import numpy as np
from torchvision import datasets, transforms
from utils.toolkit import split_images_labels
from torchvision.datasets import VisionDataset
from torch.utils.data import Dataset, DataLoader, random_split
from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
from typing import Any
import os
from PIL import Image 
import logging
import torch

class iData(object):
    train_trsf = []
    test_trsf = []
    common_trsf = []
    class_order = None


class iCIFAR10(iData):
    use_path = False
    train_trsf = [
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(p=0.5),
        transforms.ColorJitter(brightness=63 / 255),
        transforms.ToTensor(),
    ]
    test_trsf = [transforms.ToTensor()]
    common_trsf = [
        transforms.Normalize(
            mean=(0.4914, 0.4822, 0.4465), std=(0.2023, 0.1994, 0.2010)
        ),
    ]

    class_order = np.arange(10).tolist()

    def download_data(self):
        train_dataset = datasets.cifar.CIFAR10("./data", train=True, download=True)
        test_dataset = datasets.cifar.CIFAR10("./data", train=False, download=True)
        self.train_data, self.train_targets = train_dataset.data, np.array(
            train_dataset.targets
        )
        self.test_data, self.test_targets = test_dataset.data, np.array(
            test_dataset.targets
        )


class iCIFAR100(iData):
    use_path = False
    train_trsf = [
        
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ColorJitter(brightness=63 / 255),
        transforms.ToTensor()
    ]
    test_trsf = [transforms.ToTensor()]
    common_trsf = [
        transforms.Normalize(
            mean=(0.5071, 0.4867, 0.4408), std=(0.2675, 0.2565, 0.2761)
        ),
    ]

    class_order = np.arange(100).tolist()

    def download_data(self):
        train_dataset = datasets.cifar.CIFAR100("./data", train=True, download=True)
        test_dataset = datasets.cifar.CIFAR100("./data", train=False, download=True)
        
        '''print(type(train_dataset))
        print(type(test_dataset))
        print('type of each pair of data', type(train_dataset[0]))
        print(train_dataset.data[0].shape)
        print(train_dataset.targets[0])'''
    
        
        
        self.train_data, self.train_targets = train_dataset.data, np.array(
            train_dataset.targets
        )
        self.test_data, self.test_targets = test_dataset.data, np.array(
            test_dataset.targets
        )


class iImageNet1000(iData):
    use_path = True
    train_trsf = [
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ColorJitter(brightness=63 / 255),
    ]
    test_trsf = [
        transforms.Resize(256),
        transforms.CenterCrop(224),
    ]
    common_trsf = [
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ]

    class_order = np.arange(1000).tolist()

    def download_data(self):
        assert 0, "You should specify the folder of your dataset"
        train_dir = "[DATA-PATH]/train/"
        test_dir = "[DATA-PATH]/val/"

        train_dset = datasets.ImageFolder(train_dir)
        test_dset = datasets.ImageFolder(test_dir)

        self.train_data, self.train_targets = split_images_labels(train_dset.imgs)
        self.test_data, self.test_targets = split_images_labels(test_dset.imgs)


class iImageNet100(iData):
    use_path = True
    train_trsf = [
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
    ]
    test_trsf = [
        transforms.Resize(256),
        transforms.CenterCrop(224),
    ]
    common_trsf = [
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ]

    class_order = np.arange(1000).tolist()

    def download_data(self):
        assert 0, "You should specify the folder of your dataset"
        train_dir = "[DATA-PATH]/train/"
        test_dir = "[DATA-PATH]/val/"

        train_dset = datasets.ImageFolder(train_dir)
        test_dset = datasets.ImageFolder(test_dir)

        self.train_data, self.train_targets = split_images_labels(train_dset.imgs)
        self.test_data, self.test_targets = split_images_labels(test_dset.imgs)
    

class iData(object):
    train_trsf = []
    test_trsf = []
    common_trsf = []
    class_order = None


        
def split_images_labels(imgs):
    images = []
    labels = []
    for item in imgs:
        images.append(item[0])
        labels.append(item[1])

    return np.array(images), np.array(labels)


class CustomImageFolder(datasets.ImageFolder):
    def find_classes(self, directory: str) -> Tuple[List[str], Dict[str, int]]:
        classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
        if not classes:
            raise FileNotFoundError(f"Couldn't find any class folder in {directory}.")
        class_to_idx = dict()
        for i, cls_name in enumerate(classes):
            if int(cls_name) > 5:
                class_to_idx[cls_name] = int(cls_name) - 1
            else:
                class_to_idx[cls_name] = int(cls_name)
        return classes, class_to_idx

class GNSS(iData):
    use_path = True
    
    common_trsf = [
        transforms.ToTensor()
    ]

    class_order = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

    def download_data(self):
        
        train_dir = "./Dataset/GNSS/Train/"
        test_dir = "./Dataset/GNSS/Test/"
        
        train_dset = CustomImageFolder(train_dir)
        test_dset = CustomImageFolder(test_dir)
        
        self.train_data, self.train_targets = split_images_labels(train_dset.imgs)
        self.test_data, self.test_targets = split_images_labels(test_dset.imgs)
        

if __name__ == "__main__":
    obj = GNSS()
    obj.download_data()