"""
wild mixture of
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/CompVis/taming-transformers
-- merci
"""

from functools import partial
from contextlib import contextmanager
import numpy as np
from tqdm import tqdm
from einops import rearrange, repeat
import logging
mainlogger = logging.getLogger('mainlogger')
import torch
import torch.nn as nn
from torchvision.utils import make_grid
import pytorch_lightning as pl
from utils import instantiate_from_config
from lvdm.ema import LitEma
from lvdm.distributions import DiagonalGaussianDistribution
from lvdm.models.utils_diffusion import make_beta_schedule
from lvdm.modules.encoders.ip_resampler import ImageProjModel, Resampler
from lvdm.basics import disabled_train
from lvdm.common import (
    extract_into_tensor,
    noise_like,
    exists,
    default
)


__conditioning_keys__ = {'concat': 'c_concat',
                         'crossattn': 'c_crossattn',
                         'adm': 'y'}

class DDPM(pl.LightningModule):
    # classic DDPM with Gaussian diffusion, in image space
    def __init__(self,
                 unet_config,
                 timesteps=1000,
                 beta_schedule="linear",
                 loss_type="l2",
                 ckpt_path=None,
                 ignore_keys=[],
                 load_only_unet=False,
                 monitor=None,
                 use_ema=True,
                 first_stage_key="image",
                 image_size=256,
                 channels=3,
                 log_every_t=100,
                 clip_denoised=True,
                 linear_start=1e-4,
                 linear_end=2e-2,
                 cosine_s=8e-3,
                 given_betas=None,
                 original_elbo_weight=0.,
                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
                 l_simple_weight=1.,
                 conditioning_key=None,
                 parameterization="eps",  # all assuming fixed variance schedules
                 scheduler_config=None,
                 use_positional_encodings=False,
                 learn_logvar=False,
                 logvar_init=0.
                 ):
        super().__init__()
        assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
        self.parameterization = parameterization
        mainlogger.info(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
        self.cond_stage_model = None
        self.clip_denoised = clip_denoised
        self.log_every_t = log_every_t
        self.first_stage_key = first_stage_key
        self.channels = channels
        self.temporal_length = unet_config.params.temporal_length
        self.image_size = image_size 
        if isinstance(self.image_size, int):
            self.image_size = [self.image_size, self.image_size]
        self.use_positional_encodings = use_positional_encodings
        self.model = DiffusionWrapper(unet_config, conditioning_key)
        self.use_ema = use_ema
        if self.use_ema:
            self.model_ema = LitEma(self.model)
            mainlogger.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")

        self.use_scheduler = scheduler_config is not None
        if self.use_scheduler:
            self.scheduler_config = scheduler_config

        self.v_posterior = v_posterior
        self.original_elbo_weight = original_elbo_weight
        self.l_simple_weight = l_simple_weight

        if monitor is not None:
            self.monitor = monitor
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)

        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)

        self.loss_type = loss_type

        self.learn_logvar = learn_logvar
        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
        if self.learn_logvar:
            self.logvar = nn.Parameter(self.logvar, requires_grad=True)


    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 exists(given_betas):
            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 = np.cumprod(alphas, axis=0)
        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
        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'

        to_torch = partial(torch.tensor, dtype=torch.float32)

        self.register_buffer('betas', to_torch(betas))
        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))

        # calculations for posterior q(x_{t-1} | x_t, x_0)
        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
                    1. - alphas_cumprod) + self.v_posterior * betas
        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
        self.register_buffer('posterior_variance', to_torch(posterior_variance))
        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
        self.register_buffer('posterior_mean_coef1', to_torch(
            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
        self.register_buffer('posterior_mean_coef2', to_torch(
            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))

        if self.parameterization == "eps":
            lvlb_weights = self.betas ** 2 / (
                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
        elif self.parameterization == "x0":
            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
        else:
            raise NotImplementedError("mu not supported")
        # TODO how to choose this term
        lvlb_weights[0] = lvlb_weights[1]
        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
        assert not torch.isnan(self.lvlb_weights).all()

    @contextmanager
    def ema_scope(self, context=None):
        if self.use_ema:
            self.model_ema.store(self.model.parameters())
            self.model_ema.copy_to(self.model)
            if context is not None:
                mainlogger.info(f"{context}: Switched to EMA weights")
        try:
            yield None
        finally:
            if self.use_ema:
                self.model_ema.restore(self.model.parameters())
                if context is not None:
                    mainlogger.info(f"{context}: Restored training weights")

