apps/text_tools.py

import gradio as gr
import math
import json
import os
from transformers import AutoTokenizer

# --- Tokenizer 加载逻辑 ---
# 为了避免每次请求都重新加载，我们可以尝试缓存 tokenizer
# 但在 HF Spaces 中，内存有限，且模型可能很大。
# 对于 Qwen2.5-VL，我们可以使用 Qwen/Qwen2.5-VL-7B-Instruct 的 tokenizer
# 对于 Llava，通常使用 Llama-2 或 Vicuna 的 tokenizer
TOKENIZERS = {}

def get_tokenizer(model_name):
    if model_name in TOKENIZERS:
        return TOKENIZERS[model_name]
    
    try:
        if model_name == "Qwen2.5-VL / Qwen2-VL":
            # Qwen2-VL 使用 Qwen2 的 tokenizer
            # 注意：这里需要联网下载 tokenizer.json，HF Spaces 通常允许
            tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True)
        elif model_name == "Llava-1.6 (Next)":
            # Llava-1.6 基于 Vicuna/Llama-2，这里用 Llama-2 tokenizer 近似，或者直接用 llava-hf
            # 为了通用性，我们使用 llava-hf/llava-v1.6-vicuna-7b-hf
            tokenizer = AutoTokenizer.from_pretrained("llava-hf/llava-v1.6-vicuna-7b-hf", trust_remote_code=True)
        else:
            return None
        
        TOKENIZERS[model_name] = tokenizer
        return tokenizer
    except Exception as e:
        print(f"Error loading tokenizer for {model_name}: {e}")
        return None

# --- Token 计算逻辑 ---

def calculate_qwen2_vl_tokens(text, images, videos, tokenizer):
    """

    Qwen2-VL / Qwen2.5-VL Token 计算公式

    """
    text_tokens_count = 0
    image_tokens_count = 0
    video_tokens_count = 0
    
    # 1. 文本 Token (真实计算)
    text_tokens_ids = []
    if tokenizer:
        text_tokens_ids = tokenizer.encode(text)
        text_tokens_count = len(text_tokens_ids)
    else:
        # Fallback
        text_tokens_count = len(text) // 2
    
    # 2. 图片 Token
    image_details = []
    for img in images:
        width, height = img['width'], img['height']
        new_w = int(round(width / 28.0) * 28)
        new_h = int(round(height / 28.0) * 28)
        grid_w = new_w // 14
        grid_h = new_h // 14
        img_tokens = grid_h * grid_w
        
        image_tokens_count += img_tokens
        image_details.append({
            "original_size": [width, height],
            "resized_size": [new_w, new_h],
            "tokens": img_tokens
        })
        
    # 3. 视频 Token
    video_details = []
    for vid in videos:
        frames = vid['frames']
        width, height = vid['width'], vid['height']
        new_w = int(round(width / 28.0) * 28)
        new_h = int(round(height / 28.0) * 28)
        grid_w = new_w // 14
        grid_h = new_h // 14
        frame_tokens = grid_h * grid_w
        
        vid_total = frames * frame_tokens
        video_tokens_count += vid_total
        video_details.append({
            "original_size": [width, height],
            "resized_size": [new_w, new_h],
            "frames": frames,
            "tokens": vid_total
        })

    total_tokens = text_tokens_count + image_tokens_count + video_tokens_count
    
    breakdown = {
        "text_tokens": text_tokens_count,
        "image_tokens": image_tokens_count,
        "video_tokens": video_tokens_count
    }
    
    media_details = {
        "images": image_details,
        "videos": video_details
    }

    return total_tokens, text_tokens_ids, breakdown, media_details

def calculate_llava_next_tokens(text, images, tokenizer):
    """

    Llava-1.6 (Next) Token 计算公式

    """
    text_tokens_count = 0
    image_tokens_count = 0
    
    # 1. 文本 Token
    text_tokens_ids = []
    if tokenizer:
        text_tokens_ids = tokenizer.encode(text)
        text_tokens_count = len(text_tokens_ids)
    else:
        text_tokens_count = len(text) // 2
    
