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 os
  from datetime import timedelta
  from typing import Dict, List, Optional, Union
  
  from pytorch_lightning.callbacks import Callback, ModelCheckpoint, ProgressBar, ProgressBarBase
  from pytorch_lightning.callbacks.timer import Timer
  from pytorch_lightning.utilities import rank_zero_info
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  from pytorch_lightning.utilities.warnings import rank_zero_deprecation
  
  
  class CallbackConnector:
      def __init__(self, trainer):
          self.trainer = trainer
  
      def on_trainer_init(
          self,
          callbacks: Optional[Union[List[Callback], Callback]],
          checkpoint_callback: bool,
          progress_bar_refresh_rate: Optional[int],
          process_position: int,
          default_root_dir: Optional[str],
          weights_save_path: Optional[str],
          stochastic_weight_avg: bool,
          max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None,
      ):
          # init folder paths for checkpoint + weights save callbacks
          self.trainer._default_root_dir = default_root_dir or os.getcwd()
          self.trainer._weights_save_path = weights_save_path or self.trainer._default_root_dir
          self.trainer._stochastic_weight_avg = stochastic_weight_avg
  
          # init callbacks
          if isinstance(callbacks, Callback):
              callbacks = [callbacks]
          self.trainer.callbacks = callbacks or []
  
          # configure checkpoint callback
          # pass through the required args to figure out defaults
          self._configure_checkpoint_callbacks(checkpoint_callback)
  
          # configure swa callback
          self._configure_swa_callbacks()
  
          # configure the timer callback.
          # responsible to stop the training when max_time is reached.
          self._configure_timer_callback(max_time)
  
          # init progress bar
          if process_position != 0:
              rank_zero_deprecation(
                  f"Setting `Trainer(process_position={process_position})` is deprecated in v1.5 and will be removed"
                  " in v1.7. Please pass `pytorch_lightning.callbacks.progress.ProgressBar` with"
                  " `process_position` directly to the Trainer's `callbacks` argument instead."
              )
          self.trainer._progress_bar_callback = self.configure_progress_bar(progress_bar_refresh_rate, process_position)
  
          # push all checkpoint callbacks to the end
          # it is important that these are the last callbacks to run
          self.trainer.callbacks = self._reorder_callbacks(self.trainer.callbacks)
  
      def _configure_checkpoint_callbacks(self, checkpoint_callback: bool) -> None:
          # TODO: Remove this error in v1.5 so we rely purely on the type signature
          if not isinstance(checkpoint_callback, bool):
              error_msg = (
                  "Invalid type provided for checkpoint_callback:"
                  f" Expected bool but received {type(checkpoint_callback)}."
              )
              if isinstance(checkpoint_callback, Callback):
                  error_msg += " Pass callback instances to the `callbacks` argument in the Trainer constructor instead."
              raise MisconfigurationException(error_msg)
          if self._trainer_has_checkpoint_callbacks() and checkpoint_callback is False:
              raise MisconfigurationException(
                  "Trainer was configured with checkpoint_callback=False but found ModelCheckpoint in callbacks list."
              )
  
          if not self._trainer_has_checkpoint_callbacks() and checkpoint_callback is True:
              self.trainer.callbacks.append(ModelCheckpoint())
  
      def _configure_swa_callbacks(self):
          if not self.trainer._stochastic_weight_avg:
              return
  
          from pytorch_lightning.callbacks.stochastic_weight_avg import StochasticWeightAveraging
  
          existing_swa = [cb for cb in self.trainer.callbacks if isinstance(cb, StochasticWeightAveraging)]
          if not existing_swa:
              self.trainer.callbacks = [StochasticWeightAveraging()] + self.trainer.callbacks
  
      def configure_progress_bar(self, refresh_rate=None, process_position=0):
          if os.getenv("COLAB_GPU") and refresh_rate is None:
              # smaller refresh rate on colab causes crashes, choose a higher value
              refresh_rate = 20
          refresh_rate = 1 if refresh_rate is None else refresh_rate
  
          progress_bars = [c for c in self.trainer.callbacks if isinstance(c, ProgressBarBase)]
          if len(progress_bars) > 1:
              raise MisconfigurationException(
                  "You added multiple progress bar callbacks to the Trainer, but currently only one"
                  " progress bar is supported."
              )
          if len(progress_bars) == 1:
              progress_bar_callback = progress_bars[0]
          elif refresh_rate > 0:
              progress_bar_callback = ProgressBar(refresh_rate=refresh_rate, process_position=process_position)
              self.trainer.callbacks.append(progress_bar_callback)
          else:
              progress_bar_callback = None
  
          return progress_bar_callback
  
      def _configure_timer_callback(self, max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None) -> None:
          if max_time is None:
              return
          if any(isinstance(cb, Timer) for cb in self.trainer.callbacks):
              rank_zero_info("Ignoring `Trainer(max_time=...)`, callbacks list already contains a Timer.")
              return
          timer = Timer(duration=max_time, interval="step")
          self.trainer.callbacks.append(timer)
  
      def _trainer_has_checkpoint_callbacks(self):
          return len(self.trainer.checkpoint_callbacks) > 0
  
      def attach_model_logging_functions(self, model):
          for callback in self.trainer.callbacks:
              callback.log = model.log
              callback.log_dict = model.log_dict
  
      def _attach_model_callbacks(self) -> None:
          """Attaches the callbacks defined in the model.
  
          If a callback returned by the model's configure_callback method has the same type as one or several
          callbacks already present in the trainer callbacks list, it will replace them.
          In addition, all :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` callbacks
          will be pushed to the end of the list, ensuring they run last.
          """
          model_callbacks = self.trainer.call_hook("configure_callbacks")
          if not model_callbacks:
              return
          model_callback_types = {type(c) for c in model_callbacks}
          trainer_callback_types = {type(c) for c in self.trainer.callbacks}
          override_types = model_callback_types.intersection(trainer_callback_types)
          if override_types:
              rank_zero_info(
                  "The following callbacks returned in `LightningModule.configure_callbacks` will override"
                  " existing callbacks passed to Trainer:"
                  f" {', '.join(sorted(t.__name__ for t in override_types))}"
              )
          # remove all callbacks with a type that occurs in model callbacks
          all_callbacks = [c for c in self.trainer.callbacks if type(c) not in override_types]
          all_callbacks.extend(model_callbacks)
          all_callbacks = CallbackConnector._reorder_callbacks(all_callbacks)
          # TODO: connectors refactor: move callbacks list to connector and do not write Trainer state
          self.trainer.callbacks = all_callbacks
  
      @staticmethod
      def _reorder_callbacks(callbacks: List[Callback]) -> List[Callback]:
          """Moves all ModelCheckpoint callbacks to the end of the list. The sequential order within the group of
          checkpoint callbacks is preserved, as well as the order of all other callbacks.
  
          Args:
              callbacks: A list of callbacks.
  
          Return:
              A new list in which the last elements are ModelCheckpoints if there were any present in the
              input.
          """
          checkpoints = [c for c in callbacks if isinstance(c, ModelCheckpoint)]
          not_checkpoints = [c for c in callbacks if not isinstance(c, ModelCheckpoint)]
          return not_checkpoints + checkpoints
  