"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer splits moves into 3 parts:
1. Piece (e.g., WP)
2. From Square (e.g., e2)
3. To Square + Suffix (e.g., e4 or e4(x))
"""

from __future__ import annotations

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

from transformers import PreTrainedTokenizer


class ChessTokenizer(PreTrainedTokenizer):
    """
    A custom tokenizer for chess moves using a 3-part split.
    
    Splits "WPe2e4(x)" into ["WP", "e2", "e4(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,
    ):
        # 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
        
        # Load vocab
        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 a minimal default vocabulary with just special tokens."""
        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
    
    @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 3 components per move.
        """
        tokens = []
        raw_moves = text.strip().split()
        
        for move in raw_moves:
            if len(move) >= 6:
                # 1. Piece (WP)
                tokens.append(move[:2])
                # 2. From (e2)
                tokens.append(move[2:4])
                # 3. To (e4 or e4(x)) - grab the rest
                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:
        # Filter specials
        filtered = [t for t in tokens if t not in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]]
        # Join with space. Result: "WP e2 e4 BN g8 f6"
        return " ".join(filtered)
    
    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: str = "dlouapre/lichess_2025-01_1M",
        split: str = "train",
        column: str = "text",
        min_frequency: int = 100,
        max_samples: Optional[int] = 100000,
    ) -> "ChessTokenizer":
        from datasets import load_dataset
        
        print(f"Loading dataset {dataset_name} to build vocabulary...")
        dataset = load_dataset(dataset_name, split=split, streaming=True)
        
        unique_tokens = set()
        
        print("Building vocabulary...")
        count = 0
        for example in dataset:
            moves = example[column].strip().split()
            for move in moves:
                if len(move) >= 6:
                    unique_tokens.add(move[:2])    # Piece
                    unique_tokens.add(move[2:4])   # From
                    unique_tokens.add(move[4:])    # To (includes suffix like (x))
            count += 1
            if max_samples is not None and count >= max_samples:
                break

        special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
        # Sort tokens to ensure deterministic IDs
        all_tokens = special + sorted(list(unique_tokens))
        
        vocab = {token: idx for idx, token in enumerate(all_tokens)}
        print(f"Built vocabulary with {len(vocab)} tokens")
        return cls(vocab=vocab)


# Kept for compatibility if other scripts import it
def count_vocab_from_dataset(*args, **kwargs):
    return {}