import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
import torch.nn.functional as F
## Add more imports if required

####################################################################################################################
# Define your model and transform and all necessary helper functions here #
# They will be imported to the exp_recognition.py file #
####################################################################################################################

# Definition of classes as dictionary
classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5: 'SADNESS', 6: 'SURPRISE'}

# Example Network
class facExpRec(torch.nn.Module):
    def __init__(self):
        super(facExpRec, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=64, kernel_size=3)
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3)
        self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=1)  
        self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1)  
        self.conv6 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=1) 
        self.fc1 = nn.Linear(1024 * 1 * 1, 256)  
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 64)
        self.fc4 = nn.Linear(64, 7)

        self.pool = nn.MaxPool2d(kernel_size=2)

    def forward(self, x):
        x = self.pool(F.elu(self.conv1(x)))
        x = self.pool(F.elu(self.conv2(x)))
        x = self.pool(F.elu(self.conv3(x)))
        x = self.pool(F.elu(self.conv4(x)))  
        x = self.pool(F.elu(self.conv5(x))) 
        x = self.pool(F.elu(self.conv6(x)))  
        x = x.view(-1, 1024 * 1 * 1)  
        x = F.elu(self.fc1(x))
        x = F.elu(self.fc2(x))
        x = F.elu(self.fc3(x))
        x = self.fc4(x)
        x = F.log_softmax(x, dim=1)
        return x
        
# Sample Helper function
def rgb2gray(image):
    return image.convert('L')
    
# Sample Transformation function
#YOUR CODE HERE for changing the Transformation values.
trnscm = transforms.Compose([rgb2gray, transforms.Resize((100,100)), transforms.ToTensor()])