#Taken from: https://github.com/tfernd/HyperTile/

import math
from einops import rearrange
# Use torch rng for consistency across generations
from torch import randint

def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
    min_value = min(min_value, value)

    # All big divisors of value (inclusive)
    divisors = [i for i in range(min_value, value + 1) if value % i == 0]

    ns = [value // i for i in divisors[:max_options]]  # has at least 1 element

    if len(ns) - 1 > 0:
        idx = randint(low=0, high=len(ns) - 1, size=(1,)).item()
    else:
        idx = 0

    return ns[idx]

class HyperTile:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                             "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}),
                             "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}),
                             "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}),
                             "scale_depth": ("BOOLEAN", {"default": False}),
                              }}
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "model_patches/unet"

    def patch(self, model, tile_size, swap_size, max_depth, scale_depth):
        model_channels = model.model.model_config.unet_config["model_channels"]

        latent_tile_size = max(32, tile_size) // 8
        self.temp = None

        def hypertile_in(q, k, v, extra_options):
            model_chans = q.shape[-2]
            orig_shape = extra_options['original_shape']
            apply_to = []
            for i in range(max_depth + 1):
                apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i)))

            if model_chans in apply_to:
                shape = extra_options["original_shape"]
                aspect_ratio = shape[-1] / shape[-2]

                hw = q.size(1)
                h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))

                factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1
                nh = random_divisor(h, latent_tile_size * factor, swap_size)
                nw = random_divisor(w, latent_tile_size * factor, swap_size)

                if nh * nw > 1:
                    q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
                    self.temp = (nh, nw, h, w)
                return q, k, v

            return q, k, v
        def hypertile_out(out, extra_options):
            if self.temp is not None:
                nh, nw, h, w = self.temp
                self.temp = None
                out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
                out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
            return out


        m = model.clone()
        m.set_model_attn1_patch(hypertile_in)
        m.set_model_attn1_output_patch(hypertile_out)
        return (m, )

NODE_CLASS_MAPPINGS = {
    "HyperTile": HyperTile,
}
