"""Synthetic embedding layer implementation for CapibaraModel."""

import jax #type: ignore
import jax.numpy as jnp #type: ignore
from flax import linen as nn #type: ignore
import logging
from typing import Optional, List

from capibara_model.layers.bitnet import BitNet
from capibara_model.layers.sparse_capibara import SparseCapibara
from capibara_model.layers.self_attention import SelfAttention

logger = logging.getLogger(__name__)

class SyntheticEmbedding(nn.Module):
    """Capa de embedding sintético con múltiples componentes."""
    
    hidden_size: int
    num_heads: int = 8
    num_layers: int = 3
    dropout_rate: float = 0.1
    
    def setup(self):
        """Initialize model parameters."""
        # Capas de procesamiento
        self.attention = SelfAttention(
            hidden_size=self.hidden_size,
            num_heads=self.num_heads,
            dropout_rate=self.dropout_rate
        )
        
        # Capas BitNet
        self.bitnets = [
            BitNet(hidden_size=self.hidden_size)
            for _ in range(self.num_layers)
        ]
        
        # Capas Sparse
        self.sparse = SparseCapibara(
            hidden_size=self.hidden_size,
            dropout_rate=self.dropout_rate
        )
        
        # Capas de salida
        self.output = nn.Dense(self.hidden_size)
        
        # Capas auxiliares
        self.norm = nn.LayerNorm()
        self.dropout = nn.Dropout(rate=self.dropout_rate)
    
    def _apply_attention(
        self,
        x: jnp.ndarray,
        mask: Optional[jnp.ndarray] = None,
        training: bool = False
    ) -> jnp.ndarray:
        """Apply attention mechanism."""
        # Residual
        residual = x
        
        # Atención
        x = self.attention(x, mask=mask, training=training)
        
        # Normalización y dropout
        x = self.norm(x)
        if training:
            x = self.dropout(x, deterministic=not training)
        
        return x + residual
    
    def _apply_bitnets(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Apply BitNet layers."""
        # Residual
        residual = x
        
        # Procesar con BitNets
        for bitnet in self.bitnets:
            x = bitnet(x, training=training)
            x = self.norm(x)
            if training:
                x = self.dropout(x, deterministic=not training)
        
        return x + residual
    
    def __call__(
        self,
        x: jnp.ndarray,
        mask: Optional[jnp.ndarray] = None,
        training: bool = False
    ) -> jnp.ndarray:
        """Forward pass."""
        try:
            # Validar entrada
            if x.ndim != 3:
                raise ValueError(
                    f"Expected 3D input (batch, seq_len, dim), got shape {x.shape}"
                )
            
            # Aplicar atención
            x = self._apply_attention(x, mask, training)
            
            # Aplicar BitNets
            x = self._apply_bitnets(x, training)
            
            # Aplicar sparse
            x = self.sparse(x, training=training)
            
            # Proyección final
            x = self.output(x)
            x = self.norm(x)
            if training:
                x = self.dropout(x, deterministic=not training)
            
            return x
            
        except Exception as e:
            logger.error(f"Error in forward pass: {e}")
            raise
