#  # noqa: D100
# Licensed to the Apache Software Foundation (ASF) under one or more
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from typing import TYPE_CHECKING, Callable, Union, overload

from ..exception import ContributionsAcceptedError
from .column import Column
from .dataframe import DataFrame
from .functions import _to_column_expr
from .types import NumericType

# Only import symbols needed for type checking if something is type checking
if TYPE_CHECKING:
    from ._typing import ColumnOrName
    from .session import SparkSession

__all__ = ["GroupedData", "Grouping"]


def _api_internal(self: "GroupedData", name: str, *cols: str) -> DataFrame:
    expressions = ",".join(list(cols))
    group_by = str(self._grouping) if self._grouping else ""
    projections = self._grouping.get_columns()
    jdf = self._df.relation.apply(
        function_name=name,  # aggregate function
        function_aggr=expressions,  # inputs to aggregate
        group_expr=group_by,  # groups
        projected_columns=projections,  # projections
    )
    return DataFrame(jdf, self.session)


def df_varargs_api(f: Callable[..., DataFrame]) -> Callable[..., DataFrame]:
    def _api(self: "GroupedData", *cols: str) -> DataFrame:
        name = f.__name__
        return _api_internal(self, name, *cols)

    _api.__name__ = f.__name__
    _api.__doc__ = f.__doc__
    return _api


class Grouping:  # noqa: D101
    def __init__(self, *cols: "ColumnOrName", **kwargs) -> None:  # noqa: D107
        self._type = ""
        self._cols = [_to_column_expr(x) for x in cols]
        if "special" in kwargs:
            special = kwargs["special"]
            accepted_special = ["cube", "rollup"]
            assert special in accepted_special
            self._type = special

    def get_columns(self) -> str:  # noqa: D102
        columns = ",".join([str(x) for x in self._cols])
        return columns

    def __str__(self) -> str:  # noqa: D105
        columns = self.get_columns()
        if self._type:
            return self._type + "(" + columns + ")"
        return columns


class GroupedData:
    """A set of methods for aggregations on a :class:`DataFrame`,
    created by :func:`DataFrame.groupBy`.

    """  # noqa: D205

    def __init__(self, grouping: Grouping, df: DataFrame) -> None:  # noqa: D107
        self._grouping = grouping
        self._df = df
        self.session: SparkSession = df.session

    def __repr__(self) -> str:  # noqa: D105
        return str(self._df)

    def count(self) -> DataFrame:
        """Counts the number of records for each group.

        Examples:
        --------
        >>> df = spark.createDataFrame(
        ...     [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"]
        ... )
        >>> df.show()
        +---+-----+
        |age| name|
        +---+-----+
        |  2|Alice|
        |  3|Alice|
        |  5|  Bob|
        | 10|  Bob|
        +---+-----+

        Group-by name, and count each group.

        >>> df.groupBy(df.name).count().sort("name").show()
        +-----+-----+
        | name|count|
        +-----+-----+
        |Alice|    2|
        |  Bob|    2|
        +-----+-----+
        """
        return _api_internal(self, "count").withColumnRenamed("count_star()", "count")

    @df_varargs_api
    def mean(self, *cols: str) -> DataFrame:
        """Computes average values for each numeric columns for each group.

        :func:`mean` is an alias for :func:`avg`.

        Parameters
        ----------
        cols : str
            column names. Non-numeric columns are ignored.
        """

    def avg(self, *cols: str) -> DataFrame:
        """Computes average values for each numeric columns for each group.

        :func:`mean` is an alias for :func:`avg`.

        Parameters
        ----------
        cols : str
            column names. Non-numeric columns are ignored.

        Examples:
        --------
        >>> df = spark.createDataFrame(
        ...     [(2, "Alice", 80), (3, "Alice", 100), (5, "Bob", 120), (10, "Bob", 140)],
        ...     ["age", "name", "height"],
        ... )
        >>> df.show()
        +---+-----+------+
        |age| name|height|
        +---+-----+------+
        |  2|Alice|    80|
        |  3|Alice|   100|
        |  5|  Bob|   120|
        | 10|  Bob|   140|
        +---+-----+------+

        Group-by name, and calculate the mean of the age in each group.

