import os
import torch
from torchvision import transforms, datasets
from albumentations import (
    HorizontalFlip,
    VerticalFlip,
    ShiftScaleRotate,
    CLAHE,
    RandomRotate90,
    Transpose,
    ShiftScaleRotate,
    HueSaturationValue,
    GaussNoise,
    Sharpen,
    Emboss,
    RandomBrightnessContrast,
    OneOf,
    Compose,
)
import numpy as np
from PIL import Image

torch.hub.set_dir('./cache')
os.environ["HUGGINGFACE_HUB_CACHE"] = "./cache"

def strong_aug(p=0.5):
    return Compose(
        [
            RandomRotate90(p=0.2),
            Transpose(p=0.2),
            HorizontalFlip(p=0.5),
            VerticalFlip(p=0.5),
            OneOf(
                [
                    GaussNoise(),
                ],
                p=0.2,
            ),
            ShiftScaleRotate(p=0.2),
            OneOf(
                [
                    CLAHE(clip_limit=2),
                    Sharpen(),
                    Emboss(),
                    RandomBrightnessContrast(),
                ],
                p=0.2,
            ),
            HueSaturationValue(p=0.2),
        ],
        p=p,
    )


def augment(aug, image):
    return aug(image=image)["image"]


class Aug(object):
    def __call__(self, img):
        aug = strong_aug(p=0.9)
        return Image.fromarray(augment(aug, np.array(img)))


def normalize_data():
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]

    return {
        "train": transforms.Compose(
            [Aug(), transforms.ToTensor(), transforms.Normalize(mean, std)]
        ),
        "valid": transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize(mean, std)]
        ),
        "test": transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize(mean, std)]
        ),
        "vid": transforms.Compose([transforms.Normalize(mean, std)]),
    }


def load_data(data_dir="sample/", batch_size=4):
    data_dir = data_dir
    image_datasets = {
        x: datasets.ImageFolder(os.path.join(data_dir, x), normalize_data()[x])
        for x in ["train", "valid", "test"]
    }

    # dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size,
    #                                             shuffle=True, num_workers=0, pin_memory=True)
    #               for x in ['train', 'validation', 'test']}

    dataset_sizes = {x: len(image_datasets[x]) for x in ["train", "valid", "test"]}

    train_dataloaders = torch.utils.data.DataLoader(
        image_datasets["train"],
        batch_size,
        shuffle=True,
        num_workers=0,
        pin_memory=True,
    )
    validation_dataloaders = torch.utils.data.DataLoader(
        image_datasets["valid"],
        batch_size,
        shuffle=False,
        num_workers=0,
        pin_memory=True,
    )
    test_dataloaders = torch.utils.data.DataLoader(
        image_datasets["test"],
        batch_size,
        shuffle=False,
        num_workers=0,
        pin_memory=True,
    )

    dataloaders = {
        "train": train_dataloaders,
        "validation": validation_dataloaders,
        "test": test_dataloaders,
    }

    return dataloaders, dataset_sizes


# def load_checkpoint(model, optimizer, filename=None):
#     start_epoch = 0
#     log_loss = 0
#     if os.path.isfile(filename):
#         print("=> loading checkpoint '{}'".format(filename))
#         checkpoint = torch.load(filename)
#         start_epoch = checkpoint["epoch"]
#         model.load_state_dict(checkpoint["state_dict"], strict=False)
#         optimizer.load_state_dict(checkpoint["optimizer"])
#         log_loss = checkpoint["min_loss"]
#         print(
#             "=> loaded checkpoint '{}' (epoch {})".format(filename, checkpoint["epoch"])
#         )
#     else:
#         print("=> no checkpoint found at '{}'".format(filename))

#     return model, optimizer, start_epoch, log_loss