content: import json
  import os
  import re
  import shutil
  
  import cv2
  import imageio
  import matplotlib.pyplot as plt
  import numpy as np
  import torch
  import trimesh
  from matplotlib import cm
  from matplotlib.colors import LinearSegmentedColormap
  from PIL import Image, ImageDraw
  from pytorch_lightning.loggers import WandbLogger
  
  from craftsman.models.geometry.utils import Mesh
  from craftsman.utils.typing import *
  
  
  class SaverMixin:
      import wandb
      _save_dir: Optional[str] = None
      _wandb_logger: Optional[WandbLogger] = None
  
      def set_save_dir(self, save_dir: str):
          self._save_dir = save_dir
  
      def get_save_dir(self):
          if self._save_dir is None:
              raise ValueError("Save dir is not set")
          return self._save_dir
  
      def convert_data(self, data):
          if data is None:
              return None
          elif isinstance(data, np.ndarray):
              return data
          elif isinstance(data, torch.Tensor):
              return data.detach().cpu().numpy()
          elif isinstance(data, list):
              return [self.convert_data(d) for d in data]
          elif isinstance(data, dict):
              return {k: self.convert_data(v) for k, v in data.items()}
          else:
              raise TypeError(
                  "Data must be in type numpy.ndarray, torch.Tensor, list or dict, getting",
                  type(data),
              )
  
      def get_save_path(self, filename):
          save_path = os.path.join(self.get_save_dir(), filename)
          os.makedirs(os.path.dirname(save_path), exist_ok=True)
          return save_path
  
      def create_loggers(self, cfg_loggers: DictConfig) -> None:
          if "wandb" in cfg_loggers.keys() and cfg_loggers.wandb.enable:
              self._wandb_logger = WandbLogger(
                  project=cfg_loggers.wandb.project, name=cfg_loggers.wandb.name
              )
  
      def get_loggers(self) -> List:
          if self._wandb_logger:
              return [self._wandb_logger]
          else:
              return []
  
      DEFAULT_RGB_KWARGS = {"data_format": "HWC", "data_range": (0, 1)}
      DEFAULT_UV_KWARGS = {
          "data_format": "HWC",
          "data_range": (0, 1),
          "cmap": "checkerboard",
      }
      DEFAULT_GRAYSCALE_KWARGS = {"data_range": None, "cmap": "jet"}
      DEFAULT_GRID_KWARGS = {"align": "max"}
  
      def get_rgb_image_(self, img, data_format, data_range, rgba=False):
          img = self.convert_data(img)
          assert data_format in ["CHW", "HWC"]
          if data_format == "CHW":
              img = img.transpose(1, 2, 0)
          if img.dtype != np.uint8:
              img = img.clip(min=data_range[0], max=data_range[1])
              img = (
                  (img - data_range[0]) / (data_range[1] - data_range[0]) * 255.0
              ).astype(np.uint8)
          nc = 4 if rgba else 3
          imgs = [img[..., start : start + nc] for start in range(0, img.shape[-1], nc)]
          imgs = [
              img_
              if img_.shape[-1] == nc
              else np.concatenate(
                  [
                      img_,
                      np.zeros(
                          (img_.shape[0], img_.shape[1], nc - img_.shape[2]),
                          dtype=img_.dtype,
                      ),
                  ],
                  axis=-1,
              )
              for img_ in imgs
          ]
          img = np.concatenate(imgs, axis=1)
          if rgba:
              img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
          else:
              img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
          return img
  
      def _save_rgb_image(
          self,
          filename,
          img,
          data_format,
          data_range,
          name: Optional[str] = None,
          step: Optional[int] = None,
      ):
          img = self.get_rgb_image_(img, data_format, data_range)
          cv2.imwrite(filename, img)
          if name and self._wandb_logger:
              wandb.log(
                  {
                      name: wandb.Image(self.get_save_path(filename)),
                      "trainer/global_step": step,
                  }
              )
  
