content: import gc
  import os
  import re
  
  import torch
  import torch.distributed as dist
  from packaging import version
  
  from craftsman.utils.config import config_to_primitive
  from craftsman.utils.typing import *
  
  
  
  def parse_version(ver: str):
      return version.parse(ver)
  
  
  def get_rank():
      # SLURM_PROCID can be set even if SLURM is not managing the multiprocessing,
      # therefore LOCAL_RANK needs to be checked first
      rank_keys = ("RANK", "LOCAL_RANK", "SLURM_PROCID", "JSM_NAMESPACE_RANK")
      for key in rank_keys:
          rank = os.environ.get(key)
          if rank is not None:
              return int(rank)
      return 0
  
  def get_world_size():
      world_size_keys = ("WORLD_SIZE", "SLURM_NTASKS", "JSM_NAMESPACE_SIZE")
      for key in world_size_keys:
          world_size = os.environ.get(key)
          if world_size is not None:
              return int(world_size)
      return 1
  
  def get_device():
      return torch.device(f"cuda:{get_rank()}")
  
  
  def load_module_weights(
      path, module_name=None, ignore_modules=None, map_location=None
  ) -> Tuple[dict, int, int]:
      if module_name is not None and ignore_modules is not None:
          raise ValueError("module_name and ignore_modules cannot be both set")
      if map_location is None:
          map_location = get_device()
  
      ckpt = torch.load(path, map_location=map_location)
      state_dict = ckpt["state_dict"]
      state_dict_to_load = state_dict
  
      if ignore_modules is not None:
          state_dict_to_load = {}
          for k, v in state_dict.items():
              ignore = any(
                  [k.startswith(ignore_module + ".") for ignore_module in ignore_modules]
              )
              if ignore:
                  continue
              state_dict_to_load[k] = v
  
      if module_name is not None:
          state_dict_to_load = {}
          for k, v in state_dict.items():
              m = re.match(rf"^{module_name}\.(.*)$", k)
              if m is None:
                  continue
              state_dict_to_load[m.group(1)] = v
  
      return state_dict_to_load, ckpt["epoch"], ckpt["global_step"]
  
  
  def C(value: Any, epoch: int, global_step: int) -> float:
      if isinstance(value, int) or isinstance(value, float):
          pass
      else:
          value = config_to_primitive(value)
          if not isinstance(value, list):
              raise TypeError("Scalar specification only supports list, got", type(value))
          if len(value) == 3:
              value = [0] + value
          assert len(value) == 4
          start_step, start_value, end_value, end_step = value
          if isinstance(end_step, int):
              current_step = global_step
              value = start_value + (end_value - start_value) * max(
                  min(1.0, (current_step - start_step) / (end_step - start_step)), 0.0
              )
          elif isinstance(end_step, float):
              current_step = epoch
              value = start_value + (end_value - start_value) * max(
                  min(1.0, (current_step - start_step) / (end_step - start_step)), 0.0
              )
      return value
  
  
  def cleanup():
      gc.collect()
      torch.cuda.empty_cache()
      tcnn.free_temporary_memory()
  
  
  def finish_with_cleanup(func: Callable):
      def wrapper(*args, **kwargs):
          out = func(*args, **kwargs)
          cleanup()
          return out
  
      return wrapper
  
  
  def _distributed_available():
      return torch.distributed.is_available() and torch.distributed.is_initialized()
  
  
  def barrier():
      if not _distributed_available():
          return
      else:
          torch.distributed.barrier()
  
  
  def broadcast(tensor, src=0):
      if not _distributed_available():
          return tensor
      else:
          torch.distributed.broadcast(tensor, src=src)
          return tensor
  
  
  def enable_gradient(model, enabled: bool = True) -> None:
      for param in model.parameters():
          param.requires_grad_(enabled)
  
  
  def all_gather_batch(tensors):
      """
      Performs all_gather operation on the provided tensors.
      """
      # Queue the gathered tensors
      world_size = get_world_size()
      # There is no need for reduction in the single-proc case
      if world_size == 1:
          if isinstance(tensors, list):
              return tensors
          return tensors
      if not isinstance(tensors, list):
          is_list = False
          tensors = [tensors]
      else:
          is_list = True
      output_tensor = []
      tensor_list = []
      for tensor in tensors:
          tensor_all = [torch.ones_like(tensor) for _ in range(world_size)]
          dist.all_gather(
              tensor_all,
              tensor,
              async_op=False  # performance opt
          )
  
          tensor_list.append(tensor_all)
  
      for tensor_all in tensor_list:
          output_tensor.append(torch.cat(tensor_all, dim=0))
      if not is_list:
          return output_tensor[0]
      return output_tensor