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

import torch
from transformers import AutoModel, AutoProcessor

from .utils import normalize_vectors


MODEL_NAME = "Marqo/marqo-fashionCLIP"


class FashionCLIPEncoder:
    def __init__(self, normalize: bool = False):
        self.normalize = normalize

        self.device = torch.device("cpu")

        self.processor = AutoProcessor.from_pretrained(
            MODEL_NAME,
            trust_remote_code=True,
        )

        self.model = AutoModel.from_pretrained(
            MODEL_NAME,
            trust_remote_code=True,
        )

        self.model.to(self.device)
        self.model.eval()

    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()}
            vectors = self.model.get_text_features(**batch)
                
            return self._postprocess_vectors(vectors)

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

        with torch.no_grad():
            batch = {k: v.to(self.device) for k, v in inputs.items()}
            vectors = self.model.get_image_features(**batch)

            return self._postprocess_vectors(vectors)

    def _postprocess_vectors(self, vectors: torch.Tensor) -> List[List[float]]:
        if self.normalize:
            vectors = normalize_vectors(vectors)

        return vectors.detach().cpu().numpy().tolist()