import string

import numpy as np

from pandas import (
    NA,
    DataFrame,
    Index,
    MultiIndex,
    RangeIndex,
    Series,
    array,
    date_range,
)


class GetLoc:
    def setup(self):
        self.mi_large = MultiIndex.from_product(
            [np.arange(1000), np.arange(20), list(string.ascii_letters)],
            names=["one", "two", "three"],
        )
        self.mi_med = MultiIndex.from_product(
            [np.arange(1000), np.arange(10), list("A")], names=["one", "two", "three"]
        )
        self.mi_small = MultiIndex.from_product(
            [np.arange(100), list("A"), list("A")], names=["one", "two", "three"]
        )

    def time_large_get_loc(self):
        self.mi_large.get_loc((999, 19, "Z"))

    def time_large_get_loc_warm(self):
        for _ in range(1000):
            self.mi_large.get_loc((999, 19, "Z"))

    def time_med_get_loc(self):
        self.mi_med.get_loc((999, 9, "A"))

    def time_med_get_loc_warm(self):
        for _ in range(1000):
            self.mi_med.get_loc((999, 9, "A"))

    def time_string_get_loc(self):
        self.mi_small.get_loc((99, "A", "A"))

    def time_small_get_loc_warm(self):
        for _ in range(1000):
            self.mi_small.get_loc((99, "A", "A"))


class GetLocs:
    def setup(self):
        self.mi_large = MultiIndex.from_product(
            [np.arange(1000), np.arange(20), list(string.ascii_letters)],
            names=["one", "two", "three"],
        )
        self.mi_med = MultiIndex.from_product(
            [np.arange(1000), np.arange(10), list("A")], names=["one", "two", "three"]
        )
        self.mi_small = MultiIndex.from_product(
            [np.arange(100), list("A"), list("A")], names=["one", "two", "three"]
        )

    def time_large_get_locs(self):
        self.mi_large.get_locs([999, 19, "Z"])

    def time_med_get_locs(self):
        self.mi_med.get_locs([999, 9, "A"])

    def time_small_get_locs(self):
        self.mi_small.get_locs([99, "A", "A"])


class Duplicates:
    def setup(self):
        size = 65536
        arrays = [np.random.randint(0, 8192, size), np.random.randint(0, 1024, size)]
        mask = np.random.rand(size) < 0.1
        self.mi_unused_levels = MultiIndex.from_arrays(arrays)
        self.mi_unused_levels = self.mi_unused_levels[mask]

    def time_remove_unused_levels(self):
        self.mi_unused_levels.remove_unused_levels()


class Integer:
    def setup(self):
        self.mi_int = MultiIndex.from_product(
            [np.arange(1000), np.arange(1000)], names=["one", "two"]
        )
        self.obj_index = np.array(
            [
                (0, 10),
                (0, 11),
                (0, 12),
                (0, 13),
                (0, 14),
                (0, 15),
                (0, 16),
                (0, 17),
                (0, 18),
                (0, 19),
            ],
            dtype=object,
        )
        self.other_mi_many_mismatches = MultiIndex.from_tuples(
            [
                (-7, 41),
                (-2, 3),
                (-0.7, 5),
                (0, 0),
                (0, 1.5),
                (0, 340),
                (0, 1001),
                (1, -4),
                (1, 20),
                (1, 1040),
                (432, -5),
                (432, 17),
                (439, 165.5),
                (998, -4),
                (998, 24065),
                (999, 865.2),
                (999, 1000),
                (1045, -843),
            ]
        )

    def time_get_indexer(self):
        self.mi_int.get_indexer(self.obj_index)

    def time_get_indexer_and_backfill(self):
        self.mi_int.get_indexer(self.other_mi_many_mismatches, method="backfill")

    def time_get_indexer_and_pad(self):
        self.mi_int.get_indexer(self.other_mi_many_mismatches, method="pad")

    def time_is_monotonic(self):
        self.mi_int.is_monotonic_increasing


class Duplicated:
    def setup(self):
        n, k = 200, 5000
        levels = [
            np.arange(n),
            Index([f"i-{i}" for i in range(n)], dtype=object).values,
            1000 + np.arange(n),
        ]
        codes = [np.random.choice(n, (k * n)) for lev in levels]
        self.mi = MultiIndex(levels=levels, codes=codes)

