audioldm_train/modules/latent_diffusion/ddpm.py

from multiprocessing.sharedctypes import Value
import statistics
import sys
import os
# from tkinter import Ec
# sys.path.append('/home/changli/Adan')

import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
# from adan import Adan
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from qa_mdt.audioldm_train.conditional_models import *
import datetime

from qa_mdt.audioldm_train.utilities.model_util import (
    exists,
    default,
    mean_flat,
    count_params,
    instantiate_from_config,
)

from qa_mdt.audioldm_train.utilities.diffusion_util import (
    make_beta_schedule,
    extract_into_tensor,
    noise_like,
)

from qa_mdt.audioldm_train.modules.diffusionmodules.ema import LitEma
from qa_mdt.audioldm_train.modules.diffusionmodules.distributions import (
    normal_kl,
    DiagonalGaussianDistribution,
)

# from audioldm_train.modules.diffusionmodules.transport import 

from qa_mdt.audioldm_train.modules.latent_diffusion.ddim import DDIMSampler
from qa_mdt.audioldm_train.modules.latent_diffusion.plms import PLMSSampler
import soundfile as sf
import os

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

import json
with open('./qa_mdt/offset_pretrained_checkpoints.json', 'r') as config_file:
    config_data = json.load(config_file)


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,
        sampling_rate=None,
        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",
        latent_t_size=256,
        latent_f_size=16,
        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=0.0,
        v_posterior=0.0,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
        l_simple_weight=1.0,
        conditioning_key=None,
        parameterization="eps",  # all assuming fixed variance schedules
        scheduler_config=None,
        use_positional_encodings=False,
        learn_logvar=False,
        logvar_init=0.0,
        evaluator=None,
    ):
        super().__init__()
        assert parameterization in [
            "eps",
            "x0",
            "v",
        ], 'currently only supporting "eps" and "x0" and "v"'
        self.parameterization = parameterization
        self.state = None
        print(
            f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode"
        )
        assert sampling_rate is not None
        self.validation_folder_name = "temp_name"
        self.clip_denoised = clip_denoised
        self.log_every_t = log_every_t
        self.first_stage_key = first_stage_key
        self.sampling_rate = sampling_rate
        self.clap = CLAPAudioEmbeddingClassifierFreev2(
            pretrained_path=config_data["clap_music"],
            sampling_rate=self.sampling_rate,
            embed_mode="audio",
            amodel="HTSAT-base",
        )

        if self.global_rank == 0:
            self.evaluator = evaluator

        self.initialize_param_check_toolkit()

        self.latent_t_size = latent_t_size
        self.latent_f_size = latent_f_size

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

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

        self.loss_type = loss_type

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

        self.logger_save_dir = None
        self.logger_exp_name = None
        self.logger_exp_group_name = None
        self.logger_version = None

        self.label_indices_total = None
        # To avoid the system cannot find metric value for checkpoint
        self.metrics_buffer = {
            "val/kullback_leibler_divergence_sigmoid": 15.0,
            "val/kullback_leibler_divergence_softmax": 10.0,
            "val/psnr": 0.0,
            "val/ssim": 0.0,
            "val/inception_score_mean": 1.0,
            "val/inception_score_std": 0.0,
            "val/kernel_inception_distance_mean": 0.0,
            "val/kernel_inception_distance_std": 0.0,
            "val/frechet_inception_distance": 133.0,
            "val/frechet_audio_distance": 32.0,
        }
        self.initial_learning_rate = None
        self.test_data_subset_path = None

    def get_log_dir(self):
        return os.path.join(
            self.logger_save_dir, self.logger_exp_group_name, self.logger_exp_name
        )

    def set_log_dir(self, save_dir, exp_group_name, exp_name):
        self.logger_save_dir = save_dir
        self.logger_exp_group_name = exp_group_name
        self.logger_exp_name = exp_name

    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 = 1.0 - betas
        alphas_cumprod = np.cumprod(alphas, axis=0)
        alphas_cumprod_prev = np.append(1.0, 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(1.0 - alphas_cumprod))
        )
        self.register_buffer(
            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
        )
        self.register_buffer(
            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
        )
        self.register_buffer(
            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
        )

        # calculations for posterior q(x_{t-1} | x_t, x_0)
        posterior_variance = (1 - self.v_posterior) * betas * (
            1.0 - alphas_cumprod_prev
        ) / (1.0 - alphas_cumprod) + self.v_posterior * betas
        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
        self.register_buffer("posterior_variance", to_torch(posterior_variance))
        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
        self.register_buffer(
            "posterior_log_variance_clipped",
            to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
        )
        self.register_buffer(
            "posterior_mean_coef1",
            to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
        )
        self.register_buffer(
            "posterior_mean_coef2",
            to_torch(
                (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
            ),
        )

        if self.parameterization == "eps":
            lvlb_weights = self.betas**2 / (
                2
                * self.posterior_variance
                * to_torch(alphas)
                * (1 - self.alphas_cumprod)
            )
        elif self.parameterization == "x0":
            lvlb_weights = (
                0.5
                * np.sqrt(torch.Tensor(alphas_cumprod))
                / (2.0 * 1 - torch.Tensor(alphas_cumprod))
            )
        elif self.parameterization == "v":
            lvlb_weights = torch.ones_like(
                self.betas**2
                / (
                    2
                    * self.posterior_variance
                    * to_torch(alphas)
                    * (1 - self.alphas_cumprod)
                )
            )
        else:
            raise NotImplementedError("mu not supported")
        # TODO how to choose this term
        lvlb_weights[0] = lvlb_weights[1]
        self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
        assert not torch.isnan(self.lvlb_weights).all()

    @contextmanager
    def ema_scope(self, context=None):
        if self.use_ema:
            self.model_ema.store(self.model.parameters())
            self.model_ema.copy_to(self.model)
            if context is not None:
                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")

    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]
        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}")

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

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

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

    def p_mean_variance(self, x, t, clip_denoised: bool):
        model_out = self.model(x, t)
        if self.parameterization == "eps":
            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
        elif self.parameterization == "x0":
            x_recon = model_out
        if clip_denoised:
            x_recon.clamp_(-1.0, 1.0)

        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))).contiguous()
        )
        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):
        shape = (batch_size, channels, self.latent_t_size, self.latent_f_size)
        channels = self.channels
        return self.p_sample_loop(shape, 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_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 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 get_v(self, x, noise, t):
        return (
            extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise
            - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
        )

