"""Code from : https://debuggercafe.com/implementing-resnet18-in-pytorch-from-scratch/"""
import torch.nn as nn
import torch
from torchvision.ops import RoIPool
from torch import Tensor
from typing import Type

class BasicBlock(nn.Module):
    def __init__(self, in_channels: int,out_channels: int,stride: int = 1,expansion: int = 1,downsample: nn.Module = None) -> None:
        super(BasicBlock, self).__init__()
        # Multiplicative factor for the subsequent conv2d layer's output channels.
        # It is 1 for ResNet18 and ResNet34.
        self.expansion = expansion
        self.downsample = downsample
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1,bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels*self.expansion, kernel_size=3, padding=1,bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels*self.expansion)

    def forward(self, x: Tensor) -> Tensor:
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return  out

class ResNet18(nn.Module):
    def __init__(self, img_channels: int,num_layers: int,block: Type[BasicBlock],num_classes: int  = 1000) -> None:
        super(ResNet18, self).__init__()
        if num_layers == 18:
            # The following `layers` list defines the number of `BasicBlock` 
            # to use to build the network and how many basic blocks to stack
            # together.
            layers = [2, 2, 2, 2]
            self.expansion = 1
        
        self.in_channels = 64
        # All ResNets (18 to 152) contain a Conv2d => BN => ReLU for the first
        # three layers. Here, kernel size is 7.
        self.conv1 = nn.Conv2d(in_channels=img_channels,out_channels=self.in_channels,kernel_size=7, stride=2,padding=3,bias=False)
        self.bn1 = nn.BatchNorm2d(self.in_channels)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512*self.expansion, num_classes)

    def _make_layer(self, block: Type[BasicBlock],out_channels: int,blocks: int,stride: int = 1) -> nn.Sequential:
        downsample = None
        if stride != 1:
            """
            This should pass from `layer2` to `layer4` or 
            when building ResNets50 and above. Section 3.3 of the paper
            Deep Residual Learning for Image Recognition
            (https://arxiv.org/pdf/1512.03385v1.pdf).
            """
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, out_channels*self.expansion,kernel_size=1,stride=stride,bias=False),
                nn.BatchNorm2d(out_channels * self.expansion),
            )
        layers = []
        layers.append(
            block(
                self.in_channels, out_channels, stride, self.expansion, downsample
            )
        )
        self.in_channels = out_channels * self.expansion
        for i in range(1, blocks):
            layers.append(block(
                self.in_channels,
                out_channels,
                expansion=self.expansion
            ))
        return nn.Sequential(*layers)
    def forward(self, x: Tensor) -> Tensor:
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        # The spatial dimension of the final layer's feature 
        # map should be (7, 7) for all ResNets.
        #print('Dimensions of the last convolutional feature map: ', x.shape)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        #x = self.fc(x)
        return x