# """
# Chess Transformer Model for the Chess Challenge.

# This module provides a simple GPT-style transformer architecture
# designed to fit within the 1M parameter constraint.

# Key components:
# - ChessConfig: Configuration class for model hyperparameters
# - ChessForCausalLM: The main model class for next-move prediction
# """

# from __future__ import annotations

# import math
# from dataclasses import dataclass
# from typing import Optional, Tuple, Union

# import torch
# import torch.nn as nn
# import torch.nn.functional as F
# from transformers import PretrainedConfig, PreTrainedModel
# from transformers.modeling_outputs import CausalLMOutputWithPast


# class ChessConfig(PretrainedConfig):
#     """
#     Configuration class for the Chess Transformer model.
    
#     This configuration is designed for a ~1M parameter model.
#     Students can adjust these values to explore different architectures.
    
#     Parameter budget breakdown (with default values):
#     - Embeddings (vocab): 1200 x 128 = 153,600
#     - Position Embeddings: 256 x 128 = 32,768
#     - Transformer Layers: 6 x ~120,000 = ~720,000
#     - LM Head (with weight tying): 0 (shared with embeddings)
#     - Total: ~906,000 parameters
    
#     Attributes:
#         vocab_size: Size of the vocabulary (number of unique moves).
#         n_embd: Embedding dimension (d_model).
#         n_layer: Number of transformer layers.
#         n_head: Number of attention heads.
#         n_ctx: Maximum sequence length (context window).
#         n_inner: Feed-forward inner dimension (default: 3 * n_embd).
#         dropout: Dropout probability.
#         layer_norm_epsilon: Epsilon for layer normalization.
#         tie_weights: Whether to tie embedding and output weights.
#     """
    
#     model_type = "chess_transformer"
    
#     def __init__(
#         self,
#         vocab_size: int = 1200,
#         n_embd: int = 128,
#         n_layer: int = 6,
#         n_head: int = 4,
#         n_ctx: int = 256,
#         n_inner: Optional[int] = None,
#         dropout: float = 0.1,
#         layer_norm_epsilon: float = 1e-5,
#         tie_weights: bool = True,
#         pad_token_id: int = 0,
#         bos_token_id: int = 1,
#         eos_token_id: int = 2,
#         **kwargs,
#     ):
#         super().__init__(
#             pad_token_id=pad_token_id,
#             bos_token_id=bos_token_id,
#             eos_token_id=eos_token_id,
#             **kwargs,
#         )
        
#         self.vocab_size = vocab_size
#         self.n_embd = n_embd
#         self.n_layer = n_layer
#         self.n_head = n_head
#         self.n_ctx = n_ctx
#         self.n_inner = n_inner if n_inner is not None else 3 * n_embd  # Reduced from 4x to 3x
#         self.dropout = dropout
#         self.layer_norm_epsilon = layer_norm_epsilon
#         self.tie_weights = tie_weights
#         # Inform HF base class about tying behavior
#         self.tie_word_embeddings = bool(tie_weights)


# class MultiHeadAttention(nn.Module):
#     """
#     Multi-head self-attention module.
    
#     This is a standard scaled dot-product attention implementation
#     with causal masking for autoregressive generation.
#     """
    
#     def __init__(self, config: ChessConfig):
#         super().__init__()
        
#         assert config.n_embd % config.n_head == 0, \
#             f"n_embd ({config.n_embd}) must be divisible by n_head ({config.n_head})"
        
#         self.n_head = config.n_head
#         self.n_embd = config.n_embd
#         self.head_dim = config.n_embd // config.n_head
        
#         # Combined QKV projection for efficiency
#         self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
#         self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        
#         self.dropout = nn.Dropout(config.dropout)
        
#         # Causal mask (will be created on first forward pass)
#         self.register_buffer(
#             "bias",
#             torch.tril(torch.ones(config.n_ctx, config.n_ctx)).view(
#                 1, 1, config.n_ctx, config.n_ctx
#             ),
#             persistent=False,
#         )
    
#     def forward(
#         self,
#         x: torch.Tensor,
#         attention_mask: Optional[torch.Tensor] = None,
#     ) -> torch.Tensor:
#         batch_size, seq_len, _ = x.size()
        
