"""Mixture of Rookies 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, Optional

logger = logging.getLogger(__name__)

class MixtureOfRookies(nn.Module):
    """Capa de optimización con mezcla de modelos simples."""
    
    hidden_size: int
    threshold: float = 0.7
    sparsity: float = 0.5
    dropout_rate: float = 0.1
    
    def setup(self):
        """Initialize model parameters."""
        # Capas de proyección
        self.proj_in = nn.Dense(self.hidden_size)
        self.proj_out = nn.Dense(self.hidden_size)
        
        # Parámetros de mezcla
        self.alpha = self.param(
            'alpha',
            nn.initializers.ones,
            (self.hidden_size,)
        )
        self.beta = self.param(
            'beta',
            nn.initializers.normal(0.02),
            (self.hidden_size,)
        )
        
        # Capas auxiliares
        self.norm = nn.LayerNorm()
        self.dropout = nn.Dropout(rate=self.dropout_rate)
    
    def _apply_sparsity(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Apply sparsity mask."""
        # Generar máscara
        if training:
            mask = jax.random.bernoulli(
                self.make_rng('dropout'),
                1 - self.sparsity,
                x.shape
            )
            x = x * mask
        
        # Umbralización
        x = jnp.where(
            jnp.abs(x) > self.threshold,
            x,
            jnp.zeros_like(x)
        )
        
        return x
    
    def _mix_predictions(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Mix predictions from different models."""
        # Proyección inicial
        x = self.proj_in(x)
        x = self.norm(x)
        if training:
            x = self.dropout(x, deterministic=not training)
        
        # Aplicar sparsidad
        x_sparse = self._apply_sparsity(x, training)
        
        # Mezcla ponderada
        weights = jax.nn.sigmoid(self.alpha)
        x = weights * x + (1 - weights) * x_sparse
        
        # Proyección final
        x = self.proj_out(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}"
                )
            
            # Mezclar predicciones
            return self._mix_predictions(x, training)
            
        except Exception as e:
            logger.error(f"Error in forward pass: {e}")
            raise
