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

import itertools
import os
from contextlib import contextmanager, nullcontext
from functools import partial

import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from omegaconf import ListConfig
from pytorch_lightning.utilities.distributed import rank_zero_only
from torch.optim.lr_scheduler import LambdaLR
from torchvision.utils import make_grid
from tqdm import tqdm

from ....ldm.models.autoencoder import AutoencoderKL, IdentityFirstStage
from ....ldm.models.diffusion.ddim import DDIMSampler
from ....ldm.modules.diffusionmodules.util import (extract_into_tensor,
                                                   make_beta_schedule,
                                                   noise_like)
from ....ldm.modules.distributions.distributions import (
    DiagonalGaussianDistribution, normal_kl)
from ....ldm.modules.ema import LitEma
from ....ldm.util import (count_params, default, exists,
                          instantiate_from_config, isimage, ismap,
                          log_txt_as_img, mean_flat)

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


def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


def uniform_on_device(r1, r2, shape, device):
    return (r1 - r2) * torch.rand(*shape, device=device) + r2


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='val/loss',
            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.,
            make_it_fit=False,
            ucg_training=None,
            reset_ema=False,
            reset_num_ema_updates=False,
            **kwargs):
        super().__init__()
        assert parameterization in [
            'eps', 'x0', 'v'
        ], 'currently only supporting "eps" and "x0" and "v"'
        self.parameterization = parameterization
        print(
            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.image_size = image_size  # try conv?
        self.channels = channels
        self.use_positional_encodings = use_positional_encodings
        self.model = DiffusionWrapper(unet_config, conditioning_key)
        count_params(self.model, verbose=True)
        self.use_ema = use_ema
        if self.use_ema:
            self.model_ema = LitEma(self.model)
            print(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
        self.make_it_fit = make_it_fit
        if reset_ema:
            assert exists(ckpt_path)
        if ckpt_path is not None:
            self.init_from_ckpt(
                ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
            if reset_ema:
                assert self.use_ema
                print(
                    'Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.'
                )
                self.model_ema = LitEma(self.model)
        if reset_num_ema_updates:
            print(
                ' +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ '
            )
            assert self.use_ema
            self.model_ema.reset_num_updates()

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

        self.ucg_training = ucg_training or dict()
        if self.ucg_training:
            self.ucg_prng = np.random.RandomState()

    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))))
        tmp = betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)
        self.register_buffer('posterior_mean_coef1', to_torch(tmp))
        tmp = (1. - alphas_cumprod_prev) * np.sqrt(alphas)
        self.register_buffer('posterior_mean_coef2',
                             to_torch(tmp / (1. - alphas_cumprod)))

        if self.parameterization == 'eps':
            tmp = 2 * self.posterior_variance * to_torch(alphas)
            lvlb_weights = self.betas**2 / (tmp * (1 - self.alphas_cumprod))
        elif self.parameterization == 'x0':
            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (
                2. * 1 - torch.Tensor(alphas_cumprod))
        elif self.parameterization == 'v':
            tmp = 2 * self.posterior_variance * to_torch(alphas)
            tmp = self.betas**2 (tmp * (1 - self.alphas_cumprod))
            lvlb_weights = torch.ones_like(tmp)
        else:
            raise NotImplementedError('mu not supported')
        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:
                print(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:
                    print(f'{context}: Restored training weights')

    @torch.no_grad()
    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):
                    print('Deleting key {} from state_dict.'.format(k))
                    del sd[k]
        if self.make_it_fit:
            n_params = len([
                name for name, _ in itertools.chain(self.named_parameters(),
                                                    self.named_buffers())
            ])
            for name, param in tqdm(
                    itertools.chain(self.named_parameters(),
                                    self.named_buffers()),
                    desc='Fitting old weights to new weights',
                    total=n_params):
                if name not in sd:
                    continue
                old_shape = sd[name].shape
                new_shape = param.shape
                assert len(old_shape) == len(new_shape)
                if len(new_shape) > 2:
                    # we only modify first two axes
                    assert new_shape[2:] == old_shape[2:]
                # assumes first axis corresponds to output dim
                if not new_shape == old_shape:
                    new_param = param.clone()
                    old_param = sd[name]
                    if len(new_shape) == 1:
                        for i in range(new_param.shape[0]):
                            new_param[i] = old_param[i % old_shape[0]]
                    elif len(new_shape) >= 2:
                        for i in range(new_param.shape[0]):
                            for j in range(new_param.shape[1]):
                                new_param[i, j] = old_param[i % old_shape[0],
                                                            j % old_shape[1]]

                        n_used_old = torch.ones(old_shape[1])
                        for j in range(new_param.shape[1]):
                            n_used_old[j % old_shape[1]] += 1
                        n_used_new = torch.zeros(new_shape[1])
                        for j in range(new_param.shape[1]):
                            n_used_new[j] = n_used_old[j % old_shape[1]]

                        n_used_new = n_used_new[None, :]
                        while len(n_used_new.shape) < len(new_shape):
                            n_used_new = n_used_new.unsqueeze(-1)
                        new_param /= n_used_new

                    sd[name] = new_param

        missing, unexpected = self.load_state_dict(
            sd,
            strict=False) if not only_model else self.model.load_state_dict(
                sd, strict=False)
        print(
            f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
        )
        if len(missing) > 0:
            print(f'Missing Keys:\n {missing}')
        if len(unexpected) > 0:
            print(f'\nUnexpected Keys:\n {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 predict_start_from_z_and_v(self, x_t, t, v):
        # 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)))
        return (
            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t
            - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t,
                                  x_t.shape) * v)

    def predict_eps_from_z_and_v(self, x_t, t, v):
        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v
                + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t,
                                      x_t.shape) * x_t)

    def q_posterior(self, x_start, x_t, t):
        tmp1 = extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape)
        tmp2 = extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape)
        posterior_mean = (tmp1 * x_start + tmp2 * 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.sqrt_one_minus_alphas_cumprod, t,
                                      x_start.shape) * noise)

    def get_v(self, x, noise, t):
        tmp1 = extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape)
        tmp2 = extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t,
                                   x.shape)
        return (tmp1 * noise - tmp2 * x)

    def get_loss(self, pred, target, mean=True):
        if self.loss_type == 'l1':
            loss = (target - pred).abs()
            if mean:
                loss = loss.mean()
        elif self.loss_type == 'l2':
            if mean:
                loss = torch.nn.functional.mse_loss(target, pred)
            else:
                loss = torch.nn.functional.mse_loss(
                    target, pred, reduction='none')
        else:
            raise NotImplementedError("unknown loss type '{loss_type}'")

        return loss

    def p_losses(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        model_out = self.model(x_noisy, t)

        loss_dict = {}
        if self.parameterization == 'eps':
            target = noise
        elif self.parameterization == 'x0':
            target = x_start
        elif self.parameterization == 'v':
            target = self.get_v(x_start, noise, t)
        else:
            raise NotImplementedError(
                f'Parameterization {self.parameterization} not yet supported')

