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
  import os
  from functools import partial
  from typing import Optional, Type
  
  import pytorch_lightning as pl
  from pytorch_lightning.accelerators import GPUAccelerator
  from pytorch_lightning.loops import Loop
  from pytorch_lightning.loops.epoch import TrainingEpochLoop
  from pytorch_lightning.loops.epoch.training_epoch_loop import _OUTPUTS_TYPE as _EPOCH_OUTPUTS_TYPE
  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.exceptions import MisconfigurationException
  from pytorch_lightning.utilities.fetching import (
      AbstractDataFetcher,
      DataFetcher,
      DataLoaderIterDataFetcher,
      InterBatchParallelDataFetcher,
  )
  from pytorch_lightning.utilities.model_helpers import is_overridden
  from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_warn
  from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
  
  log = logging.getLogger(__name__)
  
  
  class FitLoop(Loop[None]):
      """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: int = 0,
          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
          self._outputs: _EPOCH_OUTPUTS_TYPE = []
          self._data_fetcher: Optional[AbstractDataFetcher] = None
  
      @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) -> Optional[int]:
          # TODO(@justusschock): Why aren't we using the attribute in this class?
          """Returns the minimum number of steps to run."""
          return self.epoch_loop.min_steps
  
      @min_steps.setter
      def min_steps(self, value: Optional[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
  
      @Loop.restarting.setter
      def restarting(self, restarting: bool) -> None:
          # if the last epoch completely finished, we are not actually restarting
          values = self.epoch_progress.current.ready, self.epoch_progress.current.started
          epoch_unfinished = any(v != self.epoch_progress.current.processed for v in values)
          restarting = restarting and epoch_unfinished or self._iteration_based_training()
          Loop.restarting.fset(self, restarting)  # call the parent setter
  
      @property
      def prefetch_batches(self) -> int:
          is_unsized = self.trainer.num_training_batches == float("inf")
          inter_batch_parallelism = os.getenv("PL_INTER_BATCH_PARALLELISM", "0") == "1"
          return 1 if is_unsized or inter_batch_parallelism else 0
  
      @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."""
          # TODO(@awaelchli): Move track steps inside training loop and move part of these condition inside training loop
          stop_steps = _is_max_limit_reached(self.epoch_loop.global_step, self.max_steps)
          # `processed` is increased before `on_train_epoch_end`, the hook where checkpoints are typically saved.
          # we use it here because the checkpoint data won't have `completed` increased yet
          stop_epochs = _is_max_limit_reached(self.epoch_progress.current.processed, self.max_epochs)
          if stop_epochs:
              # in case they are not equal, override so `trainer.current_epoch` has the expected value
              self.epoch_progress.current.completed = self.epoch_progress.current.processed
  
          should_stop = False
          if self.trainer.should_stop:
              # early stopping
              met_min_epochs = self.epoch_progress.current.processed >= self.min_epochs if self.min_epochs else True
              met_min_steps = self.epoch_loop.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) -> None:  # type: ignore[override]
          """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:  # type: ignore[override]
          """Calls the ``on_train_start`` hook."""
          # update the current_epoch in-case of checkpoint reload
          if not self._iteration_based_training():
              self.epoch_progress.current.completed = self.epoch_progress.current.processed
  
          # reset train dataloader and val dataloader
          self.trainer.reset_train_val_dataloaders(self.trainer.lightning_module)
  
          data_fetcher_cls = _select_data_fetcher(self.trainer)
          self._data_fetcher = data_fetcher_cls(prefetch_batches=self.prefetch_batches)
  
          self._is_fresh_start_epoch = True
          self._results.to(device=self.trainer.lightning_module.device)
  
          self.trainer._call_callback_hooks("on_train_start")
          self.trainer._call_lightning_module_hook("on_train_start")
          self.trainer._call_strategy_hook("on_train_start")
  
      def on_advance_start(self) -> None:  # type: ignore[override]
          """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._data_connector._should_reload_train_dl:
              log.detail(f"{self.__class__.__name__}: resetting train dataloader")
              self.trainer.reset_train_dataloader(model)
          self._is_fresh_start_epoch = False
  
          # reset outputs here instead of in `reset` as they are not accumulated between epochs
          self._outputs = []
  
          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.epoch_progress.current.processed)
  
