import torch
import k_diffusion.sampling

@torch.no_grad()
def restart_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'''
    from tqdm.auto import trange
    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 = dict()
    temp_list = dict()
    for key, value in restart_list.items():
        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
    restart_list = temp_list
    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([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
    last_sigma = None
    for i in trange(len(step_list), disable=disable):
        if last_sigma is None:
            last_sigma = step_list[i][0]
        elif last_sigma < step_list[i][0]:
            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5
        x = heun_step(x, step_list[i][0], step_list[i][1])
        last_sigma = step_list[i][1]
    return x