#imports

import os
import csv
import torch
from torch import nn 
from torch.utils.data import DataLoader 
from torchvision import datasets 
from torchvision.transforms import ToTensor, Normalize, RandomCrop, RandomHorizontalFlip, Compose 
from approximator import Approximator

# data transforms

transform = Compose([
RandomCrop(32, padding=4),
RandomHorizontalFlip(), 
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

])

training_data = datasets.CIFAR10(
                                       root='data',
                                       train=True,
                                       download=True,
                                       transform=transform 
                                       )

test_data = datasets.CIFAR10(
                                       root='data',
                                       train=False,
                                       download=True,
                                       transform=transform 
                                       )                                       
# create dataloaders  
                                     
batch_size = 128

train_dataloader = DataLoader(training_data, batch_size=batch_size,shuffle=True)
test_dataloader = DataLoader(test_data, batch_size=batch_size)


for X, y in test_dataloader:
    print(f"Shape of X [N,C,H,W]:{X.shape}")
    print(f"Shape of y:{y.shape}{y.dtype}")
    break



# size checking for loading images
def check_sizes(image_size, patch_size):
    sqrt_num_patches, remainder = divmod(image_size, patch_size)
    assert remainder == 0, "`image_size` must be divisibe by `patch_size`"
    num_patches = sqrt_num_patches ** 2
    return num_patches



# create model
# Get cpu or gpu device for training.

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"using {device} device") 

# model definition

class ApproximatorImageClassification(Approximator):
    def __init__(
        self,
        image_size=32,
        patch_size=4,
        in_channels=3,
        num_classes=10,
        d_model=256,
        d_ffn=512,
        num_layers=4,
        dropout=0.5
    ):
        num_patches = check_sizes(image_size, patch_size)
        super().__init__(d_model, d_ffn, num_layers,dropout)
        self.patcher = nn.Conv2d(
            in_channels, d_model, kernel_size=patch_size, stride=patch_size
        )
        self.classifier = nn.Linear(d_model, num_classes)

    def forward(self, x):
        
        patches = self.patcher(x)
        batch_size, num_channels, _, _ = patches.shape
        patches = patches.permute(0, 2, 3, 1)
        patches = patches.view(batch_size, -1, num_channels)
        embedding = self.model(patches)
        embedding = embedding.mean(dim=1) # global average pooling
        out = self.classifier(embedding)
        return out

model = ApproximatorImageClassification().to(device)
print(model)

# Optimizer

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(),lr=1e-3)


# Training Loop

def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.train()
    train_loss = 0
    correct = 0
    for batch, (X,y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)
       
        #compute prediction error
        pred = model(X)
        loss = loss_fn(pred,y)
        
        # backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        _, labels = torch.max(pred.data, 1)
        correct += labels.eq(y.data).type(torch.float).sum()

        


        if batch % 100 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {loss:>7f}   [{current:>5d}/{size:>5d}]")

    train_loss /= num_batches
    train_accuracy = 100. * correct.item() / size
    print(train_accuracy)
    return train_loss,train_accuracy 



# Test loop

def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)            
    num_batches = len(dataloader)
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for X,y in dataloader:
            X,y = X.to(device), y.to(device)
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= num_batches
    correct /= size
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")  
    test_accuracy = 100*correct      
    return test_loss, test_accuracy



# apply train and test

logname = "/PATH/Approximator/Experiments_cifar10/logs_approximator/logs_cifar10.csv"
if not os.path.exists(logname):
  with open(logname, 'w') as logfile:
    logwriter = csv.writer(logfile, delimiter=',')
    logwriter.writerow(['epoch', 'train loss', 'train acc',
                        'test loss', 'test acc'])


epochs = 100
for epoch in range(epochs):
    print(f"Epoch {epoch+1}\n-----------------------------------")
    train_loss, train_acc = train(train_dataloader, model, loss_fn, optimizer)
    
    test_loss, test_acc = test(test_dataloader, model, loss_fn)
    with open(logname, 'a') as logfile:
        logwriter = csv.writer(logfile, delimiter=',')
        logwriter.writerow([epoch+1, train_loss, train_acc,
                            test_loss, test_acc])
print("Done!")

# saving trained model

path = "/PATH/Approximator/Experiments_cifar10/weights_approximator"
model_name = "ApproximatorImageClassification_cifar10"
torch.save(model.state_dict(), f"{path}/{model_name}.pth")
print(f"Saved Model State to {path}/{model_name}.pth ")