from datetime import datetime
import re

import numpy as np
import pytest

from pandas._libs import lib
import pandas.util._test_decorators as td

import pandas as pd
from pandas import (
    Series,
    StringDtype,
    _testing as tm,
)
from pandas.tests.strings import (
    _convert_na_value,
    is_object_or_nan_string_dtype,
)

# --------------------------------------------------------------------------------------
# str.contains
# --------------------------------------------------------------------------------------


def test_contains(any_string_dtype):
    values = np.array(
        ["foo", np.nan, "fooommm__foo", "mmm_", "foommm[_]+bar"], dtype=np.object_
    )
    values = Series(values, dtype=any_string_dtype)
    pat = "mmm[_]+"

    result = values.str.contains(pat)
    if any_string_dtype == "str":
        # NaN propagates as False
        expected = Series([False, False, True, True, False], dtype=bool)
    else:
        expected_dtype = (
            "object" if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
        )
        expected = Series(
            np.array([False, np.nan, True, True, False], dtype=np.object_),
            dtype=expected_dtype,
        )

    tm.assert_series_equal(result, expected)

    result = values.str.contains(pat, regex=False)
    if any_string_dtype == "str":
        expected = Series([False, False, False, False, True], dtype=bool)
    else:
        expected = Series(
            np.array([False, np.nan, False, False, True], dtype=np.object_),
            dtype=expected_dtype,
        )
    tm.assert_series_equal(result, expected)

    values = Series(
        np.array(["foo", "xyz", "fooommm__foo", "mmm_"], dtype=object),
        dtype=any_string_dtype,
    )
    result = values.str.contains(pat)
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )
    expected = Series(np.array([False, False, True, True]), dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # case insensitive using regex
    values = Series(
        np.array(["Foo", "xYz", "fOOomMm__fOo", "MMM_"], dtype=object),
        dtype=any_string_dtype,
    )

    result = values.str.contains("FOO|mmm", case=False)
    expected = Series(np.array([True, False, True, True]), dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # case insensitive without regex
    result = values.str.contains("foo", regex=False, case=False)
    expected = Series(np.array([True, False, True, False]), dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # unicode
    values = Series(
        np.array(["foo", np.nan, "fooommm__foo", "mmm_"], dtype=np.object_),
        dtype=any_string_dtype,
    )
    pat = "mmm[_]+"

    result = values.str.contains(pat)
    if any_string_dtype == "str":
        expected = Series([False, False, True, True], dtype=bool)
    else:
        expected_dtype = (
            "object" if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
        )
        expected = Series(
            np.array([False, np.nan, True, True], dtype=np.object_),
            dtype=expected_dtype,
        )
    tm.assert_series_equal(result, expected)

    result = values.str.contains(pat, na=False)
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )
    expected = Series(np.array([False, False, True, True]), dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    values = Series(
        np.array(["foo", "xyz", "fooommm__foo", "mmm_"], dtype=np.object_),
        dtype=any_string_dtype,
    )
    result = values.str.contains(pat)
    expected = Series(np.array([False, False, True, True]), dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


def test_contains_object_mixed():
    mixed = Series(
        np.array(
            ["a", np.nan, "b", True, datetime.today(), "foo", None, 1, 2.0],
            dtype=object,
        )
    )
    result = mixed.str.contains("o")
    expected = Series(
        np.array(
            [False, np.nan, False, np.nan, np.nan, True, None, np.nan, np.nan],
            dtype=np.object_,
        )
    )
    tm.assert_series_equal(result, expected)


def test_contains_na_kwarg_for_object_category():
    # gh 22158

    # na for category
    values = Series(["a", "b", "c", "a", np.nan], dtype="category")
    result = values.str.contains("a", na=True)
    expected = Series([True, False, False, True, True])
    tm.assert_series_equal(result, expected)

    result = values.str.contains("a", na=False)
    expected = Series([True, False, False, True, False])
    tm.assert_series_equal(result, expected)

    # na for objects
    values = Series(["a", "b", "c", "a", np.nan])
    result = values.str.contains("a", na=True)
    expected = Series([True, False, False, True, True])
    tm.assert_series_equal(result, expected)

    result = values.str.contains("a", na=False)
    expected = Series([True, False, False, True, False])
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "na, expected",
    [
        (None, pd.NA),
        (True, True),
        (False, False),
        (0, False),
        (3, True),
        (np.nan, pd.NA),
    ],
)
@pytest.mark.parametrize("regex", [True, False])
def test_contains_na_kwarg_for_nullable_string_dtype(
    nullable_string_dtype, na, expected, regex
):
    # https://github.com/pandas-dev/pandas/pull/41025#issuecomment-824062416

    values = Series(["a", "b", "c", "a", np.nan], dtype=nullable_string_dtype)

    if na in [0, 3] and na is not False:
        msg = f"na must be None, pd.NA, np.nan, True, or False; got {na}"
        with pytest.raises(ValueError, match=msg):
            values.str.contains("a", na=na, regex=regex)
    else:
        result = values.str.contains("a", na=na, regex=regex)
        expected = Series([True, False, False, True, expected], dtype="boolean")
        tm.assert_series_equal(result, expected)


def test_contains_moar(any_string_dtype):
    # PR #1179
    s = Series(
        ["A", "B", "C", "Aaba", "Baca", "", np.nan, "CABA", "dog", "cat"],
        dtype=any_string_dtype,
    )

    result = s.str.contains("a")
    if any_string_dtype == "str":
        # NaN propagates as False
        expected_dtype = bool
        na_value = False
    else:
        expected_dtype = (
            "object" if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
        )
        na_value = np.nan
    expected = Series(
        [False, False, False, True, True, False, na_value, False, False, True],
        dtype=expected_dtype,
    )
    tm.assert_series_equal(result, expected)

    result = s.str.contains("a", case=False)
    expected = Series(
        [True, False, False, True, True, False, na_value, True, False, True],
        dtype=expected_dtype,
    )
    tm.assert_series_equal(result, expected)

    result = s.str.contains("Aa")
    expected = Series(
        [False, False, False, True, False, False, na_value, False, False, False],
        dtype=expected_dtype,
    )
    tm.assert_series_equal(result, expected)

    result = s.str.contains("ba")
    expected = Series(
        [False, False, False, True, False, False, na_value, False, False, False],
        dtype=expected_dtype,
    )
    tm.assert_series_equal(result, expected)

    result = s.str.contains("ba", case=False)
    expected = Series(
        [False, False, False, True, True, False, na_value, True, False, False],
        dtype=expected_dtype,
    )
    tm.assert_series_equal(result, expected)


def test_contains_nan(any_string_dtype):
    # PR #14171
    s = Series([np.nan, np.nan, np.nan], dtype=any_string_dtype)

