"""Spiking Neural Network 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 NamedTuple, Tuple, Optional

logger = logging.getLogger(__name__)

class LIFState(NamedTuple):
    """Estado de neurona LIF."""
    voltage: jnp.ndarray
    spikes: jnp.ndarray
    threshold: jnp.ndarray

class SNNSLiCell(nn.Module):
    """Red neuronal con neuronas LIF."""
    
    hidden_size: int
    tau_m: float = 20.0  # Constante de tiempo de membrana
    v_rest: float = -65.0  # Potencial de reposo
    v_reset: float = -70.0  # Potencial de reset
    v_threshold: float = -50.0  # Umbral base
    dropout_rate: float = 0.1
    
    def setup(self):
        """Initialize model parameters."""
        # Capas de procesamiento
        self.input_proj = nn.Dense(self.hidden_size)
        self.recurrent = 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 _lif_step(
        self,
        x: jnp.ndarray,
        state: LIFState,
        training: bool = False
    ) -> Tuple[jnp.ndarray, LIFState]:
        """Single LIF neuron step."""
        # Procesar entrada
        input_current = self.input_proj(x)
        if training:
            input_current = self.dropout(
                input_current,
                deterministic=not training
            )
        
        # Actualizar voltaje
        dv = (
            -(state.voltage - self.v_rest) + input_current
        ) / self.tau_m
        
        new_v = state.voltage + dv
        
        # Generar spikes
        spikes = (new_v >= state.threshold).astype(jnp.float32)
        
        # Reset post-spike
        new_v = jnp.where(spikes > 0, self.v_reset, new_v)
        
        # Actualizar umbral
        new_threshold = (
            state.threshold + 
            0.1 * spikes - 
            (state.threshold - self.v_threshold) / self.tau_m
        )
        
        return spikes, LIFState(new_v, spikes, new_threshold)
    
    def __call__(
        self,
        x: jnp.ndarray,
        initial_state: Optional[LIFState] = None,
        training: bool = False
    ) -> Tuple[jnp.ndarray, LIFState]:
        """Forward pass."""
        try:
            # Validar entrada
            if x.ndim != 3:
                raise ValueError(
                    f"Expected 3D input (batch, seq_len, dim), got shape {x.shape}"
                )
            
            batch_size = x.shape[0]
            
            # Inicializar estado si es necesario
            if initial_state is None:
                initial_state = LIFState(
                    voltage=jnp.full(
                        (batch_size, self.hidden_size),
                        self.v_rest
                    ),
                    spikes=jnp.zeros((batch_size, self.hidden_size)),
                    threshold=jnp.full(
                        (batch_size, self.hidden_size),
                        self.v_threshold
                    )
                )
            
            # Procesar secuencia
            def scan_fn(carry, x_t):
                state = carry
                spikes, new_state = self._lif_step(x_t, state, training)
                return new_state, spikes
            
            # Scan sobre la secuencia
            final_state, outputs = jax.lax.scan(
                scan_fn,
                initial_state,
                x.transpose(1, 0, 2)  # (seq_len, batch, dim)
            )
            
            # Procesar salida
            outputs = outputs.transpose(1, 0, 2)  # (batch, seq_len, dim)
            outputs = self.output(outputs)
            
            return outputs, final_state
            
        except Exception as e:
            logger.error(f"Error in forward pass: {e}")
            raise
