import torch
import numpy as np

class DQN(torch.nn.Module):
    def __init__(self, input_size=42, hidden_size=128, output_size=7):
        super(DQN, self).__init__()
        self.fc1 = torch.nn.Linear(input_size, hidden_size)
        self.relu = torch.nn.ReLU()
        self.fc2 = torch.nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        return self.fc2(x)

def load_model(path):
    model = DQN()
    model.load_state_dict(torch.load(path, map_location=torch.device('cpu')))
    model.eval()
    return model

def get_best_action(board, model):
    flat_state = torch.tensor(board.flatten(), dtype=torch.float32).unsqueeze(0)
    with torch.no_grad():
        q_values = model(flat_state)
    valid_actions = [c for c in range(7) if board[0][c] == 0]
    q_values[0, [i for i in range(7) if i not in valid_actions]] = -float('inf')
    return torch.argmax(q_values).item()