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.
  from abc import ABC
  from copy import deepcopy
  from typing import Any, Dict, List, Optional, Type, Union
  
  import torch
  from packaging.version import Version
  
  import pytorch_lightning as pl
  from pytorch_lightning.callbacks import Callback
  from pytorch_lightning.utilities import rank_zero_warn
  from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  
  
  class TrainerCallbackHookMixin(ABC):
  
      # this is just a summary on variables used in this abstract class,
      # the proper values/initialisation should be done in child class
      callbacks: List[Callback] = []
      lightning_module: "pl.LightningModule"
  
      def on_before_accelerator_backend_setup(self) -> None:
          """Called at the beginning of fit (train + validate), validate, test, or predict, or tune."""
          for callback in self.callbacks:
              callback.on_before_accelerator_backend_setup(self, self.lightning_module)
  
      def on_configure_sharded_model(self) -> None:
          """Called at the beginning of fit (train + validate), validate, test, or predict, or tune."""
          for callback in self.callbacks:
              callback.on_configure_sharded_model(self, self.lightning_module)
  
      def setup(self, stage: Optional[str]) -> None:
          """Called at the beginning of fit (train + validate), validate, test, or predict, or tune."""
          for callback in self.callbacks:
              callback.setup(self, self.lightning_module, stage=stage)
  
      def teardown(self, stage: Optional[str] = None) -> None:
          """Called at the end of fit (train + validate), validate, test, or predict, or tune."""
          for callback in self.callbacks:
              callback.teardown(self, self.lightning_module, stage=stage)
  
      def on_init_start(self):
          """Called when the trainer initialization begins, model has not yet been set."""
          for callback in self.callbacks:
              callback.on_init_start(self)
  
      def on_init_end(self):
          """Called when the trainer initialization ends, model has not yet been set."""
          for callback in self.callbacks:
              callback.on_init_end(self)
  
      def on_fit_start(self):
          """Called when the trainer initialization begins, model has not yet been set."""
          for callback in self.callbacks:
              callback.on_fit_start(self, self.lightning_module)
  
      def on_fit_end(self):
          """Called when the trainer initialization begins, model has not yet been set."""
          for callback in self.callbacks:
              callback.on_fit_end(self, self.lightning_module)
  
      def on_sanity_check_start(self):
          """Called when the validation sanity check starts."""
          for callback in self.callbacks:
              callback.on_sanity_check_start(self, self.lightning_module)
  
      def on_sanity_check_end(self):
          """Called when the validation sanity check ends."""
          for callback in self.callbacks:
              callback.on_sanity_check_end(self, self.lightning_module)
  
      def on_train_epoch_start(self):
          """Called when the epoch begins."""
          for callback in self.callbacks:
              callback.on_train_epoch_start(self, self.lightning_module)
  
      def on_train_epoch_end(self):
          """Called when the epoch ends."""
          for callback in self.callbacks:
              callback.on_train_epoch_end(self, self.lightning_module)
  
      def on_validation_epoch_start(self):
          """Called when the epoch begins."""
          for callback in self.callbacks:
              callback.on_validation_epoch_start(self, self.lightning_module)
  
      def on_validation_epoch_end(self):
          """Called when the validation epoch ends."""
          for callback in self.callbacks:
              callback.on_validation_epoch_end(self, self.lightning_module)
  
      def on_test_epoch_start(self):
          """Called when the epoch begins."""
          for callback in self.callbacks:
              callback.on_test_epoch_start(self, self.lightning_module)
  
      def on_test_epoch_end(self):
          """Called when the test epoch ends."""
          for callback in self.callbacks:
              callback.on_test_epoch_end(self, self.lightning_module)
  
      def on_predict_epoch_start(self) -> None:
          """Called when the epoch begins."""
          for callback in self.callbacks:
              callback.on_predict_epoch_start(self, self.lightning_module)
  
