a þd¿ ã@sDddlmZmZddlmZmZmZddlmZGdd„deƒZ dS)é)ÚABCÚabstractmethod)ÚAnyÚDictÚOptional)Ú_PATHc@s|eZdZdZedeeefee eddœdd„ƒZ edee eeeefdœdd„ƒZ eedd œd d „ƒZ dd œd d„Z dS)Ú CheckpointIOa$Interface to save/load checkpoints as they are saved through the ``Strategy``. Typically most plugins either use the Torch based IO Plugin; ``TorchCheckpointIO`` but may require particular handling depending on the plugin. In addition, you can pass a custom ``CheckpointIO`` by extending this class and passing it to the Trainer, i.e ``Trainer(plugins=[MyCustomCheckpointIO()])``. .. note:: For some plugins, it is not possible to use a custom checkpoint plugin as checkpointing logic is not modifiable. N)Ú checkpointÚpathÚstorage_optionsÚreturncCsdS)a&Save model/training states as a checkpoint file through state-dump and file-write. Args: checkpoint: dict containing model and trainer state path: write-target path storage_options: Optional parameters when saving the model/training states. N©)Úselfr r r r r úr/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/lightning_fabric/plugins/io/checkpoint_io.pyÚsave_checkpoint#szCheckpointIO.save_checkpoint)r Ú map_locationr cCsdS)aJLoad checkpoint from a path when resuming or loading ckpt for test/validate/predict stages. Args: path: Path to checkpoint map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage locations. Returns: The loaded checkpoint. Nr )rr rr r rÚload_checkpoint-szCheckpointIO.load_checkpoint)r r cCsdS)zhRemove checkpoint file from the filesystem. Args: path: Path to checkpoint Nr )rr r r rÚremove_checkpoint9szCheckpointIO.remove_checkpoint)r cCsdS)z.This method is called to teardown the process.Nr )rr r rÚteardownAszCheckpointIO.teardown)N)N)Ú__name__Ú __module__Ú __qualname__Ú__doc__rrÚstrrrrrrrrr r r rrs$ " rN) ÚabcrrÚtypingrrrZ lightning_fabric.utilities.typesrrr r r rÚs