content: from typing import Dict
  import numpy as np
  from omegaconf import DictConfig, ListConfig
  import torch
  from torch.utils.data import Dataset
  from pathlib import Path
  import json
  from PIL import Image
  from torchvision import transforms
  from einops import rearrange
  from typing import Literal, Tuple, Optional, Any
  import random
  
  import json
  import os, sys
  import math
  
  from .normal_utils import trans_normal, normal2img, img2normal
  
  
  class ObjaverseDataset(Dataset):
      def __init__(self,
          root_dir: str,
          num_views: int,
          bg_color: Any,
          img_wh: Tuple[int, int],
          object_list: str,
          groups_num: int=1,
          validation: bool = False,
          data_view_num: int = 6,
          num_validation_samples: int = 64,
          num_samples: Optional[int] = None,
          invalid_list: Optional[str] = None,
          trans_norm_system: bool = True,   # if True, transform all normals map into the cam system of front view
          augment_data: bool = False,
          read_normal: bool = True,
          read_color: bool = False,
          read_depth: bool = False,
          read_mask: bool = True,
          mix_color_normal: bool = False,
          suffix: str = 'png',
          subscene_tag: int = 3,
          backup_scene: str = "9438abf986c7453a9f4df7c34aa2e65b"
          ) -> None:
          """Create a dataset from a folder of images.
          If you pass in a root directory it will be searched for images
          ending in ext (ext can be a list)
          """
          self.root_dir = Path(root_dir)
          self.num_views = num_views
          self.bg_color = bg_color
          self.validation = validation
          self.num_samples = num_samples
          self.trans_norm_system = trans_norm_system
          self.augment_data = augment_data
          self.invalid_list = invalid_list
          self.groups_num = groups_num
          print("augment data: ", self.augment_data)
          self.img_wh = img_wh
          self.read_normal = read_normal
          self.read_color = read_color
          self.read_depth = read_depth
          self.read_mask = read_mask
          self.mix_color_normal = mix_color_normal  # mix load color and normal maps
          self.suffix = suffix
          self.subscene_tag = subscene_tag
  
          self.view_types  = ['front', 'front_right', 'right', 'back', 'left', 'front_left']
          self.fix_cam_pose_dir = "./Gen_3D_Modules/MVDream/data/fixed_poses/nine_views"
  
          self.fix_cam_poses = self.load_fixed_poses()  # world2cam matrix
  
          if object_list is not None:
              with open(object_list) as f:
                  self.objects = json.load(f)
              self.objects = [os.path.basename(o).replace(".glb", "") for o in self.objects]
          else:
              self.objects = os.listdir(self.root_dir)
              self.objects = sorted(self.objects)
  
          if self.invalid_list is not None:
              with open(self.invalid_list) as f:
                  self.invalid_objects = json.load(f)
              self.invalid_objects = [os.path.basename(o).replace(".glb", "") for o in self.invalid_objects]
          else:
              self.invalid_objects = []
          
          
          self.all_objects = set(self.objects) - (set(self.invalid_objects) & set(self.objects))
          self.all_objects = list(self.all_objects)
  
          if not validation:
              self.all_objects = self.all_objects[:-num_validation_samples]
          else:
              self.all_objects = self.all_objects[-num_validation_samples:]
          if num_samples is not None:
              self.all_objects = self.all_objects[:num_samples]
  
          print("loading ", len(self.all_objects), " objects in the dataset")
  
          if self.mix_color_normal:
              self.backup_data = self.__getitem_mix__(0, backup_scene)
          else:
              self.backup_data = self.__getitem_joint__(0, backup_scene) 
  
      def __len__(self):
          return len(self.objects)*self.total_view
  
      def load_fixed_poses(self):
          poses = {}
          for face in self.view_types:
              RT = np.loadtxt(os.path.join(self.fix_cam_pose_dir,'%03d_%s_RT.txt'%(0, face)))
              poses[face] = RT
  