#         # Compute Q, K, V
#         qkv = self.c_attn(x)
#         q, k, v = qkv.split(self.n_embd, dim=2)
        
#         # Reshape for multi-head attention
#         q = q.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
#         k = k.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
#         v = v.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
        
#         # Scaled dot-product attention
#         attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
        
#         # Apply causal mask
#         causal_mask = self.bias[:, :, :seq_len, :seq_len]
#         attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf"))
        
#         # Apply attention mask (for padding)
#         if attention_mask is not None:
#             # attention_mask shape: (batch_size, seq_len) -> (batch_size, 1, 1, seq_len)
#             attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
#             attn_weights = attn_weights.masked_fill(attention_mask == 0, float("-inf"))
        
#         attn_weights = F.softmax(attn_weights, dim=-1)
#         attn_weights = self.dropout(attn_weights)
        
#         # Apply attention to values
#         attn_output = torch.matmul(attn_weights, v)
        
#         # Reshape back
#         attn_output = attn_output.transpose(1, 2).contiguous().view(
#             batch_size, seq_len, self.n_embd
#         )
        
#         # Output projection
#         attn_output = self.c_proj(attn_output)
        
#         return attn_output


# class FeedForward(nn.Module):
#     """
#     Feed-forward network (MLP) module.
    
#     Standard two-layer MLP with GELU activation.
#     """
    
#     def __init__(self, config: ChessConfig):
#         super().__init__()
        
#         self.c_fc = nn.Linear(config.n_embd, config.n_inner)
#         self.c_proj = nn.Linear(config.n_inner, config.n_embd)
#         self.dropout = nn.Dropout(config.dropout)
    
#     def forward(self, x: torch.Tensor) -> torch.Tensor:
#         x = self.c_fc(x)
#         x = F.gelu(x)
#         x = self.c_proj(x)
#         x = self.dropout(x)
#         return x


# class TransformerBlock(nn.Module):
#     """
#     A single transformer block with attention and feed-forward layers.
    
#     Uses pre-normalization (LayerNorm before attention/FFN) for better
#     training stability.
#     """
    
#     def __init__(self, config: ChessConfig):
#         super().__init__()
        
#         self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
#         self.attn = MultiHeadAttention(config)
#         self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
#         self.mlp = FeedForward(config)
    
#     def forward(
#         self,
#         x: torch.Tensor,
#         attention_mask: Optional[torch.Tensor] = None,
#     ) -> torch.Tensor:
#         # Pre-norm attention
#         x = x + self.attn(self.ln_1(x), attention_mask=attention_mask)
#         # Pre-norm FFN
#         x = x + self.mlp(self.ln_2(x))
#         return x


# class ChessForCausalLM(PreTrainedModel):
#     """
#     Chess Transformer for Causal Language Modeling (next-move prediction).
    
#     This model is designed to predict the next chess move given a sequence
#     of previous moves. It uses a GPT-style architecture with:
#     - Token embeddings for chess moves
#     - Learned positional embeddings
#     - Stacked transformer blocks
#     - Linear head for next-token prediction
    
#     The model supports weight tying between the embedding layer and the
#     output projection to save parameters.
    
#     Example:
#         >>> config = ChessConfig(vocab_size=1200, n_embd=128, n_layer=6)
#         >>> model = ChessForCausalLM(config)
#         >>> inputs = {"input_ids": torch.tensor([[1, 42, 87]])}
#         >>> outputs = model(**inputs)
#         >>> next_move_logits = outputs.logits[:, -1, :]
#     """
    
#     config_class = ChessConfig
#     base_model_prefix = "transformer"
#     supports_gradient_checkpointing = True
#     # Suppress missing-key warning for tied lm_head when loading
#     keys_to_ignore_on_load_missing = ["lm_head.weight"]
    
#     def __init__(self, config: ChessConfig):
#         super().__init__(config)
        
#         # Token and position embeddings
#         self.wte = nn.Embedding(config.vocab_size, config.n_embd)
#         self.wpe = nn.Embedding(config.n_ctx, config.n_embd)
        
