import dataclasses

import torch

import k_diffusion


@dataclasses.dataclass
class Scheduler:
    name: str
    label: str
    function: any

    default_rho: float = -1
    need_inner_model: bool = False
    aliases: list = None


def uniform(n, sigma_min, sigma_max, inner_model, device):
    return inner_model.get_sigmas(n)


def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
    start = inner_model.sigma_to_t(torch.tensor(sigma_max))
    end = inner_model.sigma_to_t(torch.tensor(sigma_min))
    sigs = [
        inner_model.t_to_sigma(ts)
        for ts in torch.linspace(start, end, n + 1)[:-1]
    ]
    sigs += [0.0]
    return torch.FloatTensor(sigs).to(device)


def kl_optimal(n, sigma_min, sigma_max, device):
    alpha_min = torch.arctan(torch.tensor(sigma_min, device=device))
    alpha_max = torch.arctan(torch.tensor(sigma_max, device=device))
    sigmas = torch.empty((n+1,), device=device)
    for i in range(n+1):
        sigmas[i] = torch.tan((i/n) * alpha_min + (1.0-i/n) * alpha_max)
    return sigmas


schedulers = [
    Scheduler('automatic', 'Automatic', None),
    Scheduler('uniform', 'Uniform', uniform, need_inner_model=True),
    Scheduler('karras', 'Karras', k_diffusion.sampling.get_sigmas_karras, default_rho=7.0),
    Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential),
    Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0),
    Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
    Scheduler('kl_optimal', 'KL Optimal', kl_optimal),
]

schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}