    result = s.str.contains("foo", na=False)
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )
    expected = Series([False, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = s.str.contains("foo", na=True)
    expected = Series([True, True, True], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    msg = "na must be None, pd.NA, np.nan, True, or False; got foo"
    with pytest.raises(ValueError, match=msg):
        s.str.contains("foo", na="foo")

    result = s.str.contains("foo")
    if any_string_dtype == "str":
        # NaN propagates as False
        expected = Series([False, False, False], dtype=bool)
    else:
        expected_dtype = (
            "object" if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
        )
        expected = Series([np.nan, np.nan, np.nan], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


def test_contains_compiled_regex(any_string_dtype):
    # GH#61942
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )

    ser = Series(["foo", "bar", "Baz"], dtype=any_string_dtype)

    pat = re.compile("ba.")
    result = ser.str.contains(pat)
    expected = Series([False, True, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # TODO this currently works for pyarrow-backed dtypes but raises for python
    if any_string_dtype == "string" and any_string_dtype.storage == "pyarrow":
        result = ser.str.contains(pat, case=False)
        expected = Series([False, True, True], dtype=expected_dtype)
        tm.assert_series_equal(result, expected)
    else:
        with pytest.raises(
            ValueError, match="cannot process flags argument with a compiled pattern"
        ):
            ser.str.contains(pat, case=False)

    pat = re.compile("ba.", flags=re.IGNORECASE)
    result = ser.str.contains(pat)
    expected = Series([False, True, True], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # TODO should this be supported?
    with pytest.raises(
        ValueError, match="cannot process flags argument with a compiled pattern"
    ):
        ser.str.contains(pat, flags=re.IGNORECASE)


def test_contains_compiled_regex_flags(any_string_dtype):
    # ensure other (than ignorecase) flags are respected
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )

    ser = Series(["foobar", "foo\nbar", "Baz"], dtype=any_string_dtype)

    pat = re.compile("^ba")
    result = ser.str.contains(pat)
    expected = Series([False, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    pat = re.compile("^ba", flags=re.MULTILINE)
    result = ser.str.contains(pat)
    expected = Series([False, True, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    pat = re.compile("^ba", flags=re.MULTILINE | re.IGNORECASE)
    result = ser.str.contains(pat)
    expected = Series([False, True, True], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "pat, expected_data",
    [
        (r"a(?=b)", [False, True, False, False]),
        (r"(?<=a)b", [False, True, False, False]),
        (r"a(?!b)", [True, False, True, False]),
        (r"(?<!b)a", [True, True, False, False]),
        ("ab", [False, True, False, False]),
    ],
)
@pytest.mark.parametrize("na", [lib.no_default, True, False, np.nan, None, pd.NA])
def test_contains_lookarounds(any_string_dtype, pat, expected_data, na):
    # https://github.com/pandas-dev/pandas/issues/60833
    if any_string_dtype == "object" and not isinstance(na, bool):
        expected_dtype = "object"
    else:
        expected_dtype = (
            np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
        )
    if any_string_dtype == "object":
        # The behavior here for `na=pd.NA` looks wrong.
        if (na is lib.no_default or pd.isna(na)) and na is not pd.NA:
            na_result = None
        else:
            na_result = na
    elif na is lib.no_default or pd.isna(na):
        if any_string_dtype == "str":
            na_result = False
        elif any_string_dtype == "string":
            na_result = pd.NA
        else:
            raise ValueError(f"Unrecognized string dtype {any_string_dtype}")
    else:
        na_result = na
    expected_data = expected_data.copy()
    expected_data.append(na_result)
    ser = Series(["aa", "ab", "ba", "bb", None], dtype=any_string_dtype)
    result = ser.str.contains(pat, regex=True, na=na)
    expected = Series(expected_data, dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


def test_contains_end_of_string(any_string_dtype):
    # https://github.com/pandas-dev/pandas/pull/63613
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )

    ser = Series(["baz", "bar", "bars", "bar\n"], dtype=any_string_dtype)

    # with dollar sign
    result = ser.str.contains("bar$")
    if any_string_dtype == "string" and any_string_dtype.storage == "pyarrow":
        # pyarrow (RE2) only matches $ at the very end of the line
        expected = Series([False, True, False, False], dtype=expected_dtype)
    else:
        # python matches $ before or after an ending newline
        expected = Series([False, True, False, True], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # with \Z (ensure this is translated to \z for pyarrow)
    result = ser.str.contains(r"bar\Z")
    expected = Series([False, True, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # ensure finding a literal \Z still works
    ser = Series(
        ["bar", r"bar{}".format("\\"), r"bar\Z", r"bar\\Z", "bars", "bar\n"],
        dtype=any_string_dtype,
    )

    result = ser.str.contains(r"bar\\Z")
    expected = Series([False, False, True, False, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = ser.str.contains(r"bar\\\Z")
    expected = Series([False, True, False, False, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = ser.str.contains(r"bar\\\\Z")
    expected = Series([False, False, False, True, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


# --------------------------------------------------------------------------------------
# str.startswith
# --------------------------------------------------------------------------------------


def test_startswith_endswith_validate_na(any_string_dtype):
    # GH#59615
    ser = Series(
        ["om", np.nan, "foo_nom", "nom", "bar_foo", np.nan, "foo"],
        dtype=any_string_dtype,
    )
    msg = "na must be None, pd.NA, np.nan, True, or False; got baz"
    with pytest.raises(ValueError, match=msg):
        ser.str.startswith("kapow", na="baz")
    with pytest.raises(ValueError, match=msg):
        ser.str.endswith("bar", na="baz")


@pytest.mark.parametrize("pat", ["foo", ("foo", "baz")])
@pytest.mark.parametrize("dtype", ["object", "category"])
@pytest.mark.parametrize("null_value", [None, np.nan, pd.NA])
@pytest.mark.parametrize("na", [True, False])
def test_startswith(pat, dtype, null_value, na, using_infer_string):
    # add category dtype parametrizations for GH-36241
    values = Series(
        ["om", null_value, "foo_nom", "nom", "bar_foo", null_value, "foo"],
        dtype=dtype,
    )

    result = values.str.startswith(pat)
    exp = Series([False, np.nan, True, False, False, np.nan, True])
    if dtype == "object" and null_value is pd.NA:
        # GH#18463
        exp = exp.fillna(null_value)
    elif dtype == "object" and null_value is None:
        exp[exp.isna()] = None
    elif using_infer_string and dtype == "category":
        exp = exp.fillna(False).astype(bool)
    tm.assert_series_equal(result, exp)

    result = values.str.startswith(pat, na=na)
    exp = Series([False, na, True, False, False, na, True])
    tm.assert_series_equal(result, exp)

    # mixed
    mixed = np.array(
        ["a", np.nan, "b", True, datetime.today(), "foo", None, 1, 2.0],
        dtype=np.object_,
    )
    rs = Series(mixed).str.startswith("f")
    xp = Series([False, np.nan, False, np.nan, np.nan, True, None, np.nan, np.nan])
    tm.assert_series_equal(rs, xp)


