text: import logging
  import os
  from contextlib import contextmanager
  from typing import Any, Dict, List, Union
  
  import torch
  import torch.distributed as torch_distrib
  from torch.optim import Optimizer
  
  from pytorch_lightning.core.lightning import LightningModule
  from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
  from pytorch_lightning.plugins.plugin import LightningPlugin
  
  log = logging.getLogger(__name__)
  
  
  class DDPPlugin(LightningPlugin):
      """
      Plugin to link a custom ddp implementation to any arbitrary accelerator.
  
      This plugin forwards all constructor arguments to `LightningDistributedDataParallel`,
      which in turn forwards all args to `DistributedDataParallel`.
  
      Example::
  
          class MyDDP(DDPPlugin):
  
              def configure_ddp(self, model, device_ids):
                  model = MyDDPWrapper(model, device_ids)
                  return model
  
          my_ddp = MyDDP()
          trainer = Trainer(accelerator='ddp_x', plugins=[my_ddp])
      """
  
      def __init__(self, **kwargs):
          self._ddp_kwargs: Dict[str, Any] = kwargs
  
      def configure_ddp(
              self, model: LightningModule, device_ids: List[int]
      ) -> LightningDistributedDataParallel:
          """
          Pass through all customizations from constructor to `LightningDistributedDataParallel`.
          Override to define a custom DDP implementation.
  
          .. note:: Only requirement is that your DDP implementation subclasses LightningDistributedDataParallel
  
  
          The default implementation is::
  
              def configure_ddp(self, model, device_ids):
                  model = LightningDistributedDataParallel(
                      model, device_ids=device_ids, find_unused_parameters=False
                  )
                  return model
  
          Args:
              model: the lightningModule
              device_ids: the list of devices available
  
          Returns:
              the model wrapped in LightningDistributedDataParallel
  
          """
          # if unset, default `find_unused_parameters` `False`
          self._ddp_kwargs["find_unused_parameters"] = self._ddp_kwargs.get(
              "find_unused_parameters", False
          )
          model = LightningDistributedDataParallel(
              model,
              device_ids=device_ids,
              **self._ddp_kwargs,
          )
          return model
  
      def init_ddp_connection(
              self,
              trainer,
              cluster_environment,
              global_rank: int,
              world_size: int,
              is_slurm_managing_tasks: bool = True,
      ) -> None:
          os.environ["MASTER_ADDR"] = str(cluster_environment.master_address())
          os.environ["MASTER_PORT"] = str(cluster_environment.master_port())
          os.environ["WORLD_SIZE"] = str(cluster_environment.world_size())
          torch_backend = "nccl" if trainer.on_gpu else "gloo"
  
          if not torch_distrib.is_initialized():
              log.info(
                  f"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank + 1}/{world_size}"
              )
              torch_distrib.init_process_group(
                  torch_backend, rank=global_rank, world_size=world_size
              )
  
      @property
      def is_running_single_process_per_device(self) -> bool:
          # objects do not need to be scattered in single process per device, move objects upfront to device
          # This property is used in ``self.on_before_forward`` function.
          return self.device_ids is not None and len(self.device_ids) == 1
  
      def on_before_forward(self, model: LightningModule, *args):
          """
          Override to handle custom edge case.
  
          Args:
              args: Inputs to the model.
              model: Model to train.
          Returns: args moved to correct device if needed.
          """
          if self.is_running_single_process_per_device:
              args = model.transfer_batch_to_device(args, model.device)
          return args
  
      def optimizer_state(self, optimizer: Optimizer) -> dict:
          return optimizer.state_dict()
  
      def on_after_setup_optimizers(self, trainer):
          """
          Called after optimizers have been set-up. This is useful for doing any configuration options in RPC, or
          state sharding.
          """
  
      def get_model_from_plugin(
              self,
              model: Union[LightningDistributedDataParallel, LightningModule]
      ) -> LightningModule:
          """
          Override to modify returning base :class:`LightningModule`
          when accessing variable and functions outside of the parallel wrapper.
  
          Example::
              ref_model = ddp_plugin.get_model_from_plugin(model)
              ref_model.training_step(...)
  
          Args:
              model: Model with parallel wrapper.
  
          Returns: Reference :class:`LightningModule` within parallel wrapper.
  
          """
          if isinstance(model, LightningDistributedDataParallel):
              return model.module
          return model
  
      @contextmanager
      def block_backward_sync(self, model: LightningDistributedDataParallel):
          """
          Blocks ddp sync gradients behaviour on backwards pass.
          This is useful for skipping sync when accumulating gradients, reducing communication overhead
          Returns: context manager with sync behaviour off
          """
          yield model.no_sync()
  
      def on_before_manual_backward(self, model: LightningDistributedDataParallel, output: Any):
          model.reducer_prepare_for_backwards(output)
  
      def on_after_manual_backward(self, model: LightningDistributedDataParallel):
          model.reducer_reset_hooks()
  
      def distributed_sampler_kwargs(self, distributed_sampler_kwargs):
          return distributed_sampler_kwargs
  
      @property
      def data_parallel_group(self):
          """
          Return the group that this process exists in. By default, this is the world size.
          Useful for when additional parallel groups have been created, to select certain processes.
          Returns: The ProcessGroup this process exists in.
          """
          return torch_distrib.group.WORLD
  