import torch
import tqdm
import k_diffusion.sampling
from modules import sd_samplers_common, sd_samplers_kdiffusion, sd_samplers
from importlib import import_module

NAME = 'Restart_Test'
ALIAS = 'restart_test'


# 仅用作测试
# sampler

@torch.no_grad()
def restart_test_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):
    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)
    Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}
    If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list
    """
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    step_id = 0
    from k_diffusion.sampling import to_d, get_sigmas_karras

    def heun_step(x, old_sigma, new_sigma, second_order=True):
        nonlocal step_id
        denoised = model(x, old_sigma * s_in, **extra_args)
        d = to_d(x, old_sigma, denoised)
        if callback is not None:
            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
        dt = new_sigma - old_sigma
        if new_sigma == 0 or not second_order:
            # Euler method
            x = x + d * dt
        else:
            # Heun's method
            x_2 = x + d * dt
            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
            d_2 = to_d(x_2, new_sigma, denoised_2)
            d_prime = (d + d_2) / 2
            x = x + d_prime * dt
        step_id += 1
        return x

    steps = sigmas.shape[0] - 1
    if restart_list is None:
        if steps >= 20:
            restart_steps = 9
            restart_times = 1
            if steps >= 36:
                restart_steps = steps // 4
                restart_times = 2
            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
            restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
        else:
            restart_list = {}

    restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}

    step_list = []
    for i in range(len(sigmas) - 1):
        step_list.append((sigmas[i], sigmas[i + 1]))
        if i + 1 in restart_list:
            restart_steps, restart_times, restart_max = restart_list[i + 1]
            min_idx = i + 1
            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
            if max_idx < min_idx:
                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
                while restart_times > 0:
                    restart_times -= 1
                    step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:]))

    last_sigma = None
    for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):
        if last_sigma is None:
            last_sigma = old_sigma
        elif last_sigma < old_sigma:
            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5
        x = heun_step(x, old_sigma, new_sigma)
        last_sigma = new_sigma

    return x



# add sampler
if not NAME in [x.name for x in sd_samplers.all_samplers]:
    euler_max_samplers = [(NAME, restart_test_sampler, [ALIAS], {})]
    samplers_data_euler_max_samplers = [
        sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: sd_samplers_kdiffusion.KDiffusionSampler(funcname, model), aliases, options)
        for label, funcname, aliases, options in euler_max_samplers
        if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
    ]
    sd_samplers.all_samplers += samplers_data_euler_max_samplers
    sd_samplers.all_samplers_map = {x.name: x for x in sd_samplers.all_samplers}
    sd_samplers.set_samplers()