import os
import torch
from torchvision import transforms
from torch.utils.data import Dataset
from models import *
import torch.nn as nn
from torchvision.models import squeezenet1_0, SqueezeNet1_0_Weights
from torchvision.models import squeezenet1_0
# Constants
RANDOM_SEED = 123
BATCH_SIZE = 16
NUM_EPOCHS = 40
LEARNING_RATE = 5.488903014780378e-05
STEP_SIZE = 10
GAMMA = 0.3
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
NUM_PRINT = 100
TASK = 1
RAW_DATA_DIR = r"data/train/raw/Task "
AUG_DATA_DIR = r"data/train/augmented/Task "
EXTERNAL_DATA_DIR = r"data/train/external/Task "
TEMP_DATA_DIR = "data/temp/"
NUM_CLASSES = 7
EARLY_STOPPING_PATIENCE = 20
CLASSES = ['Alzheimer Disease', 'Cerebral Palsy', 'Dystonia', 'Essential Tremor', 'Healthy', 'Huntington Disease', 'Parkinson Disease']
MODEL_SAVE_PATH = "output/checkpoints/model.pth"


    
class SqueezeNet1_0WithDropout(nn.Module):
    def __init__(self, num_classes=1000):
        super(SqueezeNet1_0WithDropout, self).__init__()
        squeezenet = squeezenet1_0(weights=SqueezeNet1_0_Weights) 
        self.features = squeezenet.features
        self.classifier = nn.Sequential(
            nn.Conv2d(512, num_classes, kernel_size=1),
            nn.BatchNorm2d(num_classes),  # add batch normalization
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d((1, 1))
        )

    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x)
        x = torch.flatten(x, 1)
        return x
    
    
    
MODEL = SqueezeNet1_0WithDropout(num_classes=7)
print(CLASSES)

preprocess = transforms.Compose(
    [
        transforms.Resize((64, 64)),  # Resize images to 64x64
        transforms.ToTensor(),  # Convert to tensor
        # Normalize 3 channels
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
    ]
)


# Custom dataset class
class CustomDataset(Dataset):
    def __init__(self, dataset):
        self.data = dataset

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

    def __getitem__(self, idx):
        img, label = self.data[idx]
        return img, label