content: #  Copyright (c) ZenML GmbH 2022. All Rights Reserved.
  #
  #  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:
  #
  #       https://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.
  """Implementation of the PyTorch Module materializer."""
  
  import os
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Tuple, Type
  
  import cloudpickle
  import torch
  from torch.nn import Module
  
  from zenml.enums import ArtifactType
  from zenml.integrations.pytorch.materializers.base_pytorch_materializer import (
      BasePyTorchMaterializer,
  )
  from zenml.integrations.pytorch.utils import count_module_params
  from zenml.io import fileio
  
  if TYPE_CHECKING:
      from zenml.metadata.metadata_types import MetadataType
  
  DEFAULT_FILENAME = "entire_model.pt"
  CHECKPOINT_FILENAME = "checkpoint.pt"
  
  
  class PyTorchModuleMaterializer(BasePyTorchMaterializer):
      """Materializer to read/write Pytorch models.
  
      Inspired by the guide:
      https://pytorch.org/tutorials/beginner/saving_loading_models.html
      """
  
      ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (Module,)
      ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.MODEL
      FILENAME: ClassVar[str] = DEFAULT_FILENAME
  
      def save(self, model: Module) -> None:
          """Writes a PyTorch model, as a model and a checkpoint.
  
          Args:
              model: A torch.nn.Module or a dict to pass into model.save
          """
          # Save entire model to artifact directory, This is the default behavior
          # for loading model in development phase (training, evaluation)
          super().save(model)
  
          # Also save model checkpoint to artifact directory,
          # This is the default behavior for loading model in production phase (inference)
          if isinstance(model, Module):
              with fileio.open(
                  os.path.join(self.uri, CHECKPOINT_FILENAME), "wb"
              ) as f:
                  # NOTE (security): The `torch.save` function uses `cloudpickle` as
                  # the default unpickler, which is NOT secure. This materializer
                  # is intended for use with trusted data sources.
                  torch.save(model.state_dict(), f, pickle_module=cloudpickle)  # nosec
  
      def extract_metadata(self, model: Module) -> Dict[str, "MetadataType"]:
          """Extract metadata from the given `Model` object.
  
          Args:
              model: The `Model` object to extract metadata from.
  
          Returns:
              The extracted metadata as a dictionary.
          """
          return {**count_module_params(model)}
  