      def on_predict_epoch_end(self, outputs: List[Any]) -> None:
          """Called when the epoch ends."""
          for callback in self.callbacks:
              callback.on_predict_epoch_end(self, self.lightning_module, outputs)
  
      def on_epoch_start(self):
          """Called when either of train/val/test epoch begins."""
          for callback in self.callbacks:
              callback.on_epoch_start(self, self.lightning_module)
  
      def on_epoch_end(self):
          """Called when either of train/val/test epoch ends."""
          for callback in self.callbacks:
              callback.on_epoch_end(self, self.lightning_module)
  
      def on_train_start(self):
          """Called when the train begins."""
          for callback in self.callbacks:
              callback.on_train_start(self, self.lightning_module)
  
      def on_train_end(self):
          """Called when the train ends."""
          for callback in self.callbacks:
              callback.on_train_end(self, self.lightning_module)
  
      def on_pretrain_routine_start(self) -> None:
          """Called when the pre-train routine begins."""
          for callback in self.callbacks:
              callback.on_pretrain_routine_start(self, self.lightning_module)
  
      def on_pretrain_routine_end(self) -> None:
          """Called when the pre-train routine ends."""
          for callback in self.callbacks:
              callback.on_pretrain_routine_end(self, self.lightning_module)
  
      def on_batch_start(self):
          """Called when the training batch begins."""
          for callback in self.callbacks:
              callback.on_batch_start(self, self.lightning_module)
  
      def on_batch_end(self):
          """Called when the training batch ends."""
          for callback in self.callbacks:
              callback.on_batch_end(self, self.lightning_module)
  
      # TODO: Update this in v1.7 (deprecation: #9816)
      def on_train_batch_start(self, batch, batch_idx, dataloader_idx=0):
          """Called when the training batch begins."""
          for callback in self.callbacks:
              if is_param_in_hook_signature(callback.on_train_batch_start, "dataloader_idx", explicit=True):
                  callback.on_train_batch_start(self, self.lightning_module, batch, batch_idx, 0)
              else:
                  callback.on_train_batch_start(self, self.lightning_module, batch, batch_idx)
  
      # TODO: Update this in v1.7 (deprecation: #9816)
      def on_train_batch_end(self, outputs: STEP_OUTPUT, batch, batch_idx, dataloader_idx=0):
          """Called when the training batch ends."""
          for callback in self.callbacks:
              if is_param_in_hook_signature(callback.on_train_batch_end, "dataloader_idx", explicit=True):
                  callback.on_train_batch_end(self, self.lightning_module, outputs, batch, batch_idx, 0)
              else:
                  callback.on_train_batch_end(self, self.lightning_module, outputs, batch, batch_idx)
  
      def on_validation_batch_start(self, batch, batch_idx, dataloader_idx):
          """Called when the validation batch begins."""
          for callback in self.callbacks:
              callback.on_validation_batch_start(self, self.lightning_module, batch, batch_idx, dataloader_idx)
  
      def on_validation_batch_end(self, outputs: STEP_OUTPUT, batch, batch_idx, dataloader_idx):
          """Called when the validation batch ends."""
          for callback in self.callbacks:
              callback.on_validation_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
  
      def on_test_batch_start(self, batch, batch_idx, dataloader_idx):
          """Called when the test batch begins."""
          for callback in self.callbacks:
              callback.on_test_batch_start(self, self.lightning_module, batch, batch_idx, dataloader_idx)
  
      def on_test_batch_end(self, outputs: STEP_OUTPUT, batch, batch_idx, dataloader_idx):
          """Called when the test batch ends."""
          for callback in self.callbacks:
              callback.on_test_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
  
      def on_predict_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
          """Called when the predict batch begins."""
          for callback in self.callbacks:
              callback.on_predict_batch_start(self, self.lightning_module, batch, batch_idx, dataloader_idx)
  
      def on_predict_batch_end(self, outputs: STEP_OUTPUT, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
          """Called when the predict batch ends."""
          for callback in self.callbacks:
              callback.on_predict_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
  
