import torch
from torch import nn
import numpy as np
from cv2 import resize
import cv2
from pathlib import Path

from network import EfficientViT_l1_r224
from losses import IISLoss, activate
from utils import minmaxnorm, load_from_ckpt


class Busam:
    def __init__(self, checkpoint, device, side=224):
        out_channels = 16
        use_norm_params = False
        net = EfficientViT_l1_r224(
            out_channels=out_channels, use_norm_params=use_norm_params, pretrained=False
        )
        net = load_from_ckpt(net, checkpoint)
        net = net.to(device)
        net.eval()
        self.net = net
        self.device = device
        self.side = side

    def prepare_img(self, img):
        """
        assume H, W, 3 image
        """
        assert len(img.shape) == 3, "should be H, W, 3 but is " + str(img.shape)
        assert img.shape[2] == 3, "should be H, W, 3 but is " + str(img.shape)
        assert img.min() >= 0, "min should be more than 0 but is " + str(img.min())
        assert img.max() <= 255, "max should be less than 255 but is " + str(img.max())
        assert img.dtype == np.uint8, "dtype should be np.uint8 but is " + str(
            img.dtype
        )
        nimg = resize(img, (self.side, self.side))
        tensorimg = (
            (torch.from_numpy(nimg / 255).permute(2, 0, 1) - 0.5)
            .float()[None]
            .to(self.device)
        )
        return tensorimg

    def process_image(self, img, do_activate=False):
        with torch.no_grad():
            x = self.prepare_img(img)
            pred = self.net(x)
        H, W = img.shape[:2]
        if do_activate:
            B, F, pH, pW = pred.shape
            features, _, _, _ = activate(
                pred.view(F, pH * pW), None, "symlog", False, False, False
            )
            pred = features.view(B, F, pH, pW)
        return pred, (H, W)

    def get_mask(self, aux, click):
        """assume click is (row, col)"""
        pred = aux[0][0]  # remove batch dim
        oH, oW = aux[1]
        F, H, W = pred.shape
        features = pred.view(F, H * W)
        rclick = click[0] * H // oH, click[1] * W // oW
        sindex = rclick[0] * W + rclick[1]
        mask = IISLoss.get_mask_from_query(features, sindex)
        mask = mask.reshape(H, W)
        mask = (
            resize((mask.cpu().numpy() * 255).astype(np.uint8), (oW, oH)) > 100
        ).astype(bool)
        return mask

    def get_gradients(self, pred, size):
        F, H, W = pred[0].shape
        sobel_x = (
            torch.tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]).float().to(pred.device)
        )
        sobel_y = sobel_x.T
        sobel_x = sobel_x.repeat(F, 1, 1, 1)
        sobel_y = sobel_y.repeat(F, 1, 1, 1)
        edge_x = torch.nn.functional.conv2d(pred, sobel_x, padding=1, groups=F).view(
            F, H, W
        )  # 1, F, H, W
        edge_y = torch.nn.functional.conv2d(pred, sobel_y, padding=1, groups=F).view(
            F, H, W
        )
        edge_x = torch.norm(edge_x, dim=0, p=2)  # will take sqrt
        edge_y = torch.norm(edge_y, dim=0, p=2)  # H, W
        return edge_x, edge_y

    def sobel_from_pred(self, pred, size):
        edge_x, edge_y = self.get_gradients(pred, size)
        edge = torch.sqrt(edge_x**2 + edge_y**2)
        return edge

    def canny_from_pred(self, pred, size, th_low=10000, th_high=20000):
        th_low = th_low or th_high
        th_high = th_high or th_low

        edge_x, edge_y = self.get_gradients(pred, size)
        amin = min(edge_x.min(), edge_y.min())
        amax = max(edge_x.max(), edge_y.max())
        edge_x, edge_y = (edge_x - amin) / (amax - amin), (edge_y - amin) / (
            amax - amin
        )
        canny = cv2.Canny(cast_to_int16(edge_x), cast_to_int16(edge_y), th_low, th_high)
        return canny


def cast_to_int16(x):
    if isinstance(x, torch.Tensor):
        x = x.cpu().numpy()
    return (x * 32767).astype(np.int16)


# from segment_anything import sam_model_registry, SamPredictor
# class SAM:
#     sam_checkpoint = "sam_vit_b_01ec64.pth"
#     model_type = "vit_b"

#     def __init__(self, device):
#         sam = sam_model_registry[self.model_type](checkpoint=self.sam_checkpoint)
#         sam.to(device=device)
#         self.predictor = SamPredictor(sam)

#     def process_image(self, img):
#         self.predictor.set_image(img)
#         return None

#     def get_mask(self, aux, click):
#         input_point = np.array([[click[1], click[0]]])
#         input_label = np.array([1])
#         masks, scores, logits = self.predictor.predict(
#             point_coords=input_point, point_labels=input_label, multimask_output=False
#         )
#         return masks[0]