from __future__ import annotations

from typing import Any
from typing import Mapping
from typing import cast

import pytest
from hypothesis import assume
from hypothesis import given
from hypothesis import strategies as st
from pandas.testing import assert_frame_equal

import narwhals.stable.v1 as nw
from tests.utils import PANDAS_VERSION
from tests.utils import POLARS_VERSION
from tests.utils import assert_equal_data

pytest.importorskip("pandas")
pytest.importorskip("polars")
pytest.importorskip("pyarrow")

import pandas as pd
import polars as pl
import pyarrow as pa


@given(
    st.lists(
        st.integers(min_value=-9223372036854775807, max_value=9223372036854775807),
        min_size=3,
        max_size=3,
    ),
    st.lists(
        st.integers(min_value=-9223372036854775807, max_value=9223372036854775807),
        min_size=3,
        max_size=3,
    ),
    st.lists(
        st.floats(),
        min_size=3,
        max_size=3,
    ),
    st.lists(
        st.sampled_from(["a", "b", "c"]),
        min_size=1,
        max_size=3,
        unique=True,
    ),
)
@pytest.mark.skipif(POLARS_VERSION < (0, 20, 13), reason="0.0 == -0.0")
@pytest.mark.skipif(PANDAS_VERSION < (2, 0, 0), reason="requires pyarrow")
@pytest.mark.slow
def test_join(  # pragma: no cover
    integers: st.SearchStrategy[list[int]],
    other_integers: st.SearchStrategy[list[int]],
    floats: st.SearchStrategy[list[float]],
    cols: st.SearchStrategy[list[str]],
) -> None:
    data: Mapping[str, Any] = {"a": integers, "b": other_integers, "c": floats}
    join_cols = cast("list[str]", cols)

    df_polars = pl.DataFrame(data)
    df_polars2 = pl.DataFrame(data)
    df_pl = nw.from_native(df_polars, eager_only=True)
    other_pl = nw.from_native(df_polars2, eager_only=True)

    dframe_pl = df_pl.join(other_pl, left_on=join_cols, right_on=join_cols, how="inner")

    df_pandas = pd.DataFrame(data)
    df_pandas2 = pd.DataFrame(data)
    df_pd = nw.from_native(df_pandas, eager_only=True)
    other_pd = nw.from_native(df_pandas2, eager_only=True)
    dframe_pd = df_pd.join(other_pd, left_on=join_cols, right_on=join_cols, how="inner")

    dframe_pd1 = nw.to_native(dframe_pl).to_pandas()
    dframe_pd1 = dframe_pd1.sort_values(
        by=dframe_pd1.columns.to_list(), ignore_index=True, inplace=False
    )

    dframe_pd2 = nw.to_native(dframe_pd)
    dframe_pd2 = dframe_pd2.sort_values(
        by=dframe_pd2.columns.to_list(), ignore_index=True, inplace=False
    )

    assert_frame_equal(dframe_pd1, dframe_pd2)


@given(
    st.lists(
        st.integers(min_value=-9223372036854775807, max_value=9223372036854775807),
        min_size=3,
        max_size=3,
    ),
    st.lists(
        st.integers(min_value=-9223372036854775807, max_value=9223372036854775807),
        min_size=3,
        max_size=3,
    ),
)
@pytest.mark.skipif(PANDAS_VERSION < (2, 0, 0), reason="requires pyarrow")
@pytest.mark.slow
def test_cross_join(  # pragma: no cover
    integers: st.SearchStrategy[list[int]],
    other_integers: st.SearchStrategy[list[int]],
) -> None:
    data: Mapping[str, Any] = {"a": integers, "b": other_integers}

    df_polars = pl.DataFrame(data)
    df_polars2 = pl.DataFrame(data)
    df_pl = nw.from_native(df_polars, eager_only=True)
    other_pl = nw.from_native(df_polars2, eager_only=True)
    dframe_pl = df_pl.join(other_pl, how="cross")

    df_pandas = pd.DataFrame(data)
    df_pandas2 = pd.DataFrame(data)
    df_pd = nw.from_native(df_pandas, eager_only=True)
    other_pd = nw.from_native(df_pandas2, eager_only=True)
    dframe_pd = df_pd.join(other_pd, how="cross")

    dframe_pd1 = nw.to_native(dframe_pl).to_pandas()
    dframe_pd1 = dframe_pd1.sort_values(
        by=dframe_pd1.columns.to_list(), ignore_index=True, inplace=False
    )

    dframe_pd2 = nw.to_native(dframe_pd)
    dframe_pd2 = dframe_pd2.sort_values(
        by=dframe_pd2.columns.to_list(), ignore_index=True, inplace=False
    )

    assert_frame_equal(dframe_pd1, dframe_pd2)


@given(
    a_left_data=st.lists(st.integers(min_value=0, max_value=5), min_size=3, max_size=3),
    b_left_data=st.lists(st.integers(min_value=0, max_value=5), min_size=3, max_size=3),
    c_left_data=st.lists(st.integers(min_value=0, max_value=5), min_size=3, max_size=3),
    a_right_data=st.lists(st.integers(min_value=0, max_value=5), min_size=3, max_size=3),
    b_right_data=st.lists(st.integers(min_value=0, max_value=5), min_size=3, max_size=3),
    d_right_data=st.lists(st.integers(min_value=0, max_value=5), min_size=3, max_size=3),
    left_key=st.lists(
        st.sampled_from(["a", "b", "c"]), min_size=1, max_size=3, unique=True
    ),
    right_key=st.lists(
        st.sampled_from(["a", "b", "d"]), min_size=1, max_size=3, unique=True
    ),
)
@pytest.mark.slow
def test_left_join(  # pragma: no cover
    a_left_data: list[int],
    b_left_data: list[int],
    c_left_data: list[int],
    a_right_data: list[int],
    b_right_data: list[int],
    d_right_data: list[int],
    left_key: list[str],
    right_key: list[str],
) -> None:
    assume(len(left_key) == len(right_key))
    data_left: dict[str, Any] = {"a": a_left_data, "b": b_left_data, "c": c_left_data}
    data_right: dict[str, Any] = {
        "a": a_right_data,
        "b": b_right_data,
        "d": d_right_data,
    }
    result_pd = nw.from_native(pd.DataFrame(data_left), eager_only=True).join(
        nw.from_native(pd.DataFrame(data_right), eager_only=True),
        how="left",
        left_on=left_key,
        right_on=right_key,
    )
    result_pl = nw.to_native(
        nw.from_native(pl.DataFrame(data_left), eager_only=True).join(
            nw.from_native(pl.DataFrame(data_right), eager_only=True),
            how="left",
            left_on=left_key,
            right_on=right_key,
        )
    )
    assert_equal_data(
        result_pd.to_dict(as_series=False), result_pl.to_dict(as_series=False)
    )
    # For PyArrow, insert an extra sort, as the order of rows isn't guaranteed
    result_pa = (
        nw.from_native(pa.table(data_left), eager_only=True)
        .join(
            nw.from_native(pa.table(data_right), eager_only=True),
            how="left",
            left_on=left_key,
            right_on=right_key,
        )
        .select(nw.all().cast(nw.Float64))
        .pipe(lambda df: df.sort(df.columns))
    )
    assert_equal_data(
        result_pa,
        result_pd.pipe(lambda df: df.sort(df.columns)).to_dict(as_series=False),
    )