          return poses
          
      def cartesian_to_spherical(self, xyz):
          ptsnew = np.hstack((xyz, np.zeros(xyz.shape)))
          xy = xyz[:,0]**2 + xyz[:,1]**2
          z = np.sqrt(xy + xyz[:,2]**2)
          theta = np.arctan2(np.sqrt(xy), xyz[:,2]) # for elevation angle defined from Z-axis down
          #ptsnew[:,4] = np.arctan2(xyz[:,2], np.sqrt(xy)) # for elevation angle defined from XY-plane up
          azimuth = np.arctan2(xyz[:,1], xyz[:,0])
          return np.array([theta, azimuth, z])
  
      def get_T(self, target_RT, cond_RT):
          R, T = target_RT[:3, :3], target_RT[:, -1]
          T_target = -R.T @ T # change to cam2world
  
          R, T = cond_RT[:3, :3], cond_RT[:, -1]
          T_cond = -R.T @ T
  
          theta_cond, azimuth_cond, z_cond = self.cartesian_to_spherical(T_cond[None, :])
          theta_target, azimuth_target, z_target = self.cartesian_to_spherical(T_target[None, :])
          
          d_theta = theta_target - theta_cond
          d_azimuth = (azimuth_target - azimuth_cond) % (2 * math.pi)
          d_z = z_target - z_cond
          
          # d_T = torch.tensor([d_theta.item(), math.sin(d_azimuth.item()), math.cos(d_azimuth.item()), d_z.item()])
          return d_theta, d_azimuth
  
      def get_bg_color(self):
          if self.bg_color == 'white':
              bg_color = np.array([1., 1., 1.], dtype=np.float32)
          elif self.bg_color == 'black':
              bg_color = np.array([0., 0., 0.], dtype=np.float32)
          elif self.bg_color == 'gray':
              bg_color = np.array([0.5, 0.5, 0.5], dtype=np.float32)
          elif self.bg_color == 'random':
              bg_color = np.random.rand(3)
          elif self.bg_color == 'three_choices':
              white = np.array([1., 1., 1.], dtype=np.float32)
              black = np.array([0., 0., 0.], dtype=np.float32)
              gray = np.array([0.5, 0.5, 0.5], dtype=np.float32)
              bg_color = random.choice([white, black, gray])
          elif isinstance(self.bg_color, float):
              bg_color = np.array([self.bg_color] * 3, dtype=np.float32)
          else:
              raise NotImplementedError
          return bg_color
  
      
  
      def load_mask(self, img_path, return_type='np'):
          # not using cv2 as may load in uint16 format
          # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255]
          # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC)
          # pil always returns uint8
          img = np.array(Image.open(img_path).resize(self.img_wh))
          img = np.float32(img > 0)
  
          assert len(np.shape(img)) == 2
  
          if return_type == "np":
              pass
          elif return_type == "pt":
              img = torch.from_numpy(img)
          else:
              raise NotImplementedError
          
          return img
      
      def load_image(self, img_path, bg_color, alpha, return_type='np'):
          # not using cv2 as may load in uint16 format
          # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255]
          # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC)
          # pil always returns uint8
          img = np.array(Image.open(img_path).resize(self.img_wh))
          img = img.astype(np.float32) / 255. # [0, 1]
          assert img.shape[-1] == 3 or img.shape[-1] == 4 # RGB or RGBA
  
          if alpha is None and img.shape[-1] == 4:
              alpha = img[:, :, 3:]
              img = img[:, :, :3]
  
          if alpha.shape[-1] != 1:
              alpha = alpha[:, :, None]
  
          img = img[...,:3] * alpha + bg_color * (1 - alpha)
  
          if return_type == "np":
              pass
          elif return_type == "pt":
              img = torch.from_numpy(img)
          else:
              raise NotImplementedError
          
          return img
      
      def load_depth(self, img_path, bg_color, alpha, return_type='np'):
          # not using cv2 as may load in uint16 format
          # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255]
          # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC)
          # pil always returns uint8
          img = np.array(Image.open(img_path).resize(self.img_wh))
          img = img.astype(np.float32) / 65535. # [0, 1]
  
          img[img > 0.4] = 0
          img = img / 0.4
          
          assert img.ndim == 2 # depth
          img = np.stack([img]*3, axis=-1)
  
          if alpha.shape[-1] != 1:
              alpha = alpha[:, :, None]
  