        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])

        log_prefix = 'train' if self.training else 'val'

        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
        loss_simple = loss.mean() * self.l_simple_weight

        loss_vlb = (self.lvlb_weights[t] * loss).mean()
        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})

        loss = loss_simple + self.original_elbo_weight * loss_vlb

        loss_dict.update({f'{log_prefix}/loss': loss})

        return loss, loss_dict

    def forward(self, x, *args, **kwargs):
        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
        t = torch.randint(
            0, self.num_timesteps, (x.shape[0], ), device=self.device).long()
        return self.p_losses(x, t, *args, **kwargs)

    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = rearrange(x, 'b h w c -> b c h w')
        x = x.to(memory_format=torch.contiguous_format).float()
        return x

    def shared_step(self, batch):
        x = self.get_input(batch, self.first_stage_key)
        loss, loss_dict = self(x)
        return loss, loss_dict

    def training_step(self, batch, batch_idx):
        for k in self.ucg_training:
            p = self.ucg_training[k]['p']
            val = self.ucg_training[k]['val']
            if val is None:
                val = ''
            for i in range(len(batch[k])):
                if self.ucg_prng.choice(2, p=[1 - p, p]):
                    batch[k][i] = val

        loss, loss_dict = self.shared_step(batch)

        self.log_dict(
            loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True)

        self.log(
            'global_step',
            self.global_step,
            prog_bar=True,
            logger=True,
            on_step=True,
            on_epoch=False)

        if self.use_scheduler:
            lr = self.optimizers().param_groups[0]['lr']
            self.log(
                'lr_abs',
                lr,
                prog_bar=True,
                logger=True,
                on_step=True,
                on_epoch=False)

        return loss

    @torch.no_grad()
    def validation_step(self, batch, batch_idx):
        _, loss_dict_no_ema = self.shared_step(batch)
        with self.ema_scope():
            _, loss_dict_ema = self.shared_step(batch)
            loss_dict_ema = {
                key + '_ema': loss_dict_ema[key]
                for key in loss_dict_ema
            }
        self.log_dict(
            loss_dict_no_ema,
            prog_bar=False,
            logger=True,
            on_step=False,
            on_epoch=True)
        self.log_dict(
            loss_dict_ema,
            prog_bar=False,
            logger=True,
            on_step=False,
            on_epoch=True)

    def on_train_batch_end(self, *args, **kwargs):
        if self.use_ema:
            self.model_ema(self.model)

    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

    def configure_optimizers(self):
        lr = self.learning_rate
        params = list(self.model.parameters())
        if self.learn_logvar:
            params = params + [self.logvar]
        opt = torch.optim.AdamW(params, lr=lr)
        return opt


class LatentDiffusion(DDPM):
    """main class"""

    def __init__(self,
                 first_stage_config,
                 cond_stage_config,
                 num_timesteps_cond=None,
                 cond_stage_key='image',
                 cond_stage_trainable=False,
                 concat_mode=True,
                 cond_stage_forward=None,
                 conditioning_key=None,
                 scale_factor=1.0,
                 scale_by_std=False,
                 force_null_conditioning=False,
                 *args,
                 **kwargs):
        self.model_dir = kwargs.get('model_dir')
        self.force_null_conditioning = force_null_conditioning
        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
        if conditioning_key is None:
            conditioning_key = 'concat' if concat_mode else 'crossattn'
        if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:
            conditioning_key = None
        ckpt_path = kwargs.pop('ckpt_path', None)
        reset_ema = kwargs.pop('reset_ema', False)
        reset_num_ema_updates = kwargs.pop('reset_num_ema_updates', False)
        ignore_keys = kwargs.pop('ignore_keys', [])
        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
        self.concat_mode = concat_mode
        self.cond_stage_trainable = cond_stage_trainable
        self.cond_stage_key = cond_stage_key
        try:
            self.num_downs = len(
                first_stage_config.params.ddconfig.ch_mult) - 1
        except Exception:
            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.cond_stage_forward = cond_stage_forward
        self.clip_denoised = False
        self.bbox_tokenizer = None

        self.restarted_from_ckpt = False
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys)
            self.restarted_from_ckpt = True
            if reset_ema:
                assert self.use_ema
                print(
                    'Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.'
                )
                self.model_ema = LitEma(self.model)
        if reset_num_ema_updates:
            print(
                ' +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ '
            )
            assert self.use_ema
            self.model_ema.reset_num_updates()

    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

    @rank_zero_only
    @torch.no_grad()
    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
        # only for very first batch
        if (self.scale_by_std and self.current_epoch == 0
                and self.global_step == 0 and batch_idx == 0
                and not self.restarted_from_ckpt):
            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
            # set rescale weight to 1./std of encodings
            print('### USING STD-RESCALING ###')
            x = super().get_input(batch, self.first_stage_key)
            x = x.to(self.device)
            encoder_posterior = self.encode_first_stage(x)
            z = self.get_first_stage_encoding(encoder_posterior).detach()
            del self.scale_factor
            self.register_buffer('scale_factor', 1. / z.flatten().std())
            print(f'setting self.scale_factor to {self.scale_factor}')
            print('### USING STD-RESCALING ###')

    def register_schedule(self,
                          given_betas=None,
                          beta_schedule='linear',
                          timesteps=1000,
                          linear_start=1e-4,
                          linear_end=2e-2,
                          cosine_s=8e-3):
        super().register_schedule(given_betas, beta_schedule, timesteps,
                                  linear_start, linear_end, cosine_s)

        self.shorten_cond_schedule = self.num_timesteps_cond > 1
        if self.shorten_cond_schedule:
            self.make_cond_schedule()

    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):
        config.params.model_path = os.path.join(self.model_dir,
                                                config.params.model_path)
        if not self.cond_stage_trainable:
            if config == '__is_first_stage__':
                print('Using first stage also as cond stage.')
                self.cond_stage_model = self.first_stage_model
            elif config == '__is_unconditional__':
                print(
                    f'Training {self.__class__.__name__} as an unconditional model.'
                )
                self.cond_stage_model = None
                # self.be_unconditional = True
            else:
                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:
            assert config != '__is_first_stage__'
            assert config != '__is_unconditional__'
            model = instantiate_from_config(config)
            self.cond_stage_model = model

    def _get_denoise_row_from_list(self,
                                   samples,
                                   desc='',
                                   force_no_decoder_quantization=False):
        denoise_row = []
        for zd in tqdm(samples, desc=desc):
            denoise_row.append(
                self.decode_first_stage(
                    zd.to(self.device),
                    force_not_quantize=force_no_decoder_quantization))
        n_imgs_per_row = len(denoise_row)
        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
        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_imgs_per_row)
        return denoise_grid

