modeling_chessbot.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
import os
import chess
import math
import numpy as np
from typing import Optional, Tuple, Union
from torch import Tensor
from transformers import PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import BaseModelOutput

policy_index = [
    "a1b1", "a1c1", "a1d1", "a1e1", "a1f1", "a1g1", "a1h1", "a1a2", "a1b2",
    "a1c2", "a1a3", "a1b3", "a1c3", "a1a4", "a1d4", "a1a5", "a1e5", "a1a6",
    "a1f6", "a1a7", "a1g7", "a1a8", "a1h8", "b1a1", "b1c1", "b1d1", "b1e1",
    "b1f1", "b1g1", "b1h1", "b1a2", "b1b2", "b1c2", "b1d2", "b1a3", "b1b3",
    "b1c3", "b1d3", "b1b4", "b1e4", "b1b5", "b1f5", "b1b6", "b1g6", "b1b7",
    "b1h7", "b1b8", "c1a1", "c1b1", "c1d1", "c1e1", "c1f1", "c1g1", "c1h1",
    "c1a2", "c1b2", "c1c2", "c1d2", "c1e2", "c1a3", "c1b3", "c1c3", "c1d3",
    "c1e3", "c1c4", "c1f4", "c1c5", "c1g5", "c1c6", "c1h6", "c1c7", "c1c8",
    "d1a1", "d1b1", "d1c1", "d1e1", "d1f1", "d1g1", "d1h1", "d1b2", "d1c2",
    "d1d2", "d1e2", "d1f2", "d1b3", "d1c3", "d1d3", "d1e3", "d1f3", "d1a4",
    "d1d4", "d1g4", "d1d5", "d1h5", "d1d6", "d1d7", "d1d8", "e1a1", "e1b1",
    "e1c1", "e1d1", "e1f1", "e1g1", "e1h1", "e1c2", "e1d2", "e1e2", "e1f2",
    "e1g2", "e1c3", "e1d3", "e1e3", "e1f3", "e1g3", "e1b4", "e1e4", "e1h4",
    "e1a5", "e1e5", "e1e6", "e1e7", "e1e8", "f1a1", "f1b1", "f1c1", "f1d1",
    "f1e1", "f1g1", "f1h1", "f1d2", "f1e2", "f1f2", "f1g2", "f1h2", "f1d3",
    "f1e3", "f1f3", "f1g3", "f1h3", "f1c4", "f1f4", "f1b5", "f1f5", "f1a6",
    "f1f6", "f1f7", "f1f8", "g1a1", "g1b1", "g1c1", "g1d1", "g1e1", "g1f1",
    "g1h1", "g1e2", "g1f2", "g1g2", "g1h2", "g1e3", "g1f3", "g1g3", "g1h3",
    "g1d4", "g1g4", "g1c5", "g1g5", "g1b6", "g1g6", "g1a7", "g1g7", "g1g8",
    "h1a1", "h1b1", "h1c1", "h1d1", "h1e1", "h1f1", "h1g1", "h1f2", "h1g2",
    "h1h2", "h1f3", "h1g3", "h1h3", "h1e4", "h1h4", "h1d5", "h1h5", "h1c6",
    "h1h6", "h1b7", "h1h7", "h1a8", "h1h8", "a2a1", "a2b1", "a2c1", "a2b2",
    "a2c2", "a2d2", "a2e2", "a2f2", "a2g2", "a2h2", "a2a3", "a2b3", "a2c3",
    "a2a4", "a2b4", "a2c4", "a2a5", "a2d5", "a2a6", "a2e6", "a2a7", "a2f7",
    "a2a8", "a2g8", "b2a1", "b2b1", "b2c1", "b2d1", "b2a2", "b2c2", "b2d2",
    "b2e2", "b2f2", "b2g2", "b2h2", "b2a3", "b2b3", "b2c3", "b2d3", "b2a4",
    "b2b4", "b2c4", "b2d4", "b2b5", "b2e5", "b2b6", "b2f6", "b2b7", "b2g7",
    "b2b8", "b2h8", "c2a1", "c2b1", "c2c1", "c2d1", "c2e1", "c2a2", "c2b2",
    "c2d2", "c2e2", "c2f2", "c2g2", "c2h2", "c2a3", "c2b3", "c2c3", "c2d3",
    "c2e3", "c2a4", "c2b4", "c2c4", "c2d4", "c2e4", "c2c5", "c2f5", "c2c6",
    "c2g6", "c2c7", "c2h7", "c2c8", "d2b1", "d2c1", "d2d1", "d2e1", "d2f1",
    "d2a2", "d2b2", "d2c2", "d2e2", "d2f2", "d2g2", "d2h2", "d2b3", "d2c3",
    "d2d3", "d2e3", "d2f3", "d2b4", "d2c4", "d2d4", "d2e4", "d2f4", "d2a5",
    "d2d5", "d2g5", "d2d6", "d2h6", "d2d7", "d2d8", "e2c1", "e2d1", "e2e1",
    "e2f1", "e2g1", "e2a2", "e2b2", "e2c2", "e2d2", "e2f2", "e2g2", "e2h2",
    "e2c3", "e2d3", "e2e3", "e2f3", "e2g3", "e2c4", "e2d4", "e2e4", "e2f4",
    "e2g4", "e2b5", "e2e5", "e2h5", "e2a6", "e2e6", "e2e7", "e2e8", "f2d1",
    "f2e1", "f2f1", "f2g1", "f2h1", "f2a2", "f2b2", "f2c2", "f2d2", "f2e2",
    "f2g2", "f2h2", "f2d3", "f2e3", "f2f3", "f2g3", "f2h3", "f2d4", "f2e4",
    "f2f4", "f2g4", "f2h4", "f2c5", "f2f5", "f2b6", "f2f6", "f2a7", "f2f7",
