"""System tests for Sessions with Nulls and Infs."""

# SPDX-License-Identifier: Apache-2.0
# Copyright Tumult Labs 2024

import datetime
from typing import Any, Dict, List, Mapping, Tuple, Union

import pandas as pd
import pytest
from pyspark.sql import DataFrame, SparkSession
from pyspark.sql.types import StringType, StructField, StructType
from tmlt.core.measurements.interactive_measurements import SequentialQueryable
from tmlt.core.utils.testing import Case, parametrize

from tmlt.analytics import (
    AddOneRow,
    AnalyticsDefault,
    ColumnDescriptor,
    ColumnType,
    KeySet,
    PureDPBudget,
    QueryBuilder,
    Session,
    TruncationStrategy,
)
from tmlt.analytics._table_identifier import NamedTable

from ....conftest import assert_frame_equal_with_sort


@pytest.mark.usefixtures("null_session_data")
class TestSessionWithNulls:
    """Tests for sessions with Nulls."""

    pdf: pd.DataFrame
    sdf: DataFrame

    def _expected_replace(self, d: Mapping[str, Any]) -> pd.DataFrame:
        """The expected value if you replace None with default values in d."""
        new_cols: List[pd.DataFrame] = []
        for col in list(self.pdf.columns):
            if col in dict(d):
                # make sure I becomes an integer here
                if col == "I":
                    new_cols.append(self.pdf[col].fillna(dict(d)[col]).astype(int))
                else:
                    new_cols.append(self.pdf[col].fillna(dict(d)[col]))
            else:
                new_cols.append(self.pdf[col])
        # `axis=1` means that you want to "concatenate" by columns
        # i.e., you want your new table to look like this:
        # df1 | df2 | df3 | ...
        # df1 | df2 | df3 | ...
        return pd.concat(new_cols, axis=1)

    def test_expected_replace(self) -> None:
        """Test the test method _expected_replace."""
        d = {
            "A": "a999",
            "I": -999,
            "X": 99.9,
            "D": datetime.date(1999, 1, 1),
            "T": datetime.datetime(2019, 1, 1),
        }
        expected = pd.DataFrame(
            [
                [
                    "a0",
                    0,
                    0.0,
                    datetime.date(2000, 1, 1),
                    datetime.datetime(2020, 1, 1),
                ],
                [
                    "a999",
                    1,
                    1.0,
                    datetime.date(2001, 1, 1),
                    datetime.datetime(2021, 1, 1),
                ],
                [
                    "a2",
                    -999,
                    2.0,
                    datetime.date(2002, 1, 1),
                    datetime.datetime(2022, 1, 1),
                ],
                [
                    "a3",
                    3,
                    99.9,
                    datetime.date(2003, 1, 1),
                    datetime.datetime(2023, 1, 1),
                ],
                [
                    "a4",
                    4,
                    4.0,
                    datetime.date(1999, 1, 1),
                    datetime.datetime(2024, 1, 1),
                ],
                [
                    "a5",
                    5,
                    5.0,
                    datetime.date(2005, 1, 1),
                    datetime.datetime(2019, 1, 1),
                ],
            ],
            columns=["A", "I", "X", "D", "T"],
        )
        assert_frame_equal_with_sort(self.pdf, self._expected_replace({}))
        assert_frame_equal_with_sort(
            expected,
            self._expected_replace(d),
        )

    @pytest.mark.parametrize(
        "cols_to_defaults",
        [
            ({"A": "aaaaaaa"}),
            ({"I": 999}),
            (
                {
                    "A": "aaa",
                    "I": 999,
                    "X": -99.9,
                    "D": datetime.date.fromtimestamp(0),
                    "T": datetime.datetime.fromtimestamp(0),
                }
            ),
        ],
    )
    def test_replace_null_and_nan(
        self,
        cols_to_defaults: Mapping[
            str, Union[int, float, str, datetime.date, datetime.datetime]
        ],
    ) -> None:
        """Test Session.replace_null_and_nan."""
        session = Session.from_dataframe(
            PureDPBudget(float("inf")),
            "private",
            self.sdf,
            protected_change=AddOneRow(),
        )
        session.create_view(
            QueryBuilder("private").replace_null_and_nan(cols_to_defaults),
            "replaced",
            cache=False,
        )
        # pylint: disable=protected-access
        queryable = session._accountant._queryable
        assert isinstance(queryable, SequentialQueryable)
        data = queryable._data
        assert isinstance(data, dict)
        assert isinstance(data[NamedTable("replaced")], DataFrame)
        # pylint: enable=protected-access
        assert_frame_equal_with_sort(
            data[NamedTable("replaced")].toPandas(),
            self._expected_replace(cols_to_defaults),
        )

