content: # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
  # except for the third-party components listed below.
  # Hunyuan 3D does not impose any additional limitations beyond what is outlined
  # in the repsective licenses of these third-party components.
  # Users must comply with all terms and conditions of original licenses of these third-party
  # components and must ensure that the usage of the third party components adheres to
  # all relevant laws and regulations.
  
  # For avoidance of doubts, Hunyuan 3D means the large language models and
  # their software and algorithms, including trained model weights, parameters (including
  # optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
  # fine-tuning enabling code and other elements of the foregoing made publicly available
  # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
  
  import os
  
  # import ipdb
  import numpy as np
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import pytorch_lightning as pl
  from tqdm import tqdm
  from torchvision.transforms import v2
  from torchvision.utils import make_grid, save_image
  from einops import rearrange
  
  from diffusers import (
      DiffusionPipeline,
      EulerAncestralDiscreteScheduler,
      DDPMScheduler,
      UNet2DConditionModel,
      ControlNetModel,
  )
  
  from .modules import Dino_v2, UNet2p5DConditionModel
  import math
  
  
  def extract_into_tensor(a, t, x_shape):
      b, *_ = t.shape
      out = a.gather(-1, t)
      return out.reshape(b, *((1,) * (len(x_shape) - 1)))
  
  
  class HunyuanPaint(pl.LightningModule):
      def __init__(
          self,
          stable_diffusion_config,
          control_net_config=None,
          num_view=6,
          view_size=320,
          drop_cond_prob=0.1,
          with_normal_map=None,
          with_position_map=None,
          pbr_settings=["albedo", "mr"],
          **kwargs,
      ):
          """Initializes the HunyuanPaint Lightning Module.
  
          Args:
              stable_diffusion_config: Configuration for loading the Stable Diffusion pipeline
              control_net_config: Configuration for ControlNet (optional)
              num_view: Number of views to process
              view_size: Size of input views (height/width)
              drop_cond_prob: Probability of dropping conditioning input during training
              with_normal_map: Flag indicating whether normal maps are used
              with_position_map: Flag indicating whether position maps are used
              pbr_settings: List of PBR materials to generate (e.g., albedo, metallic-roughness)
              **kwargs: Additional keyword arguments
          """
          super(HunyuanPaint, self).__init__()
  
          self.num_view = num_view
          self.view_size = view_size
          self.drop_cond_prob = drop_cond_prob
          self.pbr_settings = pbr_settings
  
          # init modules
          pipeline = DiffusionPipeline.from_pretrained(**stable_diffusion_config)
          pipeline.set_pbr_settings(self.pbr_settings)
          pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
              pipeline.scheduler.config, timestep_spacing="trailing"
          )
  
          self.with_normal_map = with_normal_map
          self.with_position_map = with_position_map
  
          self.pipeline = pipeline
  
          self.pipeline.vae.use_slicing = True
  
          train_sched = DDPMScheduler.from_config(self.pipeline.scheduler.config)
  
          if isinstance(self.pipeline.unet, UNet2DConditionModel):
              self.pipeline.unet = UNet2p5DConditionModel(
                  self.pipeline.unet, train_sched, self.pipeline.scheduler, self.pbr_settings
              )
          self.train_scheduler = train_sched  # use ddpm scheduler during training
  
          self.register_schedule()
  
          pipeline.set_learned_parameters()
  
          if control_net_config is not None:
              pipeline.unet = pipeline.unet.bfloat16().requires_grad_(control_net_config.train_unet)
              self.pipeline.add_controlnet(
                  ControlNetModel.from_pretrained(control_net_config.pretrained_model_name_or_path),
                  conditioning_scale=0.75,
              )
  
          self.unet = pipeline.unet
  
          self.pipeline.set_progress_bar_config(disable=True)
          self.pipeline.vae = self.pipeline.vae.bfloat16()
          self.pipeline.text_encoder = self.pipeline.text_encoder.bfloat16()
  
          if self.unet.use_dino:
              self.dino_v2 = Dino_v2("facebook/dinov2-giant")
              self.dino_v2 = self.dino_v2.bfloat16()
  
          self.validation_step_outputs = []
  
