'''
    PM4Py â€“ A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschrÃ¤nkt)

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
import polars as pl
from enum import Enum

from pm4py.util import exec_utils, constants, xes_constants
from pm4py.util.business_hours import soj_time_business_hours_diff
from typing import Optional, Dict, Any, Union


class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
    AGGREGATION_MEASURE = "aggregationMeasure"
    BUSINESS_HOURS = "business_hours"
    BUSINESS_HOUR_SLOTS = "business_hour_slots"
    WORKCALENDAR = "workcalendar"


def apply(
    lf: pl.LazyFrame,
    parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[str, float]:
    """
    Gets the service time per activity on a Polars LazyFrame.

    Parameters
    ----------
    lf
        Polars LazyFrame
    parameters
        Parameters of the algorithm, including:

        - Parameters.ACTIVITY_KEY => activity key
        - Parameters.START_TIMESTAMP_KEY => start timestamp key
        - Parameters.TIMESTAMP_KEY => timestamp key
        - Parameters.BUSINESS_HOURS => calculates the difference of time based on the business hours, not the total time. Default: False
        - Parameters.AGGREGATION_MEASURE => performance aggregation measure (sum, min, max, mean, median)
        - Parameters.BUSINESS_HOURS_SLOTS => work schedule of the company.
          The following list defines that business hours are Mondays 07:00 - 17:00
          and Tuesdays 07:00 - 12:00 and 13:00 - 17:00::

            [
                (7 * 60 * 60, 17 * 60 * 60),
                ((24 + 7) * 60 * 60, (24 + 12) * 60 * 60),
                ((24 + 13) * 60 * 60, (24 + 17) * 60 * 60),
            ]

    Returns
    -----------------
    soj_time_dict
        Service time dictionary

    """
    if parameters is None:
        parameters = {}

    business_hours = exec_utils.get_param_value(
        Parameters.BUSINESS_HOURS, parameters, False
    )
    business_hours_slots = exec_utils.get_param_value(
        Parameters.BUSINESS_HOUR_SLOTS,
        parameters,
        constants.DEFAULT_BUSINESS_HOUR_SLOTS,
    )
    workcalendar = exec_utils.get_param_value(
        Parameters.WORKCALENDAR,
        parameters,
        constants.DEFAULT_BUSINESS_HOURS_WORKCALENDAR,
    )

    activity_key = exec_utils.get_param_value(
        Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY
    )
    start_timestamp_key = exec_utils.get_param_value(
        Parameters.START_TIMESTAMP_KEY,
        parameters,
        xes_constants.DEFAULT_TIMESTAMP_KEY,
    )
    timestamp_key = exec_utils.get_param_value(
        Parameters.TIMESTAMP_KEY,
        parameters,
        xes_constants.DEFAULT_TIMESTAMP_KEY,
    )
    aggregation_measure = exec_utils.get_param_value(
        Parameters.AGGREGATION_MEASURE, parameters, "mean"
    )

    if business_hours:
        # For business hours calculation, we need to collect the data and apply Python function
        if start_timestamp_key == timestamp_key:
            # Handle case where both keys are the same - use 0 duration
            df_collected = lf.collect()
            diff_values = [0.0] * len(df_collected)
        else:
            df_collected = lf.collect()
            diff_values = []

            for row in df_collected.iter_rows():
                start_idx = df_collected.columns.index(start_timestamp_key)
                end_idx = df_collected.columns.index(timestamp_key)

                diff = soj_time_business_hours_diff(
                    row[start_idx],
                    row[end_idx],
                    business_hours_slots,
                    workcalendar,
                )
                diff_values.append(diff)

        df_with_diff = df_collected.with_columns(
            pl.Series("__diff", diff_values)
        ).lazy()
    else:
        # Regular time difference calculation
        if start_timestamp_key == timestamp_key:
            # Handle case where both keys are the same - use 0 duration
            df_with_diff = lf.with_columns(pl.lit(0.0).alias("__diff"))
        else:
            df_with_diff = lf.with_columns(
                (pl.col(timestamp_key) - pl.col(start_timestamp_key))
                .dt.total_seconds()
                .alias("__diff")
            )

    # Group by activity and apply aggregation measure
    if aggregation_measure == "median":
        result = df_with_diff.group_by(activity_key).agg(pl.col("__diff").median())
    elif aggregation_measure == "min":
        result = df_with_diff.group_by(activity_key).agg(pl.col("__diff").min())
    elif aggregation_measure == "max":
        result = df_with_diff.group_by(activity_key).agg(pl.col("__diff").max())
    elif aggregation_measure == "sum":
        result = df_with_diff.group_by(activity_key).agg(pl.col("__diff").sum())
    else:  # mean
        result = df_with_diff.group_by(activity_key).agg(pl.col("__diff").mean())

    result_df = result.collect()

    # Convert to dictionary
    ret_dict = {}
    for row in result_df.iter_rows():
        activity = row[0]
        value = row[1]
        ret_dict[activity] = float(value) if value is not None else 0.0

    return ret_dict