"""
脚本名称: rewrite.py
功能: 批量重写 Task 1, 2, 3 数据集中的 CoT (Chain of Thought) 推理文本。

【功能描述】
该脚本读取原始 JSONL 文件，保持图像路径、几何参数、正确答案不变，
仅根据 metadata 中的几何信息（角度、步骤）重新生成 'cot_trace' 字段。
支持通过修改脚本顶部的模板列表来丰富语言的多样性。

【环境依赖】
python >= 3.6
tqdm

【如何运行】
在终端中使用以下命令运行。请确保 --input_dir 指向包含 jsonl 文件的根目录。

1. 针对 Task 1 (单步预测):
   python scripts/rewrite.py --task_type task1 --input_dir ./data/task1 --output_dir ./data/task1_new

2. 针对 Task 2 (多步指令跟随):
   python scripts/rewrite.py --task_type task2 --input_dir ./data/task2 --output_dir ./data/task2_new

3. 针对 Task 3 (序列排序):
   python scripts/rewrite.py --task_type task3 --input_dir ./data/task3 --output_dir ./data/task3_new

【参数说明】
--input_dir : 输入文件夹路径（脚本会递归查找该目录下的所有 .jsonl 文件）
--output_dir: 输出文件夹路径（保持原有的目录结构）
--task_type : 任务类型，必须是 [task1, task2, task3] 之一
--seed      : 随机种子，用于控制模板选择的随机性 (默认 42)
--debug     : 开启调试模式，打印错误信息
"""

import os
import json
import re
import argparse
from glob import glob
from tqdm import tqdm
import random

# ==========================================
# 1. 语言模板库 (Language Templates)
# ==========================================
#
# 【如何增加/修改模板】
# 1. 每个列表包含多个字符串，脚本会随机选择其中一条。
# 2. 必须保留大括号 {} 包裹的占位符，例如 {angle}, {direction}。
# 3. 如果你想增加新的表达方式，直接在对应的列表中添加字符串即可。
#
# 【通用变量说明】
# {angle}    : 旋转的角度数值 (绝对值)
# {direction}: 旋转方向 (如 clockwise, to the left)
# {img_tag}  : 图片标签，格式为 <image_start>[reasoning_image_x]<image_end>
# {label}    : 正确选项 (A, B, C, D)
# ==========================================

# --- 通用方向词汇映射 ---
# 用于将 metadata 中的 "clockwise" 替换为更多样的表达
DIRECTION_MAP = {
    "clockwise": ["clockwise", "to the right", "in a clockwise direction"],
    "anticlockwise": ["anticlockwise", "counter-clockwise", "to the left"],
    "counter-clockwise": ["anticlockwise", "counter-clockwise", "to the left"]
}

# ---------------------------------------------------------
# Task 1 模板: 单步预测 (Single-step View Prediction)
# 逻辑: 
#   1. Start: 描述初始旋转。
#   2. Middle: 描述中间的连续旋转步骤。
#   3. Final: 总结最终视图并匹配选项。
# ---------------------------------------------------------
T1_START = [
    "Starting from the initial view, I rotate the camera {angle} degrees {direction} and see {img_tag}",
    "First, let's move the camera {angle} degrees {direction}. The object now looks like this: {img_tag}",
    "Initiating a {direction} rotation of {angle} degrees reveals this perspective: {img_tag}",
]

T1_MIDDLE = [
    "Continuing the rotation by {angle} degrees {direction}, the view becomes {img_tag}",
    "Another {angle} degrees {direction} turn brings us to this angle: {img_tag}",
    "Rotating further by {angle} degrees {direction}, I observe {img_tag}",
]

T1_FINAL = [
    ", which represents the final target view. Comparing this with the options, image {label} is the best match, so the answer is {label}.",
    ". This matches the final position. Upon checking the candidates, option {label} aligns perfectly with this view. Therefore, the correct answer is {label}.",
    ", arriving at the destination angle. Among the choices, option {label} is identical to my current view. Thus, {label} is correct."
]

