from typing import  Dict, List, Any
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
import base64
from io import BytesIO


# set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

if device.type != 'cuda':
    raise ValueError("need to run on GPU")

class EndpointHandler():
    def __init__(self, path=""):
        # load the optimized model
        self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
        self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
        self.pipe = self.pipe.to(device)

    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        """
        Args:
            data (:obj:):
                includes the input data and the parameters for the inference.
        Return:
            A :obj:`dict`:. base64 encoded image
        """
        inputs = data.pop("inputs", data)

        # hyperparamters
        num_inference_steps = data.pop("num_inference_steps", 25)
        guidance_scale = data.pop("guidance_scale", 7.5)
        negative_prompt = data.pop("negative_prompt", None)
        height = data.pop("height", None)
        width = data.pop("width", None)
        manual_seed = data.pop("manual_seed", -1)

        generator = torch.Generator(device).manual_seed(manual_seed)

        # run inference pipeline
        with autocast(device.type):
            image = self.pipe(inputs,
                        generator=generator,
                        num_inference_steps=num_inference_steps,
                        guidance_scale=guidance_scale,
                        num_images_per_prompt=1,
                        negative_prompt=negative_prompt,
                        height=height,
                        width=width).images[0]  
            
        # encode image as base 64
        buffered = BytesIO()
        image.save(buffered, format="JPEG")
        img_str = base64.b64encode(buffered.getvalue())

        # postprocess the prediction
        return {"image": img_str.decode()}