@pytest.mark.parametrize("na", [None, True, False])
def test_startswith_string_dtype(any_string_dtype, na):
    values = Series(
        ["om", None, "foo_nom", "nom", "bar_foo", None, "foo", "regex", "rege."],
        dtype=any_string_dtype,
    )
    result = values.str.startswith("foo", na=na)

    expected_dtype = (
        (object if na is None else bool)
        if is_object_or_nan_string_dtype(any_string_dtype)
        else "boolean"
    )
    if any_string_dtype == "str":
        # NaN propagates as False
        expected_dtype = bool
        if na is None:
            na = False
    exp = Series(
        [False, na, True, False, False, na, True, False, False], dtype=expected_dtype
    )
    tm.assert_series_equal(result, exp)

    result = values.str.startswith("rege.", na=na)
    exp = Series(
        [False, na, False, False, False, na, False, False, True], dtype=expected_dtype
    )
    tm.assert_series_equal(result, exp)


# --------------------------------------------------------------------------------------
# str.endswith
# --------------------------------------------------------------------------------------


@pytest.mark.parametrize("pat", ["foo", ("foo", "baz")])
@pytest.mark.parametrize("dtype", ["object", "category"])
@pytest.mark.parametrize("null_value", [None, np.nan, pd.NA])
@pytest.mark.parametrize("na", [True, False])
def test_endswith(pat, dtype, null_value, na, using_infer_string):
    # add category dtype parametrizations for GH-36241
    values = Series(
        ["om", null_value, "foo_nom", "nom", "bar_foo", null_value, "foo"],
        dtype=dtype,
    )

    result = values.str.endswith(pat)
    exp = Series([False, np.nan, False, False, True, np.nan, True])
    if dtype == "object" and null_value is pd.NA:
        # GH#18463
        exp = exp.fillna(null_value)
    elif dtype == "object" and null_value is None:
        exp[exp.isna()] = None
    elif using_infer_string and dtype == "category":
        exp = exp.fillna(False).astype(bool)
    tm.assert_series_equal(result, exp)

    result = values.str.endswith(pat, na=na)
    exp = Series([False, na, False, False, True, na, True])
    tm.assert_series_equal(result, exp)

    # mixed
    mixed = np.array(
        ["a", np.nan, "b", True, datetime.today(), "foo", None, 1, 2.0],
        dtype=object,
    )
    rs = Series(mixed).str.endswith("f")
    xp = Series([False, np.nan, False, np.nan, np.nan, False, None, np.nan, np.nan])
    tm.assert_series_equal(rs, xp)


@pytest.mark.parametrize("na", [None, True, False])
def test_endswith_string_dtype(any_string_dtype, na):
    values = Series(
        ["om", None, "foo_nom", "nom", "bar_foo", None, "foo", "regex", "rege."],
        dtype=any_string_dtype,
    )
    result = values.str.endswith("foo", na=na)
    expected_dtype = (
        (object if na is None else bool)
        if is_object_or_nan_string_dtype(any_string_dtype)
        else "boolean"
    )
    if any_string_dtype == "str":
        # NaN propagates as False
        expected_dtype = bool
        if na is None:
            na = False
    exp = Series(
        [False, na, False, False, True, na, True, False, False], dtype=expected_dtype
    )
    tm.assert_series_equal(result, exp)

    result = values.str.endswith("rege.", na=na)
    exp = Series(
        [False, na, False, False, False, na, False, False, True], dtype=expected_dtype
    )
    tm.assert_series_equal(result, exp)


# --------------------------------------------------------------------------------------
# str.replace
# --------------------------------------------------------------------------------------
def test_replace_dict_invalid(any_string_dtype):
    # GH 51914
    series = Series(data=["A", "B_junk", "C_gunk"], name="my_messy_col")
    msg = "repl cannot be used when pat is a dictionary"

    with pytest.raises(ValueError, match=msg):
        series.str.replace(pat={"A": "a", "B": "b"}, repl="A")


def test_replace_dict(any_string_dtype):
    # GH 51914
    series = Series(data=["A", "B", "C"], name="my_messy_col")
    new_series = series.str.replace(pat={"A": "a", "B": "b"})
    expected = Series(data=["a", "b", "C"], name="my_messy_col")
    tm.assert_series_equal(new_series, expected)


def test_replace(any_string_dtype):
    ser = Series(["fooBAD__barBAD", np.nan], dtype=any_string_dtype)

    result = ser.str.replace("BAD[_]*", "", regex=True)
    expected = Series(["foobar", np.nan], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


def test_replace_max_replacements(any_string_dtype):
    ser = Series(["fooBAD__barBAD", np.nan], dtype=any_string_dtype)

    expected = Series(["foobarBAD", np.nan], dtype=any_string_dtype)
    result = ser.str.replace("BAD[_]*", "", n=1, regex=True)
    tm.assert_series_equal(result, expected)

    expected = Series(["foo__barBAD", np.nan], dtype=any_string_dtype)
    result = ser.str.replace("BAD", "", n=1, regex=False)
    tm.assert_series_equal(result, expected)


def test_replace_mixed_object():
    ser = Series(
        ["aBAD", np.nan, "bBAD", True, datetime.today(), "fooBAD", None, 1, 2.0]
    )
    result = Series(ser).str.replace("BAD[_]*", "", regex=True)
    expected = Series(
        ["a", np.nan, "b", np.nan, np.nan, "foo", None, np.nan, np.nan], dtype=object
    )
    tm.assert_series_equal(result, expected)


def test_replace_unicode(any_string_dtype):
    ser = Series([b"abcd,\xc3\xa0".decode("utf-8")], dtype=any_string_dtype)
    expected = Series([b"abcd, \xc3\xa0".decode("utf-8")], dtype=any_string_dtype)
    result = ser.str.replace(r"(?<=\w),(?=\w)", ", ", flags=re.UNICODE, regex=True)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("repl", [None, 3, {"a": "b"}])
@pytest.mark.parametrize("data", [["a", "b", None], ["a", "b", "c", "ad"]])
def test_replace_wrong_repl_type_raises(any_string_dtype, index_or_series, repl, data):
    # https://github.com/pandas-dev/pandas/issues/13438
    msg = "repl must be a string or callable"
    obj = index_or_series(data, dtype=any_string_dtype)
    with pytest.raises(TypeError, match=msg):
        obj.str.replace("a", repl)


def test_replace_callable(any_string_dtype):
    # GH 15055
    ser = Series(["fooBAD__barBAD", np.nan], dtype=any_string_dtype)

    # test with callable
    repl = lambda m: m.group(0).swapcase()
    result = ser.str.replace("[a-z][A-Z]{2}", repl, n=2, regex=True)
    expected = Series(["foObaD__baRbaD", np.nan], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "repl", [lambda: None, lambda m, x: None, lambda m, x, y=None: None]
)
def test_replace_callable_raises(any_string_dtype, repl):
    # GH 15055
    values = Series(["fooBAD__barBAD", np.nan], dtype=any_string_dtype)