      def save_rgb_image(
          self,
          filename,
          img,
          data_format=DEFAULT_RGB_KWARGS["data_format"],
          data_range=DEFAULT_RGB_KWARGS["data_range"],
          name: Optional[str] = None,
          step: Optional[int] = None,
      ) -> str:
          save_path = self.get_save_path(filename)
          self._save_rgb_image(save_path, img, data_format, data_range, name, step)
          return save_path
  
      def get_uv_image_(self, img, data_format, data_range, cmap):
          img = self.convert_data(img)
          assert data_format in ["CHW", "HWC"]
          if data_format == "CHW":
              img = img.transpose(1, 2, 0)
          img = img.clip(min=data_range[0], max=data_range[1])
          img = (img - data_range[0]) / (data_range[1] - data_range[0])
          assert cmap in ["checkerboard", "color"]
          if cmap == "checkerboard":
              n_grid = 64
              mask = (img * n_grid).astype(int)
              mask = (mask[..., 0] + mask[..., 1]) % 2 == 0
              img = np.ones((img.shape[0], img.shape[1], 3), dtype=np.uint8) * 255
              img[mask] = np.array([255, 0, 255], dtype=np.uint8)
              img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
          elif cmap == "color":
              img_ = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
              img_[..., 0] = (img[..., 0] * 255).astype(np.uint8)
              img_[..., 1] = (img[..., 1] * 255).astype(np.uint8)
              img_ = cv2.cvtColor(img_, cv2.COLOR_RGB2BGR)
              img = img_
          return img
  
      def save_uv_image(
          self,
          filename,
          img,
          data_format=DEFAULT_UV_KWARGS["data_format"],
          data_range=DEFAULT_UV_KWARGS["data_range"],
          cmap=DEFAULT_UV_KWARGS["cmap"],
      ) -> str:
          save_path = self.get_save_path(filename)
          img = self.get_uv_image_(img, data_format, data_range, cmap)
          cv2.imwrite(save_path, img)
          return save_path
  
      def get_grayscale_image_(self, img, data_range, cmap):
          img = self.convert_data(img)
          img = np.nan_to_num(img)
          if data_range is None:
              img = (img - img.min()) / (img.max() - img.min())
          else:
              img = img.clip(data_range[0], data_range[1])
              img = (img - data_range[0]) / (data_range[1] - data_range[0])
          assert cmap in [None, "jet", "magma", "spectral"]
          if cmap == None:
              img = (img * 255.0).astype(np.uint8)
              img = np.repeat(img[..., None], 3, axis=2)
          elif cmap == "jet":
              img = (img * 255.0).astype(np.uint8)
              img = cv2.applyColorMap(img, cv2.COLORMAP_JET)
          elif cmap == "magma":
              img = 1.0 - img
              base = cm.get_cmap("magma")
              num_bins = 256
              colormap = LinearSegmentedColormap.from_list(
                  f"{base.name}{num_bins}", base(np.linspace(0, 1, num_bins)), num_bins
              )(np.linspace(0, 1, num_bins))[:, :3]
              a = np.floor(img * 255.0)
              b = (a + 1).clip(max=255.0)
              f = img * 255.0 - a
              a = a.astype(np.uint16).clip(0, 255)
              b = b.astype(np.uint16).clip(0, 255)
              img = colormap[a] + (colormap[b] - colormap[a]) * f[..., None]
              img = (img * 255.0).astype(np.uint8)
          elif cmap == "spectral":
              colormap = plt.get_cmap("Spectral")
  
              def blend_rgba(image):
                  image = image[..., :3] * image[..., -1:] + (
                      1.0 - image[..., -1:]
                  )  # blend A to RGB
                  return image
  
              img = colormap(img)
              img = blend_rgba(img)
              img = (img * 255).astype(np.uint8)
              img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
          return img
  