    "f2f8", "g2e1", "g2f1", "g2g1", "g2h1", "g2a2", "g2b2", "g2c2", "g2d2",
    "g2e2", "g2f2", "g2h2", "g2e3", "g2f3", "g2g3", "g2h3", "g2e4", "g2f4",
    "g2g4", "g2h4", "g2d5", "g2g5", "g2c6", "g2g6", "g2b7", "g2g7", "g2a8",
    "g2g8", "h2f1", "h2g1", "h2h1", "h2a2", "h2b2", "h2c2", "h2d2", "h2e2",
    "h2f2", "h2g2", "h2f3", "h2g3", "h2h3", "h2f4", "h2g4", "h2h4", "h2e5",
    "h2h5", "h2d6", "h2h6", "h2c7", "h2h7", "h2b8", "h2h8", "a3a1", "a3b1",
    "a3c1", "a3a2", "a3b2", "a3c2", "a3b3", "a3c3", "a3d3", "a3e3", "a3f3",
    "a3g3", "a3h3", "a3a4", "a3b4", "a3c4", "a3a5", "a3b5", "a3c5", "a3a6",
    "a3d6", "a3a7", "a3e7", "a3a8", "a3f8", "b3a1", "b3b1", "b3c1", "b3d1",
    "b3a2", "b3b2", "b3c2", "b3d2", "b3a3", "b3c3", "b3d3", "b3e3", "b3f3",
    "b3g3", "b3h3", "b3a4", "b3b4", "b3c4", "b3d4", "b3a5", "b3b5", "b3c5",
    "b3d5", "b3b6", "b3e6", "b3b7", "b3f7", "b3b8", "b3g8", "c3a1", "c3b1",
    "c3c1", "c3d1", "c3e1", "c3a2", "c3b2", "c3c2", "c3d2", "c3e2", "c3a3",
    "c3b3", "c3d3", "c3e3", "c3f3", "c3g3", "c3h3", "c3a4", "c3b4", "c3c4",
    "c3d4", "c3e4", "c3a5", "c3b5", "c3c5", "c3d5", "c3e5", "c3c6", "c3f6",
    "c3c7", "c3g7", "c3c8", "c3h8", "d3b1", "d3c1", "d3d1", "d3e1", "d3f1",
    "d3b2", "d3c2", "d3d2", "d3e2", "d3f2", "d3a3", "d3b3", "d3c3", "d3e3",
    "d3f3", "d3g3", "d3h3", "d3b4", "d3c4", "d3d4", "d3e4", "d3f4", "d3b5",
    "d3c5", "d3d5", "d3e5", "d3f5", "d3a6", "d3d6", "d3g6", "d3d7", "d3h7",
    "d3d8", "e3c1", "e3d1", "e3e1", "e3f1", "e3g1", "e3c2", "e3d2", "e3e2",
    "e3f2", "e3g2", "e3a3", "e3b3", "e3c3", "e3d3", "e3f3", "e3g3", "e3h3",
    "e3c4", "e3d4", "e3e4", "e3f4", "e3g4", "e3c5", "e3d5", "e3e5", "e3f5",
    "e3g5", "e3b6", "e3e6", "e3h6", "e3a7", "e3e7", "e3e8", "f3d1", "f3e1",
    "f3f1", "f3g1", "f3h1", "f3d2", "f3e2", "f3f2", "f3g2", "f3h2", "f3a3",
    "f3b3", "f3c3", "f3d3", "f3e3", "f3g3", "f3h3", "f3d4", "f3e4", "f3f4",
    "f3g4", "f3h4", "f3d5", "f3e5", "f3f5", "f3g5", "f3h5", "f3c6", "f3f6",
    "f3b7", "f3f7", "f3a8", "f3f8", "g3e1", "g3f1", "g3g1", "g3h1", "g3e2",
    "g3f2", "g3g2", "g3h2", "g3a3", "g3b3", "g3c3", "g3d3", "g3e3", "g3f3",
    "g3h3", "g3e4", "g3f4", "g3g4", "g3h4", "g3e5", "g3f5", "g3g5", "g3h5",
    "g3d6", "g3g6", "g3c7", "g3g7", "g3b8", "g3g8", "h3f1", "h3g1", "h3h1",
    "h3f2", "h3g2", "h3h2", "h3a3", "h3b3", "h3c3", "h3d3", "h3e3", "h3f3",
    "h3g3", "h3f4", "h3g4", "h3h4", "h3f5", "h3g5", "h3h5", "h3e6", "h3h6",
    "h3d7", "h3h7", "h3c8", "h3h8", "a4a1", "a4d1", "a4a2", "a4b2", "a4c2",
    "a4a3", "a4b3", "a4c3", "a4b4", "a4c4", "a4d4", "a4e4", "a4f4", "a4g4",
    "a4h4", "a4a5", "a4b5", "a4c5", "a4a6", "a4b6", "a4c6", "a4a7", "a4d7",
    "a4a8", "a4e8", "b4b1", "b4e1", "b4a2", "b4b2", "b4c2", "b4d2", "b4a3",
    "b4b3", "b4c3", "b4d3", "b4a4", "b4c4", "b4d4", "b4e4", "b4f4", "b4g4",
    "b4h4", "b4a5", "b4b5", "b4c5", "b4d5", "b4a6", "b4b6", "b4c6", "b4d6",
    "b4b7", "b4e7", "b4b8", "b4f8", "c4c1", "c4f1", "c4a2", "c4b2", "c4c2",
    "c4d2", "c4e2", "c4a3", "c4b3", "c4c3", "c4d3", "c4e3", "c4a4", "c4b4",
    "c4d4", "c4e4", "c4f4", "c4g4", "c4h4", "c4a5", "c4b5", "c4c5", "c4d5",
    "c4e5", "c4a6", "c4b6", "c4c6", "c4d6", "c4e6", "c4c7", "c4f7", "c4c8",
    "c4g8", "d4a1", "d4d1", "d4g1", "d4b2", "d4c2", "d4d2", "d4e2", "d4f2",
    "d4b3", "d4c3", "d4d3", "d4e3", "d4f3", "d4a4", "d4b4", "d4c4", "d4e4",