    @pytest.mark.parametrize(
        "public_df,keyset,expected",
        [
            (
                pd.DataFrame(
                    [[None, 0], [None, 1], ["a2", 1], ["a2", 2]],
                    columns=["A", "new_column"],
                ),
                KeySet.from_dict({"new_column": [0, 1, 2]}),
                pd.DataFrame([[0, 1], [1, 2], [2, 1]], columns=["new_column", "count"]),
            ),
            (
                pd.DataFrame(
                    [["a0", 0, 0], [None, 1, 17], ["a5", 5, 17], ["a5", 5, 400]],
                    columns=["A", "I", "new_column"],
                ),
                KeySet.from_dict({"new_column": [0, 17, 400]}),
                pd.DataFrame(
                    [[0, 1], [17, 2], [400, 1]], columns=["new_column", "count"]
                ),
            ),
            (
                pd.DataFrame(
                    [
                        [datetime.date(2000, 1, 1), "2000"],
                        [datetime.date(2001, 1, 1), "2001"],
                        [None, "none"],
                        [None, "also none"],
                    ],
                    columns=["D", "year"],
                ),
                KeySet.from_dict(
                    {"D": [datetime.date(2000, 1, 1), datetime.date(2001, 1, 1), None]}
                ),
                pd.DataFrame(
                    [
                        [datetime.date(2000, 1, 1), 1],
                        [datetime.date(2001, 1, 1), 1],
                        [None, 2],
                    ],
                    columns=["D", "count"],
                ),
            ),
        ],
    )
    def test_join_public(
        self, spark, public_df: pd.DataFrame, keyset: KeySet, expected: pd.DataFrame
    ) -> None:
        """Test that join_public creates the correct results.

        The query used to evaluate this is a GroupByCount on the new dataframe,
        using the keyset provided.
        """
        session = Session.from_dataframe(
            PureDPBudget(float("inf")),
            "private",
            self.sdf,
            protected_change=AddOneRow(),
        )
        session.add_public_dataframe("public", spark.createDataFrame(public_df))
        result = session.evaluate(
            QueryBuilder("private").join_public("public").groupby(keyset).count(),
            privacy_budget=PureDPBudget(float("inf")),
        )
        assert_frame_equal_with_sort(result.toPandas(), expected)

    @pytest.mark.parametrize(
        "private_df,keyset,expected",
        [
            (
                pd.DataFrame(
                    [[None, 0], [None, 1], ["a2", 1], ["a2", 2]],
                    columns=["A", "new_column"],
                ),
                KeySet.from_dict({"new_column": [0, 1, 2]}),
                pd.DataFrame([[0, 1], [1, 2], [2, 1]], columns=["new_column", "count"]),
            ),
            (
                pd.DataFrame(
                    [["a0", 0, 0], [None, 1, 17], ["a5", 5, 17], ["a5", 5, 400]],
                    columns=["A", "I", "new_column"],
                ),
                KeySet.from_dict({"new_column": [0, 17, 400]}),
                pd.DataFrame(
                    [[0, 1], [17, 2], [400, 1]], columns=["new_column", "count"]
                ),
            ),
            (
                pd.DataFrame(
                    [
                        [datetime.date(2000, 1, 1), "2000"],
                        [datetime.date(2001, 1, 1), "2001"],
                        [None, "none"],
                        [None, "also none"],
                    ],
                    columns=["D", "year"],
                ),
                KeySet.from_dict(
                    {"D": [datetime.date(2000, 1, 1), datetime.date(2001, 1, 1), None]}
                ),
                pd.DataFrame(
                    [
                        [datetime.date(2000, 1, 1), 1],
                        [datetime.date(2001, 1, 1), 1],
                        [None, 2],
                    ],
                    columns=["D", "count"],
                ),
            ),
        ],
    )
    def test_join_private(
        self, spark, private_df: pd.DataFrame, keyset: KeySet, expected: pd.DataFrame
    ) -> None:
        """Test that join_private creates the correct results.

