import numpy as np


class APlayer(object):
	def __init__(self, mark):
		self.mark = mark
		self.other_mark = 'O'
		if 'O' in mark:
			self.other_mark = 'X'

		self.state = None
		self.state_last = None

	def set_debug(self, with_debug):
		pass

	@staticmethod
	def get_potential_moves(state: np.array) -> np.array:
		st = state.reshape(state.size)
		indices = [idx for idx, s in enumerate(st) if '-' in s]
		return np.array(indices)

	def move(self, state: np.array):
		return state, False

	def state_from_move(self, state: np.array, field):
		state.reshape(state.size)[field] = self.mark
		return state

	def reward(self, value):
		pass

	def new_game(self):
		pass

	@staticmethod
	def f_state_slices():
		nr = 3
		nc = 3
		result = []
		# Create row finishing states
		d1 = ()
		d2_r = ()
		for r in range(0, nr):
			d1 += (r,)
		for c in range(0, nc):
			d2 = (c,) * nc
			result.append((d1, d2))

		# Create column finishing states
		for c in range(0, nc):
			d2 = (c,) * nc
			result.append((d2, d1))
		for c in range(0, nc):
			d2_r += (nc - c - 1,)

		# Create diagonal finishing states #1
		result.append((d1, d1))

		# Create diagonal finishing states #2
		result.append((d1, d2_r))

		return result

	def has_won(self, state: np.array):
		result = False
		ref = [self.mark, self.mark, self.mark]
		for f_state_slice in APlayer.f_state_slices():
			if np.all(state[f_state_slice] == ref):
				result = True
				break

		return result