"""Game theory 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 Tuple, Optional

logger = logging.getLogger(__name__)

class GameTheory(nn.Module):
    """Capa de teoría de juegos para aprendizaje estratégico."""
    
    hidden_size: int
    num_players: int = 2
    dropout_rate: float = 0.1
    
    def setup(self):
        """Initialize model parameters."""
        # Capas de estrategia
        self.strategy = nn.Dense(self.hidden_size)
        self.payoff = nn.Dense(self.hidden_size)
        self.equilibrium = nn.Dense(self.hidden_size)
        
        # Capas auxiliares
        self.norm = nn.LayerNorm()
        self.dropout = nn.Dropout(rate=self.dropout_rate)
    
    def _compute_payoffs(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Compute payoff matrix."""
        # Proyección de estrategias
        strategies = self.strategy(x)
        strategies = self.norm(strategies)
        if training:
            strategies = self.dropout(strategies, deterministic=not training)
        
        # Matriz de pagos
        payoffs = self.payoff(strategies)
        payoffs = jax.nn.softmax(payoffs, axis=-1)
        
        return payoffs
    
    def _find_equilibrium(
        self,
        payoffs: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Find Nash equilibrium."""
        # Proyección de equilibrio
        equilibrium = self.equilibrium(payoffs)
        equilibrium = self.norm(equilibrium)
        if training:
            equilibrium = self.dropout(equilibrium, deterministic=not training)
        
        # Normalización
        equilibrium = jax.nn.softmax(equilibrium, axis=-1)
        
        return equilibrium
    
    def __call__(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> Tuple[jnp.ndarray, 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}"
                )
            
            # Calcular pagos
            payoffs = self._compute_payoffs(x, training)
            
            # Encontrar equilibrio
            equilibrium = self._find_equilibrium(payoffs, training)
            
            return payoffs, equilibrium
            
        except Exception as e:
            logger.error(f"Error in forward pass: {e}")
            raise