    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)

        mse_loss_weight = None
        alpha = extract_into_tensor(self.sqrt_alphas_cumprod, t, t.shape)
        sigma = extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, t.shape)
        snr = (alpha / sigma) ** 2
        
        # velocity = (alpha[:, None, None, None] * x_noisy - x_start) / sigma[:, None, None, None]

        # get loss weight
        if self.parameterization != "x0":
            mse_loss_weight = torch.ones_like(t)
            k = 5.0
            # min{snr, k}
            mse_loss_weight = torch.stack([snr, k * torch.ones_like(t)], dim=1).min(dim=1)[0] / snr
        else:
            k = 5.0
            # min{snr, k}
            mse_loss_weight = torch.stack([snr, k * torch.ones_like(t)], dim=1).min(dim=1)[0]
        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"Paramterization {self.parameterization} not yet supported"
            )

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

        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):
        # fbank, log_magnitudes_stft, label_indices, fname, waveform, clip_label, text = batch
        # fbank, stft, label_indices, fname, waveform, text = batch
        # a = 1/0
        fname, text, label_indices, waveform, stft, fbank, mos = (
            batch["fname"],
            batch["text"],
            batch["label_vector"],
            batch["waveform"],
            batch["stft"],
            batch["log_mel_spec"],
            batch["mos"],
            # batch
        )
        # for i in range(fbank.size(0)):
        #     fb = fbank[i].numpy()
        #     seg_lb = seg_label[i].numpy()
        #     logits = np.mean(seg_lb, axis=0)
        #     index = np.argsort(logits)[::-1][:5]
        #     plt.imshow(seg_lb[:,index], aspect="auto")
        #     plt.title(index)
        #     plt.savefig("%s_label.png" % i)
        #     plt.close()
        #     plt.imshow(fb, aspect="auto")
        #     plt.savefig("%s_fb.png" % i)
        #     plt.close()
        ret = {}

        ret["fbank"] = (
            fbank.unsqueeze(1).to(memory_format=torch.contiguous_format).float()
        )
        ret["stft"] = stft.to(memory_format=torch.contiguous_format).float()
        # ret["clip_label"] = clip_label.to(memory_format=torch.contiguous_format).float()
        ret["waveform"] = waveform.to(memory_format=torch.contiguous_format).float()
        ret["text"] = list(text)
        ret["fname"] = fname
        ret["mos"] = list(mos)

        for key in batch.keys():
            if key not in ret.keys():
                ret[key] = batch[key]

        return ret[k]

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

    def warmup_step(self):
        if self.initial_learning_rate is None:
            self.initial_learning_rate = self.learning_rate

        # Only the first parameter group
        if self.global_step <= self.warmup_steps:
            if self.global_step == 0:
                print(
                    "Warming up learning rate start with %s"
                    % self.initial_learning_rate
                )
            self.trainer.optimizers[0].param_groups[0]["lr"] = (
                self.global_step / self.warmup_steps
            ) * self.initial_learning_rate
        else:
            # TODO set learning rate here
            self.trainer.optimizers[0].param_groups[0][
                "lr"
            ] = self.initial_learning_rate

    def training_step(self, batch, batch_idx):
        # You instantiate a optimizer for the scheduler
        # But later you overwrite the optimizer by reloading its states from a checkpoint
        # So you need to replace the optimizer with the checkpoint one
        # if(self.lr_schedulers().optimizer.param_groups[0]['lr'] != self.trainer.optimizers[0].param_groups[0]['lr']):
        #     self.lr_schedulers().optimizer = self.trainer.optimizers[0]

        # if(self.ckpt is not None):
        #     self.reload_everything()
        #     self.ckpt = None

        self.random_clap_condition()
        self.warmup_step()

        # if (
        #     self.state is None
        #     and len(self.trainer.optimizers[0].state_dict()["state"].keys()) > 0
        # ):
        #     self.state = (
        #         self.trainer.optimizers[0].state_dict()["state"][0]["exp_avg"].clone()
        #     )
        # elif self.state is not None and batch_idx % 1000 == 0:
        #     assert (
        #         torch.sum(
        #             torch.abs(
        #                 self.state
        #                 - self.trainer.optimizers[0].state_dict()["state"][0]["exp_avg"]
        #             )
        #         )
        #         > 1e-7
        #     ), "Optimizer is not working"

        if len(self.metrics_buffer.keys()) > 0:
            for k in self.metrics_buffer.keys():
                self.log(
                    k,
                    self.metrics_buffer[k],
                    prog_bar=False,
                    logger=True,
                    on_step=True,
                    on_epoch=False,
                )
                # print(k, self.metrics_buffer[k])
            self.metrics_buffer = {}
        loss, loss_dict = self.shared_step(batch)

        self.log_dict(
            {k: float(v) for k, v in loss_dict.items()},
            prog_bar=True,
            logger=True,
            on_step=True,
            on_epoch=True,
        )

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

        lr = self.trainer.optimizers[0].param_groups[0]["lr"]
        self.log(
            "lr_abs",
            float(lr),
            prog_bar=True,
            logger=True,
            on_step=True,
            on_epoch=False,
        )

        return loss

    def random_clap_condition(self):
        # This function is only used during training, let the CLAP model to use both text and audio as condition
        assert self.training == True

        for key in self.cond_stage_model_metadata.keys():
            metadata = self.cond_stage_model_metadata[key]
            model_idx, cond_stage_key, conditioning_key = (
                metadata["model_idx"],
                metadata["cond_stage_key"],
                metadata["conditioning_key"],
            )

            # If we use CLAP as condition, we might use audio for training, but we also must use text for evaluation
            if isinstance(
                self.cond_stage_models[model_idx], CLAPAudioEmbeddingClassifierFreev2
            ):
                self.cond_stage_model_metadata[key][
                    "cond_stage_key_orig"
                ] = self.cond_stage_model_metadata[key]["cond_stage_key"]
                self.cond_stage_model_metadata[key][
                    "embed_mode_orig"
                ] = self.cond_stage_models[model_idx].embed_mode
                if torch.randn(1).item() < 0.5:
                    self.cond_stage_model_metadata[key]["cond_stage_key"] = "text"
                    self.cond_stage_models[model_idx].embed_mode = "text"
                else:
                    self.cond_stage_model_metadata[key]["cond_stage_key"] = "waveform"
                    self.cond_stage_models[model_idx].embed_mode = "audio"

    def on_validation_epoch_start(self) -> None:
        # Use text as condition during validation
        for key in self.cond_stage_model_metadata.keys():
            metadata = self.cond_stage_model_metadata[key]
            model_idx, cond_stage_key, conditioning_key = (
                metadata["model_idx"],
                metadata["cond_stage_key"],
                metadata["conditioning_key"],
            )

