import asyncio
import math
import os
import re
from typing import List, Union

import torch
import torchvision
from PIL import Image
from torchvision.transforms import InterpolationMode

from sglang.srt.layers.rotary_embedding import MRotaryEmbedding
from sglang.srt.models.qwen2_5_vl import Qwen2_5_VLForConditionalGeneration
from sglang.srt.models.qwen2_vl import Qwen2VLForConditionalGeneration
from sglang.srt.models.qwen3_vl import Qwen3VLForConditionalGeneration
from sglang.srt.models.qwen3_vl_moe import Qwen3VLMoeForConditionalGeneration
from sglang.srt.multimodal.processors.base_processor import (
    BaseMultimodalProcessor as SGLangBaseProcessor,
)
from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
from sglang.utils import logger

IMAGE_FACTOR = 28
MIN_PIXELS = 4 * 28 * 28
MAX_PIXELS = 16384 * 28 * 28
MAX_RATIO = 200
VIDEO_TOTAL_PIXELS = int(
    float(os.environ.get("VIDEO_MAX_PIXELS", 128000 * 28 * 28 * 0.9))
)

VIDEO_MIN_PIXELS = 128 * 28 * 28
VIDEO_MAX_PIXELS = 768 * 28 * 28
FRAME_FACTOR = 2
FPS = 2.0
FPS_MIN_FRAMES = 4
FPS_MAX_FRAMES = 768


def smart_resize(
    height: int,
    width: int,
    factor: int = IMAGE_FACTOR,
    min_pixels: int = MIN_PIXELS,
    max_pixels: int = MAX_PIXELS,
) -> tuple[int, int]:
    """
    Rescales the image so that the following conditions are met:

    1. Both dimensions (height and width) are divisible by 'factor'.

    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].

    3. The aspect ratio of the image is maintained as closely as possible.
    """
    if max(height, width) / min(height, width) > MAX_RATIO:
        raise ValueError(
            f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}"
        )
    h_bar = max(factor, round_by_factor(height, factor))
    w_bar = max(factor, round_by_factor(width, factor))
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = floor_by_factor(height / beta, factor)
        w_bar = floor_by_factor(width / beta, factor)
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = ceil_by_factor(height * beta, factor)
        w_bar = ceil_by_factor(width * beta, factor)
    return h_bar, w_bar


def resize_image(
    image,
    min_pixels: int = MIN_PIXELS,
    max_pixels: int = MAX_PIXELS,
    size_factor: int = IMAGE_FACTOR,
) -> Image.Image:
    width, height = image.size
    min_pixels = min_pixels
    max_pixels = max_pixels
    resized_height, resized_width = smart_resize(
        height,
        width,
        factor=size_factor,
        min_pixels=min_pixels,
        max_pixels=max_pixels,
    )
    image = image.resize((resized_width, resized_height))
    return image


def round_by_factor(number: int, factor: int) -> int:
    """Returns the closest integer to 'number' that is divisible by 'factor'."""
    return round(number / factor) * factor


def ceil_by_factor(number: int, factor: int) -> int:
    """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
    return math.ceil(number / factor) * factor


def floor_by_factor(number: int, factor: int) -> int:
    """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
    return math.floor(number / factor) * factor


async def resize_image_async(
    image,
    min_pixels: int = MIN_PIXELS,
    max_pixels: int = MAX_PIXELS,
    size_factor: int = IMAGE_FACTOR,
):
    return resize_image(image, min_pixels, max_pixels, size_factor)


def smart_nframes(
    ele: dict,
    total_frames: int,
    video_fps: int | float,
) -> int:
    """calculate the number of frames for video used for model inputs.

    Args:
        ele (dict): a dict contains the configuration of video.
            support either `fps` or `nframes`:
                - nframes: the number of frames to extract for model inputs.
                - fps: the fps to extract frames for model inputs.
                    - min_frames: the minimum number of frames of the video, only used when fps is provided.
                    - max_frames: the maximum number of frames of the video, only used when fps is provided.
        total_frames (int): the original total number of frames of the video.
        video_fps (int | float): the original fps of the video.

    Raises:
        ValueError: nframes should in interval [FRAME_FACTOR, total_frames].

    Returns:
        int: the number of frames for video used for model inputs.
    """
    assert not (
        "fps" in ele and "nframes" in ele
    ), "Only accept either `fps` or `nframes`"
    if "nframes" in ele:
        nframes = round_by_factor(ele["nframes"], FRAME_FACTOR)
    else:
        fps = ele.get("fps", FPS)
        min_frames = ceil_by_factor(ele.get("min_frames", FPS_MIN_FRAMES), FRAME_FACTOR)
        max_frames = floor_by_factor(
            ele.get("max_frames", min(FPS_MAX_FRAMES, total_frames)), FRAME_FACTOR
        )
        nframes = total_frames / video_fps * fps
        if nframes > total_frames:
            logger.warning(
                f"smart_nframes: nframes[{nframes}] > total_frames[{total_frames}]"
            )
        nframes = min(min(max(nframes, min_frames), max_frames), total_frames)
        nframes = floor_by_factor(nframes, FRAME_FACTOR)
    if not (FRAME_FACTOR <= nframes and nframes <= total_frames):
        raise ValueError(
            f"nframes should in interval [{FRAME_FACTOR}, {total_frames}], but got {nframes}."
        )
    return nframes


