text: # Copyright The PyTorch Lightning team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import contextlib
  from abc import ABC, abstractmethod
  from pathlib import Path
  from typing import Any, Callable, Dict, Generator, Iterable, Mapping, Optional, TypeVar, Union
  
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  
  import pytorch_lightning as pl
  from pytorch_lightning.overrides.base import unwrap_lightning_module
  from pytorch_lightning.plugins.base_plugin import Plugin
  from pytorch_lightning.utilities import rank_zero_warn
  from pytorch_lightning.utilities.cloud_io import atomic_save
  from pytorch_lightning.utilities.cloud_io import load as pl_load
  from pytorch_lightning.utilities.types import _EVALUATE_OUTPUT, _PREDICT_OUTPUT
  
  TBroadcast = TypeVar("T")
  
  
  class TrainingTypePlugin(Plugin, ABC):
      """
      Base class for all training type plugins that change the behaviour of the training, validation and test-loop.
      """
  
      def __init__(self) -> None:
          self._model: Optional[Module] = None
          self._results: Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]] = None
          self._call_configure_sharded_model_hook = True
  
      def connect(self, model: Module) -> None:
          """Called by the accelerator to connect the accelerator and the model with this plugin"""
          self.model = model
  
      def setup_environment(self) -> None:
          """
          Setup any processes or distributed connections.
          This is called before the LightningModule/DataModule setup hook
          which allows the user to access the accelerator environment before setup is complete.
          """
  
      def setup(self, model: Module) -> None:
          """Called by the accelerator to finish setup."""
  
      @property
      @abstractmethod
      def on_gpu(self) -> bool:
          """Returns whether the current process is done on GPU"""
          raise NotImplementedError
  
      @property
      @abstractmethod
      def on_tpu(self) -> bool:
          """Returns whether the current process is done on TPU"""
          raise NotImplementedError
  
      @property
      @abstractmethod
      def root_device(self) -> torch.device:
          """Returns the root device"""
          raise NotImplementedError
  
      @abstractmethod
      def model_to_device(self) -> None:
          """Moves the model to the correct device"""
  
      @property
      @abstractmethod
      def is_global_zero(self) -> bool:
          """Whether the current process is the rank zero process not only on the local node, but for all nodes."""
  
      @abstractmethod
      def reduce(self, tensor: Union[torch.Tensor, Any], *args: Any, **kwargs: Any) -> Union[torch.Tensor, Any]:
          """
          Reduces the given tensor (e.g. across GPUs/processes).
  
          Args:
              tensor: the tensor to sync and reduce
              *args: plugin-specific positional arguments
              **kwargs: plugin-specific keyword arguments
          """
  
      @abstractmethod
      def barrier(self, name: Optional[str] = None) -> None:
          """Forces all possibly joined processes to wait for each other"""
  
      @abstractmethod
      def broadcast(self, obj: TBroadcast, src: int = 0) -> TBroadcast:
          """Broadcasts an object to all processes"""
  
      @abstractmethod
      def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
          """Perform a all_gather on all processes """
  
      def reduce_boolean_decision(self, decision: bool) -> bool:
          """Reduce the early stopping decision across all processes"""
          return decision
  
      def pre_backward(self, closure_loss: torch.Tensor) -> None:
          """Run before precision plugin executes backward"""
  
      def post_backward(self, closure_loss: torch.Tensor) -> None:
          """Run after precision plugin executes backward"""
  
      def post_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int, **kwargs) -> None:
          """Hook to do something after each optimizer step."""
  
      @property
      def model(self) -> Optional[Module]:
          """Returns the potentially wrapped LightningModule"""
          return self._model
  
      @model.setter
      def model(self, new_model: Optional[Module]) -> None:
          self._model = new_model
  
      @property
      def lightning_module(self) -> 'pl.LightningModule':
          """Returns the pure LightningModule without potential wrappers"""
          return unwrap_lightning_module(self._model)
  
      @property
      def results(self) -> Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]]:
          """
          Enables plugin-agnostic access to the result returned by the training/evaluation/prediction run. The result is
          cached instead of returned directly, because some plugins require transmitting the results from one
          multiprocessing context to another in a separate step. For example, the plugins that use the "spawn"
          start-method send the result to the master process through a
          `multiprocessing queue (shared memory) <https://pytorch.org/docs/stable/multiprocessing.html>`_.
          """
          return self._results
  
      def load_checkpoint_file(self, checkpoint_path: Union[str, Path]) -> Dict[str, Any]:
          return pl_load(checkpoint_path, map_location=(lambda storage, loc: storage))
  
      def load_model_state_dict(self, checkpoint: Mapping[str, Any]) -> None:
          self.lightning_module.load_state_dict(checkpoint["state_dict"])
  
      def load_optimizer_state_dict(self, checkpoint: Mapping[str, Any]) -> None:
          optimizer_states = checkpoint["optimizer_states"]
          for optimizer, opt_state in zip(self.lightning_module.trainer.accelerator.optimizers, optimizer_states):
              optimizer.load_state_dict(opt_state)
  
      def start_training(self, trainer: 'pl.Trainer') -> None:
          # double dispatch to initiate the training loop
          self._results = trainer.run_stage()
  
      def start_evaluating(self, trainer: 'pl.Trainer') -> None:
          # double dispatch to initiate the test loop
          self._results = trainer.run_stage()
  
      def start_predicting(self, trainer: 'pl.Trainer') -> None:
          # double dispatch to initiate the predicting loop
          self._results = trainer.run_stage()
  