    "d4f4", "d4g4", "d4h4", "d4b5", "d4c5", "d4d5", "d4e5", "d4f5", "d4b6",
    "d4c6", "d4d6", "d4e6", "d4f6", "d4a7", "d4d7", "d4g7", "d4d8", "d4h8",
    "e4b1", "e4e1", "e4h1", "e4c2", "e4d2", "e4e2", "e4f2", "e4g2", "e4c3",
    "e4d3", "e4e3", "e4f3", "e4g3", "e4a4", "e4b4", "e4c4", "e4d4", "e4f4",
    "e4g4", "e4h4", "e4c5", "e4d5", "e4e5", "e4f5", "e4g5", "e4c6", "e4d6",
    "e4e6", "e4f6", "e4g6", "e4b7", "e4e7", "e4h7", "e4a8", "e4e8", "f4c1",
    "f4f1", "f4d2", "f4e2", "f4f2", "f4g2", "f4h2", "f4d3", "f4e3", "f4f3",
    "f4g3", "f4h3", "f4a4", "f4b4", "f4c4", "f4d4", "f4e4", "f4g4", "f4h4",
    "f4d5", "f4e5", "f4f5", "f4g5", "f4h5", "f4d6", "f4e6", "f4f6", "f4g6",
    "f4h6", "f4c7", "f4f7", "f4b8", "f4f8", "g4d1", "g4g1", "g4e2", "g4f2",
    "g4g2", "g4h2", "g4e3", "g4f3", "g4g3", "g4h3", "g4a4", "g4b4", "g4c4",
    "g4d4", "g4e4", "g4f4", "g4h4", "g4e5", "g4f5", "g4g5", "g4h5", "g4e6",
    "g4f6", "g4g6", "g4h6", "g4d7", "g4g7", "g4c8", "g4g8", "h4e1", "h4h1",
    "h4f2", "h4g2", "h4h2", "h4f3", "h4g3", "h4h3", "h4a4", "h4b4", "h4c4",
    "h4d4", "h4e4", "h4f4", "h4g4", "h4f5", "h4g5", "h4h5", "h4f6", "h4g6",
    "h4h6", "h4e7", "h4h7", "h4d8", "h4h8", "a5a1", "a5e1", "a5a2", "a5d2",
    "a5a3", "a5b3", "a5c3", "a5a4", "a5b4", "a5c4", "a5b5", "a5c5", "a5d5",
    "a5e5", "a5f5", "a5g5", "a5h5", "a5a6", "a5b6", "a5c6", "a5a7", "a5b7",
    "a5c7", "a5a8", "a5d8", "b5b1", "b5f1", "b5b2", "b5e2", "b5a3", "b5b3",
    "b5c3", "b5d3", "b5a4", "b5b4", "b5c4", "b5d4", "b5a5", "b5c5", "b5d5",
    "b5e5", "b5f5", "b5g5", "b5h5", "b5a6", "b5b6", "b5c6", "b5d6", "b5a7",
    "b5b7", "b5c7", "b5d7", "b5b8", "b5e8", "c5c1", "c5g1", "c5c2", "c5f2",
    "c5a3", "c5b3", "c5c3", "c5d3", "c5e3", "c5a4", "c5b4", "c5c4", "c5d4",
    "c5e4", "c5a5", "c5b5", "c5d5", "c5e5", "c5f5", "c5g5", "c5h5", "c5a6",
    "c5b6", "c5c6", "c5d6", "c5e6", "c5a7", "c5b7", "c5c7", "c5d7", "c5e7",
    "c5c8", "c5f8", "d5d1", "d5h1", "d5a2", "d5d2", "d5g2", "d5b3", "d5c3",
    "d5d3", "d5e3", "d5f3", "d5b4", "d5c4", "d5d4", "d5e4", "d5f4", "d5a5",
    "d5b5", "d5c5", "d5e5", "d5f5", "d5g5", "d5h5", "d5b6", "d5c6", "d5d6",
    "d5e6", "d5f6", "d5b7", "d5c7", "d5d7", "d5e7", "d5f7", "d5a8", "d5d8",
    "d5g8", "e5a1", "e5e1", "e5b2", "e5e2", "e5h2", "e5c3", "e5d3", "e5e3",
    "e5f3", "e5g3", "e5c4", "e5d4", "e5e4", "e5f4", "e5g4", "e5a5", "e5b5",
    "e5c5", "e5d5", "e5f5", "e5g5", "e5h5", "e5c6", "e5d6", "e5e6", "e5f6",
    "e5g6", "e5c7", "e5d7", "e5e7", "e5f7", "e5g7", "e5b8", "e5e8", "e5h8",
    "f5b1", "f5f1", "f5c2", "f5f2", "f5d3", "f5e3", "f5f3", "f5g3", "f5h3",
    "f5d4", "f5e4", "f5f4", "f5g4", "f5h4", "f5a5", "f5b5", "f5c5", "f5d5",
    "f5e5", "f5g5", "f5h5", "f5d6", "f5e6", "f5f6", "f5g6", "f5h6", "f5d7",
    "f5e7", "f5f7", "f5g7", "f5h7", "f5c8", "f5f8", "g5c1", "g5g1", "g5d2",
    "g5g2", "g5e3", "g5f3", "g5g3", "g5h3", "g5e4", "g5f4", "g5g4", "g5h4",
    "g5a5", "g5b5", "g5c5", "g5d5", "g5e5", "g5f5", "g5h5", "g5e6", "g5f6",
    "g5g6", "g5h6", "g5e7", "g5f7", "g5g7", "g5h7", "g5d8", "g5g8", "h5d1",
    "h5h1", "h5e2", "h5h2", "h5f3", "h5g3", "h5h3", "h5f4", "h5g4", "h5h4",
    "h5a5", "h5b5", "h5c5", "h5d5", "h5e5", "h5f5", "h5g5", "h5f6", "h5g6",
    "h5h6", "h5f7", "h5g7", "h5h7", "h5e8", "h5h8", "a6a1", "a6f1", "a6a2",
    "a6e2", "a6a3", "a6d3", "a6a4", "a6b4", "a6c4", "a6a5", "a6b5", "a6c5",