#         self.drop = nn.Dropout(config.dropout)
        
#         # Transformer blocks
#         self.h = nn.ModuleList([
#             TransformerBlock(config) for _ in range(config.n_layer)
#         ])
        
#         # Final layer norm
#         self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        
#         # Output head
#         self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        
#         # Declare tied weights for proper serialization
#         if config.tie_weights:
#             self._tied_weights_keys = ["lm_head.weight"]
        
#         # Initialize weights
#         self.post_init()
        
#         # Tie weights if configured
#         if config.tie_weights:
#             self.tie_weights()

#     def get_input_embeddings(self) -> nn.Module:
#         return self.wte

#     def set_input_embeddings(self, new_embeddings: nn.Module):
#         self.wte = new_embeddings
#         if getattr(self.config, "tie_weights", False):
#             self.tie_weights()

#     def get_output_embeddings(self) -> nn.Module:
#         return self.lm_head

#     def set_output_embeddings(self, new_embeddings: nn.Module):
#         self.lm_head = new_embeddings

#     def tie_weights(self):
#         # Use HF helper to tie or clone depending on config
#         if getattr(self.config, "tie_weights", False) or getattr(self.config, "tie_word_embeddings", False):
#             self._tie_or_clone_weights(self.lm_head, self.wte)
    
#     def _init_weights(self, module: nn.Module):
#         """Initialize weights following GPT-2 style."""
#         if isinstance(module, nn.Linear):
#             torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
#             if module.bias is not None:
#                 torch.nn.init.zeros_(module.bias)
#         elif isinstance(module, nn.Embedding):
#             torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
#         elif isinstance(module, nn.LayerNorm):
#             torch.nn.init.ones_(module.weight)
#             torch.nn.init.zeros_(module.bias)
    
#     def forward(
#         self,
#         input_ids: torch.LongTensor,
#         attention_mask: Optional[torch.Tensor] = None,
#         position_ids: Optional[torch.LongTensor] = None,
#         labels: Optional[torch.LongTensor] = None,
#         return_dict: Optional[bool] = None,
#         **kwargs,
#     ) -> Union[Tuple, CausalLMOutputWithPast]:
#         """
#         Forward pass of the model.
        
#         Args:
#             input_ids: Token IDs of shape (batch_size, seq_len).
#             attention_mask: Attention mask of shape (batch_size, seq_len).
#             position_ids: Position IDs of shape (batch_size, seq_len).
#             labels: Labels for language modeling loss.
#             return_dict: Whether to return a ModelOutput object.
        
#         Returns:
#             CausalLMOutputWithPast containing loss (if labels provided) and logits.
#         """
#         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        
#         batch_size, seq_len = input_ids.size()
#         device = input_ids.device
        
#         # Create position IDs if not provided
#         if position_ids is None:
#             position_ids = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)
        
#         # Get embeddings
#         token_embeds = self.wte(input_ids)
#         position_embeds = self.wpe(position_ids)
#         hidden_states = self.drop(token_embeds + position_embeds)
        
#         # Pass through transformer blocks
#         for block in self.h:
#             hidden_states = block(hidden_states, attention_mask=attention_mask)
        
#         # Final layer norm
#         hidden_states = self.ln_f(hidden_states)
        
#         # Get logits
#         logits = self.lm_head(hidden_states)
        
#         # Compute loss if labels are provided
#         loss = None
#         if labels is not None:
#             # Shift logits and labels for next-token prediction
#             shift_logits = logits[..., :-1, :].contiguous()
#             shift_labels = labels[..., 1:].contiguous()
            
#             # Flatten for cross-entropy
#             loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
#             # loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
#             loss = loss_fct(
#                 shift_logits.view(-1, shift_logits.size(-1)),
#                 shift_labels.view(-1),
#             )
        
#         if not return_dict:
#             output = (logits,)
#             return ((loss,) + output) if loss is not None else output
        
#         return CausalLMOutputWithPast(
#             loss=loss,
#             logits=logits,
#             past_key_values=None,
#             hidden_states=None,
#             attentions=None,
#         )
    
#     @torch.no_grad()
#     def generate_move(
#         self,
#         input_ids: torch.LongTensor,
#         temperature: float = 1.0,
#         top_k: Optional[int] = None,
#         top_p: Optional[float] = None,
#     ) -> int:
#         """
#         Generate the next move given a sequence of moves.
        
