import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
from PIL import Image
import torch
import imagehash
ImageCache = None

class AnimalDataset(Dataset):
    def __init__(self, df, transform=None):
        self.paths = df["path"].values
        self.targets = df["target"].values
        self.encoded_target = df['encoded_target'].values
        self.transform = transform
        self.images = []
        for path in tqdm(self.paths):
            self.images.append(Image.open(path).convert("RGB").resize((224, 224)))
        
    def __len__(self):
        return len(self.paths)
    
    def __getitem__(self, idx):
        img = self.images[idx]
        if self.transform:
            img = self.transform(img)
        target = self.targets[idx]
        encoded_target = torch.tensor(self.encoded_target[idx]).type(torch.LongTensor)
        return img, encoded_target, target

train_transform = transforms.Compose([
    transforms.Resize((224,224)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Define the transformation pipeline
transform = transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),  # Convert the images to PyTorch tensors
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

class CustomImageCache:
    def __init__(self, cache_size=50, debug=False):
        self.cache = dict()
        self.cache_size = 50
        self.debug = debug
        self.cache_hits = 0
        self.cache_misses = 0
        
    def __getitem__(self, image):
        if isinstance(image, dict):
            # Its the image and a mask as pillow both -> Combine them to one image
            image = Image.blend(image["image"], image["mask"], alpha=0.5)
        key = imagehash.average_hash(image)
        
        if key in self.cache:
            if self.debug: print("Cache hit!")
            self.cache_hits += 1
            return self.cache[key]
        else:
            if self.debug: print("Cache miss!")
            self.cache_misses += 1
            if len(self.cache.keys()) >= self.cache_size:
                if self.debug: print("Cache full, popping item!")
                self.cache.popitem()
            self.cache[key] = image
            return self.cache[key]
        
    def __len__(self):
        return len(self.cache.keys())
    
    def print_info(self):
        print(f"Cache size: {len(self)}")
        print(f"Cache hits: {self.cache_hits}")
        print(f"Cache misses: {self.cache_misses}")
        
def imageCacheWrapper(fn):
    def wrapper(image):
        return fn(ImageCache[image])
    return wrapper