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 logging
  from typing import Optional
  
  from pytorch_lightning.loops import Loop
  from pytorch_lightning.loops.epoch import TrainingEpochLoop
  from pytorch_lightning.loops.utilities import _is_max_limit_reached
  from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
  from pytorch_lightning.trainer.progress import Progress
  from pytorch_lightning.trainer.supporters import TensorRunningAccum
  from pytorch_lightning.utilities import rank_zero_deprecation
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  
  log = logging.getLogger(__name__)
  
  
  class FitLoop(Loop):
      """This Loop iterates over the epochs to run the training.
  
      Args:
          min_epochs: The minimum number of epochs
          max_epochs: The maximum number of epochs, can be set -1 to turn this limit off
      """
  
      def __init__(
          self,
          min_epochs: Optional[int] = 1,
          max_epochs: int = 1000,
      ) -> None:
          super().__init__()
          if max_epochs < -1:
              # Allow max_epochs to be zero, since this will be handled by fit_loop.done
              raise MisconfigurationException(
                  f"`max_epochs` must be a non-negative integer or -1. You passed in {max_epochs}."
              )
  
          self.max_epochs = max_epochs
          self.min_epochs = min_epochs
          self.epoch_loop = TrainingEpochLoop()
          self.epoch_progress = Progress()
          self._is_fresh_start_epoch: bool = True
  
      @property
      def current_epoch(self) -> int:
          """Return the current epoch."""
          return self.epoch_progress.current.completed
  
      @current_epoch.setter
      def current_epoch(self, value: int) -> None:
          """Setter for the current epoch."""
          self.epoch_progress.current.completed = value
  
      @property
      def global_step(self) -> int:
          """Returns the global step."""
          return self.epoch_loop.global_step
  
      @global_step.setter
      def global_step(self, value: int) -> None:
          """Sets the global step (forwards to epoch_loop)"""
          self.epoch_loop.global_step = value
  
      @property
      def total_batch_idx(self) -> int:
          """Returns the current batch index (across epochs)"""
          return self.epoch_loop.total_batch_idx
  
      @property
      def batch_idx(self) -> int:
          """Returns the current batch index (within this epoch)"""
          return self.epoch_loop.batch_idx
  
      @property
      def split_idx(self) -> int:
          """Returns the index of the current batch split (within the current batch) for bptt."""
          return self.epoch_loop.batch_loop.split_idx
  
      @property
      def min_steps(self) -> int:
          # TODO(@justusschock): Why aren't we using the attribute in this class?
          """Returns the minimum numnber of steps to run."""
          return self.epoch_loop.min_steps
  
      @min_steps.setter
      def min_steps(self, value: int) -> None:
          """Sets the minimum number of steps (forwards to epoch_loop)"""
          # TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided
          self.epoch_loop.min_steps = value
  
      @property
      def max_steps(self) -> int:
          """Returns the maximum number of steps to run."""
          return self.epoch_loop.max_steps
  
      @max_steps.setter
      def max_steps(self, value: int) -> None:
          """Sets the maximum number of steps (forwards to epoch_loop)"""
          # TODO(@awaelchli): This setter is required by debugging connector (fast dev run), should be avoided
          if value is None:
              rank_zero_deprecation(
                  "Setting `max_steps = None` is deprecated in v1.5 and will no longer be supported in v1.7."
                  " Use `max_steps = -1` instead."
              )
              value = -1
          elif value < -1:
              raise MisconfigurationException(
                  f"`max_steps` must be a non-negative integer or -1 (infinite steps). You passed in {value}."
              )
          self.epoch_loop.max_steps = value
  
      @property
      def running_loss(self) -> TensorRunningAccum:
          """Returns the running loss."""
          return self.epoch_loop.batch_loop.running_loss
  
      @property
      def _skip_backward(self) -> bool:
          """Determines whether the loop will skip backward during automatic optimization."""
          return self.epoch_loop.batch_loop.optimizer_loop._skip_backward
  
      @_skip_backward.setter
      def _skip_backward(self, value: bool) -> None:
          """Determines whether the loop will skip backward during automatic optimization."""
          self.epoch_loop.batch_loop.optimizer_loop._skip_backward = value
  
      @property
      def _results(self) -> ResultCollection:
          if self.trainer.training:
              return self.epoch_loop._results
          if self.trainer.validating:
              return self.epoch_loop.val_loop._results
          raise RuntimeError("`FitLoop._results` property isn't defined. Accessed outside of scope")
  
      @property
      def done(self) -> bool:
          """Evaluates when to leave the loop.
  
