"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets.

It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in
subclasses.

"""
import random
import numpy as np
import torch.utils.data as data
import torch
from PIL import Image
import torchvision.transforms as transforms
from abc import ABC, abstractmethod


class BaseDataset(data.Dataset, ABC):
    """This class is an abstract base class (ABC) for datasets.

    To create a subclass, you need to implement the following four functions:
    -- <__init__>:                      initialize the class, first call BaseDataset.__init__(self, opt).
    -- <__len__>:                       return the size of dataset.
    -- <__getitem__>:                   get a data point.
    -- <modify_commandline_options>:    (optionally) add dataset-specific options and set default options.
    """

    def __init__(self, opt):
        """Initialize the class; save the options in the class

        Parameters:
            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
        """
        self.opt = opt
        self.root = opt.dataroot

    @staticmethod
    def modify_commandline_options(parser, is_train):
        """用于添加针对这个数据集特定的选项，这个脚本里头只是一个样例。

        Parameters:
            parser          -- original option parser
            parser:
            is_train (bool) -- whether training phase or test phase.

        Returns:
            the modified parser.
        """
        return parser

    @abstractmethod
    def __len__(self):
        """Return the total number of images in the dataset."""
        return 0

    @abstractmethod
    def __getitem__(self, index):
        """Return a data point and its metadata information.

        Parameters:
            index - - a random integer for data indexing

        Returns:
            a dictionary of data with their names. It usually contains the data itself and its metadata information.
        """
        pass


def get_params(opt, size):
    w, h = size
    new_h = h
    new_w = w
    if opt.preprocess == "resize_and_crop":
        new_h = new_w = opt.load_size
    elif opt.preprocess == "scale_width_and_crop":
        new_w = opt.load_size
        new_h = opt.load_size * h // w

    x = random.randint(0, np.maximum(0, new_w - opt.crop_size))
    y = random.randint(0, np.maximum(0, new_h - opt.crop_size))

    flip = random.random() > 0.5

    return {"crop_pos": (x, y), "flip": flip}


def get_transform(
    opt,
    params=None,
    grayscale=False,
    convert=True,
    method=transforms.InterpolationMode.BICUBIC,
):
    """数据预处理"""
    transform_list = []

    # 灰度化
    if grayscale:
        transform_list.append(transforms.Grayscale(1))

    # 图片大小调整
    # 默认：双三次插值
    if "resize" in opt.preprocess:
        osize = [opt.load_size, opt.load_size]
        transform_list.append(transforms.Resize(osize, method))
    elif "scale_width" in opt.preprocess:
        transform_list.append(
            transforms.Lambda(
                lambda img: __scale_width(img, opt.load_size, opt.crop_size, method)
            )
        )

    # 裁剪
    if "crop" in opt.preprocess:
        if params is None:
            transform_list.append(transforms.RandomCrop(opt.crop_size))
        else:
            transform_list.append(
                transforms.Lambda(
                    lambda img: __crop(img, params["crop_pos"], opt.crop_size)
                )
            )
    if opt.preprocess == "none":
        transform_list.append(
            transforms.Lambda(lambda img: __make_power_2(img, base=4, method=method))
        )

    # 图片左右翻转
    if not opt.no_flip:
        if params is None:
            transform_list.append(transforms.RandomHorizontalFlip())
        elif params["flip"]:
            transform_list.append(
                transforms.Lambda(lambda img: __flip(img, params["flip"]))
            )

    # convert
    if convert:
        transform_list += [transforms.ToTensor()]
        transform_list += [GaussionNoise()] if opt.isTrain else []
        if grayscale:
            transform_list += [transforms.Normalize((0.5,), (0.5,))]
        else:
            transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    return transforms.Compose(transform_list)


def __transforms2pil_resize(method):
    mapper = {
        transforms.InterpolationMode.BILINEAR: Image.BILINEAR,
        transforms.InterpolationMode.BICUBIC: Image.BICUBIC,
        transforms.InterpolationMode.NEAREST: Image.NEAREST,
        transforms.InterpolationMode.LANCZOS: Image.LANCZOS,
    }
    return mapper[method]


def __make_power_2(img, base, method=transforms.InterpolationMode.BICUBIC):
    """根据给定的方法（例如：双三次插值），将图片变成指定的大小。
    其中的round函数是一种四舍五入的方法。
    """
    method = __transforms2pil_resize(method)
    ow, oh = img.size
    h = int(round(oh / base) * base)
    w = int(round(ow / base) * base)
    if h == oh and w == ow:
        return img

    __print_size_warning(ow, oh, w, h)
    return img.resize((w, h), method)


def __scale_width(
    img, target_size, crop_size, method=transforms.InterpolationMode.BICUBIC
):
    """调整大小"""
    method = __transforms2pil_resize(method)
    ow, oh = img.size
    if ow == target_size and oh >= crop_size:
        return img
    w = target_size
    h = int(max(target_size * oh / ow, crop_size))
    return img.resize((w, h), method)


def __crop(img, pos, size):
    """图片裁剪"""
    ow, oh = img.size
    x1, y1 = pos
    tw = th = size
    if ow > tw or oh > th:
        return img.crop((x1, y1, x1 + tw, y1 + th))
    return img


def __flip(img, flip):
    """图片左右翻转"""
    if flip:
        return img.transpose(Image.FLIP_LEFT_RIGHT)
    return img


def _gaussion_noise(img):
    noise = torch.randn(img.shape)
    img = img + noise * 0.1
    return img


def __print_size_warning(ow, oh, w, h):
    """Print warning information about image size(only print once)"""
    if not hasattr(__print_size_warning, "has_printed"):
        print(
            "The image size needs to be a multiple of 4. "
            "The loaded image size was (%d, %d), so it was adjusted to "
            "(%d, %d). This adjustment will be done to all images "
            "whose sizes are not multiples of 4" % (ow, oh, w, h)
        )
        __print_size_warning.has_printed = True


class GaussionNoise:
    """添加高斯噪声"""

    def __init__(self) -> None:
        pass

    def __call__(self, img):
        noise = torch.randn(img.shape)
        img_mix_noise = img + noise * 0.1
        return img_mix_noise

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}()"