import numpy as np
import pytest

import pandas as pd
from pandas import Index, IntervalIndex, MultiIndex
from pandas.api.types import is_scalar


def test_is_monotonic_increasing():
    i = MultiIndex.from_product([np.arange(10), np.arange(10)], names=["one", "two"])
    assert i.is_monotonic is True
    assert i._is_strictly_monotonic_increasing is True
    assert Index(i.values).is_monotonic is True
    assert i._is_strictly_monotonic_increasing is True

    i = MultiIndex.from_product(
        [np.arange(10, 0, -1), np.arange(10)], names=["one", "two"]
    )
    assert i.is_monotonic is False
    assert i._is_strictly_monotonic_increasing is False
    assert Index(i.values).is_monotonic is False
    assert Index(i.values)._is_strictly_monotonic_increasing is False

    i = MultiIndex.from_product(
        [np.arange(10), np.arange(10, 0, -1)], names=["one", "two"]
    )
    assert i.is_monotonic is False
    assert i._is_strictly_monotonic_increasing is False
    assert Index(i.values).is_monotonic is False
    assert Index(i.values)._is_strictly_monotonic_increasing is False

    i = MultiIndex.from_product([[1.0, np.nan, 2.0], ["a", "b", "c"]])
    assert i.is_monotonic is False
    assert i._is_strictly_monotonic_increasing is False
    assert Index(i.values).is_monotonic is False
    assert Index(i.values)._is_strictly_monotonic_increasing is False

    # string ordering
    i = MultiIndex(
        levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
        names=["first", "second"],
    )
    assert i.is_monotonic is False
    assert Index(i.values).is_monotonic is False
    assert i._is_strictly_monotonic_increasing is False
    assert Index(i.values)._is_strictly_monotonic_increasing is False

    i = MultiIndex(
        levels=[["bar", "baz", "foo", "qux"], ["mom", "next", "zenith"]],
        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
        names=["first", "second"],
    )
    assert i.is_monotonic is True
    assert Index(i.values).is_monotonic is True
    assert i._is_strictly_monotonic_increasing is True
    assert Index(i.values)._is_strictly_monotonic_increasing is True

    # mixed levels, hits the TypeError
    i = MultiIndex(
        levels=[
            [1, 2, 3, 4],
            [
                "gb00b03mlx29",
                "lu0197800237",
                "nl0000289783",
                "nl0000289965",
                "nl0000301109",
            ],
        ],
        codes=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]],
        names=["household_id", "asset_id"],
    )

    assert i.is_monotonic is False
    assert i._is_strictly_monotonic_increasing is False

    # empty
    i = MultiIndex.from_arrays([[], []])
    assert i.is_monotonic is True
    assert Index(i.values).is_monotonic is True
    assert i._is_strictly_monotonic_increasing is True
    assert Index(i.values)._is_strictly_monotonic_increasing is True


def test_is_monotonic_decreasing():
    i = MultiIndex.from_product(
        [np.arange(9, -1, -1), np.arange(9, -1, -1)], names=["one", "two"]
    )
    assert i.is_monotonic_decreasing is True
    assert i._is_strictly_monotonic_decreasing is True
    assert Index(i.values).is_monotonic_decreasing is True
    assert i._is_strictly_monotonic_decreasing is True

    i = MultiIndex.from_product(
        [np.arange(10), np.arange(10, 0, -1)], names=["one", "two"]
    )
    assert i.is_monotonic_decreasing is False
    assert i._is_strictly_monotonic_decreasing is False
    assert Index(i.values).is_monotonic_decreasing is False
    assert Index(i.values)._is_strictly_monotonic_decreasing is False

    i = MultiIndex.from_product(
        [np.arange(10, 0, -1), np.arange(10)], names=["one", "two"]
    )
    assert i.is_monotonic_decreasing is False
    assert i._is_strictly_monotonic_decreasing is False
    assert Index(i.values).is_monotonic_decreasing is False
    assert Index(i.values)._is_strictly_monotonic_decreasing is False

    i = MultiIndex.from_product([[2.0, np.nan, 1.0], ["c", "b", "a"]])
    assert i.is_monotonic_decreasing is False
    assert i._is_strictly_monotonic_decreasing is False
    assert Index(i.values).is_monotonic_decreasing is False
    assert Index(i.values)._is_strictly_monotonic_decreasing is False

    # string ordering
    i = MultiIndex(
        levels=[["qux", "foo", "baz", "bar"], ["three", "two", "one"]],
        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
        names=["first", "second"],
    )
    assert i.is_monotonic_decreasing is False
    assert Index(i.values).is_monotonic_decreasing is False
    assert i._is_strictly_monotonic_decreasing is False
    assert Index(i.values)._is_strictly_monotonic_decreasing is False

    i = MultiIndex(
        levels=[["qux", "foo", "baz", "bar"], ["zenith", "next", "mom"]],
        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
        names=["first", "second"],
    )
    assert i.is_monotonic_decreasing is True
    assert Index(i.values).is_monotonic_decreasing is True
    assert i._is_strictly_monotonic_decreasing is True
    assert Index(i.values)._is_strictly_monotonic_decreasing is True

    # mixed levels, hits the TypeError
    i = MultiIndex(
        levels=[
            [4, 3, 2, 1],
            [
                "nl0000301109",
                "nl0000289965",
                "nl0000289783",
                "lu0197800237",
                "gb00b03mlx29",
            ],
        ],
        codes=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]],
        names=["household_id", "asset_id"],
    )

    assert i.is_monotonic_decreasing is False
    assert i._is_strictly_monotonic_decreasing is False

    # empty
    i = MultiIndex.from_arrays([[], []])
    assert i.is_monotonic_decreasing is True
    assert Index(i.values).is_monotonic_decreasing is True
    assert i._is_strictly_monotonic_decreasing is True
    assert Index(i.values)._is_strictly_monotonic_decreasing is True


def test_is_strictly_monotonic_increasing():
    idx = pd.MultiIndex(
        levels=[["bar", "baz"], ["mom", "next"]], codes=[[0, 0, 1, 1], [0, 0, 0, 1]]
    )
    assert idx.is_monotonic_increasing is True
    assert idx._is_strictly_monotonic_increasing is False


def test_is_strictly_monotonic_decreasing():
    idx = pd.MultiIndex(
        levels=[["baz", "bar"], ["next", "mom"]], codes=[[0, 0, 1, 1], [0, 0, 0, 1]]
    )
    assert idx.is_monotonic_decreasing is True
    assert idx._is_strictly_monotonic_decreasing is False


def test_searchsorted_monotonic(indices):
    # GH17271
    # not implemented for tuple searches in MultiIndex
    # or Intervals searches in IntervalIndex
    if isinstance(indices, (MultiIndex, IntervalIndex)):
        return

    # nothing to test if the index is empty
    if indices.empty:
        return
    value = indices[0]

    # determine the expected results (handle dupes for 'right')
    expected_left, expected_right = 0, (indices == value).argmin()
    if expected_right == 0:
        # all values are the same, expected_right should be length
        expected_right = len(indices)

    # test _searchsorted_monotonic in all cases
    # test searchsorted only for increasing
    if indices.is_monotonic_increasing:
        ssm_left = indices._searchsorted_monotonic(value, side="left")
        assert is_scalar(ssm_left)
        assert expected_left == ssm_left

        ssm_right = indices._searchsorted_monotonic(value, side="right")
        assert is_scalar(ssm_right)
        assert expected_right == ssm_right

        ss_left = indices.searchsorted(value, side="left")
        assert is_scalar(ss_left)
        assert expected_left == ss_left

        ss_right = indices.searchsorted(value, side="right")
        assert is_scalar(ss_right)
        assert expected_right == ss_right

    elif indices.is_monotonic_decreasing:
        ssm_left = indices._searchsorted_monotonic(value, side="left")
        assert is_scalar(ssm_left)
        assert expected_left == ssm_left

        ssm_right = indices._searchsorted_monotonic(value, side="right")
        assert is_scalar(ssm_right)
        assert expected_right == ssm_right

    else:
        # non-monotonic should raise.
        with pytest.raises(ValueError):
            indices._searchsorted_monotonic(value, side="left")