Chess Challenge submission by Giu0804

tokenizer_v2.py

@@ -0,0 +1,178 @@
+"""
+Coordinate Chess Tokenizer (Vocab Size = 72).
+Compatible with Hugging Face AutoTokenizer and existing Evaluation scripts.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import re
+from typing import Dict, List, Optional, Tuple, Union
+
+from transformers import PreTrainedTokenizer
+
+class ChessTokenizer(PreTrainedTokenizer):
+    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]"
+
+    # Regex to capture coordinates and promotions from any format (UCI, SAN, Extended)
+    # Captures: "e2", "e4", "q" inside strings like "WPe2e4" or "e2e4q"
+    MOVE_REGEX = re.compile(r"([a-h][1-8])([a-h][1-8])([qrbn])?")
+
+    def __init__(
+        self,
+        vocab_file: Optional[str] = None,
+        **kwargs,
+    ):
+        # 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
+
+        # Clean kwargs to avoid duplication errors during loading
+        kwargs.pop("pad_token", None)
+        kwargs.pop("bos_token", None)
+        kwargs.pop("eos_token", None)
+        kwargs.pop("unk_token", None)
+
+        # 1. Load or Create Vocabulary
+        # If a vocab_file is provided (loading from HF), use it.
+        # Otherwise, create the fixed 72-token vocabulary.
+        if 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_fixed_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_fixed_vocab(self) -> Dict[str, int]:
+        """Creates the deterministic 72-token vocabulary."""
+        vocab = {}
+        
+        # 0-3: Special Tokens
+        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
+        for idx, token in enumerate(special_tokens):
+            vocab[token] = idx
+            
+        # 4-7: Promotions (q, r, b, n)
+        promotions = ["q", "r", "b", "n"]
+        for idx, token in enumerate(promotions):
+            vocab[token] = len(vocab)
+            
+        # 8-71: Squares (a1...h8)
+        files = "abcdefgh"
+        ranks = "12345678"
+        for r in ranks:
+            for f in files:
+                square = f + r
+                vocab[square] = len(vocab)
+                
+        return vocab
+
+    @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]:
+        """
+        Robust tokenization handling both raw coordinates and 'dirty' UCI extended strings.
+        """
+        tokens = []
+        # Split by whitespace first
+        raw_chunks = text.strip().split()
+        
+        # Set of exact match tokens to preserve special tokens
+        special_set = {self.BOS_TOKEN, self.EOS_TOKEN, self.PAD_TOKEN, self.UNK_TOKEN}
+
+        for chunk in raw_chunks:
+            # If it's explicitly a special token, keep it
+            if chunk in special_set:
+                tokens.append(chunk)
+                continue
+
+            # Otherwise, use Regex to extract coordinates
+            # This handles "WPe2e4" -> ["e2", "e4"]
+            # And "e2e4" -> ["e2", "e4"]
+            match = self.MOVE_REGEX.search(chunk)
+            if match:
+                start_sq, end_sq, promotion = match.groups()
+                tokens.append(start_sq)
+                tokens.append(end_sq)
+                if promotion:
+                    tokens.append(promotion)
+            else:
+                # If regex fails but it is in our vocab (e.g. isolated 'a1'), take it
+                if chunk in self._vocab:
+                    tokens.append(chunk)
+                else:
+                    tokens.append(self.UNK_TOKEN)
+                
+        return tokens
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN))
+
+    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:
+        """
+        Reconstructs string. Important: adds spaces between coordinates.
+        Evaluate.py handles spaces fine via regex.
+        """
+        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+        clean_tokens = [t for t in tokens if t not in special]
+        return " ".join(clean_tokens)
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Vital for Hugging Face: saves the vocab.json to the directory.
+        """
+        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,)
+    
+    @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,  # Ignored
+        max_samples: Optional[int] = 100000,  # Ignored
+    ) -> "ChessTokenizer":
+        """
+        Mock implementation to satisfy train.py API.
+        Ignores dataset scanning since vocab is fixed.
+        """
+        print(f"Coordinate Tokenizer: Using fixed vocabulary (size 72). Ignoring dataset scan.")
+        return cls()