# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import datetime, time, timedelta
from itertools import product, starmap
import operator
import warnings

import numpy as np
import pytest
import pytz

from pandas._libs.tslibs.conversion import localize_pydatetime
from pandas._libs.tslibs.offsets import shift_months
from pandas.compat.numpy import np_datetime64_compat
from pandas.errors import PerformanceWarning

import pandas as pd
from pandas import (
    DatetimeIndex,
    NaT,
    Period,
    Series,
    Timedelta,
    TimedeltaIndex,
    Timestamp,
    date_range,
)
import pandas._testing as tm
from pandas.core.arrays import DatetimeArray, TimedeltaArray
from pandas.core.ops import roperator
from pandas.tests.arithmetic.common import (
    assert_invalid_addsub_type,
    assert_invalid_comparison,
    get_upcast_box,
)

# ------------------------------------------------------------------
# Comparisons


class TestDatetime64ArrayLikeComparisons:
    # Comparison tests for datetime64 vectors fully parametrized over
    #  DataFrame/Series/DatetimeIndex/DatetimeArray.  Ideally all comparison
    #  tests will eventually end up here.

    def test_compare_zerodim(self, tz_naive_fixture, box_with_array):
        # Test comparison with zero-dimensional array is unboxed
        tz = tz_naive_fixture
        box = box_with_array
        xbox = box_with_array if box_with_array is not pd.Index else np.ndarray
        dti = date_range("20130101", periods=3, tz=tz)

        other = np.array(dti.to_numpy()[0])

        dtarr = tm.box_expected(dti, box)
        result = dtarr <= other
        expected = np.array([True, False, False])
        expected = tm.box_expected(expected, xbox)
        tm.assert_equal(result, expected)

    @pytest.mark.parametrize(
        "other",
        [
            "foo",
            -1,
            99,
            4.0,
            object(),
            timedelta(days=2),
            # GH#19800, GH#19301 datetime.date comparison raises to
            #  match DatetimeIndex/Timestamp.  This also matches the behavior
            #  of stdlib datetime.datetime
            datetime(2001, 1, 1).date(),
            # GH#19301 None and NaN are *not* cast to NaT for comparisons
            None,
            np.nan,
        ],
    )
    def test_dt64arr_cmp_scalar_invalid(self, other, tz_naive_fixture, box_with_array):
        # GH#22074, GH#15966
        tz = tz_naive_fixture

        rng = date_range("1/1/2000", periods=10, tz=tz)
        dtarr = tm.box_expected(rng, box_with_array)
        assert_invalid_comparison(dtarr, other, box_with_array)

    @pytest.mark.parametrize(
        "other",
        [
            list(range(10)),
            np.arange(10),
            np.arange(10).astype(np.float32),
            np.arange(10).astype(object),
            pd.timedelta_range("1ns", periods=10).array,
            np.array(pd.timedelta_range("1ns", periods=10)),
            list(pd.timedelta_range("1ns", periods=10)),
            pd.timedelta_range("1 Day", periods=10).astype(object),
            pd.period_range("1971-01-01", freq="D", periods=10).array,
            pd.period_range("1971-01-01", freq="D", periods=10).astype(object),
        ],
    )
    def test_dt64arr_cmp_arraylike_invalid(self, other, tz_naive_fixture):
        # We don't parametrize this over box_with_array because listlike
        #  other plays poorly with assert_invalid_comparison reversed checks
        tz = tz_naive_fixture

        dta = date_range("1970-01-01", freq="ns", periods=10, tz=tz)._data
        assert_invalid_comparison(dta, other, tm.to_array)

    def test_dt64arr_cmp_mixed_invalid(self, tz_naive_fixture):
        tz = tz_naive_fixture

        dta = date_range("1970-01-01", freq="h", periods=5, tz=tz)._data

        other = np.array([0, 1, 2, dta[3], pd.Timedelta(days=1)])
        result = dta == other
        expected = np.array([False, False, False, True, False])
        tm.assert_numpy_array_equal(result, expected)

        result = dta != other
        tm.assert_numpy_array_equal(result, ~expected)

        msg = "Invalid comparison between|Cannot compare type|not supported between"
        with pytest.raises(TypeError, match=msg):
            dta < other
        with pytest.raises(TypeError, match=msg):
            dta > other
        with pytest.raises(TypeError, match=msg):
            dta <= other
        with pytest.raises(TypeError, match=msg):
            dta >= other

    def test_dt64arr_nat_comparison(self, tz_naive_fixture, box_with_array):
        # GH#22242, GH#22163 DataFrame considered NaT == ts incorrectly
        tz = tz_naive_fixture
        box = box_with_array
        xbox = box if box is not pd.Index else np.ndarray

        ts = pd.Timestamp.now(tz)
        ser = pd.Series([ts, pd.NaT])

        obj = tm.box_expected(ser, box)

        expected = pd.Series([True, False], dtype=np.bool_)
        expected = tm.box_expected(expected, xbox)

        result = obj == ts
        tm.assert_equal(result, expected)


class TestDatetime64SeriesComparison:
    # TODO: moved from tests.series.test_operators; needs cleanup

    @pytest.mark.parametrize(
        "pair",
        [
            (
                [pd.Timestamp("2011-01-01"), NaT, pd.Timestamp("2011-01-03")],
                [NaT, NaT, pd.Timestamp("2011-01-03")],
            ),
            (
                [pd.Timedelta("1 days"), NaT, pd.Timedelta("3 days")],
                [NaT, NaT, pd.Timedelta("3 days")],
            ),
            (
                [pd.Period("2011-01", freq="M"), NaT, pd.Period("2011-03", freq="M")],
                [NaT, NaT, pd.Period("2011-03", freq="M")],
            ),
        ],
    )
    @pytest.mark.parametrize("reverse", [True, False])
    @pytest.mark.parametrize("dtype", [None, object])
    def test_nat_comparisons(self, dtype, index_or_series, reverse, pair):
        box = index_or_series
        l, r = pair
        if reverse:
            # add lhs / rhs switched data
            l, r = r, l

        left = Series(l, dtype=dtype)
        right = box(r, dtype=dtype)
        # Series, Index

        expected = Series([False, False, True])
        tm.assert_series_equal(left == right, expected)

        expected = Series([True, True, False])
        tm.assert_series_equal(left != right, expected)

        expected = Series([False, False, False])
        tm.assert_series_equal(left < right, expected)

        expected = Series([False, False, False])
        tm.assert_series_equal(left > right, expected)

        expected = Series([False, False, True])
        tm.assert_series_equal(left >= right, expected)

        expected = Series([False, False, True])
        tm.assert_series_equal(left <= right, expected)

    def test_comparison_invalid(self, tz_naive_fixture, box_with_array):
        # GH#4968
        # invalid date/int comparisons
        tz = tz_naive_fixture
        ser = Series(range(5))
        ser2 = Series(pd.date_range("20010101", periods=5, tz=tz))

        ser = tm.box_expected(ser, box_with_array)
        ser2 = tm.box_expected(ser2, box_with_array)

        assert_invalid_comparison(ser, ser2, box_with_array)

    @pytest.mark.parametrize(
        "data",
        [
            [Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")],
            [Timedelta("1 days"), NaT, Timedelta("3 days")],
            [Period("2011-01", freq="M"), NaT, Period("2011-03", freq="M")],
        ],
    )
    @pytest.mark.parametrize("dtype", [None, object])
    def test_nat_comparisons_scalar(self, dtype, data, box_with_array):
        if box_with_array is tm.to_array and dtype is object:
            # dont bother testing ndarray comparison methods as this fails
            #  on older numpys (since they check object identity)
            return

        xbox = box_with_array if box_with_array is not pd.Index else np.ndarray

        left = Series(data, dtype=dtype)
        left = tm.box_expected(left, box_with_array)

        expected = [False, False, False]
        expected = tm.box_expected(expected, xbox)
        tm.assert_equal(left == NaT, expected)
        tm.assert_equal(NaT == left, expected)

        expected = [True, True, True]
        expected = tm.box_expected(expected, xbox)
        tm.assert_equal(left != NaT, expected)
        tm.assert_equal(NaT != left, expected)

        expected = [False, False, False]
        expected = tm.box_expected(expected, xbox)
        tm.assert_equal(left < NaT, expected)
        tm.assert_equal(NaT > left, expected)
        tm.assert_equal(left <= NaT, expected)
        tm.assert_equal(NaT >= left, expected)

        tm.assert_equal(left > NaT, expected)
        tm.assert_equal(NaT < left, expected)
        tm.assert_equal(left >= NaT, expected)
        tm.assert_equal(NaT <= left, expected)

    @pytest.mark.parametrize("val", [datetime(2000, 1, 4), datetime(2000, 1, 5)])
    def test_series_comparison_scalars(self, val):
        series = Series(date_range("1/1/2000", periods=10))

        result = series > val
        expected = Series([x > val for x in series])
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "left,right", [("lt", "gt"), ("le", "ge"), ("eq", "eq"), ("ne", "ne")]
    )
    def test_timestamp_compare_series(self, left, right):
        # see gh-4982
        # Make sure we can compare Timestamps on the right AND left hand side.
        ser = pd.Series(pd.date_range("20010101", periods=10), name="dates")
        s_nat = ser.copy(deep=True)

        ser[0] = pd.Timestamp("nat")
        ser[3] = pd.Timestamp("nat")

        left_f = getattr(operator, left)
        right_f = getattr(operator, right)

        # No NaT
        expected = left_f(ser, pd.Timestamp("20010109"))
        result = right_f(pd.Timestamp("20010109"), ser)
        tm.assert_series_equal(result, expected)

        # NaT
        expected = left_f(ser, pd.Timestamp("nat"))
        result = right_f(pd.Timestamp("nat"), ser)
        tm.assert_series_equal(result, expected)

        # Compare to Timestamp with series containing NaT
        expected = left_f(s_nat, pd.Timestamp("20010109"))
        result = right_f(pd.Timestamp("20010109"), s_nat)
        tm.assert_series_equal(result, expected)

        # Compare to NaT with series containing NaT
        expected = left_f(s_nat, pd.Timestamp("nat"))
        result = right_f(pd.Timestamp("nat"), s_nat)
        tm.assert_series_equal(result, expected)

    def test_dt64arr_timestamp_equality(self, box_with_array):
        # GH#11034
        xbox = box_with_array if box_with_array is not pd.Index else np.ndarray

        ser = pd.Series([pd.Timestamp("2000-01-29 01:59:00"), "NaT"])
        ser = tm.box_expected(ser, box_with_array)

        result = ser != ser
        expected = tm.box_expected([False, True], xbox)
        tm.assert_equal(result, expected)

        result = ser != ser[0]
        expected = tm.box_expected([False, True], xbox)
        tm.assert_equal(result, expected)

        result = ser != ser[1]
        expected = tm.box_expected([True, True], xbox)
        tm.assert_equal(result, expected)

        result = ser == ser
        expected = tm.box_expected([True, False], xbox)
        tm.assert_equal(result, expected)

        result = ser == ser[0]
        expected = tm.box_expected([True, False], xbox)
        tm.assert_equal(result, expected)

        result = ser == ser[1]
        expected = tm.box_expected([False, False], xbox)
        tm.assert_equal(result, expected)


class TestDatetimeIndexComparisons:

