import operator

import numpy as np
import pytest

import pandas as pd
import pandas._testing as tm
from pandas.core import ops
from pandas.core.arrays import FloatingArray

# Basic test for the arithmetic array ops
# -----------------------------------------------------------------------------


@pytest.mark.parametrize(
    "opname, exp",
    [("add", [1, 3, None, None, 9]), ("mul", [0, 2, None, None, 20])],
    ids=["add", "mul"],
)
def test_add_mul(dtype, opname, exp):
    a = pd.array([0, 1, None, 3, 4], dtype=dtype)
    b = pd.array([1, 2, 3, None, 5], dtype=dtype)

    # array / array
    expected = pd.array(exp, dtype=dtype)

    op = getattr(operator, opname)
    result = op(a, b)
    tm.assert_extension_array_equal(result, expected)

    op = getattr(ops, "r" + opname)
    result = op(a, b)
    tm.assert_extension_array_equal(result, expected)


def test_sub(dtype):
    a = pd.array([1, 2, 3, None, 5], dtype=dtype)
    b = pd.array([0, 1, None, 3, 4], dtype=dtype)

    result = a - b
    expected = pd.array([1, 1, None, None, 1], dtype=dtype)
    tm.assert_extension_array_equal(result, expected)


def test_div(dtype):
    a = pd.array([1, 2, 3, None, 5], dtype=dtype)
    b = pd.array([0, 1, None, 3, 4], dtype=dtype)

    result = a / b
    expected = pd.array([np.inf, 2, None, None, 1.25], dtype="Float64")
    tm.assert_extension_array_equal(result, expected)


@pytest.mark.parametrize("zero, negative", [(0, False), (0.0, False), (-0.0, True)])
def test_divide_by_zero(zero, negative):
    # https://github.com/pandas-dev/pandas/issues/27398, GH#22793
    a = pd.array([0, 1, -1, None], dtype="Int64")
    result = a / zero
    expected = FloatingArray(
        np.array([np.nan, np.inf, -np.inf, 1], dtype="float64"),
        np.array([False, False, False, True]),
    )
    if negative:
        expected *= -1
    tm.assert_extension_array_equal(result, expected)


def test_floordiv(dtype):
    a = pd.array([1, 2, 3, None, 5], dtype=dtype)
    b = pd.array([0, 1, None, 3, 4], dtype=dtype)

    result = a // b
    # Series op sets 1//0 to np.inf, which IntegerArray does not do (yet)
    expected = pd.array([0, 2, None, None, 1], dtype=dtype)
    tm.assert_extension_array_equal(result, expected)


def test_floordiv_by_int_zero_no_mask(any_int_ea_dtype):
    # GH 48223: Aligns with non-masked floordiv
    # but differs from numpy
    # https://github.com/pandas-dev/pandas/issues/30188#issuecomment-564452740
    ser = pd.Series([0, 1], dtype=any_int_ea_dtype)
    result = 1 // ser
    expected = pd.Series([np.inf, 1.0], dtype="Float64")
    tm.assert_series_equal(result, expected)

    ser_non_nullable = ser.astype(ser.dtype.numpy_dtype)
    result = 1 // ser_non_nullable
    expected = expected.astype(np.float64)
    tm.assert_series_equal(result, expected)


def test_mod(dtype):
    a = pd.array([1, 2, 3, None, 5], dtype=dtype)
    b = pd.array([0, 1, None, 3, 4], dtype=dtype)

    result = a % b
    expected = pd.array([0, 0, None, None, 1], dtype=dtype)
    tm.assert_extension_array_equal(result, expected)


def test_pow_scalar():
    a = pd.array([-1, 0, 1, None, 2], dtype="Int64")
    result = a**0
    expected = pd.array([1, 1, 1, 1, 1], dtype="Int64")
    tm.assert_extension_array_equal(result, expected)

    result = a**1
    expected = pd.array([-1, 0, 1, None, 2], dtype="Int64")
    tm.assert_extension_array_equal(result, expected)

    result = a**pd.NA
    expected = pd.array([None, None, 1, None, None], dtype="Int64")
    tm.assert_extension_array_equal(result, expected)

    result = a**np.nan
    expected = FloatingArray(
        np.array([np.nan, np.nan, 1, np.nan, np.nan], dtype="float64"),
        np.array([False, False, False, True, False]),
    )
    tm.assert_extension_array_equal(result, expected)