    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
        sd = torch.load(path, map_location="cpu")
        if "state_dict" in list(sd.keys()):
            sd = sd["state_dict"]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    mainlogger.info("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
            sd, strict=False)
        mainlogger.info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
        if len(missing) > 0:
            mainlogger.info(f"Missing Keys: {missing}")
        if len(unexpected) > 0:
            mainlogger.info(f"Unexpected Keys: {unexpected}")

    def q_mean_variance(self, x_start, t):
        """
        Get the distribution q(x_t | x_0).
        :param x_start: the [N x C x ...] tensor of noiseless inputs.
        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
        """
        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
        return mean, variance, log_variance

    def predict_start_from_noise(self, x_t, t, noise):
        return (
                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
        )

    def q_posterior(self, x_start, x_t, t):
        posterior_mean = (
                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
        )
        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
        return posterior_mean, posterior_variance, posterior_log_variance_clipped

    def p_mean_variance(self, x, t, clip_denoised: bool):
        model_out = self.model(x, t)
        if self.parameterization == "eps":
            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
        elif self.parameterization == "x0":
            x_recon = model_out
        if clip_denoised:
            x_recon.clamp_(-1., 1.)

        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
        return model_mean, posterior_variance, posterior_log_variance

    @torch.no_grad()
    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
        b, *_, device = *x.shape, x.device
        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
        noise = noise_like(x.shape, device, repeat_noise)
        # no noise when t == 0
        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise

    @torch.no_grad()
    def p_sample_loop(self, shape, return_intermediates=False):
        device = self.betas.device
        b = shape[0]
        img = torch.randn(shape, device=device)
        intermediates = [img]
        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
                                clip_denoised=self.clip_denoised)
            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
                intermediates.append(img)
        if return_intermediates:
            return img, intermediates
        return img

    @torch.no_grad()
    def sample(self, batch_size=16, return_intermediates=False):
        image_size = self.image_size
        channels = self.channels
        return self.p_sample_loop((batch_size, channels, image_size, image_size),
                                  return_intermediates=return_intermediates)

    def q_sample(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start *
                extract_into_tensor(self.scale_arr, t, x_start.shape) +
                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)

    def get_input(self, batch, k):
        x = batch[k]
        x = x.to(memory_format=torch.contiguous_format).float()
        return x

    def _get_rows_from_list(self, samples):
        n_imgs_per_row = len(samples)
        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
        return denoise_grid

    @torch.no_grad()
    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
        log = dict()
        x = self.get_input(batch, self.first_stage_key)
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        x = x.to(self.device)[:N]
        log["inputs"] = x

        # get diffusion row
        diffusion_row = list()
        x_start = x[:n_row]

        for t in range(self.num_timesteps):
            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
                t = t.to(self.device).long()
                noise = torch.randn_like(x_start)
                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
                diffusion_row.append(x_noisy)

        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)

        if sample:
            # get denoise row
            with self.ema_scope("Plotting"):
                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)

            log["samples"] = samples
            log["denoise_row"] = self._get_rows_from_list(denoise_row)

        if return_keys:
            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
                return log
            else:
                return {key: log[key] for key in return_keys}
        return log


class LatentDiffusion(DDPM):
    """main class"""
    def __init__(self,
                 first_stage_config,
                 cond_stage_config,
                 num_timesteps_cond=None,
                 cond_stage_key="caption",
                 cond_stage_trainable=False,
                 cond_stage_forward=None,
                 conditioning_key=None,
                 uncond_prob=0.2,
                 uncond_type="empty_seq",
                 scale_factor=1.0,
                 scale_by_std=False,
                 encoder_type="2d",
                 only_model=False,
                 use_scale=False,
                 scale_a=1,
                 scale_b=0.3,
                 mid_step=400,
                 fix_scale_bug=False,
                 *args, **kwargs):
        self.num_timesteps_cond = default(num_timesteps_cond, 1)
        self.scale_by_std = scale_by_std
        assert self.num_timesteps_cond <= kwargs['timesteps']
        # for backwards compatibility after implementation of DiffusionWrapper
        ckpt_path = kwargs.pop("ckpt_path", None)
        ignore_keys = kwargs.pop("ignore_keys", [])
        conditioning_key = default(conditioning_key, 'crossattn')
        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)

        self.cond_stage_trainable = cond_stage_trainable
        self.cond_stage_key = cond_stage_key