    def get_first_stage_encoding(self, encoder_posterior):
        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
            z = encoder_posterior.sample()
        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

    def get_learned_conditioning(self, c):
        # c 1,3,224,224
        if self.cond_stage_forward is None:
            if hasattr(self.cond_stage_model, 'encode') and callable(
                    self.cond_stage_model.encode):
                # 1,1,1024
                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 meshgrid(self, h, w):
        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)

        arr = torch.cat([y, x], dim=-1)
        return arr

    def delta_border(self, h, w):
        """
        :param h: height
        :param w: width
        :return: normalized distance to image border,
         wtith min distance = 0 at border and max dist = 0.5 at image center
        """
        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
        arr = self.meshgrid(h, w) / lower_right_corner
        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
        edge_dist = torch.min(
            torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
        return edge_dist

    def get_weighting(self, h, w, Ly, Lx, device):
        weighting = self.delta_border(h, w)
        weighting = torch.clip(
            weighting,
            self.split_input_params['clip_min_weight'],
            self.split_input_params['clip_max_weight'],
        )
        weighting = weighting.view(1, h * w, 1).repeat(1, 1,
                                                       Ly * Lx).to(device)

        if self.split_input_params['tie_braker']:
            L_weighting = self.delta_border(Ly, Lx)
            L_weighting = torch.clip(
                L_weighting, self.split_input_params['clip_min_tie_weight'],
                self.split_input_params['clip_max_tie_weight'])

            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
            weighting = weighting * L_weighting
        return weighting

    def get_fold_unfold(self,
                        x,
                        kernel_size,
                        stride,
                        uf=1,
                        df=1):  # todo load once not every time, shorten code
        """
        :param x: img of size (bs, c, h, w)
        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
        """
        bs, nc, h, w = x.shape

        # number of crops in image
        Ly = (h - kernel_size[0]) // stride[0] + 1
        Lx = (w - kernel_size[1]) // stride[1] + 1

        if uf == 1 and df == 1:
            fold_params = dict(
                kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
            unfold = torch.nn.Unfold(**fold_params)

            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)

            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly,
                                           Lx, x.device).to(x.dtype)
            normalization = fold(weighting).view(1, 1, h,
                                                 w)  # normalizes the overlap
            weighting = weighting.view(
                (1, 1, kernel_size[0], kernel_size[1], Ly * Lx))

        elif uf > 1 and df == 1:
            fold_params = dict(
                kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
            unfold = torch.nn.Unfold(**fold_params)

            fold_params2 = dict(
                kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
                dilation=1,
                padding=0,
                stride=(stride[0] * uf, stride[1] * uf))
            fold = torch.nn.Fold(
                output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)

            weighting = self.get_weighting(kernel_size[0] * uf,
                                           kernel_size[1] * uf, Ly, Lx,
                                           x.device).to(x.dtype)
            normalization = fold(weighting).view(
                1, 1, h * uf, w * uf)  # normalizes the overlap
            weighting = weighting.view(
                (1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))

        elif df > 1 and uf == 1:
            fold_params = dict(
                kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
            unfold = torch.nn.Unfold(**fold_params)

            fold_params2 = dict(
                kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
                dilation=1,
                padding=0,
                stride=(stride[0] // df, stride[1] // df))
            fold = torch.nn.Fold(
                output_size=(x.shape[2] // df, x.shape[3] // df),
                **fold_params2)

            weighting = self.get_weighting(kernel_size[0] // df,
                                           kernel_size[1] // df, Ly, Lx,
                                           x.device).to(x.dtype)
            normalization = fold(weighting).view(
                1, 1, h // df, w // df)  # normalizes the overlap
            weighting = weighting.view(
                (1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))

        else:
            raise NotImplementedError

        return fold, unfold, normalization, weighting

    @torch.no_grad()
    def get_input(self,
                  batch,
                  k,
                  return_first_stage_outputs=False,
                  force_c_encode=False,
                  cond_key=None,
                  return_original_cond=False,
                  bs=None,
                  return_x=False):
        x = super().get_input(batch, k)
        if bs is not None:
            x = x[:bs]
        x = x.to(self.device)
        encoder_posterior = self.encode_first_stage(x)
        z = self.get_first_stage_encoding(encoder_posterior).detach()

        if self.model.conditioning_key is not None and not self.force_null_conditioning:
            if cond_key is None:
                cond_key = self.cond_stage_key
            if cond_key != self.first_stage_key:
                if cond_key in ['caption', 'coordinates_bbox', 'txt']:
                    xc = batch[cond_key]
                elif cond_key in ['class_label', 'cls']:
                    xc = batch
                else:
                    xc = super().get_input(batch, cond_key).to(self.device)
            else:
                xc = x
            if not self.cond_stage_trainable or force_c_encode:
                if isinstance(xc, dict) or isinstance(xc, list):
                    c = self.get_learned_conditioning(xc)
                else:
                    c = self.get_learned_conditioning(xc.to(self.device))
            else:
                c = xc
            if bs is not None:
                c = c[:bs]

            if self.use_positional_encodings:
                pos_x, pos_y = self.compute_latent_shifts(batch)
                ckey = __conditioning_keys__[self.model.conditioning_key]
                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}

        else:
            c = None
            xc = None
            if self.use_positional_encodings:
                pos_x, pos_y = self.compute_latent_shifts(batch)
                c = {'pos_x': pos_x, 'pos_y': pos_y}
        out = [z, c]
        if return_first_stage_outputs:
            xrec = self.decode_first_stage(z)
            out.extend([x, xrec])
        if return_x:
            out.extend([x])
        if return_original_cond:
            out.append(xc)
        return out

    @torch.no_grad()
    def decode_first_stage(self,
                           z,
                           predict_cids=False,
                           force_not_quantize=False):
        if predict_cids:
            if z.dim() == 4:
                z = torch.argmax(z.exp(), dim=1).long()
            z = self.first_stage_model.quantize.get_codebook_entry(
                z, shape=None)
            z = rearrange(z, 'b h w c -> b c h w').contiguous()

        z = 1. / self.scale_factor * z
        return self.first_stage_model.decode(z)

    @torch.no_grad()
    def encode_first_stage(self, x):
        return self.first_stage_model.encode(x)

    def shared_step(self, batch, **kwargs):
        x, c = self.get_input(batch, self.first_stage_key)
        loss = self(x, c)
        return loss

    def forward(self, x, c, *args, **kwargs):
        # t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
        t = self.time_steps.reshape((x.shape[0], )).to(self.device).long()

        if self.model.conditioning_key is not None:
            assert c is not None
            if self.cond_stage_trainable:
                c = self.get_learned_conditioning(c)
            if self.shorten_cond_schedule:  # TODO: drop this option
                tc = self.cond_ids[t].to(self.device)
                c = self.q_sample(
                    x_start=c, t=tc, noise=torch.randn_like(c.float()))
        return self.p_losses(x, c, t, *args, **kwargs)

    def apply_model(self, x_noisy, t, cond, return_ids=False):
        if isinstance(cond, dict):
            # hybrid case, cond is expected 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)

        if isinstance(x_recon, tuple) and not return_ids:
            return x_recon[0]
        else:
            return x_recon

