"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer uses sub-structural tokenization: each move is decomposed into
its components (piece, source square, destination square, suffix) instead of
treating the whole move as a single token.

Example: WPe2e4 -> [P, e2, e4]  (color is implicit from move number)
         BNg8f6(x) -> [N, g8, f6, (x)]

This approach:
- Reduces vocabulary from ~1200 to ~80 tokens
- Enables generalization across similar moves
- Eliminates [UNK] tokens for rare moves
- Saves parameters in the embedding layer

The dataset format uses:
- W/B prefix for White/Black (ignored - implicit from position)
- 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
import re
from pathlib import Path
from typing import Dict, List, Optional, Tuple

from transformers import PreTrainedTokenizer


# Regex pattern to parse extended UCI notation
# Matches: (W|B)(Piece)(src_file)(src_rank)(dst_file)(dst_rank)(suffix?)
MOVE_PATTERN = re.compile(
    r'^([WB])([PNBRQK])([a-h])([1-8])([a-h])([1-8])(\([^)]+\))?$'
)


class ChessTokenizer(PreTrainedTokenizer):
    """
    A custom tokenizer for chess moves using sub-structural tokenization.

    Each move is decomposed into components:
    - Piece type (P, N, B, R, Q, K)
    - Source square (e2, d7, etc.)
    - Destination square (e4, f6, etc.)
    - Optional suffix for captures/checks ((x), (+), (+*), (o), (O))

    The color (W/B) is NOT tokenized as it's implicit from the move order.

    Example:
        >>> tokenizer = ChessTokenizer.build_vocab()
        >>> tokenizer.encode("WPe2e4 BPe7e5")
        [1, 5, 20, 28, 5, 52, 44, 2]  # [BOS, P, e2, e4, P, e7, e5, 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 the full sub-structural vocabulary.

        The vocabulary contains:
        - 4 special tokens: [PAD], [BOS], [EOS], [UNK]
        - 6 piece tokens: P, N, B, R, Q, K
        - 64 square tokens: a1, a2, ..., h8
        - 5 suffix tokens: (x), (+), (+*), (o), (O)

        Total: 79 tokens (vs ~1200 for move-level tokenization)
        """
        tokens = []

        # Special tokens first
        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        tokens.extend(special_tokens)

        # Piece tokens
        pieces = ['P', 'N', 'B', 'R', 'Q', 'K']
        tokens.extend(pieces)

        # Square tokens (a1-h8)
        files = 'abcdefgh'
        ranks = '12345678'
        for f in files:
            for r in ranks:
                tokens.append(f + r)

        # Suffix tokens for special moves
        suffixes = ['(x)', '(+)', '(+*)', '(o)', '(O)']
        tokens.extend(suffixes)

        # Promotion tokens (pawn promotion to piece)
        # Format in dataset might be like WPe7e8Q for promotion
        promotion_pieces = ['=Q', '=R', '=B', '=N']
        tokens.extend(promotion_pieces)

        vocab = {token: idx for idx, token in enumerate(tokens)}
        return vocab
    
    @classmethod
    def build_vocab(cls) -> "ChessTokenizer":
        """
        Build a tokenizer with the pre-defined sub-structural vocabulary.

        This is the recommended way to create a tokenizer for the chess challenge.
        The vocabulary is deterministic and covers all possible moves.

        Returns:
            A ChessTokenizer with the full sub-structural vocabulary (~83 tokens).
        """
        return cls()

    @classmethod
    def build_vocab_from_iterator(
        cls,
        iterator,
        min_frequency: int = 1,
    ) -> "ChessTokenizer":
        """
        Build a tokenizer vocabulary from an iterator of game strings.

        Note: With sub-structural tokenization, this method is mainly useful
        for analyzing token frequencies. The default vocabulary already covers
        all possible moves.

        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.
        """
        # With sub-structural tokenization, we use the default vocab
        # which already contains all possible sub-tokens
        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 = 500,
        max_samples: Optional[int] = 100000,
    ) -> "ChessTokenizer":
        """
        Build a tokenizer vocabulary from a Hugging Face dataset.

