"""Aleph-TILDE 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 Tuple, Optional

logger = logging.getLogger(__name__)

class AlephTilde(nn.Module):
    """Neural implementation of Aleph-TILDE algorithm."""
    
    hidden_size: int
    dropout_rate: float = 0.1
    
    def setup(self):
        """Initialize model parameters."""
        # Capas de transformación
        self.hypothesis = nn.Dense(self.hidden_size)
        self.background = nn.Dense(self.hidden_size)
        self.rules = nn.Dense(self.hidden_size)
        
        # Capas de integración
        self.combine = nn.Dense(self.hidden_size)
        self.output = nn.Dense(self.hidden_size)
        
        # Capas auxiliares
        self.norm = nn.LayerNorm()
        self.dropout = nn.Dropout(rate=self.dropout_rate)
    
    def _generate_hypothesis(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Generate hypothesis from input."""
        x = self.hypothesis(x)
        x = self.norm(x)
        if training:
            x = self.dropout(x, deterministic=not training)
        return jax.nn.gelu(x)
    
    def _apply_background(
        self,
        x: jnp.ndarray,
        h: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Apply background knowledge."""
        b = self.background(x)
        b = self.norm(b)
        if training:
            b = self.dropout(b, deterministic=not training)
            
        # Combinar hipótesis y background
        combined = self.combine(jnp.concatenate([h, b], axis=-1))
        return jax.nn.gelu(combined)
    
    def _induce_rules(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Induce rules from combined knowledge."""
        x = self.rules(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
            hypothesis = self._generate_hypothesis(x, training)
            background = self._apply_background(x, hypothesis, training)
            rules = self._induce_rules(background, training)
            output = self.output(rules)
            
            return output
            
        except Exception as e:
            logger.error(f"Error in forward pass: {e}")
            raise