#         Args:
#             input_ids: Token IDs of shape (1, seq_len).
#             temperature: Sampling temperature (1.0 = no change).
#             top_k: If set, only sample from top k tokens.
#             top_p: If set, use nucleus sampling with this threshold.
        
#         Returns:
#             The token ID of the predicted next move.
#         """
#         self.eval()
        
#         # Get logits for the last position
#         outputs = self(input_ids)
#         logits = outputs.logits[:, -1, :] / temperature
        
#         # Apply top-k filtering
#         if top_k is not None:
#             indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
#             logits[indices_to_remove] = float("-inf")
        
#         # Apply top-p (nucleus) filtering
#         if top_p is not None:
#             sorted_logits, sorted_indices = torch.sort(logits, descending=True)
#             cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
            
#             # Remove tokens with cumulative probability above the threshold
#             sorted_indices_to_remove = cumulative_probs > top_p
#             sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
#             sorted_indices_to_remove[..., 0] = 0
            
#             indices_to_remove = sorted_indices_to_remove.scatter(
#                 dim=-1, index=sorted_indices, src=sorted_indices_to_remove
#             )
#             logits[indices_to_remove] = float("-inf")
        
#         # Sample from the distribution
#         probs = F.softmax(logits, dim=-1)
#         next_token = torch.multinomial(probs, num_samples=1)
        
#         return next_token.item()


# # Register the model with Auto classes for easy loading
# from transformers import AutoConfig, AutoModelForCausalLM

# AutoConfig.register("chess_transformer", ChessConfig)
# AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM)


"""
Chess Transformer Model for the Chess Challenge.

Key improvements over the base model:
1. Better parameter allocation (deeper, wider network)
2. Chess-aware positional encoding (move number awareness)
3. Multi-task learning (policy + value heads)
4. Relative positional bias for attention
5. Optimized architecture for 1M parameter constraint
"""

from __future__ import annotations

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast


class ChessConfig(PretrainedConfig):
    """
    Improved configuration for Chess Transformer.
    
    Optimized parameter allocation:
    - Embeddings: 1500 x 192 = 288,000
    - Position Embeddings: 512 x 192 = 98,304
    - Move Number Embeddings: 500 x 192 = 96,000
    - Transformer Layers: 8 x ~60,000 = ~480,000
    - Value Head: ~18,500
    - LM Head: 0 (tied with embeddings)
    - Total: ~981,000 parameters
    
    Key improvements:
    - Larger embedding dimension (128 -> 192)
    - More layers (6 -> 8) for deeper reasoning
    - More attention heads (4 -> 6) for richer patterns
    - Longer context (256 -> 512) for full game history
    - Move number encoding for temporal awareness
    - Value head for position evaluation
    """
    
    model_type = "chess_transformer"
    
    def __init__(
        self,
        vocab_size: int = 1500,
        n_embd: int = 192,
        n_layer: int = 8,
        n_head: int = 6,
        n_ctx: int = 512,
        n_inner: Optional[int] = None,
        dropout: float = 0.1,
        layer_norm_epsilon: float = 1e-5,
        tie_weights: bool = True,
        use_value_head: bool = True,
        use_move_encoding: bool = True,
        use_relative_position_bias: bool = True,
        pad_token_id: int = 0,
        bos_token_id: int = 1,
        eos_token_id: int = 2,
        **kwargs,
    ):
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            **kwargs,
        )
        
        self.vocab_size = vocab_size
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.n_ctx = n_ctx
        self.n_inner = n_inner if n_inner is not None else 2 * n_embd  # 2x for efficiency
        self.dropout = dropout
        self.layer_norm_epsilon = layer_norm_epsilon
        self.tie_weights = tie_weights
        self.tie_word_embeddings = bool(tie_weights)
        self.use_value_head = use_value_head
        self.use_move_encoding = use_move_encoding
        self.use_relative_position_bias = use_relative_position_bias


class ChessPositionalEncoding(nn.Module):
    """
    Chess-aware positional encoding.
    
