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# to you under the Apache License, Version 2.0 (the
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# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
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# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

import pyarrow as pa
import pytest

from datafusion import SessionContext, column, udwf, lit, functions as f
from datafusion.udf import WindowEvaluator
from datafusion.expr import WindowFrame


class ExponentialSmoothDefault(WindowEvaluator):
    def __init__(self, alpha: float = 0.9) -> None:
        self.alpha = alpha

    def evaluate_all(self, values: list[pa.Array], num_rows: int) -> pa.Array:
        results = []
        curr_value = 0.0
        values = values[0]
        for idx in range(num_rows):
            if idx == 0:
                curr_value = values[idx].as_py()
            else:
                curr_value = values[idx].as_py() * self.alpha + curr_value * (
                    1.0 - self.alpha
                )
            results.append(curr_value)

        return pa.array(results)


class ExponentialSmoothBounded(WindowEvaluator):
    def __init__(self, alpha: float = 0.9) -> None:
        self.alpha = alpha

    def supports_bounded_execution(self) -> bool:
        return True

    def get_range(self, idx: int, num_rows: int) -> tuple[int, int]:
        # Override the default range of current row since uses_window_frame is False
        # So for the purpose of this test we just smooth from the previous row to
        # current.
        if idx == 0:
            return (0, 0)
        return (idx - 1, idx)

    def evaluate(
        self, values: list[pa.Array], eval_range: tuple[int, int]
    ) -> pa.Scalar:
        (start, stop) = eval_range
        curr_value = 0.0
        values = values[0]
        for idx in range(start, stop + 1):
            if idx == start:
                curr_value = values[idx].as_py()
            else:
                curr_value = values[idx].as_py() * self.alpha + curr_value * (
                    1.0 - self.alpha
                )
        return pa.scalar(curr_value).cast(pa.float64())


class ExponentialSmoothRank(WindowEvaluator):
    def __init__(self, alpha: float = 0.9) -> None:
        self.alpha = alpha

    def include_rank(self) -> bool:
        return True

    def evaluate_all_with_rank(
        self, num_rows: int, ranks_in_partition: list[tuple[int, int]]
    ) -> pa.Array:
        results = []
        for idx in range(num_rows):
            if idx == 0:
                prior_value = 1.0
            matching_row = [
                i
                for i in range(len(ranks_in_partition))
                if ranks_in_partition[i][0] <= idx and ranks_in_partition[i][1] > idx
            ][0] + 1
            curr_value = matching_row * self.alpha + prior_value * (1.0 - self.alpha)
            results.append(curr_value)
            prior_value = matching_row

        return pa.array(results)


class ExponentialSmoothFrame(WindowEvaluator):
    def __init__(self, alpha: float = 0.9) -> None:
        self.alpha = alpha

    def uses_window_frame(self) -> bool:
        return True

    def evaluate(
        self, values: list[pa.Array], eval_range: tuple[int, int]
    ) -> pa.Scalar:
        (start, stop) = eval_range
        curr_value = 0.0
        if len(values) > 1:
            order_by = values[1]  # noqa: F841
            values = values[0]
        else:
            values = values[0]
        for idx in range(start, stop):
            if idx == start:
                curr_value = values[idx].as_py()
            else:
                curr_value = values[idx].as_py() * self.alpha + curr_value * (
                    1.0 - self.alpha
                )
        return pa.scalar(curr_value).cast(pa.float64())


class SmoothTwoColumn(WindowEvaluator):
    """This class demonstrates using two columns.

    If the second column is above a threshold, then smooth over the first column from
    the previous and next rows.
    """

    def __init__(self, alpha: float = 0.9) -> None:
        self.alpha = alpha

    def evaluate_all(self, values: list[pa.Array], num_rows: int) -> pa.Array:
        results = []
        values_a = values[0]
        values_b = values[1]
        for idx in range(num_rows):
            if values_b[idx].as_py() > 7:
                if idx == 0:
                    results.append(values_a[1].cast(pa.float64()))
                elif idx == num_rows - 1:
                    results.append(values_a[num_rows - 2].cast(pa.float64()))
                else:
                    results.append(
                        pa.scalar(
                            values_a[idx - 1].as_py() * self.alpha
                            + values_a[idx + 1].as_py() * (1.0 - self.alpha)
                        )
                    )
            else:
                results.append(values_a[idx].cast(pa.float64()))

        return pa.array(results)


class NotSubclassOfWindowEvaluator:
    pass


@pytest.fixture
def df():
    ctx = SessionContext()