        The query used to evaluate this is a GroupByCount on the joined dataframe,
        using the keyset provided.
        """
        session = (
            Session.Builder()
            .with_privacy_budget(PureDPBudget(float("inf")))
            .with_private_dataframe("private", self.sdf, AddOneRow())
            .with_private_dataframe(
                "private2", spark.createDataFrame(private_df), AddOneRow()
            )
            .build()
        )
        result = session.evaluate(
            QueryBuilder("private")
            .join_private(
                QueryBuilder("private2"),
                TruncationStrategy.DropExcess(100),
                TruncationStrategy.DropExcess(100),
            )
            .groupby(keyset)
            .count(),
            PureDPBudget(float("inf")),
        )
        assert_frame_equal_with_sort(result.toPandas(), expected)

    @parametrize(
        Case("both_allow_nulls")(
            public_schema=StructType([StructField("foo", StringType(), True)]),
            private_schema=StructType([StructField("foo", StringType(), True)]),
            expected_schema={
                "foo": ColumnDescriptor(ColumnType.VARCHAR, allow_null=True)
            },
        ),
        Case("none_allow_nulls")(
            public_schema=StructType([StructField("foo", StringType(), False)]),
            private_schema=StructType([StructField("foo", StringType(), False)]),
            expected_schema={
                "foo": ColumnDescriptor(ColumnType.VARCHAR, allow_null=False)
            },
        ),
        Case("public_only_nulls")(
            public_schema=StructType([StructField("foo", StringType(), True)]),
            private_schema=StructType([StructField("foo", StringType(), False)]),
            expected_schema={
                "foo": ColumnDescriptor(ColumnType.VARCHAR, allow_null=False)
            },
        ),
        Case("private_only_nulls")(
            public_schema=StructType([StructField("foo", StringType(), False)]),
            private_schema=StructType([StructField("foo", StringType(), True)]),
            expected_schema={
                "foo": ColumnDescriptor(ColumnType.VARCHAR, allow_null=False)
            },
        ),
    )
    def test_public_join_schema_null_propagation(
        self,
        public_schema: StructType,
        private_schema: StructType,
        expected_schema: StructType,
        spark: SparkSession,
    ):
        """Tests that join_public correctly handles schemas that allow null values."""
        public_df = spark.createDataFrame([], public_schema)
        private_df = spark.createDataFrame([], private_schema)
        sess = (
            Session.Builder()
            .with_privacy_budget(PureDPBudget(float("inf")))
            .with_private_dataframe("private", private_df, protected_change=AddOneRow())
            .with_public_dataframe("public", public_df)
            .build()
        )
        sess.create_view(
            QueryBuilder("private").join_public("public"), source_id="join", cache=False
        )
        assert sess.get_schema("join") == expected_schema

    @parametrize(
        Case("both_allow_nulls")(
            left_schema=StructType([StructField("foo", StringType(), True)]),
            right_schema=StructType([StructField("foo", StringType(), True)]),
            expected_schema={
                "foo": ColumnDescriptor(ColumnType.VARCHAR, allow_null=True)
            },
        ),
        Case("none_allow_nulls")(
            left_schema=StructType([StructField("foo", StringType(), False)]),
            right_schema=StructType([StructField("foo", StringType(), False)]),
            expected_schema={
                "foo": ColumnDescriptor(ColumnType.VARCHAR, allow_null=False)
            },
        ),
        Case("public_only_nulls")(
            left_schema=StructType([StructField("foo", StringType(), True)]),
            right_schema=StructType([StructField("foo", StringType(), False)]),
            expected_schema={
                "foo": ColumnDescriptor(ColumnType.VARCHAR, allow_null=False)
            },
        ),
        Case("private_only_nulls")(
            left_schema=StructType([StructField("foo", StringType(), False)]),
            right_schema=StructType([StructField("foo", StringType(), True)]),
            expected_schema={
                "foo": ColumnDescriptor(ColumnType.VARCHAR, allow_null=False)
            },
        ),
    )
    def test_private_join_schema_null_propagation(
        self,
        left_schema: StructType,
        right_schema: StructType,
        expected_schema: StructType,
        spark: SparkSession,
    ):
        """Tests that join_private correctly handles schemas that allow null values."""
        left_df = spark.createDataFrame([], left_schema)
        right_df = spark.createDataFrame([], right_schema)
        sess = (
            Session.Builder()
            .with_privacy_budget(PureDPBudget(float("inf")))
            .with_private_dataframe("left", left_df, protected_change=AddOneRow())
            .with_private_dataframe("right", right_df, protected_change=AddOneRow())
            .build()
        )
        sess.create_view(
            QueryBuilder("left").join_private(
                "right",
                truncation_strategy_left=TruncationStrategy.DropExcess(1),
                truncation_strategy_right=TruncationStrategy.DropExcess(1),
            ),
            source_id="join",
            cache=False,
        )
        assert sess.get_schema("join") == expected_schema


