import torch
import torch.nn as nn
import numpy as np
from PIL import Image
from mmcv.runner import force_fp32, auto_fp16

class Grid(object):
    def __init__(self, use_h, use_w, rotate = 1, offset=False, ratio = 0.5, mode=0, prob = 1.):
        self.use_h = use_h
        self.use_w = use_w
        self.rotate = rotate
        self.offset = offset
        self.ratio = ratio
        self.mode=mode
        self.st_prob = prob
        self.prob = prob

    def set_prob(self, epoch, max_epoch):
        self.prob = self.st_prob * epoch / max_epoch

    def __call__(self, img, label):
        if np.random.rand() > self.prob:
            return img, label
        h = img.size(1)
        w = img.size(2)
        self.d1 = 2
        self.d2 = min(h, w)
        hh = int(1.5*h)
        ww = int(1.5*w)
        d = np.random.randint(self.d1, self.d2)
        if self.ratio == 1:
            self.l = np.random.randint(1, d)
        else:
            self.l = min(max(int(d*self.ratio+0.5),1),d-1)
        mask = np.ones((hh, ww), np.float32)
        st_h = np.random.randint(d)
        st_w = np.random.randint(d)
        if self.use_h:
            for i in range(hh//d):
                s = d*i + st_h
                t = min(s+self.l, hh)
                mask[s:t,:] *= 0
        if self.use_w:
            for i in range(ww//d):
                s = d*i + st_w
                t = min(s+self.l, ww)
                mask[:,s:t] *= 0
       
        r = np.random.randint(self.rotate)
        mask = Image.fromarray(np.uint8(mask))
        mask = mask.rotate(r)
        mask = np.asarray(mask)
        mask = mask[(hh-h)//2:(hh-h)//2+h, (ww-w)//2:(ww-w)//2+w]

        mask = torch.from_numpy(mask).float()
        if self.mode == 1:
            mask = 1-mask

        mask = mask.expand_as(img)
        if self.offset:
            offset = torch.from_numpy(2 * (np.random.rand(h,w) - 0.5)).float()
            offset = (1 - mask) * offset
            img = img * mask + offset
        else:
            img = img * mask 

        return img, label


class GridMask(nn.Module):
    def __init__(self, use_h, use_w, rotate = 1, offset=False, ratio = 0.5, mode=0, prob = 1.):
        super(GridMask, self).__init__()
        self.use_h = use_h
        self.use_w = use_w
        self.rotate = rotate
        self.offset = offset
        self.ratio = ratio
        self.mode = mode
        self.st_prob = prob
        self.prob = prob
        self.fp16_enable = False
    def set_prob(self, epoch, max_epoch):
        self.prob = self.st_prob * epoch / max_epoch #+ 1.#0.5
    @auto_fp16()
    def forward(self, x):
        if np.random.rand() > self.prob or not self.training:
            return x
        n,c,h,w = x.size()
        x = x.view(-1,h,w)
        hh = int(1.5*h)
        ww = int(1.5*w)
        d = np.random.randint(2, h)
        self.l = min(max(int(d*self.ratio+0.5),1),d-1)
        mask = np.ones((hh, ww), np.float32)
        st_h = np.random.randint(d)
        st_w = np.random.randint(d)
        if self.use_h:
            for i in range(hh//d):
                s = d*i + st_h
                t = min(s+self.l, hh)
                mask[s:t,:] *= 0
        if self.use_w:
            for i in range(ww//d):
                s = d*i + st_w
                t = min(s+self.l, ww)
                mask[:,s:t] *= 0
       
        r = np.random.randint(self.rotate)
        mask = Image.fromarray(np.uint8(mask))
        mask = mask.rotate(r)
        mask = np.asarray(mask)
        mask = mask[(hh-h)//2:(hh-h)//2+h, (ww-w)//2:(ww-w)//2+w]

        mask = torch.from_numpy(mask).to(x.dtype).cuda()
        if self.mode == 1:
            mask = 1-mask
        mask = mask.expand_as(x)
        if self.offset:
            offset = torch.from_numpy(2 * (np.random.rand(h,w) - 0.5)).to(x.dtype).cuda()
            x = x * mask + offset * (1 - mask)
        else:
            x = x * mask 
        
        return x.view(n,c,h,w)


class GridMaskHybrid(nn.Module):
    def __init__(self, use_h, use_w, rotate = 1, offset=False, ratio = 0.5, mode=0, prob = 1.):
        super(GridMaskHybrid, self).__init__()
        self.use_h = use_h
        self.use_w = use_w
        self.rotate = rotate
        self.offset = offset
        self.ratio = ratio
        self.mode = mode
        self.st_prob = prob
        self.prob = prob
        self.fp16_enable = False
    def set_prob(self, epoch, max_epoch):
        self.prob = self.st_prob * epoch / max_epoch #+ 1.#0.5
    @auto_fp16()
    def forward(self, x, dpt=None):
        if np.random.rand() > self.prob or not self.training:
            returns = [x, ]
            if not (dpt is None):
                returns.append(dpt)
            return returns[0] if len(returns)==1 else returns
        n,c,h,w = x.size()
        hh = int(1.5*h)
        ww = int(1.5*w)
        d = np.random.randint(2, h)
        self.l = min(max(int(d*self.ratio+0.5),1),d-1)
        mask = np.ones((hh, ww), np.float32)
        st_h = np.random.randint(d)
        st_w = np.random.randint(d)
        if self.use_h:
            for i in range(hh//d):
                s = d*i + st_h
                t = min(s+self.l, hh)
                mask[s:t,:] *= 0
        if self.use_w:
            for i in range(ww//d):
                s = d*i + st_w
                t = min(s+self.l, ww)
                mask[:,s:t] *= 0
       
        r = np.random.randint(self.rotate)
        mask = Image.fromarray(np.uint8(mask))
        mask = mask.rotate(r)
        mask = np.asarray(mask)
        mask = mask[(hh-h)//2:(hh-h)//2+h, (ww-w)//2:(ww-w)//2+w]

        mask = torch.from_numpy(mask).to(x.dtype).cuda()
        if self.mode == 1:
            mask = 1-mask
        returns = []
        if self.offset:
            offset = torch.from_numpy(2 * (np.random.rand(h,w) - 0.5)).to(x.dtype).cuda()
            x = x.view(-1,h,w)
            mask_x = mask.expand_as(x)
            x = x * mask_x + offset * (1 - mask_x)
            returns.append(x.view(n,c,h,w))
            if not (dpt is None):
                _,c_dpt,_,_ = dpt.size()
                dpt = dpt.view(-1, h, w)
                mask_dpt = mask.expand_as(dpt)
                dpt = dpt * mask_dpt + offset * (1 - mask_dpt)
                returns.append(dpt.view(n,c_dpt,h,w))
        else:
            x = x.view(-1,h,w)
            mask_x = mask.expand_as(x)
            x = x * mask_x
            returns.append(x.view(n,c,h,w))
            if not (dpt is None):
                _,c_dpt,_,_ = dpt.size()
                dpt = dpt.view(-1, h, w)
                mask_dpt = mask.expand_as(dpt)
                dpt = dpt * mask_dpt
                returns.append(dpt.view(n, c_dpt, h, w))
        
        return returns[0] if len(returns)==1 else returns