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.
  r"""
  Base class used to build new callbacks.
  
  """
  
  from typing import Any, Dict, List, Optional, Type
  
  import torch
  from torch.optim import Optimizer
  
  import pytorch_lightning as pl
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  
  
  class Callback:
      r"""
      Abstract base class used to build new callbacks.
  
      Subclass this class and override any of the relevant hooks
      """
  
      @property
      def state_key(self) -> str:
          """Identifier for the state of the callback.
  
          Used to store and retrieve a callback's state from the checkpoint dictionary by
          ``checkpoint["callbacks"][state_key]``. Implementations of a callback need to provide a unique state key if 1)
          the callback has state and 2) it is desired to maintain the state of multiple instances of that callback.
          """
          return self.__class__.__qualname__
  
      @property
      def _legacy_state_key(self) -> Type["Callback"]:
          """State key for checkpoints saved prior to version 1.5.0."""
          return type(self)
  
      def _generate_state_key(self, **kwargs: Any) -> str:
          """Formats a set of key-value pairs into a state key string with the callback class name prefixed. Useful
          for defining a :attr:`state_key`.
  
          Args:
              **kwargs: A set of key-value pairs. Must be serializable to :class:`str`.
          """
          return f"{self.__class__.__qualname__}{repr(kwargs)}"
  
      def on_configure_sharded_model(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          r"""
          .. deprecated:: v1.6
              This callback hook was deprecated in v1.6 and will be removed in v1.8. Use `setup()` instead.
  
          Called before configure sharded model.
          """
  
      def on_before_accelerator_backend_setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called before accelerator is being setup."""
  
      def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
          """Called when fit, validate, test, predict, or tune begins."""
  
      def teardown(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
          """Called when fit, validate, test, predict, or tune ends."""
  
      def on_init_start(self, trainer: "pl.Trainer") -> None:
          r"""
          .. deprecated:: v1.6
              This callback hook was deprecated in v1.6 and will be removed in v1.8.
  
          Called when the trainer initialization begins, model has not yet been set.
          """
  
      def on_init_end(self, trainer: "pl.Trainer") -> None:
          r"""
          .. deprecated:: v1.6
              This callback hook was deprecated in v1.6 and will be removed in v1.8.
  
          Called when the trainer initialization ends, model has not yet been set.
          """
  
      def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when fit begins."""
  
      def on_fit_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when fit ends."""
  
      def on_sanity_check_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the validation sanity check starts."""
  
      def on_sanity_check_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the validation sanity check ends."""
  
      def on_train_batch_start(
          self,
          trainer: "pl.Trainer",
          pl_module: "pl.LightningModule",
          batch: Any,
          batch_idx: int,
          unused: int = 0,
      ) -> None:
          """Called when the train batch begins."""
  
      def on_train_batch_end(
          self,
          trainer: "pl.Trainer",
          pl_module: "pl.LightningModule",
          outputs: STEP_OUTPUT,
          batch: Any,
          batch_idx: int,
          unused: int = 0,
      ) -> None:
          """Called when the train batch ends."""
  
      def on_train_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the train epoch begins."""
  
      def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the train epoch ends.
  
          To access all batch outputs at the end of the epoch, either:
  
          1. Implement `training_epoch_end` in the `LightningModule` and access outputs via the module OR
          2. Cache data across train batch hooks inside the callback implementation to post-process in this hook.
          """
  
      def on_validation_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the val epoch begins."""
  
      def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the val epoch ends."""
  
      def on_test_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the test epoch begins."""
  
      def on_test_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the test epoch ends."""
  
      def on_predict_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the predict epoch begins."""
  
      def on_predict_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: List[Any]) -> None:
          """Called when the predict epoch ends."""
  
      def on_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when either of train/val/test epoch begins."""
  
      def on_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when either of train/val/test epoch ends."""
  
      def on_batch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          r"""
          .. deprecated:: v1.6
              This callback hook was deprecated in v1.6 and will be removed in v1.8. Use
              ``on_train_batch_start`` instead.
  