            # If we use CLAP as condition, we might use audio for training, but we also must use text for evaluation
            if isinstance(
                self.cond_stage_models[model_idx], CLAPAudioEmbeddingClassifierFreev2
            ):
                self.cond_stage_model_metadata[key][
                    "cond_stage_key_orig"
                ] = self.cond_stage_model_metadata[key]["cond_stage_key"]
                self.cond_stage_model_metadata[key][
                    "embed_mode_orig"
                ] = self.cond_stage_models[model_idx].embed_mode
                print(
                    "Change the model original cond_keyand embed_mode %s, %s to text during evaluation"
                    % (
                        self.cond_stage_model_metadata[key]["cond_stage_key_orig"],
                        self.cond_stage_model_metadata[key]["embed_mode_orig"],
                    )
                )
                self.cond_stage_model_metadata[key]["cond_stage_key"] = "text"
                self.cond_stage_models[model_idx].embed_mode = "text"

            if isinstance(
                self.cond_stage_models[model_idx], CLAPGenAudioMAECond
            ) or isinstance(self.cond_stage_models[model_idx], SequenceGenAudioMAECond):
                self.cond_stage_model_metadata[key][
                    "use_gt_mae_output_orig"
                ] = self.cond_stage_models[model_idx].use_gt_mae_output
                self.cond_stage_model_metadata[key][
                    "use_gt_mae_prob_orig"
                ] = self.cond_stage_models[model_idx].use_gt_mae_prob
                print("Change the model condition to the predicted AudioMAE tokens")
                self.cond_stage_models[model_idx].use_gt_mae_output = False
                self.cond_stage_models[model_idx].use_gt_mae_prob = 0.0
        self.validation_folder_name = self.get_validation_folder_name()
        return super().on_validation_epoch_start()

    @torch.no_grad()
    def validation_step(self, batch, batch_idx):
        self.generate_sample(
            [batch],
            name=self.validation_folder_name,
            unconditional_guidance_scale=self.evaluation_params[
                "unconditional_guidance_scale"
            ],
            ddim_steps=self.evaluation_params["ddim_sampling_steps"],
            n_gen=self.evaluation_params["n_candidates_per_samples"],
        )

    def get_validation_folder_name(self):
        now = datetime.datetime.now()
        timestamp = now.strftime("%m-%d-%H:%M")
        return "val_%s_%s_cfg_scale_%s_ddim_%s_n_cand_%s" % (
            self.global_step,
            timestamp,
            self.evaluation_params["unconditional_guidance_scale"],
            self.evaluation_params["ddim_sampling_steps"],
            self.evaluation_params["n_candidates_per_samples"],
        )

    def on_validation_epoch_end(self) -> None:
        if self.global_rank == 0 and self.evaluator is not None:
            assert (
                self.test_data_subset_path is not None
            ), "Please set test_data_subset_path before validation so that model have a target folder"
            try:

                name = self.validation_folder_name
                # import pdb 
                # pdb.set_trace()
                waveform_save_path = os.path.join(self.get_log_dir(), name)
                if (
                    os.path.exists(waveform_save_path)
                    and len(os.listdir(waveform_save_path)) > 0
                ):

                    metrics = self.evaluator.main(
                        waveform_save_path,
                        self.test_data_subset_path,
                    )

                    self.metrics_buffer = {
                        ("val/" + k): float(v) for k, v in metrics.items()
                    }
                else:
                    print(
                        "The target folder for evaluation does not exist: %s"
                        % waveform_save_path
                    )
            except Exception as e:
                print("Error encountered during evaluation: ", e)

        # Very important or the program may fail
        torch.cuda.synchronize()

        for key in self.cond_stage_model_metadata.keys():
            metadata = self.cond_stage_model_metadata[key]
            model_idx, cond_stage_key, conditioning_key = (
                metadata["model_idx"],
                metadata["cond_stage_key"],
                metadata["conditioning_key"],
            )

            if isinstance(
                self.cond_stage_models[model_idx], CLAPAudioEmbeddingClassifierFreev2
            ):
                self.cond_stage_model_metadata[key][
                    "cond_stage_key"
                ] = self.cond_stage_model_metadata[key]["cond_stage_key_orig"]
                self.cond_stage_models[
                    model_idx
                ].embed_mode = self.cond_stage_model_metadata[key]["embed_mode_orig"]
                print(
                    "Change back the embedding mode to %s %s"
                    % (
                        self.cond_stage_model_metadata[key]["cond_stage_key"],
                        self.cond_stage_models[model_idx].embed_mode,
                    )
                )

            if isinstance(
                self.cond_stage_models[model_idx], CLAPGenAudioMAECond
            ) or isinstance(self.cond_stage_models[model_idx], SequenceGenAudioMAECond):
                self.cond_stage_models[
                    model_idx
                ].use_gt_mae_output = self.cond_stage_model_metadata[key][
                    "use_gt_mae_output_orig"
                ]
                self.cond_stage_models[
                    model_idx
                ].use_gt_mae_prob = self.cond_stage_model_metadata[key][
                    "use_gt_mae_prob_orig"
                ]
                print(
                    "Change the AudioMAE condition setting to %s (Use gt) %s (gt prob)"
                    % (
                        self.cond_stage_models[model_idx].use_gt_mae_output,
                        self.cond_stage_models[model_idx].use_gt_mae_prob,
                    )
                )

        return super().on_validation_epoch_end()

    def on_train_epoch_start(self, *args, **kwargs):
        print("Log directory: ", self.get_log_dir())

    def on_train_batch_end(self, *args, **kwargs):
        # Does this affect speed?
        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)
        # opt = Adan(params, lr=lr, max_grad_norm=1, fused=True)
        return opt

    def initialize_param_check_toolkit(self):
        self.tracked_steps = 0
        self.param_dict = {}