      def _save_grayscale_image(
          self,
          filename,
          img,
          data_range,
          cmap,
          name: Optional[str] = None,
          step: Optional[int] = None,
      ):
          img = self.get_grayscale_image_(img, data_range, cmap)
          cv2.imwrite(filename, img)
          if name and self._wandb_logger:
              wandb.log(
                  {
                      name: wandb.Image(self.get_save_path(filename)),
                      "trainer/global_step": step,
                  }
              )
  
      def save_grayscale_image(
          self,
          filename,
          img,
          data_range=DEFAULT_GRAYSCALE_KWARGS["data_range"],
          cmap=DEFAULT_GRAYSCALE_KWARGS["cmap"],
          name: Optional[str] = None,
          step: Optional[int] = None,
      ) -> str:
          save_path = self.get_save_path(filename)
          self._save_grayscale_image(save_path, img, data_range, cmap, name, step)
          return save_path
  
      def get_image_grid_(self, imgs, align):
          if isinstance(imgs[0], list):
              return np.concatenate(
                  [self.get_image_grid_(row, align) for row in imgs], axis=0
              )
          cols = []
          for col in imgs:
              assert col["type"] in ["rgb", "uv", "grayscale"]
              if col["type"] == "rgb":
                  rgb_kwargs = self.DEFAULT_RGB_KWARGS.copy()
                  rgb_kwargs.update(col["kwargs"])
                  cols.append(self.get_rgb_image_(col["img"], **rgb_kwargs))
              elif col["type"] == "uv":
                  uv_kwargs = self.DEFAULT_UV_KWARGS.copy()
                  uv_kwargs.update(col["kwargs"])
                  cols.append(self.get_uv_image_(col["img"], **uv_kwargs))
              elif col["type"] == "grayscale":
                  grayscale_kwargs = self.DEFAULT_GRAYSCALE_KWARGS.copy()
                  grayscale_kwargs.update(col["kwargs"])
                  cols.append(self.get_grayscale_image_(col["img"], **grayscale_kwargs))
  
          if align == "max":
              h = max([col.shape[0] for col in cols])
              w = max([col.shape[1] for col in cols])
          elif align == "min":
              h = min([col.shape[0] for col in cols])
              w = min([col.shape[1] for col in cols])
          elif isinstance(align, int):
              h = align
              w = align
          elif (
              isinstance(align, tuple)
              and isinstance(align[0], int)
              and isinstance(align[1], int)
          ):
              h, w = align
          else:
              raise ValueError(
                  f"Unsupported image grid align: {align}, should be min, max, int or (int, int)"
              )
  
          for i in range(len(cols)):
              if cols[i].shape[0] != h or cols[i].shape[1] != w:
                  cols[i] = cv2.resize(cols[i], (w, h), interpolation=cv2.INTER_LINEAR)
          return np.concatenate(cols, axis=1)
  
      def save_image_grid(
          self,
          filename,
          imgs,
          align=DEFAULT_GRID_KWARGS["align"],
          name: Optional[str] = None,
          step: Optional[int] = None,
          texts: Optional[List[float]] = None,
      ):
          save_path = self.get_save_path(filename)
          img = self.get_image_grid_(imgs, align=align)
  
          if texts is not None:
              img = Image.fromarray(img)
              draw = ImageDraw.Draw(img)
              black, white = (0, 0, 0), (255, 255, 255)
              for i, text in enumerate(texts):
                  draw.text((2, (img.size[1] // len(texts)) * i + 1), f"{text}", white)
                  draw.text((0, (img.size[1] // len(texts)) * i + 1), f"{text}", white)
                  draw.text((2, (img.size[1] // len(texts)) * i - 1), f"{text}", white)
                  draw.text((0, (img.size[1] // len(texts)) * i - 1), f"{text}", white)
                  draw.text((1, (img.size[1] // len(texts)) * i), f"{text}", black)
              img = np.asarray(img)
  
          cv2.imwrite(save_path, img)
          if name and self._wandb_logger:
              wandb.log({name: wandb.Image(save_path), "trainer/global_step": step})
          return save_path
  
