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.
  from typing import Optional, Union
  
  import torch
  
  from pytorch_lightning.plugins.precision.native_amp import NativeMixedPrecisionPlugin
  from pytorch_lightning.utilities import _FAIRSCALE_AVAILABLE
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  
  if _FAIRSCALE_AVAILABLE:
      from fairscale.optim import OSS
      from fairscale.optim.grad_scaler import ShardedGradScaler
  
  
  class ShardedNativeMixedPrecisionPlugin(NativeMixedPrecisionPlugin):
      """Native AMP for Sharded Training."""
  
      def __init__(
          self, precision: Union[str, int], device: str, scaler: Optional[torch.cuda.amp.GradScaler] = None
      ) -> None:
          if not _FAIRSCALE_AVAILABLE:
              raise MisconfigurationException(
                  "You have asked for sharded AMP but you have not installed it."
                  " Install `fairscale` using this guide: https://https://github.com/facebookresearch/fairscale"
              )
          super().__init__(precision, device, scaler=scaler or ShardedGradScaler())
  
      def clip_grad_by_norm(self, optimizer: "OSS", clip_val: Union[int, float]) -> None:
          optimizer.clip_grad_norm(clip_val)
  