    # 2. 图片 Token
    image_details = []
    for img in images:
        width, height = img['width'], img['height']
        scale_res = 336
        patch_x = math.ceil(width / scale_res)
        patch_y = math.ceil(height / scale_res)
        num_patches = patch_x * patch_y
        img_tokens = (num_patches + 1) * 576
        
        image_tokens_count += img_tokens
        image_details.append({
            "original_size": [width, height],
            "resized_size": ["Dynamic Grid", f"{patch_x}x{patch_y} patches"],
            "tokens": img_tokens
        })
        
    total_tokens = text_tokens_count + image_tokens_count
    
    breakdown = {
        "text_tokens": text_tokens_count,
        "image_tokens": image_tokens_count,
        "video_tokens": 0
    }
    
    media_details = {
        "images": image_details,
        "videos": []
    }
        
    return total_tokens, text_tokens_ids, breakdown, media_details

# --- 实际 UI 逻辑 ---

def run_calculation(text, model, img_count, img_w, img_h, vid_count, vid_frames, vid_w, vid_h):
    # 构造虚拟数据
    images = [{'width': img_w, 'height': img_h} for _ in range(int(img_count))]
    videos = [{'width': vid_w, 'height': vid_h, 'frames': int(vid_frames)} for _ in range(int(vid_count))]
    
    # 获取 Tokenizer
    tokenizer = get_tokenizer(model)
    
    # 确定真实模型 ID
    model_id_map = {
        "Qwen2.5-VL / Qwen2-VL": "Qwen/Qwen2.5-VL-7B-Instruct",
        "Llava-1.6 (Next)": "llava-hf/llava-v1.6-vicuna-7b-hf"
    }
    real_model_id = model_id_map.get(model, model)
    
    text_tokens_ids = []
    breakdown = {}
    media_details = {}
    tokens = 0
    
    if model == "Qwen2.5-VL / Qwen2-VL":
        tokens, text_tokens_ids, breakdown, media_details = calculate_qwen2_vl_tokens(text, images, videos, tokenizer)
    elif model == "Llava-1.6 (Next)":
        tokens, text_tokens_ids, breakdown, media_details = calculate_llava_next_tokens(text, images, tokenizer)
    else:
        tokens = 0
    
    # 生成 Token 对应文件
    token_file_path = None
    if tokenizer and text_tokens_ids:
        token_data = []
        # 解码每个 token id 对应的 string
        for tid in text_tokens_ids:
            token_str = tokenizer.decode([tid])
            token_data.append({"id": tid, "token": token_str})
            
        token_file_path = "token_analysis.json"
        with open(token_file_path, "w", encoding="utf-8") as f:
            json.dump({"text": text, "tokens": token_data}, f, ensure_ascii=False, indent=2)

    # 构造最终返回的 JSON
    result = {
        "model_id": real_model_id,
        "tokenizer_loaded": tokenizer is not None,
        "total_tokens": tokens,
        "breakdown": breakdown,
        "text_stats": {
            "char_count": len(text)
        },
        "media_details": media_details
    }

    return result, token_file_path

def create_ui():
    with gr.Row():
        with gr.Column(scale=1):
            model_select = gr.Dropdown(
                choices=["Qwen2.5-VL / Qwen2-VL", "Llava-1.6 (Next)"],
                value="Qwen2.5-VL / Qwen2-VL",
                label="选择模型"
            )
            text_input = gr.Textbox(lines=5, label="输入文本 (Text)", placeholder="输入 Prompt...")
            
            with gr.Accordion("🖼️ 图片设置 (Images)", open=True):
                with gr.Row():
                    img_count = gr.Number(value=1, label="图片数量", precision=0)
                    img_w = gr.Number(value=1024, label="宽 (px)")
                    img_h = gr.Number(value=1024, label="高 (px)")
            
            with gr.Accordion("🎥 视频设置 (Videos)", open=False):
                with gr.Row():
                    vid_count = gr.Number(value=0, label="视频数量", precision=0)
                    vid_frames = gr.Number(value=16, label="总帧数/视频", precision=0)
                    vid_w = gr.Number(value=512, label="宽 (px)")
                    vid_h = gr.Number(value=512, label="高 (px)")
            
            btn = gr.Button("🚀 计算 Token", variant="primary")
            
        with gr.Column(scale=1):
            out_json = gr.JSON(label="计算结果")
            out_file = gr.File(label="下载 Token 分析 (JSON)")
            gr.Markdown("""

            ### 说明

            * **真实 Tokenizer**: 首次运行时会自动下载 `transformers` 模型配置，可能需要几秒钟。

            * **Qwen2-VL**: 基于 `H/14 * W/14` 计算，自动对齐到 28px 网格。

            * **Llava-1.6**: 基于 `(Patches + 1) * 576` 计算，Patch 大小为 336px。

            """)
    
    btn.click(
        run_calculation, 
        [text_input, model_select, img_count, img_w, img_h, vid_count, vid_frames, vid_w, vid_h], 
        [out_json, out_file]
    )