"""
Module that implements a BitNet quantizer for neural networks using JAX/Flax.

This module provides a quantization layer that can be used
to reduce the precision of weights and activations in a neural network,
resulting in more efficient models in terms of memory and computation.

Classes:
    BitNetQuantizer: Implements BitNet quantization.

Dependencies:
    - jax: For array operations and automatic differentiation.
    - flax: For neural network module definitions.
"""

import jax
import jax.numpy as jnp
from flax import linen as nn
import logging

# Set up logging
logging.basicConfig(level=logging.INFO,
                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)


class BitNetQuantizer(nn.Module):
    """
    A quantization module for BitNet-style quantization.

    This module performs quantization of input arrays to a specified bit width,
    supporting both symmetric and asymmetric quantization, and allowing for
    gradient estimation during backpropagation.

    Attributes:
        bit_width (int): The number of bits to use for quantization.
        symmetric (bool): Whether to use symmetric quantization.
        eps (float): A small value to avoid division by zero.
    """
    bit_width: int

    def quantize(self, x):
        # Lógica de cuantización aquí
        return x  # Placeholder

    @nn.compact
    def __call__(self, x):
        return jax.vmap(self.quantize)(x)

    @staticmethod
    def validate_bit_width(bit_width: int):
        """Validate the bit width."""
        if not isinstance(bit_width, int) or bit_width < 2:
            error_msg = "`bit_width` must be an integer greater than or equal to 2."
            logger.error(error_msg)
            raise ValueError(error_msg)
        logger.debug(f"Bit width {bit_width} is valid")

    def get_quantization_params(self, x: jnp.ndarray) -> dict:
        """
        Get the quantization parameters for a given array.

        This method is useful for analysis and debugging.

        Args:
            x (jnp.ndarray): The input array.

        Returns:
            dict: A dictionary containing quantization parameters.
        """
        logger.debug("Calculating quantization parameters")
        if self.symmetric:
            max_val = jnp.max(jnp.abs(x))
            min_val = -max_val
            zero_point = jnp.zeros_like(max_val)
        else:
            max_val = jnp.max(x)
            min_val = jnp.min(x)
            zero_point = min_val

        scale = (max_val - min_val) / (2**self.bit_width - 1)
        scale = jnp.maximum(scale, self.eps)

        params = {
            "max_val": float(max_val),
            "min_val": float(min_val),
            "scale": float(scale),
            "zero_point": float(zero_point),
            "bit_width": self.bit_width,
            "symmetric": self.symmetric
        }
        logger.info(f"Quantization parameters calculated: {params}")
        return params


# Example usage
if __name__ == "__main__":
    try:
        logger.info("Starting BitNetQuantizer example")

        # Create a sample array
        key = jax.random.PRNGKey(0)
        x = jax.random.normal(key, (5, 5))
        logger.info(f"Created sample array with shape: {x.shape}")

        # Initialize the quantizer
        quantizer = BitNetQuantizer(bit_width=4, symmetric=True)
        logger.info("Initialized BitNetQuantizer")

        # Perform quantization
        x_quantized = quantizer.apply({}, x)
        logger.info("Quantization performed")

        # Get quantization parameters
        params = quantizer.get_quantization_params(x)
        logger.info("Retrieved quantization parameters")

        print("Original array:")
        print(x)
        print("\nQuantized array:")
        print(x_quantized)
        print("\nQuantization parameters:")
        for key, value in params.items():
            print(f"{key}: {value}")

        logger.info("BitNetQuantizer example completed successfully")
    except Exception as e:
        logger.exception(
            f"An error occurred during the BitNetQuantizer example: {str(e)}")