    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
        tmp1 = extract_into_tensor(self.sqrt_recip_alphas_cumprod, t,
                                   x_t.shape)
        tmp2 = extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t,
                                   x_t.shape)
        return (tmp1 * x_t - pred_xstart) / tmp2

    def _prior_bpd(self, x_start):
        """
        Get the prior KL term for the variational lower-bound, measured in
        bits-per-dim.
        This term can't be optimized, as it only depends on the encoder.
        :param x_start: the [N x C x ...] tensor of inputs.
        :return: a batch of [N] KL values (in bits), one per batch element.
        """
        batch_size = x_start.shape[0]
        t = torch.tensor(
            [self.num_timesteps - 1] * batch_size, device=x_start.device)
        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
        kl_prior = normal_kl(
            mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
        return mean_flat(kl_prior) / np.log(2.0)

    def p_losses(self, x_start, cond, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        model_output = self.apply_model(x_noisy, t, cond)

        loss_dict = {}
        prefix = 'train' if self.training else 'val'

        if self.parameterization == 'x0':
            target = x_start
        elif self.parameterization == 'eps':
            target = noise
        elif self.parameterization == 'v':
            target = self.get_v(x_start, noise, t)
        else:
            raise NotImplementedError()

        loss_simple = self.get_loss(model_output, target, mean=False)
        # boundary = self.boundary.to(loss_simple.device)
        # boundary = F.interpolate(boundary, size = (64,64)) * 5 + 1.0 #16,1,64,64

        # print(loss_simple.shape) #16,4,64,64
        loss_simple = loss_simple.mean([1, 2, 3])
        # .mean([1, 2, 3])
        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})

        logvar_t = self.logvar[t].to(self.device)
        loss = loss_simple / torch.exp(logvar_t) + logvar_t
        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
        if self.learn_logvar:
            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
            loss_dict.update({'logvar': self.logvar.data.mean()})

        loss = self.l_simple_weight * loss.mean()

        loss_vlb = self.get_loss(
            model_output, target, mean=False).mean(dim=(1, 2, 3))
        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
        loss += (self.original_elbo_weight * loss_vlb)
        loss_dict.update({f'{prefix}/loss': loss})

        # print(self.parameterization, self.learn_logvar, self.original_elbo_weight, self.lvlb_weights[t])

        return loss, loss_dict

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

        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 return_codebook_ids:
            model_out, logits = model_out

        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.)
        if quantize_denoised:
            x_recon, _, [_, _,
                         indices] = self.first_stage_model.quantize(x_recon)
        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
            x_start=x_recon, x_t=x, t=t)
        if return_codebook_ids:
            return model_mean, posterior_variance, posterior_log_variance, logits
        elif 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_codebook_ids=False,
                 quantize_denoised=False,
                 return_x0=False,
                 temperature=1.,
                 noise_dropout=0.,
                 score_corrector=None,
                 corrector_kwargs=None):
        b, *_, device = *x.shape, x.device
        outputs = self.p_mean_variance(
            x=x,
            c=c,
            t=t,
            clip_denoised=clip_denoised,
            return_codebook_ids=return_codebook_ids,
            quantize_denoised=quantize_denoised,
            return_x0=return_x0,
            score_corrector=score_corrector,
            corrector_kwargs=corrector_kwargs)
        if return_codebook_ids:
            raise DeprecationWarning('Support dropped.')
            model_mean, _, model_log_variance, logits = outputs
        elif 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_codebook_ids:
            return model_mean + nonzero_mask * (
                0.5 * model_log_variance).exp() * noise, logits.argmax(dim=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 progressive_denoising(self,
                              cond,
                              shape,
                              verbose=True,
                              callback=None,
                              quantize_denoised=False,
                              img_callback=None,
                              mask=None,
                              x0=None,
                              temperature=1.,
                              noise_dropout=0.,
                              score_corrector=None,
                              corrector_kwargs=None,
                              batch_size=None,
                              x_T=None,
                              start_T=None,
                              log_every_t=None):
        if not log_every_t:
            log_every_t = self.log_every_t
        timesteps = self.num_timesteps
        if batch_size is not None:
            b = batch_size if batch_size is not None else shape[0]
            shape = [batch_size] + list(shape)
        else:
            b = batch_size = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=self.device)
        else:
            img = x_T
        intermediates = []
        if cond is not None:
            if isinstance(cond, dict):
                cond = {
                    key:
                    cond[key][:batch_size] if not isinstance(cond[key], list)
                    else list(map(lambda x: x[:batch_size], cond[key]))
                    for key in cond
                }
            else:
                cond = [c[:batch_size] for c in cond] if isinstance(
                    cond, list) else cond[:batch_size]

        if start_T is not None:
            timesteps = min(timesteps, start_T)
        iterator = tqdm(
            reversed(range(0, timesteps)),
            desc='Progressive Generation',
            total=timesteps) if verbose else reversed(range(0, timesteps))
        if type(temperature) == float:
            temperature = [temperature] * timesteps

        for i in iterator:
            ts = torch.full((b, ), i, device=self.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, x0_partial = self.p_sample(
                img,
                cond,
                ts,
                clip_denoised=self.clip_denoised,
                quantize_denoised=quantize_denoised,
                return_x0=True,
                temperature=temperature[i],
                noise_dropout=noise_dropout,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs)
            if mask is not None:
                assert x0 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(x0_partial)
            if callback:
                callback(i)
            if img_callback:
                img_callback(img, i)
        return img, intermediates

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

        if not log_every_t:
            log_every_t = self.log_every_t
        device = self.betas.device
        b = shape[0]
        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,
                quantize_denoised=quantize_denoised)
            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

    @torch.no_grad()
    def sample(self,
               cond,
               batch_size=16,
               return_intermediates=False,
               x_T=None,
               verbose=True,
               timesteps=None,
               quantize_denoised=False,
               mask=None,
               x0=None,
               shape=None,
               **kwargs):
        if shape is None:
            shape = (batch_size, self.channels, self.image_size,
                     self.image_size)
        if cond is not None:
            if isinstance(cond, dict):
                cond = {
                    key:
                    cond[key][:batch_size] if not isinstance(cond[key], list)
                    else list(map(lambda x: x[:batch_size], cond[key]))
                    for key in cond
                }
            else:
                cond = [c[:batch_size] for c in cond] if isinstance(
                    cond, list) else cond[:batch_size]
        return self.p_sample_loop(
            cond,
            shape,
            return_intermediates=return_intermediates,
            x_T=x_T,
            verbose=verbose,
            timesteps=timesteps,
            quantize_denoised=quantize_denoised,
            mask=mask,
            x0=x0)