# ---------------------------------------------------------
# Task 2 模板: 多步指令 (Multi-step Instruction Following)
# 逻辑:
#   1. Step (Intermediate): 执行指令 -> 展示 reasoning_image。
#   2. Final Step: 执行最后一步指令 -> 到达目标位置 (注意：最后一步通常没有 reasoning_image，直接对应选项)。
#   3. Conclusion: 匹配选项。
# ---------------------------------------------------------
T2_STEP = [
    "Step {i}: Rotating {angle} degrees {direction}, the view transitions to <image_start>[{img_key}]<image_end>",
    "Following the instruction to rotate {angle} degrees {direction}, I observe this intermediate view: <image_start>[{img_key}]<image_end>",
    "Next, a {angle}-degree {direction} rotation reveals: <image_start>[{img_key}]<image_end>",
]

T2_FINAL_STEP = [
    "Finally, rotating {angle} degrees {direction} brings us to the target position.",
    "The last step is a {angle}-degree {direction} rotation to reach the destination.",
    "Completing the sequence with a {angle} degrees {direction} turn.",
]

T2_CONCLUSION = [
    " Comparing the final view with the options, it matches option {label}. So the answer is {label}.",
    " This final perspective corresponds to option {label}. Therefore, {label} is correct.",
]

# ---------------------------------------------------------
# Task 3 模板: 序列排序 (View Ordering)
# 逻辑:
#   1. Start: 确定方向，开始旋转。
#   2. Middle (No Match): 旋转后展示图片，但该图片不对应任何选项图片 (只是中间过程)。
#   3. Middle (Match): 旋转后展示图片，并且该图片与选项中的某张图 (1/2/3/4) 匹配。
#   4. Conclusion: 总结正确的顺序 (如 4-2-1-3) 并选择选项。
# ---------------------------------------------------------
T3_START = [
    "Based on the images, the rotation appears to be {direction}. Starting the rotation by {angle} degrees, I see <image_start>[{img_key}]<image_end>",
    "I deduce the rotation is {direction}. First, moving {angle} degrees reveals <image_start>[{img_key}]<image_end>",
]

# 当这一步的 reasoning_image 不匹配任何选项图片时使用：
T3_MIDDLE_NO_MATCH = [
    "Continuing {angle} degrees {direction}, the view is <image_start>[{img_key}]<image_end>",
    "Rotating another {angle} degrees {direction} shows <image_start>[{img_key}]<image_end>",
    "Next, moving {angle} degrees {direction} gives us <image_start>[{img_key}]<image_end>",
]

# 当这一步的 reasoning_image 匹配了选项图片 (img_idx) 时使用：
# {img_idx} 是匹配到的图片编号 (1, 2, 3, 4)
T3_MIDDLE_MATCH = [
    "After rotating {angle} degrees {direction}, I see <image_start>[{img_key}]<image_end>. This view closely resembles image {img_idx}, so the next item in the sequence is {img_idx}.",
    "Moving {angle} degrees {direction} leads to <image_start>[{img_key}]<image_end>, which matches image {img_idx}. Thus, {img_idx} is the next step.",
    "A further {angle} degrees {direction} rotation shows <image_start>[{img_key}]<image_end>. This looks identical to image {img_idx}.",
]

T3_CONCLUSION = [
    "Combining these observations, the correct chronological order is {seq_str}. This corresponds to option {label}.",
    "Therefore, the sequence is {seq_str}, making {label} the correct choice.",
    "So, the sequence should be {seq_str}, which indicates that option {label} should be the right answer."
]


