"""
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


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]"
    EOM_TOKEN = "[EOM]"  # End of Move - marks boundary between moves
    
    def __init__(
        self,
        vocab_file: Optional[str] = None,
        vocab: Optional[Dict[str, int]] = None,
        component_mode: bool = False,
        **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).
            component_mode: If True, tokenize moves into components (WP, e2, e4).
            **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
        self._eom_token = self.EOM_TOKEN
        
        # Component mode flag (for splitting moves into parts)
        self._component_mode = component_mode

        # 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)
        kwargs.pop("eom_token", None)
        kwargs.pop("component_mode", 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,
            component_mode=component_mode,  # This gets saved to tokenizer_config.json
            **kwargs,
        )
        # Store EOM token ID for easy access
        self.eom_token_id = self._vocab.get(self.EOM_TOKEN, -1)
    
    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)
    
    @classmethod
    def build_vocab_more_detailed(
        cls,
        ) -> "ChessTokenizer":
        """
        Build a component-based tokenizer for chess moves.
        
        Instead of one token per move (WPe2e4), splits into components:
        WPe2e4 -> [WP, e2, e4]
        BNg8f6(x) -> [BN, g8, f6, (x)]
        
        This gives ~90 tokens instead of ~1200, with better generalization.
        
        Returns:
            A ChessTokenizer with component vocabulary.
        """
        # Combined color+piece tokens (avoids B collision between Black and Bishop)
        tokens_pieces = [
            "WP", "WN", "WB", "WR", "WQ", "WK",  # White pieces
            "BP", "BN", "BB", "BR", "BQ", "BK",  # Black pieces
        ]
        
        # the positions:
        files = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
        ranks = ['1', '2', '3', '4', '5', '6', '7', '8']
        tokens_positions = [f + r for f in files for r in ranks]
        
        # the special suffixes:
        tokens_suffixes = [
            "(x)",   # capture
            "(+)",   # check
            "(x+)",  # capture + check
            "(+*)",  # checkmate
            "(x+*)", # capture + checkmate
            "(o)",   # kingside castling
            "(O)",   # queenside castling
            "(xE)",  # en passant
            "=Q",    # promotion to queen
            "=R",    # promotion to rook
            "=B",    # promotion to bishop
            "=N",    # promotion to knight
        ]
        
        # Combine all tokens
        tokens = tokens_pieces + tokens_positions + tokens_suffixes
        
        # Build vocabulary with [EOM] for move boundaries
        # [EOM] helps the model understand when a move ends
        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN, cls.EOM_TOKEN]
        vocab = {token: idx for idx, token in enumerate(special_tokens + tokens)}
        for ind, token in enumerate(special_tokens+tokens):
            print(f"Token {ind}: {token}")
        # Pass component_mode=True so it gets saved to tokenizer_config.json
        return cls(vocab=vocab, component_mode=True)
  
    @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.
        
        If component_mode is enabled, splits each move into parts:
        WPe2e4 -> [W, P, e2, e4, " "]
        BNg8f6(x) -> [B, N, g8, f6, (x), " "]
        
        Args:
            text: A string of space-separated moves.
        
        Returns:
            List of tokens.
        """
        if getattr(self, '_component_mode', False):
            return self._tokenize_components(text)
        return text.strip().split()
    
    def _tokenize_components(self, text: str) -> List[str]:
        """
        Tokenize moves into component parts with [EOM] boundaries.
        
