Chess Challenge submission by Dhia-GB

README.md

@@ -0,0 +1,26 @@
+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# good_but_inefficient
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [Dhia-GB](https://huggingface.co/Dhia-GB)
+- **Parameters**: 832,896
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 85
+- **Embedding dim**: 128
+- **Layers**: 5
+- **Heads**: 4

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "../my_model_v2/checkpoint-1856/",
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "dropout": 0.1,
+  "eos_token_id": 2,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "chess_transformer",
+  "n_ctx": 512,
+  "n_embd": 128,
+  "n_head": 4,
+  "n_inner": 384,
+  "n_layer": 5,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.2",
+  "use_rmsnorm": false,
+  "use_rope": true,
+  "use_swiglu": true,
+  "vocab_size": 85
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e83ac7e86cca4090a70337acf67cca4d4d7df6ab02d6a88c0e69481d6966324
+size 3336560

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "bos_token": "[BOS]",
+  "eos_token": "[EOS]",
+  "pad_token": "[PAD]",
+  "unk_token": "[UNK]"
+}

tokenizer.py

@@ -0,0 +1,609 @@
+"""
+Custom Chess Tokenizer for the Chess Challenge.
+
+This tokenizer treats each move as a single token using the extended UCI notation
+from the Lichess dataset (e.g., WPe2e4, BNg8f6).
+
+The dataset format uses:
+- W/B prefix for White/Black
+- Piece letter: P=Pawn, N=Knight, B=Bishop, R=Rook, Q=Queen, K=King
+- Source and destination squares (e.g., e2e4)
+- Special suffixes: (x)=capture, (+)=check, (+*)=checkmate, (o)/(O)=castling
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from transformers import PreTrainedTokenizer
+
+
+class ChessTokenizer_v0(PreTrainedTokenizer):
+    """
+    A custom tokenizer for chess moves using extended UCI notation.
+    
+    This tokenizer maps each possible chess move to a unique token ID.
+    The vocabulary is built from the training dataset to ensure all moves
+    encountered during training have a corresponding token.
+    
+    Example:
+        >>> tokenizer = ChessTokenizer_v0()
+        >>> tokenizer.encode("WPe2e4 BPe7e5")
+        [1, 42, 87, 2]  # [BOS, e2e4, e7e5, EOS]
+    """
+    
+    model_input_names = ["input_ids", "attention_mask"]
+    vocab_files_names = {"vocab_file": "vocab.json"}
+    
+    # Special tokens
+    PAD_TOKEN = "[PAD]"
+    BOS_TOKEN = "[BOS]"
+    EOS_TOKEN = "[EOS]"
+    UNK_TOKEN = "[UNK]"
+    
+    def __init__(
+        self,
+        vocab_file: Optional[str] = None,
+        vocab: Optional[Dict[str, int]] = None,
+        **kwargs,
+    ):
+        """
+        Initialize the chess tokenizer.
+        
+        Args:
+            vocab_file: Path to a JSON file containing the vocabulary mapping.
+            vocab: Dictionary mapping tokens to IDs (alternative to vocab_file).
+            **kwargs: Additional arguments passed to PreTrainedTokenizer.
+        """
+        # Initialize special tokens
+        self._pad_token = self.PAD_TOKEN
+        self._bos_token = self.BOS_TOKEN
+        self._eos_token = self.EOS_TOKEN
+        self._unk_token = self.UNK_TOKEN
+
+        # Remove any duplicate special-token entries passed through kwargs
+        # to avoid "multiple values for keyword" errors when loading from disk.