    # test with wrong number of arguments, raising an error
    msg = (
        r"((takes)|(missing)) (?(2)from \d+ to )?\d+ "
        r"(?(3)required )positional arguments?"
    )
    with pytest.raises(TypeError, match=msg):
        values.str.replace("a", repl, regex=True)


@pytest.mark.parametrize(
    "repl, expected_list",
    [
        (
            r"\g<three> \g<two> \g<one>",
            ["Three Two One", "Baz Bar Foo"],
        ),
        (
            r"\3 \2 \1",
            ["Three Two One", "Baz Bar Foo"],
        ),
        (
            r"\g<3> \g<2> \g<1>",
            ["Three Two One", "Baz Bar Foo"],
        ),
        (
            r"\g<2>0",
            ["Two0", "Bar0"],
        ),
        (
            r"\g<2>0 \1",
            ["Two0 One", "Bar0 Foo"],
        ),
    ],
    ids=[
        "named_groups_full_swap",
        "numbered_groups_no_g_full_swap",
        "numbered_groups_full_swap",
        "single_group_with_literal",
        "mixed_group_reference_with_literal",
    ],
)
@pytest.mark.parametrize("use_compile", [True, False])
def test_replace_named_groups_regex_swap(
    any_string_dtype, use_compile, repl, expected_list
):
    # GH#57636
    ser = Series(["One Two Three", "Foo Bar Baz"], dtype=any_string_dtype)
    pattern = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)"
    if use_compile:
        pattern = re.compile(pattern)
    result = ser.str.replace(pattern, repl, regex=True)
    expected = Series(expected_list, dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "repl",
    [
        r"\g<20>",
        r"\20",
        r"\40",
        r"\4",
    ],
)
@pytest.mark.parametrize("use_compile", [True, False])
def test_replace_named_groups_regex_swap_expected_fail(
    any_string_dtype, repl, use_compile, request
):
    # GH#57636
    if (
        not use_compile
        and r"\g" not in repl
        and isinstance(any_string_dtype, StringDtype)
        and any_string_dtype.storage == "pyarrow"
    ):
        # calls pyarrow method directly
        if repl == r"\20":
            mark = pytest.mark.xfail(reason="PyArrow interprets as group + literal")
            request.applymarker(mark)

        pa = pytest.importorskip("pyarrow")
        error_type = pa.ArrowInvalid
        error_msg = r"only has \d parenthesized subexpressions"
    else:
        error_type = re.error
        error_msg = "invalid group reference"

    pattern = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)"
    if use_compile:
        pattern = re.compile(pattern)
    ser = Series(["One Two Three", "Foo Bar Baz"], dtype=any_string_dtype)

    with pytest.raises(error_type, match=error_msg):
        ser.str.replace(pattern, repl, regex=True)


@pytest.mark.parametrize(
    "pattern, repl",
    [
        (r"(\w+) (\w+) (\w+)", r"\20"),
        (r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)", r"\20"),
    ],
)
def test_pyarrow_ambiguous_group_references(pyarrow_string_dtype, pattern, repl):
    # GH#62653
    ser = Series(["One Two Three", "Foo Bar Baz"], dtype=pyarrow_string_dtype)

    result = ser.str.replace(pattern, repl, regex=True)
    expected = Series(["Two0", "Bar0"], dtype=pyarrow_string_dtype)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "pattern, repl, expected_list",
    [
        (
            r"\[(?P<one>\d+)\]",
            r"(\1)",
            ["var.one(0)", "var.two(1)", "var.three(2)"],
        ),
        (
            r"\[(\d+)\]",
            r"(\1)",
            ["var.one(0)", "var.two(1)", "var.three(2)"],
        ),
    ],
)
@td.skip_if_no("pyarrow")
def test_pyarrow_backend_group_replacement(pattern, repl, expected_list):
    ser = Series(["var.one[0]", "var.two[1]", "var.three[2]"]).convert_dtypes(
        dtype_backend="pyarrow"
    )
    result = ser.str.replace(pattern, repl, regex=True)
    expected = Series(expected_list).convert_dtypes(dtype_backend="pyarrow")
    tm.assert_series_equal(result, expected)


def test_replace_callable_named_groups(any_string_dtype):
    # test regex named groups
    ser = Series(["Foo Bar Baz", np.nan], dtype=any_string_dtype)
    pat = r"(?P<first>\w+) (?P<middle>\w+) (?P<last>\w+)"
    repl = lambda m: m.group("middle").swapcase()
    result = ser.str.replace(pat, repl, regex=True)
    expected = Series(["bAR", np.nan], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


def test_replace_compiled_regex(any_string_dtype):
    # GH 15446
    ser = Series(["fooBAD__barBAD", np.nan], dtype=any_string_dtype)

    # test with compiled regex
    pat = re.compile(r"BAD_*")
    result = ser.str.replace(pat, "", regex=True)
    expected = Series(["foobar", np.nan], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)

    result = ser.str.replace(pat, "", n=1, regex=True)
    expected = Series(["foobarBAD", np.nan], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


def test_replace_compiled_regex_mixed_object():
    pat = re.compile(r"BAD_*")
    ser = Series(
        ["aBAD", np.nan, "bBAD", True, datetime.today(), "fooBAD", None, 1, 2.0]
    )
    result = Series(ser).str.replace(pat, "", regex=True)
    expected = Series(
        ["a", np.nan, "b", np.nan, np.nan, "foo", None, np.nan, np.nan], dtype=object
    )
    tm.assert_series_equal(result, expected)


def test_replace_compiled_regex_unicode(any_string_dtype):
    ser = Series([b"abcd,\xc3\xa0".decode("utf-8")], dtype=any_string_dtype)
    expected = Series([b"abcd, \xc3\xa0".decode("utf-8")], dtype=any_string_dtype)
    pat = re.compile(r"(?<=\w),(?=\w)", flags=re.UNICODE)
    result = ser.str.replace(pat, ", ", regex=True)
    tm.assert_series_equal(result, expected)


def test_replace_compiled_regex_raises(any_string_dtype):
    # case and flags provided to str.replace will have no effect
    # and will produce warnings
    ser = Series(["fooBAD__barBAD__bad", np.nan], dtype=any_string_dtype)
    pat = re.compile(r"BAD_*")

    msg = "case and flags cannot be set when pat is a compiled regex"

    with pytest.raises(ValueError, match=msg):
        ser.str.replace(pat, "", flags=re.IGNORECASE, regex=True)

    with pytest.raises(ValueError, match=msg):
        ser.str.replace(pat, "", case=False, regex=True)

    with pytest.raises(ValueError, match=msg):
        ser.str.replace(pat, "", case=True, regex=True)