        Note: With sub-structural tokenization, the vocabulary is pre-defined
        and doesn't need to be built from data. This method is kept for
        compatibility but simply returns a tokenizer with the default vocab.

        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.
            max_samples: Maximum number of samples to process.

        Returns:
            A ChessTokenizer with the full sub-structural vocabulary.
        """
        # With sub-structural tokenization, we don't need to scan the dataset
        return cls()
    
    @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 _parse_move(self, move: str) -> List[str]:
        """
        Parse a single move into its sub-components.

        Args:
            move: A move in extended UCI notation (e.g., WPe2e4, BNg8f6(x))

        Returns:
            List of tokens: [piece, src_square, dst_square, suffix?]
            Color (W/B) is ignored as it's implicit from move order.
        """
        # Try standard move pattern
        match = MOVE_PATTERN.match(move)
        if match:
            color, piece, src_file, src_rank, dst_file, dst_rank, suffix = match.groups()
            tokens = [piece, src_file + src_rank, dst_file + dst_rank]
            if suffix:
                tokens.append(suffix)
            return tokens

        # Try promotion pattern: WPe7e8Q or WPe7e8Q(+)
        promo_pattern = re.match(
            r'^([WB])P([a-h])([1-8])([a-h])([1-8])([QRBN])(\([^)]+\))?$',
            move
        )
        if promo_pattern:
            color, src_file, src_rank, dst_file, dst_rank, promo_piece, suffix = promo_pattern.groups()
            tokens = ['P', src_file + src_rank, dst_file + dst_rank, '=' + promo_piece]
            if suffix:
                tokens.append(suffix)
            return tokens

        # Fallback: return as single token (will likely be UNK)
        return [move]

    def _tokenize(self, text: str) -> List[str]:
        """
        Tokenize a string of moves into sub-structural tokens.

        Each move is decomposed into:
        - Piece type (P, N, B, R, Q, K)
        - Source square (e2, d7, etc.)
        - Destination square (e4, f6, etc.)
        - Optional suffix ((x), (+), etc.)

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

        Returns:
            List of sub-tokens.

        Example:
            "WPe2e4 BPe7e5" -> ['P', 'e2', 'e4', 'P', 'e7', 'e5']
        """
        tokens = []
        moves = text.strip().split()
        for move in moves:
            tokens.extend(self._parse_move(move))
        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 a list of sub-tokens back to a string of moves.

        Reconstructs moves from their components. Each move consists of:
        - Piece token (P, N, B, R, Q, K)
        - Source square (e2, d7, etc.)
        - Destination square (e4, f6, etc.)
        - Optional suffix ((x), (+), etc.) or promotion (=Q, =R, etc.)

        Args:
            tokens: List of sub-tokens.

        Returns:
            Space-separated string of reconstructed moves.
        """
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        pieces = {'P', 'N', 'B', 'R', 'Q', 'K'}
        suffixes = {'(x)', '(+)', '(+*)', '(o)', '(O)'}
        promotions = {'=Q', '=R', '=B', '=N'}

        moves = []
        current_move = []

        for token in tokens:
            if token in special:
                continue

            if token in pieces:
                # Start of a new move - save previous if exists
                if current_move:
                    moves.append(''.join(current_move))
                current_move = [token]
            elif token in suffixes or token in promotions:
                # End of move with suffix/promotion
                current_move.append(token)
            else:
                # Square token
                current_move.append(token)

        # Don't forget the last move
        if current_move:
            moves.append(''.join(current_move))

        return " ".join(moves)
    
    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 sub-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 sub-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))))

    # Use a tokenizer instance to parse moves into sub-tokens
    tokenizer = ChessTokenizer()
    token_counts = Counter()

    for example in dataset:
        sub_tokens = tokenizer._tokenize(example[column])
        token_counts.update(sub_tokens)

    return dict(token_counts)