import numpy as np
import pytest

import pandas as pd
from pandas import (
    DataFrame,
    Index,
    MultiIndex,
    Series,
    period_range,
    timedelta_range,
)
import pandas._testing as tm
from pandas.core.util.hashing import hash_tuples
from pandas.util import (
    hash_array,
    hash_pandas_object,
)


@pytest.fixture(
    params=[
        Series([1, 2, 3] * 3, dtype="int32"),
        Series([None, 2.5, 3.5] * 3, dtype="float32"),
        Series(["a", "b", "c"] * 3, dtype="category"),
        Series(["d", "e", "f"] * 3),
        Series([True, False, True] * 3),
        Series(pd.date_range("20130101", periods=9)),
        Series(pd.date_range("20130101", periods=9, tz="US/Eastern")),
        Series(timedelta_range("2000", periods=9)),
    ]
)
def series(request):
    return request.param


@pytest.fixture(params=[True, False])
def index(request):
    return request.param


def test_consistency():
    # Check that our hash doesn't change because of a mistake
    # in the actual code; this is the ground truth.
    result = hash_pandas_object(Index(["foo", "bar", "baz"]))
    expected = Series(
        np.array(
            [3600424527151052760, 1374399572096150070, 477881037637427054],
            dtype="uint64",
        ),
        index=["foo", "bar", "baz"],
    )
    tm.assert_series_equal(result, expected)


def test_hash_array(series):
    arr = series.values
    tm.assert_numpy_array_equal(hash_array(arr), hash_array(arr))


@pytest.mark.parametrize("dtype", ["U", object])
def test_hash_array_mixed(dtype):
    result1 = hash_array(np.array(["3", "4", "All"]))
    result2 = hash_array(np.array([3, 4, "All"], dtype=dtype))

    tm.assert_numpy_array_equal(result1, result2)


@pytest.mark.parametrize("val", [5, "foo", pd.Timestamp("20130101")])
def test_hash_array_errors(val):
    msg = "must pass an ndarray-like"
    with pytest.raises(TypeError, match=msg):
        hash_array(val)


def test_hash_array_index_exception():
    # GH42003 TypeError instead of AttributeError
    obj = pd.DatetimeIndex(["2018-10-28 01:20:00"], tz="Europe/Berlin")

    msg = "Use hash_pandas_object instead"
    with pytest.raises(TypeError, match=msg):
        hash_array(obj)


def test_hash_tuples():
    tuples = [(1, "one"), (1, "two"), (2, "one")]
    result = hash_tuples(tuples)

    expected = hash_pandas_object(MultiIndex.from_tuples(tuples)).values
    tm.assert_numpy_array_equal(result, expected)

    # We only need to support MultiIndex and list-of-tuples
    msg = "|".join(["object is not iterable", "zip argument #1 must support iteration"])
    with pytest.raises(TypeError, match=msg):
        hash_tuples(tuples[0])


@pytest.mark.parametrize("val", [5, "foo", pd.Timestamp("20130101")])
def test_hash_tuples_err(val):
    msg = "must be convertible to a list-of-tuples"
    with pytest.raises(TypeError, match=msg):
        hash_tuples(val)


def test_multiindex_unique():
    mi = MultiIndex.from_tuples([(118, 472), (236, 118), (51, 204), (102, 51)])
    assert mi.is_unique is True

    result = hash_pandas_object(mi)
    assert result.is_unique is True


def test_multiindex_objects():
    mi = MultiIndex(
        levels=[["b", "d", "a"], [1, 2, 3]],
        codes=[[0, 1, 0, 2], [2, 0, 0, 1]],
        names=["col1", "col2"],
    )
    recons = mi._sort_levels_monotonic()

    # These are equal.
    assert mi.equals(recons)
    assert Index(mi.values).equals(Index(recons.values))


@pytest.mark.parametrize(
    "obj",
    [
        Series([1, 2, 3]),
        Series([1.0, 1.5, 3.2]),
        Series([1.0, 1.5, np.nan]),
        Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]),
        Series(["a", "b", "c"]),
        Series(["a", np.nan, "c"]),
        Series(["a", None, "c"]),
        Series([True, False, True]),
        Series(dtype=object),
        DataFrame({"x": ["a", "b", "c"], "y": [1, 2, 3]}),
        DataFrame(),
        DataFrame(np.full((10, 4), np.nan)),
        DataFrame(
            {
                "A": [0.0, 1.0, 2.0, 3.0, 4.0],
                "B": [0.0, 1.0, 0.0, 1.0, 0.0],
                "C": Index(["foo1", "foo2", "foo3", "foo4", "foo5"], dtype=object),
                "D": pd.date_range("20130101", periods=5),
            }
        ),
        DataFrame(range(5), index=pd.date_range("2020-01-01", periods=5)),
        Series(range(5), index=pd.date_range("2020-01-01", periods=5)),
        Series(period_range("2020-01-01", periods=10, freq="D")),
        Series(pd.date_range("20130101", periods=3, tz="US/Eastern")),
    ],
)
def test_hash_pandas_object(obj, index):
    a = hash_pandas_object(obj, index=index)
    b = hash_pandas_object(obj, index=index)
    tm.assert_series_equal(a, b)


