"""
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

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


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,
        num_kv_groups: int = 2,
        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.num_kv_groups = num_kv_groups
        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 GroupedQueryAttention(nn.Module):
    def __init__(
            self,  config: ChessConfig
    ):
        super().__init__()
        # assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
        assert config.n_embd % config.n_head == 0, \
            f"n_embd ({config.n_embd}) must be divisible by n_head ({config.n_head})"
        assert config.n_head % config.num_kv_groups == 0, \
            "num_heads must be divisible by num_kv_groups"

        self.n_embd = config.n_embd
        self.n_head = config.n_head
        self.head_dim = config.n_embd // config.n_head

        self.W_key = nn.Linear(config.n_embd, config.num_kv_groups * self.head_dim)
        self.W_value = nn.Linear(config.n_embd, config.num_kv_groups * self.head_dim)
        self.num_kv_groups = config.num_kv_groups
        self.group_size = config.n_head // config.num_kv_groups

        self.W_query = nn.Linear(config.n_embd, config.n_embd)
        self.out_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)
        self.dropout = nn.Dropout(config.dropout)

        # self.register_buffer("cache_k", None, persistent=False)
        # self.register_buffer("cache_v", None, persistent=False)
        # self.ptr_current_pos = 0
        
        # 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()

        # Apply projections
        queries = self.W_query(x)  # (b, seq_len, num_heads * head_dim)
        keys = self.W_key(x)       # (b, seq_len, num_kv_groups * head_dim)
        values = self.W_value(x)   # (b, seq_len, num_kv_groups * head_dim)

        # Reshape
        queries = queries.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2)
        keys_new = keys.view(batch_size, seq_len, self.num_kv_groups, self.head_dim).transpose(1, 2)
        values_new = (
            values.view(batch_size, seq_len, self.num_kv_groups, self.head_dim)
            .transpose(1, 2)
        )

        '''if use_cache:
            if self.cache_k is None:
                self.cache_k, self.cache_v = keys_new, values_new
            else:
                self.cache_k = torch.cat([self.cache_k, keys_new], dim=2)
                self.cache_v = torch.cat([self.cache_v, values_new], dim=2)
            keys_base, values_base = self.cache_k, self.cache_v
        else:
            keys_base, values_base = keys_new, values_new
            if self.cache_k is not None or self.cache_v is not None:
                self.cache_k, self.cache_v = None, None
                self.ptr_current_pos = 0'''

        # Expand keys and values to match the number of heads
        # Shape: (b, num_heads, seq_len, head_dim)
        keys = keys_new.repeat_interleave(self.group_size, dim=1)  
        # Shape: (b, num_heads, seq_len, head_dim)
        values = values_new.repeat_interleave(self.group_size, dim=1)  
        # Shape: (b, num_heads, seq_len, head_dim)
        # For example, before repeat_interleave along dim=1 (query groups):
        #   [K1, K2]
        # After repeat_interleave (each query group is repeated group_size times):
        #   [K1, K1, K2, K2]
        # If we used regular repeat instead of repeat_interleave, we'd get:
        #   [K1, K2, K1, K2]

        # Compute scaled dot-product attention (aka self-attention) with a causal mask
        # Shape: (b, num_heads, seq_len, seq_len)
        attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head

        ####################################################
        # causal mask
        '''num_tokens_Q = queries.shape[-2]
        num_tokens_K = keys.shape[-2]
        device = queries.device
        if use_cache:
            q_positions = torch.arange(
                self.ptr_current_pos,
                self.ptr_current_pos + num_tokens_Q,
                device=device,
                dtype=torch.long,
            )
            self.ptr_current_pos += num_tokens_Q
        else:
            q_positions = torch.arange(num_tokens_Q, device=device, dtype=torch.long)
            self.ptr_current_pos = 0
        k_positions = torch.arange(num_tokens_K, device=device, dtype=torch.long)
        mask = q_positions.unsqueeze(-1) < k_positions.unsqueeze(0)'''
        
        # Use the mask to fill attention scores
        # attn_scores = attn_scores.masked_fill(mask, -torch.inf)
        attn_weights = attn_scores / math.sqrt(self.head_dim)
        
        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)
        assert keys.shape[-1] == self.head_dim
        attn_weights = self.dropout(attn_weights)

        # Shape: (b, seq_len, num_heads, head_dim)
        context_vec = (attn_weights @ values).transpose(1, 2)

        # Combine heads, where self.d_out = self.num_heads * self.head_dim
        context_vec = context_vec.contiguous().view(batch_size, seq_len, self.n_embd)
        context_vec = self.out_proj(context_vec)  # optional projection

        return context_vec


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.RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.attn = GroupedQueryAttention(config)
        self.ln_2 = nn.RMSNorm(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)
        self.n_layer = config.n_layer

        # Transformer blocks
        '''self.h = nn.ModuleList([
            TransformerBlock(config) for _ in range(config.n_layer)
        ])'''
        
        self.shared_block = TransformerBlock(config)

        # Final layer norm
        self.ln_f = nn.RMSNorm(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.RMSNorm):
            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)
        
        for _ in range(self.n_layer):
            hidden_states = self.shared_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()


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