      def register_schedule(self):
  
          self.num_timesteps = self.train_scheduler.config.num_train_timesteps
  
          betas = self.train_scheduler.betas.detach().cpu()
  
          alphas = 1.0 - betas
          alphas_cumprod = torch.cumprod(alphas, dim=0)
          alphas_cumprod_prev = torch.cat([torch.ones(1, dtype=torch.float64), alphas_cumprod[:-1]], 0)
  
          self.register_buffer("betas", betas.float())
          self.register_buffer("alphas_cumprod", alphas_cumprod.float())
          self.register_buffer("alphas_cumprod_prev", alphas_cumprod_prev.float())
  
          # calculations for diffusion q(x_t | x_{t-1}) and others
          self.register_buffer("sqrt_alphas_cumprod", torch.sqrt(alphas_cumprod).float())
          self.register_buffer("sqrt_one_minus_alphas_cumprod", torch.sqrt(1 - alphas_cumprod).float())
  
          self.register_buffer("sqrt_recip_alphas_cumprod", torch.sqrt(1.0 / alphas_cumprod).float())
          self.register_buffer("sqrt_recipm1_alphas_cumprod", torch.sqrt(1.0 / alphas_cumprod - 1).float())
  
      def on_fit_start(self):
          device = torch.device(f"cuda:{self.local_rank}")
          self.pipeline.to(device)
          if self.global_rank == 0:
              os.makedirs(os.path.join(self.logdir, "images_val"), exist_ok=True)
  
      def prepare_batch_data(self, batch):
          """Preprocesses a batch of input data for training/inference.
  
          Args:
              batch: Raw input batch dictionary
  
          Returns:
              tuple: Contains:
                  - cond_imgs: Primary conditioning images (B, 1, C, H, W)
                  - cond_imgs_another: Secondary conditioning images (B, 1, C, H, W)
                  - target_imgs: Dictionary of target PBR images resized and clamped
                  - images_normal: Preprocessed normal maps (if available)
                  - images_position: Preprocessed position maps (if available)
          """
  
          images_cond = batch["images_cond"].to(self.device)  # (B, M, C, H, W), where M is the number of reference images
          cond_imgs, cond_imgs_another = images_cond[:, 0:1, ...], images_cond[:, 1:2, ...]
  
          cond_size = self.view_size
          cond_imgs = v2.functional.resize(cond_imgs, cond_size, interpolation=3, antialias=True).clamp(0, 1)
          cond_imgs_another = v2.functional.resize(cond_imgs_another, cond_size, interpolation=3, antialias=True).clamp(
              0, 1
          )
  
          target_imgs = {}
          for pbr_token in self.pbr_settings:
              target_imgs[pbr_token] = batch[f"images_{pbr_token}"].to(self.device)
              target_imgs[pbr_token] = v2.functional.resize(
                  target_imgs[pbr_token], self.view_size, interpolation=3, antialias=True
              ).clamp(0, 1)
  
          images_normal = None
          if "images_normal" in batch:
              images_normal = batch["images_normal"]  # (B, N, C, H, W)
              images_normal = v2.functional.resize(images_normal, self.view_size, interpolation=3, antialias=True).clamp(
                  0, 1
              )
              images_normal = [images_normal]
  
          images_position = None
          if "images_position" in batch:
              images_position = batch["images_position"]  # (B, N, C, H, W)
              images_position = v2.functional.resize(
                  images_position, self.view_size, interpolation=3, antialias=True
              ).clamp(0, 1)
              images_position = [images_position]
  
          return cond_imgs, cond_imgs_another, target_imgs, images_normal, images_position
  
      @torch.no_grad()
      def forward_text_encoder(self, prompts):
          device = next(self.pipeline.vae.parameters()).device
          text_embeds = self.pipeline.encode_prompt(prompts, device, 1, False)[0]
          return text_embeds
  
      @torch.no_grad()
      def encode_images(self, images):
          """Encodes input images into latent representations using the VAE.
  
          Handles both standard input (B, N, C, H, W) and PBR input (B, N_pbrs, N, C, H, W)
          Maintains original batch structure in output latents.
  
