tokenizer.py

# """
# 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(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()
#         >>> 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":
#     #     """
#     #     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 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_iterator(
#         cls,
#         iterator,
#         vocab_size: int = 1200,
#         min_frequency: int = 1,
#         ) -> "ChessTokenizer":
#         """
#         Build a tokenizer vocabulary from an iterator of game strings.

#         - Controls final vocab size explicitly via vocab_size.
#         - Keeps the most frequent move tokens (best coverage).
#         - Uses min_frequency as a floor, but vocab_size is the main control.
#         """
#         from collections import Counter

#         token_counts = Counter()
#         for game in iterator:
#             moves = game.strip().split()
#             token_counts.update(moves)

#         # Filter by min_frequency first
#         items = [(tok, cnt) for tok, cnt in token_counts.items() if cnt >= min_frequency]

#         # Sort by frequency desc, then token for determinism
#         items.sort(key=lambda x: (-x[1], x[0]))

#         special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
#         max_move_tokens = max(0, vocab_size - len(special_tokens))

#         move_tokens = [tok for tok, _ in items[:max_move_tokens]]
#         vocab = {token: idx for idx, token in enumerate(special_tokens + move_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":
#     #     """
#     #     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 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)
#     @classmethod
#     def build_vocab_from_dataset(
#         cls,
#         dataset_name: str = "dlouapre/lichess_2025-01_1M",
#         split: str = "train",
#         column: str = "text",
#         vocab_size: int = 1200,
#         min_frequency: int = 1,
#         max_samples: Optional[int] = 200000,
#     ) -> "ChessTokenizer":
#         """
#         Build a tokenizer vocabulary from a Hugging Face dataset.

#         Args:
#             vocab_size: Final vocab size INCLUDING special tokens.
#             min_frequency: Minimum count to consider a move (usually 1 is fine).
#             max_samples: How many games to scan to build vocab.
#         """
#         from datasets import load_dataset

#         dataset = load_dataset(dataset_name, split=split)

#         # if max_samples is not None: # v0&1
#         #     dataset = dataset.select(range(min(max_samples, len(dataset))))
         
#         if max_samples is not None: # v2
#             n = min(max_samples, len(dataset))
#             dataset = dataset.shuffle(seed=42).select(range(n))

#         def game_iterator():
#             for example in dataset:
#                 yield example[column]

#         return cls.build_vocab_from_iterator(
#             game_iterator(),
#             vocab_size=vocab_size,
#             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 build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
#     #     if token_ids_1 is not None:
#     #         # Not expected here, but handle gracefully
#     #         token_ids = token_ids_0 + token_ids_1
#     #     else:
#     #         token_ids = token_ids_0
#     #     return [self.bos_token_id] + token_ids + [self.eos_token_id]

#     # def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
#     #     if already_has_special_tokens:
#     #         return [1 if t in (self.pad_token_id, self.bos_token_id, self.eos_token_id, self.unk_token_id) else 0 for t in token_ids_0]
#     #     if token_ids_1 is not None:
#     #         token_ids = token_ids_0 + token_ids_1
#     #     else:
#     #         token_ids = token_ids_0
#     #     return [1] + [0] * len(token_ids) + [1]

# 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)

"""
Grammar-aware Chess Tokenizer for the Chess Challenge.

Goal: maximize legal move extraction in evaluate.py which searches for
two square patterns ([a-h][1-8]) in the generated text and takes the first two.

Strategy:
- Decompose each move into structured tokens:
  - CP_<color><piece>   (e.g., CP_WP, CP_BN)
  - SQ_<square>         (e.g., SQ_e2, SQ_e4)
  - EV_<event>          (e.g., EV_NONE, EV_X, EV_PLUS, EV_MATE, EV_PROMO_Q, ...)
  - SEP                 (end-of-move marker, decoded as a space)
- Deterministic vocab: no dataset-dependent OOV -> UNK for rare full moves disappears.
"""

from __future__ import annotations

import json
import os
import re
from typing import Dict, List, Optional

from transformers import PreTrainedTokenizer


class ChessTokenizer(PreTrainedTokenizer):
    model_input_names = ["input_ids", "attention_mask"]
    vocab_files_names = {"vocab_file": "vocab.json"}

    PAD_TOKEN = "[PAD]"
    BOS_TOKEN = "[BOS]"
    EOS_TOKEN = "[EOS]"
    UNK_TOKEN = "[UNK]"
    SEP_TOKEN = "[SEP]"  # end-of-move marker (decoded as a space)

    _SQUARE_RE = re.compile(r"^[a-h][1-8]$") # positions are in the format xY where x is in [a-h], y in [1-8]

    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
        self._sep_token = self.SEP_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,
        )

    #### Vocab
    def _create_default_vocab(self) -> Dict[str, int]:
        special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN, self.SEP_TOKEN]