      def save_image(self, filename, img) -> str:
          save_path = self.get_save_path(filename)
          img = self.convert_data(img)
          assert img.dtype == np.uint8 or img.dtype == np.uint16
          if img.ndim == 3 and img.shape[-1] == 3:
              img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
          elif img.ndim == 3 and img.shape[-1] == 4:
              img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
          cv2.imwrite(save_path, img)
          return save_path
  
      def save_cubemap(self, filename, img, data_range=(0, 1), rgba=False) -> str:
          save_path = self.get_save_path(filename)
          img = self.convert_data(img)
          assert img.ndim == 4 and img.shape[0] == 6 and img.shape[1] == img.shape[2]
  
          imgs_full = []
          for start in range(0, img.shape[-1], 3):
              img_ = img[..., start : start + 3]
              img_ = np.stack(
                  [
                      self.get_rgb_image_(img_[i], "HWC", data_range, rgba=rgba)
                      for i in range(img_.shape[0])
                  ],
                  axis=0,
              )
              size = img_.shape[1]
              placeholder = np.zeros((size, size, 3), dtype=np.float32)
              img_full = np.concatenate(
                  [
                      np.concatenate(
                          [placeholder, img_[2], placeholder, placeholder], axis=1
                      ),
                      np.concatenate([img_[1], img_[4], img_[0], img_[5]], axis=1),
                      np.concatenate(
                          [placeholder, img_[3], placeholder, placeholder], axis=1
                      ),
                  ],
                  axis=0,
              )
              imgs_full.append(img_full)
  
          imgs_full = np.concatenate(imgs_full, axis=1)
          cv2.imwrite(save_path, imgs_full)
          return save_path
  
      def save_data(self, filename, data) -> str:
          data = self.convert_data(data)
          if isinstance(data, dict):
              if not filename.endswith(".npz"):
                  filename += ".npz"
              save_path = self.get_save_path(filename)
              np.savez(save_path, **data)
          else:
              if not filename.endswith(".npy"):
                  filename += ".npy"
              save_path = self.get_save_path(filename)
              np.save(save_path, data)
          return save_path
  
      def save_state_dict(self, filename, data) -> str:
          save_path = self.get_save_path(filename)
          torch.save(data, save_path)
          return save_path
  
      def save_img_sequence(
          self,
          filename,
          img_dir,
          matcher,
          save_format="mp4",
          fps=30,
          name: Optional[str] = None,
          step: Optional[int] = None,
      ) -> str:
          assert save_format in ["gif", "mp4"]
          if not filename.endswith(save_format):
              filename += f".{save_format}"
          save_path = self.get_save_path(filename)
          matcher = re.compile(matcher)
          img_dir = os.path.join(self.get_save_dir(), img_dir)
          imgs = []
          for f in os.listdir(img_dir):
              if matcher.search(f):
                  imgs.append(f)
          imgs = sorted(imgs, key=lambda f: int(matcher.search(f).groups()[0]))
          imgs = [cv2.imread(os.path.join(img_dir, f)) for f in imgs]
  
          if save_format == "gif":
              imgs = [cv2.cvtColor(i, cv2.COLOR_BGR2RGB) for i in imgs]
              imageio.mimsave(save_path, imgs, fps=fps, palettesize=256)
          elif save_format == "mp4":
              imgs = [cv2.cvtColor(i, cv2.COLOR_BGR2RGB) for i in imgs]
              imageio.mimsave(save_path, imgs, fps=fps)
          if name and self._wandb_logger:
              wandb.log(
                  {
                      name: wandb.Video(save_path, format="mp4"),
                      "trainer/global_step": step,
                  }
              )
          return save_path
  
      def save_mesh(self, filename, v_pos, t_pos_idx, v_tex=None, t_tex_idx=None) -> str:
          save_path = self.get_save_path(filename)
          v_pos = self.convert_data(v_pos)
          t_pos_idx = self.convert_data(t_pos_idx)
          mesh = trimesh.Trimesh(vertices=v_pos, faces=t_pos_idx)
          mesh.export(save_path)
          return save_path
  