          Args:
              images: Input images tensor
  
          Returns:
              torch.Tensor: Latent representations with original batch dimensions preserved
          """
  
          B = images.shape[0]
          image_ndims = images.ndim
          if image_ndims != 5:
              N_pbrs, N = images.shape[1:3]
          images = (
              rearrange(images, "b n c h w -> (b n) c h w")
              if image_ndims == 5
              else rearrange(images, "b n_pbrs n c h w -> (b n_pbrs n) c h w")
          )
          dtype = next(self.pipeline.vae.parameters()).dtype
  
          images = (images - 0.5) * 2.0
          posterior = self.pipeline.vae.encode(images.to(dtype)).latent_dist
          latents = posterior.sample() * self.pipeline.vae.config.scaling_factor
  
          latents = (
              rearrange(latents, "(b n) c h w -> b n c h w", b=B)
              if image_ndims == 5
              else rearrange(latents, "(b n_pbrs n) c h w -> b n_pbrs n c h w", b=B, n_pbrs=N_pbrs)
          )
  
          return latents
  
      def forward_unet(self, latents, t, **cached_condition):
          """Runs the UNet model to predict noise/latent residuals.
  
          Args:
              latents: Noisy latent representations (B, C, H, W)
              t: Timestep tensor (B,)
              **cached_condition: Dictionary of conditioning inputs (text embeds, reference images, etc)
  
          Returns:
              torch.Tensor: UNet output (predicted noise or velocity)
          """
  
          dtype = next(self.unet.parameters()).dtype
          latents = latents.to(dtype)
          shading_embeds = cached_condition["shading_embeds"]
          pred_noise = self.pipeline.unet(latents, t, encoder_hidden_states=shading_embeds, **cached_condition)
          return pred_noise[0]
  
      def predict_start_from_z_and_v(self, x_t, t, v):
          """
          Predicts clean image (x0) from noisy latents (x_t) and
          velocity prediction (v) using the v-prediction formula.
  
          Args:
              x_t: Noisy latents at timestep t
              t: Current timestep
              v: Predicted velocity (v) from UNet
  
          Returns:
              torch.Tensor: Predicted clean image (x0)
          """
  
          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 get_v(self, x, noise, t):
          """Computes the target velocity (v) for v-prediction training.
  
          Args:
              x: Clean latents (x0)
              noise: Added noise
              t: Current timestep
  
          Returns:
              torch.Tensor: Target velocity
          """
  
          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 training_step(self, batch, batch_idx):
          """Performs a single training step with both conditioning paths.
  
          Implements:
          1. Dual-conditioning path training (main ref + secondary ref)
          2. Velocity-prediction with consistency loss
          3. Conditional dropout for robust learning
          4. PBR-specific losses (albedo/metallic-roughness)
  
          Args:
              batch: Input batch from dataloader
              batch_idx: Index of current batch
  
          Returns:
              torch.Tensor: Combined loss value
          """
  
          cond_imgs, cond_imgs_another, target_imgs, normal_imgs, position_imgs = self.prepare_batch_data(batch)
  
          B, N_ref = cond_imgs.shape[:2]
          _, N_gen, _, H, W = target_imgs["albedo"].shape
          N_pbrs = len(self.pbr_settings)
          t = torch.randint(0, self.num_timesteps, size=(B,)).long().to(self.device)
          t = t.unsqueeze(-1).repeat(1, N_pbrs, N_gen)
          t = rearrange(t, "b n_pbrs n -> (b n_pbrs n)")
  
          all_target_pbrs = []
          for pbr_token in self.pbr_settings:
              all_target_pbrs.append(target_imgs[pbr_token])
          all_target_pbrs = torch.stack(all_target_pbrs, dim=0).transpose(1, 0)
          gen_latents = self.encode_images(all_target_pbrs)  #! B, N_pbrs N C H W
          ref_latents = self.encode_images(cond_imgs)  #! B, M, C, H, W
          ref_latents_another = self.encode_images(cond_imgs_another)  #! B, M, C, H, W
  
          all_shading_tokens = []
          for token in self.pbr_settings:
              if token in ["albedo", "mr"]:
                  all_shading_tokens.append(
                      getattr(self.unet, f"learned_text_clip_{token}").unsqueeze(dim=0).repeat(B, 1, 1)
                  )
          shading_embeds = torch.stack(all_shading_tokens, dim=1)
  
          if self.unet.use_dino:
              dino_hidden_states = self.dino_v2(cond_imgs[:, :1, ...])
              dino_hidden_states_another = self.dino_v2(cond_imgs_another[:, :1, ...])
  