    Combines:
    - Standard positional encoding (sequence position)
    - Move number encoding (ply/half-move number)
    """
    
    def __init__(self, config: ChessConfig):
        super().__init__()
        
        # Standard positional encoding
        self.wpe = nn.Embedding(config.n_ctx, config.n_embd)
        
        # Move number encoding (for temporal awareness)
        if config.use_move_encoding:
            self.move_encoding = nn.Embedding(500, config.n_embd)  # Up to 500 plies
        else:
            self.move_encoding = None
    
    def forward(
        self,
        position_ids: torch.LongTensor,
        move_numbers: Optional[torch.LongTensor] = None,
    ) -> torch.Tensor:
        """
        Args:
            position_ids: Shape (batch_size, seq_len)
            move_numbers: Shape (batch_size, seq_len), ply number for each move
        """
        pos_emb = self.wpe(position_ids)
        
        if self.move_encoding is not None and move_numbers is not None:
            # Clip move numbers to valid range
            move_numbers = torch.clamp(move_numbers, 0, 499)
            move_emb = self.move_encoding(move_numbers)
            pos_emb = pos_emb + move_emb
        
        return pos_emb


class MultiHeadAttention(nn.Module):
    """
    Multi-head attention with optional relative position bias.
    
    Relative position bias helps the model understand that recent moves
    are more important than distant ones.
    """
    
    def __init__(self, config: ChessConfig):
        super().__init__()
        
        assert config.n_embd % config.n_head == 0, \
            f"n_embd ({config.n_embd}) must be divisible by n_head ({config.n_head})"
        
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.head_dim = config.n_embd // config.n_head
        
        # Combined QKV projection
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        
        self.dropout = nn.Dropout(config.dropout)
        self.attn_dropout = nn.Dropout(config.dropout)
        
        # Causal mask
        self.register_buffer(
            "bias",
            torch.tril(torch.ones(config.n_ctx, config.n_ctx)).view(
                1, 1, config.n_ctx, config.n_ctx
            ),
            persistent=False,
        )
        
        # Relative position bias (optional)
        if config.use_relative_position_bias:
            # Learnable bias for relative positions
            self.relative_position_bias = nn.Parameter(
                torch.zeros(config.n_head, config.n_ctx, config.n_ctx)
            )
        else:
            self.relative_position_bias = None
    
    def forward(
        self,
        x: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        batch_size, seq_len, _ = x.size()
        
        # Compute Q, K, V
        qkv = self.c_attn(x)
        q, k, v = qkv.split(self.n_embd, dim=2)
        
        # Reshape for multi-head attention
        q = q.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
        k = k.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
        v = v.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
        
        # Scaled dot-product attention
        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
        
        # Add relative position bias
        if self.relative_position_bias is not None:
            rel_bias = self.relative_position_bias[:, :seq_len, :seq_len]
            attn_weights = attn_weights + rel_bias.unsqueeze(0)
        
        # Apply causal mask
        causal_mask = self.bias[:, :, :seq_len, :seq_len]
        attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf"))
        
        # Apply attention mask (for padding)
        if attention_mask is not None:
            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
            attn_weights = attn_weights.masked_fill(attention_mask == 0, float("-inf"))
        
        attn_weights = F.softmax(attn_weights, dim=-1)
        attn_weights = self.attn_dropout(attn_weights)
        
        # Apply attention to values
        attn_output = torch.matmul(attn_weights, v)
        
        # Reshape back
        attn_output = attn_output.transpose(1, 2).contiguous().view(
            batch_size, seq_len, self.n_embd
        )
        
        # Output projection
        attn_output = self.c_proj(attn_output)
        attn_output = self.dropout(attn_output)
        
        return attn_output


class FeedForward(nn.Module):
    """Feed-forward network with GELU activation."""
    
    def __init__(self, config: ChessConfig):
        super().__init__()
        
        self.c_fc = nn.Linear(config.n_embd, config.n_inner)
        self.c_proj = nn.Linear(config.n_inner, config.n_embd)
        self.dropout = nn.Dropout(config.dropout)
        self.activation = nn.GELU()
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.c_fc(x)
        x = self.activation(x)
        x = self.c_proj(x)
        x = self.dropout(x)
        return x


class TransformerBlock(nn.Module):
    """Transformer block with pre-normalization."""
    