        >>> df.groupBy("name").avg("age").sort("name").show()
        +-----+--------+
        | name|avg(age)|
        +-----+--------+
        |Alice|     2.5|
        |  Bob|     7.5|
        +-----+--------+

        Calculate the mean of the age and height in all data.

        >>> df.groupBy().avg("age", "height").show()
        +--------+-----------+
        |avg(age)|avg(height)|
        +--------+-----------+
        |     5.0|      110.0|
        +--------+-----------+
        """
        columns = list(cols)
        if len(columns) == 0:
            schema = self._df.schema
            # Take only the numeric types of the relation
            columns: list[str] = [x.name for x in schema.fields if isinstance(x.dataType, NumericType)]
        return _api_internal(self, "avg", *columns)

    @df_varargs_api
    def max(self, *cols: str) -> DataFrame:
        """Computes the max value for each numeric columns for each group.

        Examples:
        --------
        >>> df = spark.createDataFrame(
        ...     [(2, "Alice", 80), (3, "Alice", 100), (5, "Bob", 120), (10, "Bob", 140)],
        ...     ["age", "name", "height"],
        ... )
        >>> df.show()
        +---+-----+------+
        |age| name|height|
        +---+-----+------+
        |  2|Alice|    80|
        |  3|Alice|   100|
        |  5|  Bob|   120|
        | 10|  Bob|   140|
        +---+-----+------+

        Group-by name, and calculate the max of the age in each group.

        >>> df.groupBy("name").max("age").sort("name").show()
        +-----+--------+
        | name|max(age)|
        +-----+--------+
        |Alice|       3|
        |  Bob|      10|
        +-----+--------+

        Calculate the max of the age and height in all data.

        >>> df.groupBy().max("age", "height").show()
        +--------+-----------+
        |max(age)|max(height)|
        +--------+-----------+
        |      10|        140|
        +--------+-----------+
        """

    @df_varargs_api
    def min(self, *cols: str) -> DataFrame:
        """Computes the min value for each numeric column for each group.

        Parameters
        ----------
        cols : str
            column names. Non-numeric columns are ignored.

        Examples:
        --------
        >>> df = spark.createDataFrame(
        ...     [(2, "Alice", 80), (3, "Alice", 100), (5, "Bob", 120), (10, "Bob", 140)],
        ...     ["age", "name", "height"],
        ... )
        >>> df.show()
        +---+-----+------+
        |age| name|height|
        +---+-----+------+
        |  2|Alice|    80|
        |  3|Alice|   100|
        |  5|  Bob|   120|
        | 10|  Bob|   140|
        +---+-----+------+

        Group-by name, and calculate the min of the age in each group.

        >>> df.groupBy("name").min("age").sort("name").show()
        +-----+--------+
        | name|min(age)|
        +-----+--------+
        |Alice|       2|
        |  Bob|       5|
        +-----+--------+

        Calculate the min of the age and height in all data.

        >>> df.groupBy().min("age", "height").show()
        +--------+-----------+
        |min(age)|min(height)|
        +--------+-----------+
        |       2|         80|
        +--------+-----------+
        """

    @df_varargs_api
    def sum(self, *cols: str) -> DataFrame:
        """Computes the sum for each numeric columns for each group.

        Parameters
        ----------
        cols : str
            column names. Non-numeric columns are ignored.

        Examples:
        --------
        >>> df = spark.createDataFrame(
        ...     [(2, "Alice", 80), (3, "Alice", 100), (5, "Bob", 120), (10, "Bob", 140)],
        ...     ["age", "name", "height"],
        ... )
        >>> df.show()
        +---+-----+------+
        |age| name|height|
        +---+-----+------+
        |  2|Alice|    80|
        |  3|Alice|   100|
        |  5|  Bob|   120|
        | 10|  Bob|   140|
        +---+-----+------+

        Group-by name, and calculate the sum of the age in each group.