        Move format: [Color][Piece][from_square][to_square][suffix] [EOM]
        Example: 
          WPe2e4 -> [WP, e2, e4, EOM]
          BNg8f6(x) -> [BN, g8, f6, (x), EOM]
        """
        import re
        
        tokens = []
        moves = text.strip().split()
        
        for i, move in enumerate(moves):
            # Skip special tokens
            if move in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN, self.EOM_TOKEN]:
                tokens.append(move)
                continue
            
            # Parse move: ColorPiece + from_square + to_square + optional suffix
            # Pattern: (W|B)(P|N|B|R|Q|K)([a-h][1-8])([a-h][1-8])(suffix)?
            pattern = r'^([WB])([PNBRQK])([a-h][1-8])([a-h][1-8])(.*)$'
            match = re.match(pattern, move)
            
            if match:
                color, piece, from_sq, to_sq, suffix = match.groups()
                # Combined color+piece token (e.g., "WP", "BN", "BB")
                tokens.append(color + piece)
                tokens.extend([from_sq, to_sq])
                
                # Handle suffix (could be combination like "(x+)" or "=Q")
                if suffix:
                    # Try to match known suffixes
                    suffix_pattern = r'(\(x\+\*\)|\(x\+\)|\(\+\*\)|\(xE\)|\(x\)|\(\+\)|\(o\)|\(O\)|=Q|=R|=B|=N)'
                    suffix_matches = re.findall(suffix_pattern, suffix)
                    tokens.extend(suffix_matches)
                
                # Add [EOM] to mark end of this move
                tokens.append(self.EOM_TOKEN)
            else:
                # Fallback: add as unknown + EOM
                tokens.append(self.UNK_TOKEN)
                tokens.append(self.EOM_TOKEN)
        
        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."""
        token = self._ids_to_tokens.get(index, self.UNK_TOKEN)
        # Convert [EOM] to whitespace for evaluator compatibility
        # This makes _generate_until_whitespace stop after one move
        if token == self.EOM_TOKEN:
            return " "
        return token
    
    # Color+piece tokens that mark the start of a new move
    _MOVE_START_TOKENS = {"WP", "WN", "WB", "WR", "WQ", "WK", "BP", "BN", "BB", "BR", "BQ", "BK"}
    
    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """Convert a list of tokens back to a string.
        
        In component mode, reconstructs moves by replacing [EOM] with spaces.
        CRITICAL: [EOM] must decode to a non-empty whitespace string so that
        the evaluator's _generate_until_whitespace stops after one move.
        """
        # Filter out special tokens except EOM for cleaner output
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        
        if getattr(self, '_component_mode', False):
            # Reconstruct with [EOM] as space delimiter
            result = []
            for token in tokens:
                if token == self.EOM_TOKEN:
                    # MUST be non-empty whitespace for evaluator
                    result.append(" ")
                elif token not in special:
                    result.append(token)
            # Don't strip! We need the trailing space from [EOM]
            return "".join(result)
        
        # Non-component mode: just join with spaces
        filtered = [t for t in tokens if t not in special]
        return " ".join(filtered)
    
    # =========================================================================
    # Structured Generation Support Methods
    # =========================================================================
    
    def get_token_category(self, token: str) -> str:
        """Categorize a token into: piece, square, suffix, eom, or special.
        
        Args:
            token: Token string to categorize.
            
        Returns:
            Category name: 'piece', 'square', 'suffix', 'eom', or 'special'.
        """
        if token in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]:
            return 'special'
        if token == self.EOM_TOKEN:
            return 'eom'
        if self.is_piece_token(token):
            return 'piece'
        if self.is_square_token(token):
            return 'square'
        if self.is_suffix_token(token):
            return 'suffix'
        return 'unknown'
    
    def is_piece_token(self, token: str) -> bool:
        """Check if token is a piece token (WP, BN, etc.)."""
        return token in ['WP', 'WN', 'WB', 'WR', 'WQ', 'WK', 'BP', 'BN', 'BB', 'BR', 'BQ', 'BK']
    
    def is_square_token(self, token: str) -> bool:
        """Check if token is a square token (e2, g8, etc.)."""
        if len(token) != 2:
            return False
        return token[0] in 'abcdefgh' and token[1] in '12345678'
    
    def is_suffix_token(self, token: str) -> bool:
        """Check if token is a suffix token ((x), (+), =Q, etc.)."""
        return token in ['(x)', '(+)', '(x+)', '(+*)', '(x+*)', '(o)', '(O)', '(xE)', '=Q', '=R', '=B', '=N']
    
    def is_eom_token(self, token: str) -> bool:
        """Check if token is the [EOM] token."""
        return token == self.EOM_TOKEN
    
    def get_token_color(self, token: str) -> Optional[str]:
        """Get the color ('W' or 'B') from a piece token, None otherwise."""
        if self.is_piece_token(token) and len(token) >= 2:
            return token[0]  # 'W' or 'B'
        return None
    
    def build_vocabulary_masks(self) -> dict:
        """Build boolean masks for each token category.
        