@pytest.mark.usefixtures("infs_test_data")
class TestSessionWithInfs:
    """Tests for Sessions with Infs."""

    pdf: pd.DataFrame
    sdf: DataFrame

    @pytest.mark.parametrize(
        "replace_with,",
        [
            ({}),
            ({"B": (-100.0, 100.0)}),
            ({"B": (123.45, 678.90)}),
            ({"B": (999.9, 111.1)}),
        ],
    )
    def test_replace_infinity(
        self, replace_with: Dict[str, Tuple[float, float]]
    ) -> None:
        """Test replace_infinity query."""
        session = Session.from_dataframe(
            PureDPBudget(float("inf")),
            "private",
            self.sdf,
            protected_change=AddOneRow(),
        )
        session.create_view(
            QueryBuilder("private").replace_infinity(replace_with),
            "replaced",
            cache=False,
        )
        # pylint: disable=protected-access
        queryable = session._accountant._queryable
        assert isinstance(queryable, SequentialQueryable)
        data = queryable._data
        assert isinstance(data, dict)
        assert isinstance(data[NamedTable("replaced")], DataFrame)
        # pylint: enable=protected-access
        (replace_negative, replace_positive) = replace_with.get(
            "B", (AnalyticsDefault.DECIMAL, AnalyticsDefault.DECIMAL)
        )
        expected = self.pdf.replace(float("-inf"), replace_negative).replace(
            float("inf"), replace_positive
        )
        assert_frame_equal_with_sort(data[NamedTable("replaced")].toPandas(), expected)

    @pytest.mark.parametrize(
        "replace_with,expected",
        [
            ({}, pd.DataFrame([["a0", 2.0], ["a1", 5.0]], columns=["A", "sum"])),
            (
                {"B": (-100.0, 100.0)},
                pd.DataFrame([["a0", -98.0], ["a1", 105.0]], columns=["A", "sum"]),
            ),
            (
                {"B": (500.0, 100.0)},
                pd.DataFrame([["a0", 502.0], ["a1", 105.0]], columns=["A", "sum"]),
            ),
        ],
    )
    def test_sum(
        self, replace_with: Dict[str, Tuple[float, float]], expected: pd.DataFrame
    ) -> None:
        """Test GroupByBoundedSum after replacing infinite values."""
        session = Session.from_dataframe(
            PureDPBudget(float("inf")),
            "private",
            self.sdf,
            protected_change=AddOneRow(),
        )
        result = session.evaluate(
            QueryBuilder("private")
            .replace_infinity(replace_with)
            .groupby(KeySet.from_dict({"A": ["a0", "a1"]}))
            .sum("B", low=-1000, high=1000, name="sum"),
            PureDPBudget(float("inf")),
        )
        assert_frame_equal_with_sort(result.toPandas(), expected)

    def test_drop_infinity(self):
        """Test GroupByBoundedSum after dropping infinite values."""
        session = Session.from_dataframe(
            PureDPBudget(float("inf")),
            "private",
            self.sdf,
            protected_change=AddOneRow(),
        )
        result = session.evaluate(
            QueryBuilder("private")
            .drop_infinity(columns=["B"])
            .groupby(KeySet.from_dict({"A": ["a0", "a1"]}))
            .sum("B", low=-1000, high=1000, name="sum"),
            PureDPBudget(float("inf")),
        )
        expected = pd.DataFrame([["a0", 2.0], ["a1", 5.0]], columns=["A", "sum"])
        assert_frame_equal_with_sort(result.toPandas(), expected)