        >>> df.groupBy("name").sum("age").sort("name").show()
        +-----+--------+
        | name|sum(age)|
        +-----+--------+
        |Alice|       5|
        |  Bob|      15|
        +-----+--------+

        Calculate the sum of the age and height in all data.

        >>> df.groupBy().sum("age", "height").show()
        +--------+-----------+
        |sum(age)|sum(height)|
        +--------+-----------+
        |      20|        440|
        +--------+-----------+
        """

    @overload
    def agg(self, *exprs: Column) -> DataFrame: ...

    @overload
    def agg(self, __exprs: dict[str, str]) -> DataFrame: ...  # noqa: PYI063

    def agg(self, *exprs: Union[Column, dict[str, str]]) -> DataFrame:
        """Compute aggregates and returns the result as a :class:`DataFrame`.

        The available aggregate functions can be:

        1. built-in aggregation functions, such as `avg`, `max`, `min`, `sum`, `count`

        2. group aggregate pandas UDFs, created with :func:`pyspark.sql.functions.pandas_udf`

           .. note:: There is no partial aggregation with group aggregate UDFs, i.e.,
               a full shuffle is required. Also, all the data of a group will be loaded into
               memory, so the user should be aware of the potential OOM risk if data is skewed
               and certain groups are too large to fit in memory.

           .. seealso:: :func:`pyspark.sql.functions.pandas_udf`

        If ``exprs`` is a single :class:`dict` mapping from string to string, then the key
        is the column to perform aggregation on, and the value is the aggregate function.

        Alternatively, ``exprs`` can also be a list of aggregate :class:`Column` expressions.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        exprs : dict
            a dict mapping from column name (string) to aggregate functions (string),
            or a list of :class:`Column`.

        Notes:
        -----
        Built-in aggregation functions and group aggregate pandas UDFs cannot be mixed
        in a single call to this function.

        Examples:
        --------
        >>> from pyspark.sql import functions as F
        >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
        >>> df = spark.createDataFrame(
        ...     [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"]
        ... )
        >>> df.show()
        +---+-----+
        |age| name|
        +---+-----+
        |  2|Alice|
        |  3|Alice|
        |  5|  Bob|
        | 10|  Bob|
        +---+-----+

        Group-by name, and count each group.

        >>> df.groupBy(df.name)
        GroupedData[grouping...: [name...], value: [age: bigint, name: string], type: GroupBy]

        >>> df.groupBy(df.name).agg({"*": "count"}).sort("name").show()
        +-----+--------+
        | name|count(1)|
        +-----+--------+
        |Alice|       2|
        |  Bob|       2|
        +-----+--------+

        Group-by name, and calculate the minimum age.

        >>> df.groupBy(df.name).agg(F.min(df.age)).sort("name").show()
        +-----+--------+
        | name|min(age)|
        +-----+--------+
        |Alice|       2|
        |  Bob|       5|
        +-----+--------+

        Same as above but uses pandas UDF.

        >>> @pandas_udf("int", PandasUDFType.GROUPED_AGG)  # doctest: +SKIP
        ... def min_udf(v):
        ...     return v.min()
        >>> df.groupBy(df.name).agg(min_udf(df.age)).sort("name").show()  # doctest: +SKIP
        +-----+------------+
        | name|min_udf(age)|
        +-----+------------+
        |Alice|           2|
        |  Bob|           5|
        +-----+------------+
        """
        assert exprs, "exprs should not be empty"
        if len(exprs) == 1 and isinstance(exprs[0], dict):
            raise ContributionsAcceptedError
        else:
            # Columns
            assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column"
            expressions = list(self._grouping._cols)
            expressions.extend([x.expr for x in exprs])
            group_by = str(self._grouping)
            rel = self._df.relation.select(*expressions, groups=group_by)
        return DataFrame(rel, self.session)

    # TODO: add 'pivot'  # noqa: TD002, TD003
