import torch.optim as optim
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.models
from torch.autograd import Variable
from torch.utils.data import random_split
import os
import time
import numpy as np
import pandas as pd
import torch.nn.functional as F
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from PIL import ImageS
import torchvision.datasets as dsets



class ModifiedCIFAR10(Dataset):
    def __init__(self, root, train=True, transform=None, target_classes=[0, 1, 2, 3,4,5,6,7,8,9], num_samples=[500, 500, 2500, 2500,5000,5000,5000,5000,5000,5000]):
        self.original_dataset = dsets.CIFAR10(root=root, train=train, download=True, transform=transform)
        self.target_classes = target_classes
        self.num_samples = num_samples

        self.sample_indices = []
        for target_class, num_sample in zip(target_classes, num_samples):
            class_indices = [i for i, label in enumerate(self.original_dataset.targets) if label == target_class]
            self.sample_indices += class_indices[:num_sample]

    def __len__(self):
        return len(self.sample_indices)

    def __getitem__(self, idx):
        original_idx = self.sample_indices[idx]
        return self.original_dataset[original_idx]





#training parameters
modellr = 1e-4
BATCH_SIZE = 64
EPOCHS = 20
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Add these variables to keep track of the best accuracy and epoch number
best_accuracy = 0
best_epoch = 0

np.random.seed(42)
torch.manual_seed(42)


#data preprocess
mean, std = [0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261]
# These values are mostly used by researchers as found to very useful in fast convergence

transform_train = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(30), 
    #newly added
    transforms.ColorJitter(brightness = 0.1, # Randomly adjust color jitter of the images
                           contrast = 0.1, 
                           saturation = 0.1), 
    transforms.RandomAdjustSharpness(sharpness_factor = 2, p = 0.1), # Randomly adjust sharpness
    transforms.ToTensor(),
    transforms.Normalize(mean, std),
    transforms.RandomErasing()
])
transform_test = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize(mean, std),
])


test_dataset = dsets.CIFAR10(root='./data', train=False, download=True, transform = transform_test)

# Modify the number of samples for class 0 from 5000 to 500
modified_train_dataset = ModifiedCIFAR10(
    root='./data',
    train=True,
    transform=transform_train,
    target_classes=[0, 1, 2, 3,4,5,6,7,8,9],
    num_samples=[500, 500, 2500, 2500,5000,5000,5000,5000,5000,5000]
)


# Split the dataset into training and validation sets
train_size = int(0.9 * len(modified_train_dataset))
val_size = len(modified_train_dataset) - train_size

torch.manual_seed(42)
train_dataset, validation_dataset = random_split(modified_train_dataset, [train_size, val_size])



train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=BATCH_SIZE,  shuffle=False)

val_loader = torch.utils.data.DataLoader(dataset=validation_dataset, batch_size=BATCH_SIZE, shuffle=False)





#model & training settings


criterion = nn.CrossEntropyLoss()
#First balance method

# Calculate class weights
#class_weights = torch.FloatTensor([num_samples[i] / len(modified_train_dataset) for i in range(10)])
# Instantiate CrossEntropyLoss with class weights
#criterion = nn.CrossEntropyLoss(weight=class_weights.to(DEVICE))


model = torchvision.models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 10)
model.to(DEVICE)

optimizer = optim.Adam(model.parameters(), lr=modellr)


#Learning rate adjust (no need)
def adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    modellrnew = modellr * (0.1 ** (epoch // 50))
    print("lr:", modellrnew)
    for param_group in optimizer.param_groups:
        param_group['lr'] = modellrnew



model = torch.load("666cifar_model_resnet18_lr0.0001_unbalanced_crossentropy.pth")






from sklearn.metrics import confusion_matrix
import seaborn as sn
import matplotlib.pyplot as plt

def get_predictions(model, device, data_loader):
    model.eval()
    model.to(device)
    all_predictions = []
    all_targets = []

    with torch.no_grad():
        for data, target in data_loader:
            data, target = data.to(device), target.to(device)
            outputs = model(data)
            _, predicted = torch.max(outputs.data, 1)
            all_predictions.extend(predicted.cpu().numpy())
            all_targets.extend(target.cpu().numpy())

    return np.array(all_predictions), np.array(all_targets)

# Get predictions and targets
predictions, targets = get_predictions(model, DEVICE, test_loader)

# Create confusion matrix
conf_matrix = confusion_matrix(targets, predictions)

# Plot heatmap
plt.figure(figsize=(10, 8))
sn.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=range(10), yticklabels=range(10))
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.title('Confusion Matrix')
plt.show()