from __future__ import annotations

import base64
import math
from io import BytesIO

import requests
import torch
from PIL import Image
from torchvision import io, transforms
from torchvision.transforms import InterpolationMode


IMAGE_FACTOR = 28
MIN_PIXELS = 4 * 28 * 28
MAX_PIXELS = 300 * 28 * 28  # adjust for reducing the number of visual tokens
# MAX_PIXELS = 5000 * 28 * 28
MAX_RATIO = 200 

VIDEO_MIN_PIXELS = 128 * 28 * 28
VIDEO_MAX_PIXELS = 768 * 28 * 28
VIDEO_TOTAL_PIXELS = 24576 * 28 * 28
FRAME_FACTOR = 2
FPS = 2.0
FPS_MIN_FRAMES = 4
FPS_MAX_FRAMES = 768

# 定义全局变量，用于动态设置最大像素值
MAX_PIXELS = 300 * 28 * 28  # 默认值

def set_max_pixels(value: int):
    """设置 MAX_PIXELS 的值"""
    global MAX_PIXELS
    MAX_PIXELS = value


def set_video_max_pixels(value: int):
    """设置 VIDEO_MAX_PIXELS 的值"""
    global VIDEO_MAX_PIXELS
    VIDEO_MAX_PIXELS = value

def set_video_total_pixels(value: int):
    """设置 VIDEO_TOTAL_PIXELS 的值"""
    global VIDEO_TOTAL_PIXELS
    VIDEO_TOTAL_PIXELS = value

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


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.
    """
    # print(f"当前 MAX_PIXELS 的值: {MAX_PIXELS}")  # 调试用
    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)
    
    if h_bar / w_bar > MAX_RATIO:
        h_bar = w_bar * MAX_RATIO    
    elif w_bar / h_bar > MAX_RATIO:
        w_bar = h_bar * MAX_RATIO
    return h_bar, w_bar


def fetch_image(ele: dict[str, str | Image.Image], size_factor: int = IMAGE_FACTOR) -> Image.Image:
    if "image" in ele:
        image = ele["image"]
    else:
        image = ele["image_url"]
    image_obj = None
    if isinstance(image, Image.Image):
        image_obj = image
    elif image.startswith("http://") or image.startswith("https://"):
        image_obj = Image.open(requests.get(image, stream=True).raw)
    elif image.startswith("file://"):
        image_obj = Image.open(image[7:])
    elif image.startswith("data:image"):
        data = image.split(";", 1)[1]
        if data.startswith("base64,"):
            data = base64.b64decode(data[7:])
            image_obj = Image.open(BytesIO(data))
    else:
        image_obj = Image.open(image)
    if image_obj is None:
        raise ValueError(f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}")
    image = image_obj.convert("RGB")
    ## resize
    if "resized_height" in ele and "resized_width" in ele:
        resized_height, resized_width = smart_resize(
            ele["resized_height"],
            ele["resized_width"],
            factor=size_factor,
        )
    else:
        width, height = image.size
        min_pixels = ele.get("min_pixels", MIN_PIXELS)
        max_pixels = ele.get("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 fetch_video(ele: dict, image_factor: int = IMAGE_FACTOR) -> torch.Tensor | list[Image.Image]:
    if isinstance(ele["video"], str):
        # TODO: support http url

        video = ele["video"]
        if video.startswith("file://"):
            video = video[7:]

        video, audio, info = io.read_video(
            video,
            start_pts=ele.get("video_start", 0.0),
            end_pts=ele.get("video_end", None),
            pts_unit="sec",
            output_format="TCHW",
        )

        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, video.size(0))), FRAME_FACTOR)
            nframes = video.size(0) / info["video_fps"] * fps
            nframes = min(max(nframes, min_frames), max_frames)
            nframes = round_by_factor(nframes, FRAME_FACTOR)
        if not (FRAME_FACTOR <= nframes and nframes <= video.size(0)):
            raise ValueError(f"nframes should in interval [{FRAME_FACTOR}, {video.size(0)}], but got {nframes}.")

        idx = torch.linspace(0, video.size(0) - 1, nframes).round().long()
        height, width = video.shape[2:]
        video = video[idx]

        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 = ele.get("max_pixels", 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 = transforms.functional.resize(
            video,
            [resized_height, resized_width],
            interpolation=InterpolationMode.BICUBIC,
            antialias=True,
        ).float()
        return video
    else:
        assert isinstance(ele["video"], (list, tuple))
        process_info = ele.copy()
        process_info.pop("type", None)
        process_info.pop("video", None)
        images = [
            fetch_image({"image": video_element, **process_info}, size_factor=image_factor)
            for video_element in ele["video"]
        ]
        nframes = ceil_by_factor(len(images), FRAME_FACTOR)
        if len(images) < nframes:
            images.extend([images[-1]] * (nframes - len(images)))
        return images


def extract_vision_info(conversations: list[dict] | list[list[dict]]) -> list[dict]:
    vision_infos = []
    if isinstance(conversations[0], dict):
        conversations = [conversations]
    for conversation in conversations:
        for message in conversation:
            if isinstance(message["content"], list):
                for ele in message["content"]:
                    if (
                        "image" in ele
                        or "image_url" in ele
                        or "video" in ele
                        or ele["type"] in ("image", "image_url", "video")
                    ):
                        vision_infos.append(ele)
    return vision_infos


def process_vision_info(
    conversations: list[dict] | list[list[dict]],
) -> tuple[list[Image.Image] | None, list[torch.Tensor | list[Image.Image]] | None]:
    vision_infos = extract_vision_info(conversations)
    ## Read images or videos
    image_inputs = []
    video_inputs = []
    for vision_info in vision_infos:
        if "image" in vision_info or "image_url" in vision_info:
            image_inputs.append(fetch_image(vision_info))
        elif "video" in vision_info:
            video_inputs.append(fetch_video(vision_info))
        else:
            raise ValueError("image, image_url or video should in content.")
    if len(image_inputs) == 0:
        image_inputs = None
    if len(video_inputs) == 0:
        video_inputs = None
    return image_inputs, video_inputs