          gen_latents = rearrange(gen_latents, "b n_pbrs n c h w -> (b n_pbrs n) c h w")
          noise = torch.randn_like(gen_latents).to(self.device)
          latents_noisy = self.train_scheduler.add_noise(gen_latents, noise, t).to(self.device)
          latents_noisy = rearrange(latents_noisy, "(b n_pbrs n) c h w -> b n_pbrs n c h w", b=B, n_pbrs=N_pbrs)
  
          cached_condition = {}
  
          if normal_imgs is not None:
              normal_embeds = self.encode_images(normal_imgs[0])
              cached_condition["embeds_normal"] = normal_embeds  #! B, N, C, H, W
  
          if position_imgs is not None:
              position_embeds = self.encode_images(position_imgs[0])
              cached_condition["embeds_position"] = position_embeds  #! B, N, C, H, W
              cached_condition["position_maps"] = position_imgs[0]  #! B, N, C, H, W
  
          for b in range(B):
              prob = np.random.rand()
              if prob < self.drop_cond_prob:
                  if "normal_imgs" in cached_condition:
                      cached_condition["embeds_normal"][b, ...] = torch.zeros_like(
                          cached_condition["embeds_normal"][b, ...]
                      )
                  if "position_imgs" in cached_condition:
                      cached_condition["embeds_position"][b, ...] = torch.zeros_like(
                          cached_condition["embeds_position"][b, ...]
                      )
  
              prob = np.random.rand()
              if prob < self.drop_cond_prob:
                  if "position_maps" in cached_condition:
                      cached_condition["position_maps"][b, ...] = torch.zeros_like(
                          cached_condition["position_maps"][b, ...]
                      )
  
              prob = np.random.rand()
              if prob < self.drop_cond_prob:
                  dino_hidden_states[b, ...] = torch.zeros_like(dino_hidden_states[b, ...])
              prob = np.random.rand()
              if prob < self.drop_cond_prob:
                  dino_hidden_states_another[b, ...] = torch.zeros_like(dino_hidden_states_another[b, ...])
  
          # MVA & Ref Attention
          prob = np.random.rand()
          cached_condition["mva_scale"] = 1.0
          cached_condition["ref_scale"] = 1.0
          if prob < self.drop_cond_prob:
              cached_condition["mva_scale"] = 0.0
              cached_condition["ref_scale"] = 0.0
          elif prob > 1.0 - self.drop_cond_prob:
              prob = np.random.rand()
              if prob < 0.5:
                  cached_condition["mva_scale"] = 0.0
              else:
                  cached_condition["ref_scale"] = 0.0
          else:
              pass
  
          if self.train_scheduler.config.prediction_type == "v_prediction":
  
              cached_condition["shading_embeds"] = shading_embeds
              cached_condition["ref_latents"] = ref_latents
              cached_condition["dino_hidden_states"] = dino_hidden_states
              v_pred = self.forward_unet(latents_noisy, t, **cached_condition)
              v_pred_albedo, v_pred_mr = torch.split(
                  rearrange(
                      v_pred, "(b n_pbr n) c h w -> b n_pbr n c h w", n_pbr=len(self.pbr_settings), n=self.num_view
                  ),
                  1,
                  dim=1,
              )
              v_target = self.get_v(gen_latents, noise, t)
              v_target_albedo, v_target_mr = torch.split(
                  rearrange(
                      v_target, "(b n_pbr n) c h w -> b n_pbr n c h w", n_pbr=len(self.pbr_settings), n=self.num_view
                  ),
                  1,
                  dim=1,
              )
  
              albedo_loss_1, _ = self.compute_loss(v_pred_albedo, v_target_albedo)
              mr_loss_1, _ = self.compute_loss(v_pred_mr, v_target_mr)
  