    "a6b6", "a6c6", "a6d6", "a6e6", "a6f6", "a6g6", "a6h6", "a6a7", "a6b7",
    "a6c7", "a6a8", "a6b8", "a6c8", "b6b1", "b6g1", "b6b2", "b6f2", "b6b3",
    "b6e3", "b6a4", "b6b4", "b6c4", "b6d4", "b6a5", "b6b5", "b6c5", "b6d5",
    "b6a6", "b6c6", "b6d6", "b6e6", "b6f6", "b6g6", "b6h6", "b6a7", "b6b7",
    "b6c7", "b6d7", "b6a8", "b6b8", "b6c8", "b6d8", "c6c1", "c6h1", "c6c2",
    "c6g2", "c6c3", "c6f3", "c6a4", "c6b4", "c6c4", "c6d4", "c6e4", "c6a5",
    "c6b5", "c6c5", "c6d5", "c6e5", "c6a6", "c6b6", "c6d6", "c6e6", "c6f6",
    "c6g6", "c6h6", "c6a7", "c6b7", "c6c7", "c6d7", "c6e7", "c6a8", "c6b8",
    "c6c8", "c6d8", "c6e8", "d6d1", "d6d2", "d6h2", "d6a3", "d6d3", "d6g3",
    "d6b4", "d6c4", "d6d4", "d6e4", "d6f4", "d6b5", "d6c5", "d6d5", "d6e5",
    "d6f5", "d6a6", "d6b6", "d6c6", "d6e6", "d6f6", "d6g6", "d6h6", "d6b7",
    "d6c7", "d6d7", "d6e7", "d6f7", "d6b8", "d6c8", "d6d8", "d6e8", "d6f8",
    "e6e1", "e6a2", "e6e2", "e6b3", "e6e3", "e6h3", "e6c4", "e6d4", "e6e4",
    "e6f4", "e6g4", "e6c5", "e6d5", "e6e5", "e6f5", "e6g5", "e6a6", "e6b6",
    "e6c6", "e6d6", "e6f6", "e6g6", "e6h6", "e6c7", "e6d7", "e6e7", "e6f7",
    "e6g7", "e6c8", "e6d8", "e6e8", "e6f8", "e6g8", "f6a1", "f6f1", "f6b2",
    "f6f2", "f6c3", "f6f3", "f6d4", "f6e4", "f6f4", "f6g4", "f6h4", "f6d5",
    "f6e5", "f6f5", "f6g5", "f6h5", "f6a6", "f6b6", "f6c6", "f6d6", "f6e6",
    "f6g6", "f6h6", "f6d7", "f6e7", "f6f7", "f6g7", "f6h7", "f6d8", "f6e8",
    "f6f8", "f6g8", "f6h8", "g6b1", "g6g1", "g6c2", "g6g2", "g6d3", "g6g3",
    "g6e4", "g6f4", "g6g4", "g6h4", "g6e5", "g6f5", "g6g5", "g6h5", "g6a6",
    "g6b6", "g6c6", "g6d6", "g6e6", "g6f6", "g6h6", "g6e7", "g6f7", "g6g7",
    "g6h7", "g6e8", "g6f8", "g6g8", "g6h8", "h6c1", "h6h1", "h6d2", "h6h2",
    "h6e3", "h6h3", "h6f4", "h6g4", "h6h4", "h6f5", "h6g5", "h6h5", "h6a6",
    "h6b6", "h6c6", "h6d6", "h6e6", "h6f6", "h6g6", "h6f7", "h6g7", "h6h7",
    "h6f8", "h6g8", "h6h8", "a7a1", "a7g1", "a7a2", "a7f2", "a7a3", "a7e3",
    "a7a4", "a7d4", "a7a5", "a7b5", "a7c5", "a7a6", "a7b6", "a7c6", "a7b7",
    "a7c7", "a7d7", "a7e7", "a7f7", "a7g7", "a7h7", "a7a8", "a7b8", "a7c8",
    "b7b1", "b7h1", "b7b2", "b7g2", "b7b3", "b7f3", "b7b4", "b7e4", "b7a5",
    "b7b5", "b7c5", "b7d5", "b7a6", "b7b6", "b7c6", "b7d6", "b7a7", "b7c7",
    "b7d7", "b7e7", "b7f7", "b7g7", "b7h7", "b7a8", "b7b8", "b7c8", "b7d8",
    "c7c1", "c7c2", "c7h2", "c7c3", "c7g3", "c7c4", "c7f4", "c7a5", "c7b5",
    "c7c5", "c7d5", "c7e5", "c7a6", "c7b6", "c7c6", "c7d6", "c7e6", "c7a7",
    "c7b7", "c7d7", "c7e7", "c7f7", "c7g7", "c7h7", "c7a8", "c7b8", "c7c8",
    "c7d8", "c7e8", "d7d1", "d7d2", "d7d3", "d7h3", "d7a4", "d7d4", "d7g4",
    "d7b5", "d7c5", "d7d5", "d7e5", "d7f5", "d7b6", "d7c6", "d7d6", "d7e6",
    "d7f6", "d7a7", "d7b7", "d7c7", "d7e7", "d7f7", "d7g7", "d7h7", "d7b8",
    "d7c8", "d7d8", "d7e8", "d7f8", "e7e1", "e7e2", "e7a3", "e7e3", "e7b4",
    "e7e4", "e7h4", "e7c5", "e7d5", "e7e5", "e7f5", "e7g5", "e7c6", "e7d6",
    "e7e6", "e7f6", "e7g6", "e7a7", "e7b7", "e7c7", "e7d7", "e7f7", "e7g7",
    "e7h7", "e7c8", "e7d8", "e7e8", "e7f8", "e7g8", "f7f1", "f7a2", "f7f2",
    "f7b3", "f7f3", "f7c4", "f7f4", "f7d5", "f7e5", "f7f5", "f7g5", "f7h5",
    "f7d6", "f7e6", "f7f6", "f7g6", "f7h6", "f7a7", "f7b7", "f7c7", "f7d7",
    "f7e7", "f7g7", "f7h7", "f7d8", "f7e8", "f7f8", "f7g8", "f7h8", "g7a1",