# ==========================================
# 2. 核心处理逻辑
# ==========================================

def get_direction_variations(direction_str):
    """根据方向关键词返回同义词列表"""
    key = direction_str.lower()
    if "counter" in key or "anti" in key:
        return DIRECTION_MAP["anticlockwise"]
    return DIRECTION_MAP["clockwise"]

def process_task1(line_data):
    """处理 Task 1: 单步预测"""
    metadata = line_data.get("metadata", {})
    old_cot = metadata.get("cot_trace", "")
    direction = metadata.get("direction", "clockwise")
    
    gt_answer = line_data.get("gt_answer", "")
    label_match = re.search(r'<answer>([A-D])</answer>', gt_answer)
    correct_label = label_match.group(1) if label_match else "A"

    # 从旧文本中提取所有数字作为角度步骤
    steps = [int(m) for m in re.findall(r'(\d+)\s+degrees', old_cot)]
    
    images = line_data.get("images", {})
    # 按索引排序 reasoning images
    reasoning_keys = sorted([k for k in images.keys() if k.startswith("reasoning_image_")], 
                            key=lambda x: int(x.split('_')[-1]))
    
    # 简单校验：步骤数应等于中间图数量
    if len(steps) != len(reasoning_keys):
        return line_data, False 

    cot_parts = []
    dir_vars = get_direction_variations(direction)
    
    for i, (step, img_key) in enumerate(zip(steps, reasoning_keys)):
        img_tag = f"<image_start>[{img_key}]<image_end>"
        cur_dir = random.choice(dir_vars)
        
        if i == 0:
            tmpl = random.choice(T1_START)
        else:
            tmpl = random.choice(T1_MIDDLE)
        
        cot_parts.append(tmpl.format(angle=step, direction=cur_dir, img_tag=img_tag))

    new_cot = "; ".join(cot_parts)
    new_cot += random.choice(T1_FINAL).format(label=correct_label)
    
    line_data['metadata']['cot_trace'] = new_cot
    return line_data, True

def process_task2(line_data):
    """处理 Task 2: 多步指令"""
    metadata = line_data.get("metadata", {})
    
    # 优先从 metadata 获取准确的角度列表
    steps_degrees = metadata.get("steps_degrees", [])
    if not steps_degrees:
        instr = metadata.get("instruction_sequence", "")
        steps_degrees = [int(m) for m in re.findall(r'(\d+)\s+degrees', instr)]
    
    instruction_seq = metadata.get("instruction_sequence", "")
    # 提取指令中的方向序列
    directions = re.findall(r'(clockwise|anticlockwise|counter-clockwise)', instruction_seq)
    
    gt_answer = line_data.get("gt_answer", "")
    label_match = re.search(r'<answer>([A-D])</answer>', gt_answer)
    correct_label = label_match.group(1) if label_match else "D"

    images = line_data.get("images", {})
    reasoning_keys = sorted([k for k in images.keys() if k.startswith("reasoning_image_")], 
                            key=lambda x: int(x.split('_')[-1]))
    
    # Task 2 逻辑：N 个步骤，通常有 N-1 张中间图 (最后一步直接到结果)
    if len(steps_degrees) != len(reasoning_keys) + 1:
        # 容错：如果步骤数不匹配，不修改
        return line_data, False

    cot_parts = []
    
    for i, angle in enumerate(steps_degrees):
        abs_angle = abs(angle)
        # 获取当前步骤对应的方向
        cur_dir_raw = directions[i] if i < len(directions) else "clockwise"
        cur_dir = random.choice(get_direction_variations(cur_dir_raw))
        
        if i < len(reasoning_keys):
            # 中间步骤：有 reasoning_image
            img_key = reasoning_keys[i]
            tmpl = random.choice(T2_STEP)
            cot_parts.append(tmpl.format(i=i+1, angle=abs_angle, direction=cur_dir, img_key=img_key))
        else:
            # 最后一步：没有 reasoning_image，直接得出结论
            tmpl = random.choice(T2_FINAL_STEP)
            cot_parts.append(tmpl.format(angle=abs_angle, direction=cur_dir))

    new_cot = " ".join(cot_parts)
    new_cot += random.choice(T2_CONCLUSION).format(label=correct_label)
    
    line_data['metadata']['cot_trace'] = new_cot
    return line_data, True

def process_task3(line_data):
    """处理 Task 3: 序列排序"""
    metadata = line_data.get("metadata", {})
    old_cot = metadata.get("cot_trace", "")
    
