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
        self.CurrentScore = self.Current.score[1]-self.Current.score[2]
        self.children = {}
    def update_score(self):
         self.CurrentScore = self.Current.score[1]-self.Current.score[2]
    def Make(self, r,c,o, player): # Function for generating a child node
        self.children[(r,c,o)] = ABNode(self.Current)
        move=(r,c,o)
        self.children[(r,c,o)].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(State, Ply_num):  # Function for the minimax algorithm

        for i in range(State.Current.dimY):
            for j in range(State.Current.dimX):
                if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
                    State.Make(j, i, True)
                    if Ply_num < 2:
                        return (i, j)

        Minimum_Score = 1000
        i = 0

        j = 0
        for k, z in State.children.items():
            Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
            if Minimum_Score > Result:
                Minimum_Score = Result
                i = k[0]
                j = k[1]

        return (i, j)

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

        for i in range(State.Current.dimY):
            for j in range(State.Current.dimX):
                if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
                    State.Make(j, i, False)

        Maximum_Score = -1000
        i = 0
        j = 0
        for k, z in State.children.items():
            Result = Algo.Minimum(z, Ply_num - 1, Maximum_Score)
            if Maximum_Score < Result:
                Maximum_Score = Result
            if Result > Alpha:
                return Result

        return Maximum_Score

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

        for i in range(State.Current.dimY):
            for j in range(State.Current.dimX):
                if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
                    State.Make(j, i, True)

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

        return Minimum_Score