    @torch.no_grad()
    def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
        if ddim:
            ddim_sampler = DDIMSampler(self)
            shape = (self.channels, self.image_size, self.image_size)
            samples, intermediates = ddim_sampler.sample(
                ddim_steps, batch_size, shape, cond, verbose=False, **kwargs)

        else:
            samples, intermediates = self.sample(
                cond=cond,
                batch_size=batch_size,
                return_intermediates=True,
                **kwargs)

        return samples, intermediates

    @torch.no_grad()
    def get_unconditional_conditioning(self, batch_size, null_label=None):
        if null_label is not None:
            xc = null_label
            if isinstance(xc, ListConfig):
                xc = list(xc)
            if isinstance(xc, dict) or isinstance(xc, list):
                c = self.get_learned_conditioning(xc)
            else:
                if hasattr(xc, 'to'):
                    xc = xc.to(self.device)
                c = self.get_learned_conditioning(xc)
        else:
            if self.cond_stage_key in ['class_label', 'cls']:
                xc = self.cond_stage_model.get_unconditional_conditioning(
                    batch_size, device=self.device)
                return self.get_learned_conditioning(xc)
            else:
                raise NotImplementedError('todo')
        if isinstance(c, list):  # in case the encoder gives us a list
            for i in range(len(c)):
                c[i] = repeat(
                    c[i], '1 ... -> b ...', b=batch_size).to(self.device)
        else:
            c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
        return c

    @torch.no_grad()
    def log_images(self,
                   batch,
                   N=8,
                   n_row=4,
                   sample=True,
                   ddim_steps=50,
                   ddim_eta=0.,
                   return_keys=None,
                   quantize_denoised=True,
                   inpaint=True,
                   plot_denoise_rows=False,
                   plot_progressive_rows=True,
                   plot_diffusion_rows=True,
                   unconditional_guidance_scale=1.,
                   unconditional_guidance_label=None,
                   use_ema_scope=True,
                   **kwargs):
        ema_scope = self.ema_scope if use_ema_scope else nullcontext
        use_ddim = ddim_steps is not None

        log = dict()
        z, c, x, xrec, xc = self.get_input(
            batch,
            self.first_stage_key,
            return_first_stage_outputs=True,
            force_c_encode=True,
            return_original_cond=True,
            bs=N)
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        log['inputs'] = x
        log['reconstruction'] = xrec
        if self.model.conditioning_key is not None:
            if hasattr(self.cond_stage_model, 'decode'):
                xc = self.cond_stage_model.decode(c)
                log['conditioning'] = xc
            elif self.cond_stage_key in ['caption', 'txt']:
                xc = log_txt_as_img((x.shape[2], x.shape[3]),
                                    batch[self.cond_stage_key],
                                    size=x.shape[2] // 25)
                log['conditioning'] = xc
            elif self.cond_stage_key in ['class_label', 'cls']:
                try:
                    xc = log_txt_as_img((x.shape[2], x.shape[3]),
                                        batch['human_label'],
                                        size=x.shape[2] // 25)
                    log['conditioning'] = xc
                except KeyError:
                    # probably no "human_label" in batch
                    pass
            elif isimage(xc):
                log['conditioning'] = xc
            if ismap(xc):
                log['original_conditioning'] = self.to_rgb(xc)

        if plot_diffusion_rows:
            # get diffusion row
            diffusion_row = list()
            z_start = z[: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(z_start)
                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
                    diffusion_row.append(self.decode_first_stage(z_noisy))

            diffusion_row = torch.stack(
                diffusion_row)  # n_log_step, n_row, C, H, W
            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
            diffusion_grid = rearrange(diffusion_grid,
                                       'b n c h w -> (b n) c h w')
            diffusion_grid = make_grid(
                diffusion_grid, nrow=diffusion_row.shape[0])
            log['diffusion_row'] = diffusion_grid

        if sample:
            # get denoise row
            with ema_scope('Sampling'):
                samples, z_denoise_row = self.sample_log(
                    cond=c,
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta)
                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
            x_samples = self.decode_first_stage(samples)
            log['samples'] = x_samples
            if plot_denoise_rows:
                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
                log['denoise_row'] = denoise_grid

            if quantize_denoised and not isinstance(
                    self.first_stage_model, AutoencoderKL) and not isinstance(
                        self.first_stage_model, IdentityFirstStage):
                # also display when quantizing x0 while sampling
                with ema_scope('Plotting Quantized Denoised'):
                    samples, z_denoise_row = self.sample_log(
                        cond=c,
                        batch_size=N,
                        ddim=use_ddim,
                        ddim_steps=ddim_steps,
                        eta=ddim_eta,
                        quantize_denoised=True)
                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
                    #                                      quantize_denoised=True)
                x_samples = self.decode_first_stage(samples.to(self.device))
                log['samples_x0_quantized'] = x_samples

        if unconditional_guidance_scale > 1.0:
            uc = self.get_unconditional_conditioning(
                N, unconditional_guidance_label)
            if self.model.conditioning_key == 'crossattn-adm':
                uc = {'c_crossattn': [uc], 'c_adm': c['c_adm']}
            with ema_scope('Sampling with classifier-free guidance'):
                samples_cfg, _ = self.sample_log(
                    cond=c,
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta,
                    unconditional_guidance_scale=unconditional_guidance_scale,
                    unconditional_conditioning=uc,
                )
                x_samples_cfg = self.decode_first_stage(samples_cfg)
                log[f'samples_cfg_scale_{unconditional_guidance_scale:.2f}'] = x_samples_cfg

        if inpaint:
            # make a simple center square
            _, h, w = z.shape[0], z.shape[2], z.shape[3]
            mask = torch.ones(N, h, w).to(self.device)
            # zeros will be filled in
            mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
            mask = mask[:, None, ...]
            with ema_scope('Plotting Inpaint'):
                samples, _ = self.sample_log(
                    cond=c,
                    batch_size=N,
                    ddim=use_ddim,
                    eta=ddim_eta,
                    ddim_steps=ddim_steps,
                    x0=z[:N],
                    mask=mask)
            x_samples = self.decode_first_stage(samples.to(self.device))
            log['samples_inpainting'] = x_samples
            log['mask'] = mask

