import comfy.samplers
import comfy.utils
import torch
import numpy as np
from tqdm.auto import trange, tqdm
import math


@torch.no_grad()
def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None):
    extra_args = {} if extra_args is None else extra_args

    if upscale_steps is None:
        upscale_steps = max(len(sigmas) // 2 + 1, 2)
    else:
        upscale_steps += 1
        upscale_steps = min(upscale_steps, len(sigmas) + 1)

    upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:]

    orig_shape = x.size()
    s_in = x.new_ones([x.shape[0]])
    for i in trange(len(sigmas) - 1, disable=disable):
        denoised = model(x, sigmas[i] * s_in, **extra_args)
        if callback is not None:
            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})

        x = denoised
        if i < len(upscales):
            x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled")

        if sigmas[i + 1] > 0:
            x += sigmas[i + 1] * torch.randn_like(x)
    return x


class SamplerLCMUpscale:
    upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"]

    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}),
                     "scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}),
                     "upscale_method": (s.upscale_methods,),
                      }
               }
    RETURN_TYPES = ("SAMPLER",)
    CATEGORY = "sampling/custom_sampling/samplers"

    FUNCTION = "get_sampler"

    def get_sampler(self, scale_ratio, scale_steps, upscale_method):
        if scale_steps < 0:
            scale_steps = None
        sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
        return (sampler, )

NODE_CLASS_MAPPINGS = {
    "SamplerLCMUpscale": SamplerLCMUpscale,
}