    # reversed
    a = a[1:]  # Can't raise integers to negative powers.

    result = 0**a
    expected = pd.array([1, 0, None, 0], dtype="Int64")
    tm.assert_extension_array_equal(result, expected)

    result = 1**a
    expected = pd.array([1, 1, 1, 1], dtype="Int64")
    tm.assert_extension_array_equal(result, expected)

    result = pd.NA**a
    expected = pd.array([1, None, None, None], dtype="Int64")
    tm.assert_extension_array_equal(result, expected)

    result = np.nan**a
    expected = FloatingArray(
        np.array([1, np.nan, np.nan, np.nan], dtype="float64"),
        np.array([False, False, True, False]),
    )
    tm.assert_extension_array_equal(result, expected)


def test_pow_array():
    a = pd.array([0, 0, 0, 1, 1, 1, None, None, None])
    b = pd.array([0, 1, None, 0, 1, None, 0, 1, None])
    result = a**b
    expected = pd.array([1, 0, None, 1, 1, 1, 1, None, None])
    tm.assert_extension_array_equal(result, expected)


def test_rpow_one_to_na():
    # https://github.com/pandas-dev/pandas/issues/22022
    # https://github.com/pandas-dev/pandas/issues/29997
    arr = pd.array([np.nan, np.nan], dtype="Int64")
    result = np.array([1.0, 2.0]) ** arr
    expected = pd.array([1.0, np.nan], dtype="Float64")
    tm.assert_extension_array_equal(result, expected)


@pytest.mark.parametrize("other", [0, 0.5])
def test_numpy_zero_dim_ndarray(other):
    arr = pd.array([1, None, 2])
    result = arr + np.array(other)
    expected = arr + other
    tm.assert_equal(result, expected)


# Test generic characteristics / errors
# -----------------------------------------------------------------------------


def test_error_invalid_values(data, all_arithmetic_operators):
    op = all_arithmetic_operators
    s = pd.Series(data)
    ops = getattr(s, op)

    # invalid scalars
    msg = "|".join(
        [
            r"can only perform ops with numeric values",
            r"IntegerArray cannot perform the operation mod",
            r"unsupported operand type",
            r"can only concatenate str \(not \"int\"\) to str",
            "not all arguments converted during string",
            "ufunc '.*' not supported for the input types, and the inputs could not",
            "ufunc '.*' did not contain a loop with signature matching types",
            "Addition/subtraction of integers and integer-arrays with Timestamp",
        ]
    )
    with pytest.raises(TypeError, match=msg):
        ops("foo")
    with pytest.raises(TypeError, match=msg):
        ops(pd.Timestamp("20180101"))

    # invalid array-likes
    str_ser = pd.Series("foo", index=s.index)
    # with pytest.raises(TypeError, match=msg):
    if all_arithmetic_operators in [
        "__mul__",
        "__rmul__",
    ]:  # (data[~data.isna()] >= 0).all():
        res = ops(str_ser)
        expected = pd.Series(["foo" * x for x in data], index=s.index)
        expected = expected.fillna(np.nan)
        # TODO: doing this fillna to keep tests passing as we make
        #  assert_almost_equal stricter, but the expected with pd.NA seems
        #  more-correct than np.nan here.
        tm.assert_series_equal(res, expected)
    else:
        with pytest.raises(TypeError, match=msg):
            ops(str_ser)

    msg = "|".join(
        [
            "can only perform ops with numeric values",
            "cannot perform .* with this index type: DatetimeArray",
            "Addition/subtraction of integers and integer-arrays "
            "with DatetimeArray is no longer supported. *",
            "unsupported operand type",
            r"can only concatenate str \(not \"int\"\) to str",
            "not all arguments converted during string",
            "cannot subtract DatetimeArray from ndarray",
        ]
    )
    with pytest.raises(TypeError, match=msg):
        ops(pd.Series(pd.date_range("20180101", periods=len(s))))