    def statistic_require_grad_tensor_number(self, module, name=None):
        requires_grad_num = 0
        total_num = 0
        require_grad_tensor = None
        for p in module.parameters():
            if p.requires_grad:
                requires_grad_num += 1
                if require_grad_tensor is None:
                    require_grad_tensor = p
            total_num += 1
        print(
            "Module: [%s] have %s trainable parameters out of %s total parameters (%.2f)"
            % (name, requires_grad_num, total_num, requires_grad_num / total_num)
        )
        return require_grad_tensor

    def check_module_param_update(self):
        if self.tracked_steps == 0:
            for name, module in self.named_children():
                try:
                    require_grad_tensor = self.statistic_require_grad_tensor_number(
                        module, name=name
                    )
                    if require_grad_tensor is not None:
                        self.param_dict[name] = require_grad_tensor.clone()
                    else:
                        print("==> %s does not requires grad" % name)
                except Exception as e:
                    print("%s does not have trainable parameters: %s" % (name, e))
                    continue

        if self.tracked_steps % 5000 == 0:
            for name, module in self.named_children():
                try:
                    require_grad_tensor = self.statistic_require_grad_tensor_number(
                        module, name=name
                    )

                    if require_grad_tensor is not None:
                        print(
                            "===> Param diff %s: %s; Size: %s"
                            % (
                                name,
                                torch.sum(
                                    torch.abs(
                                        self.param_dict[name] - require_grad_tensor
                                    )
                                ),
                                require_grad_tensor.size(),
                            )
                        )
                    else:
                        print("%s does not requires grad" % name)
                except Exception as e:
                    print("%s does not have trainable parameters: %s" % (name, e))
                    continue

        self.tracked_steps += 1


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

    def __init__(
        self,
        first_stage_config,
        cond_stage_config=None,
        num_timesteps_cond=None,
        cond_stage_key="image",
        optimize_ddpm_parameter=True,
        unconditional_prob_cfg=0.1,
        warmup_steps=10000,
        cond_stage_trainable=False,
        concat_mode=True,
        cond_stage_forward=None,
        conditioning_key=None,
        scale_factor=1.0,
        batchsize=None,
        evaluation_params={},
        scale_by_std=False,
        base_learning_rate=None,
        *args,
        **kwargs,
    ):
        self.learning_rate = base_learning_rate
        self.num_timesteps_cond = default(num_timesteps_cond, 1)
        self.scale_by_std = scale_by_std
        self.warmup_steps = warmup_steps


        if optimize_ddpm_parameter:
            if unconditional_prob_cfg == 0.0:
                "You choose to optimize DDPM. The classifier free guidance scale should be 0.1"
                unconditional_prob_cfg = 0.1
        else:
            if unconditional_prob_cfg == 0.1:
                "You choose not to optimize DDPM. The classifier free guidance scale should be 0.0"
                unconditional_prob_cfg = 0.0

        self.evaluation_params = evaluation_params
        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__":
        #     conditioning_key = None

        conditioning_key = list(cond_stage_config.keys())

        self.conditioning_key = conditioning_key

        ckpt_path = kwargs.pop("ckpt_path", None)
        ignore_keys = kwargs.pop("ignore_keys", [])
        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)

        self.optimize_ddpm_parameter = optimize_ddpm_parameter
        # if(not optimize_ddpm_parameter):
        #     print("Warning: Close the optimization of the latent diffusion model")
        #     for p in self.model.parameters():
        #         p.requires_grad=False

        self.concat_mode = concat_mode
        self.cond_stage_key = cond_stage_key
        self.cond_stage_key_orig = cond_stage_key
        try:
            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
        except:
            self.num_downs = 0
        if not scale_by_std:
            self.scale_factor = scale_factor
        else:
            self.register_buffer("scale_factor", torch.tensor(scale_factor))
        self.instantiate_first_stage(first_stage_config)
        self.unconditional_prob_cfg = unconditional_prob_cfg
        self.cond_stage_models = nn.ModuleList([])
        self.instantiate_cond_stage(cond_stage_config)
        self.cond_stage_forward = cond_stage_forward
        self.clip_denoised = False
        self.bbox_tokenizer = None
        self.conditional_dry_run_finished = False
        self.restarted_from_ckpt = False
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys)
            self.restarted_from_ckpt = True

    def configure_optimizers(self):
        lr = self.learning_rate
        params = list(self.model.parameters())

        for each in self.cond_stage_models:
            params = params + list(
                each.parameters()
            )  # Add the parameter from the conditional stage

        if self.learn_logvar:
            print("Diffusion model optimizing logvar")
            params.append(self.logvar)
        # opt = Adan(params, lr=lr, max_grad_norm=1, fused=True)
        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

    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):

        # only for very first batch
        if (
            self.scale_factor == 1
            and 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", 1.0 / 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 make_decision(self, probability):
        if float(torch.rand(1)) < probability:
            return True
        else:
            return False

    def instantiate_cond_stage(self, config):
        self.cond_stage_model_metadata = {}
        for i, cond_model_key in enumerate(config.keys()):
            model = instantiate_from_config(config[cond_model_key])
            self.cond_stage_models.append(model)
            self.cond_stage_model_metadata[cond_model_key] = {
                "model_idx": i,
                "cond_stage_key": config[cond_model_key]["cond_stage_key"],
                "conditioning_key": config[cond_model_key]["conditioning_key"],
            }

    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, key, unconditional_cfg):
        assert key in self.cond_stage_model_metadata.keys()

        # Classifier-free guidance
        if not unconditional_cfg:
            c = self.cond_stage_models[
                self.cond_stage_model_metadata[key]["model_idx"]
            ](c)
        else:
            # when the cond_stage_key is "all", pick one random element out
            if isinstance(c, dict):
                c = c[list(c.keys())[0]]

            if isinstance(c, torch.Tensor):
                batchsize = c.size(0)
            elif isinstance(c, list):
                batchsize = len(c)
            else:
                raise NotImplementedError()

            c = self.cond_stage_models[
                self.cond_stage_model_metadata[key]["model_idx"]
            ].get_unconditional_condition(batchsize)

        return c

    def get_input(
        self,
        batch,
        k,
        return_first_stage_encode=True,
        return_decoding_output=False,
        return_encoder_input=False,
        return_encoder_output=False,
        unconditional_prob_cfg=0.1,
    ):
        # print(self.cond_stage_model_metadata.keys())
        x = super().get_input(batch, k)

        x = x.to(self.device)

