from typing import Dict, List, Any
from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution, Swin2SRModel
import torch
import base64
import logging
import numpy as np
import gc
from PIL import Image
from io import BytesIO
import subprocess


logger = logging.getLogger()
logger.setLevel(logging.DEBUG)


class EndpointHandler:
    def __init__(self, path=""):
        # load the model
        self.processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
        Swin2SRModel._no_split_modules = ["Swin2SREmbeddings", "Swin2SRStage"]
        Swin2SRForImageSuperResolution._no_split_modules = ["Swin2SREmbeddings", "Swin2SRStage"]
        model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64", device_map="auto")
        logger.info(model.hf_device_map)
        model.hf_device_map["swin2sr.conv_after_body"] = model.hf_device_map["swin2sr.embeddings"]
        model.hf_device_map["upsample"] = model.hf_device_map["swin2sr.embeddings"]
        self.model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64", device_map=model.hf_device_map)
        print(subprocess.run(["nvidia-smi"]))

    def __call__(self, data: Any):
        image = data["inputs"]
        inputs = self.processor(image, return_tensors="pt")

        try:
            with torch.no_grad():
                outputs = self.model(**inputs)

            print(subprocess.run(["nvidia-smi"]))
            
            output = outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy()
            output = np.moveaxis(output, source=0, destination=-1)
            output = (output * 255.0).round().astype(np.uint8)
    
            img = Image.fromarray(output)
            buffered = BytesIO()
            img.save(buffered, format="JPEG")
            img_str = base64.b64encode(buffered.getvalue())
    
            return img_str.decode()
        
        except Exception as e:
            logger.error(str(e))
            del inputs
            gc.collect()
            torch.cuda.empty_cache()

            return {"error": str(e)}