      def training_step(self, *args, **kwargs):
          return self.model.training_step(*args, **kwargs)
  
      def post_training_step(self):
          pass
  
      def validation_step(self, *args, **kwargs):
          return self.model.validation_step(*args, **kwargs)
  
      def test_step(self, *args, **kwargs):
          return self.model.test_step(*args, **kwargs)
  
      def predict_step(self, *args, **kwargs):
          return self.model.predict_step(*args, **kwargs)
  
      def training_step_end(self, output):
          return output
  
      def validation_step_end(self, output):
          return output
  
      def test_step_end(self, output):
          return output
  
      def on_save(self, checkpoint: Dict[str, Union[Any, torch.Tensor]]) -> Dict[str, Union[Any, torch.Tensor]]:
          return checkpoint
  
      def process_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
          """Wraps the dataloader if necessary
  
          Args:
              dataloader: iterable. Ideally of type: :class:`torch.utils.data.DataLoader`
          """
          return dataloader
  
      def on_reset_train_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
          """Called before resetting the train dataloader."""
          return dataloader
  
      def on_reset_val_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
          """Called before resetting the val dataloader."""
          return dataloader
  
      def on_reset_test_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
          """Called before resetting the test dataloader."""
          return dataloader
  
      def on_reset_predict_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
          """Called before resetting the predict dataloader."""
          return dataloader
  
      def init_optimizers(self, trainer: 'pl.Trainer', model: 'pl.LightningModule'):
          return trainer.init_optimizers(model)
  
      def optimizer_step(self, optimizer: torch.optim.Optimizer, lambda_closure: Callable, **kwargs):
          optimizer.step(closure=lambda_closure, **kwargs)
  
      @property
      def setup_optimizers_in_pre_dispatch(self) -> bool:
          """
          Override to delay setting optimizers and schedulers till after dispatch.
          This is useful when the `TrainingTypePlugin` requires operating on the wrapped accelerator model.
          However this may break certain precision plugins such as APEX which require optimizers to be set.
          Returns: If True, delay setup optimizers till pre_dispatch, else call within setup.
          """
          return False
  
      def update_global_step(self, total_batch_idx: int, current_global_step: int) -> int:
          """
          Provide a hook to count optimizer step calls.
  
          Args:
              total_batch_idx: Total number of batches seen for training
              current_global_step: Current number of optimizer step calls
  
          Returns: New optimizer step calls
          """
          return current_global_step + 1
  
      def lightning_module_state_dict(self) -> Dict[str, Union[Any, Tensor]]:
          """Returns model state."""
          model = self.lightning_module
          return model.state_dict()
  
      def save_checkpoint(self, checkpoint: Dict[str, Any], filepath: str) -> None:
          """Save model/training states as a checkpoint file through state-dump and file-write.
  
          Args:
              checkpoint: dict containing model and trainer state
              filepath: write-target file's path
          """
          # dump states as a checkpoint dictionary object
          checkpoint = self.on_save(checkpoint)
          if self.is_global_zero:
              try:
                  # write the checkpoint dictionary on the file
                  atomic_save(checkpoint, filepath)
              except AttributeError as err:
                  key = pl.LightningModule.CHECKPOINT_HYPER_PARAMS_KEY
                  checkpoint.pop(key, None)
                  rank_zero_warn(f'Warning, `{key}` dropped from checkpoint. An attribute is not picklable: {err}')
                  atomic_save(checkpoint, filepath)
  
      @contextlib.contextmanager
      def model_sharded_context(self) -> Generator:
          """
          Provide hook to create modules in a distributed aware context. This is useful for when we'd like to
          shard the model instantly, which is useful for extremely large models which can save memory and
          initialization time.
  
          Returns: Model parallel context.
          """
          yield
  
      @property
      def call_configure_sharded_model_hook(self) -> bool:
          """
          Allow model parallel hook to be called in suitable environments determined by the training type plugin.
          This is useful for when we want to shard the model once within fit.
          Returns: True if we want to call the model parallel setup hook.
          """
          return self._call_configure_sharded_model_hook
  
      @call_configure_sharded_model_hook.setter
      def call_configure_sharded_model_hook(self, mode: bool) -> None:
          self._call_configure_sharded_model_hook = mode
  
      @abstractmethod
      def teardown(self) -> None:
          """
          This method is called to teardown the training process.
          It is the right place to release memory and free other resources.
          """
          raise NotImplementedError
  
      @classmethod
      def register_plugins(cls, plugin_registry):
          pass
  
      @property
      def should_rank_save_checkpoint(self) -> bool:
          """Returns whether the checkpoint should be saved (rank based)"""
          return self.is_global_zero
  
      def on_train_start(self) -> None:
          """Called when train begins."""
          pass
  
      def on_validation_start(self) -> None:
          """Called when validation begins."""
          pass
  
      def on_test_start(self) -> None:
          """Called when test begins."""
          pass
  
      def on_predict_start(self) -> None:
          """Called when predict begins."""
          pass
  
      def on_train_end(self) -> None:
          """Called when train ends."""
          pass
  
      def on_validation_end(self) -> None:
          """Called when validation ends."""
          pass
  
      def on_test_end(self) -> None:
          """Called when test end."""
          pass
  
      def on_predict_end(self):
          """Called when predict ends."""
          pass
  
      def on_train_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
          """
          Called in the training loop before anything happens for that batch.
          """
          pass
  