        if return_first_stage_encode:
            encoder_posterior = self.encode_first_stage(x)
            z = self.get_first_stage_encoding(encoder_posterior).detach()
        else:
            z = None
        cond_dict = {}
        if len(self.cond_stage_model_metadata.keys()) > 0:
            unconditional_cfg = False
            if self.conditional_dry_run_finished and self.make_decision(
                unconditional_prob_cfg
            ):
                unconditional_cfg = True
            for cond_model_key in self.cond_stage_model_metadata.keys():
                cond_stage_key = self.cond_stage_model_metadata[cond_model_key][
                    "cond_stage_key"
                ]

                if cond_model_key in cond_dict.keys():
                    continue

                if not self.training:
                    if isinstance(
                        self.cond_stage_models[
                            self.cond_stage_model_metadata[cond_model_key]["model_idx"]
                        ],
                        CLAPAudioEmbeddingClassifierFreev2,
                    ):
                        print(
                            "Warning: CLAP model normally should use text for evaluation"
                        )

                # The original data for conditioning
                # If cond_model_key is "all", that means the conditional model need all the information from a batch

                if cond_stage_key != "all":
                    xc = super().get_input(batch, cond_stage_key)
                    if type(xc) == torch.Tensor:
                        xc = xc.to(self.device)
                else:
                    xc = batch
                
                # batch inference BUG
                #if cond_stage_key == 'text':
                #    xc = xc[0]


                # if cond_stage_key is "all", xc will be a dictionary containing all keys
                # Otherwise xc will be an entry of the dictionary
                c = self.get_learned_conditioning(
                    xc, key=cond_model_key, unconditional_cfg=unconditional_cfg
                )
                
                # cond_dict will be used to condition the diffusion model
                # If one conditional model return multiple conditioning signal
                if isinstance(c, dict):
                    for k in c.keys():
                        cond_dict[k] = c[k]
                else:
                    cond_dict[cond_model_key] = c

        # If the key is accidently added to the dictionary and not in the condition list, remove the condition
        # for k in list(cond_dict.keys()):
        #     if(k not in self.cond_stage_model_metadata.keys()):
        #         del cond_dict[k]

        cond_dict['mos'] = batch['mos']
        out = [z, cond_dict]

        if return_decoding_output:
            xrec = self.decode_first_stage(z)
            out += [xrec]

        if return_encoder_input:
            out += [x]

        if return_encoder_output:
            out += [encoder_posterior]

        if not self.conditional_dry_run_finished:
            self.conditional_dry_run_finished = True

        # Output is a dictionary, where the value could only be tensor or tuple
        return out

    def decode_first_stage(self, z):
        with torch.no_grad():
            z = 1.0 / self.scale_factor * z
            decoding = self.first_stage_model.decode(z)
        return decoding

    def mel_spectrogram_to_waveform(
        self, mel, savepath=".", bs=None, name="outwav", save=True, n_gen=1
    ):
        # Mel: [bs, 1, t-steps, fbins]
        if len(mel.size()) == 4:
            mel = mel.squeeze(1)
        mel = mel.permute(0, 2, 1)
        waveform = self.first_stage_model.vocoder(mel)
        waveform = waveform.cpu().detach().numpy()
        if save:
            self.save_waveform(waveform, savepath="./")
        return waveform

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

    def extract_possible_loss_in_cond_dict(self, cond_dict):
        # This function enable the conditional module to return loss function that can optimize them

        assert isinstance(cond_dict, dict)
        losses = {}

        for cond_key in cond_dict.keys():
            if "loss" in cond_key and "noncond" in cond_key:
                assert cond_key not in losses.keys()
                losses[cond_key] = cond_dict[cond_key]

        return losses

    def filter_useful_cond_dict(self, cond_dict):
        new_cond_dict = {}
        for key in cond_dict.keys():
            if key in self.cond_stage_model_metadata.keys():
                new_cond_dict[key] = cond_dict[key]

        # All the conditional key in the metadata should be used
        for key in self.cond_stage_model_metadata.keys():
            assert key in new_cond_dict.keys(), "%s, %s" % (
                key,
                str(new_cond_dict.keys()),
            )
        try:
            new_cond_dict['mos'] = cond_dict['mos']
        except:
            pass

        return new_cond_dict

    def shared_step(self, batch, **kwargs):
        # self.check_module_param_update()
        if self.training:
            # Classifier-free guidance
            unconditional_prob_cfg = self.unconditional_prob_cfg
        else:
            unconditional_prob_cfg = 0.0  # TODO possible bug here

        x, c = self.get_input(
            batch, self.first_stage_key, unconditional_prob_cfg=unconditional_prob_cfg
        )

        if self.optimize_ddpm_parameter:
            loss, loss_dict = self(x, self.filter_useful_cond_dict(c))
        else:
            loss_dict = {}
            loss = None

        additional_loss_for_cond_modules = self.extract_possible_loss_in_cond_dict(c)
        assert isinstance(additional_loss_for_cond_modules, dict)

        loss_dict.update(additional_loss_for_cond_modules)

        if len(additional_loss_for_cond_modules.keys()) > 0:
            for k in additional_loss_for_cond_modules.keys():
                if loss is None:
                    loss = additional_loss_for_cond_modules[k]
                else:
                    loss = loss + additional_loss_for_cond_modules[k]

        # for k,v in additional_loss_for_cond_modules.items():
        #     self.log(
        #         "cond_stage/"+k,
        #         float(v),
        #         prog_bar=True,
        #         logger=True,
        #         on_step=True,
        #         on_epoch=True,
        #     )
        if self.training:
            assert loss is not None

        return loss, loss_dict

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

        # assert c is not None
        # c = self.get_learned_conditioning(c)

        loss, loss_dict = self.p_losses(x, c, t, *args, **kwargs)
        return loss, loss_dict

    def reorder_cond_dict(self, cond_dict):
        # To make sure the order is correct
        new_cond_dict = {}
        for key in self.conditioning_key:
            new_cond_dict[key] = cond_dict[key]
        new_cond_dict['mos'] = cond_dict['mos']
        return new_cond_dict

    def apply_model(self, x_noisy, t, cond, return_ids=False):
        cond = self.reorder_cond_dict(cond)
        # import pdb; pdb.set_trace()
        x_recon = self.model(x_noisy, t, cond_dict=cond)