+        kwargs.pop("pad_token", None)
+        kwargs.pop("bos_token", None)
+        kwargs.pop("eos_token", None)
+        kwargs.pop("unk_token", None)
+        
+        # Load or create vocabulary
+        if vocab is not None:
+            self._vocab = vocab
+        elif vocab_file is not None and os.path.exists(vocab_file):
+            with open(vocab_file, "r", encoding="utf-8") as f:
+                self._vocab = json.load(f)
+        else:
+            # Create a minimal vocabulary with just special tokens
+            # The full vocabulary should be built from the dataset
+            self._vocab = self._create_default_vocab()
+        
+        # Create reverse mapping
+        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
+        
+        # Call parent init AFTER setting up vocab
+        super().__init__(
+            pad_token=self._pad_token,
+            bos_token=self._bos_token,
+            eos_token=self._eos_token,
+            unk_token=self._unk_token,
+            **kwargs,
+        )
+    
+    def _create_default_vocab(self) -> Dict[str, int]:
+        """
+        Create a minimal default vocabulary with just special tokens.
+        
+        For the full vocabulary, use `build_vocab_from_dataset()`.
+        This minimal vocab is just a placeholder - you should build from data.
+        """
+        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
+        vocab = {token: idx for idx, token in enumerate(special_tokens)}
+        return vocab
+    
+    @classmethod
+    def build_vocab_from_iterator(
+        cls,
+        iterator,
+        min_frequency: int = 1,
+    ) -> "ChessTokenizer_v0":
+        """
+        Build a tokenizer vocabulary from an iterator of game strings.
+        
+        Args:
+            iterator: An iterator yielding game strings (space-separated moves).
+            min_frequency: Minimum frequency for a token to be included.
+        
+        Returns:
+            A ChessTokenizer_v0 with the built vocabulary.
+        """
+        from collections import Counter
+        
+        token_counts = Counter()
+        
+        for game in iterator:
+            moves = game.strip().split()
+            token_counts.update(moves)
+        
+        # Filter by frequency
+        tokens = [
+            token for token, count in token_counts.items()
+            if count >= min_frequency
+        ]
+        
+        # Sort for reproducibility
+        tokens = sorted(tokens)
+        
+        # Build vocabulary
+        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
+        vocab = {token: idx for idx, token in enumerate(special_tokens + tokens)}
+        
+        return cls(vocab=vocab)
+    
+    @classmethod
+    def build_vocab_from_dataset(
+        cls,
+        dataset_name: str = "dlouapre/lichess_2025-01_1M",
+        split: str = "train",
+        column: str = "text",
+        min_frequency: int = 500,
+        max_samples: Optional[int] = 100000,
+    ) -> "ChessTokenizer_v0":
+        """
+        Build a tokenizer vocabulary from a Hugging Face dataset.
+        
+        Args:
+            dataset_name: Name of the dataset on Hugging Face Hub.
+            split: Dataset split to use.
+            column: Column containing the game strings.
+            min_frequency: Minimum frequency for a token to be included (default: 500).
+            max_samples: Maximum number of samples to process (default: 100k).
+        
+        Returns:
+            A ChessTokenizer_v0 with the built vocabulary.
+        """
+        from datasets import load_dataset
+        
+        dataset = load_dataset(dataset_name, split=split)
+        
+        if max_samples is not None:
+            dataset = dataset.select(range(min(max_samples, len(dataset))))
+        
+        def game_iterator():
+            for example in dataset:
+                yield example[column]
+        
+        return cls.build_vocab_from_iterator(game_iterator(), min_frequency=min_frequency)
+    
+    @property
+    def vocab_size(self) -> int:
+        """Return the size of the vocabulary."""
+        return len(self._vocab)
+    
+    def get_vocab(self) -> Dict[str, int]:
+        """Return the vocabulary as a dictionary."""
+        return dict(self._vocab)
+    
+    def _tokenize(self, text: str) -> List[str]:
+        """
+        Tokenize a string of moves into a list of tokens.
+        
+        Args:
+            text: A string of space-separated moves.
+        
+        Returns:
+            List of move tokens.
+        """
+        return text.strip().split()
+    
+    def _convert_token_to_id(self, token: str) -> int:
+        """Convert a token to its ID."""
