from typing import List, Dict
from PIL.Image import Image
import os

import torch
from transformers import AutoModel, AutoProcessor


MODEL_NAME = "Marqo/marqo-fashionCLIP"
HF_TOKEN = os.environ.get("HF_TOKEN")


class FashionCLIPEncoder:
    def __init__(self):
        self.device = torch.device("cpu")
        
        self.processor = AutoProcessor.from_pretrained(
            MODEL_NAME, 
            trust_remote_code=True,
            token=HF_TOKEN
        )
        
        try:
            self.model = AutoModel.from_pretrained(
                MODEL_NAME,
                trust_remote_code=True,
                device_map=None,
                token=HF_TOKEN
            )
            self.model = self.model.to(self.device)
            self.model.eval()
            
        except Exception as e:
            print(f"Error initializing model: {str(e)}")
            raise

    def encode_text(self, texts: List[str]) -> List[List[float]]:
        kwargs = {
            "padding": "max_length",
            "return_tensors": "pt",
            "truncation": True,
        }
        inputs = self.processor(text=texts, **kwargs)

        with torch.no_grad():
            batch = {k: v.to(self.device) for k, v in inputs.items()}
            return self._encode_text(batch)

    def encode_images(self, images: List[Image]) -> List[List[float]]:
        kwargs = {
            "return_tensors": "pt",
        }
        inputs = self.processor(images=images, **kwargs)

        with torch.no_grad():
            batch = {k: v.to(self.device) for k, v in inputs.items()}
            return self._encode_images(batch)

    def _encode_text(self, batch: Dict) -> List[List[float]]:
        return self.model.get_text_features(**batch).detach().cpu().numpy().tolist()

    def _encode_images(self, batch: Dict) -> List[List[float]]:
        return self.model.get_image_features(**batch).detach().cpu().numpy().tolist()