        # scale factor
        self.use_scale=use_scale
        if self.use_scale:
            self.scale_a=scale_a
            self.scale_b=scale_b
            if fix_scale_bug:
                scale_step=self.num_timesteps-mid_step
            else: #bug
                scale_step = self.num_timesteps

            scale_arr1 = np.linspace(scale_a, scale_b, mid_step)
            scale_arr2 = np.full(scale_step, scale_b)
            scale_arr = np.concatenate((scale_arr1, scale_arr2))
            scale_arr_prev = np.append(scale_a, scale_arr[:-1])
            to_torch = partial(torch.tensor, dtype=torch.float32)
            self.register_buffer('scale_arr', to_torch(scale_arr))

        try:
            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
        except:
            self.num_downs = 0
        if not scale_by_std:
            self.scale_factor = scale_factor
        else:
            self.register_buffer('scale_factor', torch.tensor(scale_factor))
        self.instantiate_first_stage(first_stage_config)
        self.instantiate_cond_stage(cond_stage_config)
        self.first_stage_config = first_stage_config
        self.cond_stage_config = cond_stage_config        
        self.clip_denoised = False

        self.cond_stage_forward = cond_stage_forward
        self.encoder_type = encoder_type
        assert(encoder_type in ["2d", "3d"])
        self.uncond_prob = uncond_prob
        self.classifier_free_guidance = True if uncond_prob > 0 else False
        assert(uncond_type in ["zero_embed", "empty_seq"])
        self.uncond_type = uncond_type


        self.restarted_from_ckpt = False
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model)
            self.restarted_from_ckpt = True
                

    def make_cond_schedule(self, ):
        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
        self.cond_ids[:self.num_timesteps_cond] = ids

    def q_sample(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        if self.use_scale:  
            return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start *
                extract_into_tensor(self.scale_arr, t, x_start.shape) +
                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
        else:
            return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)


    def _freeze_model(self):
        for name, para in self.model.diffusion_model.named_parameters():
            para.requires_grad = False

    def instantiate_first_stage(self, config):
        model = instantiate_from_config(config)
        self.first_stage_model = model.eval()
        self.first_stage_model.train = disabled_train
        for param in self.first_stage_model.parameters():
            param.requires_grad = False

    def instantiate_cond_stage(self, config):
        if not self.cond_stage_trainable:
            model = instantiate_from_config(config)
            self.cond_stage_model = model.eval()
            self.cond_stage_model.train = disabled_train
            for param in self.cond_stage_model.parameters():
                param.requires_grad = False
        else:
            model = instantiate_from_config(config)
            self.cond_stage_model = model
    
    def get_learned_conditioning(self, c):
        if self.cond_stage_forward is None:
            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
                c = self.cond_stage_model.encode(c)
                if isinstance(c, DiagonalGaussianDistribution):
                    c = c.mode()
            else:
                c = self.cond_stage_model(c)
        else:
            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
        return c

    def get_first_stage_encoding(self, encoder_posterior, noise=None):
        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
            z = encoder_posterior.sample(noise=noise)
        elif isinstance(encoder_posterior, torch.Tensor):
            z = encoder_posterior
        else:
            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
        return self.scale_factor * z
   
    @torch.no_grad()
    def encode_first_stage(self, x):
        if self.encoder_type == "2d" and x.dim() == 5:
            b, _, t, _, _ = x.shape
            x = rearrange(x, 'b c t h w -> (b t) c h w')
            reshape_back = True
        else:
            reshape_back = False
        
        encoder_posterior = self.first_stage_model.encode(x)
        results = self.get_first_stage_encoding(encoder_posterior).detach()
        
        if reshape_back:
            results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t)
        
        return results
    
    @torch.no_grad()
    def encode_first_stage_2DAE(self, x):

        b, _, t, _, _ = x.shape
        results = torch.cat([self.get_first_stage_encoding(self.first_stage_model.encode(x[:,:,i])).detach().unsqueeze(2) for i in range(t)], dim=2)
        
        return results
    
    def decode_core(self, z, **kwargs):
        if self.encoder_type == "2d" and z.dim() == 5:
            b, _, t, _, _ = z.shape
            z = rearrange(z, 'b c t h w -> (b t) c h w')
            reshape_back = True
        else:
            reshape_back = False
            
        z = 1. / self.scale_factor * z

        results = self.first_stage_model.decode(z, **kwargs)
            
        if reshape_back:
            results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t)
        return results

