import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm

train_batch_size = 64
test_batch_size = 1000
learning_rate = 0.0001

train_dataset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())

test_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transforms.ToTensor())

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)

test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=test_batch_size, shuffle=False)

print(len(train_dataset))


class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.pool = nn.MaxPool2d(2, 2)
        self.conv1 = nn.Conv2d(1, 32, 3)
        self.conv2 = nn.Conv2d(32, 64, 2)
        self.fc1 = nn.Linear(2304, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        # -> n, 1, 28, 28
        x = self.conv1(x)
        x = F.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.pool(x) # 64 x 6 x 6
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        output = F.softmax(x)
        return output
    


def train(model, device, train_loader, optimizer, criterion, epochs, interval=500):
    for epoch in range(epochs):
        print(f'Starting #{epoch}')
        for batch_idx, (images, labels) in tqdm(enumerate(train_loader)):
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            loss = criterion(outputs, labels)
            
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            if batch_idx % interval == 0 or batch_idx * len(images) ==  len(train_loader.dataset):
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(images), len(train_loader.dataset),
                    100. * batch_idx / len(train_loader), loss.item()))
        print(f'Finishing Epoch #{epoch}')


def test(model, device, test_loader):
    with torch.no_grad():
        correct_imgs = 0
        all_imgs = 0
        matrix = [[0] * 10] * 10 # TODO
        for images, labels in test_loader:
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            predictions = outputs.argmax()
            for pred, label in list(zip(predictions, labels)):
            
                matrix[pred][label] += 1
                all_imgs += 1
                if label == pred:
                    correct_imgs += 1
        
        return correct_imgs / all_imgs, matrix

model = ConvNet().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate)






import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm

train_batch_size = 64
test_batch_size = 1000
learning_rate = 0.0001

train_dataset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())

test_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transforms.ToTensor())

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)

test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=test_batch_size, shuffle=False)

print(len(train_dataset))


class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.pool = nn.MaxPool2d(2, 2)
        self.conv1 = nn.Conv2d(1, 32, 3)
        self.conv2 = nn.Conv2d(32, 64, 2)
        self.fc1 = nn.Linear(2304, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        # -> n, 1, 28, 28
        x = self.conv1(x)
        x = F.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.pool(x) # 64 x 6 x 6
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        output = F.softmax(x)
        return output
    


def train(model, device, train_loader, optimizer, criterion, epochs, interval=500):
    for epoch in range(epochs):
        print(f'Starting #{epoch}')
        for batch_idx, (images, labels) in tqdm(enumerate(train_loader)):
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            loss = criterion(outputs, labels)
            
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            if batch_idx % interval == 0 or batch_idx * len(images) ==  len(train_loader.dataset):
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(images), len(train_loader.dataset),
                    100. * batch_idx / len(train_loader), loss.item()))
        print(f'Finishing Epoch #{epoch}')


def test(model, device, test_loader):
    with torch.no_grad():
        correct_imgs = 0
        all_imgs = 0
        matrix = [[0] * 10] * 10 # TODO
        for images, labels in test_loader:
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            predictions = outputs.argmax()
            for pred, label in list(zip(predictions, labels)):
            
                matrix[pred][label] += 1
                all_imgs += 1
                if label == pred:
                    correct_imgs += 1
        
        return correct_imgs / all_imgs, matrix

model = ConvNet().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adagrad(model.parameters(), lr=learning_rate)






# -> n, 1, 28, 28 # 64 x 6 x 6 #{epoch}') #{epoch}') # TODO