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

# Sample Transformation function
# YOUR CODE HERE for changing the Transformation values.
trnscm = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()])

##Example Network
class Siamese(torch.nn.Module):
    def __init__(self):
        super(Siamese, self).__init__()
        #YOUR CODE HERE
        self.cnn1 = nn.Sequential(
            nn.ReflectionPad2d(1),       #Pads the input tensor using the reflection of the input boundary, it similar to the padding.
            nn.Conv2d(1, 4, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(4),

            nn.ReflectionPad2d(1),
            nn.Conv2d(4, 8, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(8),


            nn.ReflectionPad2d(1),
            nn.Conv2d(8, 8, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(8),
        )

        self.fc1 = nn.Sequential(
            nn.Linear(8*100*100, 500),
            nn.ReLU(inplace=True),

            nn.Linear(500, 500),
            nn.ReLU(inplace=True),

            nn.Linear(500, 5))

    # forward_once is for one image. This can be used while classifying the face images
    def forward_once(self, x):
        output = self.cnn1(x)
        output = output.view(output.size()[0], -1)
        output = self.fc1(output)
        return output

    def forward(self, input1, input2):
        output1 = self.forward_once(input1)
        output2 = self.forward_once(input2)
        return output1, output2

   # def forward(self, x):
       # pass   # remove 'pass' once you have written your code
        #YOUR CODE HERE
        
##########################################################################################################
## Sample classification network (Specify if you are using a pytorch classifier during the training)    ##
## classifier = nn.Sequential(nn.Linear(64, 64), nn.BatchNorm1d(64), nn.ReLU(), nn.Linear...)           ##
##########################################################################################################

# YOUR CODE HERE for pytorch classifier
classes = ['person1','person2','person6','person7']

num_of_classes = len(classes)

classifier = nn.Sequential(nn.Linear(256, 64), nn.BatchNorm1d(64), nn.ReLU(),
                           nn.Linear(64, 32), nn.BatchNorm1d(32), nn.ReLU(),
                           nn.Linear(32, num_of_classes))

#classifier = classifier.to(device) -error thrown
print(classifier)

class MLPClassifier(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(MLPClassifier, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, 2048)
        self.relu2 = nn.ReLU()
        self.fc3 = nn.Linear(2048, 1024)
        self.relu3 = nn.ReLU()
        self.fc4 = nn.Linear(1024, num_classes)
    def forward(self, x):
        x = self.fc1(x)
        x = self.relu1(x)
        x = self.fc2(x)
        x = self.relu2(x)
        x = self.fc3(x)
        x = self.relu2(x)
        x = self.fc4(x)
        return x