import logging
from random import randint,uniform
from time import time
from typing import cast

from geniusweb.actions.Accept import Accept
from geniusweb.actions.Action import Action
from geniusweb.actions.LearningDone import LearningDone
from geniusweb.actions.Offer import Offer
from geniusweb.actions.PartyId import PartyId
from geniusweb.actions.Vote import Vote
from geniusweb.actions.Votes import Votes
from geniusweb.bidspace.AllBidsList import AllBidsList
from geniusweb.inform.ActionDone import ActionDone
from geniusweb.inform.Finished import Finished
from geniusweb.inform.Inform import Inform
from geniusweb.inform.OptIn import OptIn
from geniusweb.inform.Settings import Settings
from geniusweb.inform.Voting import Voting
from geniusweb.inform.YourTurn import YourTurn
from geniusweb.issuevalue.Bid import Bid
from geniusweb.issuevalue.Domain import Domain
from geniusweb.issuevalue.Value import Value
from geniusweb.issuevalue.ValueSet import ValueSet
from geniusweb.party.Capabilities import Capabilities
from geniusweb.party.DefaultParty import DefaultParty
from geniusweb.profile.utilityspace.UtilitySpace import UtilitySpace
from geniusweb.profile.utilityspace.LinearAdditiveUtilitySpace import (
    LinearAdditiveUtilitySpace,
)
from geniusweb.profileconnection.ProfileConnectionFactory import (
    ProfileConnectionFactory,
)
from geniusweb.progress.ProgressTime import ProgressTime
from geniusweb.references.Parameters import Parameters
from tudelft_utilities_logging.ReportToLogger import ReportToLogger

from .utils.opponent_model import OpponentModel

from geniusweb.progress.ProgressRounds import ProgressRounds
from geniusweb.utils import val
from geniusweb.profileconnection.ProfileInterface import ProfileInterface
from geniusweb.profile.utilityspace.LinearAdditive import LinearAdditive
from geniusweb.progress.Progress import Progress
from tudelft.utilities.immutablelist.ImmutableList import ImmutableList
from decimal import Decimal
import sys
from .extended_util_space import ExtendedUtilSpace
from tudelft_utilities_logging.Reporter import Reporter


class AgentFish(DefaultParty):
    """
    Template of a Python geniusweb agent.
    """

    def __init__(self):
        super().__init__()
        self.logger: ReportToLogger = self.getReporter()

        self.domain: Domain = None
        self.parameters: Parameters = None
        self.profile: LinearAdditiveUtilitySpace = None
        self.progress: ProgressTime = None
        self.me: PartyId = None
        self.other: str = None
        self.settings: Settings = None
        self.storage_dir: str = None

        self.last_received_bid: Bid = None
        self.opponent_model: OpponentModel = None
        self.logger.log(logging.INFO, "party is initialized")

        self.profileint: ProfileInterface = None  # type:ignore
        self.utilspace: LinearAdditive = None  # type:ignore
        self.extendedspace: ExtendedUtilSpace = None  # type:ignore
        self.e: float = 1.2
        self.lastvotes: Votes = None  # type:ignore
        self.opponent_max = 0.0

    def notifyChange(self, data: Inform):
        """MUST BE IMPLEMENTED
        This is the entry point of all interaction with your agent after is has been initialised.
        How to handle the received data is based on its class type.

        Args:
            info (Inform): Contains either a request for action or information.
        """

        # a Settings message is the first message that will be send to your
        # agent containing all the information about the negotiation session.
        if isinstance(data, Settings):
            self.settings = cast(Settings, data)
            self.me = self.settings.getID()

            # progress towards the deadline has to be tracked manually through the use of the Progress object
            self.progress = self.settings.getProgress()

            self.parameters = self.settings.getParameters()
            self.storage_dir = self.parameters.get("storage_dir")

            # the profile contains the preferences of the agent over the domain
            profile_connection = ProfileConnectionFactory.create(
                data.getProfile().getURI(), self.getReporter()
            )
            self.profile = profile_connection.getProfile()
            self.domain = self.profile.getDomain()
            profile_connection.close()

        # ActionDone informs you of an action (an offer or an accept)
        # that is performed by one of the agents (including yourself).
        elif isinstance(data, ActionDone):
            action = cast(ActionDone, data).getAction()
            actor = action.getActor()

            # ignore action if it is our action
            if actor != self.me:
                # obtain the name of the opponent, cutting of the position ID.
                self.other = str(actor).rsplit("_", 1)[0]

                # process action done by opponent
                self.opponent_action(action)
        # YourTurn notifies you that it is your turn to act
        elif isinstance(data, YourTurn):
            # execute a turn
            self.my_turn()

        # Finished will be send if the negotiation has ended (through agreement or deadline)
        elif isinstance(data, Finished):
            self.save_data()
            # terminate the agent MUST BE CALLED
            self.logger.log(logging.INFO, "party is terminating:")
            super().terminate()
        else:
            self.logger.log(logging.WARNING, "Ignoring unknown info " + str(data))

    def getE(self) -> float:

        return self.e

    def getCapabilities(self) -> Capabilities:
        """MUST BE IMPLEMENTED
        Method to indicate to the protocol what the capabilities of this agent are.
        Leave it as is for the ANL 2022 competition

        Returns:
            Capabilities: Capabilities representation class
        """
        return Capabilities(
            set(["SAOP"]),
            set(["geniusweb.profile.utilityspace.LinearAdditive"]),
        )

    def send_action(self, action: Action):
        """Sends an action to the opponent(s)

        Args:
            action (Action): action of this agent
        """
        self.getConnection().send(action)

    # give a description of your agent
    def getDescription(self) -> str:
        """MUST BE IMPLEMENTED
        Returns a description of your agent. 1 or 2 sentences.