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

    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()
        # print(model_output.size(), target.size())
        mse_loss_weight = None
        alpha = extract_into_tensor(self.sqrt_alphas_cumprod, t, t.shape)
        sigma = extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, t.shape)
        snr = (alpha / sigma) ** 2
        
        # velocity = (alpha[:, None, None, None] * x_t - x_start) / sigma[:, None, None, None]

        # get loss weight
        if self.parameterization != "x0":
            mse_loss_weight = torch.ones_like(t)
            k = 5.0
            # min{snr, k}
            mse_loss_weight = torch.stack([snr, k * torch.ones_like(t)], dim=1).min(dim=1)[0] / snr
        else:
            k = 5.0
            # min{snr, k}
            mse_loss_weight = torch.stack([snr, k * torch.ones_like(t)], dim=1).min(dim=1)[0]
        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
        loss_simple = loss_simple * mse_loss_weight
        # import pdb
        # pdb.set_trace()
        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})

        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_(-1.0, 1.0)
        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=1.0,
        noise_dropout=0.0,
        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 > 0.0:
            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))).contiguous()
        )

        # 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=1.0,
        noise_dropout=0.0,
        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 + (1.0 - 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))

            # import pdb
            # pdb.set_trace()
            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 + (1.0 - 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.latent_t_size, self.latent_f_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,
            **kwargs,
        )

    def save_waveform(self, waveform, savepath="./", name="awesome.wav", n_gen=1):
      print(f'debug_name : {name}')
      
      # If `name` is a list, join the elements into a string or select the first element
      if isinstance(name, list):
          name = "_".join(name)  # Joins the list elements with an underscore
          name += ".wav"  # Ensures the file has a `.wav` extension
      elif not isinstance(name, str):
          raise TypeError("Name must be a string or list")
      
      # Normalize the energy of the waveform
      todo_waveform = waveform[0, 0]  # Assuming you are only saving the first waveform
      todo_waveform = (todo_waveform / np.max(np.abs(todo_waveform))) * 0.8
      
      # Define the path where to save the file
      path = os.path.join(savepath, name)
      
      try:
          # Save the waveform to the specified path
          sf.write(path, todo_waveform, samplerate=self.sampling_rate)
          print(f'Waveform saved at -> {path}')
      except Exception as e:
          print(f'Error saving waveform: {e}')



    @torch.no_grad()
    def sample_log(
        self,
        cond,
        batch_size,
        ddim,
        ddim_steps,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        use_plms=False,
        mask=None,
        **kwargs,
    ):
        if mask is not None:
            shape = (self.channels, mask.size()[-2], mask.size()[-1])
        else:
            shape = (self.channels, self.latent_t_size, self.latent_f_size)

        intermediate = None
        if ddim and not use_plms:
            print("Use ddim sampler")

            ddim_sampler = DDIMSampler(self)
            samples, intermediates = ddim_sampler.sample(
                ddim_steps,
                batch_size,
                shape,
                cond,
                verbose=False,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
                mask=mask,
                **kwargs,
            )
        elif use_plms:
            print("Use plms sampler")
            plms_sampler = PLMSSampler(self)
            samples, intermediates = plms_sampler.sample(
                ddim_steps,
                batch_size,
                shape,
                cond,
                verbose=False,
                unconditional_guidance_scale=unconditional_guidance_scale,
                mask=mask,
                unconditional_conditioning=unconditional_conditioning,
                **kwargs,
            )

        else:
            print("Use DDPM sampler")
            samples, intermediates = self.sample(
                cond=cond,
                batch_size=batch_size,
                return_intermediates=True,
                unconditional_guidance_scale=unconditional_guidance_scale,
                mask=mask,
                unconditional_conditioning=unconditional_conditioning,
                **kwargs,
            )

        return samples, intermediate

    @torch.no_grad()
    def generate_sample(
        self,
        batchs,
        ddim_steps=200,
        ddim_eta=1.0,
        x_T=None,
        n_gen=1,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        name=None,
        use_plms=False,
        limit_num=None,
        **kwargs,
    ):
        # Generate n_gen times and select the best
        # Batch: audio, text, fnames
        # import pdb 
        # pdb.set_trace()
        assert x_T is None
        try:
            batchs = iter(batchs)
        except TypeError:
            raise ValueError("The first input argument should be an iterable object")

        if use_plms:
            assert ddim_steps is not None

        use_ddim = ddim_steps is not None
        if name is None:
            name = self.get_validation_folder_name()

        waveform_save_path = os.path.join(self.get_log_dir(), name)
        os.makedirs(waveform_save_path, exist_ok=True)
        print("Waveform save path: ", waveform_save_path)

        # if (
        #     "audiocaps" in waveform_save_path
        #     and len(os.listdir(waveform_save_path)) >= 964
        # ):
        #     print("The evaluation has already been done at %s" % waveform_save_path)
        #     return waveform_save_path

        with self.ema_scope("Plotting"):
            for i, batch in enumerate(batchs):
                #print(batch)
                z, c = self.get_input(
                    batch,
                    self.first_stage_key,
                    unconditional_prob_cfg=0.0,  # Do not output unconditional information in the c
                )
              
                # import pdb; pdb.set_trace()
                if limit_num is not None and i * z.size(0) > limit_num:
                    break

                c = self.filter_useful_cond_dict(c)
                
                text = super().get_input(batch, "text")
                mos = super().get_input(batch, "mos")
                # for cond_key in c.keys():
                #     c[cond_key] = self.cond_stage_models[self.cond_stage_model_metadata[cond_key]["model_idx"]](text[0])

                # Generate multiple samples
                batch_size = z.shape[0] * n_gen

                # Generate multiple samples at a time and filter out the best
                # The condition to the diffusion wrapper can have many format
                # import pdb 
                # pdb.set_trace()
                for cond_key in c.keys():
                    if isinstance(c[cond_key], list):
                       
                        for i in range(len(c[cond_key])):
                            c[cond_key][i] = torch.cat([c[cond_key][i]] * n_gen, dim=0)
                          
                    elif isinstance(c[cond_key], dict):
                        for k in c[cond_key].keys():
                            c[cond_key][k] = torch.cat([c[cond_key][k]] * n_gen, dim=0)
                    else:
                        c[cond_key] = torch.cat([c[cond_key]] * n_gen, dim=0)
                        
