import warnings

import numpy as np
import pytest

import pandas as pd
from pandas import (
    Categorical,
    DataFrame,
    Series,
    date_range,
)
import pandas._testing as tm
from pandas.tests.arrays.categorical.common import TestCategorical


class TestCategoricalOpsWithFactor(TestCategorical):
    def test_categories_none_comparisons(self):
        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
        tm.assert_categorical_equal(factor, self.factor)

    def test_comparisons(self):
        result = self.factor[self.factor == "a"]
        expected = self.factor[np.asarray(self.factor) == "a"]
        tm.assert_categorical_equal(result, expected)

        result = self.factor[self.factor != "a"]
        expected = self.factor[np.asarray(self.factor) != "a"]
        tm.assert_categorical_equal(result, expected)

        result = self.factor[self.factor < "c"]
        expected = self.factor[np.asarray(self.factor) < "c"]
        tm.assert_categorical_equal(result, expected)

        result = self.factor[self.factor > "a"]
        expected = self.factor[np.asarray(self.factor) > "a"]
        tm.assert_categorical_equal(result, expected)

        result = self.factor[self.factor >= "b"]
        expected = self.factor[np.asarray(self.factor) >= "b"]
        tm.assert_categorical_equal(result, expected)

        result = self.factor[self.factor <= "b"]
        expected = self.factor[np.asarray(self.factor) <= "b"]
        tm.assert_categorical_equal(result, expected)

        n = len(self.factor)

        other = self.factor[np.random.permutation(n)]
        result = self.factor == other
        expected = np.asarray(self.factor) == np.asarray(other)
        tm.assert_numpy_array_equal(result, expected)

        result = self.factor == "d"
        expected = np.zeros(len(self.factor), dtype=bool)
        tm.assert_numpy_array_equal(result, expected)

        # comparisons with categoricals
        cat_rev = Categorical(["a", "b", "c"], categories=["c", "b", "a"], ordered=True)
        cat_rev_base = Categorical(
            ["b", "b", "b"], categories=["c", "b", "a"], ordered=True
        )
        cat = Categorical(["a", "b", "c"], ordered=True)
        cat_base = Categorical(["b", "b", "b"], categories=cat.categories, ordered=True)

        # comparisons need to take categories ordering into account
        res_rev = cat_rev > cat_rev_base
        exp_rev = np.array([True, False, False])
        tm.assert_numpy_array_equal(res_rev, exp_rev)

        res_rev = cat_rev < cat_rev_base
        exp_rev = np.array([False, False, True])
        tm.assert_numpy_array_equal(res_rev, exp_rev)

        res = cat > cat_base
        exp = np.array([False, False, True])
        tm.assert_numpy_array_equal(res, exp)

        # Only categories with same categories can be compared
        msg = "Categoricals can only be compared if 'categories' are the same"
        with pytest.raises(TypeError, match=msg):
            cat > cat_rev

        cat_rev_base2 = Categorical(["b", "b", "b"], categories=["c", "b", "a", "d"])

        with pytest.raises(TypeError, match=msg):
            cat_rev > cat_rev_base2

        # Only categories with same ordering information can be compared
        cat_unorderd = cat.set_ordered(False)
        assert not (cat > cat).any()

        with pytest.raises(TypeError, match=msg):
            cat > cat_unorderd

        # comparison (in both directions) with Series will raise
        s = Series(["b", "b", "b"])
        msg = (
            "Cannot compare a Categorical for op __gt__ with type "
            r"<class 'numpy\.ndarray'>"
        )
        with pytest.raises(TypeError, match=msg):
            cat > s
        with pytest.raises(TypeError, match=msg):
            cat_rev > s
        with pytest.raises(TypeError, match=msg):
            s < cat
        with pytest.raises(TypeError, match=msg):
            s < cat_rev

        # comparison with numpy.array will raise in both direction, but only on
        # newer numpy versions
        a = np.array(["b", "b", "b"])
        with pytest.raises(TypeError, match=msg):
            cat > a
        with pytest.raises(TypeError, match=msg):
            cat_rev > a

        # Make sure that unequal comparison take the categories order in
        # account
        cat_rev = Categorical(list("abc"), categories=list("cba"), ordered=True)
        exp = np.array([True, False, False])
        res = cat_rev > "b"
        tm.assert_numpy_array_equal(res, exp)

