import torch
import torch.nn as nn
import torch.nn.functional as F

class ConvBnRelu(nn.Module):
    def __init__(self, in_ch, out_ch, k=3, s=1, p=1):
        super().__init__()
        self.block = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, k, s, p, bias=False),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True)
        )
    def forward(self, x): return self.block(x)

class LightCNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.b1 = nn.Sequential(ConvBnRelu(1,32),ConvBnRelu(32,32),nn.MaxPool2d(2,2))
        self.b2 = nn.Sequential(ConvBnRelu(32,64),ConvBnRelu(64,64),nn.MaxPool2d(2,2))
        self.b3 = nn.Sequential(ConvBnRelu(64,128),ConvBnRelu(128,128),nn.MaxPool2d((2,1)))
        self.b4 = nn.Sequential(ConvBnRelu(128,256),ConvBnRelu(256,256),nn.MaxPool2d((2,1)))
        self.b5 = nn.Sequential(ConvBnRelu(256,256),ConvBnRelu(256,256))
        self.pool = nn.AdaptiveAvgPool2d((1, None))
    def forward(self, x):
        for b in [self.b1,self.b2,self.b3,self.b4,self.b5]: x = b(x)
        return self.pool(x).squeeze(2)

class BidirectionalLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.rnn = nn.LSTM(input_size, hidden_size, bidirectional=True, batch_first=True)
        self.linear = nn.Linear(hidden_size*2, output_size)
    def forward(self, x):
        out, _ = self.rnn(x)
        return self.linear(out)

class Model(nn.Module):
    """EasyOCR requires the class to be named Model"""
    def __init__(self, input_channel, output_channel, hidden_size, num_class):
        super().__init__()
        self.cnn = LightCNN()
        self.rnn = nn.Sequential(
            BidirectionalLSTM(256, hidden_size, hidden_size),
            BidirectionalLSTM(hidden_size, hidden_size, num_class)
        )
    def forward(self, x):
        f = self.cnn(x)
        f = f.permute(0,2,1)
        o = self.rnn(f)
        o = o.permute(1,0,2)
        return F.log_softmax(o, dim=2)