      def save_obj(
          self,
          filename: str,
          mesh: Mesh,
          save_mat: bool = False,
          save_normal: bool = False,
          save_uv: bool = False,
          save_vertex_color: bool = False,
          map_Kd: Optional[Float[Tensor, "H W 3"]] = None,
          map_Ks: Optional[Float[Tensor, "H W 3"]] = None,
          map_Bump: Optional[Float[Tensor, "H W 3"]] = None,
          map_Pm: Optional[Float[Tensor, "H W 1"]] = None,
          map_Pr: Optional[Float[Tensor, "H W 1"]] = None,
          map_format: str = "jpg",
      ) -> List[str]:
          save_paths: List[str] = []
          if not filename.endswith(".obj"):
              filename += ".obj"
  
          v_pos, t_pos_idx = self.convert_data(mesh.v_pos), self.convert_data(
              mesh.t_pos_idx
          )
          v_nrm, v_tex, t_tex_idx, v_rgb = None, None, None, None
          if save_normal:
              v_nrm = self.convert_data(mesh.v_nrm)
          if save_uv:
              v_tex, t_tex_idx = self.convert_data(mesh.v_tex), self.convert_data(
                  mesh.t_tex_idx
              )
          if save_vertex_color:
              v_rgb = self.convert_data(mesh.v_rgb)
          matname, mtllib = None, None
          if save_mat:
              matname = "default"
              mtl_filename = filename.replace(".obj", ".mtl")
              mtllib = os.path.basename(mtl_filename)
              mtl_save_paths = self._save_mtl(
                  mtl_filename,
                  matname,
                  map_Kd=self.convert_data(map_Kd),
                  map_Ks=self.convert_data(map_Ks),
                  map_Bump=self.convert_data(map_Bump),
                  map_Pm=self.convert_data(map_Pm),
                  map_Pr=self.convert_data(map_Pr),
                  map_format=map_format,
              )
              save_paths += mtl_save_paths
          obj_save_path = self._save_obj(
              filename,
              v_pos,
              t_pos_idx,
              v_nrm=v_nrm,
              v_tex=v_tex,
              t_tex_idx=t_tex_idx,
              v_rgb=v_rgb,
              matname=matname,
              mtllib=mtllib,
          )
          save_paths.append(obj_save_path)
          return save_paths
  
      def _save_obj(
          self,
          filename,
          v_pos,
          t_pos_idx,
          v_nrm=None,
          v_tex=None,
          t_tex_idx=None,
          v_rgb=None,
          matname=None,
          mtllib=None,
      ) -> str:
          obj_str = ""
          if matname is not None:
              obj_str += f"mtllib {mtllib}\n"
              obj_str += f"g object\n"
              obj_str += f"usemtl {matname}\n"
          for i in range(len(v_pos)):
              obj_str += f"v {v_pos[i][0]} {v_pos[i][1]} {v_pos[i][2]}"
              if v_rgb is not None:
                  obj_str += f" {v_rgb[i][0]} {v_rgb[i][1]} {v_rgb[i][2]}"
              obj_str += "\n"
          if v_nrm is not None:
              for v in v_nrm:
                  obj_str += f"vn {v[0]} {v[1]} {v[2]}\n"
          if v_tex is not None:
              for v in v_tex:
                  obj_str += f"vt {v[0]} {1.0 - v[1]}\n"
  
          for i in range(len(t_pos_idx)):
              obj_str += "f"
              for j in range(3):
                  obj_str += f" {t_pos_idx[i][j] + 1}/"
                  if v_tex is not None:
                      obj_str += f"{t_tex_idx[i][j] + 1}"
                  obj_str += "/"
                  if v_nrm is not None:
                      obj_str += f"{t_pos_idx[i][j] + 1}"
              obj_str += "\n"
  
