.upper_encoder_extra_2 = nn.Sequential(
            ResidualBlock(400, 600),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        
    def forward(self, x):
        # First Encoder Branch (Upper)
        upper_features_1 = self.upper_encoder.apply_resnet_encoder(x)
        upper_features_1 = self.upper_encoder_extra_1(upper_features_1)
        upper_features_2 = self.upper_encoder_extra_2(upper_features_1)
        
        # Second Encoder Branch (Lower)
        lower_feature, skip_list = self.lower_encoder(x)
        
        # Merge and decode features
        merged_feature = self.merge_decoder(lower_feature, skip_list, [upper_features_1, upper_features_2])
        return merged_feature