from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
import torch
from PIL import Image
from typing import Dict, List, Any

class EndpointHandler():
    def __init__(self, path=""):
        model = VisionEncoderDecoderModel.from_pretrained(
            "nlpconnect/vit-gpt2-image-captioning")
        feature_extractor = ViTImageProcessor.from_pretrained(
            "nlpconnect/vit-gpt2-image-captioning")
        tokenizer = AutoTokenizer.from_pretrained(
            "nlpconnect/vit-gpt2-image-captioning")

        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        model.to(device)
        self.model = model
        self.feature_extractor = feature_extractor
        self.tokenizer = tokenizer

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
       data args:
            inputs (:obj: `str`)
            date (:obj: `str`)
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """
        # get inputs
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        max_length = 128
        num_beams = 4
        gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
        image_paths = data.pop("image_paths", data)
        images = []
        for image_path in image_paths:
            i_image = Image.open(image_path)
            if i_image.mode != "RGB":
                i_image = i_image.convert(mode="RGB")

            images.append(i_image)

        pixel_values = self.feature_extractor(
            images=images, return_tensors="pt").pixel_values
        pixel_values = pixel_values.to(device)

        output_ids = self.model.generate(pixel_values, **gen_kwargs)

        preds = self.tokenizer.batch_decode(
            output_ids, skip_special_tokens=True)
        preds = [pred.strip() for pred in preds]
        return preds