import numpy as np
import pytest

from pandas import DataFrame, Series
import pandas._testing as tm
from pandas.api.indexers import BaseIndexer
from pandas.core.window.indexers import ExpandingIndexer


def test_bad_get_window_bounds_signature():
    class BadIndexer(BaseIndexer):
        def get_window_bounds(self):
            return None

    indexer = BadIndexer()
    with pytest.raises(ValueError, match="BadIndexer does not implement"):
        Series(range(5)).rolling(indexer)


def test_expanding_indexer():
    s = Series(range(10))
    indexer = ExpandingIndexer()
    result = s.rolling(indexer).mean()
    expected = s.expanding().mean()
    tm.assert_series_equal(result, expected)


def test_indexer_constructor_arg():
    # Example found in computation.rst
    use_expanding = [True, False, True, False, True]
    df = DataFrame({"values": range(5)})

    class CustomIndexer(BaseIndexer):
        def get_window_bounds(self, num_values, min_periods, center, closed):
            start = np.empty(num_values, dtype=np.int64)
            end = np.empty(num_values, dtype=np.int64)
            for i in range(num_values):
                if self.use_expanding[i]:
                    start[i] = 0
                    end[i] = i + 1
                else:
                    start[i] = i
                    end[i] = i + self.window_size
            return start, end

    indexer = CustomIndexer(window_size=1, use_expanding=use_expanding)
    result = df.rolling(indexer).sum()
    expected = DataFrame({"values": [0.0, 1.0, 3.0, 3.0, 10.0]})
    tm.assert_frame_equal(result, expected)


def test_indexer_accepts_rolling_args():
    df = DataFrame({"values": range(5)})

    class CustomIndexer(BaseIndexer):
        def get_window_bounds(self, num_values, min_periods, center, closed):
            start = np.empty(num_values, dtype=np.int64)
            end = np.empty(num_values, dtype=np.int64)
            for i in range(num_values):
                if center and min_periods == 1 and closed == "both" and i == 2:
                    start[i] = 0
                    end[i] = num_values
                else:
                    start[i] = i
                    end[i] = i + self.window_size
            return start, end

    indexer = CustomIndexer(window_size=1)
    result = df.rolling(indexer, center=True, min_periods=1, closed="both").sum()
    expected = DataFrame({"values": [0.0, 1.0, 10.0, 3.0, 4.0]})
    tm.assert_frame_equal(result, expected)


def test_win_type_not_implemented():
    class CustomIndexer(BaseIndexer):
        def get_window_bounds(self, num_values, min_periods, center, closed):
            return np.array([0, 1]), np.array([1, 2])

    df = DataFrame({"values": range(2)})
    indexer = CustomIndexer()
    with pytest.raises(NotImplementedError, match="BaseIndexer subclasses not"):
        df.rolling(indexer, win_type="boxcar")