          # 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.reset(window_length=self.trainer.accumulate_grad_batches)
  
          self.epoch_progress.increment_ready()
  
          self.trainer._logger_connector.on_epoch_start()
  
          self.trainer._call_callback_hooks("on_epoch_start")
          self.trainer._call_lightning_module_hook("on_epoch_start")
  
          self.trainer._call_callback_hooks("on_train_epoch_start")
          self.trainer._call_lightning_module_hook("on_train_epoch_start")
  
          self.epoch_progress.increment_started()
  
      def advance(self) -> None:  # type: ignore[override]
          """Runs one whole epoch."""
          log.detail(f"{self.__class__.__name__}: advancing loop")
          assert self.trainer.train_dataloader is not None
          dataloader = self.trainer.train_dataloader
          assert self._data_fetcher is not None
          self._data_fetcher.setup(
              dataloader, batch_to_device=partial(self.trainer._call_strategy_hook, "batch_to_device", dataloader_idx=0)
          )
          with self.trainer.profiler.profile("run_training_epoch"):
              self._outputs = self.epoch_loop.run(self._data_fetcher)
  
      def on_advance_end(self) -> None:
          # inform logger the batch loop has finished
          self.trainer._logger_connector.epoch_end_reached()
  
          # get the model and call model.training_epoch_end
          model = self.trainer.lightning_module
          if is_overridden("training_epoch_end", model) and self._outputs:
              epoch_end_outputs = self.epoch_loop._prepare_outputs_training_epoch_end(
                  self._outputs,
                  lightning_module=model,
                  num_optimizers=len(self.trainer.optimizers),
              )
              # run lightning module hook training_epoch_end
              # refresh the result for custom logging at the epoch level
              epoch_end_outputs = self.trainer._call_lightning_module_hook("training_epoch_end", epoch_end_outputs)
              if epoch_end_outputs is not None:
                  raise MisconfigurationException(
                      "`training_epoch_end` expects a return of None. "
                      "HINT: remove the return statement in `training_epoch_end`."
                  )
          # free memory
          self._outputs = []
  
          self.epoch_progress.increment_processed()
  
          # call train epoch end hooks
          self.trainer._call_callback_hooks("on_train_epoch_end")
          self.trainer._call_lightning_module_hook("on_train_epoch_end")
  
          self.trainer._call_callback_hooks("on_epoch_end")
          self.trainer._call_lightning_module_hook("on_epoch_end")
  
          self.trainer._logger_connector.on_epoch_end()
  
          if self.epoch_loop._num_ready_batches_reached():
              self.epoch_loop.update_lr_schedulers("epoch", update_plateau_schedulers=True)
  
          self.epoch_progress.increment_completed()
  
          # we manually decrease here because loggers expect that the same step is used when logging epoch-end metrics
          # even when the batch loop has finished
          self.epoch_loop._batches_that_stepped -= 1
          # log epoch metrics
          self.trainer._logger_connector.update_train_epoch_metrics()
          self.epoch_loop._batches_that_stepped += 1
  
          # if fault tolerant is enabled and process has been notified, exit.
          self.trainer._exit_gracefully_on_signal()
  
      def on_run_end(self) -> None:
          """Calls the ``on_train_end`` hook."""
          log.detail(f"{self.__class__.__name__}: train run ended")
  
          # hook
          self.trainer._call_callback_hooks("on_train_end")
          self.trainer._call_lightning_module_hook("on_train_end")
          self.trainer._call_strategy_hook("on_train_end")
  
      def teardown(self) -> None:
          if self._data_fetcher is not None:
              self._data_fetcher.teardown()
              self._data_fetcher = None
          self.epoch_loop.teardown()
  
      def _should_accumulate(self) -> bool:
          """Whether the gradients should be accumulated."""
          return self.epoch_loop._should_accumulate()
  
      def _iteration_based_training(self) -> bool:
          return self.trainer.max_steps != -1
  
  
  def _select_data_fetcher(trainer: "pl.Trainer") -> Type[AbstractDataFetcher]:
      training_step_fx = getattr(trainer.lightning_module, "training_step")
      if is_param_in_hook_signature(training_step_fx, "dataloader_iter", explicit=True):
          rank_zero_warn(
              "Found `dataloader_iter` argument in the `training_step`. Note that the support for "
              "this signature is experimental and the behavior is subject to change."
          )
          return DataLoaderIterDataFetcher
      elif os.getenv("PL_INTER_BATCH_PARALLELISM", "0") == "1":
          if not isinstance(trainer.accelerator, GPUAccelerator):
              raise MisconfigurationException("Inter batch parallelism is available only when using Nvidia GPUs.")
          return InterBatchParallelDataFetcher
      return DataFetcher
  