    @torch.no_grad()
    def decode_first_stage(self, z, **kwargs):
        return self.decode_core(z, **kwargs)

    def apply_model(self, x_noisy, t, cond, **kwargs):
        if isinstance(cond, dict):
            # hybrid case, cond is exptected to be a dict
            pass
        else:
            if not isinstance(cond, list):
                cond = [cond]
            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
            cond = {key: cond}

        x_recon = self.model(x_noisy, t, **cond, **kwargs)

        if isinstance(x_recon, tuple):
            return x_recon[0]
        else:
            return x_recon

    def _get_denoise_row_from_list(self, samples, desc=''):
        denoise_row = []
        for zd in tqdm(samples, desc=desc):
            denoise_row.append(self.decode_first_stage(zd.to(self.device)))
        n_log_timesteps = len(denoise_row)

        denoise_row = torch.stack(denoise_row)  # n_log_timesteps, b, C, H, W
        
        if denoise_row.dim() == 5:
            # img, num_imgs= n_log_timesteps * bs, grid_size=[bs,n_log_timesteps]
            denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
            denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
            denoise_grid = make_grid(denoise_grid, nrow=n_log_timesteps)
        elif denoise_row.dim() == 6:
            # video, grid_size=[n_log_timesteps*bs, t]
            video_length = denoise_row.shape[3]
            denoise_grid = rearrange(denoise_row, 'n b c t h w -> b n c t h w')
            denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w')
            denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w')
            denoise_grid = make_grid(denoise_grid, nrow=video_length)
        else:
            raise ValueError

        return denoise_grid
 

    @torch.no_grad()
    def decode_first_stage_2DAE(self, z, **kwargs):

        b, _, t, _, _ = z.shape
        z = 1. / self.scale_factor * z
        results = torch.cat([self.first_stage_model.decode(z[:,:,i], **kwargs).unsqueeze(2) for i in range(t)], dim=2)

        return results


    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=False, score_corrector=None, corrector_kwargs=None, **kwargs):
        t_in = t
        model_out = self.apply_model(x, t_in, c, **kwargs)

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

        if self.parameterization == "eps":
            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
        elif self.parameterization == "x0":
            x_recon = model_out
        else:
            raise NotImplementedError()

        if clip_denoised:
            x_recon.clamp_(-1., 1.)

        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)

        if return_x0:
            return model_mean, posterior_variance, posterior_log_variance, x_recon
        else:
            return model_mean, posterior_variance, posterior_log_variance

    @torch.no_grad()
    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_x0=False, \
                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, **kwargs):
        b, *_, device = *x.shape, x.device
        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_x0=return_x0, \
                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, **kwargs)
        if return_x0:
            model_mean, _, model_log_variance, x0 = outputs
        else:
            model_mean, _, model_log_variance = outputs

        noise = noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
        # no noise when t == 0
        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))

        if return_x0:
            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
        else:
            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise

    @torch.no_grad()
    def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, \
                      timesteps=None, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None, **kwargs):

        if not log_every_t:
            log_every_t = self.log_every_t
        device = self.betas.device
        b = shape[0]        
        # sample an initial noise
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T

        intermediates = [img]
        if timesteps is None:
            timesteps = self.num_timesteps
        if start_T is not None:
            timesteps = min(timesteps, start_T)