    def time_duplicated(self):
        self.mi.duplicated()


class Sortlevel:
    def setup(self):
        n = 1182720
        low, high = -4096, 4096
        arrs = [
            np.repeat(np.random.randint(low, high, (n // k)), k)
            for k in [11, 7, 5, 3, 1]
        ]
        self.mi_int = MultiIndex.from_arrays(arrs)[np.random.permutation(n)]

        a = np.repeat(np.arange(100), 1000)
        b = np.tile(np.arange(1000), 100)
        self.mi = MultiIndex.from_arrays([a, b])
        self.mi = self.mi.take(np.random.permutation(np.arange(100000)))

    def time_sortlevel_int64(self):
        self.mi_int.sortlevel()

    def time_sortlevel_zero(self):
        self.mi.sortlevel(0)

    def time_sortlevel_one(self):
        self.mi.sortlevel(1)


class SortValues:
    params = ["int64", "Int64"]
    param_names = ["dtype"]

    def setup(self, dtype):
        a = array(np.tile(np.arange(100), 1000), dtype=dtype)
        b = array(np.tile(np.arange(1000), 100), dtype=dtype)
        self.mi = MultiIndex.from_arrays([a, b])

    def time_sort_values(self, dtype):
        self.mi.sort_values()


class Values:
    def setup_cache(self):
        level1 = range(1000)
        level2 = date_range(start="1/1/2012", periods=100)
        mi = MultiIndex.from_product([level1, level2])
        return mi

    def time_datetime_level_values_copy(self, mi):
        mi.copy().values

    def time_datetime_level_values_sliced(self, mi):
        mi[:10].values


class CategoricalLevel:
    def setup(self):
        self.df = DataFrame(
            {
                "a": np.arange(1_000_000, dtype=np.int32),
                "b": np.arange(1_000_000, dtype=np.int64),
                "c": np.arange(1_000_000, dtype=float),
            }
        ).astype({"a": "category", "b": "category"})

    def time_categorical_level(self):
        self.df.set_index(["a", "b"])


class Equals:
    def setup(self):
        self.mi = MultiIndex.from_product(
            [
                date_range("2000-01-01", periods=1000),
                RangeIndex(1000),
            ]
        )
        self.mi_deepcopy = self.mi.copy(deep=True)
        self.idx_non_object = RangeIndex(1)

    def time_equals_deepcopy(self):
        self.mi.equals(self.mi_deepcopy)

    def time_equals_non_object_index(self):
        self.mi.equals(self.idx_non_object)


class SetOperations:
    params = [
        ("monotonic", "non_monotonic"),
        ("datetime", "int", "string", "ea_int"),
        ("intersection", "union", "symmetric_difference"),
        (False, None),
    ]
    param_names = ["index_structure", "dtype", "method", "sort"]

    def setup(self, index_structure, dtype, method, sort):
        N = 10**5
        level1 = range(1000)

        level2 = date_range(start="1/1/2000", periods=N // 1000)
        dates_left = MultiIndex.from_product([level1, level2])

        level2 = range(N // 1000)
        int_left = MultiIndex.from_product([level1, level2])

        level2 = Index([f"i-{i}" for i in range(N // 1000)], dtype=object).values
        str_left = MultiIndex.from_product([level1, level2])

        level2 = range(N // 1000)
        ea_int_left = MultiIndex.from_product([level1, Series(level2, dtype="Int64")])

        data = {
            "datetime": dates_left,
            "int": int_left,
            "string": str_left,
            "ea_int": ea_int_left,
        }

        if index_structure == "non_monotonic":
            data = {k: mi[::-1] for k, mi in data.items()}

        data = {k: {"left": mi, "right": mi[:-1]} for k, mi in data.items()}
        self.left = data[dtype]["left"]
        self.right = data[dtype]["right"]

    def time_operation(self, index_structure, dtype, method, sort):
        getattr(self.left, method)(self.right, sort=sort)


class Difference:
    params = [
        ("datetime", "int", "string", "ea_int"),
    ]
    param_names = ["dtype"]

    def setup(self, dtype):
        N = 10**4 * 2
        level1 = range(1000)

        level2 = date_range(start="1/1/2000", periods=N // 1000)
        dates_left = MultiIndex.from_product([level1, level2])

        level2 = range(N // 1000)
        int_left = MultiIndex.from_product([level1, level2])

        level2 = Series(range(N // 1000), dtype="Int64")
        level2[0] = NA
        ea_int_left = MultiIndex.from_product([level1, level2])

        level2 = Index([f"i-{i}" for i in range(N // 1000)], dtype=object).values
        str_left = MultiIndex.from_product([level1, level2])

        data = {
            "datetime": dates_left,
            "int": int_left,
            "ea_int": ea_int_left,
            "string": str_left,
        }

        data = {k: {"left": mi, "right": mi[:5]} for k, mi in data.items()}
        self.left = data[dtype]["left"]
        self.right = data[dtype]["right"]

    def time_difference(self, dtype):
        self.left.difference(self.right)


class Unique:
    params = [
        (("Int64", NA), ("int64", 0)),
    ]
    param_names = ["dtype_val"]

    def setup(self, dtype_val):
        level = Series(
            [1, 2, dtype_val[1], dtype_val[1], *list(range(1000000))],
            dtype=dtype_val[0],
        )
        self.midx = MultiIndex.from_arrays([level, level])

        level_dups = Series(
            [1, 2, dtype_val[1], dtype_val[1]] + list(range(500_000)) * 2,
            dtype=dtype_val[0],
        )

        self.midx_dups = MultiIndex.from_arrays([level_dups, level_dups])

    def time_unique(self, dtype_val):
        self.midx.unique()

    def time_unique_dups(self, dtype_val):
        self.midx_dups.unique()


class Isin:
    params = [
        ("string", "int", "datetime"),
    ]
    param_names = ["dtype"]

    def setup(self, dtype):
        N = 10**5
        level1 = range(1000)

        level2 = date_range(start="1/1/2000", periods=N // 1000)
        dates_midx = MultiIndex.from_product([level1, level2])

        level2 = range(N // 1000)
        int_midx = MultiIndex.from_product([level1, level2])

        level2 = Index([f"i-{i}" for i in range(N // 1000)], dtype=object).values
        str_midx = MultiIndex.from_product([level1, level2])

        data = {
            "datetime": dates_midx,
            "int": int_midx,
            "string": str_midx,
        }

        self.midx = data[dtype]
        self.values_small = self.midx[:100]
        self.values_large = self.midx[100:]

    def time_isin_small(self, dtype):
        self.midx.isin(self.values_small)

    def time_isin_large(self, dtype):
        self.midx.isin(self.values_large)


class Putmask:
    def setup(self):
        N = 10**5
        level1 = range(1_000)

        level2 = date_range(start="1/1/2000", periods=N // 1000)
        self.midx = MultiIndex.from_product([level1, level2])

        level1 = range(1_000, 2_000)
        self.midx_values = MultiIndex.from_product([level1, level2])

        level2 = date_range(start="1/1/2010", periods=N // 1000)
        self.midx_values_different = MultiIndex.from_product([level1, level2])
        self.mask = np.array([True, False] * (N // 2))

    def time_putmask(self):
        self.midx.putmask(self.mask, self.midx_values)

    def time_putmask_all_different(self):
        self.midx.putmask(self.mask, self.midx_values_different)


class Append:
    params = ["datetime64[ns]", "int64", "string"]
    param_names = ["dtype"]

    def setup(self, dtype):
        N1 = 1000
        N2 = 500
        left_level1 = range(N1)
        right_level1 = range(N1, N1 + N1)

        if dtype == "datetime64[ns]":
            level2 = date_range(start="2000-01-01", periods=N2)
        elif dtype == "int64":
            level2 = range(N2)
        elif dtype == "string":
            level2 = Index([f"i-{i}" for i in range(N2)], dtype=object)
        else:
            raise NotImplementedError

        self.left = MultiIndex.from_product([left_level1, level2])
        self.right = MultiIndex.from_product([right_level1, level2])

    def time_append(self, dtype):
        self.left.append(self.right)


from .pandas_vb_common import setup  # noqa: F401 isort:skip
