from collections import deque
import string

import numpy as np
import pytest

from pandas.core.dtypes.common import is_dtype_equal

import pandas as pd
import pandas._testing as tm
from pandas.arrays import SparseArray

BINARY_UFUNCS = [np.add, np.logaddexp]  # dunder op
SPARSE = [True, False]
SPARSE_IDS = ["sparse", "dense"]


@pytest.fixture
def arrays_for_binary_ufunc():
    """
    A pair of random, length-100 integer-dtype arrays, that are mostly 0.
    """
    a1 = np.random.randint(0, 10, 100, dtype="int64")
    a2 = np.random.randint(0, 10, 100, dtype="int64")
    a1[::3] = 0
    a2[::4] = 0
    return a1, a2


@pytest.mark.parametrize("ufunc", [np.positive, np.floor, np.exp])
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
def test_unary_ufunc(ufunc, sparse):
    # Test that ufunc(pd.Series) == pd.Series(ufunc)
    arr = np.random.randint(0, 10, 10, dtype="int64")
    arr[::2] = 0
    if sparse:
        arr = SparseArray(arr, dtype=pd.SparseDtype("int64", 0))

    index = list(string.ascii_letters[:10])
    name = "name"
    series = pd.Series(arr, index=index, name=name)

    result = ufunc(series)
    expected = pd.Series(ufunc(arr), index=index, name=name)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
def test_binary_ufunc_with_array(flip, sparse, ufunc, arrays_for_binary_ufunc):
    # Test that ufunc(pd.Series(a), array) == pd.Series(ufunc(a, b))
    a1, a2 = arrays_for_binary_ufunc
    if sparse:
        a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
        a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))

    name = "name"  # op(pd.Series, array) preserves the name.
    series = pd.Series(a1, name=name)
    other = a2

    array_args = (a1, a2)
    series_args = (series, other)  # ufunc(series, array)

    if flip:
        array_args = reversed(array_args)
        series_args = reversed(series_args)  # ufunc(array, series)

    expected = pd.Series(ufunc(*array_args), name=name)
    result = ufunc(*series_args)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
def test_binary_ufunc_with_index(flip, sparse, ufunc, arrays_for_binary_ufunc):
    # Test that
    #   * func(pd.Series(a), pd.Series(b)) == pd.Series(ufunc(a, b))
    #   * ufunc(Index, pd.Series) dispatches to pd.Series (returns a pd.Series)
    a1, a2 = arrays_for_binary_ufunc
    if sparse:
        a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
        a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))

    name = "name"  # op(pd.Series, array) preserves the name.
    series = pd.Series(a1, name=name)

    warn = None if not sparse else FutureWarning
    with tm.assert_produces_warning(warn):
        other = pd.Index(a2, name=name).astype("int64")

    array_args = (a1, a2)
    series_args = (series, other)  # ufunc(series, array)

    if flip:
        array_args = reversed(array_args)
        series_args = reversed(series_args)  # ufunc(array, series)

    expected = pd.Series(ufunc(*array_args), name=name)
    result = ufunc(*series_args)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
@pytest.mark.parametrize("shuffle", [True, False], ids=["unaligned", "aligned"])
@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"])
def test_binary_ufunc_with_series(
    flip, shuffle, sparse, ufunc, arrays_for_binary_ufunc
):
    # Test that
    #   * func(pd.Series(a), pd.Series(b)) == pd.Series(ufunc(a, b))
    #   with alignment between the indices
    a1, a2 = arrays_for_binary_ufunc
    if sparse:
        a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
        a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))

    name = "name"  # op(pd.Series, array) preserves the name.
    series = pd.Series(a1, name=name)
    other = pd.Series(a2, name=name)

    idx = np.random.permutation(len(a1))

    if shuffle:
        other = other.take(idx)
        if flip:
            index = other.align(series)[0].index
        else:
            index = series.align(other)[0].index
    else:
        index = series.index

    array_args = (a1, a2)
    series_args = (series, other)  # ufunc(series, array)

    if flip:
        array_args = tuple(reversed(array_args))
        series_args = tuple(reversed(series_args))  # ufunc(array, series)

    expected = pd.Series(ufunc(*array_args), index=index, name=name)
    result = ufunc(*series_args)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
@pytest.mark.parametrize("flip", [True, False])
def test_binary_ufunc_scalar(ufunc, sparse, flip, arrays_for_binary_ufunc):
    # Test that
    #   * ufunc(pd.Series, scalar) == pd.Series(ufunc(array, scalar))
    #   * ufunc(pd.Series, scalar) == ufunc(scalar, pd.Series)
    arr, _ = arrays_for_binary_ufunc
    if sparse:
        arr = SparseArray(arr)
    other = 2
    series = pd.Series(arr, name="name")

    series_args = (series, other)
    array_args = (arr, other)

    if flip:
        series_args = tuple(reversed(series_args))
        array_args = tuple(reversed(array_args))

    expected = pd.Series(ufunc(*array_args), name="name")
    result = ufunc(*series_args)