        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(range(0, timesteps))

        if mask is not None:
            assert x0 is not None
            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match

        for i in iterator:
            ts = torch.full((b,), i, device=device, dtype=torch.long)
            if self.shorten_cond_schedule:
                assert self.model.conditioning_key != 'hybrid'
                tc = self.cond_ids[ts].to(cond.device)
                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))

            img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, **kwargs)
            if mask is not None:
                img_orig = self.q_sample(x0, ts)
                img = img_orig * mask + (1. - mask) * img

            if i % log_every_t == 0 or i == timesteps - 1:
                intermediates.append(img)
            if callback: callback(i)
            if img_callback: img_callback(img, i)

        if return_intermediates:
            return img, intermediates
        return img


class LatentVisualDiffusion(LatentDiffusion):
    def __init__(self, cond_img_config, finegrained=False, random_cond=False, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.random_cond = random_cond
        self.instantiate_img_embedder(cond_img_config, freeze=True)
        num_tokens = 16 if finegrained else 4
        self.image_proj_model = self.init_projector(use_finegrained=finegrained, num_tokens=num_tokens, input_dim=1024,\
                                            cross_attention_dim=1024, dim=1280)    

    def instantiate_img_embedder(self, config, freeze=True):
        embedder = instantiate_from_config(config)
        if freeze:
            self.embedder = embedder.eval()
            self.embedder.train = disabled_train
            for param in self.embedder.parameters():
                param.requires_grad = False

    def init_projector(self, use_finegrained, num_tokens, input_dim, cross_attention_dim, dim):
        if not use_finegrained:
            image_proj_model = ImageProjModel(clip_extra_context_tokens=num_tokens, cross_attention_dim=cross_attention_dim,
                clip_embeddings_dim=input_dim
            )
        else:
            image_proj_model = Resampler(dim=input_dim, depth=4, dim_head=64, heads=12, num_queries=num_tokens,
                embedding_dim=dim, output_dim=cross_attention_dim, ff_mult=4
            )
        return image_proj_model

    ## Never delete this func: it is used in log_images() and inference stage
    def get_image_embeds(self, batch_imgs):
        ## img: b c h w
        img_token = self.embedder(batch_imgs)
        img_emb = self.image_proj_model(img_token)
        return img_emb


class DiffusionWrapper(pl.LightningModule):
    def __init__(self, diff_model_config, conditioning_key):
        super().__init__()
        self.diffusion_model = instantiate_from_config(diff_model_config)
        self.conditioning_key = conditioning_key

    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None,
                c_adm=None, s=None, mask=None, **kwargs):
        # temporal_context = fps is foNone
        if self.conditioning_key is None:
            out = self.diffusion_model(x, t)
        elif self.conditioning_key == 'concat':
            xc = torch.cat([x] + c_concat, dim=1)
            out = self.diffusion_model(xc, t, **kwargs)
        elif self.conditioning_key == 'crossattn':
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(x, t, context=cc, **kwargs)
        elif self.conditioning_key == 'hybrid':
            ## it is just right [b,c,t,h,w]: concatenate in channel dim
            xc = torch.cat([x] + c_concat, dim=1)
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc)
        elif self.conditioning_key == 'resblockcond':
            cc = c_crossattn[0]
            out = self.diffusion_model(x, t, context=cc)
        elif self.conditioning_key == 'adm':
            cc = c_crossattn[0]
            out = self.diffusion_model(x, t, y=cc)
        elif self.conditioning_key == 'hybrid-adm':
            assert c_adm is not None
            xc = torch.cat([x] + c_concat, dim=1)
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc, y=c_adm)
        elif self.conditioning_key == 'hybrid-time':
            assert s is not None
            xc = torch.cat([x] + c_concat, dim=1)
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc, s=s)
        elif self.conditioning_key == 'concat-time-mask':
            # assert s is not None
            # mainlogger.info('x & mask:',x.shape,c_concat[0].shape)
            xc = torch.cat([x] + c_concat, dim=1)
            out = self.diffusion_model(xc, t, context=None, s=s, mask=mask)
        elif self.conditioning_key == 'concat-adm-mask':
            # assert s is not None
            # mainlogger.info('x & mask:',x.shape,c_concat[0].shape)
            if c_concat is not None:
                xc = torch.cat([x] + c_concat, dim=1)
            else:
                xc = x
            out = self.diffusion_model(xc, t, context=None, y=s, mask=mask)
        elif self.conditioning_key == 'hybrid-adm-mask':
            cc = torch.cat(c_crossattn, 1)
            if c_concat is not None:
                xc = torch.cat([x] + c_concat, dim=1)
            else:
                xc = x
            out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask)
        elif self.conditioning_key == 'hybrid-time-adm': # adm means y, e.g., class index
            # assert s is not None
            assert c_adm is not None
            xc = torch.cat([x] + c_concat, dim=1)
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm)
        else:
            raise NotImplementedError()

        return out