+        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
+    
+    def _convert_id_to_token(self, index: int) -> str:
+        """Convert an ID to its token."""
+        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
+    
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """Convert a list of tokens back to a string."""
+        # Filter out special tokens for cleaner output
+        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+        return " ".join(t for t in tokens if t not in special)
+    
+    def save_vocabulary(
+        self,
+        save_directory: str,
+        filename_prefix: Optional[str] = None,
+    ) -> tuple:
+        """
+        Save the vocabulary to a JSON file.
+        
+        Args:
+            save_directory: Directory to save the vocabulary.
+            filename_prefix: Optional prefix for the filename.
+        
+        Returns:
+            Tuple containing the path to the saved vocabulary file.
+        """
+        if not os.path.isdir(save_directory):
+            os.makedirs(save_directory, exist_ok=True)
+        
+        vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") + "vocab.json",
+        )
+        
+        with open(vocab_file, "w", encoding="utf-8") as f:
+            json.dump(self._vocab, f, ensure_ascii=False, indent=2)
+        
+        return (vocab_file,)
+
+
+def count_vocab_from_dataset(
+    dataset_name: str = "dlouapre/lichess_2025-01_1M",
+    split: str = "train",
+    column: str = "text",
+    max_samples: Optional[int] = 10000,
+) -> Dict[str, int]:
+    """
+    Count token frequencies in a dataset (useful for vocabulary analysis).
+    
+    Args:
+        dataset_name: Name of the dataset on Hugging Face Hub.
+        split: Dataset split to use.
+        column: Column containing the game strings.
+        max_samples: Maximum number of samples to process.
+    
+    Returns:
+        Dictionary mapping tokens to their frequencies.
+    """
+    from collections import Counter
+    from datasets import load_dataset
+    
+    dataset = load_dataset(dataset_name, split=split)
+    
+    if max_samples is not None:
+        dataset = dataset.select(range(min(max_samples, len(dataset))))
+    
+    token_counts = Counter()
+    
+    for example in dataset:
+        moves = example[column].strip().split()
+        token_counts.update(moves)
+    
+    return dict(token_counts)
+
+
+# ============================================================================
+# V1 IMPROVEMENTS: Sub-word tokenizer that decomposes moves into components
+# ============================================================================
+
+import re
+
+# Regex to parse extended UCI move format: WPe2e4(x)(+) etc.
+MOVE_PATTERN = re.compile(
+    r"^(?P<side>[WB])"
+    r"(?P<piece>[PNBRQK])"
+    r"(?P<src>[a-h][1-8])"
+    r"(?P<dst>[a-h][1-8])"
+    r"(?P<suffix>.*)$"
+)
+
+
+class ChessTokenizer(PreTrainedTokenizer):
+    """
+    Sub-word chess tokenizer that decomposes moves into components.
