fix cyclic import

pytorch_lightning/accelerators/accelerator.py

@@ -18,7 +18,6 @@
 from torch.utils.data import DataLoader
 
 from pytorch_lightning.core import LightningModule
-from pytorch_lightning.trainer.trainer import Trainer
 from pytorch_lightning.plugins.precision import (
     ApexMixedPrecisionPlugin,
     NativeMixedPrecisionPlugin,
@@ -63,7 +62,7 @@ def __init__(
         self.lr_schedulers = None
         self.optimizer_frequencies = None
 
-    def setup(self, trainer: "Trainer", model: LightningModule) -> None:
+    def setup(self, trainer, model: LightningModule) -> None:
         """
         Connects the plugins to the training process, creates optimizers
 
@@ -302,7 +301,7 @@ def on_train_end(self) -> None:
         """Hook to do something at the end of the training"""
         pass
 
-    def setup_optimizers(self, trainer: "Trainer"):
+    def setup_optimizers(self, trainer):
         """creates optimizers and schedulers
 
         Args:

pytorch_lightning/plugins/training_type/rpc_sequential.py

@@ -21,7 +21,6 @@
 from torch.nn.parallel import DistributedDataParallel
 from torch.optim import Optimizer
 
-from pytorch_lightning.trainer.trainer import Trainer
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.overrides.distributed import LightningDistributedModule
 from pytorch_lightning.plugins.training_type.rpc import DEFAULT_RPC_TIMEOUT_SEC, RPCPlugin
@@ -330,11 +329,11 @@ def post_training(self):
         if self.main_rpc_process:
             super().post_training()
 
-    def start_training(self, trainer: 'Trainer') -> None:
+    def start_training(self, trainer) -> None:
         if self.main_rpc_process:
             super().start_training(trainer)
 
-    def start_testing(self, trainer: 'Trainer') -> None:
+    def start_testing(self, trainer) -> None:
         if self.main_rpc_process:
             super().start_testing(trainer)