    "g7g1", "g7b2", "g7g2", "g7c3", "g7g3", "g7d4", "g7g4", "g7e5", "g7f5",
    "g7g5", "g7h5", "g7e6", "g7f6", "g7g6", "g7h6", "g7a7", "g7b7", "g7c7",
    "g7d7", "g7e7", "g7f7", "g7h7", "g7e8", "g7f8", "g7g8", "g7h8", "h7b1",
    "h7h1", "h7c2", "h7h2", "h7d3", "h7h3", "h7e4", "h7h4", "h7f5", "h7g5",
    "h7h5", "h7f6", "h7g6", "h7h6", "h7a7", "h7b7", "h7c7", "h7d7", "h7e7",
    "h7f7", "h7g7", "h7f8", "h7g8", "h7h8", "a8a1", "a8h1", "a8a2", "a8g2",
    "a8a3", "a8f3", "a8a4", "a8e4", "a8a5", "a8d5", "a8a6", "a8b6", "a8c6",
    "a8a7", "a8b7", "a8c7", "a8b8", "a8c8", "a8d8", "a8e8", "a8f8", "a8g8",
    "a8h8", "b8b1", "b8b2", "b8h2", "b8b3", "b8g3", "b8b4", "b8f4", "b8b5",
    "b8e5", "b8a6", "b8b6", "b8c6", "b8d6", "b8a7", "b8b7", "b8c7", "b8d7",
    "b8a8", "b8c8", "b8d8", "b8e8", "b8f8", "b8g8", "b8h8", "c8c1", "c8c2",
    "c8c3", "c8h3", "c8c4", "c8g4", "c8c5", "c8f5", "c8a6", "c8b6", "c8c6",
    "c8d6", "c8e6", "c8a7", "c8b7", "c8c7", "c8d7", "c8e7", "c8a8", "c8b8",
    "c8d8", "c8e8", "c8f8", "c8g8", "c8h8", "d8d1", "d8d2", "d8d3", "d8d4",
    "d8h4", "d8a5", "d8d5", "d8g5", "d8b6", "d8c6", "d8d6", "d8e6", "d8f6",
    "d8b7", "d8c7", "d8d7", "d8e7", "d8f7", "d8a8", "d8b8", "d8c8", "d8e8",
    "d8f8", "d8g8", "d8h8", "e8e1", "e8e2", "e8e3", "e8a4", "e8e4", "e8b5",
    "e8e5", "e8h5", "e8c6", "e8d6", "e8e6", "e8f6", "e8g6", "e8c7", "e8d7",
    "e8e7", "e8f7", "e8g7", "e8a8", "e8b8", "e8c8", "e8d8", "e8f8", "e8g8",
    "e8h8", "f8f1", "f8f2", "f8a3", "f8f3", "f8b4", "f8f4", "f8c5", "f8f5",
    "f8d6", "f8e6", "f8f6", "f8g6", "f8h6", "f8d7", "f8e7", "f8f7", "f8g7",
    "f8h7", "f8a8", "f8b8", "f8c8", "f8d8", "f8e8", "f8g8", "f8h8", "g8g1",
    "g8a2", "g8g2", "g8b3", "g8g3", "g8c4", "g8g4", "g8d5", "g8g5", "g8e6",
    "g8f6", "g8g6", "g8h6", "g8e7", "g8f7", "g8g7", "g8h7", "g8a8", "g8b8",
    "g8c8", "g8d8", "g8e8", "g8f8", "g8h8", "h8a1", "h8h1", "h8b2", "h8h2",
    "h8c3", "h8h3", "h8d4", "h8h4", "h8e5", "h8h5", "h8f6", "h8g6", "h8h6",
    "h8f7", "h8g7", "h8h7", "h8a8", "h8b8", "h8c8", "h8d8", "h8e8", "h8f8",
    "h8g8", "a7a8q", "a7a8r", "a7a8b", "a7b8q", "a7b8r", "a7b8b", "b7a8q",
    "b7a8r", "b7a8b", "b7b8q", "b7b8r", "b7b8b", "b7c8q", "b7c8r", "b7c8b",
    "c7b8q", "c7b8r", "c7b8b", "c7c8q", "c7c8r", "c7c8b", "c7d8q", "c7d8r",
    "c7d8b", "d7c8q", "d7c8r", "d7c8b", "d7d8q", "d7d8r", "d7d8b", "d7e8q",
    "d7e8r", "d7e8b", "e7d8q", "e7d8r", "e7d8b", "e7e8q", "e7e8r", "e7e8b",
    "e7f8q", "e7f8r", "e7f8b", "f7e8q", "f7e8r", "f7e8b", "f7f8q", "f7f8r",
    "f7f8b", "f7g8q", "f7g8r", "f7g8b", "g7f8q", "g7f8r", "g7f8b", "g7g8q",
    "g7g8r", "g7g8b", "g7h8q", "g7h8r", "g7h8b", "h7g8q", "h7g8r", "h7g8b",
    "h7h8q", "h7h8r", "h7h8b", #add the promotions for black
    "a2a1q","a2a1r","a2a1b","a2b1q","a2b1r","a2b1b",
    "b2a1q","b2a1r","b2a1b","b2b1q","b2b1r","b2b1b","b2c1q","b2c1r","b2c1b",
    "c2b1q","c2b1r","c2b1b","c2c1q","c2c1r","c2c1b","c2d1q","c2d1r","c2d1b",
    "d2c1q","d2c1r","d2c1b","d2d1q","d2d1r","d2d1b","d2e1q","d2e1r","d2e1b",
    "e2d1q","e2d1r","e2d1b","e2e1q","e2e1r","e2e1b","e2f1q","e2f1r","e2f1b",
    "f2e1q","f2e1r","f2e1b","f2f1q","f2f1r","f2f1b","f2g1q","f2g1r","f2g1b",
    "g2f1q","g2f1r","g2f1b","g2g1q","g2g1r","g2g1b","g2h1q","g2h1r","g2h1b",
    "h2g1q","h2g1r","h2g1b","h2h1q","h2h1r","h2h1b",#add special tokens
    "<thinking>","</thinking>","end_variation","end","padding_token"
]