              cached_condition["ref_latents"] = ref_latents_another
              cached_condition["dino_hidden_states"] = dino_hidden_states_another
              v_pred_another = self.forward_unet(latents_noisy, t, **cached_condition)
              v_pred_another_albedo, v_pred_another_mr = torch.split(
                  rearrange(
                      v_pred_another,
                      "(b n_pbr n) c h w -> b n_pbr n c h w",
                      n_pbr=len(self.pbr_settings),
                      n=self.num_view,
                  ),
                  1,
                  dim=1,
              )
  
              albedo_loss_2, _ = self.compute_loss(v_pred_another_albedo, v_target_albedo)
              mr_loss_2, _ = self.compute_loss(v_pred_another_mr, v_target_mr)
  
              consistency_loss, _ = self.compute_loss(v_pred_another, v_pred)
  
              albedo_loss = (albedo_loss_1 + albedo_loss_2) * 0.5
              mr_loss = (mr_loss_1 + mr_loss_2) * 0.5
  
              log_loss_dict = {}
              log_loss_dict.update({f"train/albedo_loss": albedo_loss})
              log_loss_dict.update({f"train/mr_loss": mr_loss})
              log_loss_dict.update({f"train/cons_loss": consistency_loss})
  
              loss_dict = log_loss_dict
  
          elif self.train_scheduler.config.prediction_type == "epsilon":
              e_pred = self.forward_unet(latents_noisy, t, **cached_condition)
              loss, loss_dict = self.compute_loss(e_pred, noise)
          else:
              raise f"No {self.train_scheduler.config.prediction_type}"
  
          # logging
          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)
          lr = self.optimizers().param_groups[0]["lr"]
          self.log("lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
  
          return 0.85 * (albedo_loss + mr_loss) + 0.15 * consistency_loss
  
      def compute_loss(self, noise_pred, noise_gt):
          loss = F.mse_loss(noise_pred, noise_gt)
          prefix = "train"
          loss_dict = {}
          loss_dict.update({f"{prefix}/loss": loss})
          return loss, loss_dict
  
      @torch.no_grad()
      def validation_step(self, batch, batch_idx):
          """Performs validation on a single batch.
  
          Generates predicted images using:
          1. Reference conditioning images
          2. Optional normal/position maps
          3. Frozen DINO features (if enabled)
          4. Text prompt conditioning
  
          Compares predictions against ground truth targets and prepares visualization.
          Stores results for epoch-level aggregation.
  
          Args:
              batch: Input batch from validation dataloader
              batch_idx: Index of current batch
          """
          # [Validation image generation and comparison logic...]
          # Key steps:
          # 1. Preprocess conditioning images to PIL format
          # 2. Set up conditioning inputs (normal maps, position maps, DINO features)
          # 3. Run pipeline inference with fixed prompt ("high quality")
          # 4. Decode latent outputs to image space
          # 5. Arrange predictions and ground truths for visualization
  
          cond_imgs_tensor, _, target_imgs, normal_imgs, position_imgs = self.prepare_batch_data(batch)
          resolution = self.view_size
          image_pils = []
          for i in range(cond_imgs_tensor.shape[0]):
              image_pils.append([])
              for j in range(cond_imgs_tensor.shape[1]):
                  image_pils[-1].append(v2.functional.to_pil_image(cond_imgs_tensor[i, j, ...]))
  
          outputs, gts = [], []
          for idx in range(len(image_pils)):
              cond_imgs = image_pils[idx]
  
              cached_condition = dict(num_in_batch=self.num_view, N_pbrs=len(self.pbr_settings))
              if normal_imgs is not None:
                  cached_condition["images_normal"] = normal_imgs[0][idx, ...].unsqueeze(0)
              if position_imgs is not None:
                  cached_condition["images_position"] = position_imgs[0][idx, ...].unsqueeze(0)
              if self.pipeline.unet.use_dino:
                  dino_hidden_states = self.dino_v2([cond_imgs][0])
                  cached_condition["dino_hidden_states"] = dino_hidden_states
  
