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from pyspark import SparkContext
from pyspark.sql.column import Column, _to_java_column


def vector_to_array(col, dtype="float64"):
    """
    Converts a column of MLlib sparse/dense vectors into a column of dense arrays.

    .. versionadded:: 3.0.0

    Parameters
    ----------
    col : :py:class:`pyspark.sql.Column` or str
        Input column
    dtype : str, optional
        The data type of the output array. Valid values: "float64" or "float32".

    Returns
    -------
    :py:class:`pyspark.sql.Column`
        The converted column of dense arrays.

    Examples
    --------
    >>> 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"])
    >>> df1 = df.select(vector_to_array("vec").alias("vec"),
    ...                 vector_to_array("oldVec").alias("oldVec"))
    >>> df1.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])]
    >>> df2 = df.select(vector_to_array("vec", "float32").alias("vec"),
    ...                 vector_to_array("oldVec", "float32").alias("oldVec"))
    >>> df2.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])]
    >>> df1.schema.fields
    [StructField(vec,ArrayType(DoubleType,false),false),
    StructField(oldVec,ArrayType(DoubleType,false),false)]
    >>> df2.schema.fields
    [StructField(vec,ArrayType(FloatType,false),false),
    StructField(oldVec,ArrayType(FloatType,false),false)]
    """
    sc = SparkContext._active_spark_context
    return Column(
        sc._jvm.org.apache.spark.ml.functions.vector_to_array(_to_java_column(col), dtype))


def array_to_vector(col):
    """
    Converts a column of array of numeric type into a column of dense vectors in MLlib

    .. versionadded:: 3.1.0

    Parameters
    ----------
    col : :py:class:`pyspark.sql.Column` or str
        Input column

    Returns
    -------
    :py:class:`pyspark.sql.Column`
        The converted column of MLlib dense vectors.

    Examples
    --------
    >>> from pyspark.ml.functions import array_to_vector
    >>> df1 = spark.createDataFrame([([1.5, 2.5],),], schema='v1 array<double>')
    >>> df1.select(array_to_vector('v1').alias('vec1')).collect()
    [Row(vec1=DenseVector([1.5, 2.5]))]
    >>> df2 = spark.createDataFrame([([1.5, 3.5],),], schema='v1 array<float>')
    >>> df2.select(array_to_vector('v1').alias('vec1')).collect()
    [Row(vec1=DenseVector([1.5, 3.5]))]
    >>> df3 = spark.createDataFrame([([1, 3],),], schema='v1 array<int>')
    >>> df3.select(array_to_vector('v1').alias('vec1')).collect()
    [Row(vec1=DenseVector([1.0, 3.0]))]
    """
    sc = SparkContext._active_spark_context
    return Column(
        sc._jvm.org.apache.spark.ml.functions.array_to_vector(_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()