# process video, qwen-specific
async def preprocess_video(
    vr,
    image_factor: int = IMAGE_FACTOR,
    # vr: VideoReader, image_factor: int = IMAGE_FACTOR
) -> torch.Tensor:
    ele = {}
    total_frames, video_fps = len(vr), vr.get_avg_fps()
    nframes = smart_nframes({}, total_frames=total_frames, video_fps=video_fps)
    idx = torch.linspace(0, total_frames - 1, nframes).round().long().tolist()
    video = vr.get_batch(idx).asnumpy()
    video = torch.tensor(video).permute(0, 3, 1, 2)  # Convert to TCHW format
    nframes, _, height, width = video.shape
    min_pixels = ele.get("min_pixels", VIDEO_MIN_PIXELS)
    total_pixels = ele.get("total_pixels", VIDEO_TOTAL_PIXELS)
    max_pixels = max(
        min(VIDEO_MAX_PIXELS, total_pixels / nframes * FRAME_FACTOR),
        int(min_pixels * 1.05),
    )
    max_pixels_supposed = ele.get("max_pixels", max_pixels)
    if max_pixels_supposed > max_pixels:
        logger.warning(
            f"The given max_pixels[{max_pixels_supposed}] exceeds limit[{max_pixels}]."
        )
    max_pixels = min(max_pixels_supposed, max_pixels)
    if "resized_height" in ele and "resized_width" in ele:
        resized_height, resized_width = smart_resize(
            ele["resized_height"],
            ele["resized_width"],
            factor=image_factor,
        )
    else:
        resized_height, resized_width = smart_resize(
            height,
            width,
            factor=image_factor,
            min_pixels=min_pixels,
            max_pixels=max_pixels,
        )
    video = torchvision.transforms.functional.resize(
        video,
        [resized_height, resized_width],
        interpolation=InterpolationMode.BICUBIC,
        antialias=True,
    ).float()
    return video


# Compatible with Qwen2VL and Qwen2_5VL
class Qwen2_5VLImageProcessor(SGLangBaseProcessor):
    models = [
        Qwen2VLForConditionalGeneration,
        Qwen2_5_VLForConditionalGeneration,
        Qwen3VLForConditionalGeneration,
        Qwen3VLMoeForConditionalGeneration,
    ]

    def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
        super().__init__(hf_config, server_args, _processor, *args, **kwargs)
        # The regex that matches expanded image tokens.
        self.IM_START_TOKEN_ID = hf_config.vision_start_token_id
        self.IM_END_TOKEN_ID = hf_config.vision_end_token_id
        self.vision_start_token_id = hf_config.vision_start_token_id
        self.vision_end_token_id = hf_config.vision_end_token_id
        self.NUM_TOKEN_PER_FRAME = 770
        self.IMAGE_FACTOR = 28
        self.MIN_PIXELS = 4 * 28 * 28
        self.MAX_PIXELS = 16384 * 28 * 28
        self.MAX_RATIO = 200
        self.mm_tokens = MultimodalSpecialTokens(
            image_token="<|vision_start|><|image_pad|><|vision_end|>",
            image_token_id=hf_config.image_token_id,
            image_token_regex=re.compile(
                r"<\|vision_start\|>(?:<\|image_pad\|>)+<\|vision_end\|>"
            ),
            video_token_id=hf_config.video_token_id,
        ).build(_processor)

    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,
        )

        # Qwen-specific: resize images if they are raw Image objects
        if base_output.images and isinstance(base_output.images[0], Image.Image):
            resize_tasks = [resize_image_async(image) for image in base_output.images]
            base_output.images = await asyncio.gather(*resize_tasks)

        if base_output.videos:
            base_output.videos = [
                await preprocess_video(video) for video in base_output.videos
            ]

        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(
            spatial_merge_size=self.hf_config.vision_config.spatial_merge_size,
            image_token_id=self.mm_tokens.image_token_id,
            video_token_id=self.mm_tokens.video_token_id,
            vision_start_token_id=self.vision_start_token_id,
            model_type=self.hf_config.model_type,
            tokens_per_second=getattr(
                self.hf_config.vision_config, "tokens_per_second", None
            ),
            input_ids=input_ids.unsqueeze(0),
            image_grid_thw=getattr(ret, "image_grid_thw", None),
            video_grid_thw=getattr(ret, "video_grid_thw", None),
            second_per_grid_ts=getattr(ret, "second_per_grid_ts", None),
        )
        mrope_positions = mrope_positions.squeeze(1)

        return {
            "input_ids": input_ids.tolist(),
            "mm_items": mm_items,
            "im_start_id": self.IM_START_TOKEN_ID,
            "im_end_id": self.IM_END_TOKEN_ID,
            "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,
        }
