"""
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





"""
Custom Chess Tokenizer (Split Move Version).

Splits moves into 3 components:
1. Piece (e.g., "WP")
2. From Square (e.g., "e2")
3. To Square (e.g., "e4")
"""



class ChessTokenizer(PreTrainedTokenizer):
    model_input_names = ["input_ids", "attention_mask"]
    
    # Special tokens
    PAD_TOKEN = "[PAD]"
    BOS_TOKEN = "[BOS]" # Beginning of Sequence (Start of Game)
    EOS_TOKEN = "[EOS]" # End of Sequence (End of Game)
    UNK_TOKEN = "[UNK]"

    vocab_files_names = {
        "vocab_file": "vocab.json"
    }
    
    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
        
        # Clean kwargs to avoid conflicts
        kwargs.pop("pad_token", None)
        kwargs.pop("bos_token", None)
        kwargs.pop("eos_token", None)
        kwargs.pop("unk_token", None)

        self.vocab_file = vocab_file
        
        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]:
        """Creates basic vocab. Use build_vocab_from_dataset for full vocab."""
        # 4 Special + 12 Pieces + 64 Squares = 80 tokens total
        special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        vocab = {t: i for i, t in enumerate(special)}
        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]:
        """
        Splits text "WPe2e4 BNg8f6" into ["WP", "e2", "e4", "BN", "g8", "f6"]
        """
        tokens = []
        # Split by space to get individual moves first
        raw_moves = text.strip().split()

        
        for move in raw_moves:
            # Check length to ensure it's a valid move string
            if len(move) >= 6:
                # Part 1: Player + Piece (Indices 0-2, e.g., "WP")
                tokens.append(move[:2])
                
                # Part 2: Start Square (Indices 2-4, e.g., "e2")
                tokens.append(move[2:4])
                
                # Part 3: End Square (Indices 4-6, e.g., "e4")
                tokens.append(move[4:])



            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 the move string. 
        Note: This simply joins them. You might need custom logic 
        if you want to strictly recreate 'WPe2e4' from ['WP','e2','e4'].
        """
        return " ".join(t for t in tokens if t not in [
            self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN
        ])

    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,)

    @classmethod
    def build_vocab_from_dataset(cls, dataset_name="dlouapre/lichess_2025-01_1M", split="train", max_samples=10000):
        """Scans dataset to find all unique pieces and squares."""
        from datasets import load_dataset
        dataset = load_dataset(dataset_name, split=split, streaming=True)
        
        pieces = set()
        squares = set()
        endings = set()
        
        print("Building vocabulary...")
        count = 0
        for example in dataset:
            moves = example["text"].split()
            for move in moves:
                if len(move) >= 6:
                    pieces.add(move[:2])    # WP, BN, etc.
                    squares.add(move[2:4])  # e2
                    squares.add(move[4:])  # e4


            count += 1
            if count >= max_samples:
                break

        # Combine into vocab structure
        special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
        all_tokens = special + sorted(list(pieces)) + sorted(list(endings)) + sorted(list(squares))
        
        vocab = {token: idx for idx, token in enumerate(all_tokens)}
        return cls(vocab=vocab)