    # TODO: moved from tests.indexes.test_base; parametrize and de-duplicate
    @pytest.mark.parametrize(
        "op",
        [operator.eq, operator.ne, operator.gt, operator.lt, operator.ge, operator.le],
    )
    def test_comparators(self, op):
        index = tm.makeDateIndex(100)
        element = index[len(index) // 2]
        element = Timestamp(element).to_datetime64()

        arr = np.array(index)
        arr_result = op(arr, element)
        index_result = op(index, element)

        assert isinstance(index_result, np.ndarray)
        tm.assert_numpy_array_equal(arr_result, index_result)

    @pytest.mark.parametrize(
        "other",
        [datetime(2016, 1, 1), Timestamp("2016-01-01"), np.datetime64("2016-01-01")],
    )
    def test_dti_cmp_datetimelike(self, other, tz_naive_fixture):
        tz = tz_naive_fixture
        dti = pd.date_range("2016-01-01", periods=2, tz=tz)
        if tz is not None:
            if isinstance(other, np.datetime64):
                # no tzaware version available
                return
            other = localize_pydatetime(other, dti.tzinfo)

        result = dti == other
        expected = np.array([True, False])
        tm.assert_numpy_array_equal(result, expected)

        result = dti > other
        expected = np.array([False, True])
        tm.assert_numpy_array_equal(result, expected)

        result = dti >= other
        expected = np.array([True, True])
        tm.assert_numpy_array_equal(result, expected)

        result = dti < other
        expected = np.array([False, False])
        tm.assert_numpy_array_equal(result, expected)

        result = dti <= other
        expected = np.array([True, False])
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize("dtype", [None, object])
    def test_dti_cmp_nat(self, dtype, box_with_array):
        if box_with_array is tm.to_array and dtype is object:
            # dont bother testing ndarray comparison methods as this fails
            #  on older numpys (since they check object identity)
            return

        xbox = box_with_array if box_with_array is not pd.Index else np.ndarray

        left = pd.DatetimeIndex(
            [pd.Timestamp("2011-01-01"), pd.NaT, pd.Timestamp("2011-01-03")]
        )
        right = pd.DatetimeIndex([pd.NaT, pd.NaT, pd.Timestamp("2011-01-03")])

        left = tm.box_expected(left, box_with_array)
        right = tm.box_expected(right, box_with_array)

        lhs, rhs = left, right
        if dtype is object:
            lhs, rhs = left.astype(object), right.astype(object)

        result = rhs == lhs
        expected = np.array([False, False, True])
        expected = tm.box_expected(expected, xbox)
        tm.assert_equal(result, expected)

        result = lhs != rhs
        expected = np.array([True, True, False])
        expected = tm.box_expected(expected, xbox)
        tm.assert_equal(result, expected)

        expected = np.array([False, False, False])
        expected = tm.box_expected(expected, xbox)
        tm.assert_equal(lhs == pd.NaT, expected)
        tm.assert_equal(pd.NaT == rhs, expected)

        expected = np.array([True, True, True])
        expected = tm.box_expected(expected, xbox)
        tm.assert_equal(lhs != pd.NaT, expected)
        tm.assert_equal(pd.NaT != lhs, expected)

        expected = np.array([False, False, False])
        expected = tm.box_expected(expected, xbox)
        tm.assert_equal(lhs < pd.NaT, expected)
        tm.assert_equal(pd.NaT > lhs, expected)

    def test_dti_cmp_nat_behaves_like_float_cmp_nan(self):
        fidx1 = pd.Index([1.0, np.nan, 3.0, np.nan, 5.0, 7.0])
        fidx2 = pd.Index([2.0, 3.0, np.nan, np.nan, 6.0, 7.0])

        didx1 = pd.DatetimeIndex(
            ["2014-01-01", pd.NaT, "2014-03-01", pd.NaT, "2014-05-01", "2014-07-01"]
        )
        didx2 = pd.DatetimeIndex(
            ["2014-02-01", "2014-03-01", pd.NaT, pd.NaT, "2014-06-01", "2014-07-01"]
        )
        darr = np.array(
            [
                np_datetime64_compat("2014-02-01 00:00Z"),
                np_datetime64_compat("2014-03-01 00:00Z"),
                np_datetime64_compat("nat"),
                np.datetime64("nat"),
                np_datetime64_compat("2014-06-01 00:00Z"),
                np_datetime64_compat("2014-07-01 00:00Z"),
            ]
        )

        cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)]

        # Check pd.NaT is handles as the same as np.nan
        with tm.assert_produces_warning(None):
            for idx1, idx2 in cases:

                result = idx1 < idx2
                expected = np.array([True, False, False, False, True, False])
                tm.assert_numpy_array_equal(result, expected)

                result = idx2 > idx1
                expected = np.array([True, False, False, False, True, False])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 <= idx2
                expected = np.array([True, False, False, False, True, True])
                tm.assert_numpy_array_equal(result, expected)

                result = idx2 >= idx1
                expected = np.array([True, False, False, False, True, True])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 == idx2
                expected = np.array([False, False, False, False, False, True])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 != idx2
                expected = np.array([True, True, True, True, True, False])
                tm.assert_numpy_array_equal(result, expected)

        with tm.assert_produces_warning(None):
            for idx1, val in [(fidx1, np.nan), (didx1, pd.NaT)]:
                result = idx1 < val
                expected = np.array([False, False, False, False, False, False])
                tm.assert_numpy_array_equal(result, expected)
                result = idx1 > val
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 <= val
                tm.assert_numpy_array_equal(result, expected)
                result = idx1 >= val
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 == val
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 != val
                expected = np.array([True, True, True, True, True, True])
                tm.assert_numpy_array_equal(result, expected)

        # Check pd.NaT is handles as the same as np.nan
        with tm.assert_produces_warning(None):
            for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]:
                result = idx1 < val
                expected = np.array([True, False, False, False, False, False])
                tm.assert_numpy_array_equal(result, expected)
                result = idx1 > val
                expected = np.array([False, False, False, False, True, True])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 <= val
                expected = np.array([True, False, True, False, False, False])
                tm.assert_numpy_array_equal(result, expected)
                result = idx1 >= val
                expected = np.array([False, False, True, False, True, True])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 == val
                expected = np.array([False, False, True, False, False, False])
                tm.assert_numpy_array_equal(result, expected)

                result = idx1 != val
                expected = np.array([True, True, False, True, True, True])
                tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize(
        "op",
        [operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le],
    )
    def test_comparison_tzawareness_compat(self, op, box_with_array):
        # GH#18162
        box = box_with_array

        dr = pd.date_range("2016-01-01", periods=6)
        dz = dr.tz_localize("US/Pacific")

        dr = tm.box_expected(dr, box)
        dz = tm.box_expected(dz, box)

        msg = "Cannot compare tz-naive and tz-aware"
        with pytest.raises(TypeError, match=msg):
            op(dr, dz)

        if box is pd.DataFrame:
            tolist = lambda x: x.astype(object).values.tolist()[0]
        else:
            tolist = list

        with pytest.raises(TypeError, match=msg):
            op(dr, tolist(dz))
        with pytest.raises(TypeError, match=msg):
            op(dr, np.array(tolist(dz), dtype=object))
        with pytest.raises(TypeError, match=msg):
            op(dz, dr)

        with pytest.raises(TypeError, match=msg):
            op(dz, tolist(dr))
        with pytest.raises(TypeError, match=msg):
            op(dz, np.array(tolist(dr), dtype=object))

        # The aware==aware and naive==naive comparisons should *not* raise
        assert np.all(dr == dr)
        assert np.all(dr == tolist(dr))
        assert np.all(tolist(dr) == dr)
        assert np.all(np.array(tolist(dr), dtype=object) == dr)
        assert np.all(dr == np.array(tolist(dr), dtype=object))

        assert np.all(dz == dz)
        assert np.all(dz == tolist(dz))
        assert np.all(tolist(dz) == dz)
        assert np.all(np.array(tolist(dz), dtype=object) == dz)
        assert np.all(dz == np.array(tolist(dz), dtype=object))

    @pytest.mark.parametrize(
        "op",
        [operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le],
    )
    def test_comparison_tzawareness_compat_scalars(self, op, box_with_array):
        # GH#18162
        dr = pd.date_range("2016-01-01", periods=6)
        dz = dr.tz_localize("US/Pacific")

        dr = tm.box_expected(dr, box_with_array)
        dz = tm.box_expected(dz, box_with_array)

        # Check comparisons against scalar Timestamps
        ts = pd.Timestamp("2000-03-14 01:59")
        ts_tz = pd.Timestamp("2000-03-14 01:59", tz="Europe/Amsterdam")

        assert np.all(dr > ts)
        msg = "Cannot compare tz-naive and tz-aware"
        with pytest.raises(TypeError, match=msg):
            op(dr, ts_tz)

        assert np.all(dz > ts_tz)
        with pytest.raises(TypeError, match=msg):
            op(dz, ts)

        # GH#12601: Check comparison against Timestamps and DatetimeIndex
        with pytest.raises(TypeError, match=msg):
            op(ts, dz)

    @pytest.mark.parametrize(
        "op",
        [operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le],
    )
    @pytest.mark.parametrize(
        "other",
        [datetime(2016, 1, 1), Timestamp("2016-01-01"), np.datetime64("2016-01-01")],
    )
    # Bug in NumPy? https://github.com/numpy/numpy/issues/13841
    # Raising in __eq__ will fallback to NumPy, which warns, fails,
    # then re-raises the original exception. So we just need to ignore.
    @pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
    @pytest.mark.filterwarnings("ignore:Converting timezone-aware:FutureWarning")
    def test_scalar_comparison_tzawareness(
        self, op, other, tz_aware_fixture, box_with_array
    ):
        tz = tz_aware_fixture
        dti = pd.date_range("2016-01-01", periods=2, tz=tz)

        dtarr = tm.box_expected(dti, box_with_array)
        msg = "Cannot compare tz-naive and tz-aware"
        with pytest.raises(TypeError, match=msg):
            op(dtarr, other)
        with pytest.raises(TypeError, match=msg):
            op(other, dtarr)