          Called when the training batch begins.
          """
  
      def on_batch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          r"""
          .. deprecated:: v1.6
              This callback hook was deprecated in v1.6 and will be removed in v1.8. Use
              ``on_train_batch_end`` instead.
  
          Called when the training batch ends.
          """
  
      def on_validation_batch_start(
          self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
      ) -> None:
          """Called when the validation batch begins."""
  
      def on_validation_batch_end(
          self,
          trainer: "pl.Trainer",
          pl_module: "pl.LightningModule",
          outputs: Optional[STEP_OUTPUT],
          batch: Any,
          batch_idx: int,
          dataloader_idx: int,
      ) -> None:
          """Called when the validation batch ends."""
  
      def on_test_batch_start(
          self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
      ) -> None:
          """Called when the test batch begins."""
  
      def on_test_batch_end(
          self,
          trainer: "pl.Trainer",
          pl_module: "pl.LightningModule",
          outputs: Optional[STEP_OUTPUT],
          batch: Any,
          batch_idx: int,
          dataloader_idx: int,
      ) -> None:
          """Called when the test batch ends."""
  
      def on_predict_batch_start(
          self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
      ) -> None:
          """Called when the predict batch begins."""
  
      def on_predict_batch_end(
          self,
          trainer: "pl.Trainer",
          pl_module: "pl.LightningModule",
          outputs: Any,
          batch: Any,
          batch_idx: int,
          dataloader_idx: int,
      ) -> None:
          """Called when the predict batch ends."""
  
      def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the train begins."""
  
      def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the train ends."""
  
      def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the pretrain routine begins."""
  
      def on_pretrain_routine_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the pretrain routine ends."""
  
      def on_validation_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the validation loop begins."""
  
      def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the validation loop ends."""
  
      def on_test_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the test begins."""
  
      def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the test ends."""
  
      def on_predict_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when the predict begins."""
  
      def on_predict_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called when predict ends."""
  
      def on_keyboard_interrupt(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          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.
          """
  
      def on_exception(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", exception: BaseException) -> None:
          """Called when any trainer execution is interrupted by an exception."""
  
      def on_save_checkpoint(
          self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any]
      ) -> dict:
          """Called when saving a model checkpoint, use to persist state.
  
          Args:
              trainer: the current :class:`~pytorch_lightning.trainer.Trainer` instance.
              pl_module: the current :class:`~pytorch_lightning.core.lightning.LightningModule` instance.
              checkpoint: the checkpoint dictionary that will be saved.
  
          Returns:
              The callback state.
          """
  
      def on_load_checkpoint(
          self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", callback_state: Dict[str, Any]
      ) -> None:
          """Called when loading a model checkpoint, use to reload state.
  
          Args:
              trainer: the current :class:`~pytorch_lightning.trainer.Trainer` instance.
              pl_module: the current :class:`~pytorch_lightning.core.lightning.LightningModule` instance.
              callback_state: the callback state returned by ``on_save_checkpoint``.
  
          Note:
              The ``on_load_checkpoint`` won't be called with an undefined state.
              If your ``on_load_checkpoint`` hook behavior doesn't rely on a state,
              you will still need to override ``on_save_checkpoint`` to return a ``dummy state``.
          """
  
      def on_before_backward(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", loss: torch.Tensor) -> None:
          """Called before ``loss.backward()``."""
  
      def on_after_backward(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
          """Called after ``loss.backward()`` and before optimizers are stepped."""
  
      def on_before_optimizer_step(
          self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", optimizer: Optimizer, opt_idx: int
      ) -> None:
          """Called before ``optimizer.step()``."""
  
      def on_before_zero_grad(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", optimizer: Optimizer) -> None:
          """Called before ``optimizer.zero_grad()``."""
  