import numpy as np
import torch
from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN


def carga_modelo(model_name="ceyda/butterfly_cropped_uniq1K_512", model_version=None):
    """
    Loads a pre-trained LightweightGAN model from Hugging Face Model Hub.

    Args:
        model_name (str): The name of the pre-trained model to load. Defaults to "ceyda/butterfly_cropped_uniq1K_512".
        model_version (str): The version of the pre-trained model to load. Defaults to None.

    Returns:
        LightweightGAN: The loaded pre-trained model.
    """
    gan = LightweightGAN.from_pretrained(model_name, version=model_version)
    gan.eval()
    return gan


def genera(gan, batch_size=1):
    """
    Generates images using the given GAN model.

    Args:
        gan (nn.Module): The GAN model to use for generating images.
        batch_size (int, optional): The number of images to generate in each batch. Defaults to 1.

    Returns:
        numpy.ndarray: A numpy array of generated images.
    """
    with torch.no_grad():
        ims = gan.G(torch.randn(batch_size, gan.latent_dim)).clamp_(0.0, 1.0) * 255
        ims = ims.permute(0, 2, 3, 1).detach().cpu().numpy().astype(np.uint8)
    return ims