old_contents: # 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"""
  Gradient Accumulator
  ====================
  
  Change gradient accumulation factor according to scheduling.
  Trainer also calls ``optimizer.step()`` for the last indivisible step number.
  
  """
  
  from pytorch_lightning.callbacks.base import Callback
  
  
  class GradientAccumulationScheduler(Callback):
      r"""
      Change gradient accumulation factor according to scheduling.
  
      Args:
          scheduling: scheduling in format {epoch: accumulation_factor}
  
      Example::
  
          >>> from pytorch_lightning import Trainer
          >>> from pytorch_lightning.callbacks import GradientAccumulationScheduler
  
          # at epoch 5 start accumulating every 2 batches
          >>> accumulator = GradientAccumulationScheduler(scheduling={5: 2})
          >>> trainer = Trainer(callbacks=[accumulator])
  
          # alternatively, pass the scheduling dict directly to the Trainer
          >>> trainer = Trainer(accumulate_grad_batches={5: 2})
      """
  
      def __init__(self, scheduling: dict):
          super().__init__()
  
          if not scheduling:  # empty dict error
              raise TypeError("Empty dict cannot be interpreted correct")
  
          for key in scheduling:
              if not isinstance(key, int) or not isinstance(scheduling[key], int):
                  raise TypeError("All epoches and accumulation factor must be integers")
  
          minimal_epoch = min(scheduling.keys())
          if minimal_epoch < 0:
              raise IndexError(f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct")
          if minimal_epoch != 0:  # if user didnt define first epoch accumulation factor
              scheduling.update({0: 1})
  
          self.scheduling = scheduling
          self.epochs = sorted(scheduling.keys())
  
      def on_epoch_start(self, trainer, pl_module):
          epoch = trainer.current_epoch
          for i in reversed(range(len(self.epochs))):
              if epoch >= self.epochs[i]:
                  trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i])
                  break
  
new_contents: # 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"""
  Gradient Accumulator
  ====================
  
  Change gradient accumulation factor according to scheduling.
  Trainer also calls ``optimizer.step()`` for the last indivisible step number.
  
  """
  
  from typing import Dict
  
  from pytorch_lightning.callbacks.base import Callback
  
  
  class GradientAccumulationScheduler(Callback):
      r"""
      Change gradient accumulation factor according to scheduling.
  
      Args:
          scheduling: scheduling in format {epoch: accumulation_factor}
  
      Example::
  
          >>> from pytorch_lightning import Trainer
          >>> from pytorch_lightning.callbacks import GradientAccumulationScheduler
  
          # at epoch 5 start accumulating every 2 batches
          >>> accumulator = GradientAccumulationScheduler(scheduling={5: 2})
          >>> trainer = Trainer(callbacks=[accumulator])
  
          # alternatively, pass the scheduling dict directly to the Trainer
          >>> trainer = Trainer(accumulate_grad_batches={5: 2})
      """
  
      def __init__(self, scheduling: Dict[int, int]):
          super().__init__()
  
          if not scheduling:  # empty dict error
              raise TypeError("Empty dict cannot be interpreted correct")
  
          for key in scheduling:
              if not isinstance(key, int) or not isinstance(scheduling[key], int):
                  raise TypeError("All epoches and accumulation factor must be integers")
  
          minimal_epoch = min(scheduling.keys())
          if minimal_epoch < 0:
              raise IndexError(
                  f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct"
              )
          if minimal_epoch != 0:  # if user didnt define first epoch accumulation factor
              scheduling.update({0: 1})
  
          self.scheduling = scheduling
          self.epochs = sorted(scheduling.keys())
  
      def on_epoch_start(self, trainer, pl_module):
          epoch = trainer.current_epoch
          for i in reversed(range(len(self.epochs))):
              if epoch >= self.epochs[i]:
                  trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i])
                  break
  
subject: Add stronger typing to gradient accumulation scheduler callback (#3558)