            # outpaint
            mask = 1. - mask
            with ema_scope('Plotting Outpaint'):
                samples, _ = self.sample_log(
                    cond=c,
                    batch_size=N,
                    ddim=use_ddim,
                    eta=ddim_eta,
                    ddim_steps=ddim_steps,
                    x0=z[:N],
                    mask=mask)
            x_samples = self.decode_first_stage(samples.to(self.device))
            log['samples_outpainting'] = x_samples

        if plot_progressive_rows:
            with ema_scope('Plotting Progressives'):
                img, progressives = self.progressive_denoising(
                    c,
                    shape=(self.channels, self.image_size, self.image_size),
                    batch_size=N)
            prog_row = self._get_denoise_row_from_list(
                progressives, desc='Progressive Generation')
            log['progressive_row'] = prog_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

    def configure_optimizers(self):
        lr = self.learning_rate
        params = list(self.model.parameters())
        if self.cond_stage_trainable:
            print(
                f'{self.__class__.__name__}: Also optimizing conditioner params!'
            )
            params = params + list(self.cond_stage_model.parameters())
        if self.learn_logvar:
            print('Diffusion model optimizing logvar')
            params.append(self.logvar)
        opt = torch.optim.AdamW(params, lr=lr)
        if self.use_scheduler:
            assert 'target' in self.scheduler_config
            scheduler = instantiate_from_config(self.scheduler_config)

            print('Setting up LambdaLR scheduler...')
            scheduler = [{
                'scheduler':
                LambdaLR(opt, lr_lambda=scheduler.schedule),
                'interval':
                'step',
                'frequency':
                1
            }]
            return [opt], scheduler
        return opt

    @torch.no_grad()
    def to_rgb(self, x):
        x = x.float()
        if not hasattr(self, 'colorize'):
            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
        x = nn.functional.conv2d(x, weight=self.colorize)
        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
        return x


class DiffusionWrapper(pl.LightningModule):

    def __init__(self, diff_model_config, conditioning_key):
        super().__init__()
        self.sequential_cross_attn = diff_model_config.pop(
            'sequential_crossattn', False)
        self.diffusion_model = instantiate_from_config(diff_model_config)
        self.conditioning_key = conditioning_key
        assert self.conditioning_key in [
            None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm',
            'crossattn-adm'
        ]

    def forward(self,
                x,
                t,
                c_concat: list = None,
                c_crossattn: list = None,
                c_adm=None):
        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)
        elif self.conditioning_key == 'crossattn':
            if not self.sequential_cross_attn:
                cc = torch.cat(c_crossattn, 1)
            else:
                cc = c_crossattn
            out = self.diffusion_model(x, t, context=cc)
        elif self.conditioning_key == 'hybrid':
            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 == '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 == 'crossattn-adm':
            assert c_adm is not None
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(x, t, context=cc, y=c_adm)
        elif self.conditioning_key == 'adm':
            cc = c_crossattn[0]
            out = self.diffusion_model(x, t, y=cc)
        else:
            raise NotImplementedError()

        return out


class LatentUpscaleDiffusion(LatentDiffusion):

    def __init__(self,
                 *args,
                 low_scale_config,
                 low_scale_key='LR',
                 noise_level_key=None,
                 **kwargs):
        super().__init__(*args, **kwargs)
        # assumes that neither the cond_stage nor the low_scale_model contain trainable params
        assert not self.cond_stage_trainable
        self.instantiate_low_stage(low_scale_config)
        self.low_scale_key = low_scale_key
        self.noise_level_key = noise_level_key

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

    @torch.no_grad()
    def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
        if not log_mode:
            z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
        else:
            z, c, x, xrec, xc = super().get_input(
                batch,
                self.first_stage_key,
                return_first_stage_outputs=True,
                force_c_encode=True,
                return_original_cond=True,
                bs=bs)
        x_low = batch[self.low_scale_key][:bs]
        x_low = rearrange(x_low, 'b h w c -> b c h w')
        x_low = x_low.to(memory_format=torch.contiguous_format).float()
        zx, noise_level = self.low_scale_model(x_low)
        if self.noise_level_key is not None:
            # get noise level from batch instead, e.g. when extracting a custom noise level for bsr
            raise NotImplementedError('TODO')

        all_conds = {
            'c_concat': [zx],
            'c_crossattn': [c],
            'c_adm': noise_level
        }
        if log_mode:
            # TODO: maybe disable if too expensive
            x_low_rec = self.low_scale_model.decode(zx)
            return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
        return z, all_conds

    @torch.no_grad()
    def log_images(self,
                   batch,
                   N=8,
                   n_row=4,
                   sample=True,
                   ddim_steps=200,
                   ddim_eta=1.,
                   return_keys=None,
                   plot_denoise_rows=False,
                   plot_progressive_rows=True,
                   plot_diffusion_rows=True,
                   unconditional_guidance_scale=1.,
                   unconditional_guidance_label=None,
                   use_ema_scope=True,
                   **kwargs):
        ema_scope = self.ema_scope if use_ema_scope else nullcontext
        use_ddim = ddim_steps is not None

        log = dict()
        z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(
            batch, self.first_stage_key, bs=N, log_mode=True)
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        log['inputs'] = x
        log['reconstruction'] = xrec
        log['x_lr'] = x_low
        log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec
        if self.model.conditioning_key is not None:
            if hasattr(self.cond_stage_model, 'decode'):
                xc = self.cond_stage_model.decode(c)
                log['conditioning'] = xc
            elif self.cond_stage_key in ['caption', 'txt']:
                xc = log_txt_as_img((x.shape[2], x.shape[3]),
                                    batch[self.cond_stage_key],
                                    size=x.shape[2] // 25)
                log['conditioning'] = xc
            elif self.cond_stage_key in ['class_label', 'cls']:
                xc = log_txt_as_img((x.shape[2], x.shape[3]),
                                    batch['human_label'],
                                    size=x.shape[2] // 25)
                log['conditioning'] = xc
            elif isimage(xc):
                log['conditioning'] = xc
            if ismap(xc):
                log['original_conditioning'] = self.to_rgb(xc)

        if plot_diffusion_rows:
            # get diffusion row
            diffusion_row = list()
            z_start = z[: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(z_start)
                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
                    diffusion_row.append(self.decode_first_stage(z_noisy))

            diffusion_row = torch.stack(
                diffusion_row)  # n_log_step, n_row, C, H, W
            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
            diffusion_grid = rearrange(diffusion_grid,
                                       'b n c h w -> (b n) c h w')
            diffusion_grid = make_grid(
                diffusion_grid, nrow=diffusion_row.shape[0])
            log['diffusion_row'] = diffusion_grid

        if sample:
            # get denoise row
            with ema_scope('Sampling'):
                samples, z_denoise_row = self.sample_log(
                    cond=c,
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta)
                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
            x_samples = self.decode_first_stage(samples)
            log['samples'] = x_samples
            if plot_denoise_rows:
                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
                log['denoise_row'] = denoise_grid