        Returns:
            Dictionary with keys: 'piece', 'square', 'suffix', 'eom', 'white_piece', 'black_piece'.
            Each value is a boolean list/tensor of length vocab_size.
        """
        import torch
        
        vocab_size = len(self._vocab)
        masks = {
            'piece': [False] * vocab_size,
            'square': [False] * vocab_size,
            'suffix': [False] * vocab_size,
            'eom': [False] * vocab_size,
            'white_piece': [False] * vocab_size,
            'black_piece': [False] * vocab_size,
        }
        
        for token, token_id in self._vocab.items():
            if self.is_piece_token(token):
                masks['piece'][token_id] = True
                color = self.get_token_color(token)
                if color == 'W':
                    masks['white_piece'][token_id] = True
                elif color == 'B':
                    masks['black_piece'][token_id] = True
            elif self.is_square_token(token):
                masks['square'][token_id] = True
            elif self.is_suffix_token(token):
                masks['suffix'][token_id] = True
            elif self.is_eom_token(token):
                masks['eom'][token_id] = True
        
        # Convert to tensors
        return {k: torch.tensor(v, dtype=torch.bool) for k, v in masks.items()}
    
    def analyze_generation_state(self, input_ids: torch.Tensor) -> dict:
        """Analyze the current generation state to determine next expected token.
        
        Args:
            input_ids: Tensor of shape (batch_size, seq_len) with token IDs.
            
        Returns:
            Dictionary with:
            - 'position': 0 (piece), 1 (from_square), 2 (to_square), 3 (suffix/eom)
            - 'expected_color': 'W' or 'B'
            - 'last_eom_idx': Index of last [EOM] token in sequence
        """
        batch_size = input_ids.shape[0]
        results = []
        
        for b in range(batch_size):
            seq = input_ids[b].tolist()
            
            # Find last [EOM] or [BOS]
            last_eom_idx = -1
            for i in range(len(seq) - 1, -1, -1):
                token = self._ids_to_tokens.get(seq[i], self.UNK_TOKEN)
                if token in [self.EOM_TOKEN, self.BOS_TOKEN]:
                    last_eom_idx = i
                    break
            
            # Count tokens since last [EOM]/[BOS] (excluding padding)
            tokens_since_boundary = []
            for i in range(last_eom_idx + 1, len(seq)):
                token = self._ids_to_tokens.get(seq[i], self.UNK_TOKEN)
                if token != self.PAD_TOKEN:
                    tokens_since_boundary.append(token)
            
            # Determine position in move structure: [Piece][Square][Square][Suffix?][EOM]
            num_tokens = len(tokens_since_boundary)
            
            if num_tokens == 0:
                position = 0  # Expect piece
            elif num_tokens == 1:
                position = 1  # Expect from_square
            elif num_tokens == 2:
                position = 2  # Expect to_square
            else:
                position = 3  # Expect suffix or [EOM]
            
            # Determine expected color by counting complete moves
            # Count [EOM] tokens to get move number
            eom_count = sum(1 for i in seq if self._ids_to_tokens.get(i, '') == self.EOM_TOKEN)
            expected_color = 'W' if eom_count % 2 == 0 else 'B'
            
            results.append({
                'position': position,
                'expected_color': expected_color,
                'last_eom_idx': last_eom_idx,
            })
        
        # For single batch, return dict directly; for multi-batch, return list
        return results[0] if batch_size == 1 else results
    
    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)