+    
+    Instead of treating each move as a single token (requiring ~1500 tokens),
+    this tokenizer breaks moves into:
+    - Side: [W], [B]
+    - Piece: [P], [N], [B], [R], [Q], [K]
+    - Source square: [a1] through [h8]
+    - Destination square: [a1] through [h8]
+    - Optional suffixes: [x] (capture), [+] (check), [#] (checkmate), 
+                        [O-O], [O-O-O], [=Q], [=R], [=B], [=N]
+    
+    Total vocabulary: ~90 tokens (vs ~1500 for whole-move tokenizer)
+    
+    Trade-off: Each move becomes 4-6 tokens instead of 1, but:
+    - Saves ~100-200K embedding parameters
+    - Model learns piece/square patterns independently
+    - Zero OOV - can represent any legal move
+    
+    Example:
+        "WPe2e4" -> ["[W]", "[P]", "[e2]", "[e4]"]
+        "BNg8f6(x)(+)" -> ["[B]", "[N]", "[g8]", "[f6]", "[x]", "[+]"]
+    """
+    
+    model_input_names = ["input_ids", "attention_mask"]
+    vocab_files_names = {"vocab_file": "vocab.json"}
+    
+    # Special tokens
+    PAD_TOKEN = "[PAD]"
+    BOS_TOKEN = "[BOS]"
+    EOS_TOKEN = "[EOS]"
+    UNK_TOKEN = "[UNK]"
+    
+    def __init__(
+        self,
+        vocab_file: Optional[str] = None,
+        vocab: Optional[Dict[str, int]] = None,
+        **kwargs,
+    ):
+        self._pad_token = self.PAD_TOKEN
+        self._bos_token = self.BOS_TOKEN
+        self._eos_token = self.EOS_TOKEN
+        self._unk_token = self.UNK_TOKEN
+
+        kwargs.pop("pad_token", None)
+        kwargs.pop("bos_token", None)
+        kwargs.pop("eos_token", None)
+        kwargs.pop("unk_token", None)
+        
+        if vocab is not None:
+            self._vocab = vocab
+        elif vocab_file is not None and os.path.exists(vocab_file):
+            with open(vocab_file, "r", encoding="utf-8") as f:
+                self._vocab = json.load(f)
+        else:
+            self._vocab = self._create_default_vocab()
+        
+        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
+        
+        super().__init__(
+            pad_token=self._pad_token,
+            bos_token=self._bos_token,
+            eos_token=self._eos_token,
+            unk_token=self._unk_token,
+            **kwargs,
+        )
+    
+    def _create_default_vocab(self) -> Dict[str, int]:
+        """
+        Create the fixed sub-word vocabulary.
+        
+        This vocabulary is complete - no need to build from data.
+        """
+        vocab_list = []
+        
+        # 1. Special tokens (4)
+        vocab_list.extend([self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])
+        
+        # 2. Side tokens (2)
+        vocab_list.extend(["[W]", "[B]"])
+        
+        # 3. Piece tokens (6)
+        vocab_list.extend(["[P]", "[N]", "[Bi]", "[R]", "[Q]", "[K]"])
+        
+        # 4. Square tokens (64)
+        for rank in "12345678":
+            for file in "abcdefgh":
+                vocab_list.append(f"[{file}{rank}]")
+        
+        # 5. Suffix tokens
+        vocab_list.extend([
+            "[x]",      # capture
+            "[+]",      # check
+            "[#]",      # checkmate
+            "[O-O]",    # kingside castling
+            "[O-O-O]",  # queenside castling
+            "[=Q]",     # promotion to queen
+            "[=R]",     # promotion to rook
+            "[=B]",     # promotion to bishop
+            "[=N]",     # promotion to knight
+        ])
+        
+        return {token: idx for idx, token in enumerate(vocab_list)}
+    
+    @property
+    def vocab_size(self) -> int:
+        return len(self._vocab)
+    
+    def get_vocab(self) -> Dict[str, int]:
+        return dict(self._vocab)
+    
+    def _tokenize(self, text: str) -> List[str]:
+        """
+        Tokenize a string of moves into sub-word tokens.
+        
+        Args:
+            text: A string of space-separated moves (e.g., "WPe2e4 BPe7e5")
+        
+        Returns:
+            List of sub-word tokens
+        """
+        tokens = []
+        moves = text.strip().split()
+        
+        for move in moves:
+            tokens.extend(self._tokenize_move(move))
+        
+        return tokens
+    
+    def _tokenize_move(self, move: str) -> List[str]:
+        """Parse a single move into component tokens."""
