"""
Paper: "UTRNet: High-Resolution Urdu Text Recognition In Printed Documents" presented at ICDAR 2023
Authors: Abdur Rahman, Arjun Ghosh, Chetan Arora
GitHub Repository: https://github.com/abdur75648/UTRNet-High-Resolution-Urdu-Text-Recognition
Project Website: https://abdur75648.github.io/UTRNet/
Copyright (c) 2023-present: This work is licensed under the Creative Commons Attribution-NonCommercial
4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/)
"""

import torch.nn as nn
import torch

'''
Source - https://github.com/rishikksh20/ResUnet/blob/master/core/res_unet.py
An implementation of this paper - https://arxiv.org/pdf/1711.10684.pdf
'''

class ResidualConv(nn.Module):
    def __init__(self, input_dim, output_dim, stride, padding):
        super(ResidualConv, self).__init__()

        self.conv_block = nn.Sequential(
            nn.BatchNorm2d(input_dim),
            nn.ReLU(),
            nn.Conv2d(
                input_dim, output_dim, kernel_size=3, stride=stride, padding=padding
            ),
            nn.BatchNorm2d(output_dim),
            nn.ReLU(),
            nn.Conv2d(output_dim, output_dim, kernel_size=3, padding=1),
        )
        self.conv_skip = nn.Sequential(
            nn.Conv2d(input_dim, output_dim, kernel_size=3, stride=stride, padding=1),
            nn.BatchNorm2d(output_dim),
        )

    def forward(self, x):

        return self.conv_block(x) + self.conv_skip(x)


class Upsample(nn.Module):
    def __init__(self, input_dim, output_dim, kernel, stride):
        super(Upsample, self).__init__()

        self.upsample = nn.ConvTranspose2d(
            input_dim, output_dim, kernel_size=kernel, stride=stride
        )

    def forward(self, x):
        return self.upsample(x)
    
class ResUnet(nn.Module):
    def __init__(self, in_channel=1,out_channel=512, filters=[64, 128, 256, 512]):
        super(ResUnet, self).__init__()

        self.input_layer = nn.Sequential(
            nn.Conv2d(in_channel, filters[0], kernel_size=3, padding=1),
            nn.BatchNorm2d(filters[0]),
            nn.ReLU(),
            nn.Conv2d(filters[0], filters[0], kernel_size=3, padding=1),
        )
        self.input_skip = nn.Sequential(
            nn.Conv2d(in_channel, filters[0], kernel_size=3, padding=1)
        )

        self.residual_conv_1 = ResidualConv(filters[0], filters[1], 2, 1)
        self.residual_conv_2 = ResidualConv(filters[1], filters[2], 2, 1)

        self.bridge = ResidualConv(filters[2], filters[3], 2, 1)

        self.upsample_1 = Upsample(filters[3], filters[3], 2, 2)
        self.up_residual_conv1 = ResidualConv(filters[3] + filters[2], filters[2], 1, 1)

        self.upsample_2 = Upsample(filters[2], filters[2], 2, 2)
        self.up_residual_conv2 = ResidualConv(filters[2] + filters[1], filters[1], 1, 1)

        self.upsample_3 = Upsample(filters[1], filters[1], 2, 2)
        self.up_residual_conv3 = ResidualConv(filters[1] + filters[0], filters[0], 1, 1)

        self.output_layer = nn.Sequential(
            nn.Conv2d(filters[0], out_channel, 1, 1),
            nn.Sigmoid(),
        )

    def forward(self, x):
        # Encode
        x1 = self.input_layer(x) + self.input_skip(x)
        x2 = self.residual_conv_1(x1)
        x3 = self.residual_conv_2(x2)
        # Bridge
        x4 = self.bridge(x3)
        # Decode
        x4 = self.upsample_1(x4)
        x5 = torch.cat([x4, x3], dim=1)

        x6 = self.up_residual_conv1(x5)

        x6 = self.upsample_2(x6)
        x7 = torch.cat([x6, x2], dim=1)

        x8 = self.up_residual_conv2(x7)

        x8 = self.upsample_3(x8)
        x9 = torch.cat([x8, x1], dim=1)

        x10 = self.up_residual_conv3(x9)

        output = self.output_layer(x10)

        return output

# x = torch.randn(1, 1, 32, 400)
# net = ResUnet(1,512)
# out = net(x)
# print(out.shape)