content: #  Copyright (c) ZenML GmbH 2024. 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.
  """Polars materializer."""
  
  import os
  from typing import Any, ClassVar, Tuple, Type, Union
  
  import polars as pl
  import pyarrow as pa  # type: ignore
  import pyarrow.parquet as pq  # type: ignore
  
  from zenml.enums import ArtifactType
  from zenml.materializers.base_materializer import BaseMaterializer
  from zenml.utils import io_utils
  
  
  class PolarsMaterializer(BaseMaterializer):
      """Materializer to read/write Polars dataframes."""
  
      ASSOCIATED_TYPES: ClassVar[Tuple[Type[Any], ...]] = (
          pl.DataFrame,
          pl.Series,
      )
      ASSOCIATED_ARTIFACT_TYPE = ArtifactType.DATA
  
      def load(self, data_type: Type[Any]) -> Any:
          """Reads and returns Polars data after copying it to temporary path.
  
          Args:
              data_type: The type of the data to read.
  
          Returns:
              A Polars data frame or series.
          """
          with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
              io_utils.copy_dir(self.uri, temp_dir)
  
              # Load the data from the temporary directory
              table = pq.read_table(
                  os.path.join(temp_dir, "dataframe.parquet").replace("\\", "/")
              )
  
              # If the data is of type pl.Series, convert it back to a pyarrow array
              # instead of a table.
              if (
                  table.schema.metadata
                  and b"zenml_is_pl_series" in table.schema.metadata
              ):
                  isinstance_bytes = table.schema.metadata[b"zenml_is_pl_series"]
                  isinstance_series = bool.from_bytes(isinstance_bytes, "big")
                  if isinstance_series:
                      table = table.column(0)
  
              # Convert the table to a Polars data frame or series
              data = pl.from_arrow(table)
  
              return data
  
      def save(self, data: Union[pl.DataFrame, pl.Series]) -> None:
          """Writes Polars data to the artifact store.
  
          Args:
              data: The data to write.
  
          Raises:
              TypeError: If the data is not of type pl.DataFrame or pl.Series.
          """
          # Data type check
          if not isinstance(data, self.ASSOCIATED_TYPES):
              raise TypeError(
                  f"Expected data of type {self.ASSOCIATED_TYPES}, "
                  f"got {type(data)}"
              )
  
          # Convert the data to an Apache Arrow Table
          if isinstance(data, pl.DataFrame):
              table = data.to_arrow()
          else:
              # Construct a PyArrow Table with schema from the individual pl.Series
              # array if it is a single pl.Series.
              array = data.to_arrow()
              table = pa.Table.from_arrays([array], names=[data.name])
  
          # Register whether data is of type pl.Series, so that the materializer read step can
          # convert it back appropriately.
          isinstance_bytes = isinstance(data, pl.Series).to_bytes(1, "big")
          table = table.replace_schema_metadata(
              {b"zenml_is_pl_series": isinstance_bytes}
          )
  
          with self.get_temporary_directory(delete_at_exit=True) as temp_dir:
              # Write the table to a Parquet file
              path = os.path.join(temp_dir, "dataframe.parquet").replace(
                  "\\", "/"
              )
              pq.write_table(table, path)  # Uses lz4 compression by default
              io_utils.copy_dir(temp_dir, self.uri)
  