# """
# 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 osz
# 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_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)
    
#     @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)

"""
Decomposed Chess Tokenizer (Idea 1)

Each move in extended UCI is split into structured subtokens:

Example:
  WPe2e4            -> ["W", "P", "e2", "e4"]
  BNg8f6(x)         -> ["B", "N", "g8", "f6", "(x)"]
  WPe7e8=Q(+)       -> ["W", "P", "e7", "e8", "=Q", "(+)"]
  WKe1g1(o)         -> ["W", "K", "e1", "g1", "(o)"]

A full game string (space-separated moves) is expanded move-by-move.
"""

from __future__ import annotations

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

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]"

    # Allowed atomic tokens
    COLORS = ["W", "B"]
    PIECES = ["P", "N", "B", "R", "Q", "K"]

    # Common suffixes in the dataset/template utils
    SUFFIXES = [
        "(x)", "(+)", "(+*)",
        "(x+)", "(x+*)",
        "(o)", "(O)",
    ]

    PROMOTIONS = ["=Q", "=R", "=B", "=N"]

    def __init__(
        self,
        vocab_file: Optional[str] = None,
        vocab: Optional[Dict[str, int]] = None,
        **kwargs,
    ):
        # 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

        # Avoid duplicate kwargs when loading
        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 building
    # -----------------------
    def _create_default_vocab(self) -> Dict[str, int]:
        special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]

        squares = [f"{file}{rank}" for rank in "12345678" for file in "abcdefgh"]

        tokens = []
        tokens += self.COLORS
        tokens += self.PIECES
        tokens += squares
        tokens += self.PROMOTIONS
        tokens += self.SUFFIXES

        vocab_tokens = special + tokens
        return {tok: i for i, tok in enumerate(vocab_tokens)}

    @classmethod
    def build_vocab_from_iterator(cls, iterator, min_frequency: int = 1) -> "ChessTokenizer":
        # For this decomposed tokenizer, vocab is fixed (structured tokens),
        # so iterator/frequency are ignored, but kept for API compatibility.
        return cls()

    @classmethod
    def build_vocab_from_dataset(
        cls,
        dataset_name: str = "dlouapre/lichess_2025-01_1M",
        split: str = "train",
        column: str = "text",
        min_frequency: int = 1,
        max_samples: Optional[int] = None,
    ) -> "ChessTokenizer":
        # Fixed vocab, dataset not needed.
        return cls()

    # -----------------------
    # Required tokenizer API
    # -----------------------
    @property
    def vocab_size(self) -> int:
        return len(self._vocab)

    def get_vocab(self) -> Dict[str, int]:
        return dict(self._vocab)

    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 save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        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,)

    # -----------------------
    # Tokenization logic
    # -----------------------
    def _tokenize(self, text: str) -> List[str]:
        """
        Split a full game string into atomic subtokens.

        Input format is typically:
          "[BOS] WPe2e4 BPe7e5 WNg1f3 ..."
        or just
          "WPe2e4 BPe7e5 ..."
        """
        parts = text.strip().split()
        out: List[str] = []
        for part in parts:
            if part in (self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN):
                out.append(part)
                continue

            # Expand one move token into subtokens
            out.extend(self._split_move_token(part))
        return out

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """
        Reconstruct a space-separated move string from atomic tokens.

        We group tokens into moves:
          COLOR PIECE FROM TO [PROMO] [SUFFIX]
        """
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        toks = [t for t in tokens if t not in special]

        moves: List[str] = []
        i = 0
        while i < len(toks):
            # Need at least 4 tokens for base move
            if i + 3 >= len(toks):
                break

            color, piece, from_sq, to_sq = toks[i], toks[i + 1], toks[i + 2], toks[i + 3]
            i += 4

            # Basic sanity: if structure broken, fall back to raw join
            if color not in self.COLORS or piece not in self.PIECES or not self._is_square(from_sq) or not self._is_square(to_sq):
                # fallback: join remaining tokens
                return " ".join(toks)

            move = f"{color}{piece}{from_sq}{to_sq}"

            # Optional promotion
            if i < len(toks) and toks[i] in self.PROMOTIONS:
                move += toks[i]
                i += 1

            # Optional suffix
            if i < len(toks) and toks[i] in self.SUFFIXES:
                move += toks[i]
                i += 1

            moves.append(move)

        return " ".join(moves)

    # -----------------------
    # Helpers
    # -----------------------
    def _is_square(self, s: str) -> bool:
        return (
            len(s) == 2 and
            s[0] in "abcdefgh" and
            s[1] in "12345678"
        )

    def _split_move_token(self, move: str) -> List[str]:
        """
        Parse one extended-UCI move token.

        Expected minimum length is 6: [W|B][Piece][from][to]
        Suffix/promotion may appear after that.
        """
        if len(move) < 6:
            return [self.UNK_TOKEN]

        color = move[0]
        piece = move[1]
        from_sq = move[2:4]
        to_sq = move[4:6]

        if color not in self.COLORS or piece not in self.PIECES or not self._is_square(from_sq) or not self._is_square(to_sq):
            return [self.UNK_TOKEN]

        tokens = [color, piece, from_sq, to_sq]

        # Promotion like "=Q"
        promo = None
        if "=" in move:
            eq = move.index("=")
            if eq + 1 < len(move):
                promo = "=" + move[eq + 1].upper()
                if promo in self.PROMOTIONS:
                    tokens.append(promo)

        # Suffix like "(x)", "(+)", "(x+*)", "(o)", "(O)"
        if "(" in move:
            suf = move[move.index("("):]
            if suf in self.SUFFIXES:
                tokens.append(suf)

        return tokens