          save_path = self.get_save_path(filename)
          with open(save_path, "w") as f:
              f.write(obj_str)
          return save_path
  
      def _save_mtl(
          self,
          filename,
          matname,
          Ka=(0.0, 0.0, 0.0),
          Kd=(1.0, 1.0, 1.0),
          Ks=(0.0, 0.0, 0.0),
          map_Kd=None,
          map_Ks=None,
          map_Bump=None,
          map_Pm=None,
          map_Pr=None,
          map_format="jpg",
          step: Optional[int] = None,
      ) -> List[str]:
          mtl_save_path = self.get_save_path(filename)
          save_paths = [mtl_save_path]
          mtl_str = f"newmtl {matname}\n"
          mtl_str += f"Ka {Ka[0]} {Ka[1]} {Ka[2]}\n"
          if map_Kd is not None:
              map_Kd_save_path = os.path.join(
                  os.path.dirname(mtl_save_path), f"texture_kd.{map_format}"
              )
              mtl_str += f"map_Kd texture_kd.{map_format}\n"
              self._save_rgb_image(
                  map_Kd_save_path,
                  map_Kd,
                  data_format="HWC",
                  data_range=(0, 1),
                  name=f"{matname}_Kd",
                  step=step,
              )
              save_paths.append(map_Kd_save_path)
          else:
              mtl_str += f"Kd {Kd[0]} {Kd[1]} {Kd[2]}\n"
          if map_Ks is not None:
              map_Ks_save_path = os.path.join(
                  os.path.dirname(mtl_save_path), f"texture_ks.{map_format}"
              )
              mtl_str += f"map_Ks texture_ks.{map_format}\n"
              self._save_rgb_image(
                  map_Ks_save_path,
                  map_Ks,
                  data_format="HWC",
                  data_range=(0, 1),
                  name=f"{matname}_Ks",
                  step=step,
              )
              save_paths.append(map_Ks_save_path)
          else:
              mtl_str += f"Ks {Ks[0]} {Ks[1]} {Ks[2]}\n"
          if map_Bump is not None:
              map_Bump_save_path = os.path.join(
                  os.path.dirname(mtl_save_path), f"texture_nrm.{map_format}"
              )
              mtl_str += f"map_Bump texture_nrm.{map_format}\n"
              self._save_rgb_image(
                  map_Bump_save_path,
                  map_Bump,
                  data_format="HWC",
                  data_range=(0, 1),
                  name=f"{matname}_Bump",
                  step=step,
              )
              save_paths.append(map_Bump_save_path)
          if map_Pm is not None:
              map_Pm_save_path = os.path.join(
                  os.path.dirname(mtl_save_path), f"texture_metallic.{map_format}"
              )
              mtl_str += f"map_Pm texture_metallic.{map_format}\n"
              self._save_grayscale_image(
                  map_Pm_save_path,
                  map_Pm,
                  data_range=(0, 1),
                  cmap=None,
                  name=f"{matname}_refl",
                  step=step,
              )
              save_paths.append(map_Pm_save_path)
          if map_Pr is not None:
              map_Pr_save_path = os.path.join(
                  os.path.dirname(mtl_save_path), f"texture_roughness.{map_format}"
              )
              mtl_str += f"map_Pr texture_roughness.{map_format}\n"
              self._save_grayscale_image(
                  map_Pr_save_path,
                  map_Pr,
                  data_range=(0, 1),
                  cmap=None,
                  name=f"{matname}_Ns",
                  step=step,
              )
              save_paths.append(map_Pr_save_path)
          with open(self.get_save_path(filename), "w") as f:
              f.write(mtl_str)
          return save_paths
  
      def save_file(self, filename, src_path) -> str:
          save_path = self.get_save_path(filename)
          shutil.copyfile(src_path, save_path)
          return save_path
  
      def save_json(self, filename, payload) -> str:
          save_path = self.get_save_path(filename)
          with open(save_path, "w") as f:
              f.write(json.dumps(payload))
          return save_path
  