"""Quineana 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 Dict, List, Tuple

logger = logging.getLogger(__name__)

class Quineana(nn.Module):
    """Capa para evaluación epistémica y pragmática."""
    
    hidden_size: int
    num_concepts: int = 8
    threshold: float = 0.6
    dropout_rate: float = 0.1
    
    def setup(self):
        """Initialize model parameters."""
        # Capas de codificación
        self.encoder = nn.Dense(self.hidden_size)
        self.context_proj = nn.Dense(self.hidden_size)
        self.knowledge_proj = nn.Dense(self.hidden_size)
        
        # Base de conocimiento
        self.knowledge_base = self.param(
            'knowledge_base',
            nn.initializers.normal(0.02),
            (self.num_concepts, self.hidden_size)
        )
        
        # Capas auxiliares
        self.norm = nn.LayerNorm()
        self.dropout = nn.Dropout(rate=self.dropout_rate)
    
    def _compute_consistency(
        self,
        x: jnp.ndarray,
        knowledge: jnp.ndarray
    ) -> jnp.ndarray:
        """Compute epistemic consistency."""
        # Normalizar vectores
        x_norm = x / jnp.linalg.norm(x, axis=-1, keepdims=True)
        k_norm = knowledge / jnp.linalg.norm(knowledge, axis=-1, keepdims=True)
        
        # Calcular similitud
        similarities = jnp.einsum('bsd,cd->bsc', x_norm, k_norm)
        
        # Aplicar umbral
        mask = similarities > self.threshold
        return similarities * mask
    
    def _evaluate_relevance(
        self,
        x: jnp.ndarray,
        context: jnp.ndarray
    ) -> jnp.ndarray:
        """Evaluate contextual relevance."""
        # Normalizar vectores
        x_norm = x / jnp.linalg.norm(x, axis=-1, keepdims=True)
        c_norm = context / jnp.linalg.norm(context, axis=-1, keepdims=True)
        
        # Calcular relevancia
        return jnp.einsum('bsd,bsd->bs', x_norm, c_norm)
    
    def __call__(
        self,
        x: jnp.ndarray,
        context: jnp.ndarray,
        training: bool = False
    ) -> Tuple[jnp.ndarray, Dict[str, jnp.ndarray]]:
        """Forward pass."""
        try:
            # Validar entrada
            if x.ndim != 3 or context.ndim != 3:
                raise ValueError(
                    f"Expected 3D inputs, got shapes {x.shape}, {context.shape}"
                )
            
            # Codificar entradas
            encoded = self.encoder(x)
            encoded = self.norm(encoded)
            if training:
                encoded = self.dropout(encoded, deterministic=not training)
            
            context_encoded = self.context_proj(context)
            knowledge = self.knowledge_proj(self.knowledge_base)
            
            # Evaluar consistencia y relevancia
            consistency = self._compute_consistency(encoded, knowledge)
            relevance = self._evaluate_relevance(encoded, context_encoded)
            
            # Combinar resultados
            output = encoded * consistency.mean(axis=-1, keepdims=True)
            output = output * relevance[..., None]
            
            metrics = {
                'consistency': consistency.mean(),
                'relevance': relevance.mean()
            }
            
            return output, metrics
            
        except Exception as e:
            logger.error(f"Error in forward pass: {e}")
            raise