        # check that zero-dim array gets unboxed
        res = cat_rev > np.array("b")
        tm.assert_numpy_array_equal(res, exp)


class TestCategoricalOps:
    def test_compare_frame(self):
        # GH#24282 check that Categorical.__cmp__(DataFrame) defers to frame
        data = ["a", "b", 2, "a"]
        cat = Categorical(data)

        df = DataFrame(cat)

        result = cat == df.T
        expected = DataFrame([[True, True, True, True]])
        tm.assert_frame_equal(result, expected)

        result = cat[::-1] != df.T
        expected = DataFrame([[False, True, True, False]])
        tm.assert_frame_equal(result, expected)

    def test_compare_frame_raises(self, comparison_op):
        # alignment raises unless we transpose
        op = comparison_op
        cat = Categorical(["a", "b", 2, "a"])
        df = DataFrame(cat)
        msg = "Unable to coerce to Series, length must be 1: given 4"
        with pytest.raises(ValueError, match=msg):
            op(cat, df)

    def test_datetime_categorical_comparison(self):
        dt_cat = Categorical(date_range("2014-01-01", periods=3), ordered=True)
        tm.assert_numpy_array_equal(dt_cat > dt_cat[0], np.array([False, True, True]))
        tm.assert_numpy_array_equal(dt_cat[0] < dt_cat, np.array([False, True, True]))

    def test_reflected_comparison_with_scalars(self):
        # GH8658
        cat = Categorical([1, 2, 3], ordered=True)
        tm.assert_numpy_array_equal(cat > cat[0], np.array([False, True, True]))
        tm.assert_numpy_array_equal(cat[0] < cat, np.array([False, True, True]))

    def test_comparison_with_unknown_scalars(self):
        # https://github.com/pandas-dev/pandas/issues/9836#issuecomment-92123057
        # and following comparisons with scalars not in categories should raise
        # for unequal comps, but not for equal/not equal
        cat = Categorical([1, 2, 3], ordered=True)

        msg = "Invalid comparison between dtype=category and int"
        with pytest.raises(TypeError, match=msg):
            cat < 4
        with pytest.raises(TypeError, match=msg):
            cat > 4
        with pytest.raises(TypeError, match=msg):
            4 < cat
        with pytest.raises(TypeError, match=msg):
            4 > cat

        tm.assert_numpy_array_equal(cat == 4, np.array([False, False, False]))
        tm.assert_numpy_array_equal(cat != 4, np.array([True, True, True]))

    def test_comparison_with_tuple(self):
        cat = Categorical(np.array(["foo", (0, 1), 3, (0, 1)], dtype=object))

        result = cat == "foo"
        expected = np.array([True, False, False, False], dtype=bool)
        tm.assert_numpy_array_equal(result, expected)

        result = cat == (0, 1)
        expected = np.array([False, True, False, True], dtype=bool)
        tm.assert_numpy_array_equal(result, expected)

        result = cat != (0, 1)
        tm.assert_numpy_array_equal(result, ~expected)

    def test_comparison_of_ordered_categorical_with_nan_to_scalar(
        self, compare_operators_no_eq_ne
    ):
        # https://github.com/pandas-dev/pandas/issues/26504
        # BUG: fix ordered categorical comparison with missing values (#26504 )
        # and following comparisons with scalars in categories with missing
        # values should be evaluated as False

        cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)
        scalar = 2
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
            expected = getattr(np.array(cat), compare_operators_no_eq_ne)(scalar)
        actual = getattr(cat, compare_operators_no_eq_ne)(scalar)
        tm.assert_numpy_array_equal(actual, expected)

    def test_comparison_of_ordered_categorical_with_nan_to_listlike(
        self, compare_operators_no_eq_ne
    ):
        # https://github.com/pandas-dev/pandas/issues/26504
        # and following comparisons of missing values in ordered Categorical
        # with listlike should be evaluated as False

        cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)
        other = Categorical([2, 2, 2, 2], categories=[1, 2, 3], ordered=True)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", RuntimeWarning)
            expected = getattr(np.array(cat), compare_operators_no_eq_ne)(2)
        actual = getattr(cat, compare_operators_no_eq_ne)(other)
        tm.assert_numpy_array_equal(actual, expected)

    @pytest.mark.parametrize(
        "data,reverse,base",
        [(list("abc"), list("cba"), list("bbb")), ([1, 2, 3], [3, 2, 1], [2, 2, 2])],
    )
    def test_comparisons(self, data, reverse, base):
        cat_rev = Series(Categorical(data, categories=reverse, ordered=True))
        cat_rev_base = Series(Categorical(base, categories=reverse, ordered=True))
        cat = Series(Categorical(data, ordered=True))
        cat_base = Series(
            Categorical(base, categories=cat.cat.categories, ordered=True)
        )
        s = Series(base)
        a = np.array(base)

        # comparisons need to take categories ordering into account
        res_rev = cat_rev > cat_rev_base
        exp_rev = Series([True, False, False])
        tm.assert_series_equal(res_rev, exp_rev)

        res_rev = cat_rev < cat_rev_base
        exp_rev = Series([False, False, True])
        tm.assert_series_equal(res_rev, exp_rev)

        res = cat > cat_base
        exp = Series([False, False, True])
        tm.assert_series_equal(res, exp)

        scalar = base[1]
        res = cat > scalar
        exp = Series([False, False, True])
        exp2 = cat.values > scalar
        tm.assert_series_equal(res, exp)
        tm.assert_numpy_array_equal(res.values, exp2)
        res_rev = cat_rev > scalar
        exp_rev = Series([True, False, False])
        exp_rev2 = cat_rev.values > scalar
        tm.assert_series_equal(res_rev, exp_rev)
        tm.assert_numpy_array_equal(res_rev.values, exp_rev2)