# Various
# -----------------------------------------------------------------------------


# TODO test unsigned overflow


def test_arith_coerce_scalar(data, all_arithmetic_operators):
    op = tm.get_op_from_name(all_arithmetic_operators)
    s = pd.Series(data)
    other = 0.01

    result = op(s, other)
    expected = op(s.astype(float), other)
    expected = expected.astype("Float64")

    # rmod results in NaN that wasn't NA in original nullable Series -> unmask it
    if all_arithmetic_operators == "__rmod__":
        mask = (s == 0).fillna(False).to_numpy(bool)
        expected.array._mask[mask] = False

    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("other", [1.0, np.array(1.0)])
def test_arithmetic_conversion(all_arithmetic_operators, other):
    # if we have a float operand we should have a float result
    # if that is equal to an integer
    op = tm.get_op_from_name(all_arithmetic_operators)

    s = pd.Series([1, 2, 3], dtype="Int64")
    result = op(s, other)
    assert result.dtype == "Float64"


def test_cross_type_arithmetic():
    df = pd.DataFrame(
        {
            "A": pd.Series([1, 2, np.nan], dtype="Int64"),
            "B": pd.Series([1, np.nan, 3], dtype="UInt8"),
            "C": [1, 2, 3],
        }
    )

    result = df.A + df.C
    expected = pd.Series([2, 4, np.nan], dtype="Int64")
    tm.assert_series_equal(result, expected)

    result = (df.A + df.C) * 3 == 12
    expected = pd.Series([False, True, None], dtype="boolean")
    tm.assert_series_equal(result, expected)

    result = df.A + df.B
    expected = pd.Series([2, np.nan, np.nan], dtype="Int64")
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("op", ["mean"])
def test_reduce_to_float(op):
    # some reduce ops always return float, even if the result
    # is a rounded number
    df = pd.DataFrame(
        {
            "A": ["a", "b", "b"],
            "B": [1, None, 3],
            "C": pd.array([1, None, 3], dtype="Int64"),
        }
    )

    # op
    result = getattr(df.C, op)()
    assert isinstance(result, float)

    # groupby
    result = getattr(df.groupby("A"), op)()

    expected = pd.DataFrame(
        {"B": np.array([1.0, 3.0]), "C": pd.array([1, 3], dtype="Float64")},
        index=pd.Index(["a", "b"], name="A"),
    )
    tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
    "source, neg_target, abs_target",
    [
        ([1, 2, 3], [-1, -2, -3], [1, 2, 3]),
        ([1, 2, None], [-1, -2, None], [1, 2, None]),
        ([-1, 0, 1], [1, 0, -1], [1, 0, 1]),
    ],
)
def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
    dtype = any_signed_int_ea_dtype
    arr = pd.array(source, dtype=dtype)
    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
    neg_target = pd.array(neg_target, dtype=dtype)
    abs_target = pd.array(abs_target, dtype=dtype)

    tm.assert_extension_array_equal(neg_result, neg_target)
    tm.assert_extension_array_equal(pos_result, arr)
    assert not tm.shares_memory(pos_result, arr)
    tm.assert_extension_array_equal(abs_result, abs_target)


def test_values_multiplying_large_series_by_NA():
    # GH#33701

    result = pd.NA * pd.Series(np.zeros(10001))
    expected = pd.Series([pd.NA] * 10001)

    tm.assert_series_equal(result, expected)


def test_bitwise(dtype):
    left = pd.array([1, None, 3, 4], dtype=dtype)
    right = pd.array([None, 3, 5, 4], dtype=dtype)

    result = left | right
    expected = pd.array([None, None, 3 | 5, 4 | 4], dtype=dtype)
    tm.assert_extension_array_equal(result, expected)

    result = left & right
    expected = pd.array([None, None, 3 & 5, 4 & 4], dtype=dtype)
    tm.assert_extension_array_equal(result, expected)

    result = left ^ right
    expected = pd.array([None, None, 3 ^ 5, 4 ^ 4], dtype=dtype)
    tm.assert_extension_array_equal(result, expected)

    # TODO: desired behavior when operating with boolean?  defer?

    floats = right.astype("Float64")
    with pytest.raises(TypeError, match="unsupported operand type"):
        left | floats
    with pytest.raises(TypeError, match="unsupported operand type"):
        left & floats
    with pytest.raises(TypeError, match="unsupported operand type"):
        left ^ floats