                
                text = text * n_gen
                mos = mos * n_gen
                c['mos'] = torch.stack(mos).unsqueeze(1)

                if unconditional_guidance_scale != 1.0:
                    unconditional_conditioning = {}
                    for key in self.cond_stage_model_metadata:
                        model_idx = self.cond_stage_model_metadata[key]["model_idx"]
                        unconditional_conditioning[key] = self.cond_stage_models[
                            model_idx
                        ].get_unconditional_condition(batch_size)

                fnames = list(super().get_input(batch, "fname"))
                # import pdb; pdb.set_trace()
                samples, _ = self.sample_log(
                    cond=c,
                    batch_size=batch_size,
                    x_T=x_T,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta,
                    unconditional_guidance_scale=unconditional_guidance_scale,
                    unconditional_conditioning=unconditional_conditioning,
                    use_plms=use_plms,
                )

                mel = self.decode_first_stage(samples)

                # mel = super().get_input(batch, "log_mel_spec")

                waveform = self.mel_spectrogram_to_waveform(
                    mel, savepath=waveform_save_path, bs=None, name=fnames, save=False, n_gen=n_gen
                )

                if n_gen > 1:
                    try:
                        best_index = []
                        similarity = self.clap.cos_similarity(
                            torch.FloatTensor(waveform).squeeze(1), text
                        )
                        for i in range(z.shape[0]):
                            candidates = similarity[i :: z.shape[0]]
                            max_index = torch.argmax(candidates).item()
                            best_index.append(i + max_index * z.shape[0])

                        waveform = waveform[best_index]

                        print("Similarity between generated audio and text", similarity)
                        print("Choose the following indexes:", best_index)
                    except Exception as e:
                        print("Warning: while calculating CLAP score (not fatal), ", e)
                self.save_waveform(waveform, savepath="./")
        return waveform_save_path


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

        self.conditioning_key = conditioning_key

        for key in self.conditioning_key:
            if (
                "concat" in key
                or "crossattn" in key
                or "hybrid" in key
                or "film" in key
                or "noncond" in key
            ):
                continue
            else:
                raise Value("The conditioning key %s is illegal" % key)

        self.being_verbosed_once = False

    def forward(self, x, t, cond_dict: dict = {}):
        x = x.contiguous()
        t = t.contiguous()

        # import pdb
        # pdb.set_trace()
        # x with condition (or maybe not)
        xc = x

        y = None
        context_list, attn_mask_list = [], []
        conditional_keys = cond_dict.keys()
        
        for key in conditional_keys:
            if "concat" in key:
                xc = torch.cat([x, cond_dict[key].unsqueeze(1)], dim=1)
            elif "film" in key:
                if y is None:
                    y = cond_dict[key].squeeze(1)
                else:
                    y = torch.cat([y, cond_dict[key].squeeze(1)], dim=-1)
            elif "crossattn" in key:
                # assert context is None, "You can only have one context matrix, got %s" % (cond_dict.keys())
                if isinstance(cond_dict[key], dict):
                    for k in cond_dict[key].keys():
                        if "crossattn" in k:
                            context, attn_mask = cond_dict[key][
                                k
                            ]  # crossattn_audiomae_pooled: torch.Size([12, 128, 768])
                else:
                    assert len(cond_dict[key]) == 2, (
                        "The context condition for %s you returned should have two element, one context one mask"
                        % (key)
                    )
                    context, attn_mask = cond_dict[key]

                # The input to the UNet model is a list of context matrix
                context_list.append(context)
                attn_mask_list.append(attn_mask)

            elif (
                "noncond" in key
            ):  # If you use loss function in the conditional module, include the keyword "noncond" in the return dictionary
                continue
            elif "mos" in key:
                mos = cond_dict['mos']
            else:
                raise NotImplementedError()

        if not self.being_verbosed_once:
            print("The input shape to the diffusion model is as follows:")
            print("xc", xc.size())
            print("t", t.size())
            for i in range(len(context_list)):
                print(
                    "context_%s" % i, context_list[i].size(), attn_mask_list[i].size()
                )
            if y is not None:
                print("y", y.size())
            self.being_verbosed_once = True
        # try:
        #     out = self.diffusion_model.forward_with_dpmsolver(
        #         xc, timestep=t, y=context_list, mask=attn_mask_list
        #     )
        # except:
        out = self.diffusion_model.forward(
            xc, timestep=t, context_list=context_list, context_mask_list=attn_mask_list, mos=mos
        )
       
        return out
    
    @torch.no_grad()
    def forward_with_cfg(self, x, t, cond_dict: dict = {}, cfg_scale=4.0, **model_kwargs):

        x = x.contiguous()
        t = t.contiguous()

        # x with condition (or maybe not)
        xc = x

        y = None
        context_list, attn_mask_list = [], []
        conditional_keys = cond_dict.keys()

        for key in conditional_keys:
            if "concat" in key:
                xc = torch.cat([x, cond_dict[key].unsqueeze(1)], dim=1)
            elif "film" in key:
                if y is None:
                    y = cond_dict[key].squeeze(1)
                else:
                    y = torch.cat([y, cond_dict[key].squeeze(1)], dim=-1)
            elif "crossattn" in key:
                # assert context is None, "You can only have one context matrix, got %s" % (cond_dict.keys())
                if isinstance(cond_dict[key], dict):
                    for k in cond_dict[key].keys():
                        if "crossattn" in k:
                            context, attn_mask = cond_dict[key][
                                k
                            ]  # crossattn_audiomae_pooled: torch.Size([12, 128, 768])
                else:
                    assert len(cond_dict[key]) == 2, (
                        "The context condition for %s you returned should have two element, one context one mask"
                        % (key)
                    )
                    context, attn_mask = cond_dict[key]

