import torch
import torchvision

from torch import nn
class TinyCNN(nn.Module):
  
  
  def __init__(self, input_shape: int, hidden_units: int, output_shape: int) -> None:
      super().__init__()
      self.conv_block_1 = nn.Sequential(
        nn.Conv2d(in_channels=input_shape, 
                out_channels=hidden_units, 
                kernel_size=3, 
                stride=1, 
                padding=0), 
        nn.ReLU(),
        nn.Conv2d(in_channels=hidden_units, 
                out_channels=128,
                kernel_size=3,
                stride=1,
                padding=0),
        nn.BatchNorm2d(128),
        nn.ReLU(),
        nn.MaxPool2d(kernel_size=2,
                        stride=2), 
        nn.Dropout(p=0.25)
      )
      self.conv_block_2 = nn.Sequential(
          nn.Conv2d(128, 128, kernel_size=3, padding=0),
          nn.ReLU(),
          nn.Conv2d(128, 128, kernel_size=3, padding=0),
          nn.BatchNorm2d(128),
          nn.ReLU(),
          nn.MaxPool2d(2),
          nn.Dropout(p=0.25)
      )
      
      self.conv_block_3 = nn.Sequential(
          nn.Conv2d(128, 128, kernel_size=3, padding=0),
          nn.ReLU(),
          nn.Conv2d(128, 512, kernel_size=3, padding=0),
          nn.BatchNorm2d(512),
          nn.ReLU(),
          nn.MaxPool2d(2),
          nn.Dropout(p=0.25)
      )
      
      self.conv_block_4 = nn.Sequential(
          nn.Conv2d(512, 512, kernel_size=3, padding=0),
          nn.ReLU(),
          nn.Conv2d(512, 512, kernel_size=3, padding=0),
          nn.BatchNorm2d(512),
          nn.ReLU(),
          nn.MaxPool2d(2),
          nn.Dropout(p=0.25)
      )
      
      self.fc_1 = nn.Sequential(
          nn.Flatten(),
          nn.Linear(in_features=512*16, out_features = 512),
          nn.BatchNorm1d(512),
          nn.ReLU(),
          nn.Dropout(p=0.25)
      )
      
      self.fc_2 = nn.Sequential(
          

          nn.Linear(in_features=512,
                    out_features=256),
          nn.BatchNorm1d(256),
          nn.ReLU(),
          nn.Dropout(p=0.25)
      )
      
      self.classifier = nn.Sequential(
          nn.Linear(in_features=256,
                  out_features=output_shape)
      )
    
  def forward(self, x):
      x = self.conv_block_1(x)
      x = self.conv_block_2(x)
      x = self.conv_block_3(x)
      x = self.conv_block_4(x)
      x = self.fc_1(x)
      x = self.fc_2(x)
      x = self.classifier(x)
      return x