import math
from copy import deepcopy
from time import perf_counter
from random import choice

from GameState import GameState
class GameController(object):
    def get_next_move(self, state):
        # when you get a new move, it is assumed that the game is not ended yet
        assert state.get_moves()

class ABNode(object): # A class for Node related operations
    def __init__(self, state):
        self.Current = state
        if state.player == 1:
            self.CurrentScore = self.Current.score[2]-self.Current.score[1]
        elif state.player ==2:
            self.CurrentScore = self.Current.score[1]-self.Current.score[2]
        self.children = {}


    def update_score(self):
        if self.Current.player == 2:
            self.CurrentScore = self.Current.score[1]-self.Current.score[2]
        elif self.Current.player == 1:
            self.CurrentScore = self.Current.score[2]-self.Current.score[1]
            
    def Make(self, r,c,o): # Function for generating a child node
        self.children[(r,c,o)] = ABNode(self.Current)
        move=(r,c,o)
        self.children[(r,c,o)].Current.play_move(move)
        self.children[(r,c,o)].update_score()

    def Populate(self, r,c,o, Child): # Function for adding a node
        self.children[(r,c,o)] = Child

    def Draw(self): # function for drawing the board
        self.Current.Draw_mat()

# A class for defining algorithms used (minimax and alpha-beta pruning)
class AlphaBeta(object):

    def miniMax(self, State, Ply_num):  # Function for the minimax algorithm
        start=ABNode(State)
        possiblemoves=State.get_moves()
        for x in possiblemoves:
            if (x[0],x[1],x[2]) not in start.children:
                start.Make(x[0],x[1],x[2]) 
        if Ply_num < 2:
            return (i, j)

        Minimum_Score = 1000
        r = 0
        c = 0
        o = ""
        for k, z in start.children.items():
            Result = self.Maximum(z, Ply_num - 1, Minimum_Score)
            if Minimum_Score > Result:
                Minimum_Score = Result
                r = k[0]
                c = k[1]
                o = k[2]

        return (r,c,o)

    # Alpha-beta pruning function for taking care of Alpha values
    def Maximum(self, State, Ply_num, Alpha):
        if Ply_num == 0:
            return State.CurrentScore

        possiblemoves=State.Current.get_moves()
        for x in possiblemoves:
            if (x[0],x[1],x[2]) not in State.children:
                State.Make(x[0],x[1],x[2]) 

        Maximum_Score = -1000
        r = 0
        c = 0
        o=""
        for k, z in State.children.items():
            Result = AlphaBeta.Minimum(z, Ply_num - 1, Maximum_Score)
            if Maximum_Score < Result:
                Maximum_Score = Result
            if Result > Alpha:
                return Result

        return Maximum_Score

    def Minimum(self, State, Ply_num, Beta):  # Alpha-beta pruning function for taking care of Beta values
        if Ply_num == 0:
            return State.CurrentScore

        possiblemoves = State.Current.get_moves()
        for x in possiblemoves:
            if (x[0],x[1],x[2]) not in State.children:
                State.Make(x[0],x[1],x[2])

        Minimum_Score = 1000
        i = 0
        j = 0
        for k, z in State.children.items():
            Result = AlphaBeta.Maximum(z, Ply_num - 1, Minimum_Score)
            if Minimum_Score > Result:
                Minimum_Score = Result
            if Result < Beta:
                return Result

        return Minimum_Score