    @pytest.mark.parametrize(
        "op",
        [operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le],
    )
    def test_nat_comparison_tzawareness(self, op):
        # GH#19276
        # tzaware DatetimeIndex should not raise when compared to NaT
        dti = pd.DatetimeIndex(
            ["2014-01-01", pd.NaT, "2014-03-01", pd.NaT, "2014-05-01", "2014-07-01"]
        )
        expected = np.array([op == operator.ne] * len(dti))
        result = op(dti, pd.NaT)
        tm.assert_numpy_array_equal(result, expected)

        result = op(dti.tz_localize("US/Pacific"), pd.NaT)
        tm.assert_numpy_array_equal(result, expected)

    def test_dti_cmp_str(self, tz_naive_fixture):
        # GH#22074
        # regardless of tz, we expect these comparisons are valid
        tz = tz_naive_fixture
        rng = date_range("1/1/2000", periods=10, tz=tz)
        other = "1/1/2000"

        result = rng == other
        expected = np.array([True] + [False] * 9)
        tm.assert_numpy_array_equal(result, expected)

        result = rng != other
        expected = np.array([False] + [True] * 9)
        tm.assert_numpy_array_equal(result, expected)

        result = rng < other
        expected = np.array([False] * 10)
        tm.assert_numpy_array_equal(result, expected)

        result = rng <= other
        expected = np.array([True] + [False] * 9)
        tm.assert_numpy_array_equal(result, expected)

        result = rng > other
        expected = np.array([False] + [True] * 9)
        tm.assert_numpy_array_equal(result, expected)

        result = rng >= other
        expected = np.array([True] * 10)
        tm.assert_numpy_array_equal(result, expected)

    def test_dti_cmp_list(self):
        rng = date_range("1/1/2000", periods=10)

        result = rng == list(rng)
        expected = rng == rng
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize(
        "other",
        [
            pd.timedelta_range("1D", periods=10),
            pd.timedelta_range("1D", periods=10).to_series(),
            pd.timedelta_range("1D", periods=10).asi8.view("m8[ns]"),
        ],
        ids=lambda x: type(x).__name__,
    )
    def test_dti_cmp_tdi_tzawareness(self, other):
        # GH#22074
        # reversion test that we _don't_ call _assert_tzawareness_compat
        # when comparing against TimedeltaIndex
        dti = date_range("2000-01-01", periods=10, tz="Asia/Tokyo")

        result = dti == other
        expected = np.array([False] * 10)
        tm.assert_numpy_array_equal(result, expected)

        result = dti != other
        expected = np.array([True] * 10)
        tm.assert_numpy_array_equal(result, expected)
        msg = "Invalid comparison between"
        with pytest.raises(TypeError, match=msg):
            dti < other
        with pytest.raises(TypeError, match=msg):
            dti <= other
        with pytest.raises(TypeError, match=msg):
            dti > other
        with pytest.raises(TypeError, match=msg):
            dti >= other

    def test_dti_cmp_object_dtype(self):
        # GH#22074
        dti = date_range("2000-01-01", periods=10, tz="Asia/Tokyo")

        other = dti.astype("O")

        result = dti == other
        expected = np.array([True] * 10)
        tm.assert_numpy_array_equal(result, expected)

        other = dti.tz_localize(None)
        msg = "Cannot compare tz-naive and tz-aware"
        with pytest.raises(TypeError, match=msg):
            # tzawareness failure
            dti != other

        other = np.array(list(dti[:5]) + [Timedelta(days=1)] * 5)
        result = dti == other
        expected = np.array([True] * 5 + [False] * 5)
        tm.assert_numpy_array_equal(result, expected)
        msg = ">=' not supported between instances of 'Timestamp' and 'Timedelta'"
        with pytest.raises(TypeError, match=msg):
            dti >= other


# ------------------------------------------------------------------
# Arithmetic


class TestDatetime64Arithmetic:
    # This class is intended for "finished" tests that are fully parametrized
    #  over DataFrame/Series/Index/DatetimeArray

    # -------------------------------------------------------------
    # Addition/Subtraction of timedelta-like

    def test_dt64arr_add_timedeltalike_scalar(
        self, tz_naive_fixture, two_hours, box_with_array
    ):
        # GH#22005, GH#22163 check DataFrame doesn't raise TypeError
        tz = tz_naive_fixture

        rng = pd.date_range("2000-01-01", "2000-02-01", tz=tz)
        expected = pd.date_range("2000-01-01 02:00", "2000-02-01 02:00", tz=tz)

        rng = tm.box_expected(rng, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        result = rng + two_hours
        tm.assert_equal(result, expected)

    def test_dt64arr_iadd_timedeltalike_scalar(
        self, tz_naive_fixture, two_hours, box_with_array
    ):
        tz = tz_naive_fixture

        rng = pd.date_range("2000-01-01", "2000-02-01", tz=tz)
        expected = pd.date_range("2000-01-01 02:00", "2000-02-01 02:00", tz=tz)

        rng = tm.box_expected(rng, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        rng += two_hours
        tm.assert_equal(rng, expected)

    def test_dt64arr_sub_timedeltalike_scalar(
        self, tz_naive_fixture, two_hours, box_with_array
    ):
        tz = tz_naive_fixture

        rng = pd.date_range("2000-01-01", "2000-02-01", tz=tz)
        expected = pd.date_range("1999-12-31 22:00", "2000-01-31 22:00", tz=tz)

        rng = tm.box_expected(rng, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        result = rng - two_hours
        tm.assert_equal(result, expected)

    def test_dt64arr_isub_timedeltalike_scalar(
        self, tz_naive_fixture, two_hours, box_with_array
    ):
        tz = tz_naive_fixture

        rng = pd.date_range("2000-01-01", "2000-02-01", tz=tz)
        expected = pd.date_range("1999-12-31 22:00", "2000-01-31 22:00", tz=tz)

        rng = tm.box_expected(rng, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        rng -= two_hours
        tm.assert_equal(rng, expected)

    # TODO: redundant with test_dt64arr_add_timedeltalike_scalar
    def test_dt64arr_add_td64_scalar(self, box_with_array):
        # scalar timedeltas/np.timedelta64 objects
        # operate with np.timedelta64 correctly
        ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])

        expected = Series(
            [Timestamp("20130101 9:01:01"), Timestamp("20130101 9:02:01")]
        )

        dtarr = tm.box_expected(ser, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        result = dtarr + np.timedelta64(1, "s")
        tm.assert_equal(result, expected)
        result = np.timedelta64(1, "s") + dtarr
        tm.assert_equal(result, expected)

        expected = Series(
            [Timestamp("20130101 9:01:00.005"), Timestamp("20130101 9:02:00.005")]
        )
        expected = tm.box_expected(expected, box_with_array)

        result = dtarr + np.timedelta64(5, "ms")
        tm.assert_equal(result, expected)
        result = np.timedelta64(5, "ms") + dtarr
        tm.assert_equal(result, expected)

    def test_dt64arr_add_sub_td64_nat(self, box_with_array, tz_naive_fixture):
        # GH#23320 special handling for timedelta64("NaT")
        tz = tz_naive_fixture

        dti = pd.date_range("1994-04-01", periods=9, tz=tz, freq="QS")
        other = np.timedelta64("NaT")
        expected = pd.DatetimeIndex(["NaT"] * 9, tz=tz)

        obj = tm.box_expected(dti, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        result = obj + other
        tm.assert_equal(result, expected)
        result = other + obj
        tm.assert_equal(result, expected)
        result = obj - other
        tm.assert_equal(result, expected)
        msg = "cannot subtract"
        with pytest.raises(TypeError, match=msg):
            other - obj

    def test_dt64arr_add_sub_td64ndarray(self, tz_naive_fixture, box_with_array):

        tz = tz_naive_fixture
        dti = pd.date_range("2016-01-01", periods=3, tz=tz)
        tdi = pd.TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"])
        tdarr = tdi.values

        expected = pd.date_range("2015-12-31", "2016-01-02", periods=3, tz=tz)

        dtarr = tm.box_expected(dti, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        result = dtarr + tdarr
        tm.assert_equal(result, expected)
        result = tdarr + dtarr
        tm.assert_equal(result, expected)

        expected = pd.date_range("2016-01-02", "2016-01-04", periods=3, tz=tz)
        expected = tm.box_expected(expected, box_with_array)

        result = dtarr - tdarr
        tm.assert_equal(result, expected)
        msg = "cannot subtract|(bad|unsupported) operand type for unary"
        with pytest.raises(TypeError, match=msg):
            tdarr - dtarr

    # -----------------------------------------------------------------
    # Subtraction of datetime-like scalars

    @pytest.mark.parametrize(
        "ts",
        [
            pd.Timestamp("2013-01-01"),
            pd.Timestamp("2013-01-01").to_pydatetime(),
            pd.Timestamp("2013-01-01").to_datetime64(),
        ],
    )
    def test_dt64arr_sub_dtscalar(self, box_with_array, ts):
        # GH#8554, GH#22163 DataFrame op should _not_ return dt64 dtype
        idx = pd.date_range("2013-01-01", periods=3)._with_freq(None)
        idx = tm.box_expected(idx, box_with_array)

        expected = pd.TimedeltaIndex(["0 Days", "1 Day", "2 Days"])
        expected = tm.box_expected(expected, box_with_array)

        result = idx - ts
        tm.assert_equal(result, expected)

    def test_dt64arr_sub_datetime64_not_ns(self, box_with_array):
        # GH#7996, GH#22163 ensure non-nano datetime64 is converted to nano
        #  for DataFrame operation
        dt64 = np.datetime64("2013-01-01")
        assert dt64.dtype == "datetime64[D]"

        dti = pd.date_range("20130101", periods=3)._with_freq(None)
        dtarr = tm.box_expected(dti, box_with_array)

        expected = pd.TimedeltaIndex(["0 Days", "1 Day", "2 Days"])
        expected = tm.box_expected(expected, box_with_array)

        result = dtarr - dt64
        tm.assert_equal(result, expected)

        result = dt64 - dtarr
        tm.assert_equal(result, -expected)

    def test_dt64arr_sub_timestamp(self, box_with_array):
        ser = pd.date_range("2014-03-17", periods=2, freq="D", tz="US/Eastern")
        ser = ser._with_freq(None)
        ts = ser[0]

        ser = tm.box_expected(ser, box_with_array)

        delta_series = pd.Series([np.timedelta64(0, "D"), np.timedelta64(1, "D")])
        expected = tm.box_expected(delta_series, box_with_array)

        tm.assert_equal(ser - ts, expected)
        tm.assert_equal(ts - ser, -expected)

    def test_dt64arr_sub_NaT(self, box_with_array):
        # GH#18808
        dti = pd.DatetimeIndex([pd.NaT, pd.Timestamp("19900315")])
        ser = tm.box_expected(dti, box_with_array)

        result = ser - pd.NaT
        expected = pd.Series([pd.NaT, pd.NaT], dtype="timedelta64[ns]")
        expected = tm.box_expected(expected, box_with_array)
        tm.assert_equal(result, expected)

        dti_tz = dti.tz_localize("Asia/Tokyo")
        ser_tz = tm.box_expected(dti_tz, box_with_array)

        result = ser_tz - pd.NaT
        expected = pd.Series([pd.NaT, pd.NaT], dtype="timedelta64[ns]")
        expected = tm.box_expected(expected, box_with_array)
        tm.assert_equal(result, expected)

