"""BitNet quantizer 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

logger = logging.getLogger(__name__)

class BitNetQuantizer(nn.Module):
    """Cuantizador de bits para reducir precisión."""
    
    bit_width: int
    symmetric: bool = True
    eps: float = 1e-5
    
    def setup(self):
        """Validate parameters."""
        if not isinstance(self.bit_width, int) or self.bit_width < 2:
            raise ValueError("bit_width debe ser entero >= 2")
    
    def _get_scale_params(
        self,
        x: jnp.ndarray
    ) -> Dict[str, jnp.ndarray]:
        """Calculate quantization parameters."""
        if self.symmetric:
            max_val = jnp.max(jnp.abs(x))
            min_val = -max_val
            zero_point = jnp.array(0.0)
        else:
            max_val = jnp.max(x)
            min_val = jnp.min(x)
            scale = (max_val - min_val) / (2**self.bit_width - 1 + self.eps)
            zero_point = -min_val / scale
        
        scale = (max_val - min_val) / (2**self.bit_width - 1 + self.eps)
        
        return {
            'scale': scale,
            'zero_point': zero_point,
            'min_val': min_val,
            'max_val': max_val
        }
    
    def _quantize(
        self,
        x: jnp.ndarray,
        params: Dict[str, jnp.ndarray]
    ) -> jnp.ndarray:
        """Quantize values."""
        if self.symmetric:
            # Cuantización simétrica
            x_scaled = x / params['scale']
            x_clipped = jnp.clip(
                jnp.round(x_scaled),
                -2**(self.bit_width - 1),
                2**(self.bit_width - 1) - 1
            )
            return x_clipped * params['scale']
        else:
            # Cuantización asimétrica
            x_scaled = x / params['scale'] + params['zero_point']
            x_clipped = jnp.clip(
                jnp.round(x_scaled),
                0,
                2**self.bit_width - 1
            )
            return (x_clipped - params['zero_point']) * params['scale']
    
    def __call__(
        self,
        x: jnp.ndarray,
        training: bool = False
    ) -> jnp.ndarray:
        """Forward pass."""
        try:
            # Calcular parámetros
            params = self._get_scale_params(x)
            
            # Cuantizar valores
            x_quantized = self._quantize(x, params)
            
            # Straight-through estimator para gradientes
            if training:
                x_quantized = x + jax.lax.stop_gradient(x_quantized - x)
            
            return x_quantized
            
        except Exception as e:
            logger.error(f"Error in quantization: {e}")
            raise