          Returns True if trainer.should_stop was set (e.g. by early stopping) or if the maximum number of steps or epochs
          is reached.
          """
          # TODO(@awaelchli): Move track steps inside training loop and move part of these condition inside training loop
          stop_steps = _is_max_limit_reached(self.global_step, self.max_steps)
          stop_epochs = _is_max_limit_reached(self.current_epoch, self.max_epochs)
  
          should_stop = False
          if self.trainer.should_stop:
              # early stopping
              met_min_epochs = self.current_epoch >= self.min_epochs if self.min_epochs else True
              met_min_steps = self.global_step >= self.min_steps if self.min_steps else True
              if met_min_epochs and met_min_steps:
                  should_stop = True
              else:
                  log.info(
                      "Trainer was signaled to stop but required minimum epochs"
                      f" ({self.min_epochs}) or minimum steps ({self.min_steps}) has"
                      " not been met. Training will continue..."
                  )
          self.trainer.should_stop = should_stop
  
          return stop_steps or should_stop or stop_epochs or self.trainer.num_training_batches == 0
  
      @property
      def skip(self) -> bool:
          """Whether we should skip the training and immediately return from the call to :meth:`run`."""
          # since `trainer.num_training_batches` depends on the `train_dataloader` but that won't be called
          # until `on_run_start`, we use `limit_train_batches` instead
          return self.done or self.trainer.limit_train_batches == 0
  
      def connect(self, epoch_loop: TrainingEpochLoop):
          """Connects a training epoch loop to this fit loop."""
          self.epoch_loop = epoch_loop
  
      def reset(self) -> None:
          """Resets the internal state of this loop."""
          if self.restarting:
              self.epoch_progress.reset_on_restart()
  
      def on_run_start(self) -> None:
          """Calls the ``on_train_start`` hook."""
          # reset train dataloader and val dataloader
          self.trainer.reset_train_val_dataloaders(self.trainer.lightning_module)
          self._is_fresh_start_epoch = True
          self._results.to(device=self.trainer.lightning_module.device)
          self.trainer.call_hook("on_train_start")
  
      def on_advance_start(self) -> None:
          """Prepares the dataloader for training and calls the hooks ``on_epoch_start`` and
          ``on_train_epoch_start``"""
          model = self.trainer.lightning_module
  
          # reset train dataloader
          if not self._is_fresh_start_epoch and self.trainer._should_reload_dl_epoch:
              self.trainer.reset_train_dataloader(model)
          self._is_fresh_start_epoch = False
  
          if self.trainer.train_dataloader is not None and callable(
              getattr(self.trainer.train_dataloader.sampler, "set_epoch", None)
          ):
              # set seed for distributed sampler (enables shuffling for each epoch)
              self.trainer.train_dataloader.sampler.set_epoch(self.current_epoch)
  
          # changing gradient according accumulation_scheduler
          self.trainer.accumulation_scheduler.on_train_epoch_start(self.trainer, self.trainer.lightning_module)
  
          # stores accumulated grad fractions per batch
          self.epoch_loop.batch_loop.accumulated_loss = TensorRunningAccum(
              window_length=self.trainer.accumulate_grad_batches
          )
  
          self.epoch_progress.increment_ready()
  
      def advance(self) -> None:
          """Runs one whole epoch."""
          dataloader = self.trainer.training_type_plugin.process_dataloader(self.trainer.train_dataloader)
          data_fetcher = self.trainer._data_connector.get_profiled_dataloader(dataloader)
  
          with self.trainer.profiler.profile("run_training_epoch"):
              self.epoch_loop.run(data_fetcher)
  
              # the global step is manually decreased here due to backwards compatibility with existing loggers
              # as they expect that the same step is used when logging epoch end metrics even when the batch loop has
              # finished. this means the attribute does not exactly track the number of optimizer steps applied.
              # TODO(@carmocca): deprecate and rename so users don't get confused
              self.global_step -= 1
              # log epoch metrics
              self.trainer.logger_connector.update_train_epoch_metrics()
              self.global_step += 1
  
      def on_advance_end(self) -> None:
          self.epoch_progress.increment_completed()
  
      def on_run_end(self) -> None:
          """Calls the ``on_train_end`` hook."""
          # NOTE: the current_epoch is already incremented
          # Lightning today does not increment the current epoch at the last epoch run in Trainer.fit
          # To simulate that current behavior, we decrement here.
          # TODO: must be fixed by https://github.com/PyTorchLightning/pytorch-lightning/issues/5007
          self.current_epoch = max(self.current_epoch - 1, 0)
  
          # hook
          self.trainer.call_hook("on_train_end")
  
          # give accelerators a chance to finish
          self.trainer.training_type_plugin.on_train_end()
  
      def teardown(self) -> None:
          self.epoch_loop.teardown()
  
      def _should_accumulate(self) -> bool:
          """Whether the gradients should be accumulated."""
          return self.epoch_loop._should_accumulate()
  