import torch
import nltk
import io
import base64

from pytorch_pretrained_biggan import BigGAN, one_hot_from_names, truncated_noise_sample

class PreTrainedPipeline():
    def __init__(self, path=""):
        """
        Initialize model
        """
        nltk.download('wordnet')
        self.model = BigGAN.from_pretrained(path)
        self.truncation = 0.1


    def __call__(self, inputs: str) -> str:
        """
        Args:
            inputs (:obj:`str`):
                a string containing some text
        Return:
            A :obj:`np.array`. A np.array containing the image information.
        """
        class_vector = one_hot_from_names([inputs], batch_size=1)
        if type(class_vector) == type(None):
            raise ValueError("Input is not in ImageNet")

        noise_vector = truncated_noise_sample(truncation=self.truncation, batch_size=1)

        noise_vector = torch.from_numpy(noise_vector)
        class_vector = torch.from_numpy(class_vector)

        with torch.no_grad():
            output = self.model(noise_vector, class_vector, self.truncation)

        img = transforms.ToPILImage()(output[0])
        buf = io.BytesIO()
        img.save(buf, format="JPEG")

        return base64.encodebytes(buf.getvalue()).decode('utf-8')