# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Copyright the Hypothesis Authors.
# Individual contributors are listed in AUTHORS.rst and the git log.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.

import numpy as np
import pandas as pd
import pytest

from hypothesis import HealthCheck, given, reject, settings, strategies as st
from hypothesis.errors import InvalidArgument
from hypothesis.extra import numpy as npst, pandas as pdst
from hypothesis.extra.pandas.impl import IntegerDtype

from tests.common.debug import (
    assert_all_examples,
    check_can_generate_examples,
    find_any,
)
from tests.pandas.helpers import supported_by_pandas


@given(pdst.data_frames([pdst.column("a", dtype=int), pdst.column("b", dtype=float)]))
def test_can_have_columns_of_distinct_types(df):
    assert df["a"].dtype == np.dtype(int)
    assert df["b"].dtype == np.dtype(float)


@given(
    pdst.data_frames(
        [pdst.column(dtype=int)], index=pdst.range_indexes(min_size=1, max_size=5)
    )
)
def test_respects_size_bounds(df):
    assert 1 <= len(df) <= 5


@given(pdst.data_frames(pdst.columns(["A", "B"], dtype=float)))
def test_can_specify_just_column_names(df):
    df["A"]
    df["B"]


@given(pdst.data_frames(pdst.columns(2, dtype=float)))
def test_can_specify_just_column_count(df):
    df[0]
    df[1]


@given(
    pdst.data_frames(
        rows=st.fixed_dictionaries({"A": st.integers(1, 10), "B": st.floats()})
    )
)
def test_gets_the_correct_data_shape_for_just_rows(table):
    assert table["A"].dtype == np.dtype("int64")
    assert table["B"].dtype == np.dtype(float)


@given(
    pdst.data_frames(
        columns=pdst.columns(["A", "B"], dtype=int),
        rows=st.lists(st.integers(0, 1000), min_size=2, max_size=2).map(sorted),
    )
)
def test_can_specify_both_rows_and_columns_list(d):
    assert d["A"].dtype == np.dtype(int)
    assert d["B"].dtype == np.dtype(int)
    for _, r in d.iterrows():
        assert r["A"] <= r["B"]


@given(
    pdst.data_frames(
        columns=pdst.columns(["A", "B"], dtype=int),
        rows=st.lists(st.integers(0, 1000), min_size=2, max_size=2)
        .map(sorted)
        .map(tuple),
    )
)
def test_can_specify_both_rows_and_columns_tuple(d):
    assert d["A"].dtype == np.dtype(int)
    assert d["B"].dtype == np.dtype(int)
    for _, r in d.iterrows():
        assert r["A"] <= r["B"]


@given(
    pdst.data_frames(
        columns=pdst.columns(["A", "B"], dtype=int),
        rows=st.lists(st.integers(0, 1000), min_size=2, max_size=2).map(
            lambda x: {"A": min(x), "B": max(x)}
        ),
    )
)
def test_can_specify_both_rows_and_columns_dict(d):
    assert d["A"].dtype == np.dtype(int)
    assert d["B"].dtype == np.dtype(int)
    for _, r in d.iterrows():
        assert r["A"] <= r["B"]


@given(
    pdst.data_frames(
        [
            pdst.column(
                "A",
                fill=st.just(np.nan),
                dtype=float,
                elements=st.floats(allow_nan=False),
            )
        ],
        rows=st.builds(dict),
    )
)
def test_can_fill_in_missing_elements_from_dict(df):
    assert np.isnan(df["A"]).all()


@st.composite
def column_strategy(draw):
    name = draw(st.none() | st.text())
    dtype = draw(npst.scalar_dtypes().filter(supported_by_pandas))
    pass_dtype = not draw(st.booleans())
    if pass_dtype:
        pass_elements = not draw(st.booleans())
    else:
        pass_elements = True
    if pass_elements:
        elements = npst.from_dtype(dtype)
    else:
        elements = None

    unique = draw(st.booleans())
    fill = st.nothing() if draw(st.booleans()) else None

    return pdst.column(
        name=name, dtype=dtype, unique=unique, fill=fill, elements=elements
    )


@given(pdst.data_frames(pdst.columns(1, dtype=np.dtype("M8[ns]"))))
def test_data_frames_with_timestamp_columns(df):
    pass


@given(
    pdst.data_frames(
        pdst.columns(["A"], dtype=float, fill=st.just(np.nan), unique=True)
    )
)
def test_unique_column_with_fill(df):
    assert len(set(df["A"])) == len(df["A"])


