"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer uses a DECOMPOSED format compatible with the evaluator:
    "WPe2e4" -> ["WP", "e2_f", "e4_t"]

The decomposed format uses:
- Piece token: "WP", "BN", etc. (color + piece)
- Source square with _f suffix: "e2_f", "g1_f", etc.
- Destination square with _t suffix: "e4_t", "f3_t", etc.
- Optional suffix for annotations: "(x)", "(+)", "(+*)", "(o)", "(O)"

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 DECOMPOSED format.

    This tokenizer decomposes each move into sub-tokens:
    - Piece: "WP", "BN", etc.
    - Source square with _f suffix: "e2_f", "g1_f", etc.
    - Destination square with _t suffix: "e4_t", "f3_t", etc.
    - Optional suffix: "(x)", "(+)", etc.

    This format is compatible with the evaluator's 'decomposed' detection.

    Example:
        >>> tokenizer = ChessTokenizer.build_vocab_from_dataset()
        >>> tokenizer.tokenize("WPe2e4 BPe7e5")
        ['WP', 'e2_f', 'e4_t', 'BP', 'e7_f', 'e5_t']
    """
    
    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.

        Decomposes each move into tokens: piece, source_f, dest_t, and optional suffix.

        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()
            for move in moves:
                if len(move) < 6:
                    token_counts[move] += 1
                    continue

                # Decompose move into tokens
                piece = move[:2]           # e.g., "WP", "BN"
                source = move[2:4] + "_f"  # e.g., "e2_f"
                dest = move[4:6] + "_t"    # e.g., "e4_t"
                suffix = move[6:] if len(move) > 6 else None

                token_counts[piece] += 1
                token_counts[source] += 1
                token_counts[dest] += 1
                if suffix:
                    token_counts[suffix] += 1

        # 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 decomposed tokens.

        Each move like "WPe2e4" becomes ["WP", "e2_f", "e4_t"].
        Moves with suffixes like "WPe2e4(x)" become ["WP", "e2_f", "e4_t", "(x)"].

        Args:
            text: A string of space-separated moves.

        Returns:
            List of decomposed tokens.
        """
        moves = text.strip().split()
        tokens = []

        for move in moves:
            if len(move) < 6:
                # Invalid move format, add as unknown
                tokens.append(move)
                continue

            # Split move into components
            piece = move[:2]           # e.g., "WP", "BN"
            source = move[2:4] + "_f"  # e.g., "e2_f", "g1_f"
            dest = move[4:6] + "_t"    # e.g., "e4_t", "f3_t"
            suffix = move[6:] if len(move) > 6 else None  # e.g., "(x)", "(+)"

            tokens.extend([piece, source, dest])
            if suffix:
                tokens.append(suffix)

        return tokens
    
    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 decomposed tokens back to a string of moves.

        Reconstructs moves from [piece, source_f, dest_t, optional_suffix] format.
        E.g., ["WP", "e2_f", "e4_t"] -> "WP e2_f e4_t"

        For the evaluator's decomposed format, we keep the tokens space-separated.
        """
        # Filter out special tokens
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        filtered = [t for t in tokens if t not in special]
        return " ".join(filtered)
    
    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 decomposed 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 decomposed 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()
        for move in moves:
            if len(move) < 6:
                token_counts[move] += 1
                continue

            # Decompose move
            piece = move[:2]
            source = move[2:4] + "_f"
            dest = move[4:6] + "_t"
            suffix = move[6:] if len(move) > 6 else None

            token_counts[piece] += 1
            token_counts[source] += 1
            token_counts[dest] += 1
            if suffix:
                token_counts[suffix] += 1

    return dict(token_counts)