      def on_validation_start(self):
          """Called when the validation loop begins."""
          for callback in self.callbacks:
              callback.on_validation_start(self, self.lightning_module)
  
      def on_validation_end(self):
          """Called when the validation loop ends."""
          for callback in self.callbacks:
              callback.on_validation_end(self, self.lightning_module)
  
      def on_test_start(self):
          """Called when the test begins."""
          for callback in self.callbacks:
              callback.on_test_start(self, self.lightning_module)
  
      def on_test_end(self):
          """Called when the test ends."""
          for callback in self.callbacks:
              callback.on_test_end(self, self.lightning_module)
  
      def on_predict_start(self) -> None:
          """Called when predict begins."""
          for callback in self.callbacks:
              callback.on_predict_start(self, self.lightning_module)
  
      def on_predict_end(self) -> None:
          """Called when predict ends."""
          for callback in self.callbacks:
              callback.on_predict_end(self, self.lightning_module)
  
      def on_keyboard_interrupt(self):
          r"""
          .. deprecated:: v1.5
              This callback hook was deprecated in v1.5 in favor of `on_exception` and will be removed in v1.7.
  
          Called when any trainer execution is interrupted by KeyboardInterrupt.
          """
          for callback in self.callbacks:
              callback.on_keyboard_interrupt(self, self.lightning_module)
  
      def on_exception(self, exception: BaseException) -> None:
          """Called when any trainer execution is interrupted by an exception."""
          for callback in self.callbacks:
              callback.on_exception(self, self.lightning_module, exception)
  
      def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> Dict[str, dict]:
          """Called when saving a model checkpoint."""
          callback_states = {}
          for callback in self.callbacks:
              state = callback.on_save_checkpoint(self, self.lightning_module, checkpoint)
              if state:
                  callback_states[callback.state_key] = state
          return callback_states
  
      def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
          """Called when loading a model checkpoint."""
          # Todo: the `callback_states` are dropped with TPUSpawn as they
          # can't be saved using `xm.save`
          # https://github.com/pytorch/xla/issues/2773
          callback_states: Dict[Union[Type, str], Dict] = checkpoint.get("callbacks")
  
          if callback_states is None:
              return
  
          is_legacy_ckpt = Version(checkpoint["pytorch-lightning_version"]) < Version("1.5.0dev")
          current_callbacks_keys = {cb._legacy_state_key if is_legacy_ckpt else cb.state_key for cb in self.callbacks}
          difference = callback_states.keys() - current_callbacks_keys
          if difference:
              rank_zero_warn(
                  "Be aware that when using `resume_from_checkpoint`,"
                  " callbacks used to create the checkpoint need to be provided."
                  f" Please add the following callbacks: {list(difference)}.",
                  UserWarning,
              )
  
          for callback in self.callbacks:
              state = callback_states.get(callback.state_key, callback_states.get(callback._legacy_state_key))
              if state:
                  state = deepcopy(state)
                  callback.on_load_checkpoint(self, self.lightning_module, state)
  
      def on_before_backward(self, loss: torch.Tensor) -> None:
          """Called before ``loss.backward()``."""
          for callback in self.callbacks:
              callback.on_before_backward(self, self.lightning_module, loss)
  
      def on_after_backward(self):
          """Called after loss.backward() and before optimizers do anything."""
          for callback in self.callbacks:
              callback.on_after_backward(self, self.lightning_module)
  
      def on_before_optimizer_step(self, optimizer, optimizer_idx):
          """Called after on_after_backward() once the gradient is accumulated and before optimizer.step()."""
          for callback in self.callbacks:
              callback.on_before_optimizer_step(self, self.lightning_module, optimizer, optimizer_idx)
  
      def on_before_zero_grad(self, optimizer):
          """Called after optimizer.step() and before optimizer.zero_grad()."""
          for callback in self.callbacks:
              callback.on_before_zero_grad(self, self.lightning_module, optimizer)
  