class RelativeMultiHeadAttention2(nn.Module):
    """
    Multi-head attention with relative positional encoding.
    This concept was proposed in the "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"

    Args:
        d_model (int): The dimension of model
        num_heads (int): The number of attention heads.
        dropout_p (float): probability of dropout

    Inputs: query, key, value, pos_embedding, mask
        - **query** (batch, time, dim): Tensor containing query vector
        - **key** (batch, time, dim): Tensor containing key vector
        - **value** (batch, time, dim): Tensor containing value vector
        - **pos_embedding** (batch, time, dim): Positional embedding tensor
        - **mask** (batch, 1, time2) or (batch, time1, time2): Tensor containing indices to be masked

    Returns:
        - **outputs**: Tensor produces by relative multi head attention module.
    """
    def __init__(
            self,
            d_model: int = 512,
            num_heads: int = 16,
            dropout_p: float = 0.1,
    ):
        super(RelativeMultiHeadAttention2, self).__init__()
        assert d_model % num_heads == 0, "d_model % num_heads should be zero."
        self.d_model = d_model
        self.d_head = int(d_model / num_heads)
        self.num_heads = num_heads
        self.sqrt_dim = math.sqrt(d_model)

        self.query_proj = nn.Linear(d_model, d_model)
        self.key_proj = nn.Linear(d_model, d_model)
        self.value_proj = nn.Linear(d_model, d_model)
        self.pos_proj = nn.Linear(d_model, d_model, bias=False)

        self.dropout = nn.Dropout(p=dropout_p)
        self.u_bias = nn.Parameter(torch.Tensor(self.num_heads, self.d_head))
        self.v_bias = nn.Parameter(torch.Tensor(self.num_heads, self.d_head))
        torch.nn.init.xavier_uniform_(self.u_bias)
        torch.nn.init.xavier_uniform_(self.v_bias)

        self.out_proj = nn.Linear(d_model, d_model)

    def forward(
            self,
            query: Tensor,
            key: Tensor,
            value: Tensor,
            pos_embedding: Tensor,
            mask: Optional[Tensor] = None,
    ) -> Tensor:
        batch_size = value.size(0)

        query = self.query_proj(query).view(batch_size, -1, self.num_heads, self.d_head)
        key = self.key_proj(key).view(batch_size, -1, self.num_heads, self.d_head).permute(0, 2, 1, 3)
        value = self.value_proj(value).view(batch_size, -1, self.num_heads, self.d_head).permute(0, 2, 1, 3)
        
        pos_embedding = self.pos_proj(pos_embedding).view(batch_size, -1, self.num_heads, self.d_head)

        content_score = torch.matmul((query + self.u_bias).transpose(1, 2), key.transpose(2, 3))
        pos_score = torch.matmul((query + self.v_bias).transpose(1, 2), pos_embedding.permute(0, 2, 3, 1))
        pos_score = self._compute_relative_positional_encoding(pos_score)

        score = (content_score + pos_score) / self.sqrt_dim

        if mask is not None:
            mask = mask.unsqueeze(1)
            score.masked_fill_(mask, -1e9)

        attn = F.softmax(score, -1)
        attn = self.dropout(attn)

        context = torch.matmul(attn, value).transpose(1, 2)
        context = context.contiguous().view(batch_size, -1, self.d_model)

        return self.out_proj(context)

    def _compute_relative_positional_encoding(self, pos_score: Tensor) -> Tensor:
        batch_size, num_heads, seq_length1, seq_length2 = pos_score.size()
        zeros = pos_score.new_zeros(batch_size, num_heads, seq_length1, 1)
        padded_pos_score = torch.cat([zeros, pos_score], dim=-1)

        padded_pos_score = padded_pos_score.view(batch_size, num_heads, seq_length2 + 1, seq_length1)
        pos_score = padded_pos_score[:, :, 1:].view_as(pos_score)

        return pos_score
def fen_to_tensor(fen: str):
    board = chess.Board(fen)
    P = 19  # 12 planes for pieces + 1 for side to play + 1 for en passant + 4 for castling + 1 for 50-move rule
    tensor = np.zeros((8, 8, P), dtype=np.float32)
    
    piece_map = {
        'P': 0, 'N': 1, 'B': 2, 'R': 3, 'Q': 4, 'K': 5,  # White pieces
        'p': 6, 'n': 7, 'b': 8, 'r': 9, 'q': 10, 'k': 11  # Black pieces
    }
    
    # Populate piece planes
    for square, piece in board.piece_map().items():
        rank, file = divmod(square, 8)
        plane = piece_map[piece.symbol()]
        tensor[7 - rank, file, plane] = 1.0  # Flip rank to align with standard board representation
    
    # Side to play plane
    tensor[:, :, 12] = 1.0 if board.turn == chess.WHITE else 0.0

    # En passant plane
    if board.ep_square is not None:
        rank, file = divmod(board.ep_square, 8)
        tensor[7 - rank, file, 13] = 1.0
    
    # Castling rights planes (4 total: white kingside, white queenside, black kingside, black queenside)
    tensor[:, :, 14] = 1.0 if board.has_kingside_castling_rights(chess.WHITE) else 0.0
    tensor[:, :, 15] = 1.0 if board.has_queenside_castling_rights(chess.WHITE) else 0.0
    tensor[:, :, 16] = 1.0 if board.has_kingside_castling_rights(chess.BLACK) else 0.0
    tensor[:, :, 17] = 1.0 if board.has_queenside_castling_rights(chess.BLACK) else 0.0
    
    # 50-move rule plane (normalized to [0,1])
    tensor[:, :, 18] = min(board.halfmove_clock / 100.0, 1.0)
    
    return tensor


class ChessBotConfig(PretrainedConfig):
    model_type = "chessbot"
    
    def __init__(
        self,
        num_layers=10,
        d_model=512,
        d_ff=1024,
        num_heads=8,
        max_position_embeddings=64,
        vocab_size=1929,
        torch_dtype="float32",
        **kwargs
    ):
        self.num_layers = num_layers
        self.d_model = d_model
        self.d_ff = d_ff
        self.num_heads = num_heads
        self.max_position_embeddings = max_position_embeddings
        self.vocab_size = vocab_size
        self.torch_dtype = torch_dtype
        super().__init__(**kwargs)


class MaGating(nn.Module):
    def __init__(self, d_model):
        super().__init__()
        self.a = nn.Parameter(torch.zeros(64, d_model))
        self.b = nn.Parameter(torch.ones(64, d_model))

    def forward(self, x):
        return x * torch.exp(self.a) + self.b


class EncoderLayer(nn.Module):
    def __init__(self, d_model, d_ff, num_heads):
        super().__init__()
        self.attention = RelativeMultiHeadAttention2(d_model, num_heads, 0)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.ff1 = nn.Linear(d_model, d_ff)
        self.ff2 = nn.Linear(d_ff, d_model)
        self.gelu = nn.GELU()
    
    def forward(self, x, pos_enc):
        attn_out = self.attention(x, x, x, pos_enc)
        x = attn_out + x
        x = self.norm1(x)

        y = self.ff1(x)
        y = self.gelu(y)
        y = self.ff2(y)
        y = y + x
        y = self.norm2(y)

        return y


class AbsolutePositionalEncoder(nn.Module):
    def __init__(self, d_model):
        super(AbsolutePositionalEncoder, self).__init__()
        self.d_model = d_model
        # Don't register as buffers since these are computed values
        position = torch.arange(64).unsqueeze(1).float()
        
        positional_encoding = torch.zeros(1, 64, d_model)
        _2i = torch.arange(0, d_model, step=2).float()
        positional_encoding[:, :, 0::2] = torch.sin(position / (10000 ** (_2i / d_model)))
        positional_encoding[:, :, 1::2] = torch.cos(position / (10000 ** (_2i / d_model)))
        
