import torch
from transformers import PretrainedConfig, SiglipImageProcessor, SiglipVisionModel

from llava.model.multimodal_encoder.vision_encoder import VisionTower, VisionTowerS2


class SiglipVisionTower(VisionTower):
    def __init__(self, model_name_or_path: str, config: PretrainedConfig, state_dict=None):
        super().__init__(model_name_or_path, config)
        self.image_processor = SiglipImageProcessor.from_pretrained(model_name_or_path)
        self.vision_tower = SiglipVisionModel.from_pretrained(
            # TODO(ligeng): why pass config here leading to errors?
            model_name_or_path,
            torch_dtype=eval(config.model_dtype),
            state_dict=state_dict,
        )
        self.is_loaded = True


class SiglipVisionTowerS2(VisionTowerS2):
    def __init__(self, model_name_or_path: str, config: PretrainedConfig):
        super().__init__(model_name_or_path, config)
        self.image_processor = SiglipImageProcessor.from_pretrained(model_name_or_path)
        self.vision_tower = SiglipVisionModel.from_pretrained(model_name_or_path, torch_dtype=eval(config.model_dtype))

        # Make sure it crops/resizes the image to the largest scale in self.scales to maintain high-res information
        self.image_processor.size["height"] = self.image_processor.size["width"] = self.scales[-1]

        self.is_loaded = True