    # -------------------------------------------------------------
    # Subtraction of datetime-like array-like

    def test_dt64arr_sub_dt64object_array(self, box_with_array, tz_naive_fixture):
        dti = pd.date_range("2016-01-01", periods=3, tz=tz_naive_fixture)
        expected = dti - dti

        obj = tm.box_expected(dti, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        warn = None
        if box_with_array is not pd.DataFrame or tz_naive_fixture is None:
            warn = PerformanceWarning
        with tm.assert_produces_warning(warn):
            result = obj - obj.astype(object)
        tm.assert_equal(result, expected)

    def test_dt64arr_naive_sub_dt64ndarray(self, box_with_array):
        dti = pd.date_range("2016-01-01", periods=3, tz=None)
        dt64vals = dti.values

        dtarr = tm.box_expected(dti, box_with_array)

        expected = dtarr - dtarr
        result = dtarr - dt64vals
        tm.assert_equal(result, expected)
        result = dt64vals - dtarr
        tm.assert_equal(result, expected)

    def test_dt64arr_aware_sub_dt64ndarray_raises(
        self, tz_aware_fixture, box_with_array
    ):

        tz = tz_aware_fixture
        dti = pd.date_range("2016-01-01", periods=3, tz=tz)
        dt64vals = dti.values

        dtarr = tm.box_expected(dti, box_with_array)
        msg = "subtraction must have the same timezones or"
        with pytest.raises(TypeError, match=msg):
            dtarr - dt64vals
        with pytest.raises(TypeError, match=msg):
            dt64vals - dtarr

    # -------------------------------------------------------------
    # Addition of datetime-like others (invalid)

    def test_dt64arr_add_dt64ndarray_raises(self, tz_naive_fixture, box_with_array):

        tz = tz_naive_fixture
        dti = pd.date_range("2016-01-01", periods=3, tz=tz)
        dt64vals = dti.values

        dtarr = tm.box_expected(dti, box_with_array)
        msg = "cannot add"
        with pytest.raises(TypeError, match=msg):
            dtarr + dt64vals
        with pytest.raises(TypeError, match=msg):
            dt64vals + dtarr

    def test_dt64arr_add_timestamp_raises(self, box_with_array):
        # GH#22163 ensure DataFrame doesn't cast Timestamp to i8
        idx = DatetimeIndex(["2011-01-01", "2011-01-02"])
        idx = tm.box_expected(idx, box_with_array)
        msg = "cannot add"
        with pytest.raises(TypeError, match=msg):
            idx + Timestamp("2011-01-01")
        with pytest.raises(TypeError, match=msg):
            Timestamp("2011-01-01") + idx

    # -------------------------------------------------------------
    # Other Invalid Addition/Subtraction

    @pytest.mark.parametrize(
        "other",
        [
            3.14,
            np.array([2.0, 3.0]),
            # GH#13078 datetime +/- Period is invalid
            pd.Period("2011-01-01", freq="D"),
            # https://github.com/pandas-dev/pandas/issues/10329
            time(1, 2, 3),
        ],
    )
    @pytest.mark.parametrize("dti_freq", [None, "D"])
    def test_dt64arr_add_sub_invalid(self, dti_freq, other, box_with_array):
        dti = DatetimeIndex(["2011-01-01", "2011-01-02"], freq=dti_freq)
        dtarr = tm.box_expected(dti, box_with_array)
        msg = "|".join(
            [
                "unsupported operand type",
                "cannot (add|subtract)",
                "cannot use operands with types",
                "ufunc '?(add|subtract)'? cannot use operands with types",
                "Concatenation operation is not implemented for NumPy arrays",
            ]
        )
        assert_invalid_addsub_type(dtarr, other, msg)

    @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "H"])
    @pytest.mark.parametrize("dti_freq", [None, "D"])
    def test_dt64arr_add_sub_parr(
        self, dti_freq, pi_freq, box_with_array, box_with_array2
    ):
        # GH#20049 subtracting PeriodIndex should raise TypeError
        dti = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], freq=dti_freq)
        pi = dti.to_period(pi_freq)

        dtarr = tm.box_expected(dti, box_with_array)
        parr = tm.box_expected(pi, box_with_array2)
        msg = "|".join(
            [
                "cannot (add|subtract)",
                "unsupported operand",
                "descriptor.*requires",
                "ufunc.*cannot use operands",
            ]
        )
        assert_invalid_addsub_type(dtarr, parr, msg)

    def test_dt64arr_addsub_time_objects_raises(self, box_with_array, tz_naive_fixture):
        # https://github.com/pandas-dev/pandas/issues/10329

        tz = tz_naive_fixture

        obj1 = pd.date_range("2012-01-01", periods=3, tz=tz)
        obj2 = [time(i, i, i) for i in range(3)]

        obj1 = tm.box_expected(obj1, box_with_array)
        obj2 = tm.box_expected(obj2, box_with_array)

        with warnings.catch_warnings(record=True):
            # pandas.errors.PerformanceWarning: Non-vectorized DateOffset being
            # applied to Series or DatetimeIndex
            # we aren't testing that here, so ignore.
            warnings.simplefilter("ignore", PerformanceWarning)

            # If `x + y` raises, then `y + x` should raise here as well

            msg = (
                r"unsupported operand type\(s\) for -: "
                "'(Timestamp|DatetimeArray)' and 'datetime.time'"
            )
            with pytest.raises(TypeError, match=msg):
                obj1 - obj2

            msg = "|".join(
                [
                    "cannot subtract DatetimeArray from ndarray",
                    "ufunc (subtract|'subtract') cannot use operands with types "
                    r"dtype\('O'\) and dtype\('<M8\[ns\]'\)",
                ]
            )
            with pytest.raises(TypeError, match=msg):
                obj2 - obj1

            msg = (
                r"unsupported operand type\(s\) for \+: "
                "'(Timestamp|DatetimeArray)' and 'datetime.time'"
            )
            with pytest.raises(TypeError, match=msg):
                obj1 + obj2

            msg = "|".join(
                [
                    r"unsupported operand type\(s\) for \+: "
                    "'(Timestamp|DatetimeArray)' and 'datetime.time'",
                    "ufunc (add|'add') cannot use operands with types "
                    r"dtype\('O'\) and dtype\('<M8\[ns\]'\)",
                ]
            )
            with pytest.raises(TypeError, match=msg):
                obj2 + obj1


class TestDatetime64DateOffsetArithmetic:

    # -------------------------------------------------------------
    # Tick DateOffsets

    # TODO: parametrize over timezone?
    def test_dt64arr_series_add_tick_DateOffset(self, box_with_array):
        # GH#4532
        # operate with pd.offsets
        ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])
        expected = Series(
            [Timestamp("20130101 9:01:05"), Timestamp("20130101 9:02:05")]
        )

        ser = tm.box_expected(ser, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        result = ser + pd.offsets.Second(5)
        tm.assert_equal(result, expected)

        result2 = pd.offsets.Second(5) + ser
        tm.assert_equal(result2, expected)

    def test_dt64arr_series_sub_tick_DateOffset(self, box_with_array):
        # GH#4532
        # operate with pd.offsets
        ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])
        expected = Series(
            [Timestamp("20130101 9:00:55"), Timestamp("20130101 9:01:55")]
        )

        ser = tm.box_expected(ser, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        result = ser - pd.offsets.Second(5)
        tm.assert_equal(result, expected)

        result2 = -pd.offsets.Second(5) + ser
        tm.assert_equal(result2, expected)
        msg = "(bad|unsupported) operand type for unary"
        with pytest.raises(TypeError, match=msg):
            pd.offsets.Second(5) - ser

    @pytest.mark.parametrize(
        "cls_name", ["Day", "Hour", "Minute", "Second", "Milli", "Micro", "Nano"]
    )
    def test_dt64arr_add_sub_tick_DateOffset_smoke(self, cls_name, box_with_array):
        # GH#4532
        # smoke tests for valid DateOffsets
        ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])
        ser = tm.box_expected(ser, box_with_array)

        offset_cls = getattr(pd.offsets, cls_name)
        ser + offset_cls(5)
        offset_cls(5) + ser
        ser - offset_cls(5)

    def test_dti_add_tick_tzaware(self, tz_aware_fixture, box_with_array):
        # GH#21610, GH#22163 ensure DataFrame doesn't return object-dtype
        tz = tz_aware_fixture
        if tz == "US/Pacific":
            dates = date_range("2012-11-01", periods=3, tz=tz)
            offset = dates + pd.offsets.Hour(5)
            assert dates[0] + pd.offsets.Hour(5) == offset[0]

        dates = date_range("2010-11-01 00:00", periods=3, tz=tz, freq="H")
        expected = DatetimeIndex(
            ["2010-11-01 05:00", "2010-11-01 06:00", "2010-11-01 07:00"],
            freq="H",
            tz=tz,
        )

        dates = tm.box_expected(dates, box_with_array)
        expected = tm.box_expected(expected, box_with_array)

        # TODO: parametrize over the scalar being added?  radd?  sub?
        offset = dates + pd.offsets.Hour(5)
        tm.assert_equal(offset, expected)
        offset = dates + np.timedelta64(5, "h")
        tm.assert_equal(offset, expected)
        offset = dates + timedelta(hours=5)
        tm.assert_equal(offset, expected)

    # -------------------------------------------------------------
    # RelativeDelta DateOffsets

    def test_dt64arr_add_sub_relativedelta_offsets(self, box_with_array):
        # GH#10699
        vec = DatetimeIndex(
            [
                Timestamp("2000-01-05 00:15:00"),
                Timestamp("2000-01-31 00:23:00"),
                Timestamp("2000-01-01"),
                Timestamp("2000-03-31"),
                Timestamp("2000-02-29"),
                Timestamp("2000-12-31"),
                Timestamp("2000-05-15"),
                Timestamp("2001-06-15"),
            ]
        )
        vec = tm.box_expected(vec, box_with_array)
        vec_items = vec.squeeze() if box_with_array is pd.DataFrame else vec

        # DateOffset relativedelta fastpath
        relative_kwargs = [
            ("years", 2),
            ("months", 5),
            ("days", 3),
            ("hours", 5),
            ("minutes", 10),
            ("seconds", 2),
            ("microseconds", 5),
        ]
        for i, kwd in enumerate(relative_kwargs):
            off = pd.DateOffset(**dict([kwd]))

            expected = DatetimeIndex([x + off for x in vec_items])
            expected = tm.box_expected(expected, box_with_array)
            tm.assert_equal(expected, vec + off)

            expected = DatetimeIndex([x - off for x in vec_items])
            expected = tm.box_expected(expected, box_with_array)
            tm.assert_equal(expected, vec - off)

            off = pd.DateOffset(**dict(relative_kwargs[: i + 1]))

            expected = DatetimeIndex([x + off for x in vec_items])
            expected = tm.box_expected(expected, box_with_array)
            tm.assert_equal(expected, vec + off)

            expected = DatetimeIndex([x - off for x in vec_items])
            expected = tm.box_expected(expected, box_with_array)
            tm.assert_equal(expected, vec - off)
            msg = "(bad|unsupported) operand type for unary"
            with pytest.raises(TypeError, match=msg):
                off - vec