              latent = self.pipeline(
                  cond_imgs,
                  prompt="high quality",
                  num_inference_steps=30,
                  output_type="latent",
                  height=resolution,
                  width=resolution,
                  **cached_condition,
              ).images
  
              image = self.pipeline.vae.decode(latent / self.pipeline.vae.config.scaling_factor, return_dict=False)[
                  0
              ]  # [-1, 1]
              image = (image * 0.5 + 0.5).clamp(0, 1)
  
              image = rearrange(
                  image, "(b n_pbr n) c h w -> b n_pbr n c h w", n_pbr=len(self.pbr_settings), n=self.num_view
              )
              image = torch.cat((torch.ones_like(image[:, :, :1, ...]) * 0.5, image), dim=2)
              image = rearrange(image, "b n_pbr n c h w -> (b n_pbr n) c h w")
              image = rearrange(
                  image,
                  "(b n_pbr n) c h w -> b c (n_pbr h) (n w)",
                  b=1,
                  n_pbr=len(self.pbr_settings),
                  n=self.num_view + 1,
              )
              outputs.append(image)
  
          all_target_pbrs = []
          for pbr_token in self.pbr_settings:
              all_target_pbrs.append(target_imgs[pbr_token])
          all_target_pbrs = torch.stack(all_target_pbrs, dim=0).transpose(1, 0)
          all_target_pbrs = torch.cat(
              (cond_imgs_tensor.unsqueeze(1).repeat(1, len(self.pbr_settings), 1, 1, 1, 1), all_target_pbrs), dim=2
          )
          all_target_pbrs = rearrange(all_target_pbrs, "b n_pbrs n c h w -> b c (n_pbrs h) (n w)")
          gts = all_target_pbrs
          outputs = torch.cat(outputs, dim=0).to(self.device)
          images = torch.cat([gts, outputs], dim=-2)
          self.validation_step_outputs.append(images)
  
      @torch.no_grad()
      def on_validation_epoch_end(self):
          """Aggregates validation results at epoch end.
  
          Gathers outputs from all GPUs (if distributed training),
          creates a unified visualization grid, and saves to disk.
          Only rank 0 process performs saving.
          """
          # [Result aggregation and visualization...]
          # Key steps:
          # 1. Gather validation outputs from all processes
          # 2. Create image grid combining ground truths and predictions
          # 3. Save visualization with step-numbered filename
          # 4. Clear memory for next validation cycle
  
          images = torch.cat(self.validation_step_outputs, dim=0)
          all_images = self.all_gather(images)
          all_images = rearrange(all_images, "r b c h w -> (r b) c h w")
  
          if self.global_rank == 0:
              grid = make_grid(all_images, nrow=8, normalize=True, value_range=(0, 1))
              save_image(grid, os.path.join(self.logdir, "images_val", f"val_{self.global_step:07d}.png"))
  
          self.validation_step_outputs.clear()  # free memory
  
      def configure_optimizers(self):
          lr = self.learning_rate
          optimizer = torch.optim.AdamW(self.unet.parameters(), lr=lr)
  
          def lr_lambda(step):
              warm_up_step = 1000
              T_step = 9000
              gamma = 0.9
              min_lr = 0.1 if step >= warm_up_step else 0.0
              max_lr = 1.0
              normalized_step = step % (warm_up_step + T_step)
              current_max_lr = max_lr * gamma ** (step // (warm_up_step + T_step))
              if current_max_lr < min_lr:
                  current_max_lr = min_lr
              if normalized_step < warm_up_step:
                  lr_step = min_lr + (normalized_step / warm_up_step) * (current_max_lr - min_lr)
              else:
                  step_wc_wp = normalized_step - warm_up_step
                  ratio = step_wc_wp / T_step
                  lr_step = min_lr + 0.5 * (current_max_lr - min_lr) * (1 + math.cos(math.pi * ratio))
              return lr_step
  
          lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
  
          lr_scheduler_config = {
              "scheduler": lr_scheduler,
              "interval": "step",
              "frequency": 1,
              "monitor": "val_loss",
              "strict": False,
              "name": None,
          }
  
          return {"optimizer": optimizer, "lr_scheduler": lr_scheduler_config}
  