content: #  Copyright (c) ZenML GmbH 2021. 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
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  """Implementation of the TensorFlow Keras materializer."""
  
  import os
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Tuple, Type
  
  import tensorflow as tf
  from tensorflow.python import keras as tf_keras
  from tensorflow.python.keras.utils.layer_utils import count_params
  
  from zenml.enums import ArtifactType
  from zenml.materializers.base_materializer import BaseMaterializer
  from zenml.utils import io_utils
  
  if TYPE_CHECKING:
      from zenml.metadata.metadata_types import MetadataType
  
  
  class KerasMaterializer(BaseMaterializer):
      """Materializer to read/write Keras models."""
  
      ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (
          tf.keras.Model,
          tf_keras.Model,
      )
      ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.MODEL
      MODEL_FILE_NAME = "model.keras"
  
      def load(self, data_type: Type[Any]) -> tf_keras.Model:
          """Reads and returns a Keras model after copying it to temporary path.
  
          Args:
              data_type: The type of the data to read.
  
          Returns:
              A keras.Model model.
          """
          with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
              # Copy from artifact store to temporary directory
              temp_model_file = os.path.join(temp_dir, self.MODEL_FILE_NAME)
              io_utils.copy_dir(self.uri, temp_dir)
  
              # Load the model from the temporary directory
              model = tf.keras.models.load_model(temp_model_file)
  
              return model
  
      def save(self, model: tf_keras.Model) -> None:
          """Writes a keras model to the artifact store.
  
          Args:
              model: A keras.Model model.
          """
          with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
              temp_model_file = os.path.join(temp_dir, self.MODEL_FILE_NAME)
              model.save(temp_model_file)
              io_utils.copy_dir(temp_dir, self.uri)
  
      def extract_metadata(
          self, model: tf_keras.Model
      ) -> 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 {
              "num_layers": len(model.layers),
              "num_params": count_params(model.weights),
              "num_trainable_params": count_params(model.trainable_weights),
          }
  