        if unconditional_guidance_scale > 1.0:
            uc_tmp = self.get_unconditional_conditioning(
                N, unconditional_guidance_label)
            # TODO explore better "unconditional" choices for the other keys
            # maybe guide away from empty text label and highest noise level and maximally degraded zx?
            uc = dict()
            for k in c:
                if k == 'c_crossattn':
                    assert isinstance(c[k], list) and len(c[k]) == 1
                    uc[k] = [uc_tmp]
                elif k == 'c_adm':  # todo: only run with text-based guidance?
                    assert isinstance(c[k], torch.Tensor)
                    # uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
                    uc[k] = c[k]
                elif isinstance(c[k], list):
                    uc[k] = [c[k][i] for i in range(len(c[k]))]
                else:
                    uc[k] = c[k]

            with ema_scope('Sampling with classifier-free guidance'):
                samples_cfg, _ = self.sample_log(
                    cond=c,
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta,
                    unconditional_guidance_scale=unconditional_guidance_scale,
                    unconditional_conditioning=uc,
                )
                x_samples_cfg = self.decode_first_stage(samples_cfg)
                log[f'samples_cfg_scale_{unconditional_guidance_scale:.2f}'] = x_samples_cfg

        if plot_progressive_rows:
            with ema_scope('Plotting Progressives'):
                img, progressives = self.progressive_denoising(
                    c,
                    shape=(self.channels, self.image_size, self.image_size),
                    batch_size=N)
            prog_row = self._get_denoise_row_from_list(
                progressives, desc='Progressive Generation')
            log['progressive_row'] = prog_row

        return log


class LatentFinetuneDiffusion(LatentDiffusion):
    """
         Basis for different finetunas, such as inpainting or depth2image
         To disable finetuning mode, set finetune_keys to None
    """

    def __init__(
            self,
            concat_keys: tuple,
            finetune_keys=('model.diffusion_model.input_blocks.0.0.weight',
                           'model_ema.diffusion_modelinput_blocks00weight'),
            keep_finetune_dims=4,
            # if model was trained without concat mode before and we would like to keep these channels
            c_concat_log_start=None,  # to log reconstruction of c_concat codes
            c_concat_log_end=None,
            *args,
            **kwargs):
        ckpt_path = kwargs.pop('ckpt_path', None)
        ignore_keys = kwargs.pop('ignore_keys', list())
        super().__init__(*args, **kwargs)
        self.finetune_keys = finetune_keys
        self.concat_keys = concat_keys
        self.keep_dims = keep_finetune_dims
        self.c_concat_log_start = c_concat_log_start
        self.c_concat_log_end = c_concat_log_end
        if exists(self.finetune_keys):
            assert exists(
                ckpt_path), 'can only finetune from a given checkpoint'
        if exists(ckpt_path):
            self.init_from_ckpt(ckpt_path, ignore_keys)

    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):
                    print('Deleting key {} from state_dict.'.format(k))
                    del sd[k]

            # make it explicit, finetune by including extra input channels
            if exists(self.finetune_keys) and k in self.finetune_keys:
                new_entry = None
                for name, param in self.named_parameters():
                    if name in self.finetune_keys:
                        print(
                            f"modifying key '{name}' and keeping its "
                            f'original {self.keep_dims} (channels) dimensions only'
                        )
                        new_entry = torch.zeros_like(param)  # zero init
                assert exists(
                    new_entry), 'did not find matching parameter to modify'
                new_entry[:, :self.keep_dims, ...] = sd[k]
                sd[k] = new_entry

        missing, unexpected = self.load_state_dict(
            sd,
            strict=False) if not only_model else self.model.load_state_dict(
                sd, strict=False)
        print(
            f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
        )
        if len(missing) > 0:
            print(f'Missing Keys: {missing}')
        if len(unexpected) > 0:
            print(f'Unexpected Keys: {unexpected}')

    @torch.no_grad()
    def log_images(self,
                   batch,
                   N=8,
                   n_row=4,
                   sample=True,
                   ddim_steps=200,
                   ddim_eta=1.,
                   return_keys=None,
                   quantize_denoised=True,
                   inpaint=True,
                   plot_denoise_rows=False,
                   plot_progressive_rows=True,
                   plot_diffusion_rows=True,
                   unconditional_guidance_scale=1.,
                   unconditional_guidance_label=None,
                   use_ema_scope=True,
                   **kwargs):
        ema_scope = self.ema_scope if use_ema_scope else nullcontext
        use_ddim = ddim_steps is not None

        log = dict()
        z, c, x, xrec, xc = self.get_input(
            batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
        c_cat, c = c['c_concat'][0], c['c_crossattn'][0]
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        log['inputs'] = x
        log['reconstruction'] = xrec
        if self.model.conditioning_key is not None:
            if hasattr(self.cond_stage_model, 'decode'):
                xc = self.cond_stage_model.decode(c)
                log['conditioning'] = xc
            elif self.cond_stage_key in ['caption', 'txt']:
                xc = log_txt_as_img((x.shape[2], x.shape[3]),
                                    batch[self.cond_stage_key],
                                    size=x.shape[2] // 25)
                log['conditioning'] = xc
            elif self.cond_stage_key in ['class_label', 'cls']:
                xc = log_txt_as_img((x.shape[2], x.shape[3]),
                                    batch['human_label'],
                                    size=x.shape[2] // 25)
                log['conditioning'] = xc
            elif isimage(xc):
                log['conditioning'] = xc
            if ismap(xc):
                log['original_conditioning'] = self.to_rgb(xc)

        if not (self.c_concat_log_start is None
                and self.c_concat_log_end is None):
            log['c_concat_decoded'] = self.decode_first_stage(
                c_cat[:, self.c_concat_log_start:self.c_concat_log_end])

        if plot_diffusion_rows:
            # get diffusion row
            diffusion_row = list()
            z_start = z[: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(z_start)
                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
                    diffusion_row.append(self.decode_first_stage(z_noisy))

            diffusion_row = torch.stack(
                diffusion_row)  # n_log_step, n_row, C, H, W
            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
            diffusion_grid = rearrange(diffusion_grid,
                                       'b n c h w -> (b n) c h w')
            diffusion_grid = make_grid(
                diffusion_grid, nrow=diffusion_row.shape[0])
            log['diffusion_row'] = diffusion_grid

        if sample:
            # get denoise row
            with ema_scope('Sampling'):
                samples, z_denoise_row = self.sample_log(
                    cond={
                        'c_concat': [c_cat],
                        'c_crossattn': [c]
                    },
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta)
                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
            x_samples = self.decode_first_stage(samples)
            log['samples'] = x_samples
            if plot_denoise_rows:
                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
                log['denoise_row'] = denoise_grid

        if unconditional_guidance_scale > 1.0:
            uc_cross = self.get_unconditional_conditioning(
                N, unconditional_guidance_label)
            uc_cat = c_cat
            uc_full = {'c_concat': [uc_cat], 'c_crossattn': [uc_cross]}
            with ema_scope('Sampling with classifier-free guidance'):
                samples_cfg, _ = self.sample_log(
                    cond={
                        'c_concat': [c_cat],
                        'c_crossattn': [c]
                    },
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta,
                    unconditional_guidance_scale=unconditional_guidance_scale,
                    unconditional_conditioning=uc_full,
                )
                x_samples_cfg = self.decode_first_stage(samples_cfg)
                log[f'samples_cfg_scale_{unconditional_guidance_scale:.2f}'] = x_samples_cfg

        return log


class LatentInpaintDiffusion(LatentFinetuneDiffusion):
    """
    can either run as pure inpainting model (only concat mode) or with mixed conditionings,
    e.g. mask as concat and text via cross-attn.
    To disable finetuning mode, set finetune_keys to None
     """

    def __init__(self,
                 concat_keys=('mask', 'masked_image'),
                 masked_image_key='masked_image',
                 *args,
                 **kwargs):
        super().__init__(concat_keys, *args, **kwargs)
        self.masked_image_key = masked_image_key
        assert self.masked_image_key in concat_keys

