from typing import List, Union

from transformers.utils import logging

from sglang.srt.managers.multimodal_processor import (
    BaseMultimodalProcessor as SGLangBaseProcessor,
)
from sglang.srt.managers.multimodal_processors.base_processor import (
    MultimodalSpecialTokens,
)
from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
from sglang.srt.models.gemma3_mm import Gemma3ForConditionalGeneration

# Copied from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gemma3/image_processing_gemma3_fast.py
# will be removed in the future
logger = logging.get_logger(__name__)


class Gemma3SGLangImageProcessor(SGLangBaseProcessor):
    models = [Gemma3ForConditionalGeneration]

    def __init__(self, hf_config, server_args, _processor):
        super().__init__(hf_config, server_args, _processor)
        self.IMAGE_TOKEN = "<start_of_image>"
        self.IM_START_TOKEN_ID = hf_config.boi_token_index
        self.IM_END_TOKEN_ID = hf_config.eoi_token_index

    async def process_mm_data_async(
        self,
        image_data: List[Union[str, bytes]],
        input_ids,
        request_obj,
        max_req_input_len,
        *args,
        **kwargs,
    ):
        if not image_data:
            return None
        if isinstance(image_data, str):
            image_data = [image_data]

        image_token = self.IMAGE_TOKEN
        base_output = self.load_mm_data(
            prompt=input_ids,
            image_data=image_data,
            multimodal_tokens=MultimodalSpecialTokens(image_token=image_token),
            max_req_input_len=max_req_input_len,
            discard_alpha_channel=True,
        )

        ret = self.process_mm_data(
            input_text=base_output.input_text, images=base_output.images
        )

        items = []
        for i, image in enumerate(base_output.images):
            item = MultimodalDataItem(
                pixel_values=ret["pixel_values"][i],
                modality=Modality.IMAGE,
            )
            items += [item]

        return {
            "mm_items": items,
            "input_ids": ret["input_ids"].flatten().tolist(),
            "im_start_id": self.IM_START_TOKEN_ID,
            "im_end_id": self.IM_END_TOKEN_ID,
        }
