import torch

import ldm.models.diffusion.ddpm
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms

from ldm.models.diffusion.ddim import noise_like
from ldm.models.diffusion.sampling_util import norm_thresholding


@torch.no_grad()
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                  unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):
    b, *_, device = *x.shape, x.device

    def get_model_output(x, t):
        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
            e_t = self.model.apply_model(x, t, c)
        else:
            x_in = torch.cat([x] * 2)
            t_in = torch.cat([t] * 2)

            if isinstance(c, dict):
                assert isinstance(unconditional_conditioning, dict)
                c_in = {}
                for k in c:
                    if isinstance(c[k], list):
                        c_in[k] = [
                            torch.cat([unconditional_conditioning[k][i], c[k][i]])
                            for i in range(len(c[k]))
                        ]
                    else:
                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
            else:
                c_in = torch.cat([unconditional_conditioning, c])

            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

        if score_corrector is not None:
            assert self.model.parameterization == "eps"
            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)

        return e_t

    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas

    def get_x_prev_and_pred_x0(e_t, index):
        # select parameters corresponding to the currently considered timestep
        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)

        # current prediction for x_0
        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        if quantize_denoised:
            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
        if dynamic_threshold is not None:
            pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
        # direction pointing to x_t
        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
        return x_prev, pred_x0

    e_t = get_model_output(x, t)
    if len(old_eps) == 0:
        # Pseudo Improved Euler (2nd order)
        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
        e_t_next = get_model_output(x_prev, t_next)
        e_t_prime = (e_t + e_t_next) / 2
    elif len(old_eps) == 1:
        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
        e_t_prime = (3 * e_t - old_eps[-1]) / 2
    elif len(old_eps) == 2:
        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
    elif len(old_eps) >= 3:
        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24

    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)

    return x_prev, pred_x0, e_t


def do_inpainting_hijack():
    # p_sample_plms is needed because PLMS can't work with dicts as conditionings

    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms