from __future__ import annotations

from datetime import datetime
from datetime import timedelta
from decimal import Decimal
from typing import Any

import numpy as np
import pandas as pd
import pyarrow as pa
import pytest

import narwhals.stable.v1 as nw
from narwhals.stable.v1.dependencies import get_cudf
from tests.utils import PANDAS_VERSION


@pytest.mark.parametrize(
    ("input_value", "expected"),
    [
        (1, 1),
        (pa.scalar(1), 1),
        (np.int64(1), 1),
        (Decimal("1.1"), Decimal("1.1")),
        (1.0, 1.0),
        (None, None),
        ("a", "a"),
        (True, True),
        (b"a", b"a"),
        (datetime(2021, 1, 1), datetime(2021, 1, 1)),
        (timedelta(days=1), timedelta(days=1)),
        (pd.Timestamp("2020-01-01"), datetime(2020, 1, 1)),
        (pd.Timedelta(days=3), timedelta(days=3)),
        (np.datetime64("2020-01-01", "s"), datetime(2020, 1, 1)),
        (np.datetime64("2020-01-01", "ms"), datetime(2020, 1, 1)),
        (np.datetime64("2020-01-01", "us"), datetime(2020, 1, 1)),
        (np.datetime64("2020-01-01", "ns"), datetime(2020, 1, 1)),
    ],
)
def test_to_py_scalar(
    input_value: Any,
    expected: Any,
) -> None:
    output = nw.to_py_scalar(input_value)
    if expected == 1:
        assert not isinstance(output, np.generic)
    assert output == expected


@pytest.mark.skipif(
    PANDAS_VERSION < (1,), reason="there was a (better?) time when there was no pd.NA"
)
def test_na_to_py_scalar() -> None:
    assert nw.to_py_scalar(pd.NA) is None


@pytest.mark.parametrize(
    "input_value",
    [np.array([1, 2]), [1, 2, 3], {"a": [1, 2, 3]}],
)
def test_to_py_scalar_value_error(input_value: Any) -> None:
    with pytest.raises(ValueError, match="Expected object convertible to a scalar"):
        nw.to_py_scalar(input_value)


def test_to_py_scalar_value_error_cudf() -> None:
    if cudf := get_cudf():  # pragma: no cover
        df = nw.from_native(cudf.DataFrame({"a": [1, 2, 3]}))

        with pytest.raises(ValueError, match="Expected object convertible to a scalar"):
            nw.to_py_scalar(df["a"])
