import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
from transformers.utils import logging

logging.set_verbosity_info()
logger = logging.get_logger("transformers")

## 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, out_features=7):
    super().__init__()
    self.conv1 = self.convlayer(in_channels=1, out_channels=64, kernel_size=3, max_pool=2)
    self.conv2 = self.convlayer(in_channels=64, out_channels=128, kernel_size=3, max_pool=2)
    self.conv3 = self.convlayer(in_channels=128, out_channels=512, kernel_size=3, max_pool=2)
    self.conv4 = self.convlayer(in_channels=512, out_channels=512, kernel_size=3, max_pool=1)
    self.fc1 = self.fclayer(2048, 512)
    self.fc2 = nn.Linear(512, 7)
  
  def convlayer(self, in_channels, out_channels, kernel_size, max_pool=2):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=1),
        nn.BatchNorm2d(out_channels),
        nn.ReLU(),
        # nn.Dropout2d(),
        nn.MaxPool2d(kernel_size=max_pool),
    )

  def fclayer(self, in_features, out_features):
    return nn.Sequential(
        nn.Linear(in_features, out_features),
        nn.BatchNorm1d(out_features),
        # nn.Dropout1d(0.4),
        nn.ReLU(),
    )

  def forward(self, x):
    x = self.conv1(x)
    x = self.conv2(x)
    x = self.conv3(x)
    x = self.conv4(x)
    x = x.view(-1, 2048)
    x = self.fc1(x)
    x = self.fc2(x)
    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([transforms.Resize((48,48)),transforms.Grayscale(), transforms.ToTensor(), transforms.Normalize((0.5), (0.5))])