"""Meta BAMDP 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 Dict, Any

logger = logging.getLogger(__name__)

class MetaBAMDP(nn.Module):
    """Meta-learning layer with BAMDP updates."""
    
    hidden_size: int
    dropout_rate: float = 0.1
    
    def setup(self):
        """Initialize model parameters."""
        # Capas principales
        self.encoder = nn.Dense(self.hidden_size)
        self.processor = nn.Dense(self.hidden_size)
        self.decoder = nn.Dense(self.hidden_size)
        
        # Capas auxiliares
        self.norm = nn.LayerNorm()
        self.dropout = nn.Dropout(rate=self.dropout_rate)
    
    def _encode(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Encode inputs."""
        x = self.encoder(x)
        x = self.norm(x)
        if training:
            x = self.dropout(x, deterministic=not training)
        return jax.nn.gelu(x)
    
    def _process(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Process encoded representations."""
        x = self.processor(x)
        x = self.norm(x)
        if training:
            x = self.dropout(x, deterministic=not training)
        return jax.nn.gelu(x)
    
    def _decode(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Decode processed representations."""
        x = self.decoder(x)
        x = self.norm(x)
        if training:
            x = self.dropout(x, deterministic=not training)
        return x
    
    def __call__(
        self,
        x: jnp.ndarray,
        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}"
                )
            
            # Pipeline de procesamiento
            encoded = self._encode(x, training)
            processed = self._process(encoded, training)
            output = self._decode(processed, training)
            
            return output
            
        except Exception as e:
            logger.error(f"Error in forward pass: {e}")
            raise