    # -------------------------------------------------------------
    # Non-Tick, Non-RelativeDelta DateOffsets

    # TODO: redundant with test_dt64arr_add_sub_DateOffset?  that includes
    #  tz-aware cases which this does not
    @pytest.mark.parametrize(
        "cls_and_kwargs",
        [
            "YearBegin",
            ("YearBegin", {"month": 5}),
            "YearEnd",
            ("YearEnd", {"month": 5}),
            "MonthBegin",
            "MonthEnd",
            "SemiMonthEnd",
            "SemiMonthBegin",
            "Week",
            ("Week", {"weekday": 3}),
            "Week",
            ("Week", {"weekday": 6}),
            "BusinessDay",
            "BDay",
            "QuarterEnd",
            "QuarterBegin",
            "CustomBusinessDay",
            "CDay",
            "CBMonthEnd",
            "CBMonthBegin",
            "BMonthBegin",
            "BMonthEnd",
            "BusinessHour",
            "BYearBegin",
            "BYearEnd",
            "BQuarterBegin",
            ("LastWeekOfMonth", {"weekday": 2}),
            (
                "FY5253Quarter",
                {
                    "qtr_with_extra_week": 1,
                    "startingMonth": 1,
                    "weekday": 2,
                    "variation": "nearest",
                },
            ),
            ("FY5253", {"weekday": 0, "startingMonth": 2, "variation": "nearest"}),
            ("WeekOfMonth", {"weekday": 2, "week": 2}),
            "Easter",
            ("DateOffset", {"day": 4}),
            ("DateOffset", {"month": 5}),
        ],
    )
    @pytest.mark.parametrize("normalize", [True, False])
    @pytest.mark.parametrize("n", [0, 5])
    def test_dt64arr_add_sub_DateOffsets(
        self, box_with_array, n, normalize, cls_and_kwargs
    ):
        # GH#10699
        # assert vectorized operation matches pointwise operations

        if isinstance(cls_and_kwargs, tuple):
            # If cls_name param is a tuple, then 2nd entry is kwargs for
            # the offset constructor
            cls_name, kwargs = cls_and_kwargs
        else:
            cls_name = cls_and_kwargs
            kwargs = {}

        if n == 0 and cls_name in [
            "WeekOfMonth",
            "LastWeekOfMonth",
            "FY5253Quarter",
            "FY5253",
        ]:
            # passing n = 0 is invalid for these offset classes
            return

        vec = DatetimeIndex(
            [
                Timestamp("2000-01-05 00:15:00"),
                Timestamp("2000-01-31 00:23:00"),
                Timestamp("2000-01-01"),
                Timestamp("2000-03-31"),
                Timestamp("2000-02-29"),
                Timestamp("2000-12-31"),
                Timestamp("2000-05-15"),
                Timestamp("2001-06-15"),
            ]
        )
        vec = tm.box_expected(vec, box_with_array)
        vec_items = vec.squeeze() if box_with_array is pd.DataFrame else vec

        offset_cls = getattr(pd.offsets, cls_name)

        with warnings.catch_warnings(record=True):
            # pandas.errors.PerformanceWarning: Non-vectorized DateOffset being
            # applied to Series or DatetimeIndex
            # we aren't testing that here, so ignore.
            warnings.simplefilter("ignore", PerformanceWarning)

            offset = offset_cls(n, normalize=normalize, **kwargs)

            expected = DatetimeIndex([x + offset for x in vec_items])
            expected = tm.box_expected(expected, box_with_array)
            tm.assert_equal(expected, vec + offset)

            expected = DatetimeIndex([x - offset for x in vec_items])
            expected = tm.box_expected(expected, box_with_array)
            tm.assert_equal(expected, vec - offset)

            expected = DatetimeIndex([offset + x for x in vec_items])
            expected = tm.box_expected(expected, box_with_array)
            tm.assert_equal(expected, offset + vec)
            msg = "(bad|unsupported) operand type for unary"
            with pytest.raises(TypeError, match=msg):
                offset - vec

    def test_dt64arr_add_sub_DateOffset(self, box_with_array):
        # GH#10699
        s = date_range("2000-01-01", "2000-01-31", name="a")
        s = tm.box_expected(s, box_with_array)
        result = s + pd.DateOffset(years=1)
        result2 = pd.DateOffset(years=1) + s
        exp = date_range("2001-01-01", "2001-01-31", name="a")._with_freq(None)
        exp = tm.box_expected(exp, box_with_array)
        tm.assert_equal(result, exp)
        tm.assert_equal(result2, exp)

        result = s - pd.DateOffset(years=1)
        exp = date_range("1999-01-01", "1999-01-31", name="a")._with_freq(None)
        exp = tm.box_expected(exp, box_with_array)
        tm.assert_equal(result, exp)

        s = DatetimeIndex(
            [
                Timestamp("2000-01-15 00:15:00", tz="US/Central"),
                Timestamp("2000-02-15", tz="US/Central"),
            ],
            name="a",
        )
        s = tm.box_expected(s, box_with_array)
        result = s + pd.offsets.Day()
        result2 = pd.offsets.Day() + s
        exp = DatetimeIndex(
            [
                Timestamp("2000-01-16 00:15:00", tz="US/Central"),
                Timestamp("2000-02-16", tz="US/Central"),
            ],
            name="a",
        )
        exp = tm.box_expected(exp, box_with_array)
        tm.assert_equal(result, exp)
        tm.assert_equal(result2, exp)

        s = DatetimeIndex(
            [
                Timestamp("2000-01-15 00:15:00", tz="US/Central"),
                Timestamp("2000-02-15", tz="US/Central"),
            ],
            name="a",
        )
        s = tm.box_expected(s, box_with_array)
        result = s + pd.offsets.MonthEnd()
        result2 = pd.offsets.MonthEnd() + s
        exp = DatetimeIndex(
            [
                Timestamp("2000-01-31 00:15:00", tz="US/Central"),
                Timestamp("2000-02-29", tz="US/Central"),
            ],
            name="a",
        )
        exp = tm.box_expected(exp, box_with_array)
        tm.assert_equal(result, exp)
        tm.assert_equal(result2, exp)

    @pytest.mark.parametrize(
        "other",
        [
            np.array([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)]),
            np.array([pd.offsets.DateOffset(years=1), pd.offsets.MonthEnd()]),
            np.array(  # matching offsets
                [pd.offsets.DateOffset(years=1), pd.offsets.DateOffset(years=1)]
            ),
        ],
    )
    @pytest.mark.parametrize("op", [operator.add, roperator.radd, operator.sub])
    @pytest.mark.parametrize("box_other", [True, False])
    def test_dt64arr_add_sub_offset_array(
        self, tz_naive_fixture, box_with_array, box_other, op, other
    ):
        # GH#18849
        # GH#10699 array of offsets

        tz = tz_naive_fixture
        dti = pd.date_range("2017-01-01", periods=2, tz=tz)
        dtarr = tm.box_expected(dti, box_with_array)

        other = np.array([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)])
        expected = DatetimeIndex([op(dti[n], other[n]) for n in range(len(dti))])
        expected = tm.box_expected(expected, box_with_array)

        if box_other:
            other = tm.box_expected(other, box_with_array)

        warn = PerformanceWarning
        if box_with_array is pd.DataFrame and tz is not None:
            warn = None
        with tm.assert_produces_warning(warn):
            res = op(dtarr, other)

        tm.assert_equal(res, expected)

    @pytest.mark.parametrize(
        "op, offset, exp, exp_freq",
        [
            (
                "__add__",
                pd.DateOffset(months=3, days=10),
                [
                    Timestamp("2014-04-11"),
                    Timestamp("2015-04-11"),
                    Timestamp("2016-04-11"),
                    Timestamp("2017-04-11"),
                ],
                None,
            ),
            (
                "__add__",
                pd.DateOffset(months=3),
                [
                    Timestamp("2014-04-01"),
                    Timestamp("2015-04-01"),
                    Timestamp("2016-04-01"),
                    Timestamp("2017-04-01"),
                ],
                "AS-APR",
            ),
            (
                "__sub__",
                pd.DateOffset(months=3, days=10),
                [
                    Timestamp("2013-09-21"),
                    Timestamp("2014-09-21"),
                    Timestamp("2015-09-21"),
                    Timestamp("2016-09-21"),
                ],
                None,
            ),
            (
                "__sub__",
                pd.DateOffset(months=3),
                [
                    Timestamp("2013-10-01"),
                    Timestamp("2014-10-01"),
                    Timestamp("2015-10-01"),
                    Timestamp("2016-10-01"),
                ],
                "AS-OCT",
            ),
        ],
    )
    def test_dti_add_sub_nonzero_mth_offset(
        self, op, offset, exp, exp_freq, tz_aware_fixture, box_with_array
    ):
        # GH 26258
        tz = tz_aware_fixture
        date = date_range(start="01 Jan 2014", end="01 Jan 2017", freq="AS", tz=tz)
        date = tm.box_expected(date, box_with_array, False)
        mth = getattr(date, op)
        result = mth(offset)

        expected = pd.DatetimeIndex(exp, tz=tz)
        expected = tm.box_expected(expected, box_with_array, False)
        tm.assert_equal(result, expected)


class TestDatetime64OverflowHandling:
    # TODO: box + de-duplicate

    def test_dt64_overflow_masking(self, box_with_array):
        # GH#25317
        left = Series([Timestamp("1969-12-31")])
        right = Series([NaT])

        left = tm.box_expected(left, box_with_array)
        right = tm.box_expected(right, box_with_array)

        expected = TimedeltaIndex([NaT])
        expected = tm.box_expected(expected, box_with_array)

        result = left - right
        tm.assert_equal(result, expected)

    def test_dt64_series_arith_overflow(self):
        # GH#12534, fixed by GH#19024
        dt = pd.Timestamp("1700-01-31")
        td = pd.Timedelta("20000 Days")
        dti = pd.date_range("1949-09-30", freq="100Y", periods=4)
        ser = pd.Series(dti)
        msg = "Overflow in int64 addition"
        with pytest.raises(OverflowError, match=msg):
            ser - dt
        with pytest.raises(OverflowError, match=msg):
            dt - ser
        with pytest.raises(OverflowError, match=msg):
            ser + td
        with pytest.raises(OverflowError, match=msg):
            td + ser