          # print(np.max(img[:, :, 0]))
  
          img = img[...,:3] * alpha + bg_color * (1 - alpha)
  
          if return_type == "np":
              pass
          elif return_type == "pt":
              img = torch.from_numpy(img)
          else:
              raise NotImplementedError
          
          return img
      
      def load_normal(self, img_path, bg_color, alpha, RT_w2c=None, RT_w2c_cond=None, return_type='np'):
          # not using cv2 as may load in uint16 format
          # img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255]
          # img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC)
          # pil always returns uint8
          normal = np.array(Image.open(img_path).resize(self.img_wh))
  
          assert normal.shape[-1] == 3 or normal.shape[-1] == 4 # RGB or RGBA
  
          if alpha is None and normal.shape[-1] == 4:
              alpha = normal[:, :, 3:] / 255.
              normal = normal[:, :, :3]
  
          normal = trans_normal(img2normal(normal), RT_w2c, RT_w2c_cond)
  
          img = (normal*0.5 + 0.5).astype(np.float32)  # [0, 1]
  
          if alpha.shape[-1] != 1:
              alpha = alpha[:, :, None]
  
          img = img[...,:3] * alpha + bg_color * (1 - alpha)
  
          if return_type == "np":
              pass
          elif return_type == "pt":
              img = torch.from_numpy(img)
          else:
              raise NotImplementedError
          
          return img
  
      def __len__(self):
          return len(self.all_objects)
  
      def __getitem_mix__(self, index, debug_object=None):
          if debug_object is not None:
              object_name =  debug_object #
              set_idx = random.sample(range(0, self.groups_num), 1)[0] # without replacement
          else:
              object_name = self.all_objects[index%len(self.all_objects)]
              set_idx = 0
  
          if self.augment_data:
              cond_view = random.sample(self.view_types, k=1)[0]
          else:
              cond_view = 'front'
  
  
          # ! if you would like predict depth; modify here
          if random.random() < 0.5:
              read_color, read_normal, read_depth = True, False, False
          else:
              read_color, read_normal, read_depth = False, True, False
  
          read_normal = read_normal & self.read_normal
          read_depth = read_depth & self.read_depth
  
          assert (read_color and (read_normal or read_depth)) is False
          
          view_types = self.view_types
  
          cond_w2c = self.fix_cam_poses[cond_view]
  
          tgt_w2cs = [self.fix_cam_poses[view] for view in view_types]
  
          elevations = []
          azimuths = []
  
          # get the bg color
          bg_color = self.get_bg_color()
  
          if self.read_mask:
              cond_alpha = self.load_mask(os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "mask_%03d_%s.%s" % (set_idx, cond_view, self.suffix)), return_type='np')
          else:
              cond_alpha = None
          img_tensors_in = [
              self.load_image(os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "rgb_%03d_%s.%s" % (set_idx, cond_view, self.suffix)), bg_color, cond_alpha, return_type='pt').permute(2, 0, 1)
          ] * self.num_views
          img_tensors_out = []
  
          for view, tgt_w2c in zip(view_types, tgt_w2cs):
              img_path = os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "rgb_%03d_%s.%s" % (set_idx, view, self.suffix))
              mask_path = os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "mask_%03d_%s.%s" % (set_idx, view, self.suffix))
              normal_path = os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "normals_%03d_%s.%s" % (set_idx, view, self.suffix))
              depth_path = os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "depth_%03d_%s.%s" % (set_idx, view, self.suffix))
              if self.read_mask:
                  alpha = self.load_mask(mask_path, return_type='np')
              else:
                  alpha = None
  