        # Color+piece (12 tokens)
        cp = [f"CP_{c}{p}" for c in "WB" for p in "PNBRQK"]

        # Squares (64 tokens)
        squares = [f"SQ_{f}{r}" for f in "abcdefgh" for r in "12345678"]

        # Events: keep small & canonical (you can extend later)
        events = [
            "EV_NONE",
            "EV_X",
            "EV_PLUS",
            "EV_MATE",
            "EV_XPLUS",
            "EV_XMATE",
            "EV_O",    # kingside castle
            "EV_OO",   # queenside castle
            "EV_PROMO_N",
            "EV_PROMO_B",
            "EV_PROMO_R",
            "EV_PROMO_Q",
            "EV_XPROMO_N",
            "EV_XPROMO_B",
            "EV_XPROMO_R",
            "EV_XPROMO_Q",
        ]

        vocab_list = special + cp + squares + events # this vocabulary has size 12 + 64 + 16 + 5 = 97 tokens
        return {tok: i for i, tok 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)

    #### Core tokenization
    def _tokenize(self, text: str) -> List[str]:
        """
        Input is a space-separated list of moves in extended UCI, e.g.
        "WPe2e4 BPe7e5 ..."

        Output is a sequence of structured tokens:
        CP_WP SQ_e2 SQ_e4 EV_NONE [SEP] ...
        """
        moves = text.strip().split()
        tokens: List[str] = []

        for mv in moves:
            toks = self._tokenize_one_move(mv)
            tokens.extend(toks)
            tokens.append(self.SEP_TOKEN)

        return tokens

    def _tokenize_one_move(self, mv: str) -> List[str]:
        # Minimal sanity: needs at least "WPe2e4" length 6
        if len(mv) < 6:
            return [self.UNK_TOKEN]

        color = mv[0]  # W/B
        piece = mv[1]  # P/N/B/R/Q/K
        from_sq = mv[2:4]
        to_sq = mv[4:6]
        suffix = mv[6:]  # can include capture/check/mate/castle/promo etc. => cf events tokens

        cp_tok = f"CP_{color}{piece}"
        from_tok = f"SQ_{from_sq}"
        to_tok = f"SQ_{to_sq}"

        if cp_tok not in self._vocab or from_tok not in self._vocab or to_tok not in self._vocab:
            return [self.UNK_TOKEN]

        ev_tok = self._event_token(piece, from_sq, to_sq, suffix)

        return [cp_tok, from_tok, to_tok, ev_tok]

    def _event_token(self, piece: str, from_sq: str, to_sq: str, suffix: str) -> str:
        """
        Canonicalize suffix into one of EV_* tokens.
        Keep it simple: evaluator does not need these, but they help learning.
        """
        # Castling (dataset uses (o)/(O))
        if "(o)" in suffix: # kingside
            return "EV_O"
        if "(O)" in suffix: # queenside
            return "EV_OO"

        capture = "(x" in suffix  # covers (x), (x+), (x+*), (x+) etc.
        mate = "+*" in suffix
        check = "(+)" in suffix or "(x+)" in suffix or "(+)" in suffix  # tolerant

        promo = None
        m = re.search(r"=([NBRQ])", suffix)
        if m:
            promo = m.group(1)

        if promo is not None:
            base = f"EV_PROMO_{promo}"
            if capture:
                base = f"EV_XPROMO_{promo}"
            return base if base in self._vocab else "EV_NONE"

        if capture and mate:
            return "EV_XMATE"
        if capture and check:
            return "EV_XPLUS"
        if capture:
            return "EV_X"
        if mate:
            return "EV_MATE"
        if check:
            return "EV_PLUS"
        return "EV_NONE"

    #### Conversions
    def _convert_token_to_id(self, token: str) -> int:
        return self._vocab.get(token, self._vocab[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:
        """
        Decode to a string that contains squares early and clearly.
        We intentionally emit raw squares like "e2" "e4" separated by spaces,
        so evaluate.py will reliably extract them.
        """
        out: List[str] = []
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}

        for tok in tokens:
            if tok in special:
                continue
            if tok == self.SEP_TOKEN:
                out.append(" ")
                continue
            if tok.startswith("SQ_"):
                out.append(tok[3:])      # "SQ_e2" -> "e2"
                out.append(" ")
                continue
            if tok.startswith("CP_"):
                # Optional: keep CP to help model conditioning; does not hurt extraction
                out.append(tok[3:])      # "CP_WP" -> "WP"
                out.append(" ")
                continue
            if tok.startswith("EV_"):
                # Optional: keep events; ensure no squares are embedded here
                out.append(tok[3:])      # "EV_X" -> "X"
                out.append(" ")
                continue
            # fallback
            out.append(tok)
            out.append(" ")

        return "".join(out).strip()

    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,)