    @torch.no_grad()
    def get_input(self,
                  batch,
                  k,
                  cond_key=None,
                  bs=None,
                  return_first_stage_outputs=False):
        # note: restricted to non-trainable encoders currently
        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting'
        z, c, x, xrec, xc = super().get_input(
            batch,
            self.first_stage_key,
            return_first_stage_outputs=True,
            force_c_encode=True,
            return_original_cond=True,
            bs=bs)

        assert exists(self.concat_keys)
        c_cat = list()
        for ck in self.concat_keys:
            cc = rearrange(batch[ck], 'b h w c -> b c h w').to(
                memory_format=torch.contiguous_format).float()
            if bs is not None:
                cc = cc[:bs]
                cc = cc.to(self.device)
            bchw = z.shape
            if ck != self.masked_image_key:
                cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
            else:
                cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
            c_cat.append(cc)
        c_cat = torch.cat(c_cat, dim=1)
        all_conds = {'c_concat': [c_cat], 'c_crossattn': [c]}
        if return_first_stage_outputs:
            return z, all_conds, x, xrec, xc
        return z, all_conds

    @torch.no_grad()
    def log_images(self, *args, **kwargs):
        log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
        log['masked_image'] = rearrange(
            args[0]['masked_image'], 'b h w c -> b c h w').to(
                memory_format=torch.contiguous_format).float()
        return log


class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
    """
    condition on monocular depth estimation
    """

    def __init__(self,
                 depth_stage_config,
                 concat_keys=('midas_in', ),
                 *args,
                 **kwargs):
        super().__init__(concat_keys=concat_keys, *args, **kwargs)
        self.depth_model = instantiate_from_config(depth_stage_config)
        self.depth_stage_key = concat_keys[0]

    @torch.no_grad()
    def get_input(self,
                  batch,
                  k,
                  cond_key=None,
                  bs=None,
                  return_first_stage_outputs=False):
        # note: restricted to non-trainable encoders currently
        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img'
        z, c, x, xrec, xc = super().get_input(
            batch,
            self.first_stage_key,
            return_first_stage_outputs=True,
            force_c_encode=True,
            return_original_cond=True,
            bs=bs)

        assert exists(self.concat_keys)
        assert len(self.concat_keys) == 1
        c_cat = list()
        for ck in self.concat_keys:
            cc = batch[ck]
            if bs is not None:
                cc = cc[:bs]
                cc = cc.to(self.device)
            cc = self.depth_model(cc)
            cc = torch.nn.functional.interpolate(
                cc,
                size=z.shape[2:],
                mode='bicubic',
                align_corners=False,
            )

            depth_min, depth_max = torch.amin(
                cc, dim=[1, 2, 3], keepdim=True), torch.amax(
                    cc, dim=[1, 2, 3], keepdim=True)
            cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.
            c_cat.append(cc)
        c_cat = torch.cat(c_cat, dim=1)
        all_conds = {'c_concat': [c_cat], 'c_crossattn': [c]}
        if return_first_stage_outputs:
            return z, all_conds, x, xrec, xc
        return z, all_conds

    @torch.no_grad()
    def log_images(self, *args, **kwargs):
        log = super().log_images(*args, **kwargs)
        depth = self.depth_model(args[0][self.depth_stage_key])
        depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \
            torch.amax(depth, dim=[1, 2, 3], keepdim=True)
        log['depth'] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1.
        return log


class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
    """
        condition on low-res image (and optionally on some spatial noise augmentation)
    """

    def __init__(self,
                 concat_keys=('lr', ),
                 reshuffle_patch_size=None,
                 low_scale_config=None,
                 low_scale_key=None,
                 *args,
                 **kwargs):
        super().__init__(concat_keys=concat_keys, *args, **kwargs)
        self.reshuffle_patch_size = reshuffle_patch_size
        self.low_scale_model = None
        if low_scale_config is not None:
            print('Initializing a low-scale model')
            assert exists(low_scale_key)
            self.instantiate_low_stage(low_scale_config)
            self.low_scale_key = low_scale_key

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

    @torch.no_grad()
    def get_input(self,
                  batch,
                  k,
                  cond_key=None,
                  bs=None,
                  return_first_stage_outputs=False):
        # note: restricted to non-trainable encoders currently
        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft'
        z, c, x, xrec, xc = super().get_input(
            batch,
            self.first_stage_key,
            return_first_stage_outputs=True,
            force_c_encode=True,
            return_original_cond=True,
            bs=bs)

        assert exists(self.concat_keys)
        assert len(self.concat_keys) == 1
        # optionally make spatial noise_level here
        c_cat = list()
        noise_level = None
        for ck in self.concat_keys:
            cc = batch[ck]
            cc = rearrange(cc, 'b h w c -> b c h w')
            if exists(self.reshuffle_patch_size):
                assert isinstance(self.reshuffle_patch_size, int)
                cc = rearrange(
                    cc,
                    'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
                    p1=self.reshuffle_patch_size,
                    p2=self.reshuffle_patch_size)
            if bs is not None:
                cc = cc[:bs]
                cc = cc.to(self.device)
            if exists(self.low_scale_model) and ck == self.low_scale_key:
                cc, noise_level = self.low_scale_model(cc)
            c_cat.append(cc)
        c_cat = torch.cat(c_cat, dim=1)
        if exists(noise_level):
            all_conds = {
                'c_concat': [c_cat],
                'c_crossattn': [c],
                'c_adm': noise_level
            }
        else:
            all_conds = {'c_concat': [c_cat], 'c_crossattn': [c]}
        if return_first_stage_outputs:
            return z, all_conds, x, xrec, xc
        return z, all_conds

    @torch.no_grad()
    def log_images(self, *args, **kwargs):
        log = super().log_images(*args, **kwargs)
        log['lr'] = rearrange(args[0]['lr'], 'b h w c -> b c h w')
        return log