                # The input to the UNet model is a list of context matrix
                context_list.append(context)
                attn_mask_list.append(attn_mask)

            elif (
                "noncond" in key
            ):  # If you use loss function in the conditional module, include the keyword "noncond" in the return dictionary
                continue
            else:
                raise NotImplementedError()

        if not self.being_verbosed_once:
            print("The input shape to the diffusion model is as follows:")
            print("xc", xc.size())
            print("t", t.size())
            for i in range(len(context_list)):
                print(
                    "context_%s" % i, context_list[i].size(), attn_mask_list[i].size()
                )
            if y is not None:
                print("y", y.size())
            self.being_verbosed_once = True
        # try:
        #     out = self.diffusion_model.forward_with_dpmsolver(
        #         xc, timestep=t, y=context_list, mask=attn_mask_list
        #     )
        # except:
        out = self.diffusion_model.forward_with_cfg(
            xc, timestep=t, context_list=context_list, context_mask_list=attn_mask_list, cfg_scale=cfg_scale, **model_kwargs
        )
        # import pdb
        # pdb.set_trace()
       
        return out


class LatentDiffusionSpeedTest(pl.LightningModule):
    """main class"""

    def __init__(
        self,
        first_stage_config,
        cond_stage_config=None,
        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,
        batchsize=None,
        evaluation_params={},
        scale_by_std=False,
        base_learning_rate=None,
        *args,
        **kwargs,
    ):
        super().__init__()
        self.l1 = nn.Linear(1, 1)
        self.logger_save_dir = None
        self.logger_exp_group_name = None
        self.logger_exp_name = None
        self.test_data_subset_path = None

    def set_log_dir(self, save_dir, exp_group_name, exp_name):
        self.logger_save_dir = save_dir
        self.logger_exp_group_name = exp_group_name
        self.logger_exp_name = exp_name

    def forward(self, x):
        return self.l1(x.permute(0, 2, 1)).permute(0, 2, 1)

    def training_step(self, batch, batch_idx):
        x = batch["waveform"]
        loss = self(x)
        return torch.mean(loss)

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.02)


class LatentDiffusionVAELearnable(LatentDiffusion):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.automatic_optimization = False

    def configure_optimizers(self):
        lr = self.learning_rate
        params = list(self.model.parameters())

        for each in self.cond_stage_models:
            params = params + list(
                each.parameters()
            )  # Add the parameter from the conditional stage

        if self.learn_logvar:
            print("Diffusion model optimizing logvar")
            params.append(self.logvar)
        ldm_opt = torch.optim.AdamW(params, lr=lr)

        opt_autoencoder, opt_scheduler = self.first_stage_model.configure_optimizers()
        opt_ae, opt_disc = opt_autoencoder

        return [ldm_opt, opt_ae, opt_disc], []

    def encode_first_stage(self, x):
        # with torch.no_grad():
        encoding = self.first_stage_model.encode(x)
        return encoding

    def decode_first_stage(self, z):
        # with torch.no_grad():
        z = 1.0 / self.scale_factor * z
        decoding = self.first_stage_model.decode(z)
        return decoding

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

    def shared_step(self, batch, **kwargs):
        ldm_opt, g_opt, d_opt = self.optimizers()

        if self.training:
            # Classifier-free guidance
            unconditional_prob_cfg = self.unconditional_prob_cfg
        else:
            unconditional_prob_cfg = 0.0

        x, c, decoder_xrec, encoder_x, encoder_posterior = self.get_input(
            batch,
            self.first_stage_key,
            unconditional_prob_cfg=unconditional_prob_cfg,
            return_decoding_output=True,
            return_encoder_input=True,
            return_encoder_output=True,
        )

        loss, loss_dict = self(x, self.filter_useful_cond_dict(c))

        additional_loss_for_cond_modules = self.extract_possible_loss_in_cond_dict(c)

        assert isinstance(additional_loss_for_cond_modules, dict)

        loss_dict.update(additional_loss_for_cond_modules)

        if len(additional_loss_for_cond_modules.keys()) > 0:
            for k in additional_loss_for_cond_modules.keys():
                loss = loss + additional_loss_for_cond_modules[k]

        for k, v in additional_loss_for_cond_modules.items():
            self.log(
                "cond_stage/" + k,
                float(v),
                prog_bar=True,
                logger=True,
                on_step=True,
                on_epoch=True,
            )

        aeloss, log_dict_ae = self.first_stage_model.loss(
            encoder_x,
            decoder_xrec,
            encoder_posterior,
            optimizer_idx=0,
            global_step=self.first_stage_model.global_step,
            last_layer=self.first_stage_model.get_last_layer(),
            split="train",
        )

        self.manual_backward(loss + aeloss)

        ldm_opt.step()
        ldm_opt.zero_grad()

        g_opt.step()
        g_opt.zero_grad()

        discloss, log_dict_disc = self.first_stage_model.loss(
            encoder_x,
            decoder_xrec,
            encoder_posterior,
            optimizer_idx=1,
            global_step=self.first_stage_model.global_step,
            last_layer=self.first_stage_model.get_last_layer(),
            split="train",
        )

        self.manual_backward(discloss)
        d_opt.step()
        d_opt.zero_grad()

        self.log(
            "aeloss",
            aeloss,
            prog_bar=True,
            logger=True,
            on_step=True,
            on_epoch=False,
        )
        self.log(
            "posterior_std",
            torch.mean(encoder_posterior.var),
            prog_bar=True,
            logger=True,
            on_step=True,
            on_epoch=False,
        )
        loss_dict.update(log_dict_disc)
        loss_dict.update(log_dict_ae)

        return None, loss_dict

    def training_step(self, batch, batch_idx):
        self.warmup_step()
        self.check_module_param_update()

        if (
            self.state is None
            and len(self.trainer.optimizers[0].state_dict()["state"].keys()) > 0
        ):
            self.state = (
                self.trainer.optimizers[0].state_dict()["state"][0]["exp_avg"].clone()
            )
        elif self.state is not None and batch_idx % 1000 == 0:
            assert (
                torch.sum(
                    torch.abs(
                        self.state
                        - self.trainer.optimizers[0].state_dict()["state"][0]["exp_avg"]
                    )
                )
                > 1e-7
            ), "Optimizer is not working"

        if len(self.metrics_buffer.keys()) > 0:
            for k in self.metrics_buffer.keys():
                self.log(
                    k,
                    self.metrics_buffer[k],
                    prog_bar=False,
                    logger=True,
                    on_step=True,
                    on_epoch=False,
                )
                print(k, self.metrics_buffer[k])
            self.metrics_buffer = {}

        loss, loss_dict = self.shared_step(batch)

        self.log_dict(
            {k: float(v) for k, v in loss_dict.items()},
            prog_bar=True,
            logger=True,
            on_step=True,
            on_epoch=True,
        )

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

        lr = self.trainer.optimizers[0].param_groups[0]["lr"]
        self.log(
            "lr_abs",
            float(lr),
            prog_bar=True,
            logger=True,
            on_step=True,
            on_epoch=False,
        )