'''
    PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

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
'''

from enum import Enum
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd
from typing import Optional, Dict, Any, Union
from pm4py.util import exec_utils, nx_utils
from pm4py.objects.conversion.log.variants import to_event_log


class Parameters(Enum):
    INCLUDE_DF = "include_df"
    CASE_ID_ATTRIBUTE = "case_id_attribute"
    OTHER_CASE_ATTRIBUTES_AS_NODES = "other_case_attributes_as_nodes"
    EVENT_ATTRIBUTES_AS_NODES = "event_attributes_as_nodes"


def apply(
    log_obj: Union[EventLog, EventStream, pd.DataFrame],
    parameters: Optional[Dict[Any, Any]] = None,
):
    """
    Converts an event log object to a NetworkX DiGraph object.
    The nodes of the graph are the events, the cases (and possibly the attributes of the log).
    The edges are:
    - Connecting each event to the corresponding case (BELONGS_TO type)
    - Connecting every event to the directly-following one (DF type, if enabled)
    - Connecting every case/event to the given attribute values (ATTRIBUTE_EDGE type)

    Parameters
    ----------------
    log_obj
        Log object (EventLog, EventStream, Pandas dataframe)
    parameters
        Parameters of the conversion, including:
        - Parameters.INCLUDE_DF => include the directly-follows graph relation in the graph
        - Parameters.CASE_ID_ATTRIBUTE => specify which attribute at the case level should be considered the case ID
        - Parameters.OTHER_CASE_ATTRIBUTES_AS_NODES => specify which attributes at the case level should be inserted in the graph as nodes (other than the caseID) (list, default empty)
        - Parameters.EVENT_ATTRIBUTES_AS_NODES => specify which attributes at the event level should be inserted in the graph as nodes (list, default empty)

    Returns
    ----------------
    nx_digraph
        NetworkX DiGraph object
    """
    if parameters is None:
        parameters = {}

    include_df = exec_utils.get_param_value(
        Parameters.INCLUDE_DF, parameters, True
    )
    case_id_attribute = exec_utils.get_param_value(
        Parameters.CASE_ID_ATTRIBUTE, parameters, "concept:name"
    )
    other_case_attributes_as_nodes = exec_utils.get_param_value(
        Parameters.OTHER_CASE_ATTRIBUTES_AS_NODES, parameters, None
    )
    event_attributes_as_nodes = exec_utils.get_param_value(
        Parameters.EVENT_ATTRIBUTES_AS_NODES, parameters, None
    )

    parameters["stream_postprocessing"] = True
    log_obj = to_event_log.apply(log_obj, parameters=parameters)

    if event_attributes_as_nodes is None:
        event_attributes_as_nodes = []
    if other_case_attributes_as_nodes is None:
        other_case_attributes_as_nodes = []

    nx_digraph = nx_utils.DiGraph()

    for case in log_obj:
        case_id = "CASE=" + str(case.attributes[case_id_attribute])
        dct_case = {"type": "CASE"}
        for att in case.attributes:
            dct_case[att] = case.attributes[att]
        nx_digraph.add_node(case_id, attr=dct_case)

        for index, event in enumerate(case):
            dct_ev = {"type": "EVENT"}
            for att in event:
                dct_ev[att] = event[att]

            ev_id = (
                "EVENT="
                + str(case.attributes[case_id_attribute])
                + "_"
                + str(index)
            )
            nx_digraph.add_node(ev_id, attr=dct_ev)
            nx_digraph.add_edge(ev_id, case_id, attr={"type": "BELONGS_TO"})

            for ev_att in event_attributes_as_nodes:
                if ev_att in event:
                    node_id = event[ev_att]
                    if node_id not in nx_digraph.nodes:
                        nx_digraph.add_node(
                            node_id, attr={"type": "ATTRIBUTE_NODE"}
                        )
                    nx_digraph.add_edge(
                        ev_id,
                        node_id,
                        attr={"type": "ATTRIBUTE_EDGE", "name": ev_att},
                    )

        if include_df:
            for index in range(len(case) - 1):
                curr_ev = (
                    "EVENT="
                    + str(case.attributes[case_id_attribute])
                    + "_"
                    + str(index)
                )
                next_ev = (
                    "EVENT="
                    + str(case.attributes[case_id_attribute])
                    + "_"
                    + str(index + 1)
                )

                nx_digraph.add_edge(curr_ev, next_ev, attr={"type": "DF"})

        for case_att in other_case_attributes_as_nodes:
            if case_att in case.attributes:
                node_id = case.attributes[case_att]
                if node_id not in nx_digraph.nodes:
                    nx_digraph.add_node(
                        node_id, attr={"type": "ATTRIBUTE_NODE"}
                    )
                nx_digraph.add_edge(
                    case_id,
                    node_id,
                    attr={"type": "ATTRIBUTE_EDGE", "name": case_att},
                )

    return nx_digraph
