#
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
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# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
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from pyspark import since, SparkContext
from pyspark.sql.column import Column, _to_java_column


@since(3.0)
def vector_to_array(col):
    """
    Converts a column of MLlib sparse/dense vectors into a column of dense arrays.

    >>> from pyspark.ml.linalg import Vectors
    >>> from pyspark.ml.functions import vector_to_array
    >>> from pyspark.mllib.linalg import Vectors as OldVectors
    >>> df = spark.createDataFrame([
    ...     (Vectors.dense(1.0, 2.0, 3.0), OldVectors.dense(10.0, 20.0, 30.0)),
    ...     (Vectors.sparse(3, [(0, 2.0), (2, 3.0)]),
    ...      OldVectors.sparse(3, [(0, 20.0), (2, 30.0)]))],
    ...     ["vec", "oldVec"])
    >>> df.select(vector_to_array("vec").alias("vec"),
    ...           vector_to_array("oldVec").alias("oldVec")).collect()
    [Row(vec=[1.0, 2.0, 3.0], oldVec=[10.0, 20.0, 30.0]),
     Row(vec=[2.0, 0.0, 3.0], oldVec=[20.0, 0.0, 30.0])]
    """
    sc = SparkContext._active_spark_context
    return Column(
        sc._jvm.org.apache.spark.ml.functions.vector_to_array(_to_java_column(col)))


def _test():
    import doctest
    from pyspark.sql import SparkSession
    import pyspark.ml.functions
    import sys
    globs = pyspark.ml.functions.__dict__.copy()
    spark = SparkSession.builder \
        .master("local[2]") \
        .appName("ml.functions tests") \
        .getOrCreate()
    sc = spark.sparkContext
    globs['sc'] = sc
    globs['spark'] = spark

    (failure_count, test_count) = doctest.testmod(
        pyspark.ml.functions, globs=globs,
        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
    spark.stop()
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()