    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("ufunc", [np.divmod])  # TODO: np.modf, np.frexp
@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
@pytest.mark.parametrize("shuffle", [True, False])
@pytest.mark.filterwarnings("ignore:divide by zero:RuntimeWarning")
def test_multiple_output_binary_ufuncs(ufunc, sparse, shuffle, arrays_for_binary_ufunc):
    # Test that
    #  the same conditions from binary_ufunc_scalar apply to
    #  ufuncs with multiple outputs.

    a1, a2 = arrays_for_binary_ufunc
    # work around https://github.com/pandas-dev/pandas/issues/26987
    a1[a1 == 0] = 1
    a2[a2 == 0] = 1

    if sparse:
        a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0))
        a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0))

    s1 = pd.Series(a1)
    s2 = pd.Series(a2)

    if shuffle:
        # ensure we align before applying the ufunc
        s2 = s2.sample(frac=1)

    expected = ufunc(a1, a2)
    assert isinstance(expected, tuple)

    result = ufunc(s1, s2)
    assert isinstance(result, tuple)
    tm.assert_series_equal(result[0], pd.Series(expected[0]))
    tm.assert_series_equal(result[1], pd.Series(expected[1]))


@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
def test_multiple_output_ufunc(sparse, arrays_for_binary_ufunc):
    # Test that the same conditions from unary input apply to multi-output
    # ufuncs
    arr, _ = arrays_for_binary_ufunc

    if sparse:
        arr = SparseArray(arr)

    series = pd.Series(arr, name="name")
    result = np.modf(series)
    expected = np.modf(arr)

    assert isinstance(result, tuple)
    assert isinstance(expected, tuple)

    tm.assert_series_equal(result[0], pd.Series(expected[0], name="name"))
    tm.assert_series_equal(result[1], pd.Series(expected[1], name="name"))


@pytest.mark.parametrize("sparse", SPARSE, ids=SPARSE_IDS)
@pytest.mark.parametrize("ufunc", BINARY_UFUNCS)
def test_binary_ufunc_drops_series_name(ufunc, sparse, arrays_for_binary_ufunc):
    # Drop the names when they differ.
    a1, a2 = arrays_for_binary_ufunc
    s1 = pd.Series(a1, name="a")
    s2 = pd.Series(a2, name="b")

    result = ufunc(s1, s2)
    assert result.name is None


def test_object_series_ok():
    class Dummy:
        def __init__(self, value) -> None:
            self.value = value

        def __add__(self, other):
            return self.value + other.value

    arr = np.array([Dummy(0), Dummy(1)])
    ser = pd.Series(arr)
    tm.assert_series_equal(np.add(ser, ser), pd.Series(np.add(ser, arr)))
    tm.assert_series_equal(np.add(ser, Dummy(1)), pd.Series(np.add(ser, Dummy(1))))


@pytest.fixture(
    params=[
        pd.array([1, 3, 2], dtype=np.int64),
        pd.array([1, 3, 2], dtype="Int64"),
        pd.array([1, 3, 2], dtype="Float32"),
        pd.array([1, 10, 2], dtype="Sparse[int]"),
        pd.to_datetime(["2000", "2010", "2001"]),
        pd.to_datetime(["2000", "2010", "2001"]).tz_localize("CET"),
        pd.to_datetime(["2000", "2010", "2001"]).to_period(freq="D"),
        pd.to_timedelta(["1 Day", "3 Days", "2 Days"]),
        pd.IntervalIndex([pd.Interval(0, 1), pd.Interval(2, 3), pd.Interval(1, 2)]),
    ],
    ids=lambda x: str(x.dtype),
)
def values_for_np_reduce(request):
    # min/max tests assume that these are monotonic increasing
    return request.param


class TestNumpyReductions:
    # TODO: cases with NAs, axis kwarg for DataFrame

    def test_multiply(self, values_for_np_reduce, box_with_array, request):
        box = box_with_array
        values = values_for_np_reduce

        warn = None
        if is_dtype_equal(values.dtype, "Sparse[int]") and box is pd.Index:
            warn = FutureWarning
        msg = "passing a SparseArray to pd.Index"
        with tm.assert_produces_warning(warn, match=msg):
            obj = box(values)

        if isinstance(values, pd.core.arrays.SparseArray) and box is not pd.Index:
            mark = pytest.mark.xfail(reason="SparseArray has no 'prod'")
            request.node.add_marker(mark)

        if values.dtype.kind in "iuf":
            result = np.multiply.reduce(obj)
            if box is pd.DataFrame:
                expected = obj.prod(numeric_only=False)
                tm.assert_series_equal(result, expected)
            elif box is pd.Index:
                # Int64Index, Index has no 'prod'
                expected = obj._values.prod()
                assert result == expected
            else:

                expected = obj.prod()
                assert result == expected
        else:
            msg = "|".join(
                [
                    "does not support reduction",
                    "unsupported operand type",
                    "ufunc 'multiply' cannot use operands",
                ]
            )
            with pytest.raises(TypeError, match=msg):
                np.multiply.reduce(obj)

    def test_add(self, values_for_np_reduce, box_with_array):
        box = box_with_array
        values = values_for_np_reduce

        warn = None
        if is_dtype_equal(values.dtype, "Sparse[int]") and box is pd.Index:
            warn = FutureWarning
        msg = "passing a SparseArray to pd.Index"
        with tm.assert_produces_warning(warn, match=msg):
            obj = box(values)

        if values.dtype.kind in "miuf":
            result = np.add.reduce(obj)
            if box is pd.DataFrame:
                expected = obj.sum(numeric_only=False)
                tm.assert_series_equal(result, expected)
            elif box is pd.Index:
                # Int64Index, Index has no 'sum'
                expected = obj._values.sum()
                assert result == expected
            else:
                expected = obj.sum()
                assert result == expected
        else:
            msg = "|".join(
                [
                    "does not support reduction",
                    "unsupported operand type",
                    "ufunc 'add' cannot use operands",
                ]
            )
            with pytest.raises(TypeError, match=msg):
                np.add.reduce(obj)

    def test_max(self, values_for_np_reduce, box_with_array):
        box = box_with_array
        values = values_for_np_reduce

        same_type = True
        if box is pd.Index and values.dtype.kind in ["i", "f"]:
            # ATM Index casts to object, so we get python ints/floats
            same_type = False

        warn = None
        if is_dtype_equal(values.dtype, "Sparse[int]") and box is pd.Index:
            warn = FutureWarning
        msg = "passing a SparseArray to pd.Index"
        with tm.assert_produces_warning(warn, match=msg):
            obj = box(values)

        result = np.maximum.reduce(obj)
        if box is pd.DataFrame:
            # TODO: cases with axis kwarg
            expected = obj.max(numeric_only=False)
            tm.assert_series_equal(result, expected)
        else:
            expected = values[1]
            assert result == expected
            if same_type:
                # check we have e.g. Timestamp instead of dt64
                assert type(result) == type(expected)

    def test_min(self, values_for_np_reduce, box_with_array):
        box = box_with_array
        values = values_for_np_reduce

        same_type = True
        if box is pd.Index and values.dtype.kind in ["i", "f"]:
            # ATM Index casts to object, so we get python ints/floats
            same_type = False

        warn = None
        if is_dtype_equal(values.dtype, "Sparse[int]") and box is pd.Index:
            warn = FutureWarning
        msg = "passing a SparseArray to pd.Index"
        with tm.assert_produces_warning(warn, match=msg):
            obj = box(values)

        result = np.minimum.reduce(obj)
        if box is pd.DataFrame:
            expected = obj.min(numeric_only=False)
            tm.assert_series_equal(result, expected)
        else:
            expected = values[0]
            assert result == expected
            if same_type:
                # check we have e.g. Timestamp instead of dt64
                assert type(result) == type(expected)


@pytest.mark.parametrize("type_", [list, deque, tuple])
def test_binary_ufunc_other_types(type_):
    a = pd.Series([1, 2, 3], name="name")
    b = type_([3, 4, 5])

    result = np.add(a, b)
    expected = pd.Series(np.add(a.to_numpy(), b), name="name")
    tm.assert_series_equal(result, expected)


def test_object_dtype_ok():
    class Thing:
        def __init__(self, value) -> None:
            self.value = value

        def __add__(self, other):
            other = getattr(other, "value", other)
            return type(self)(self.value + other)

        def __eq__(self, other) -> bool:
            return type(other) is Thing and self.value == other.value

        def __repr__(self) -> str:
            return f"Thing({self.value})"

    s = pd.Series([Thing(1), Thing(2)])
    result = np.add(s, Thing(1))
    expected = pd.Series([Thing(2), Thing(3)])
    tm.assert_series_equal(result, expected)


def test_outer():
    # https://github.com/pandas-dev/pandas/issues/27186
    ser = pd.Series([1, 2, 3])
    obj = np.array([1, 2, 3])

    with pytest.raises(NotImplementedError, match=tm.EMPTY_STRING_PATTERN):
        np.subtract.outer(ser, obj)


def test_np_matmul():
    # GH26650
    df1 = pd.DataFrame(data=[[-1, 1, 10]])
    df2 = pd.DataFrame(data=[-1, 1, 10])
    expected_result = pd.DataFrame(data=[102])

    with tm.assert_produces_warning(FutureWarning, match="on non-aligned"):
        result = np.matmul(df1, df2)
    tm.assert_frame_equal(
        expected_result,
        result,
    )