@settings(suppress_health_check=[HealthCheck.too_slow, HealthCheck.filter_too_much])
@given(st.data())
def test_arbitrary_data_frames(data):
    columns = data.draw(
        st.lists(
            column_strategy(),
            unique_by=lambda c: c.name if c.name is not None else np.nan,
        )
    )

    try:
        # Use raw data to work around pandas bug in repr. See
        # https://github.com/pandas-dev/pandas/issues/27484
        df = data.conjecture_data.draw(pdst.data_frames(columns))
    except Exception as e:
        if type(e).__name__ == "OutOfBoundsDatetime":
            # See https://github.com/HypothesisWorks/hypothesis/pull/826
            reject()
        else:
            raise
    data_frame_columns = list(df)

    assert len(data_frame_columns) == len(columns)

    for i, (c, n) in enumerate(zip(columns, df, strict=True)):
        if c.name is None:
            assert n == i
        else:
            assert c.name == n

    for i, c in enumerate(columns):
        column_name = data_frame_columns[i]
        values = df[column_name]
        if c.unique:
            # NA values should always be treated as unique to each other, so we
            # just ignore them here. Note NA values in the ecosystem can have
            # different identity behaviours, e.g.
            #
            #     >>> set([float("nan"), float("nan")])
            #     {nan, nan}
            #     >>> set([pd.NaT, pd.NaT])
            #     {NaT}
            #
            non_na_values = values.dropna()
            assert len(set(non_na_values)) == len(non_na_values)


@given(
    pdst.data_frames(
        pdst.columns(["A"], unique=True, dtype=int), rows=st.tuples(st.integers(0, 10))
    )
)
def test_can_specify_unique_with_rows(df):
    column = df["A"]
    assert len(set(column)) == len(column)


def test_uniqueness_does_not_affect_other_rows_1():
    data_frames = pdst.data_frames(
        [
            pdst.column("A", dtype=int, unique=True),
            pdst.column("B", dtype=int, unique=False),
        ],
        rows=st.tuples(st.integers(0, 10), st.integers(0, 10)),
        index=pdst.range_indexes(2, 2),
    )
    find_any(data_frames, lambda x: x["B"][0] == x["B"][1])


def test_uniqueness_does_not_affect_other_rows_2():
    data_frames = pdst.data_frames(
        [
            pdst.column("A", dtype=bool, unique=False),
            pdst.column("B", dtype=int, unique=True),
        ],
        rows=st.tuples(st.booleans(), st.integers(0, 10)),
        index=pdst.range_indexes(2, 2),
    )
    find_any(data_frames, lambda x: x["A"][0] == x["A"][1])


@given(
    pdst.data_frames(pdst.columns(["A"], dtype=int, fill=st.just(7)), rows=st.tuples())
)
def test_will_fill_missing_columns_in_tuple_row(df):
    for d in df["A"]:
        assert d == 7


@settings(suppress_health_check=[HealthCheck.filter_too_much, HealthCheck.too_slow])
@given(
    pdst.data_frames(
        index=pdst.range_indexes(10, 10),
        columns=[pdst.column(elements=st.integers(0, 9), fill=None, unique=True)],
    )
)
def test_can_generate_unique_columns(df):
    assert set(df[0]) == set(range(10))


@pytest.mark.skip(reason="Just works on Pandas 1.4, though the changelog is silent")
@pytest.mark.parametrize("dtype", [None, object])
def test_expected_failure_from_omitted_object_dtype(dtype):
    # See https://github.com/HypothesisWorks/hypothesis/issues/3133
    col = pdst.column(elements=st.sets(st.text(), min_size=1), dtype=dtype)

    @given(pdst.data_frames(columns=[col]))
    def works_with_object_dtype(df):
        pass

    if dtype is object:
        works_with_object_dtype()
    else:
        assert dtype is None
        with pytest.raises(ValueError, match="Maybe passing dtype=object would help"):
            works_with_object_dtype()


@pytest.mark.skipif(
    not IntegerDtype, reason="Nullable types not available in this version of Pandas"
)
def test_pandas_nullable_types():
    st = pdst.data_frames(pdst.columns(2, dtype=pd.core.arrays.integer.Int8Dtype()))
    df = find_any(st, lambda s: s.isna().any().any())
    for s in df.columns:
        assert type(df[s].dtype) == pd.core.arrays.integer.Int8Dtype


@given(pdst.data_frames(columns=[pdst.column("col", dtype=object)]))
def test_object_columns_are_of_type_object(df):
    assert df["col"].dtype == np.dtype("O")


def test_class_instances_not_allowed_in_scalar_columns():
    class A:
        pass

    s = pdst.data_frames(
        columns=[
            pdst.column(
                "col",
                elements=st.just(A()),
                dtype=npst.scalar_dtypes(),
            )
        ]
    )

    with pytest.raises(InvalidArgument):
        check_can_generate_examples(s)


def test_can_generate_object_arrays_with_mixed_dtype_elements():
    class A:
        pass

    s = pdst.data_frames(
        columns=[pdst.column("col", st.just(A()) | st.integers(), dtype=object)],
        index=pdst.range_indexes(1),
    )
    assert_all_examples(s, lambda df: df["col"].dtype == np.dtype("O"))
    find_any(s, lambda df: len({type(x) for x in df["col"].values}) > 1)


@given(st.data())
@pytest.mark.xfail(
    strict=False,
    reason="not actually true due to pandas conversion. see "
    "https://github.com/HypothesisWorks/hypothesis/pull/4444#issuecomment-3413951478",
)
def test_object_dataframe_can_hold_arbitrary_class_instances(data):
    instance = data.draw(st.from_type(type).flatmap(st.from_type))
    s = pdst.data_frames(
        columns=[pdst.column("col", elements=st.just(instance), dtype=object)]
    )
    df = data.draw(s)

    assert all(v is instance for v in df["col"].values)
