"""Ethics module 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

from capibara_model.modules.contextual_activation import ContextualActivation

logger = logging.getLogger(__name__)

class EthicsModule(nn.Module):
    """Módulo de evaluación ética con conceptos platónicos."""
    
    hidden_size: int
    num_heads: int = 8
    threshold: float = 0.5
    dropout_rate: float = 0.1
    
    def setup(self):
        """Initialize model parameters."""
        # Capas de codificación
        self.text_encoder = nn.Dense(self.hidden_size)
        self.concept_encoder = nn.Dense(self.hidden_size)
        
        # Capas de evaluación
        self.ethics = nn.Dense(self.hidden_size)
        self.score = nn.Dense(1)
        
        # Activación contextual
        self.activation = ContextualActivation(
            hidden_size=self.hidden_size,
            num_heads=self.num_heads,
            threshold=self.threshold,
            dropout_rate=self.dropout_rate
        )
        
        # Capas auxiliares
        self.norm = nn.LayerNorm()
        self.dropout = nn.Dropout(rate=self.dropout_rate)
    
    def _evaluate_ethics(
        self,
        x: jnp.ndarray,
        concepts: jnp.ndarray,
        training: bool = False
    ) -> Dict[str, jnp.ndarray]:
        """Evaluate ethical alignment."""
        # Codificar entradas
        x_encoded = self.text_encoder(x)
        c_encoded = self.concept_encoder(concepts)
        
        # Normalización y dropout
        x_encoded = self.norm(x_encoded)
        c_encoded = self.norm(c_encoded)
        if training:
            x_encoded = self.dropout(x_encoded, deterministic=not training)
            c_encoded = self.dropout(c_encoded, deterministic=not training)
        
        # Activación contextual
        activation = self.activation(x_encoded, c_encoded, training)
        
        # Evaluación ética
        combined = jnp.concatenate([
            x_encoded,
            c_encoded,
            x_encoded * c_encoded
        ], axis=-1)
        
        ethics = self.ethics(combined)
        ethics = self.norm(ethics)
        if training:
            ethics = self.dropout(ethics, deterministic=not training)
        ethics = jax.nn.gelu(ethics)
        
        scores = jax.nn.sigmoid(self.score(ethics))
        
        return {
            'scores': scores,
            'is_ethical': scores > self.threshold,
            'activation': activation['is_active']
        }
    
    def __call__(
        self,
        x: jnp.ndarray,
        concepts: jnp.ndarray,
        training: bool = False
    ) -> Dict[str, jnp.ndarray]:
        """Forward pass."""
        try:
            # Validar entrada
            if x.ndim != 3 or concepts.ndim != 3:
                raise ValueError(
                    f"Expected 3D inputs, got shapes {x.shape}, {concepts.shape}"
                )
            
            # Evaluar ética
            return self._evaluate_ethics(x, concepts, training)
            
        except Exception as e:
            logger.error(f"Error in ethics evaluation: {e}")
            raise