        Returns:
            str: Agent description
        """
        return "Foot in the Door agent for the ANL 2022 competition"

    def opponent_action(self, action):
        """Process an action that was received from the opponent.

        Args:
            action (Action): action of opponent
        """
        # if it is an offer, set the last received bid
        if isinstance(action, Offer):
            # create opponent model if it was not yet initialised
            if self.opponent_model is None:
                self.opponent_model = OpponentModel(self.domain)

            bid = cast(Offer, action).getBid()

            # update opponent model with bid
            self.opponent_model.update(bid)
            # set bid as last received
            self.last_received_bid = bid

            if self.opponent_max < self.profile.getUtility(bid):
                self.opponent_max = self.profile.getUtility(bid)

    def my_turn(self):
        """This method is called when it is our turn. It should decide upon an action
        to perform and send this action to the opponent.
        """
        # check if the last received offer is good enough
        self.updateUtilSpace()
        if self.accept_condition(self.last_received_bid):
            # if so, accept the offer
            action = Accept(self.me, self.last_received_bid)
        else:
            # if not, find a bid to propose as counter offer
            bid = self.makeBid()
            action = Offer(self.me, bid)

        # send the action
        self.send_action(action)

    def updateUtilSpace(self) -> LinearAdditive:  # throws IOException
        newutilspace = self.profile
        if not newutilspace == self.utilspace:
            self.utilspace = cast(LinearAdditive, newutilspace)
            self.extendedspace = ExtendedUtilSpace(self.utilspace)
        return self.utilspace

    def makeBid(self) -> Bid:
        """
        @return next possible bid with current target utility, or null if no such
                bid.
        """
        time_to_deadline = self.progress.get(time() * 1000)

        utilityGoal = self.getUtilityGoal(
            time_to_deadline,
            self.getE(),
            self.extendedspace.getMin(),
            self.extendedspace.getMax(),
        )
        options: ImmutableList[Bid] = self.extendedspace.getBids(utilityGoal)
        if options.size() == 0:
            # if we can't find good bid, get max util bid....
            options = self.extendedspace.getBids(self.extendedspace.getMax())
        # pick a random one.
        return options.get(randint(0, options.size() - 1))

    def getUtilityGoal(
        self, t: float, e: float, minUtil: Decimal, maxUtil: Decimal
    ) -> Decimal:
        """
        @param t       the time in [0,1] where 0 means start of nego and 1 the
                       end of nego (absolute time/round limit)
        @param e       the e value that determinses how fast the party makes
                       concessions with time. Typically around 1. 0 means no
                       concession, 1 linear concession, &gt;1 faster than linear
                       concession.
        @param minUtil the minimum utility possible in our profile
        @param maxUtil the maximum utility possible in our profile
        @return the utility goal for this time and e value
        """

        ft1 = Decimal(1)
        if e != 0:
            ft1 = round(Decimal(1 - pow(t, 1 / e)), 6)  # defaults ROUND_HALF_UP
            myoffer =  max(min((maxUtil - (maxUtil - minUtil) * ft1), maxUtil), minUtil)
            return max(myoffer, self.opponent_max)


    def save_data(self):
        """This method is called after the negotiation is finished. It can be used to store data
        for learning capabilities. Note that no extensive calculations can be done within this method.
        Taking too much time might result in your agent being killed, so use it for storage only.
        """
        data = "Data for learning (see README.md)"
        with open(f"{self.storage_dir}/data.md", "w") as f:
            f.write(data)

    ###########################################################################################
    ################################## Example methods below ##################################
    ###########################################################################################

    def accept_condition(self, bid: Bid) -> bool:
        if bid is None:
            return False

        # progress of the negotiation session between 0 and 1 (1 is deadline)
        progress = self.progress.get(time() * 1000)

        # very basic approach that accepts if the offer is valued above 0.7 and
        # 95% of the time towards the deadline has passed
        maxAccept = 0.8
        minAccept = 0.6
        ft = round(Decimal(1 - progress), 6)  # defaults ROUND_HALF_UP
        acceptline =  max(min((minAccept + (maxAccept - minAccept) * float(ft)), maxAccept), minAccept)
        conditions = [
            float(self.profile.getUtility(bid)) > acceptline,
            progress > uniform(0.8, 0.95),
            progress > 0.95,
        ]
        return all([any(conditions), progress > 0.1])

    def score_bid(self, bid: Bid, alpha: float = 0.95, eps: float = 0.1) -> float:
        """Calculate heuristic score for a bid

        Args:
            bid (Bid): Bid to score
            alpha (float, optional): Trade-off factor between self interested and
                altruistic behaviour. Defaults to 0.95.
            eps (float, optional): Time pressure factor, balances between conceding
                and Boulware behaviour over time. Defaults to 0.1.

        Returns:
            float: score
        """
        progress = self.progress.get(time() * 1000)

        our_utility = float(self.profile.getUtility(bid))

        time_pressure = 1.0 - progress ** (1 / eps)
        score = alpha * time_pressure * our_utility

        if self.opponent_model is not None:
            opponent_utility = self.opponent_model.get_predicted_utility(bid)
            opponent_score = (1.0 - alpha * time_pressure) * opponent_utility
            score += opponent_score

        return score