import numpy as np
import pytest

from pandas._libs.missing import NA

from pandas.core.dtypes.common import is_scalar

import pandas as pd
import pandas._testing as tm


def test_singleton():
    assert NA is NA
    new_NA = type(NA)()
    assert new_NA is NA


def test_repr():
    assert repr(NA) == "<NA>"
    assert str(NA) == "<NA>"


def test_truthiness():
    with pytest.raises(TypeError):
        bool(NA)

    with pytest.raises(TypeError):
        not NA


def test_hashable():
    assert hash(NA) == hash(NA)
    d = {NA: "test"}
    assert d[NA] == "test"


def test_arithmetic_ops(all_arithmetic_functions):
    op = all_arithmetic_functions

    for other in [NA, 1, 1.0, "a", np.int64(1), np.nan]:
        if op.__name__ in ("pow", "rpow", "rmod") and isinstance(other, str):
            continue
        if op.__name__ in ("divmod", "rdivmod"):
            assert op(NA, other) is (NA, NA)
        else:
            if op.__name__ == "rpow":
                # avoid special case
                other += 1
            assert op(NA, other) is NA


def test_comparison_ops():

    for other in [NA, 1, 1.0, "a", np.int64(1), np.nan, np.bool_(True)]:
        assert (NA == other) is NA
        assert (NA != other) is NA
        assert (NA > other) is NA
        assert (NA >= other) is NA
        assert (NA < other) is NA
        assert (NA <= other) is NA
        assert (other == NA) is NA
        assert (other != NA) is NA
        assert (other > NA) is NA
        assert (other >= NA) is NA
        assert (other < NA) is NA
        assert (other <= NA) is NA


@pytest.mark.parametrize(
    "value",
    [
        0,
        0.0,
        -0,
        -0.0,
        False,
        np.bool_(False),
        np.int_(0),
        np.float_(0),
        np.int_(-0),
        np.float_(-0),
    ],
)
@pytest.mark.parametrize("asarray", [True, False])
def test_pow_special(value, asarray):
    if asarray:
        value = np.array([value])
    result = pd.NA ** value

    if asarray:
        result = result[0]
    else:
        # this assertion isn't possible for ndarray.
        assert isinstance(result, type(value))
    assert result == 1


@pytest.mark.parametrize(
    "value", [1, 1.0, True, np.bool_(True), np.int_(1), np.float_(1)],
)
@pytest.mark.parametrize("asarray", [True, False])
def test_rpow_special(value, asarray):
    if asarray:
        value = np.array([value])
    result = value ** pd.NA

    if asarray:
        result = result[0]
    elif not isinstance(value, (np.float_, np.bool_, np.int_)):
        # this assertion isn't possible with asarray=True
        assert isinstance(result, type(value))

    assert result == value


@pytest.mark.parametrize(
    "value", [-1, -1.0, np.int_(-1), np.float_(-1)],
)
@pytest.mark.parametrize("asarray", [True, False])
def test_rpow_minus_one(value, asarray):
    if asarray:
        value = np.array([value])
    result = value ** pd.NA

    if asarray:
        result = result[0]

    assert pd.isna(result)


def test_unary_ops():
    assert +NA is NA
    assert -NA is NA
    assert abs(NA) is NA
    assert ~NA is NA


def test_logical_and():

    assert NA & True is NA
    assert True & NA is NA
    assert NA & False is False
    assert False & NA is False
    assert NA & NA is NA

    with pytest.raises(TypeError):
        NA & 5


def test_logical_or():

    assert NA | True is True
    assert True | NA is True
    assert NA | False is NA
    assert False | NA is NA
    assert NA | NA is NA

    with pytest.raises(TypeError):
        NA | 5


def test_logical_xor():

    assert NA ^ True is NA
    assert True ^ NA is NA
    assert NA ^ False is NA
    assert False ^ NA is NA
    assert NA ^ NA is NA

    with pytest.raises(TypeError):
        NA ^ 5


def test_logical_not():
    assert ~NA is NA


@pytest.mark.parametrize(
    "shape", [(3,), (3, 3), (1, 2, 3)],
)
def test_arithmetic_ndarray(shape, all_arithmetic_functions):
    op = all_arithmetic_functions
    a = np.zeros(shape)
    if op.__name__ == "pow":
        a += 5
    result = op(pd.NA, a)
    expected = np.full(a.shape, pd.NA, dtype=object)
    tm.assert_numpy_array_equal(result, expected)


def test_is_scalar():
    assert is_scalar(NA) is True


def test_isna():
    assert pd.isna(NA) is True
    assert pd.notna(NA) is False


def test_series_isna():
    s = pd.Series([1, NA], dtype=object)
    expected = pd.Series([False, True])
    tm.assert_series_equal(s.isna(), expected)


def test_ufunc():
    assert np.log(pd.NA) is pd.NA
    assert np.add(pd.NA, 1) is pd.NA
    result = np.divmod(pd.NA, 1)
    assert result[0] is pd.NA and result[1] is pd.NA

    result = np.frexp(pd.NA)
    assert result[0] is pd.NA and result[1] is pd.NA


def test_ufunc_raises():
    with pytest.raises(ValueError, match="ufunc method 'at'"):
        np.log.at(pd.NA, 0)


def test_binary_input_not_dunder():
    a = np.array([1, 2, 3])
    expected = np.array([pd.NA, pd.NA, pd.NA], dtype=object)
    result = np.logaddexp(a, pd.NA)
    tm.assert_numpy_array_equal(result, expected)

    result = np.logaddexp(pd.NA, a)
    tm.assert_numpy_array_equal(result, expected)

    # all NA, multiple inputs
    assert np.logaddexp(pd.NA, pd.NA) is pd.NA

    result = np.modf(pd.NA, pd.NA)
    assert len(result) == 2
    assert all(x is pd.NA for x in result)


def test_divmod_ufunc():
    # binary in, binary out.
    a = np.array([1, 2, 3])
    expected = np.array([pd.NA, pd.NA, pd.NA], dtype=object)

    result = np.divmod(a, pd.NA)
    assert isinstance(result, tuple)
    for arr in result:
        tm.assert_numpy_array_equal(arr, expected)
        tm.assert_numpy_array_equal(arr, expected)

    result = np.divmod(pd.NA, a)
    for arr in result:
        tm.assert_numpy_array_equal(arr, expected)
        tm.assert_numpy_array_equal(arr, expected)


def test_integer_hash_collision_dict():
    # GH 30013
    result = {NA: "foo", hash(NA): "bar"}

    assert result[NA] == "foo"
    assert result[hash(NA)] == "bar"


def test_integer_hash_collision_set():
    # GH 30013
    result = {NA, hash(NA)}

    assert len(result) == 2
    assert NA in result
    assert hash(NA) in result
