import torch
import torchvision

import torch.nn.functional as F
from torch import nn, Tensor

from torchvision.ops import boxes as box_ops
from torchvision.ops import roi_align

from typing import Optional, List, Dict, Tuple

from model.utils import BoxCoder, Matcher


def expand_boxes(boxes, scale):
    # type: (Tensor, float) -> Tensor
    w_half = (boxes[:, 2] - boxes[:, 0]) * .5
    h_half = (boxes[:, 3] - boxes[:, 1]) * .5
    x_c = (boxes[:, 2] + boxes[:, 0]) * .5
    y_c = (boxes[:, 3] + boxes[:, 1]) * .5

    w_half *= scale
    h_half *= scale

    boxes_exp = torch.zeros_like(boxes)
    boxes_exp[:, 0] = x_c - w_half
    boxes_exp[:, 2] = x_c + w_half
    boxes_exp[:, 1] = y_c - h_half
    boxes_exp[:, 3] = y_c + h_half
    return boxes_exp


def expand_masks(mask, padding):
    # type: (Tensor, int) -> Tuple[Tensor, float]
    M = mask.shape[-1]
    scale = float(M + 2 * padding) / M
    padded_mask = F.pad(mask, (padding,) * 4)
    return padded_mask, scale


def paste_mask_in_image(mask, box, im_h, im_w):
    # type: (Tensor, Tensor, int, int) -> Tensor
    TO_REMOVE = 1
    w = int(box[2] - box[0] + TO_REMOVE)
    h = int(box[3] - box[1] + TO_REMOVE)
    w = max(w, 1)
    h = max(h, 1)

    # Set shape to [batchxCxHxW]
    mask = mask.expand((1, 1, -1, -1))

    # Resize mask
    mask = F.interpolate(mask, size=(h, w), mode='bilinear', align_corners=False)
    mask = mask[0][0]

    im_mask = torch.zeros((im_h, im_w), dtype=mask.dtype, device=mask.device)
    x_0 = max(box[0], 0)
    x_1 = min(box[2] + 1, im_w)
    y_0 = max(box[1], 0)
    y_1 = min(box[3] + 1, im_h)

    im_mask[y_0:y_1, x_0:x_1] = mask[
        (y_0 - box[1]):(y_1 - box[1]), (x_0 - box[0]):(x_1 - box[0])
    ]
    return im_mask


def paste_masks_in_image(masks, boxes, img_shape, padding=1):
    # type: (Tensor, Tensor, Tuple[int, int], int) -> Tensor
    masks, scale = expand_masks(masks, padding=padding)
    boxes = expand_boxes(boxes, scale).to(dtype=torch.int64)
    im_h, im_w = img_shape
    res = [
        paste_mask_in_image(m[0], b, im_h, im_w)
        for m, b in zip(masks, boxes)
    ]
    if len(res) > 0:
        ret = torch.stack(res, dim=0)[:, None]
    else:
        ret = masks.new_empty((0, 1, im_h, im_w))
    return ret
