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


## Cargamos el modelo desde el Hub de Hugging Face
def carga_modelo(repo_id, token):
    return AutoModel.from_pretrained(repo_id, use_auth_token=token)


# def carga_modelo(model_name='ceyda/butterfly_cropped_uniq1K_512', model_version=None):
#     gan = LightweightGAN.from_pretrained(model_name, version=model_version)
#     gan.eval()
#     return gan

def genera(gan, batch_size=1):
    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).deatch().cpu().numpy().astype(np.unit8)
    return ims