"""Standard Transformer language model implementation."""

import math
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F

from taoTrain.core import BaseModel
from taoTrain.config import ModelConfig
from .registry import register_architecture


# ============================================================================
# Components
# ============================================================================


class PositionalEmbedding(nn.Module):
    """Sinusoidal positional embeddings."""
    
    def __init__(self, dim: int, max_seq_length: int = 2048):
        """Initialize positional embeddings."""
        super().__init__()
        self.dim = dim
        self.max_seq_length = max_seq_length
        
        # Precompute positional embeddings
        pe = torch.zeros(max_seq_length, dim)
        pos = torch.arange(0, max_seq_length, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, dim, 2).float() * (-math.log(10000.0) / dim))
        
        pe[:, 0::2] = torch.sin(pos * div_term)
        if dim % 2 == 1:
            pe[:, 1::2] = torch.cos(pos * div_term[:-1])
        else:
            pe[:, 1::2] = torch.cos(pos * div_term)
        
        self.register_buffer("pe", pe, persistent=False)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Add positional embeddings to input.
        
        Args:
            x: Input tensor (batch, seq_len, hidden_dim)
        
        Returns:
            Input + positional embeddings
        """
        seq_len = x.shape[1]
        return x + self.pe[:seq_len]


class Attention(nn.Module):
    """Multi-head self-attention using scaled dot-product attention."""
    
    def __init__(self, config: ModelConfig):
        """Initialize attention."""
        super().__init__()
        self.hidden_dim = config.hidden_dim
        self.num_heads = config.num_heads
        self.head_dim = config.head_dim
        
        assert self.hidden_dim % self.num_heads == 0
        
        # Linear projections
        self.q_proj = nn.Linear(self.hidden_dim, self.hidden_dim)
        self.k_proj = nn.Linear(self.hidden_dim, self.hidden_dim)
        self.v_proj = nn.Linear(self.hidden_dim, self.hidden_dim)
        self.out_proj = nn.Linear(self.hidden_dim, self.hidden_dim)
        
        self.dropout_p = config.dropout
    
    def forward(
        self,
        x: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """
        Forward pass using scaled_dot_product_attention.
        
        Args:
            x: Shape (batch, seq_len, hidden_dim)
            attention_mask: Shape (batch, seq_len)
        
        Returns:
            Output: Shape (batch, seq_len, hidden_dim)
        """
        batch_size, seq_len, _ = x.shape
        
        # Project to Q, K, V
        q = self.q_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
        k = self.k_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
        v = self.v_proj(x).reshape(batch_size, seq_len, self.num_heads, self.head_dim)
        
        # Transpose for attention: (batch, num_heads, seq_len, head_dim)
        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)
        
        # NOTE: PyTorch's scaled_dot_product_attention does NOT support both
        # explicit attn_mask AND is_causal=True together.
        # When is_causal=True, PyTorch handles causal masking automatically.
        # Padding positions are handled separately via loss computation (labels=-100).
        # See: https://github.com/pytorch/pytorch/issues/96099
        
        # Compute attention using scaled_dot_product_attention
        # is_causal=True automatically applies causal masking
        # We do NOT pass attn_mask when is_causal=True
        out = F.scaled_dot_product_attention(
            q, k, v,
            attn_mask=None,  # Must be None when is_causal=True
            dropout_p=self.dropout_p if self.training else 0.0,
            is_causal=True,
            scale=None  # Uses default scale of 1/sqrt(head_dim)
        )  # (batch, num_heads, seq_len, head_dim)
        
        # Transpose back and reshape
        out = out.transpose(1, 2).contiguous()  # (batch, seq_len, num_heads, head_dim)
        out = out.reshape(batch_size, seq_len, self.hidden_dim)
        
        # Output projection
        out = self.out_proj(out)
        
        return out


class SwiGLU(nn.Module):
    """Swish Gated Linear Unit activation."""
    
    def __init__(self, in_dim: int, out_dim: int, dropout: float = 0.0):
        """
        Initialize SwiGLU.
        
        Args:
            in_dim: Input dimension
            out_dim: Intermediate/hidden dimension
            dropout: Dropout rate
        """
        super().__init__()
        # Project to 2x the intermediate dimension (for value and gate)
        self.fc1 = nn.Linear(in_dim, 2 * out_dim)
        self.fc2 = nn.Linear(out_dim, in_dim)  # Project back to input dimension
        self.dropout = nn.Dropout(dropout)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass with SwiGLU activation.
        