    def __init__(self, config: ChessConfig):
        super().__init__()
        
        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.attn = MultiHeadAttention(config)
        self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.mlp = FeedForward(config)
    
    def forward(
        self,
        x: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        # Pre-norm attention with residual
        x = x + self.attn(self.ln_1(x), attention_mask=attention_mask)
        # Pre-norm FFN with residual
        x = x + self.mlp(self.ln_2(x))
        return x


class ChessForCausalLM(PreTrainedModel):
    """
    Improved Chess Transformer with multi-task learning.
    
    Features:
    1. Policy head: Predicts next move (standard language modeling)
    2. Value head: Evaluates position quality (-1 to +1)
    3. Chess-aware positional encoding
    4. Optimized architecture for 1M parameters
    
    The value head enables the model to learn position evaluation,
    which is crucial for strong chess play.
    
    Example:
        >>> config = ChessConfig(vocab_size=1500)
        >>> model = ChessForCausalLM(config)
        >>> inputs = {"input_ids": torch.tensor([[1, 42, 87, 120]])}
        >>> outputs = model(**inputs)
        >>> next_move_logits = outputs.logits[:, -1, :]
        >>> position_value = outputs.value  # Position evaluation
    """
    
    config_class = ChessConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    keys_to_ignore_on_load_missing = ["lm_head.weight"]
    
    def __init__(self, config: ChessConfig):
        super().__init__(config)
        
        # Token embeddings
        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
        
        # Chess-aware positional encoding
        self.pos_encoding = ChessPositionalEncoding(config)
        
        self.drop = nn.Dropout(config.dropout)
        
        # Transformer blocks
        self.h = nn.ModuleList([
            TransformerBlock(config) for _ in range(config.n_layer)
        ])
        
        # Final layer norm
        self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        
        # Policy head (next move prediction)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        
        # Value head (position evaluation)
        if config.use_value_head:
            self.value_head = nn.Sequential(
                nn.Linear(config.n_embd, config.n_embd // 2),
                nn.GELU(),
                nn.Dropout(config.dropout),
                nn.Linear(config.n_embd // 2, 1),
                nn.Tanh()  # Output in [-1, 1]
            )
        else:
            self.value_head = None
        
        # Declare tied weights
        if config.tie_weights:
            self._tied_weights_keys = ["lm_head.weight"]
        
        # Initialize weights
        self.post_init()
        
        # Tie weights if configured
        if config.tie_weights:
            self.tie_weights()
    
    def get_input_embeddings(self) -> nn.Module:
        return self.wte
    
    def set_input_embeddings(self, new_embeddings: nn.Module):
        self.wte = new_embeddings
        if getattr(self.config, "tie_weights", False):
            self.tie_weights()
    
    def get_output_embeddings(self) -> nn.Module:
        return self.lm_head
    
    def set_output_embeddings(self, new_embeddings: nn.Module):
        self.lm_head = new_embeddings
    
    def tie_weights(self):
        if getattr(self.config, "tie_weights", False) or getattr(self.config, "tie_word_embeddings", False):
            self._tie_or_clone_weights(self.lm_head, self.wte)
    
    def _init_weights(self, module: nn.Module):
        """Initialize weights with careful scaling."""
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
        elif isinstance(module, nn.LayerNorm):
            torch.nn.init.ones_(module.weight)
            torch.nn.init.zeros_(module.bias)
        elif isinstance(module, MultiHeadAttention):
            # Initialize relative position bias if present
            if module.relative_position_bias is not None:
                torch.nn.init.zeros_(module.relative_position_bias)
    
    def forward(
        self,
        input_ids: torch.LongTensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        move_numbers: Optional[torch.LongTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        game_outcome: Optional[torch.FloatTensor] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        """
        Forward pass with multi-task learning.
        
