import random
import ctypes as C
from binding.python_c_ffi import ChessFFI
from binding.python_c_ffi import Move


class BaseEvaluation:
    def __init__(self, chess_ffi=None):
        if chess_ffi is None:
            chess_ffi = ChessFFI()
        self.chess_ffi = chess_ffi


    def get_best_move(self, board, legal_moves):
        pass


    def get_params(self):
        pass


"""
    We will use a random move evaluation as our base AI. This
    is expected to be the worst performing strategy. We can
    play our other AI evaluation methods against this one to
    confirm if our other strategies are at least better than
    a simplistic approach.
"""
class RandomEval(BaseEvaluation):
    def __init__(self, chess_ffi=None):
        super().__init__(chess_ffi)


    def get_best_move(self, board, legal_moves):
        return random.choice(legal_moves)


class NegaMaxEval(BaseEvaluation):
    def __init__(self, depth=6, cp_window=20, chess_ffi=None):
        super().__init__(chess_ffi)
        self.depth = depth
        self.window = cp_window


    def get_best_move(self, board, legal_moves):
        best = Move()
        ok = self.chess_ffi._c_ai_find_best_move_with_window(
            board,
            self.depth,
            self.window,
            best
        )

        if ok:
            return best
        return legal_moves[0]
    

    def get_params(self):
        return [self.depth, self.window]