def test_replace_compiled_regex_callable(any_string_dtype):
    # test with callable
    ser = Series(["fooBAD__barBAD", np.nan], dtype=any_string_dtype)
    repl = lambda m: m.group(0).swapcase()
    pat = re.compile("[a-z][A-Z]{2}")
    result = ser.str.replace(pat, repl, n=2, regex=True)
    expected = Series(["foObaD__baRbaD", np.nan], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("regex,expected_val", [(True, "bao"), (False, "foo")])
def test_replace_literal(regex, expected_val, any_string_dtype):
    # GH16808 literal replace (regex=False vs regex=True)
    ser = Series(["f.o", "foo", np.nan], dtype=any_string_dtype)
    expected = Series(["bao", expected_val, np.nan], dtype=any_string_dtype)
    result = ser.str.replace("f.", "ba", regex=regex)
    tm.assert_series_equal(result, expected)


def test_replace_literal_callable_raises(any_string_dtype):
    ser = Series([], dtype=any_string_dtype)
    repl = lambda m: m.group(0).swapcase()

    msg = "Cannot use a callable replacement when regex=False"
    with pytest.raises(ValueError, match=msg):
        ser.str.replace("abc", repl, regex=False)


def test_replace_literal_compiled_raises(any_string_dtype):
    ser = Series([], dtype=any_string_dtype)
    pat = re.compile("[a-z][A-Z]{2}")

    msg = "Cannot use a compiled regex as replacement pattern with regex=False"
    with pytest.raises(ValueError, match=msg):
        ser.str.replace(pat, "", regex=False)


def test_replace_moar(any_string_dtype):
    # PR #1179
    ser = Series(
        ["A", "B", "C", "Aaba", "Baca", "", np.nan, "CABA", "dog", "cat"],
        dtype=any_string_dtype,
    )

    result = ser.str.replace("A", "YYY")
    expected = Series(
        ["YYY", "B", "C", "YYYaba", "Baca", "", np.nan, "CYYYBYYY", "dog", "cat"],
        dtype=any_string_dtype,
    )
    tm.assert_series_equal(result, expected)

    result = ser.str.replace("A", "YYY", case=False)
    expected = Series(
        [
            "YYY",
            "B",
            "C",
            "YYYYYYbYYY",
            "BYYYcYYY",
            "",
            np.nan,
            "CYYYBYYY",
            "dog",
            "cYYYt",
        ],
        dtype=any_string_dtype,
    )
    tm.assert_series_equal(result, expected)

    result = ser.str.replace("^.a|dog", "XX-XX ", case=False, regex=True)
    expected = Series(
        [
            "A",
            "B",
            "C",
            "XX-XX ba",
            "XX-XX ca",
            "",
            np.nan,
            "XX-XX BA",
            "XX-XX ",
            "XX-XX t",
        ],
        dtype=any_string_dtype,
    )
    tm.assert_series_equal(result, expected)


def test_replace_not_case_sensitive_not_regex(any_string_dtype):
    # https://github.com/pandas-dev/pandas/issues/41602
    ser = Series(["A.", "a.", "Ab", "ab", np.nan], dtype=any_string_dtype)

    result = ser.str.replace("a", "c", case=False, regex=False)
    expected = Series(["c.", "c.", "cb", "cb", np.nan], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)

    result = ser.str.replace("a.", "c.", case=False, regex=False)
    expected = Series(["c.", "c.", "Ab", "ab", np.nan], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


def test_replace_regex(any_string_dtype):
    # https://github.com/pandas-dev/pandas/pull/24809
    s = Series(["a", "b", "ac", np.nan, ""], dtype=any_string_dtype)
    result = s.str.replace("^.$", "a", regex=True)
    expected = Series(["a", "a", "ac", np.nan, ""], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("regex", [True, False])
def test_replace_regex_single_character(regex, any_string_dtype):
    # https://github.com/pandas-dev/pandas/pull/24809, enforced in 2.0
    # GH 24804
    s = Series(["a.b", ".", "b", np.nan, ""], dtype=any_string_dtype)

    result = s.str.replace(".", "a", regex=regex)
    if regex:
        expected = Series(["aaa", "a", "a", np.nan, ""], dtype=any_string_dtype)
    else:
        expected = Series(["aab", "a", "b", np.nan, ""], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "pat, expected_data",
    [
        (r"a(?=b)", ["aa", "xb", "ba", "bb"]),
        (r"(?<=a)b", ["aa", "ax", "ba", "bb"]),
        (r"a(?!b)", ["xx", "ab", "bx", "bb"]),
        (r"(?<!b)a", ["xx", "xb", "ba", "bb"]),
        ("ab", ["aa", "x", "ba", "bb"]),
    ],
)
def test_replace_lookarounds(any_string_dtype, pat, expected_data):
    # https://github.com/pandas-dev/pandas/issues/60833
    ser = Series(["aa", "ab", "ba", "bb", None], dtype=any_string_dtype)
    result = ser.str.replace(pat, "x", regex=True)
    if any_string_dtype == "object":
        null_result = None
    elif any_string_dtype == "str":
        null_result = np.nan
    elif any_string_dtype == "string":
        null_result = pd.NA
    else:
        raise ValueError(f"Unrecognized dtype: {any_string_dtype}")
    expected = Series([*expected_data, null_result], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


def test_replace_end_of_string(any_string_dtype):
    # https://github.com/pandas-dev/pandas/pull/63613
    ser = Series(["baz", "bar", "bars", "bar\n"], dtype=any_string_dtype)

    # with dollar sign
    result = ser.str.replace("bar$", "x", regex=True)
    if any_string_dtype == "string" and any_string_dtype.storage == "pyarrow":
        # pyarrow (RE2) only matches $ at the very end of the line
        expected = Series(["baz", "x", "bars", "bar\n"], dtype=any_string_dtype)
    else:
        # python matches $ before or after an ending newline
        expected = Series(["baz", "x", "bars", "x\n"], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)

    # with \Z (ensure this is translated to \z for pyarrow)
    result = ser.str.replace(r"bar\Z", "x", regex=True)
    expected = Series(["baz", "x", "bars", "bar\n"], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)

    # ensure finding a literal \Z still works
    ser = Series([r"bar\Z", "bar", "bars", "bar\n"], dtype=any_string_dtype)
    result = ser.str.replace(r"bar\\Z", "x", regex=True)
    expected = Series(["x", "bar", "bars", "bar\n"], dtype=any_string_dtype)
    tm.assert_series_equal(result, expected)


# --------------------------------------------------------------------------------------
# str.match
# --------------------------------------------------------------------------------------


def test_match(any_string_dtype):
    if any_string_dtype == "str":
        # NaN propagates as False
        expected_dtype = bool
        na_value = False
    else:
        expected_dtype = (
            "object" if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
        )
        na_value = np.nan

    values = Series(["fooBAD__barBAD", np.nan, "foo"], dtype=any_string_dtype)
    result = values.str.match(".*(BAD[_]+).*(BAD)")
    expected = Series([True, na_value, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    values = Series(
        ["fooBAD__barBAD", "BAD_BADleroybrown", np.nan, "foo"], dtype=any_string_dtype
    )
    result = values.str.match(".*BAD[_]+.*BAD")
    expected = Series([True, True, na_value, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = values.str.match("BAD[_]+.*BAD")
    expected = Series([False, True, na_value, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    values = Series(
        ["fooBAD__barBAD", "^BAD_BADleroybrown", np.nan, "foo"], dtype=any_string_dtype
    )
    result = values.str.match("^BAD[_]+.*BAD")
    expected = Series([False, False, na_value, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = values.str.match("\\^BAD[_]+.*BAD")
    expected = Series([False, True, na_value, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


def test_match_mixed_object():
    mixed = Series(
        [
            "aBAD_BAD",
            np.nan,
            "BAD_b_BAD",
            True,
            datetime.today(),
            "foo",
            None,
            1,
            2.0,
        ]
    )
    result = Series(mixed).str.match(".*(BAD[_]+).*(BAD)")
    expected = Series([True, np.nan, True, np.nan, np.nan, False, None, np.nan, np.nan])
    assert isinstance(result, Series)
    tm.assert_series_equal(result, expected)


def test_match_na_kwarg(any_string_dtype):
    # GH #6609
    s = Series(["a", "b", np.nan], dtype=any_string_dtype)

    result = s.str.match("a", na=False)
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )
    expected = Series([True, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = s.str.match("a")
    if any_string_dtype == "str":
        # NaN propagates as False
        expected_dtype = bool
        na_value = False
    else:
        expected_dtype = (
            "object" if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
        )
        na_value = np.nan

    expected = Series([True, False, na_value], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


def test_match_case_kwarg(any_string_dtype):
    values = Series(["ab", "AB", "abc", "ABC"], dtype=any_string_dtype)
    result = values.str.match("ab", case=False)
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )
    expected = Series([True, True, True, True], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


def test_match_compiled_regex(any_string_dtype):
    # GH#61952
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )

    values = Series(["ab", "AB", "abc", "ABC"], dtype=any_string_dtype)

    result = values.str.match(re.compile("ab"))
    expected = Series([True, False, True, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    msg = (
        "Cannot both specify 'case' and pass a compiled "
        "regexp object with conflicting case-sensitivity"
    )
    with pytest.raises(ValueError, match=msg):
        values.str.match(re.compile("ab"), case=False)

    result = values.str.match(re.compile("ab", flags=re.IGNORECASE))
    expected = Series([True, True, True, True], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    msg = (
        "Cannot both specify 'flags' and pass a compiled "
        "regexp object with conflicting flags"
    )
    with pytest.raises(ValueError, match=msg):
        values.str.match(re.compile("ab"), flags=re.IGNORECASE)

    # But if the flags match you're OK
    values.str.match(re.compile("ab", flags=re.IGNORECASE), flags=re.IGNORECASE)


@pytest.mark.parametrize(
    "pat, case, exp",
    [
        ["ab", False, [True, False]],
        ["Ab", True, [False, False]],
        ["bc", True, [False, False]],
        ["a[a-z]{1}", False, [True, False]],
        ["A[a-z]{1}", True, [False, False]],
        # https://github.com/pandas-dev/pandas/issues/61072
        ["(bc)|(ab)", True, [True, False]],
        ["((bc)|(ab))", True, [True, False]],
    ],
)
def test_str_match_extra_cases(any_string_dtype, pat, case, exp):
    ser = Series(["abc", "Xab"], dtype=any_string_dtype)
    result = ser.str.match(pat, case=case)

    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )
    expected = Series(exp, dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "pat, expected_data",
    [
        (r"a(?=b)", [False, True, False, False]),
        (r"(?<=a)b", [False, False, False, False]),
        (r"a(?!b)", [True, False, False, False]),
        (r"(?<!b)a", [True, True, False, False]),
        ("ab", [False, True, False, False]),
    ],
)
def test_match_lookarounds(any_string_dtype, pat, expected_data):
    # https://github.com/pandas-dev/pandas/issues/60833
    if any_string_dtype == "object":
        expected_dtype, null_result = "object", None
    elif any_string_dtype == "str":
        expected_dtype, null_result = "bool", False
    elif any_string_dtype == "string":
        expected_dtype, null_result = "boolean", pd.NA
    else:
        raise ValueError(f"Unrecognized dtype: {any_string_dtype}")
    ser = Series(["aa", "ab", "ba", "bb", None], dtype=any_string_dtype)
    result = ser.str.match(pat)
    expected = Series([*expected_data, null_result], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


def test_match_end_of_string(any_string_dtype):
    # https://github.com/pandas-dev/pandas/pull/63613
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )

    ser = Series(["baz", "bar", "bars", "bar\n"], dtype=any_string_dtype)

    # with dollar sign
    result = ser.str.match("bar$")
    if any_string_dtype == "string" and any_string_dtype.storage == "pyarrow":
        # pyarrow (RE2) only matches $ at the very end of the line
        expected = Series([False, True, False, False], dtype=expected_dtype)
    else:
        # python matches $ before or after an ending newline
        expected = Series([False, True, False, True], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # with \Z (ensure this is translated to \z for pyarrow)
    result = ser.str.match(r"bar\Z")
    expected = Series([False, True, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # ensure finding a literal \Z still works
    ser = Series([r"bar\Z", "bar", "bars", "bar\n"], dtype=any_string_dtype)
    result = ser.str.match(r"bar\\Z")
    expected = Series([True, False, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


# --------------------------------------------------------------------------------------
# str.fullmatch
# --------------------------------------------------------------------------------------


def test_fullmatch(any_string_dtype):
    # GH 32806
    ser = Series(
        ["fooBAD__barBAD", "BAD_BADleroybrown", np.nan, "foo"], dtype=any_string_dtype
    )
    result = ser.str.fullmatch(".*BAD[_]+.*BAD")
    if any_string_dtype == "str":
        # NaN propagates as False
        expected = Series([True, False, False, False], dtype=bool)
    else:
        expected_dtype = (
            "object" if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
        )
        expected = Series([True, False, np.nan, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


def test_fullmatch_dollar_literal(any_string_dtype):
    # GH 56652
    ser = Series(["foo", "foo$foo", np.nan, "foo$"], dtype=any_string_dtype)
    result = ser.str.fullmatch("foo\\$")
    if any_string_dtype == "str":
        # NaN propagates as False
        expected = Series([False, False, False, True], dtype=bool)
    else:
        expected_dtype = (
            "object" if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
        )
        expected = Series([False, False, np.nan, True], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


def test_fullmatch_na_kwarg(any_string_dtype):
    ser = Series(
        ["fooBAD__barBAD", "BAD_BADleroybrown", np.nan, "foo"], dtype=any_string_dtype
    )
    result = ser.str.fullmatch(".*BAD[_]+.*BAD", na=False)
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )
    expected = Series([True, False, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


def test_fullmatch_case_kwarg(any_string_dtype):
    ser = Series(["ab", "AB", "abc", "ABC"], dtype=any_string_dtype)
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )

    expected = Series([True, False, False, False], dtype=expected_dtype)

    result = ser.str.fullmatch("ab", case=True)
    tm.assert_series_equal(result, expected)

    expected = Series([True, True, False, False], dtype=expected_dtype)

    result = ser.str.fullmatch("ab", case=False)
    tm.assert_series_equal(result, expected)

    result = ser.str.fullmatch("ab", flags=re.IGNORECASE)
    tm.assert_series_equal(result, expected)


def test_fullmatch_compiled_regex(any_string_dtype):
    # GH#61952
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )

    values = Series(["ab", "AB", "abc", "ABC"], dtype=any_string_dtype)

    result = values.str.fullmatch(re.compile("ab"))
    expected = Series([True, False, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # TODO this currently works for pyarrow-backed dtypes but raises for python
    if any_string_dtype == "string" and any_string_dtype.storage == "pyarrow":
        result = values.str.fullmatch(re.compile("ab"), case=False)
        expected = Series([True, True, False, False], dtype=expected_dtype)
        tm.assert_series_equal(result, expected)
    else:
        with pytest.raises(
            ValueError, match="cannot process flags argument with a compiled pattern"
        ):
            values.str.fullmatch(re.compile("ab"), case=False)

    result = values.str.fullmatch(re.compile("ab", flags=re.IGNORECASE))
    expected = Series([True, True, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    with pytest.raises(
        ValueError, match="cannot process flags argument with a compiled pattern"
    ):
        values.str.fullmatch(re.compile("ab"), flags=re.IGNORECASE)


@pytest.mark.parametrize(
    "pat, case, na, exp",
    # Note: keep cases in sync with
    # pandas/tests/extension/test_arrow.py::test_str_fullmatch
    [
        ["abc", False, None, [True, False, False, None]],
        ["Abc", True, None, [False, False, False, None]],
        ["bc", True, None, [False, False, False, None]],
        ["ab", False, None, [False, False, False, None]],
        ["a[a-z]{2}", False, None, [True, False, False, None]],
        ["A[a-z]{1}", True, None, [False, False, False, None]],
        # GH Issue: #56652
        ["abc$", False, None, [True, False, False, None]],
        ["abc\\$", False, None, [False, True, False, None]],
        ["Abc$", True, None, [False, False, False, None]],
        ["Abc\\$", True, None, [False, False, False, None]],
        # https://github.com/pandas-dev/pandas/issues/61072
        ["(abc)|(abx)", True, None, [True, False, False, None]],
        ["((abc)|(abx))", True, None, [True, False, False, None]],
    ],
)
def test_str_fullmatch_extra_cases(any_string_dtype, pat, case, na, exp):
    ser = Series(["abc", "abc$", "$abc", None], dtype=any_string_dtype)
    result = ser.str.fullmatch(pat, case=case, na=na)

    if any_string_dtype == "str":
        # NaN propagates as False
        exp[-1] = False
        expected_dtype = bool
    else:
        expected_dtype = (
            "object" if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
        )
    expected = Series(exp, dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "pat",
    [(r"a(?=b)"), (r"(?<=a)b"), (r"a(?!b)"), (r"(?<!b)a"), ("ab")],
)
def test_fullmatch_lookarounds(any_string_dtype, pat):
    # https://github.com/pandas-dev/pandas/issues/60833
    # Note: By definition, any match with a lookaround is not a full match.
    if any_string_dtype == "object":
        expected_dtype, null_result = "object", None
    elif any_string_dtype == "str":
        expected_dtype, null_result = "bool", False
    elif any_string_dtype == "string":
        expected_dtype, null_result = "boolean", pd.NA
    else:
        raise ValueError(f"Unrecognized dtype: {any_string_dtype}")
    ser = Series(["aa", "ab", "ba", "bb", None], dtype=any_string_dtype)
    result = ser.str.fullmatch(pat)
    expected = Series(
        [False, True if pat == "ab" else False, False, False, null_result],
        dtype=expected_dtype,
    )
    tm.assert_series_equal(result, expected)


def test_fullmatch_end_of_string(any_string_dtype):
    # https://github.com/pandas-dev/pandas/pull/63613
    expected_dtype = (
        np.bool_ if is_object_or_nan_string_dtype(any_string_dtype) else "boolean"
    )

    ser = Series(["baz", "bar", "bars", "bar\n"], dtype=any_string_dtype)

    # with dollar sign (for fullmatch, no difference between python and pyarrow)
    result = ser.str.fullmatch("bar$")
    expected = Series([False, True, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    # with \Z (ensure this is translated to \z for pyarrow)
    result = ser.str.fullmatch(r"bar\Z")
    tm.assert_series_equal(result, expected)

    # ensure finding a literal \Z still works
    ser = Series([r"bar\Z", "bar", "bars", "bar\n"], dtype=any_string_dtype)
    result = ser.str.fullmatch(r"bar\\Z")
    expected = Series([True, False, False, False], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


# --------------------------------------------------------------------------------------
# str.findall
# --------------------------------------------------------------------------------------


def test_findall(any_string_dtype):
    ser = Series(["fooBAD__barBAD", np.nan, "foo", "BAD"], dtype=any_string_dtype)
    result = ser.str.findall("BAD[_]*")
    expected = Series([["BAD__", "BAD"], np.nan, [], ["BAD"]])
    expected = _convert_na_value(ser, expected)
    tm.assert_series_equal(result, expected)


def test_findall_mixed_object():
    ser = Series(
        [
            "fooBAD__barBAD",
            np.nan,
            "foo",
            True,
            datetime.today(),
            "BAD",
            None,
            1,
            2.0,
        ]
    )

    result = ser.str.findall("BAD[_]*")
    expected = Series(
        [
            ["BAD__", "BAD"],
            np.nan,
            [],
            np.nan,
            np.nan,
            ["BAD"],
            None,
            np.nan,
            np.nan,
        ]
    )

    tm.assert_series_equal(result, expected)