@pytest.mark.parametrize(
    "obj",
    [
        Series([1, 2, 3]),
        Series([1.0, 1.5, 3.2]),
        Series([1.0, 1.5, np.nan]),
        Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]),
        Series(["a", "b", "c"]),
        Series(["a", np.nan, "c"]),
        Series(["a", None, "c"]),
        Series([True, False, True]),
        DataFrame({"x": ["a", "b", "c"], "y": [1, 2, 3]}),
        DataFrame(np.full((10, 4), np.nan)),
        DataFrame(
            {
                "A": [0.0, 1.0, 2.0, 3.0, 4.0],
                "B": [0.0, 1.0, 0.0, 1.0, 0.0],
                "C": Index(["foo1", "foo2", "foo3", "foo4", "foo5"], dtype=object),
                "D": pd.date_range("20130101", periods=5),
            }
        ),
        DataFrame(range(5), index=pd.date_range("2020-01-01", periods=5)),
        Series(range(5), index=pd.date_range("2020-01-01", periods=5)),
        Series(period_range("2020-01-01", periods=10, freq="D")),
        Series(pd.date_range("20130101", periods=3, tz="US/Eastern")),
    ],
)
def test_hash_pandas_object_diff_index_non_empty(obj):
    a = hash_pandas_object(obj, index=True)
    b = hash_pandas_object(obj, index=False)
    assert not (a == b).all()


@pytest.mark.parametrize(
    "obj",
    [
        Index([1, 2, 3]),
        Index([True, False, True]),
        timedelta_range("1 day", periods=2),
        period_range("2020-01-01", freq="D", periods=2),
        MultiIndex.from_product(
            [range(5), ["foo", "bar", "baz"], pd.date_range("20130101", periods=2)]
        ),
        MultiIndex.from_product([pd.CategoricalIndex(list("aabc")), range(3)]),
    ],
)
def test_hash_pandas_index(obj, index):
    a = hash_pandas_object(obj, index=index)
    b = hash_pandas_object(obj, index=index)
    tm.assert_series_equal(a, b)


def test_hash_pandas_series(series, index):
    a = hash_pandas_object(series, index=index)
    b = hash_pandas_object(series, index=index)
    tm.assert_series_equal(a, b)


def test_hash_pandas_series_diff_index(series):
    a = hash_pandas_object(series, index=True)
    b = hash_pandas_object(series, index=False)
    assert not (a == b).all()


@pytest.mark.parametrize("klass", [Index, Series])
@pytest.mark.parametrize("dtype", ["float64", "object"])
def test_hash_pandas_empty_object(klass, dtype, index):
    # These are by-definition the same with
    # or without the index as the data is empty.
    obj = klass([], dtype=dtype)
    a = hash_pandas_object(obj, index=index)
    b = hash_pandas_object(obj, index=index)
    tm.assert_series_equal(a, b)


@pytest.mark.parametrize(
    "s1",
    [
        ["a", "b", "c", "d"],
        [1000, 2000, 3000, 4000],
        pd.date_range(0, periods=4),
    ],
)
@pytest.mark.parametrize("categorize", [True, False])
def test_categorical_consistency(s1, categorize):
    # see gh-15143
    #
    # Check that categoricals hash consistent with their values,
    # not codes. This should work for categoricals of any dtype.
    s1 = Series(s1)
    s2 = s1.astype("category").cat.set_categories(s1)
    s3 = s2.cat.set_categories(list(reversed(s1)))

    # These should all hash identically.
    h1 = hash_pandas_object(s1, categorize=categorize)
    h2 = hash_pandas_object(s2, categorize=categorize)
    h3 = hash_pandas_object(s3, categorize=categorize)

    tm.assert_series_equal(h1, h2)
    tm.assert_series_equal(h1, h3)


def test_categorical_with_nan_consistency(unit):
    dti = pd.date_range("2012-01-01", periods=5, name="B", unit=unit)
    cat = pd.Categorical.from_codes([-1, 0, 1, 2, 3, 4], categories=dti)
    expected = hash_array(cat, categorize=False)

    ts = pd.Timestamp("2012-01-01").as_unit(unit)
    cat2 = pd.Categorical.from_codes([-1, 0], categories=[ts])
    result = hash_array(cat2, categorize=False)

