content: import os
  from dataclasses import dataclass, field
  
  import pytorch_lightning as pl
  import torch.nn.functional as F
  
  from craftsman.utils.base import (
      Updateable,
      update_end_if_possible,
      update_if_possible,
  )
  from craftsman.utils.scheduler import parse_optimizer, parse_scheduler
  from craftsman.utils.config import parse_structured
  from craftsman.utils.misc import C, cleanup, get_device, load_module_weights
  #from craftsman.utils.saving import SaverMixin
  from craftsman.utils.typing import *
  
  
  #class BaseSystem(pl.LightningModule, Updateable, SaverMixin):
  class BaseSystem(pl.LightningModule, Updateable):
      @dataclass
      class Config:
          loggers: dict = field(default_factory=dict)
          loss: dict = field(default_factory=dict)
          optimizer: dict = field(default_factory=dict)
          scheduler: Optional[dict] = None
          weights: Optional[str] = None
          weights_ignore_modules: Optional[List[str]] = None
          cleanup_after_validation_step: bool = False
          cleanup_after_test_step: bool = False
  
          pretrained_model_path: Optional[str] = None
          strict_load: bool = True
      cfg: Config
  
      def __init__(self, cfg, resumed=False) -> None:
          super().__init__()
          self.cfg = parse_structured(self.Config, cfg)
          self._save_dir: Optional[str] = None
          self._resumed: bool = resumed
          self._resumed_eval: bool = False
          self._resumed_eval_status: dict = {"global_step": 0, "current_epoch": 0}
          #if "loggers" in cfg:
          #    self.create_loggers(cfg.loggers)
  
          self.configure()
          if self.cfg.weights is not None:
              self.load_weights(self.cfg.weights, self.cfg.weights_ignore_modules)
          self.post_configure()
  
      def load_weights(self, weights: str, ignore_modules: Optional[List[str]] = None):
          state_dict, epoch, global_step = load_module_weights(
              weights, ignore_modules=ignore_modules, map_location="cpu"
          )
          self.load_state_dict(state_dict, strict=False)
          # restore step-dependent states
          self.do_update_step(epoch, global_step, on_load_weights=True)
  
      def set_resume_status(self, current_epoch: int, global_step: int):
          # restore correct epoch and global step in eval
          self._resumed_eval = True
          self._resumed_eval_status["current_epoch"] = current_epoch
          self._resumed_eval_status["global_step"] = global_step
  
      @property
      def resumed(self):
          # whether from resumed checkpoint
          return self._resumed
  
      @property
      def true_global_step(self):
          if self._resumed_eval:
              return self._resumed_eval_status["global_step"]
          else:
              return self.global_step
  
      @property
      def true_current_epoch(self):
          if self._resumed_eval:
              return self._resumed_eval_status["current_epoch"]
          else:
              return self.current_epoch
  
      def configure(self) -> None:
          pass
  
      def post_configure(self) -> None:
          """
          executed after weights are loaded
          """
          pass
  
      def C(self, value: Any) -> float:
          return C(value, self.true_current_epoch, self.true_global_step)
  
      def configure_optimizers(self):
          optim = parse_optimizer(self.cfg.optimizer, self)
          ret = {
              "optimizer": optim,
          }
          if self.cfg.scheduler is not None:
              ret.update(
                  {
                      "lr_scheduler": parse_scheduler(self.cfg.scheduler, optim),
                  }
              )
          return ret
  
      def training_step(self, batch, batch_idx):
          raise NotImplementedError
  
      def validation_step(self, batch, batch_idx):
          raise NotImplementedError
  
      def on_train_batch_end(self, outputs, batch, batch_idx):
          self.dataset = self.trainer.train_dataloader.dataset
          update_end_if_possible(
              self.dataset, self.true_current_epoch, self.true_global_step
          )
          self.do_update_step_end(self.true_current_epoch, self.true_global_step)
  
      def on_validation_batch_end(self, outputs, batch, batch_idx):
          self.dataset = self.trainer.val_dataloaders.dataset
          update_end_if_possible(
              self.dataset, self.true_current_epoch, self.true_global_step
          )
          self.do_update_step_end(self.true_current_epoch, self.true_global_step)
          if self.cfg.cleanup_after_validation_step:
              # cleanup to save vram
              cleanup()
  
      def on_validation_epoch_end(self):
          raise NotImplementedError
  
      def test_step(self, batch, batch_idx):
          raise NotImplementedError
  
      def on_test_batch_end(self, outputs, batch, batch_idx):
          self.dataset = self.trainer.test_dataloaders.dataset
          update_end_if_possible(
              self.dataset, self.true_current_epoch, self.true_global_step
          )
          self.do_update_step_end(self.true_current_epoch, self.true_global_step)
          if self.cfg.cleanup_after_test_step:
              # cleanup to save vram
              cleanup()
  
      def on_test_epoch_end(self):
          pass
  
      def predict_step(self, batch, batch_idx):
          raise NotImplementedError
  
      def on_predict_batch_end(self, outputs, batch, batch_idx):
          self.dataset = self.trainer.predict_dataloaders.dataset
          update_end_if_possible(
              self.dataset, self.true_current_epoch, self.true_global_step
          )
          self.do_update_step_end(self.true_current_epoch, self.true_global_step)
          if self.cfg.cleanup_after_test_step:
              # cleanup to save vram
              cleanup()
  
      def on_predict_epoch_end(self):
          pass
  
      def preprocess_data(self, batch, stage):
          pass
  
      """
      Implementing on_after_batch_transfer of DataModule does the same.
      But on_after_batch_transfer does not support DP.
      """
  
      def on_train_batch_start(self, batch, batch_idx, unused=0):
          self.preprocess_data(batch, "train")
          self.dataset = self.trainer.train_dataloader.dataset
          update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step)
          self.do_update_step(self.true_current_epoch, self.true_global_step)
  
      def on_validation_batch_start(self, batch, batch_idx, dataloader_idx=0):
          self.preprocess_data(batch, "validation")
          self.dataset = self.trainer.val_dataloaders.dataset
          update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step)
          self.do_update_step(self.true_current_epoch, self.true_global_step)
  
      def on_test_batch_start(self, batch, batch_idx, dataloader_idx=0):
          self.preprocess_data(batch, "test")
          self.dataset = self.trainer.test_dataloaders.dataset
          update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step)
          self.do_update_step(self.true_current_epoch, self.true_global_step)
  
      def on_predict_batch_start(self, batch, batch_idx, dataloader_idx=0):
          self.preprocess_data(batch, "predict")
          self.dataset = self.trainer.predict_dataloaders.dataset
          update_if_possible(self.dataset, self.true_current_epoch, self.true_global_step)
          self.do_update_step(self.true_current_epoch, self.true_global_step)
  
      def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
          pass
  
      def on_before_optimizer_step(self, optimizer):
          """
          # some gradient-related debugging goes here, example:
          from lightning.pytorch.utilities import grad_norm
          norms = grad_norm(self.geometry, norm_type=2)
          print(norms)
          """
          pass
  