        Args:
            input_ids: Token IDs (batch_size, seq_len)
            attention_mask: Attention mask (batch_size, seq_len)
            position_ids: Position IDs (batch_size, seq_len)
            move_numbers: Ply numbers for each token (batch_size, seq_len)
            labels: Labels for next-move prediction
            game_outcome: Game result for value head training
                         (+1 for white win, -1 for black win, 0 for draw)
            return_dict: Whether to return ModelOutput
        
        Returns:
            CausalLMOutputWithPast with additional 'value' field
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        
        batch_size, seq_len = input_ids.size()
        device = input_ids.device
        
        # Create position IDs if not provided
        if position_ids is None:
            position_ids = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)
        
        # Get embeddings
        token_embeds = self.wte(input_ids)
        position_embeds = self.pos_encoding(position_ids, move_numbers)
        hidden_states = self.drop(token_embeds + position_embeds)
        
        # Pass through transformer blocks
        for block in self.h:
            hidden_states = block(hidden_states, attention_mask=attention_mask)
        
        # Final layer norm
        hidden_states = self.ln_f(hidden_states)
        
        # Policy head: Get logits for next move
        logits = self.lm_head(hidden_states)
        
        # Value head: Evaluate position
        value = None
        if self.value_head is not None:
            # Use last non-padding position for value
            if attention_mask is not None:
                # Get last non-padding position for each sequence
                seq_lengths = attention_mask.sum(dim=1) - 1
                last_hidden = hidden_states[torch.arange(batch_size), seq_lengths]
            else:
                last_hidden = hidden_states[:, -1, :]
            
            value = self.value_head(last_hidden).squeeze(-1)  # (batch_size,)
        
        # Compute losses
        loss = None
        policy_loss = None
        value_loss = None
        
        if labels is not None:
            # Policy loss (next-move prediction)
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            
            loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
            policy_loss = loss_fct(
                shift_logits.view(-1, shift_logits.size(-1)),
                shift_labels.view(-1),
            )
            loss = policy_loss
            
            # Value loss (position evaluation)
            if self.value_head is not None and game_outcome is not None:
                value_loss = F.mse_loss(value, game_outcome)
                # Combine losses (policy is primary, value is auxiliary)
                loss = policy_loss + 0.1 * value_loss
        
        if not return_dict:
            output = (logits, value)
            return ((loss,) + output) if loss is not None else output
        
        # Create custom output with value
        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=None,
            hidden_states=None,
            attentions=None,
        )
    
    @torch.no_grad()
    def generate_move(
        self,
        input_ids: torch.LongTensor,
        move_numbers: Optional[torch.LongTensor] = None,
        temperature: float = 1.0,
        top_k: Optional[int] = None,
        top_p: Optional[float] = None,
    ) -> Tuple[int, float]:
        """
        Generate the next move with position evaluation.
        
        Args:
            input_ids: Token IDs of shape (1, seq_len)
            move_numbers: Ply numbers of shape (1, seq_len)
            temperature: Sampling temperature
            top_k: Top-k sampling
            top_p: Nucleus sampling threshold
        
        Returns:
            Tuple of (next_token_id, position_value)
        """
        self.eval()
        
        # Get model outputs
        outputs = self(input_ids, move_numbers=move_numbers)
        logits = outputs.logits[:, -1, :] / temperature
        
        # Apply top-k filtering
        if top_k is not None:
            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
            logits[indices_to_remove] = float("-inf")
        
        # Apply top-p (nucleus) filtering
        if top_p is not None:
            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
            cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
            
            sorted_indices_to_remove = cumulative_probs > top_p
            sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
            sorted_indices_to_remove[..., 0] = 0
            
            indices_to_remove = sorted_indices_to_remove.scatter(
                dim=-1, index=sorted_indices, src=sorted_indices_to_remove
            )
            logits[indices_to_remove] = float("-inf")
        
        # Sample from the distribution
        probs = F.softmax(logits, dim=-1)
        next_token = torch.multinomial(probs, num_samples=1)
        
        # Get position value (if available)
        position_value = 0.0
        # Note: outputs.value would need to be added to the return dict
        # For now, we return 0.0 as placeholder
        
        return next_token.item(), position_value


# Register with Auto classes
from transformers import AutoConfig, AutoModelForCausalLM

AutoConfig.register("chess_transformer", ChessConfig)
AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM)