import torch
import numpy as np
from fcbh.ldm.modules.diffusionmodules.util import make_beta_schedule


class EPS:
    def calculate_input(self, sigma, noise):
        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
        return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5

    def calculate_denoised(self, sigma, model_output, model_input):
        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
        return model_input - model_output * sigma


class V_PREDICTION(EPS):
    def calculate_denoised(self, sigma, model_output, model_input):
        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5


class ModelSamplingDiscrete(torch.nn.Module):
    def __init__(self, model_config=None):
        super().__init__()
        beta_schedule = "linear"
        if model_config is not None:
            beta_schedule = model_config.sampling_settings.get("beta_schedule", beta_schedule)
        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
        self.sigma_data = 1.0

    def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
        if given_betas is not None:
            betas = given_betas
        else:
            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
        alphas = 1. - betas
        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
        # alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])

        timesteps, = betas.shape
        self.num_timesteps = int(timesteps)
        self.linear_start = linear_start
        self.linear_end = linear_end

        # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
        # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
        # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))

        sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
        self.set_sigmas(sigmas)

    def set_sigmas(self, sigmas):
        self.register_buffer('sigmas', sigmas)
        self.register_buffer('log_sigmas', sigmas.log())

    @property
    def sigma_min(self):
        return self.sigmas[0]

    @property
    def sigma_max(self):
        return self.sigmas[-1]

    def timestep(self, sigma):
        log_sigma = sigma.log()
        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
        return dists.abs().argmin(dim=0).view(sigma.shape)

    def sigma(self, timestep):
        t = torch.clamp(timestep.float(), min=0, max=(len(self.sigmas) - 1))
        low_idx = t.floor().long()
        high_idx = t.ceil().long()
        w = t.frac()
        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
        return log_sigma.exp()

    def percent_to_sigma(self, percent):
        if percent <= 0.0:
            return 999999999.9
        if percent >= 1.0:
            return 0.0
        percent = 1.0 - percent
        return self.sigma(torch.tensor(percent * 999.0)).item()