              if read_color:                        
                  img_tensor = self.load_image(img_path, bg_color, alpha, return_type="pt")
                  img_tensor = img_tensor.permute(2, 0, 1)
                  img_tensors_out.append(img_tensor)
  
              if read_normal:
                  normal_tensor = self.load_normal(normal_path, bg_color, alpha, RT_w2c=tgt_w2c, RT_w2c_cond=cond_w2c, return_type="pt").permute(2, 0, 1)
                  img_tensors_out.append(normal_tensor)
              if read_depth:
                  depth_tensor = self.load_depth(depth_path, bg_color, alpha, return_type="pt").permute(2, 0, 1)
                  img_tensors_out.append(depth_tensor)
  
              # evelations, azimuths
              elevation, azimuth = self.get_T(tgt_w2c, cond_w2c)
              elevations.append(elevation)
              azimuths.append(azimuth)
  
          img_tensors_in = torch.stack(img_tensors_in, dim=0).float() # (Nv, 3, H, W)
          img_tensors_out = torch.stack(img_tensors_out, dim=0).float() # (Nv, 3, H, W)
  
  
          elevations = torch.as_tensor(elevations).float().squeeze(1)
          azimuths = torch.as_tensor(azimuths).float().squeeze(1)
          elevations_cond = torch.as_tensor([0] * self.num_views).float()  # fixed only use 4 views to train
          camera_embeddings = torch.stack([elevations_cond, elevations, azimuths], dim=-1) # (Nv, 3)
  
          normal_class = torch.tensor([1, 0]).float()
          normal_task_embeddings = torch.stack([normal_class]*self.num_views, dim=0)  # (Nv, 2)
          color_class = torch.tensor([0, 1]).float()
          color_task_embeddings = torch.stack([color_class]*self.num_views, dim=0)  # (Nv, 2)
          if read_normal or read_depth:
              task_embeddings = normal_task_embeddings
          if read_color:
              task_embeddings = color_task_embeddings
          # print(elevations)
          # print(azimuths)
          return {
              'elevations_cond': elevations_cond,
              'elevations_cond_deg': torch.rad2deg(elevations_cond),
              'elevations': elevations,
              'azimuths': azimuths,
              'elevations_deg': torch.rad2deg(elevations),
              'azimuths_deg': torch.rad2deg(azimuths),
              'imgs_in': img_tensors_in,
              'imgs_out': img_tensors_out,
              'camera_embeddings': camera_embeddings,
              'task_embeddings': task_embeddings
          }
      
  
      def __getitem_joint__(self, index, debug_object=None):
          if debug_object is not  None:
              object_name =  debug_object #
              set_idx = random.sample(range(0, self.groups_num), 1)[0] # without replacement
          else:
              object_name = self.all_objects[index%len(self.all_objects)]
              set_idx = 0
  
          if self.augment_data:
              cond_view = random.sample(self.view_types, k=1)[0]
          else:
              cond_view = 'front'
  
          view_types = self.view_types
  
          cond_w2c = self.fix_cam_poses[cond_view]
  
          tgt_w2cs = [self.fix_cam_poses[view] for view in view_types]
  
          elevations = []
          azimuths = []
  
          # get the bg color
          bg_color = self.get_bg_color()
  
          if self.read_mask:
              cond_alpha = self.load_mask(os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "mask_%03d_%s.%s" % (set_idx, cond_view, self.suffix)), return_type='np')
          else:
              cond_alpha = None
          img_tensors_in = [
              self.load_image(os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "rgb_%03d_%s.%s" % (set_idx, cond_view, self.suffix)), bg_color, cond_alpha, return_type='pt').permute(2, 0, 1)
          ] * self.num_views
          img_tensors_out = []
          normal_tensors_out = []
          for view, tgt_w2c in zip(view_types, tgt_w2cs):
              img_path = os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "rgb_%03d_%s.%s" % (set_idx, view, self.suffix))
              mask_path = os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "mask_%03d_%s.%s" % (set_idx, view, self.suffix))
              if self.read_mask:
                  alpha = self.load_mask(mask_path, return_type='np')
              else:
                  alpha = None
  
