from typing import  Dict, List, Any
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline, AutoPipelineForInpainting, AutoPipelineForImage2Image, StableDiffusionXLImg2ImgPipeline
from PIL import Image
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=""):

        #self.fast_pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16").to("cuda")
        #self.generator = torch.Generator(device="cuda").manual_seed(0)


        # self.smooth_pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
        #     "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
        # )
        # self.smooth_pipe.to("cuda")

        
        # load StableDiffusionInpaintPipeline pipeline
        self.pipe = AutoPipelineForInpainting.from_pretrained(
            "kandinsky-community/kandinsky-2-2-decoder-inpaint",
            torch_dtype=torch.float16,
        )
        # use DPMSolverMultistepScheduler
        # self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)

        self.pipe.enable_model_cpu_offload()

        self.pipe.enable_xformers_memory_efficient_attention()
        
        # move to device
        self.pipe = self.pipe.to(device)

        # self.pipe2 = AutoPipelineForInpainting.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
        # self.pipe2.to("cuda")

        # self.pipe3 = AutoPipelineForImage2Image.from_pipe(self.pipe2)
        


    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        """
        :param data: A dictionary contains `inputs` and optional `image` field.
        :return: A dictionary with `image` field contains image in base64.
        """
        encoded_image = data.pop("image", None)
        encoded_mask_image = data.pop("mask_image", None)
        
        prompt = data.pop("prompt", "")

        negative_prompt = data.pop("negative_prompt", "")

        method = data.pop("method", "slow")
        strength = data.pop("strength", 0.2)
        guidance_scale = data.pop("guidance_scale", 8.0)
        num_inference_steps = data.pop("num_inference_steps", 20)
        """
        if(method == "smooth"):
            if encoded_image is not None:
                image = self.decode_base64_image(encoded_image)
                out = self.smooth_pipe(prompt, image=image).images[0]
    
                return out
        """
        
        # process image
        if encoded_image is not None and encoded_mask_image is not None:
            image = self.decode_base64_image(encoded_image).convert("RGB")
            mask_image = self.decode_base64_image(encoded_mask_image).convert("RGB")
        else:
            image = None
            mask_image = None 
        """
        if(method == "fast"):
            image = self.fast_pipe(
                prompt=prompt,
                negative_prompt=negative_prompt,
                image=image,
                mask_image=mask_image,
                guidance_scale=guidance_scale,
                num_inference_steps=num_inference_steps,  # steps between 15 and 30 work well for us
                strength=strength,  # make sure to use `strength` below 1.0
                generator=self.generator,
            ).images[0]
    
            return image
        """

        #pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16").to("cuda")
        
        # run inference pipeline
        out = self.pipe(prompt=prompt, negative_prompt=negative_prompt, image=image, mask_image=mask_image)

        print("1st pipeline part successful!")

        image = out.images[0].resize((1024, 1024))

        print("image resizing successful!")
"""
        self.pipe2.enable_xformers_memory_efficient_attention()

        image = self.pipe2(
            prompt=prompt,
            negative_prompt=negative_prompt,
            image=image,
            mask_image=mask_image,
            guidance_scale=guidance_scale, #8.0
            num_inference_steps=num_inference_steps, #100
            strength=strength, #0.2
            output_type="latent",  # let's keep in latent to save some VRAM
        ).images[0]

        print("2nd pipeline part successful!")
        
        self.pipe3.enable_xformers_memory_efficient_attention()
        
        image2 = self.pipe3(
            prompt=prompt,
            image=image,
            guidance_scale=guidance_scale, #8.0
            num_inference_steps=num_inference_steps, #100
            strength=strength, #0.2
        ).images[0]

        print("3rd pipeline part successful!")
        """
            
        # return first generate PIL image
        return image
    
    # helper to decode input image
    def decode_base64_image(self, image_string):
        base64_image = base64.b64decode(image_string)
        buffer = BytesIO(base64_image)
        image = Image.open(buffer)
        return image