        Args:
            x: Input tensor
        
        Returns:
            Gated activation output (same dimension as input)
        """
        # Project to 2x intermediate dimension
        x = self.fc1(x)
        
        # Split into value and gate
        x, gate = x.chunk(2, dim=-1)
        
        # SwiGLU: value * swish(gate) = value * gate * sigmoid(gate)
        x = x * F.silu(gate)  # SiLU is Swish: x * sigmoid(x)
        
        x = self.dropout(x)
        x = self.fc2(x)  # Project back to input dimension
        
        return x


class FeedForward(nn.Module):
    """Feed-forward network with SwiGLU activation."""
    
    def __init__(self, config: ModelConfig):
        """Initialize FFN with SwiGLU."""
        super().__init__()
        self.swiglu = SwiGLU(
            in_dim=config.hidden_dim,
            out_dim=config.intermediate_dim,
            dropout=config.dropout
        )
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass with SwiGLU activation."""
        return self.swiglu(x)


class TransformerBlock(nn.Module):
    """Single transformer block with attention and FFN."""
    
    def __init__(self, config: ModelConfig):
        """Initialize transformer block."""
        super().__init__()
        self.norm1 = nn.LayerNorm(config.hidden_dim)
        self.attn = Attention(config)
        self.norm2 = nn.LayerNorm(config.hidden_dim)
        self.ffn = FeedForward(config)
    
    def forward(
        self,
        x: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Forward pass with pre-norm residual connections."""
        # Attention with residual
        x = x + self.attn(self.norm1(x), attention_mask=attention_mask)
        
        # FFN with residual
        x = x + self.ffn(self.norm2(x))
        
        return x


# ============================================================================
# Transformer LM
# ============================================================================


@register_architecture("transformer")
class TransformerLM(BaseModel):
    """Standard Transformer language model."""
    
    def __init__(self, config: ModelConfig):
        """Initialize Transformer LM."""
        super().__init__(config)
        
        # Embeddings
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_dim)
        self.pos_embed = PositionalEmbedding(config.hidden_dim, max_seq_length=config.max_seq_length)
        self.dropout = nn.Dropout(config.dropout)
        
        # Transformer blocks
        self.blocks = nn.ModuleList([
            TransformerBlock(config) for _ in range(config.num_layers)
        ])
        
        # Final layer norm
        self.final_norm = nn.LayerNorm(config.hidden_dim)
        
        # Output projection (shared with input embeddings for efficiency)
        self.lm_head = nn.Linear(config.hidden_dim, config.vocab_size, bias=False)
        
        # Weight tying (optional)
        self.lm_head.weight = self.embed_tokens.weight
        
        # Initialize weights
        self._init_weights()
    
    def _init_weights(self):
        """Initialize model weights."""
        for module in self.modules():
            if isinstance(module, nn.Linear):
                nn.init.normal_(module.weight, std=self.config.init_std)
                if module.bias is not None:
                    nn.init.zeros_(module.bias)
            elif isinstance(module, nn.Embedding):
                nn.init.normal_(module.weight, std=self.config.init_std)
    
    def forward(
        self,
        input_ids: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
    ) -> dict[str, torch.Tensor]:
        """
        Forward pass.
        
        Args:
            input_ids: (batch_size, seq_len)
            attention_mask: (batch_size, seq_len)
            labels: (batch_size, seq_len) for loss computation
        
        Returns:
            Dict with 'logits' and optionally 'loss'
        """
        batch_size, seq_len = input_ids.shape
        
        # Embedding
        x = self.embed_tokens(input_ids)
        
        # Add positional embeddings
        x = self.pos_embed(x)
        
        x = self.dropout(x)
        
        # Transformer blocks
        for block in self.blocks:
            x = block(x, attention_mask=attention_mask)
        
        # Final normalization
        x = self.final_norm(x)
        
        # LM head
        logits = self.lm_head(x)  # (batch, seq_len, vocab_size)
        
        # Loss computation
        loss = None
        if labels is not None:
            # Flatten for loss computation
            logits_flat = logits.view(-1, logits.size(-1))  # (batch * seq_len, vocab_size)
            labels_flat = labels.view(-1)
            
            # Only compute loss on valid targets (ignore -100 tokens)
            loss = F.cross_entropy(
                logits_flat,
                labels_flat,
                reduction='mean',
                ignore_index=-100
            )
        
        return {
            'logits': logits,
            'loss': loss,
        }