    assert result[0] in expected
    assert result[1] in expected


def test_pandas_errors():
    msg = "Unexpected type for hashing"
    with pytest.raises(TypeError, match=msg):
        hash_pandas_object(pd.Timestamp("20130101"))


def test_hash_keys():
    # Using different hash keys, should have
    # different hashes for the same data.
    #
    # This only matters for object dtypes.
    obj = Series(list("abc"))

    a = hash_pandas_object(obj, hash_key="9876543210123456")
    b = hash_pandas_object(obj, hash_key="9876543210123465")

    assert (a != b).all()


def test_df_hash_keys():
    # DataFrame version of the test_hash_keys.
    # https://github.com/pandas-dev/pandas/issues/41404
    obj = DataFrame({"x": np.arange(3), "y": list("abc")})

    a = hash_pandas_object(obj, hash_key="9876543210123456")
    b = hash_pandas_object(obj, hash_key="9876543210123465")

    assert (a != b).all()


def test_df_encoding():
    # Check that DataFrame recognizes optional encoding.
    # https://github.com/pandas-dev/pandas/issues/41404
    # https://github.com/pandas-dev/pandas/pull/42049
    obj = DataFrame({"x": np.arange(3), "y": list("a+c")})

    a = hash_pandas_object(obj, encoding="utf8")
    b = hash_pandas_object(obj, encoding="utf7")

    # Note that the "+" is encoded as "+-" in utf-7.
    assert a[0] == b[0]
    assert a[1] != b[1]
    assert a[2] == b[2]


def test_invalid_key():
    # This only matters for object dtypes.
    msg = "key should be a 16-byte string encoded"

    with pytest.raises(ValueError, match=msg):
        hash_pandas_object(Series(list("abc")), hash_key="foo")


def test_already_encoded(index):
    # If already encoded, then ok.
    obj = Series(list("abc")).str.encode("utf8")
    a = hash_pandas_object(obj, index=index)
    b = hash_pandas_object(obj, index=index)
    tm.assert_series_equal(a, b)


def test_alternate_encoding(index):
    obj = Series(list("abc"))
    a = hash_pandas_object(obj, index=index)
    b = hash_pandas_object(obj, index=index)
    tm.assert_series_equal(a, b)


@pytest.mark.parametrize("l_exp", range(8))
@pytest.mark.parametrize("l_add", [0, 1])
def test_same_len_hash_collisions(l_exp, l_add):
    length = 2 ** (l_exp + 8) + l_add
    idx = np.array([str(i) for i in range(length)], dtype=object)

    result = hash_array(idx, "utf8")
    assert not result[0] == result[1]


def test_hash_collisions():
    # Hash collisions are bad.
    #
    # https://github.com/pandas-dev/pandas/issues/14711#issuecomment-264885726
    hashes = [
        "Ingrid-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",
        "Tim-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",
    ]

    # These should be different.
    result1 = hash_array(np.asarray(hashes[0:1], dtype=object), "utf8")
    expected1 = np.array([14963968704024874985], dtype=np.uint64)
    tm.assert_numpy_array_equal(result1, expected1)

    result2 = hash_array(np.asarray(hashes[1:2], dtype=object), "utf8")
    expected2 = np.array([16428432627716348016], dtype=np.uint64)
    tm.assert_numpy_array_equal(result2, expected2)

    result = hash_array(np.asarray(hashes, dtype=object), "utf8")
    tm.assert_numpy_array_equal(result, np.concatenate([expected1, expected2], axis=0))


@pytest.mark.parametrize(
    "data, result_data",
    [
        [[tuple("1"), tuple("2")], [10345501319357378243, 8331063931016360761]],
        [[(1,), (2,)], [9408946347443669104, 3278256261030523334]],
    ],
)
def test_hash_with_tuple(data, result_data):
    # GH#28969 array containing a tuple raises on call to arr.astype(str)
    #  apparently a numpy bug github.com/numpy/numpy/issues/9441

    df = DataFrame({"data": data})
    result = hash_pandas_object(df)
    expected = Series(result_data, dtype=np.uint64)
    tm.assert_series_equal(result, expected)


def test_hashable_tuple_args():
    # require that the elements of such tuples are themselves hashable

    df3 = DataFrame(
        {
            "data": [
                (
                    1,
                    [],
                ),
                (
                    2,
                    {},
                ),
            ]
        }
    )
    with pytest.raises(TypeError, match="unhashable type: 'list'"):
        hash_pandas_object(df3)


def test_hash_object_none_key():
    # https://github.com/pandas-dev/pandas/issues/30887
    result = pd.util.hash_pandas_object(Series(["a", "b"]), hash_key=None)
    expected = Series([4578374827886788867, 17338122309987883691], dtype="uint64")
    tm.assert_series_equal(result, expected)
