from typing import List, Union

from sglang.srt.layers.rotary_embedding import MRotaryEmbedding
from sglang.srt.managers.schedule_batch import MultimodalProcessorOutput
from sglang.srt.models.glm4v import Glm4vForConditionalGeneration
from sglang.srt.models.glm4v_moe import Glm4vMoeForConditionalGeneration
from sglang.srt.multimodal.processors.base_processor import (
    BaseMultimodalProcessor as SGLangBaseProcessor,
)
from sglang.srt.multimodal.processors.base_processor import (
    MultimodalSpecialTokens,
)

try:
    from sglang.srt.models.glm_ocr import GlmOcrForConditionalGeneration
except ImportError:
    GlmOcrForConditionalGeneration = None


class Glm4vImageProcessor(SGLangBaseProcessor):
    models = [
        m
        for m in [
            Glm4vForConditionalGeneration,
            Glm4vMoeForConditionalGeneration,
            GlmOcrForConditionalGeneration,
        ]
        if m is not None
    ]

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

        # GLM-V specific tokens
        self.IMAGE_TOKEN = "<|image|>"
        self.VIDEO_TOKEN = "<|video|>"
        self.IMAGE_START_TOKEN = "<|begin_of_image|>"
        self.IMAGE_END_TOKEN = "<|end_of_image|>"
        self.VIDEO_START_TOKEN = "<|begin_of_video|>"
        self.VIDEO_END_TOKEN = "<|end_of_video|>"

        # Token IDs
        self.IM_TOKEN_ID = hf_config.image_token_id
        self.VIDEO_TOKEN_ID = hf_config.video_token_id
        self.IMAGE_START_TOKEN_ID = hf_config.image_start_token_id
        self.IMAGE_END_TOKEN_ID = hf_config.image_end_token_id
        self.VIDEO_START_TOKEN_ID = hf_config.video_start_token_id
        self.VIDEO_END_TOKEN_ID = hf_config.video_end_token_id

        # Vision config
        self.IMAGE_FACTOR = 28
        self.MIN_PIXELS = 112 * 112
        self.MAX_PIXELS = 30000 * 28 * 28 * 2

        self.mm_tokens = MultimodalSpecialTokens(
            image_token=self.IMAGE_TOKEN,
            image_token_id=self.IM_TOKEN_ID,
            video_token=self.VIDEO_TOKEN,
            # Note: For GLM4v videos, it uses the video token before tokenization but uses image token after tokenization
            video_token_id=self.IM_TOKEN_ID,
        ).build(_processor)

    def compute_mrope_positions(self, input_ids, mm_items):
        image_grid_thw = None
        video_grid_thw = None
        for item in mm_items:
            if "image_grid_thw" in item.model_specific_data:
                image_grid_thw = item.model_specific_data["image_grid_thw"]
            if "video_grid_thw" in item.model_specific_data:
                video_grid_thw = item.model_specific_data["video_grid_thw"]

        import torch

        input_ids_tensor = torch.tensor(input_ids, dtype=torch.long).unsqueeze(0)
        attention_mask = torch.ones_like(input_ids_tensor)
        mrope_positions, mrope_position_delta = MRotaryEmbedding.get_rope_index_glm4v(
            input_ids=input_ids_tensor,
            hf_config=self.hf_config,
            image_grid_thw=image_grid_thw,
            video_grid_thw=video_grid_thw,
            attention_mask=attention_mask,
        )
        return mrope_positions.squeeze(1), mrope_position_delta

    async def process_mm_data_async(
        self,
        image_data: List[Union[str, bytes]],
        input_text,
        request_obj,
        *args,
        **kwargs,
    ):
        base_output = self.load_mm_data(
            prompt=input_text,
            image_data=image_data,
            video_data=request_obj.video_data,
            multimodal_tokens=self.mm_tokens,
        )

        if base_output.videos:
            base_output.videos = request_obj.video_data
        mm_items, input_ids, ret = self.process_and_combine_mm_data(
            base_output, self.mm_tokens
        )

        input_ids = input_ids.flatten()
        mrope_positions, mrope_position_delta = MRotaryEmbedding.get_rope_index_glm4v(
            input_ids=input_ids.unsqueeze(0),
            hf_config=self.hf_config,
            image_grid_thw=getattr(ret, "image_grid_thw", None),
            video_grid_thw=getattr(ret, "video_grid_thw", None),
            attention_mask=getattr(ret, "attention_mask", None),
        )
        mrope_positions = mrope_positions.squeeze(1)

        return MultimodalProcessorOutput(
            input_ids=input_ids.tolist(),
            mm_items=mm_items,
            im_token_id=self.mm_tokens.image_token_id,
            video_token_id=self.mm_tokens.video_token_id,
            mrope_positions=mrope_positions,
            mrope_position_delta=mrope_position_delta,
        )