        # Register as non-persistent buffer (won't be saved/loaded)
        self.register_buffer('positional_encoding', positional_encoding, persistent=False)
    
    def forward(self, x):
        batch_size, _, _ = x.size()
        return self.positional_encoding.expand(batch_size, -1, -1)


class LearnedPositionalEncoder(nn.Module):
    def __init__(self, d_model=1929, max_len=64):
        super(LearnedPositionalEncoder, self).__init__()
        self.d_model = d_model
        self.max_len = max_len
        self.positional_embedding = nn.Embedding(max_len, d_model)

    def forward(self, x):
        batch_size, seq_len, _ = x.size()
        positions = torch.arange(seq_len, device=x.device).unsqueeze(0)
        pos_embed = self.positional_embedding(positions)
        pos_embed = pos_embed.expand(batch_size, -1, -1)
        return pos_embed


class ValueHead(nn.Module):
    def __init__(self, d_model):
        super().__init__()
        self.dense1 = nn.Linear(d_model, 128)
        self.dense2 = nn.Linear(128 * 64, 128)
        self.dense3 = nn.Linear(128, 3)

    def forward(self, x):
        b, _, _ = x.size()
        x = self.dense1(x)
        x = F.gelu(x)
        x = x.view(b, -1)
        x = self.dense2(x)
        x = F.gelu(x)
        x = self.dense3(x)
        return x


class ValueHeadQ(nn.Module):
    def __init__(self, d_model):
        super().__init__()
        self.dense1 = nn.Linear(d_model, 128)
        self.dense2 = nn.Linear(128 * 64, 128)
        self.dense3 = nn.Linear(128, 3)

    def forward(self, x):
        b, _, _ = x.size()
        x = self.dense1(x)
        x = F.gelu(x)
        x = x.view(b, -1)
        x = self.dense2(x)
        x = F.gelu(x)
        x = self.dense3(x)
        return x


class ChessBotModel(PreTrainedModel):
    config_class = ChessBotConfig
    
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.is_thinking_model = False
        self.d_model = config.d_model
        self.num_layers = config.num_layers

        self.layers = nn.ModuleList([
            EncoderLayer(config.d_model, config.d_ff, config.num_heads) 
            for _ in range(config.num_layers)
        ])

        self.linear1 = nn.Linear(19, config.d_model)
        self.layernorm1 = nn.LayerNorm(config.d_model)
        self.policy_tokens_lin = nn.Linear(config.d_model, config.d_model)
        self.queries_pol = nn.Linear(config.d_model, config.d_model)
        self.keys_pol = nn.Linear(config.d_model, config.d_model)
        self.positional = AbsolutePositionalEncoder(config.d_model)
        self.ma_gating = MaGating(config.d_model)
        self.policy_head = nn.Linear(64 * 64, 1929, bias=False)
        self.value_head = ValueHead(config.d_model)
        self.value_head_q = ValueHeadQ(config.d_model)

    def forward(
        self,
        input_ids=None,
        inputs_embeds=None,
        compute_loss=False,
        step=None,
        **kwargs
    ):
        # Handle both old-style input format and new HF format
        if input_ids is not None:
            inp = input_ids
        elif inputs_embeds is not None:
            inp = inputs_embeds
        else:
            raise ValueError("Either input_ids or inputs_embeds must be provided")

        if isinstance(inp, (list, tuple)):
            x = inp[0]
        else:
            x = inp

        b, seq_len, _, _, emb = x.size()
        x = x.view(b * seq_len, 64, emb)
        x = self.linear1(x)
        x = F.gelu(x)
        x = self.layernorm1(x)
        x = self.ma_gating(x)
        
        pos_enc = self.positional(x)
        for i in range(self.num_layers):
            x = self.layers[i](x, pos_enc)

        value_h = self.value_head(x)
        value_h = value_h.view(b, seq_len, 3)
        value_h_q = self.value_head_q(x)
        value_h_q = value_h_q.view(b, seq_len, 3)
        
        policy_tokens = self.policy_tokens_lin(x)
        policy_tokens = F.gelu(policy_tokens)
        policy_tokens = policy_tokens + pos_enc
        queries = self.queries_pol(policy_tokens)
        keys = self.keys_pol(policy_tokens)

        matmul_qk = torch.matmul(queries, torch.transpose(keys, -2, -1))
        dk = torch.sqrt(torch.tensor(self.d_model, dtype=torch.float32, device=x.device))
        policy_attn_logits = matmul_qk / dk
        policy_attn_logits = policy_attn_logits.view(b, seq_len, 64 * 64)
        policy = self.policy_head(policy_attn_logits)

        if compute_loss and isinstance(inp, (list, tuple)) and len(inp) > 1:
            targets = inp[1]
            true_values = inp[3] if len(inp) > 3 else None
            q_values = inp[4] if len(inp) > 4 else None
            
            if true_values is not None and q_values is not None:
                true_values = q_values
                z = torch.argmax(true_values, dim=-1)
                q = torch.argmax(q_values, dim=-1)
                value_h_q_softmax = torch.softmax(value_h_q, dim=-1)
                
                loss_policy = F.cross_entropy(policy.view(-1, policy.size(-1)), targets.view(-1), ignore_index=1928)
                
                valid_mask = (true_values.sum(dim=-1) != 0) & (q_values.sum(dim=-1) != 0)
                
                if valid_mask.any():
                    valid_value_h = value_h[valid_mask]
                    valid_value_h_q = value_h_q_softmax[valid_mask]
                    valid_z = z[valid_mask]
                    valid_q_values = q_values[valid_mask]
                    
                    loss_value = F.cross_entropy(valid_value_h.view(-1, valid_value_h.size(-1)), valid_z.view(-1))
                    loss_q = F.mse_loss(valid_value_h_q.view(-1, valid_value_h_q.size(-1)), valid_q_values.view(-1, 3))
                else:
                    loss_value = torch.tensor(0.0, device=value_h.device, requires_grad=True)
                    loss_q = torch.tensor(0.0, device=value_h_q.device, requires_grad=True)
                
