# Copyright 2025 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

from typing import Dict, List, Optional, Union

from sglang.srt.managers.multimodal_processor import (
    BaseMultimodalProcessor as SGLangBaseProcessor,
)
from sglang.srt.managers.schedule_batch import MultimodalProcessorOutput
from sglang.srt.models.gemma3n_mm import Gemma3nForConditionalGeneration
from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens


class Gemma3nSGLangProcessor(SGLangBaseProcessor):
    """Multimodal processor for Gemma3n supporting image and audio inputs."""

    models = [Gemma3nForConditionalGeneration]

    def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
        super().__init__(hf_config, server_args, _processor, *args, **kwargs)

        self.IM_START_TOKEN_ID = hf_config.boi_token_id
        self.IM_END_TOKEN_ID = hf_config.eoi_token_id

        self.AUDIO_START_TOKEN_ID = hf_config.boa_token_id
        self.AUDIO_END_TOKEN_ID = hf_config.eoa_token_id
        self.mm_tokens = MultimodalSpecialTokens(
            image_token="<image_soft_token>",
            image_token_id=hf_config.image_token_id,
            audio_token="<audio_soft_token>",
            audio_token_id=hf_config.audio_token_id,
        ).build(_processor)

    async def process_mm_data_async(
        self,
        image_data: Optional[List[Union[str, bytes, Dict]]] = None,
        audio_data: Optional[List[Union[str, bytes, Dict]]] = None,
        input_text: str = "",
        request_obj=None,
        *args,
        **kwargs,
    ):
        """Process multimodal data including images and audio."""
        base_output = await self.load_mm_data(
            prompt=input_text,
            image_data=image_data,
            audio_data=audio_data,
            multimodal_tokens=self.mm_tokens,
        )

        mm_items, input_ids, _ = self.process_and_combine_mm_data(
            base_output, self.mm_tokens
        )

        return MultimodalProcessorOutput(
            input_ids=input_ids.tolist(),
            mm_items=mm_items,
            im_token_id=self.mm_tokens.image_token_id,
            audio_token_id=self.mm_tokens.audio_token_id,
        )