        ser.iloc[-1] = pd.NaT
        expected = pd.Series(
            ["2004-10-03", "2104-10-04", "2204-10-04", "NaT"], dtype="datetime64[ns]"
        )
        res = ser + td
        tm.assert_series_equal(res, expected)
        res = td + ser
        tm.assert_series_equal(res, expected)

        ser.iloc[1:] = pd.NaT
        expected = pd.Series(
            ["91279 Days", "NaT", "NaT", "NaT"], dtype="timedelta64[ns]"
        )
        res = ser - dt
        tm.assert_series_equal(res, expected)
        res = dt - ser
        tm.assert_series_equal(res, -expected)

    def test_datetimeindex_sub_timestamp_overflow(self):
        dtimax = pd.to_datetime(["now", pd.Timestamp.max])
        dtimin = pd.to_datetime(["now", pd.Timestamp.min])

        tsneg = Timestamp("1950-01-01")
        ts_neg_variants = [
            tsneg,
            tsneg.to_pydatetime(),
            tsneg.to_datetime64().astype("datetime64[ns]"),
            tsneg.to_datetime64().astype("datetime64[D]"),
        ]

        tspos = Timestamp("1980-01-01")
        ts_pos_variants = [
            tspos,
            tspos.to_pydatetime(),
            tspos.to_datetime64().astype("datetime64[ns]"),
            tspos.to_datetime64().astype("datetime64[D]"),
        ]
        msg = "Overflow in int64 addition"
        for variant in ts_neg_variants:
            with pytest.raises(OverflowError, match=msg):
                dtimax - variant

        expected = pd.Timestamp.max.value - tspos.value
        for variant in ts_pos_variants:
            res = dtimax - variant
            assert res[1].value == expected

        expected = pd.Timestamp.min.value - tsneg.value
        for variant in ts_neg_variants:
            res = dtimin - variant
            assert res[1].value == expected

        for variant in ts_pos_variants:
            with pytest.raises(OverflowError, match=msg):
                dtimin - variant

    def test_datetimeindex_sub_datetimeindex_overflow(self):
        # GH#22492, GH#22508
        dtimax = pd.to_datetime(["now", pd.Timestamp.max])
        dtimin = pd.to_datetime(["now", pd.Timestamp.min])

        ts_neg = pd.to_datetime(["1950-01-01", "1950-01-01"])
        ts_pos = pd.to_datetime(["1980-01-01", "1980-01-01"])

        # General tests
        expected = pd.Timestamp.max.value - ts_pos[1].value
        result = dtimax - ts_pos
        assert result[1].value == expected

        expected = pd.Timestamp.min.value - ts_neg[1].value
        result = dtimin - ts_neg
        assert result[1].value == expected
        msg = "Overflow in int64 addition"
        with pytest.raises(OverflowError, match=msg):
            dtimax - ts_neg

        with pytest.raises(OverflowError, match=msg):
            dtimin - ts_pos

        # Edge cases
        tmin = pd.to_datetime([pd.Timestamp.min])
        t1 = tmin + pd.Timedelta.max + pd.Timedelta("1us")
        with pytest.raises(OverflowError, match=msg):
            t1 - tmin

        tmax = pd.to_datetime([pd.Timestamp.max])
        t2 = tmax + pd.Timedelta.min - pd.Timedelta("1us")
        with pytest.raises(OverflowError, match=msg):
            tmax - t2


class TestTimestampSeriesArithmetic:
    def test_empty_series_add_sub(self):
        # GH#13844
        a = Series(dtype="M8[ns]")
        b = Series(dtype="m8[ns]")
        tm.assert_series_equal(a, a + b)
        tm.assert_series_equal(a, a - b)
        tm.assert_series_equal(a, b + a)
        msg = "cannot subtract"
        with pytest.raises(TypeError, match=msg):
            b - a

    def test_operators_datetimelike(self):

        # ## timedelta64 ###
        td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
        td1.iloc[2] = np.nan

        # ## datetime64 ###
        dt1 = Series(
            [
                pd.Timestamp("20111230"),
                pd.Timestamp("20120101"),
                pd.Timestamp("20120103"),
            ]
        )
        dt1.iloc[2] = np.nan
        dt2 = Series(
            [
                pd.Timestamp("20111231"),
                pd.Timestamp("20120102"),
                pd.Timestamp("20120104"),
            ]
        )
        dt1 - dt2
        dt2 - dt1

        # datetime64 with timetimedelta
        dt1 + td1
        td1 + dt1
        dt1 - td1

        # timetimedelta with datetime64
        td1 + dt1
        dt1 + td1

    def test_dt64ser_sub_datetime_dtype(self):
        ts = Timestamp(datetime(1993, 1, 7, 13, 30, 00))
        dt = datetime(1993, 6, 22, 13, 30)
        ser = Series([ts])
        result = pd.to_timedelta(np.abs(ser - dt))
        assert result.dtype == "timedelta64[ns]"

    # -------------------------------------------------------------
    # TODO: This next block of tests came from tests.series.test_operators,
    # needs to be de-duplicated and parametrized over `box` classes

    def test_operators_datetimelike_invalid(self, all_arithmetic_operators):
        # these are all TypeEror ops
        op_str = all_arithmetic_operators

        def check(get_ser, test_ser):

            # check that we are getting a TypeError
            # with 'operate' (from core/ops.py) for the ops that are not
            # defined
            op = getattr(get_ser, op_str, None)
            # Previously, _validate_for_numeric_binop in core/indexes/base.py
            # did this for us.
            with pytest.raises(
                TypeError, match="operate|[cC]annot|unsupported operand"
            ):
                op(test_ser)

        # ## timedelta64 ###
        td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
        td1.iloc[2] = np.nan

        # ## datetime64 ###
        dt1 = Series(
            [Timestamp("20111230"), Timestamp("20120101"), Timestamp("20120103")]
        )
        dt1.iloc[2] = np.nan
        dt2 = Series(
            [Timestamp("20111231"), Timestamp("20120102"), Timestamp("20120104")]
        )
        if op_str not in ["__sub__", "__rsub__"]:
            check(dt1, dt2)

        # ## datetime64 with timetimedelta ###
        # TODO(jreback) __rsub__ should raise?
        if op_str not in ["__add__", "__radd__", "__sub__"]:
            check(dt1, td1)

        # 8260, 10763
        # datetime64 with tz
        tz = "US/Eastern"
        dt1 = Series(date_range("2000-01-01 09:00:00", periods=5, tz=tz), name="foo")
        dt2 = dt1.copy()
        dt2.iloc[2] = np.nan
        td1 = Series(pd.timedelta_range("1 days 1 min", periods=5, freq="H"))
        td2 = td1.copy()
        td2.iloc[1] = np.nan

        if op_str not in ["__add__", "__radd__", "__sub__", "__rsub__"]:
            check(dt2, td2)

    def test_sub_single_tz(self):
        # GH#12290
        s1 = Series([pd.Timestamp("2016-02-10", tz="America/Sao_Paulo")])
        s2 = Series([pd.Timestamp("2016-02-08", tz="America/Sao_Paulo")])
        result = s1 - s2
        expected = Series([Timedelta("2days")])
        tm.assert_series_equal(result, expected)
        result = s2 - s1
        expected = Series([Timedelta("-2days")])
        tm.assert_series_equal(result, expected)

    def test_dt64tz_series_sub_dtitz(self):
        # GH#19071 subtracting tzaware DatetimeIndex from tzaware Series
        # (with same tz) raises, fixed by #19024
        dti = pd.date_range("1999-09-30", periods=10, tz="US/Pacific")
        ser = pd.Series(dti)
        expected = pd.Series(pd.TimedeltaIndex(["0days"] * 10))

        res = dti - ser
        tm.assert_series_equal(res, expected)
        res = ser - dti
        tm.assert_series_equal(res, expected)

    def test_sub_datetime_compat(self):
        # see GH#14088
        s = Series([datetime(2016, 8, 23, 12, tzinfo=pytz.utc), pd.NaT])
        dt = datetime(2016, 8, 22, 12, tzinfo=pytz.utc)
        exp = Series([Timedelta("1 days"), pd.NaT])
        tm.assert_series_equal(s - dt, exp)
        tm.assert_series_equal(s - Timestamp(dt), exp)

    def test_dt64_series_add_mixed_tick_DateOffset(self):
        # GH#4532
        # operate with pd.offsets
        s = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")])

        result = s + pd.offsets.Milli(5)
        result2 = pd.offsets.Milli(5) + s
        expected = Series(
            [Timestamp("20130101 9:01:00.005"), Timestamp("20130101 9:02:00.005")]
        )
        tm.assert_series_equal(result, expected)
        tm.assert_series_equal(result2, expected)

        result = s + pd.offsets.Minute(5) + pd.offsets.Milli(5)
        expected = Series(
            [Timestamp("20130101 9:06:00.005"), Timestamp("20130101 9:07:00.005")]
        )
        tm.assert_series_equal(result, expected)

    def test_datetime64_ops_nat(self):
        # GH#11349
        datetime_series = Series([NaT, Timestamp("19900315")])
        nat_series_dtype_timestamp = Series([NaT, NaT], dtype="datetime64[ns]")
        single_nat_dtype_datetime = Series([NaT], dtype="datetime64[ns]")

        # subtraction
        tm.assert_series_equal(-NaT + datetime_series, nat_series_dtype_timestamp)
        msg = "Unary negative expects"
        with pytest.raises(TypeError, match=msg):
            -single_nat_dtype_datetime + datetime_series

        tm.assert_series_equal(
            -NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp
        )
        with pytest.raises(TypeError, match=msg):
            -single_nat_dtype_datetime + nat_series_dtype_timestamp

        # addition
        tm.assert_series_equal(
            nat_series_dtype_timestamp + NaT, nat_series_dtype_timestamp
        )
        tm.assert_series_equal(
            NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp
        )

        tm.assert_series_equal(
            nat_series_dtype_timestamp + NaT, nat_series_dtype_timestamp
        )
        tm.assert_series_equal(
            NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp
        )

    # -------------------------------------------------------------
    # Invalid Operations
    # TODO: this block also needs to be de-duplicated and parametrized

    @pytest.mark.parametrize(
        "dt64_series",
        [
            Series([Timestamp("19900315"), Timestamp("19900315")]),
            Series([pd.NaT, Timestamp("19900315")]),
            Series([pd.NaT, pd.NaT], dtype="datetime64[ns]"),
        ],
    )
    @pytest.mark.parametrize("one", [1, 1.0, np.array(1)])
    def test_dt64_mul_div_numeric_invalid(self, one, dt64_series):
        # multiplication
        msg = "cannot perform .* with this index type"
        with pytest.raises(TypeError, match=msg):
            dt64_series * one
        with pytest.raises(TypeError, match=msg):
            one * dt64_series

        # division
        with pytest.raises(TypeError, match=msg):
            dt64_series / one
        with pytest.raises(TypeError, match=msg):
            one / dt64_series