        # Only categories with same categories can be compared
        msg = "Categoricals can only be compared if 'categories' are the same"
        with pytest.raises(TypeError, match=msg):
            cat > cat_rev

        # categorical cannot be compared to Series or numpy array, and also
        # not the other way around
        msg = (
            "Cannot compare a Categorical for op __gt__ with type "
            r"<class 'numpy\.ndarray'>"
        )
        with pytest.raises(TypeError, match=msg):
            cat > s
        with pytest.raises(TypeError, match=msg):
            cat_rev > s
        with pytest.raises(TypeError, match=msg):
            cat > a
        with pytest.raises(TypeError, match=msg):
            cat_rev > a

        with pytest.raises(TypeError, match=msg):
            s < cat
        with pytest.raises(TypeError, match=msg):
            s < cat_rev

        with pytest.raises(TypeError, match=msg):
            a < cat
        with pytest.raises(TypeError, match=msg):
            a < cat_rev

    @pytest.mark.parametrize(
        "ctor",
        [
            lambda *args, **kwargs: Categorical(*args, **kwargs),
            lambda *args, **kwargs: Series(Categorical(*args, **kwargs)),
        ],
    )
    def test_unordered_different_order_equal(self, ctor):
        # https://github.com/pandas-dev/pandas/issues/16014
        c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False)
        c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False)
        assert (c1 == c2).all()

        c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False)
        c2 = ctor(["b", "a"], categories=["b", "a"], ordered=False)
        assert (c1 != c2).all()

        c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False)
        c2 = ctor(["b", "b"], categories=["b", "a"], ordered=False)
        assert (c1 != c2).all()

        c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False)
        c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False)
        result = c1 == c2
        tm.assert_numpy_array_equal(np.array(result), np.array([True, False]))

    def test_unordered_different_categories_raises(self):
        c1 = Categorical(["a", "b"], categories=["a", "b"], ordered=False)
        c2 = Categorical(["a", "c"], categories=["c", "a"], ordered=False)

        with pytest.raises(TypeError, match=("Categoricals can only be compared")):
            c1 == c2

    def test_compare_different_lengths(self):
        c1 = Categorical([], categories=["a", "b"])
        c2 = Categorical([], categories=["a"])

        msg = "Categoricals can only be compared if 'categories' are the same."
        with pytest.raises(TypeError, match=msg):
            c1 == c2

    def test_compare_unordered_different_order(self):
        # https://github.com/pandas-dev/pandas/issues/16603#issuecomment-
        # 349290078
        a = Categorical(["a"], categories=["a", "b"])
        b = Categorical(["b"], categories=["b", "a"])
        assert not a.equals(b)

    def test_numeric_like_ops(self):

        df = DataFrame({"value": np.random.randint(0, 10000, 100)})
        labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]
        cat_labels = Categorical(labels, labels)

        df = df.sort_values(by=["value"], ascending=True)
        df["value_group"] = pd.cut(
            df.value, range(0, 10500, 500), right=False, labels=cat_labels
        )

        # numeric ops should not succeed
        for op, str_rep in [
            ("__add__", r"\+"),
            ("__sub__", "-"),
            ("__mul__", r"\*"),
            ("__truediv__", "/"),
        ]:
            msg = f"Series cannot perform the operation {str_rep}|unsupported operand"
            with pytest.raises(TypeError, match=msg):
                getattr(df, op)(df)

        # reduction ops should not succeed (unless specifically defined, e.g.
        # min/max)
        s = df["value_group"]
        for op in ["kurt", "skew", "var", "std", "mean", "sum", "median"]:
            msg = f"does not support reduction '{op}'"
            with pytest.raises(TypeError, match=msg):
                getattr(s, op)(numeric_only=False)

        # mad technically works because it takes always the numeric data

        # numpy ops
        s = Series(Categorical([1, 2, 3, 4]))
        with pytest.raises(TypeError, match="does not support reduction 'sum'"):
            np.sum(s)

        # numeric ops on a Series
        for op, str_rep in [
            ("__add__", r"\+"),
            ("__sub__", "-"),
            ("__mul__", r"\*"),
            ("__truediv__", "/"),
        ]:
            msg = f"Series cannot perform the operation {str_rep}|unsupported operand"
            with pytest.raises(TypeError, match=msg):
                getattr(s, op)(2)

        # invalid ufunc
        msg = "Object with dtype category cannot perform the numpy op log"
        with pytest.raises(TypeError, match=msg):
            np.log(s)
