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 dataset materializer."""
  
  import os
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Tuple, Type
  
  import tensorflow as tf
  
  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
  
  DEFAULT_FILENAME = "saved_data"
  
  
  class TensorflowDatasetMaterializer(BaseMaterializer):
      """Materializer to read data to and from tf.data.Dataset."""
  
      ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (tf.data.Dataset,)
      ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.DATA
  
      def load(self, data_type: Type[Any]) -> Any:
          """Reads data into tf.data.Dataset.
  
          Args:
              data_type: The type of the data to read.
  
          Returns:
              A tf.data.Dataset object.
          """
          with self.get_temporary_directory(delete_at_exit=False) as temp_dir:
              io_utils.copy_dir(self.uri, temp_dir)
              path = os.path.join(temp_dir, DEFAULT_FILENAME)
              dataset = tf.data.Dataset.load(path)
              return dataset
  
      def save(self, dataset: tf.data.Dataset) -> None:
          """Persists a tf.data.Dataset object.
  
          Args:
              dataset: The dataset to persist.
          """
          with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
              path = os.path.join(temp_dir, DEFAULT_FILENAME)
              tf.data.Dataset.save(
                  dataset, path, compression=None, shard_func=None
              )
              io_utils.copy_dir(temp_dir, self.uri)
  
      def extract_metadata(
          self, dataset: tf.data.Dataset
      ) -> Dict[str, "MetadataType"]:
          """Extract metadata from the given `Dataset` object.
  
          Args:
              dataset: The `Dataset` object to extract metadata from.
  
          Returns:
              The extracted metadata as a dictionary.
          """
          return {"length": len(dataset)}
  