+        # Handle castling first
+        if "O-O-O" in move or "o-o-o" in move:
+            side = "[W]" if move.startswith("W") else "[B]"
+            return [side, "[O-O-O]"]
+        
+        if "O-O" in move or "o-o" in move:
+            side = "[W]" if move.startswith("W") else "[B]"
+            return [side, "[O-O]"]
+        
+        # Parse regular move
+        match = MOVE_PATTERN.match(move)
+        if not match:
+            return [self.UNK_TOKEN]
+        
+        tokens = []
+        
+        # Side
+        side = match.group("side")
+        tokens.append(f"[{side}]")
+        
+        # Piece (use [Bi] for bishop to avoid confusion with [B] for black)
+        piece = match.group("piece")
+        if piece == "B":
+            tokens.append("[Bi]")
+        else:
+            tokens.append(f"[{piece}]")
+        
+        # Source and destination squares
+        tokens.append(f"[{match.group('src')}]")
+        tokens.append(f"[{match.group('dst')}]")
+        
+        # Parse suffix for capture, check, checkmate, promotion
+        suffix = match.group("suffix") or ""
+        
+        if "x" in suffix:
+            tokens.append("[x]")
+        
+        # Checkmate before check (since checkmate contains +)
+        if "*" in suffix or "#" in suffix:
+            tokens.append("[#]")
+        elif "+" in suffix:
+            tokens.append("[+]")
+        
+        # Promotion
+        if "=" in suffix:
+            idx = suffix.find("=")
+            if idx + 1 < len(suffix):
+                promo_piece = suffix[idx + 1].upper()
+                if promo_piece in "QRBN":
+                    tokens.append(f"[={promo_piece}]")
+        
+        return tokens
+    
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
+    
+    def _convert_id_to_token(self, index: int) -> str:
+        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
+    
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """
+        Convert tokens back to a readable string.
+        
+        This reconstructs moves from their component tokens.
+        """
+        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+        
+        # Filter special tokens
+        filtered = [t for t in tokens if t not in special]
+        
+        # Simple approach: just join with spaces
+        # A more sophisticated approach would reconstruct full moves
+        return " ".join(filtered)
+    
+    def decode_to_moves(self, token_ids: List[int]) -> List[str]:
+        """
+        Decode token IDs back to chess moves.
+        
+        Returns a list of reconstructed moves like ["WPe2e4", "BPe7e5"].
+        """
+        tokens = [self._convert_id_to_token(tid) for tid in token_ids]
+        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+        
+        moves = []
+        current_move = []
+        
+        for token in tokens:
+            if token in special:
+                continue
+            
+            # Start new move on side token
+            if token in ("[W]", "[B]"):
+                if current_move:
+                    moves.append(self._reconstruct_move(current_move))
+                current_move = [token]
+            else:
+                current_move.append(token)
+        
+        # Don't forget last move
+        if current_move:
+            moves.append(self._reconstruct_move(current_move))
+        
+        return moves
+    
+    def _reconstruct_move(self, tokens: List[str]) -> str:
+        """Reconstruct a move string from component tokens."""
+        if not tokens:
+            return ""
+        
+        # Handle castling
+        if "[O-O-O]" in tokens:
+            side = "W" if "[W]" in tokens else "B"
+            return f"{side}KO-O-O"
+        if "[O-O]" in tokens:
+            side = "W" if "[W]" in tokens else "B"
+            return f"{side}KO-O"
+        
+        move = ""
+        
+        for token in tokens:
+            # Strip brackets
+            inner = token[1:-1] if token.startswith("[") and token.endswith("]") else token
+            
+            if inner in ("W", "B"):
+                move += inner
+            elif inner == "Bi":
+                move += "B"  # Bishop
+            elif inner in "PNRQK":
+                move += inner
+            elif len(inner) == 2 and inner[0] in "abcdefgh" and inner[1] in "12345678":
+                move += inner
+            elif inner == "x":
+                move += "(x)"
+            elif inner == "+":
+                move += "(+)"
+            elif inner == "#":
+                move += "(+*)"
+            elif inner.startswith("="):
+                move += f"({inner})"
+        
+        return move
+    
+    def save_vocabulary(
+        self,
+        save_directory: str,
+        filename_prefix: Optional[str] = None,
+    ) -> tuple:
+        if not os.path.isdir(save_directory):
+            os.makedirs(save_directory, exist_ok=True)
+        
+        vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") + "vocab.json",
+        )
+        
+        with open(vocab_file, "w", encoding="utf-8") as f:
+            json.dump(self._vocab, f, ensure_ascii=False, indent=2)
+        
+        return (vocab_file,)
+    
+    def get_vocab_stats(self) -> Dict[str, int]:
+        """Get statistics about vocabulary composition."""
