import torch
import torch.nn.functional as F

class GaussianBlur:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "kernel_size": ("INT", {
                    "default": 5,
                    "min": 1,
                    "max": 31,
                    "step": 1
                }),
                "sigma": ("FLOAT", {
                    "default": 1.0,
                    "min": 0.1,
                    "max": 10.0,
                    "step": 0.1
                }),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "blur"

    CATEGORY = "postprocessing"

    def gaussian_kernel(self, kernel_size: int, sigma: float):
        x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size))
        d = torch.sqrt(x * x + y * y)
        g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
        return g / g.sum()

    def blur(self, image: torch.Tensor, kernel_size: int, sigma: float):
        batch_size, height, width, channels = image.shape

        kernel = self.gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)

        image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
        blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels)
        blurred = blurred.permute(0, 2, 3, 1)

        return (blurred,)

NODE_CLASS_MAPPINGS = {
    "GaussianBlur": GaussianBlur
}