    # TODO: parametrize over box
    @pytest.mark.parametrize("op", ["__add__", "__radd__", "__sub__", "__rsub__"])
    def test_dt64_series_add_intlike(self, tz_naive_fixture, op):
        # GH#19123
        tz = tz_naive_fixture
        dti = pd.DatetimeIndex(["2016-01-02", "2016-02-03", "NaT"], tz=tz)
        ser = Series(dti)

        other = Series([20, 30, 40], dtype="uint8")

        method = getattr(ser, op)
        msg = "|".join(
            [
                "Addition/subtraction of integers and integer-arrays",
                "cannot subtract .* from ndarray",
            ]
        )
        with pytest.raises(TypeError, match=msg):
            method(1)
        with pytest.raises(TypeError, match=msg):
            method(other)
        with pytest.raises(TypeError, match=msg):
            method(np.array(other))
        with pytest.raises(TypeError, match=msg):
            method(pd.Index(other))

    # -------------------------------------------------------------
    # Timezone-Centric Tests

    def test_operators_datetimelike_with_timezones(self):
        tz = "US/Eastern"
        dt1 = Series(date_range("2000-01-01 09:00:00", periods=5, tz=tz), name="foo")
        dt2 = dt1.copy()
        dt2.iloc[2] = np.nan

        td1 = Series(pd.timedelta_range("1 days 1 min", periods=5, freq="H"))
        td2 = td1.copy()
        td2.iloc[1] = np.nan

        result = dt1 + td1[0]
        exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz)
        tm.assert_series_equal(result, exp)

        result = dt2 + td2[0]
        exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz)
        tm.assert_series_equal(result, exp)

        # odd numpy behavior with scalar timedeltas
        result = td1[0] + dt1
        exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz)
        tm.assert_series_equal(result, exp)

        result = td2[0] + dt2
        exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz)
        tm.assert_series_equal(result, exp)

        result = dt1 - td1[0]
        exp = (dt1.dt.tz_localize(None) - td1[0]).dt.tz_localize(tz)
        tm.assert_series_equal(result, exp)
        msg = "(bad|unsupported) operand type for unary"
        with pytest.raises(TypeError, match=msg):
            td1[0] - dt1

        result = dt2 - td2[0]
        exp = (dt2.dt.tz_localize(None) - td2[0]).dt.tz_localize(tz)
        tm.assert_series_equal(result, exp)
        with pytest.raises(TypeError, match=msg):
            td2[0] - dt2

        result = dt1 + td1
        exp = (dt1.dt.tz_localize(None) + td1).dt.tz_localize(tz)
        tm.assert_series_equal(result, exp)

        result = dt2 + td2
        exp = (dt2.dt.tz_localize(None) + td2).dt.tz_localize(tz)
        tm.assert_series_equal(result, exp)

        result = dt1 - td1
        exp = (dt1.dt.tz_localize(None) - td1).dt.tz_localize(tz)
        tm.assert_series_equal(result, exp)

        result = dt2 - td2
        exp = (dt2.dt.tz_localize(None) - td2).dt.tz_localize(tz)
        tm.assert_series_equal(result, exp)
        msg = "cannot (add|subtract)"
        with pytest.raises(TypeError, match=msg):
            td1 - dt1
        with pytest.raises(TypeError, match=msg):
            td2 - dt2


class TestDatetimeIndexArithmetic:

    # -------------------------------------------------------------
    # Binary operations DatetimeIndex and int

    def test_dti_addsub_int(self, tz_naive_fixture, one):
        # Variants of `one` for #19012
        tz = tz_naive_fixture
        rng = pd.date_range("2000-01-01 09:00", freq="H", periods=10, tz=tz)
        msg = "Addition/subtraction of integers"

        with pytest.raises(TypeError, match=msg):
            rng + one
        with pytest.raises(TypeError, match=msg):
            rng += one
        with pytest.raises(TypeError, match=msg):
            rng - one
        with pytest.raises(TypeError, match=msg):
            rng -= one

    # -------------------------------------------------------------
    # __add__/__sub__ with integer arrays

    @pytest.mark.parametrize("freq", ["H", "D"])
    @pytest.mark.parametrize("int_holder", [np.array, pd.Index])
    def test_dti_add_intarray_tick(self, int_holder, freq):
        # GH#19959
        dti = pd.date_range("2016-01-01", periods=2, freq=freq)
        other = int_holder([4, -1])

        msg = "Addition/subtraction of integers|cannot subtract DatetimeArray from"
        assert_invalid_addsub_type(dti, other, msg)

    @pytest.mark.parametrize("freq", ["W", "M", "MS", "Q"])
    @pytest.mark.parametrize("int_holder", [np.array, pd.Index])
    def test_dti_add_intarray_non_tick(self, int_holder, freq):
        # GH#19959
        dti = pd.date_range("2016-01-01", periods=2, freq=freq)
        other = int_holder([4, -1])

        msg = "Addition/subtraction of integers|cannot subtract DatetimeArray from"
        assert_invalid_addsub_type(dti, other, msg)

    @pytest.mark.parametrize("int_holder", [np.array, pd.Index])
    def test_dti_add_intarray_no_freq(self, int_holder):
        # GH#19959
        dti = pd.DatetimeIndex(["2016-01-01", "NaT", "2017-04-05 06:07:08"])
        other = int_holder([9, 4, -1])
        msg = "|".join(
            ["cannot subtract DatetimeArray from", "Addition/subtraction of integers"]
        )
        assert_invalid_addsub_type(dti, other, msg)

    # -------------------------------------------------------------
    # Binary operations DatetimeIndex and TimedeltaIndex/array

    def test_dti_add_tdi(self, tz_naive_fixture):
        # GH#17558
        tz = tz_naive_fixture
        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
        tdi = pd.timedelta_range("0 days", periods=10)
        expected = pd.date_range("2017-01-01", periods=10, tz=tz)
        expected = expected._with_freq(None)

        # add with TimdeltaIndex
        result = dti + tdi
        tm.assert_index_equal(result, expected)

        result = tdi + dti
        tm.assert_index_equal(result, expected)

        # add with timedelta64 array
        result = dti + tdi.values
        tm.assert_index_equal(result, expected)

        result = tdi.values + dti
        tm.assert_index_equal(result, expected)

    def test_dti_iadd_tdi(self, tz_naive_fixture):
        # GH#17558
        tz = tz_naive_fixture
        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
        tdi = pd.timedelta_range("0 days", periods=10)
        expected = pd.date_range("2017-01-01", periods=10, tz=tz)
        expected = expected._with_freq(None)

        # iadd with TimdeltaIndex
        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
        result += tdi
        tm.assert_index_equal(result, expected)

        result = pd.timedelta_range("0 days", periods=10)
        result += dti
        tm.assert_index_equal(result, expected)

        # iadd with timedelta64 array
        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
        result += tdi.values
        tm.assert_index_equal(result, expected)

        result = pd.timedelta_range("0 days", periods=10)
        result += dti
        tm.assert_index_equal(result, expected)

    def test_dti_sub_tdi(self, tz_naive_fixture):
        # GH#17558
        tz = tz_naive_fixture
        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
        tdi = pd.timedelta_range("0 days", periods=10)
        expected = pd.date_range("2017-01-01", periods=10, tz=tz, freq="-1D")
        expected = expected._with_freq(None)

        # sub with TimedeltaIndex
        result = dti - tdi
        tm.assert_index_equal(result, expected)

        msg = "cannot subtract .*TimedeltaArray"
        with pytest.raises(TypeError, match=msg):
            tdi - dti

        # sub with timedelta64 array
        result = dti - tdi.values
        tm.assert_index_equal(result, expected)

        msg = "cannot subtract DatetimeArray from"
        with pytest.raises(TypeError, match=msg):
            tdi.values - dti

    def test_dti_isub_tdi(self, tz_naive_fixture):
        # GH#17558
        tz = tz_naive_fixture
        dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
        tdi = pd.timedelta_range("0 days", periods=10)
        expected = pd.date_range("2017-01-01", periods=10, tz=tz, freq="-1D")
        expected = expected._with_freq(None)

        # isub with TimedeltaIndex
        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
        result -= tdi
        tm.assert_index_equal(result, expected)

        msg = "cannot subtract .* from a TimedeltaArray"
        with pytest.raises(TypeError, match=msg):
            tdi -= dti

        # isub with timedelta64 array
        result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10)
        result -= tdi.values
        tm.assert_index_equal(result, expected)

        msg = "|".join(
            [
                "cannot perform __neg__ with this index type:",
                "ufunc subtract cannot use operands with types",
                "cannot subtract DatetimeArray from",
            ]
        )
        with pytest.raises(TypeError, match=msg):
            tdi.values -= dti

    # -------------------------------------------------------------
    # Binary Operations DatetimeIndex and datetime-like
    # TODO: A couple other tests belong in this section.  Move them in
    # A PR where there isn't already a giant diff.

    @pytest.mark.parametrize(
        "addend",
        [
            datetime(2011, 1, 1),
            DatetimeIndex(["2011-01-01", "2011-01-02"]),
            DatetimeIndex(["2011-01-01", "2011-01-02"]).tz_localize("US/Eastern"),
            np.datetime64("2011-01-01"),
            Timestamp("2011-01-01"),
        ],
        ids=lambda x: type(x).__name__,
    )
    @pytest.mark.parametrize("tz", [None, "US/Eastern"])
    def test_add_datetimelike_and_dtarr(self, box_with_array, addend, tz):
        # GH#9631
        dti = DatetimeIndex(["2011-01-01", "2011-01-02"]).tz_localize(tz)
        dtarr = tm.box_expected(dti, box_with_array)
        msg = "cannot add DatetimeArray and"

        with pytest.raises(TypeError, match=msg):
            dtarr + addend
        with pytest.raises(TypeError, match=msg):
            addend + dtarr

    # -------------------------------------------------------------

    def test_dta_add_sub_index(self, tz_naive_fixture):
        # Check that DatetimeArray defers to Index classes
        dti = date_range("20130101", periods=3, tz=tz_naive_fixture)
        dta = dti.array
        result = dta - dti
        expected = dti - dti
        tm.assert_index_equal(result, expected)

        tdi = result
        result = dta + tdi
        expected = dti + tdi
        tm.assert_index_equal(result, expected)

        result = dta - tdi
        expected = dti - tdi
        tm.assert_index_equal(result, expected)

    def test_sub_dti_dti(self):
        # previously performed setop (deprecated in 0.16.0), now changed to
        # return subtraction -> TimeDeltaIndex (GH ...)