              if self.read_color:                        
                  img_tensor = self.load_image(img_path, bg_color, alpha, return_type="pt")
                  img_tensor = img_tensor.permute(2, 0, 1)
                  img_tensors_out.append(img_tensor)
  
              if self.read_normal:
                  normal_path = os.path.join(self.root_dir,  object_name[:self.subscene_tag], object_name, "normals_%03d_%s.%s" % (set_idx, view, self.suffix))
                  normal_tensor = self.load_normal(normal_path, bg_color, alpha, RT_w2c=tgt_w2c, RT_w2c_cond=cond_w2c, return_type="pt").permute(2, 0, 1)
                  normal_tensors_out.append(normal_tensor)
  
              # evelations, azimuths
              elevation, azimuth = self.get_T(tgt_w2c, cond_w2c)
              elevations.append(elevation)
              azimuths.append(azimuth)
  
          img_tensors_in = torch.stack(img_tensors_in, dim=0).float() # (Nv, 3, H, W)
          if self.read_color:
              img_tensors_out = torch.stack(img_tensors_out, dim=0).float() # (Nv, 3, H, W)
          if self.read_normal:
              normal_tensors_out = torch.stack(normal_tensors_out, dim=0).float() # (Nv, 3, H, W)
  
          elevations = torch.as_tensor(elevations).float().squeeze(1)
          azimuths = torch.as_tensor(azimuths).float().squeeze(1)
          elevations_cond = torch.as_tensor([0] * self.num_views).float()  # fixed only use 4 views to train
  
          camera_embeddings = torch.stack([elevations_cond, elevations, azimuths], dim=-1) # (Nv, 3)
  
          normal_class = torch.tensor([1, 0]).float()
          normal_task_embeddings = torch.stack([normal_class]*self.num_views, dim=0)  # (Nv, 2)
          color_class = torch.tensor([0, 1]).float()
          color_task_embeddings = torch.stack([color_class]*self.num_views, dim=0)  # (Nv, 2)
  
          return {
              'elevations_cond': elevations_cond,
              'elevations_cond_deg': torch.rad2deg(elevations_cond),
              'elevations': elevations,
              'azimuths': azimuths,
              'elevations_deg': torch.rad2deg(elevations),
              'azimuths_deg': torch.rad2deg(azimuths),
              'imgs_in': img_tensors_in,
              'imgs_out': img_tensors_out,
              'normals_out': normal_tensors_out,
              'camera_embeddings': camera_embeddings,
              'normal_task_embeddings': normal_task_embeddings,
              'color_task_embeddings': color_task_embeddings
          }
  
      def __getitem__(self, index):
          try:
              if self.mix_color_normal:
                  data = self.__getitem_mix__(index)
              else:
                  data = self.__getitem_joint__(index)
              return data
          except:
              print("load error ", self.all_objects[index%len(self.all_objects)] )
              return self.backup_data
          
  
  class ConcatDataset(torch.utils.data.Dataset):
      def __init__(self, datasets, weights):
          self.datasets = datasets
          self.weights = weights
          self.num_datasets = len(datasets)
  
      def __getitem__(self, i):
  
          chosen = random.choices(self.datasets, self.weights, k=1)[0]
          return chosen[i]
  
      def __len__(self):
          return max(len(d) for d in self.datasets)
  
  if __name__ == "__main__":
      train_dataset = ObjaverseDataset(
          root_dir="/ghome/l5/xxlong/.objaverse/hf-objaverse-v1/renderings",
          size=(128, 128),
          ext="hdf5",
          default_trans=torch.zeros(3),
          return_paths=False,
          total_view=8,
          validation=False,
          object_list=None,
          views_mode='fourviews'
      )
      data0 = train_dataset[0]
      data1  = train_dataset[50]
      # print(data)
  