+        return {
+            "special": 4,
+            "sides": 2,
+            "pieces": 6,
+            "squares": 64,
+            "suffixes": 9,
+            "total": self.vocab_size,
+        }
+    
+    # For compatibility - no need to build vocab from data anymore
+    @classmethod
+    def build_vocab_from_dataset(cls, **kwargs) -> "ChessTokenizer":
+        """Return a tokenizer with the fixed vocabulary (no data needed)."""
+        return cls()
+    
+    @classmethod
+    def build_vocab_from_iterator(cls, iterator, **kwargs) -> "ChessTokenizer":
+        """Return a tokenizer with the fixed vocabulary (no data needed)."""
+        return cls()

tokenizer_config.json

@@ -0,0 +1,50 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[BOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[EOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.ChessTokenizer",
+      null
+    ]
+  },
+  "bos_token": "[BOS]",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "[EOS]",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "ChessTokenizer",
+  "unk_token": "[UNK]"
+}

vocab.json

@@ -0,0 +1,87 @@
+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  "[W]": 4,
+  "[B]": 5,
+  "[P]": 6,
+  "[N]": 7,
+  "[Bi]": 8,
+  "[R]": 9,
+  "[Q]": 10,
+  "[K]": 11,
+  "[a1]": 12,
+  "[b1]": 13,
+  "[c1]": 14,
+  "[d1]": 15,
+  "[e1]": 16,
+  "[f1]": 17,
+  "[g1]": 18,
+  "[h1]": 19,
+  "[a2]": 20,
+  "[b2]": 21,
+  "[c2]": 22,
+  "[d2]": 23,
+  "[e2]": 24,
+  "[f2]": 25,
+  "[g2]": 26,
+  "[h2]": 27,
+  "[a3]": 28,
+  "[b3]": 29,
+  "[c3]": 30,
+  "[d3]": 31,
+  "[e3]": 32,
+  "[f3]": 33,
+  "[g3]": 34,
+  "[h3]": 35,
+  "[a4]": 36,
+  "[b4]": 37,
+  "[c4]": 38,
+  "[d4]": 39,
+  "[e4]": 40,
+  "[f4]": 41,
+  "[g4]": 42,
+  "[h4]": 43,
+  "[a5]": 44,
+  "[b5]": 45,
+  "[c5]": 46,
+  "[d5]": 47,
+  "[e5]": 48,
+  "[f5]": 49,
+  "[g5]": 50,
+  "[h5]": 51,
+  "[a6]": 52,
+  "[b6]": 53,
+  "[c6]": 54,
+  "[d6]": 55,
+  "[e6]": 56,
+  "[f6]": 57,
+  "[g6]": 58,
+  "[h6]": 59,
+  "[a7]": 60,
+  "[b7]": 61,
+  "[c7]": 62,
+  "[d7]": 63,
+  "[e7]": 64,
+  "[f7]": 65,
+  "[g7]": 66,
+  "[h7]": 67,
+  "[a8]": 68,
+  "[b8]": 69,
+  "[c8]": 70,
+  "[d8]": 71,
+  "[e8]": 72,
+  "[f8]": 73,
+  "[g8]": 74,
+  "[h8]": 75,
+  "[x]": 76,
+  "[+]": 77,
+  "[#]": 78,
+  "[O-O]": 79,
+  "[O-O-O]": 80,
+  "[=Q]": 81,
+  "[=R]": 82,
+  "[=B]": 83,
+  "[=N]": 84
+}