        dti = date_range("20130101", periods=3)
        dti_tz = date_range("20130101", periods=3).tz_localize("US/Eastern")
        dti_tz2 = date_range("20130101", periods=3).tz_localize("UTC")
        expected = TimedeltaIndex([0, 0, 0])

        result = dti - dti
        tm.assert_index_equal(result, expected)

        result = dti_tz - dti_tz
        tm.assert_index_equal(result, expected)
        msg = "DatetimeArray subtraction must have the same timezones or"
        with pytest.raises(TypeError, match=msg):
            dti_tz - dti

        with pytest.raises(TypeError, match=msg):
            dti - dti_tz

        with pytest.raises(TypeError, match=msg):
            dti_tz - dti_tz2

        # isub
        dti -= dti
        tm.assert_index_equal(dti, expected)

        # different length raises ValueError
        dti1 = date_range("20130101", periods=3)
        dti2 = date_range("20130101", periods=4)
        msg = "cannot add indices of unequal length"
        with pytest.raises(ValueError, match=msg):
            dti1 - dti2

        # NaN propagation
        dti1 = DatetimeIndex(["2012-01-01", np.nan, "2012-01-03"])
        dti2 = DatetimeIndex(["2012-01-02", "2012-01-03", np.nan])
        expected = TimedeltaIndex(["1 days", np.nan, np.nan])
        result = dti2 - dti1
        tm.assert_index_equal(result, expected)

    # -------------------------------------------------------------------
    # TODO: Most of this block is moved from series or frame tests, needs
    # cleanup, box-parametrization, and de-duplication

    @pytest.mark.parametrize("op", [operator.add, operator.sub])
    def test_timedelta64_equal_timedelta_supported_ops(self, op):
        ser = Series(
            [
                Timestamp("20130301"),
                Timestamp("20130228 23:00:00"),
                Timestamp("20130228 22:00:00"),
                Timestamp("20130228 21:00:00"),
            ]
        )

        intervals = ["D", "h", "m", "s", "us"]

        def timedelta64(*args):
            # see casting notes in NumPy gh-12927
            return np.sum(list(starmap(np.timedelta64, zip(args, intervals))))

        for d, h, m, s, us in product(*([range(2)] * 5)):
            nptd = timedelta64(d, h, m, s, us)
            pytd = timedelta(days=d, hours=h, minutes=m, seconds=s, microseconds=us)
            lhs = op(ser, nptd)
            rhs = op(ser, pytd)

            tm.assert_series_equal(lhs, rhs)

    def test_ops_nat_mixed_datetime64_timedelta64(self):
        # GH#11349
        timedelta_series = Series([NaT, Timedelta("1s")])
        datetime_series = Series([NaT, Timestamp("19900315")])
        nat_series_dtype_timedelta = Series([NaT, NaT], dtype="timedelta64[ns]")
        nat_series_dtype_timestamp = Series([NaT, NaT], dtype="datetime64[ns]")
        single_nat_dtype_datetime = Series([NaT], dtype="datetime64[ns]")
        single_nat_dtype_timedelta = Series([NaT], dtype="timedelta64[ns]")

        # subtraction
        tm.assert_series_equal(
            datetime_series - single_nat_dtype_datetime, nat_series_dtype_timedelta
        )

        tm.assert_series_equal(
            datetime_series - single_nat_dtype_timedelta, nat_series_dtype_timestamp
        )
        tm.assert_series_equal(
            -single_nat_dtype_timedelta + datetime_series, nat_series_dtype_timestamp
        )

        # without a Series wrapping the NaT, it is ambiguous
        # whether it is a datetime64 or timedelta64
        # defaults to interpreting it as timedelta64
        tm.assert_series_equal(
            nat_series_dtype_timestamp - single_nat_dtype_datetime,
            nat_series_dtype_timedelta,
        )

        tm.assert_series_equal(
            nat_series_dtype_timestamp - single_nat_dtype_timedelta,
            nat_series_dtype_timestamp,
        )
        tm.assert_series_equal(
            -single_nat_dtype_timedelta + nat_series_dtype_timestamp,
            nat_series_dtype_timestamp,
        )
        msg = "cannot subtract a datelike"
        with pytest.raises(TypeError, match=msg):
            timedelta_series - single_nat_dtype_datetime

        # addition
        tm.assert_series_equal(
            nat_series_dtype_timestamp + single_nat_dtype_timedelta,
            nat_series_dtype_timestamp,
        )
        tm.assert_series_equal(
            single_nat_dtype_timedelta + nat_series_dtype_timestamp,
            nat_series_dtype_timestamp,
        )

        tm.assert_series_equal(
            nat_series_dtype_timestamp + single_nat_dtype_timedelta,
            nat_series_dtype_timestamp,
        )
        tm.assert_series_equal(
            single_nat_dtype_timedelta + nat_series_dtype_timestamp,
            nat_series_dtype_timestamp,
        )

        tm.assert_series_equal(
            nat_series_dtype_timedelta + single_nat_dtype_datetime,
            nat_series_dtype_timestamp,
        )
        tm.assert_series_equal(
            single_nat_dtype_datetime + nat_series_dtype_timedelta,
            nat_series_dtype_timestamp,
        )

    def test_ufunc_coercions(self):
        idx = date_range("2011-01-01", periods=3, freq="2D", name="x")

        delta = np.timedelta64(1, "D")
        exp = date_range("2011-01-02", periods=3, freq="2D", name="x")
        for result in [idx + delta, np.add(idx, delta)]:
            assert isinstance(result, DatetimeIndex)
            tm.assert_index_equal(result, exp)
            assert result.freq == "2D"

        exp = date_range("2010-12-31", periods=3, freq="2D", name="x")

        for result in [idx - delta, np.subtract(idx, delta)]:
            assert isinstance(result, DatetimeIndex)
            tm.assert_index_equal(result, exp)
            assert result.freq == "2D"

        # When adding/subtracting an ndarray (which has no .freq), the result
        #  does not infer freq
        idx = idx._with_freq(None)
        delta = np.array(
            [np.timedelta64(1, "D"), np.timedelta64(2, "D"), np.timedelta64(3, "D")]
        )
        exp = DatetimeIndex(["2011-01-02", "2011-01-05", "2011-01-08"], name="x")

        for result in [idx + delta, np.add(idx, delta)]:
            tm.assert_index_equal(result, exp)
            assert result.freq == exp.freq

        exp = DatetimeIndex(["2010-12-31", "2011-01-01", "2011-01-02"], name="x")
        for result in [idx - delta, np.subtract(idx, delta)]:
            assert isinstance(result, DatetimeIndex)
            tm.assert_index_equal(result, exp)
            assert result.freq == exp.freq

    def test_dti_add_series(self, tz_naive_fixture, names):
        # GH#13905
        tz = tz_naive_fixture
        index = DatetimeIndex(
            ["2016-06-28 05:30", "2016-06-28 05:31"], tz=tz, name=names[0]
        )
        ser = Series([Timedelta(seconds=5)] * 2, index=index, name=names[1])
        expected = Series(index + Timedelta(seconds=5), index=index, name=names[2])

        # passing name arg isn't enough when names[2] is None
        expected.name = names[2]
        assert expected.dtype == index.dtype
        result = ser + index
        tm.assert_series_equal(result, expected)
        result2 = index + ser
        tm.assert_series_equal(result2, expected)

        expected = index + Timedelta(seconds=5)
        result3 = ser.values + index
        tm.assert_index_equal(result3, expected)
        result4 = index + ser.values
        tm.assert_index_equal(result4, expected)

    @pytest.mark.parametrize("op", [operator.add, roperator.radd, operator.sub])
    def test_dti_addsub_offset_arraylike(
        self, tz_naive_fixture, names, op, index_or_series
    ):
        # GH#18849, GH#19744
        box = pd.Index
        other_box = index_or_series

        tz = tz_naive_fixture
        dti = pd.date_range("2017-01-01", periods=2, tz=tz, name=names[0])
        other = other_box([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)], name=names[1])

        xbox = get_upcast_box(box, other)

        with tm.assert_produces_warning(PerformanceWarning):
            res = op(dti, other)

        expected = DatetimeIndex(
            [op(dti[n], other[n]) for n in range(len(dti))], name=names[2], freq="infer"
        )
        expected = tm.box_expected(expected, xbox)
        tm.assert_equal(res, expected)

    @pytest.mark.parametrize("other_box", [pd.Index, np.array])
    def test_dti_addsub_object_arraylike(
        self, tz_naive_fixture, box_with_array, other_box
    ):
        tz = tz_naive_fixture

        dti = pd.date_range("2017-01-01", periods=2, tz=tz)
        dtarr = tm.box_expected(dti, box_with_array)
        other = other_box([pd.offsets.MonthEnd(), pd.Timedelta(days=4)])
        xbox = get_upcast_box(box_with_array, other)

        expected = pd.DatetimeIndex(["2017-01-31", "2017-01-06"], tz=tz_naive_fixture)
        expected = tm.box_expected(expected, xbox)

        warn = PerformanceWarning
        if box_with_array is pd.DataFrame and tz is not None:
            warn = None

        with tm.assert_produces_warning(warn):
            result = dtarr + other
        tm.assert_equal(result, expected)

        expected = pd.DatetimeIndex(["2016-12-31", "2016-12-29"], tz=tz_naive_fixture)
        expected = tm.box_expected(expected, xbox)

        with tm.assert_produces_warning(warn):
            result = dtarr - other
        tm.assert_equal(result, expected)


@pytest.mark.parametrize("years", [-1, 0, 1])
@pytest.mark.parametrize("months", [-2, 0, 2])
def test_shift_months(years, months):
    dti = DatetimeIndex(
        [
            Timestamp("2000-01-05 00:15:00"),
            Timestamp("2000-01-31 00:23:00"),
            Timestamp("2000-01-01"),
            Timestamp("2000-02-29"),
            Timestamp("2000-12-31"),
        ]
    )
    actual = DatetimeIndex(shift_months(dti.asi8, years * 12 + months))

    raw = [x + pd.offsets.DateOffset(years=years, months=months) for x in dti]
    expected = DatetimeIndex(raw)
    tm.assert_index_equal(actual, expected)


def test_dt64arr_addsub_object_dtype_2d():
    # block-wise DataFrame operations will require operating on 2D
    #  DatetimeArray/TimedeltaArray, so check that specifically.
    dti = pd.date_range("1994-02-13", freq="2W", periods=4)
    dta = dti._data.reshape((4, 1))

    other = np.array([[pd.offsets.Day(n)] for n in range(4)])
    assert other.shape == dta.shape

    with tm.assert_produces_warning(PerformanceWarning):
        result = dta + other
    with tm.assert_produces_warning(PerformanceWarning):
        expected = (dta[:, 0] + other[:, 0]).reshape(-1, 1)

    assert isinstance(result, DatetimeArray)
    assert result.freq is None
    tm.assert_numpy_array_equal(result._data, expected._data)

    with tm.assert_produces_warning(PerformanceWarning):
        # Case where we expect to get a TimedeltaArray back
        result2 = dta - dta.astype(object)

    assert isinstance(result2, TimedeltaArray)
    assert result2.shape == (4, 1)
    assert result2.freq is None
    assert (result2.asi8 == 0).all()