    # create a RecordBatch and a new DataFrame from it
    batch = pa.RecordBatch.from_arrays(
        [
            pa.array([0, 1, 2, 3, 4, 5, 6]),
            pa.array([7, 4, 3, 8, 9, 1, 6]),
            pa.array(["A", "A", "A", "A", "B", "B", "B"]),
        ],
        names=["a", "b", "c"],
    )
    return ctx.create_dataframe([[batch]])


def test_udwf_errors(df):
    with pytest.raises(TypeError):
        udwf(
            NotSubclassOfWindowEvaluator,
            pa.float64(),
            pa.float64(),
            volatility="immutable",
        )


smooth_default = udwf(
    ExponentialSmoothDefault,
    pa.float64(),
    pa.float64(),
    volatility="immutable",
)

smooth_w_arguments = udwf(
    lambda: ExponentialSmoothDefault(0.8),
    pa.float64(),
    pa.float64(),
    volatility="immutable",
)

smooth_bounded = udwf(
    ExponentialSmoothBounded,
    pa.float64(),
    pa.float64(),
    volatility="immutable",
)

smooth_rank = udwf(
    ExponentialSmoothRank,
    pa.utf8(),
    pa.float64(),
    volatility="immutable",
)

smooth_frame = udwf(
    ExponentialSmoothFrame,
    pa.float64(),
    pa.float64(),
    volatility="immutable",
)

smooth_two_col = udwf(
    SmoothTwoColumn,
    [pa.int64(), pa.int64()],
    pa.float64(),
    volatility="immutable",
)

data_test_udwf_functions = [
    (
        "default_udwf_no_arguments",
        smooth_default(column("a")),
        [0, 0.9, 1.89, 2.889, 3.889, 4.889, 5.889],
    ),
    (
        "default_udwf_w_arguments",
        smooth_w_arguments(column("a")),
        [0, 0.8, 1.76, 2.752, 3.75, 4.75, 5.75],
    ),
    (
        "default_udwf_partitioned",
        smooth_default(column("a")).partition_by(column("c")).build(),
        [0, 0.9, 1.89, 2.889, 4.0, 4.9, 5.89],
    ),
    (
        "default_udwf_ordered",
        smooth_default(column("a")).order_by(column("b")).build(),
        [0.551, 1.13, 2.3, 2.755, 3.876, 5.0, 5.513],
    ),
    (
        "bounded_udwf",
        smooth_bounded(column("a")),
        [0, 0.9, 1.9, 2.9, 3.9, 4.9, 5.9],
    ),
    (
        "bounded_udwf_ignores_frame",
        smooth_bounded(column("a"))
        .window_frame(WindowFrame("rows", None, None))
        .build(),
        [0, 0.9, 1.9, 2.9, 3.9, 4.9, 5.9],
    ),
    (
        "rank_udwf",
        smooth_rank(column("c")).order_by(column("c")).build(),
        [1, 1, 1, 1, 1.9, 2, 2],
    ),
    (
        "frame_unbounded_udwf",
        smooth_frame(column("a")).window_frame(WindowFrame("rows", None, None)).build(),
        [5.889, 5.889, 5.889, 5.889, 5.889, 5.889, 5.889],
    ),
    (
        "frame_bounded_udwf",
        smooth_frame(column("a")).window_frame(WindowFrame("rows", None, 0)).build(),
        [0.0, 0.9, 1.89, 2.889, 3.889, 4.889, 5.889],
    ),
    (
        "frame_bounded_udwf",
        smooth_frame(column("a"))
        .window_frame(WindowFrame("rows", None, 0))
        .order_by(column("b"))
        .build(),
        [0.551, 1.13, 2.3, 2.755, 3.876, 5.0, 5.513],
    ),
    (
        "two_column_udwf",
        smooth_two_col(column("a"), column("b")),
        [0.0, 1.0, 2.0, 2.2, 3.2, 5.0, 6.0],
    ),
]


@pytest.mark.parametrize("name,expr,expected", data_test_udwf_functions)
def test_udwf_functions(df, name, expr, expected):
    df = df.select("a", "b", f.round(expr, lit(3)).alias(name))

    # execute and collect the first (and only) batch
    result = df.sort(column("a")).select(column(name)).collect()[0]

    assert result.column(0) == pa.array(expected)