import torch
import nltk
import io
import base64
import shutil
from torchvision import transforms

from pytorch_pretrained_biggan import BigGAN, one_hot_from_names, truncated_noise_sample

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

    def __call__(self, inputs: str):
        """
        Args:
            inputs (:obj:`str`):
                a string containing some text
        Return:
            A :obj:`PIL.Image` with the raw image representation as PIL.
        """
        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)

        # Scale image
        img = output[0]
        img = (img + 1) / 2.0
        img = transforms.ToPILImage()(img)
        return img