from tugo.agents.base import Agent
from tugo.agents.helpers import is_point_an_eye
from game_logic import goboard
from tugo.models import AlphaGoModel

import numpy as np
import torch

class DeepLearningAgent(Agent):
    def __init__(self, model, encoder):
        super().__init__()
        self.model = model
        self.encoder = encoder

    def predict(self, game_state):
        encoded_state = self.encoder.encode(game_state)
        input_tensor = torch.tensor([encoded_state], dtype=torch.float32).to('cuda')
        with torch.no_grad():
            output_tensor = self.model(input_tensor)
        return output_tensor.cpu().numpy()[0]

    def select_move(self, game_state):
        num_moves = self.encoder.board_width * self.encoder.board_height
        move_probs = self.predict(game_state)

        move_probs = move_probs ** 3
        eps = 1e-6
        move_probs = np.clip(move_probs, eps, 1 - eps)
        move_probs = move_probs / np.sum(move_probs)

        candidates = np.arange(num_moves)
        ranked_moves = np.random.choice(
            candidates, num_moves, replace=False, p=move_probs)
        for point_idx in ranked_moves:
            point = self.encoder.decode_point_index(point_idx)
            if game_state.is_valid_move(goboard.Move.play(point)) and \
                    not is_point_an_eye(game_state.board, point, game_state.next_player):
                return goboard.Move.play(point)
        return goboard.Move.pass_turn()

    def save(self, path):
        torch.save(self.model.state_dict(), path)

    @classmethod
    def load(cls, path, encoder):
        device = 'cuda' if torch.cuda.is_available() else 'cpu'
        model = AlphaGoModel(encoder.get_input_shape(), is_policy_net=True).to(device)
        model.load_state_dict(torch.load(path))
        return cls(model, encoder)