@pytest.mark.parametrize(
    "pat, expected_data",
    [
        (r"a(?=b)", [[], ["a"], [], []]),
        (r"(?<=a)b", [[], ["b"], [], []]),
        (r"a(?!b)", [["a", "a"], [], ["a"], []]),
        (r"(?<!b)a", [["a", "a"], ["a"], [], []]),
        ("ab", [[], ["ab"], [], []]),
    ],
)
def test_findall_lookarounds(any_string_dtype, pat, expected_data):
    # https://github.com/pandas-dev/pandas/issues/60833
    ser = Series(["aa", "ab", "ba", "bb", None], dtype=any_string_dtype)
    result = ser.str.findall(pat)
    if any_string_dtype == "object":
        null_result = None
    elif any_string_dtype == "str":
        null_result = np.nan
    elif any_string_dtype == "string":
        null_result = pd.NA
    else:
        raise ValueError(f"Unrecognized dtype: {any_string_dtype}")
    expected = Series([*expected_data, null_result])
    tm.assert_series_equal(result, expected)


def test_findall_end_of_string(any_string_dtype):
    # https://github.com/pandas-dev/pandas/pull/63613
    ser = Series(["baz", "bar", "bars", "bar\n"], dtype=any_string_dtype)

    # with dollar sign
    result = ser.str.findall("bar$")
    expected = Series([[], ["bar"], [], ["bar"]], dtype=object)
    tm.assert_series_equal(result, expected)

    # with \Z (ensure this is translated to \z for pyarrow)
    result = ser.str.findall(r"bar\Z")
    expected = Series([[], ["bar"], [], []], dtype=object)
    tm.assert_series_equal(result, expected)

    # ensure finding a literal \Z still works
    ser = Series([r"bar\Z", "bar", "bars", "bar\n"], dtype=any_string_dtype)
    result = ser.str.findall(r"bar\\Z")
    expected = Series([["bar\\Z"], [], [], []], dtype=object)
    tm.assert_series_equal(result, expected)


# --------------------------------------------------------------------------------------
# str.find
# --------------------------------------------------------------------------------------


def test_find(any_string_dtype):
    ser = Series(
        ["ABCDEFG", "BCDEFEF", "DEFGHIJEF", "EFGHEF", "XXXX"], dtype=any_string_dtype
    )
    expected_dtype = (
        np.int64 if is_object_or_nan_string_dtype(any_string_dtype) else "Int64"
    )

    result = ser.str.find("EF")
    expected = Series([4, 3, 1, 0, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)
    expected = np.array([v.find("EF") for v in np.array(ser)], dtype=np.int64)
    tm.assert_numpy_array_equal(np.array(result, dtype=np.int64), expected)

    result = ser.str.rfind("EF")
    expected = Series([4, 5, 7, 4, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)
    expected = np.array([v.rfind("EF") for v in np.array(ser)], dtype=np.int64)
    tm.assert_numpy_array_equal(np.array(result, dtype=np.int64), expected)

    result = ser.str.find("EF", 3)
    expected = Series([4, 3, 7, 4, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)
    expected = np.array([v.find("EF", 3) for v in np.array(ser)], dtype=np.int64)
    tm.assert_numpy_array_equal(np.array(result, dtype=np.int64), expected)

    result = ser.str.rfind("EF", 3)
    expected = Series([4, 5, 7, 4, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)
    expected = np.array([v.rfind("EF", 3) for v in np.array(ser)], dtype=np.int64)
    tm.assert_numpy_array_equal(np.array(result, dtype=np.int64), expected)

    result = ser.str.find("EF", 3, 6)
    expected = Series([4, 3, -1, 4, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)
    expected = np.array([v.find("EF", 3, 6) for v in np.array(ser)], dtype=np.int64)
    tm.assert_numpy_array_equal(np.array(result, dtype=np.int64), expected)

    result = ser.str.rfind("EF", 3, 6)
    expected = Series([4, 3, -1, 4, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)
    expected = np.array([v.rfind("EF", 3, 6) for v in np.array(ser)], dtype=np.int64)
    tm.assert_numpy_array_equal(np.array(result, dtype=np.int64), expected)


def test_find_bad_arg_raises(any_string_dtype):
    ser = Series([], dtype=any_string_dtype)
    with pytest.raises(TypeError, match="expected a string object, not int"):
        ser.str.find(0)

    with pytest.raises(TypeError, match="expected a string object, not int"):
        ser.str.rfind(0)


def test_find_nan(any_string_dtype):
    ser = Series(
        ["ABCDEFG", np.nan, "DEFGHIJEF", np.nan, "XXXX"], dtype=any_string_dtype
    )
    if is_object_or_nan_string_dtype(any_string_dtype):
        expected_dtype = np.float64
        item = np.nan
    else:
        expected_dtype = "Int64"
        item = pd.NA

    result = ser.str.find("EF")
    expected = Series([4, item, 1, item, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = ser.str.rfind("EF")
    expected = Series([4, item, 7, item, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = ser.str.find("EF", 3)
    expected = Series([4, item, 7, item, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = ser.str.rfind("EF", 3)
    expected = Series([4, item, 7, item, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = ser.str.find("EF", 3, 6)
    expected = Series([4, item, -1, item, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)

    result = ser.str.rfind("EF", 3, 6)
    expected = Series([4, item, -1, item, -1], dtype=expected_dtype)
    tm.assert_series_equal(result, expected)


# --------------------------------------------------------------------------------------
# str.translate
# --------------------------------------------------------------------------------------


@pytest.mark.parametrize(
    "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
)
def test_translate(index_or_series, any_string_dtype, infer_string):
    obj = index_or_series(
        ["abcdefg", "abcc", "cdddfg", "cdefggg"], dtype=any_string_dtype
    )
    table = str.maketrans("abc", "cde")
    result = obj.str.translate(table)
    expected = index_or_series(
        ["cdedefg", "cdee", "edddfg", "edefggg"], dtype=any_string_dtype
    )
    tm.assert_equal(result, expected)


def test_translate_mixed_object():
    # Series with non-string values
    s = Series(["a", "b", "c", 1.2])
    table = str.maketrans("abc", "cde")
    expected = Series(["c", "d", "e", np.nan], dtype=object)
    result = s.str.translate(table)
    tm.assert_series_equal(result, expected)


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


def test_flags_kwarg(any_string_dtype):
    data = {
        "Dave": "dave@google.com",
        "Steve": "steve@gmail.com",
        "Rob": "rob@gmail.com",
        "Wes": np.nan,
    }
    data = Series(data, dtype=any_string_dtype)

    pat = r"([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})"

    result = data.str.extract(pat, flags=re.IGNORECASE, expand=True)
    assert result.iloc[0].tolist() == ["dave", "google", "com"]

    result = data.str.match(pat, flags=re.IGNORECASE)
    assert result.iloc[0]

    result = data.str.fullmatch(pat, flags=re.IGNORECASE)
    assert result.iloc[0]

    result = data.str.findall(pat, flags=re.IGNORECASE)
    assert result.iloc[0][0] == ("dave", "google", "com")

    result = data.str.count(pat, flags=re.IGNORECASE)
    assert result.iloc[0] == 1

    msg = "has match groups"
    with tm.assert_produces_warning(UserWarning, match=msg):
        result = data.str.contains(pat, flags=re.IGNORECASE)
    assert result.iloc[0]