#!/usr/bin/env python3
"""
最小测试脚本：测试 Manifest -> TextProcessor -> LLMClient 的数据流
验证 text_with_placeholders 是否在传递过程中丢失或损坏
"""
import sys
import json
import asyncio
from pathlib import Path
from loguru import logger

# 添加 src 到路径
sys.path.insert(0, '.')

from src.manifest_manager import ManifestManager, ManifestItem
from src.text_processor import TextProcessor
from src.llm_client import LLMClient
# Mock config
from src.utils import load_config

# 配置日志
logger.remove()
logger.add(sys.stdout, level="DEBUG")

# 1. 创建临时的 Manifest 文件，模拟包含问题的真实数据
# 模拟一个带有换行符的占位符文本，这是我们怀疑的根源
mock_manifest_data = {
    "metadata": {"book_id": "test_book"},
    "items": [
        {
            "global_id": "p_00006",
            "source_file": "test.html",
            "original_html": "<p>in the name of <span id='page_vi'></span> abundance</p>",
            "clean_text": "in the name of abundance",
            "text_hash": "hash1",
            "tag": "p",
            # 模拟包含换行符的情况 (FormatExtractor 之前的问题)
            "text_with_placeholders": "in the name of \nφ1φ\n abundance", 
            "placeholder_map": {"1": "<span id='page_vi'></span>"},
            "paragraph_type": "BODY",
            "status": "pending"
        },
        {
            "global_id": "p_00058",
            "source_file": "test.html",
            "original_html": "<p>all-<span id='a536'></span>hands</p>",
            "clean_text": "all-hands",
            "text_hash": "hash2",
            "tag": "p",
            # 正常情况
            "text_with_placeholders": "all-φ1φhands",
            "placeholder_map": {"1": "<span id='a536'></span>"},
            "paragraph_type": "BODY",
            "status": "pending"
        }
    ]
}

manifest_path = Path("cache/test_manifest.json")
manifest_path.parent.mkdir(parents=True, exist_ok=True)
with open(manifest_path, 'w') as f:
    json.dump(mock_manifest_data, f)

print(f"=== Created Mock Manifest at {manifest_path} ===")

async def run_test():
    # 2. 加载 Manifest
    manifest = ManifestManager(str(manifest_path))
    manifest.load()
    print(f"Loaded {len(manifest.get_items())} items")
    
    # 3. 创建 TextProcessor 和 Chunks
    # Mock config for processor
    processor = TextProcessor({"translation": {"chunk_size": 1000}})
    
    # 这一步会从 manifest 读取 item
    chunks = processor.create_chunks_from_manifest(manifest, mode="chinese")
    print(f"Created {len(chunks)} chunks")
    
    chunk = chunks[0]
    print(f"Chunk 0 has {len(chunk)} items")
    
    # 4. 模拟 LLMClient 构建 Prompt
    # 不需要真正的 API key，只需要测试 _build_prompt
    # 添加 dummy key 和 rate_limits 防止初始化报错
    client = LLMClient({
        "providers": {}, 
        "llm": {
            "api_key": "dummy_key", 
            "models": {"fast": "dummy_model", "smart": "dummy_model"},
            "rate_limits": {"requests_per_minute": 60, "concurrent_requests": 2}
        }
    }) 
    
    # 强制重新加载 prompts (确保我们使用最新的代码逻辑)
    # 注意：我们之前修了 _load_prompts，如果 prompts.json 不存在会报错
    # 这里我们假设 config/prompts.json 存在
    
    print("\n=== 构建 Prompt (Mode: Chinese) ===")
    prompt = client._build_prompt(chunk, mode="chinese")
    
    print("-" * 40)
    print(prompt)
    print("-" * 40)
    
    # 验证关键点
    print("\n=== 验证结果 ===")
    
    # 检查 p_00006
    # 注意：我们之前修了 FormatExtractor，但那是针对**新提取**的内容。
    # 这里我们测试的是**从旧 Manifest 读取**的内容。
    # ManifestManager 读取时并不会自动清理换行符！
    # 所以如果旧 manifest 里有换行，这里应该能复现出带换行的 prompt。
    
    has_p00006 = "p_00006 [BODY] in the name of \nφ1φ\n abundance" in prompt
    print(f"p_00006 has newlines (bad): {has_p00006}")
    
    has_p00006_clean = "p_00006 [BODY] in the name of φ1φ abundance" in prompt
    print(f"p_00006 is clean (good): {has_p00006_clean}")
    
    has_p00058 = "p_00058 [BODY] all-φ1φhands" in prompt
    print(f"p_00058 is correct: {has_p00058}")

if __name__ == "__main__":
    asyncio.run(run_test())