                return policy, value_h, value_h_q, loss_policy, loss_value, loss_q, targets, z, q
            else:
                loss_policy = F.cross_entropy(policy.view(-1, policy.size(-1)), targets.view(-1), ignore_index=1928)
                return policy, value_h, value_h_q, loss_policy

        return BaseModelOutput(
            last_hidden_state=policy,
            hidden_states=(value_h, value_h_q)
        )

    def get_move_from_fen_no_thinking(self, fen, T=1, device="cuda", force_legal=True, return_probs=False):
        board = chess.Board()
        board.set_fen(fen)
        x = torch.from_numpy(fen_to_tensor(fen)).to(device).to(torch.float32)
        x = x.view(1, 1, 8, 8, 19)

        output = self(x)
        if hasattr(output, 'last_hidden_state'):
            logits = output.last_hidden_state
        else:
            logits = output
        
        logits = logits.view(-1, 1929)
        legal_move_mask = torch.zeros((1, 1929), device=device)
        for legal_move in board.legal_moves:
            if legal_move.uci()[-1] == 'n':
                legal_move_uci = legal_move.uci()[:-1]
            else:
                legal_move_uci = legal_move.uci()
            legal_move_mask[0][policy_index.index(legal_move_uci)] = 1

        if force_legal:
            logits = logits + (1 - legal_move_mask) * -999
            
        if T == 0:
            sampled = torch.argmax(logits, dim=-1, keepdim=True)
        else:
            probs = F.softmax(logits / T, dim=-1)
            sampled = torch.multinomial(probs, num_samples=1)
            if return_probs:
                # Map to legal moves
                legal_move_probs = {}
                for move in board.legal_moves:
                    idx = policy_index.index(move.uci())
                    legal_move_probs[move.uci()] = probs[0,idx].item()

                return legal_move_probs
                
        move = policy_index[sampled.item()]
        return move

    def get_position_value(self, fen, device="cuda"):
        """Get the value evaluation for a given FEN position."""
        x = torch.from_numpy(fen_to_tensor(fen)).to(device).to(torch.float32)
        x = x.view(1, 1, 8, 8, 19)
        
        with torch.no_grad():
            b, seq_len, _, _, emb = x.size()
            x_processed = x.view(b * seq_len, 64, emb)
            x_processed = self.linear1(x_processed)
            x_processed = F.gelu(x_processed)
            x_processed = self.layernorm1(x_processed)
            x_processed = self.ma_gating(x_processed)
            
            pos_enc = self.positional(x_processed)
            for i in range(self.num_layers):
                x_processed = self.layers[i](x_processed, pos_enc)
            
            value_logits = self.value_head_q(x_processed)
            value_logits = value_logits.view(b, seq_len, 3)
            value = torch.softmax(value_logits, dim=-1)
            
        return value.squeeze()

    def get_batch_position_values(self, fens, device="cuda"):
        """Get the value evaluation for a batch of FEN positions efficiently."""
        if len(fens) == 0:
            return torch.empty(0, 3, device=device)
        
        position_tensors = []
        for fen in fens:
            x = torch.from_numpy(fen_to_tensor(fen)).to(device).to(torch.float32)
            position_tensors.append(x)
        
        batch_x = torch.stack(position_tensors, dim=0)
        batch_x = batch_x.unsqueeze(1)
        
        with torch.no_grad():
            b, seq_len, _, _, emb = batch_x.size()
            x_processed = batch_x.view(b * seq_len, 64, emb)
            x_processed = self.linear1(x_processed)
            x_processed = F.gelu(x_processed)
            x_processed = self.layernorm1(x_processed)
            x_processed = self.ma_gating(x_processed)
            
            pos_enc = self.positional(x_processed)
            for i in range(self.num_layers):
                x_processed = self.layers[i](x_processed, pos_enc)
            
            value_logits = self.value_head_q(x_processed)
            value_logits = value_logits.view(b, seq_len, 3)
            value_logits = torch.softmax(value_logits, dim=-1)
            
        return value_logits.squeeze(1)

    def calculate_move_values(self, fen, device="cuda"):
        """Calculate the value for each legal move from the given position efficiently using batching."""
        board = chess.Board()
        board.set_fen(fen)
        
        is_white_turn = board.turn == chess.WHITE
        legal_moves = list(board.legal_moves)
        if len(legal_moves) == 0:
            return [], torch.empty(0, device=device)
        
        resulting_fens = []
        for move in legal_moves:
            board.push(move)
            resulting_fens.append(board.fen())
            board.pop()
        
        batch_value_q = self.get_batch_position_values(resulting_fens, device)
        batch_value_q = batch_value_q[:, 2] - batch_value_q[:, 0]
        
        if is_white_turn:
            player_values = batch_value_q
        else:
            player_values = -batch_value_q
        
        return legal_moves, player_values

    def get_best_move_value(self, fen, T=1, device="cuda", return_probs=False, to_fall_back_to_policy=False):
        """Determine the best move based on the value of resulting positions using efficient batching."""
        value = self.get_position_value(fen, device)
        board = chess.Board()
        board.set_fen(fen)
        
        is_white_turn = board.turn == chess.WHITE
        if is_white_turn:
            value = value[2] - value[0]
        else:
            value = value[0] - value[2]
            
        if value > 0.9 and to_fall_back_to_policy:
            self.fall_back_to_policy = True
        if to_fall_back_to_policy and hasattr(self, 'fall_back_to_policy') and self.fall_back_to_policy:
            return self.get_move_from_fen_no_thinking(fen, T, device, force_legal=True, return_probs=return_probs)

        legal_moves, move_values = self.calculate_move_values(fen, device)
        
        if len(legal_moves) == 0:
            raise ValueError("No legal moves available")
        
        if T == 0:
            best_idx = torch.argmax(move_values)
            selected_move = legal_moves[best_idx]
        else:
            probs = F.softmax(move_values / T, dim=0)
            sampled_idx = torch.multinomial(probs, num_samples=1)
            selected_move = legal_moves[sampled_idx.item()]
        
        move_uci = selected_move.uci()
        
        if return_probs:
            if T == 0:
                probs = torch.zeros_like(move_values)
                probs[best_idx] = 1.0
            else:
                probs = F.softmax(move_values / T, dim=0)
                
            return probs.cpu().numpy()
        
        return move_uci