import cv2
import numpy as np
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from random import random, choice
from io import BytesIO
from PIL import Image
from PIL import ImageFile
from scipy.ndimage.filters import gaussian_filter
from torchvision.transforms import InterpolationMode
import torch
import torch.nn.functional as F
import PIL
ImageFile.LOAD_TRUNCATED_IMAGES = True

def dataset_folder(opt, root):
    if opt.mode == 'binary' or opt.mode == 'binary_elsa':
        return binary_dataset(opt, root)
    if opt.mode == 'filename':
        return FileNameDataset(opt, root)
    raise ValueError('opt.mode needs to be binary, binary_elsa or filename.')

def binary_dataset(opt, root):
    if opt.isTrain:
        crop_func = transforms.RandomCrop(opt.cropSize)
    elif opt.no_crop:
        crop_func = transforms.Lambda(lambda img: img)
    else:
        crop_func = transforms.CenterCrop(opt.cropSize)

    if opt.isTrain and not opt.no_flip:
        flip_func = transforms.RandomHorizontalFlip()
    else:
        flip_func = transforms.Lambda(lambda img: img)
    if not opt.isTrain and opt.no_resize:
        rz_func = transforms.Lambda(lambda img: img)
    else:
        rz_func = transforms.Resize((opt.loadSize, opt.loadSize))
    if opt.is_aug:
        print("aug in train")
        aug_func = transforms.Lambda(lambda img: data_augment(img, opt))
    else:
        print("NO aug")
        aug_func = transforms.Lambda(lambda img: img)

    transform_pipeline = transforms.Compose([
            rz_func,
            crop_func,
            flip_func,
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ])

    if opt.mode == 'binary_elsa':
        dset = BinaryFakeDataset(root, transform=transform_pipeline, is_valid_file=is_valid_file_func)
    else:
        dset = datasets.ImageFolder(root, transform=transform_pipeline, is_valid_file=is_valid_file_func)

    print(f"the dataset len is: {len(dset)}")
    paths = [sample[0] for sample in dset.samples]
    return dset, paths


def is_valid_file_func(filename):
    extensions = (".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm", ".tif", ".tiff", ".webp", ".jfif")
    return filename.lower().endswith(extensions if isinstance(extensions, str) else tuple(extensions))


class BinaryFakeDataset(datasets.ImageFolder):
    def name(self):
        return 'BinaryFakeDataset'

    def __init__(self, root, transform, is_valid_file):
        super().__init__(root, transform, is_valid_file)
    
    def __getitem__(self, index):
        path, label = self.samples[index]
        img = self.loader(path)
        if self.transform:
            img = self.transform(img)
        class_name = self.classes[label]
        binary_label = 0 if class_name == 'real' else 1
        return img, binary_label


class FileNameDataset(datasets.ImageFolder):
    def name(self):
        return 'FileNameDataset'

    def __init__(self, opt, root):
        self.opt = opt
        super().__init__(root)

    def __getitem__(self, index):
        # Loading sample
        path, target = self.samples[index]
        return path


def data_augment(img, opt):
    img = np.array(img)

    if random() < opt.blur_prob:
        sig = sample_continuous(opt.blur_sig)
        gaussian_blur(img, sig)

    if random() < opt.jpg_prob:
        method = sample_discrete(opt.jpg_method)
        qual = sample_discrete(opt.jpg_qual)
        img = jpeg_from_key(img, qual, method)

    return Image.fromarray(img)


def sample_continuous(s):
    if len(s) == 1:
        return s[0]
    if len(s) == 2:
        rg = s[1] - s[0]
        return random() * rg + s[0]
    raise ValueError("Length of iterable s should be 1 or 2.")


def sample_discrete(s):
    if len(s) == 1:
        return s[0]
    return choice(s)


def gaussian_blur(img, sigma):
    gaussian_filter(img[:,:,0], output=img[:,:,0], sigma=sigma)
    gaussian_filter(img[:,:,1], output=img[:,:,1], sigma=sigma)
    gaussian_filter(img[:,:,2], output=img[:,:,2], sigma=sigma)


def cv2_jpg(img, compress_val):
    img_cv2 = img[:,:,::-1]
    encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), compress_val]
    result, encimg = cv2.imencode('.jpg', img_cv2, encode_param)
    decimg = cv2.imdecode(encimg, 1)
    return decimg[:,:,::-1]


def pil_jpg(img, compress_val):
    out = BytesIO()
    img = Image.fromarray(img)
    img.save(out, format='jpeg', quality=compress_val)
    img = Image.open(out)
    # load from memory before ByteIO closes
    img = np.array(img)
    out.close()
    return img


jpeg_dict = {'cv2': cv2_jpg, 'pil': pil_jpg}
def jpeg_from_key(img, compress_val, key):
    method = jpeg_dict[key]
    return method(img, compress_val)


rz_dict = {'bilinear': InterpolationMode.BILINEAR,
           'bicubic': InterpolationMode.BICUBIC,
           'lanczos': InterpolationMode.LANCZOS,
           'nearest': InterpolationMode.NEAREST}
def custom_resize(img, opt):
    interp = sample_discrete(opt.rz_interp)
    return TF.resize(img, (opt.loadSize,opt.loadSize), interpolation=rz_dict[interp])
