import os
from typing import Dict

import huggingface_hub
import torch
from hpsv2.src.open_clip import create_model_and_transforms, get_tokenizer
from hpsv2.utils import hps_version_map, root_path
from PIL import Image


class HPSMetric:
    def __init__(self):
        self.hps_version = "v2.1"
        self.device = torch.device(
            "cuda"
            if torch.cuda.is_available()
            else "mps"
            if torch.backends.mps.is_available()
            else "cpu"
        )
        self.model_dict = {}
        self._initialize_model()

    def _initialize_model(self):
        if not self.model_dict:
            model, preprocess_train, preprocess_val = create_model_and_transforms(
                "ViT-H-14",
                "laion2B-s32B-b79K",
                precision="amp",
                device=self.device,
                jit=False,
                force_quick_gelu=False,
                force_custom_text=False,
                force_patch_dropout=False,
                force_image_size=None,
                pretrained_image=False,
                image_mean=None,
                image_std=None,
                light_augmentation=True,
                aug_cfg={},
                output_dict=True,
                with_score_predictor=False,
                with_region_predictor=False,
            )
            self.model_dict["model"] = model
            self.model_dict["preprocess_val"] = preprocess_val

            # Load checkpoint
            if not os.path.exists(root_path):
                os.makedirs(root_path)
            cp = huggingface_hub.hf_hub_download(
                "xswu/HPSv2", hps_version_map[self.hps_version]
            )

            checkpoint = torch.load(cp, map_location=self.device)
            model.load_state_dict(checkpoint["state_dict"])
            self.tokenizer = get_tokenizer("ViT-H-14")
            model = model.to(self.device)
            model.eval()

    @property
    def name(self) -> str:
        return "hps"

    def compute_score(
        self,
        image: Image.Image,
        prompt: str,
    ) -> Dict[str, float]:
        model = self.model_dict["model"]
        preprocess_val = self.model_dict["preprocess_val"]

        with torch.no_grad():
            # Process the image
            image_tensor = (
                preprocess_val(image)
                .unsqueeze(0)
                .to(device=self.device, non_blocking=True)
            )
            # Process the prompt
            text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True)
            # Calculate the HPS
            with torch.cuda.amp.autocast():
                outputs = model(image_tensor, text)
                image_features, text_features = (
                    outputs["image_features"],
                    outputs["text_features"],
                )
                logits_per_image = image_features @ text_features.T
                hps_score = torch.diagonal(logits_per_image).cpu().numpy()

        return {"hps": float(hps_score[0])}