# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
import random


class BaseDataset(data.Dataset):
    def __init__(self):
        super(BaseDataset, self).__init__()

    @staticmethod
    def modify_commandline_options(parser, is_train):
        return parser

    def initialize(self, opt):
        pass


def get_params(opt, size):
    w, h = size
    new_h = h
    new_w = w
    if opt.preprocess_mode == "resize_and_crop":
        new_h = new_w = opt.load_size
    elif opt.preprocess_mode == "scale_width_and_crop":
        new_w = opt.load_size
        new_h = opt.load_size * h // w
    elif opt.preprocess_mode == "scale_shortside_and_crop":
        ss, ls = min(w, h), max(w, h)  # shortside and longside
        width_is_shorter = w == ss
        ls = int(opt.load_size * ls / ss)
        new_w, new_h = (ss, ls) if width_is_shorter else (ls, ss)

    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, method=Image.BICUBIC, normalize=True, toTensor=True):
    transform_list = []
    if "resize" in opt.preprocess_mode:
        osize = [opt.load_size, opt.load_size]
        transform_list.append(transforms.Resize(osize, interpolation=method))
    elif "scale_width" in opt.preprocess_mode:
        transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.load_size, method)))
    elif "scale_shortside" in opt.preprocess_mode:
        transform_list.append(transforms.Lambda(lambda img: __scale_shortside(img, opt.load_size, method)))

    if "crop" in opt.preprocess_mode:
        transform_list.append(transforms.Lambda(lambda img: __crop(img, params["crop_pos"], opt.crop_size)))

    if opt.preprocess_mode == "none":
        base = 32
        transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method)))

    if opt.preprocess_mode == "fixed":
        w = opt.crop_size
        h = round(opt.crop_size / opt.aspect_ratio)
        transform_list.append(transforms.Lambda(lambda img: __resize(img, w, h, method)))

    if opt.isTrain and not opt.no_flip:
        transform_list.append(transforms.Lambda(lambda img: __flip(img, params["flip"])))

    if toTensor:
        transform_list += [transforms.ToTensor()]

    if normalize:
        transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    return transforms.Compose(transform_list)


def normalize():
    return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))


def __resize(img, w, h, method=Image.BICUBIC):
    return img.resize((w, h), method)


def __make_power_2(img, base, method=Image.BICUBIC):
    ow, oh = img.size
    h = int(round(oh / base) * base)
    w = int(round(ow / base) * base)
    if (h == oh) and (w == ow):
        return img
    return img.resize((w, h), method)


def __scale_width(img, target_width, method=Image.BICUBIC):
    ow, oh = img.size
    if ow == target_width:
        return img
    w = target_width
    h = int(target_width * oh / ow)
    return img.resize((w, h), method)


def __scale_shortside(img, target_width, method=Image.BICUBIC):
    ow, oh = img.size
    ss, ls = min(ow, oh), max(ow, oh)  # shortside and longside
    width_is_shorter = ow == ss
    if ss == target_width:
        return img
    ls = int(target_width * ls / ss)
    nw, nh = (ss, ls) if width_is_shorter else (ls, ss)
    return img.resize((nw, nh), method)


def __crop(img, pos, size):
    ow, oh = img.size
    x1, y1 = pos
    tw = th = size
    return img.crop((x1, y1, x1 + tw, y1 + th))


def __flip(img, flip):
    if flip:
        return img.transpose(Image.FLIP_LEFT_RIGHT)
    return img