    # 1. 确定总体旋转方向
    if "counter-clockwise" in old_cot or "anticlockwise" in old_cot:
        direction_raw = "anticlockwise"
    else:
        direction_raw = "clockwise"
    
    gt_answer = line_data.get("gt_answer", "")
    label_match = re.search(r'<answer>([A-D])</answer>', gt_answer)
    correct_label = label_match.group(1) if label_match else "A"
    
    # 2. 提取最终的排序结果 (如 "4-2-1-3")
    seq_match = re.search(r'sequence should be ([\d-]+)', old_cot)
    final_seq_str = seq_match.group(1) if seq_match else "UNKNOWN"

    # 3. 解析旧 CoT，提取 (角度, 图片key, 是否匹配) 的三元组
    # 策略：按 "Then," 或 "After" 分割句子，逐句分析
    segments = re.split(r'(?:Then,|After)', old_cot)
    parsed_steps = []
    
    for seg in segments:
        # 提取角度
        angle_m = re.search(r'rotat\w+\s+(\d+)\s+degrees', seg)
        if not angle_m: continue
        angle = int(angle_m.group(1))
        
        # 提取 reasoning_image 编号
        img_m = re.search(r'reasoning_image_(\d+)', seg)
        if not img_m: continue
        r_img_idx = img_m.group(1)
        r_img_key = f"reasoning_image_{r_img_idx}"
        
        # 提取匹配信息 (matches image X)
        match_m = re.search(r'(?:matches|resembles)\s+image\s+(\d+)', seg)
        matched_idx = match_m.group(1) if match_m else None
        
        parsed_steps.append({
            "angle": angle,
            "img_key": r_img_key,
            "matched_idx": matched_idx
        })
    
    images = line_data.get("images", {})
    reasoning_keys = [k for k in images.keys() if k.startswith("reasoning_image_")]
    
    # 校验解析出的步骤数是否与图片数一致
    if len(parsed_steps) != len(reasoning_keys):
        return line_data, False

    # 4. 生成新文本
    cot_parts = []
    dir_vars = get_direction_variations(direction_raw)
    
    for i, step in enumerate(parsed_steps):
        cur_dir = random.choice(dir_vars)
        angle = step['angle']
        img_key = step['img_key']
        matched_idx = step['matched_idx']
        
        if i == 0:
            # 第一步通常只是展示，不匹配
            tmpl = random.choice(T3_START)
            text = tmpl.format(angle=angle, direction=cur_dir, img_key=img_key)
        else:
            if matched_idx:
                # 如果这一步有匹配
                tmpl = random.choice(T3_MIDDLE_MATCH)
                text = tmpl.format(angle=angle, direction=cur_dir, img_key=img_key, img_idx=matched_idx)
            else:
                # 如果这一步只是中间过渡
                tmpl = random.choice(T3_MIDDLE_NO_MATCH)
                text = tmpl.format(angle=angle, direction=cur_dir, img_key=img_key)
        
        cot_parts.append(text)

    new_cot = " ".join(cot_parts)
    new_cot += random.choice(T3_CONCLUSION).format(seq_str=final_seq_str, label=correct_label)
    
    line_data['metadata']['cot_trace'] = new_cot
    return line_data, True


# ==========================================
# 3. 主程序入口
# ==========================================

def process_file(file_path, out_path, task_type, debug=False):
    new_lines = []
    modified_count = 0
    total_count = 0
    
    with open(file_path, 'r', encoding='utf-8') as f:
        for line in f:
            if not line.strip(): continue
            total_count += 1
            data = json.loads(line)
            
            is_modified = False
            try:
                if task_type == "task1":
                    data, is_modified = process_task1(data)
                elif task_type == "task2":
                    data, is_modified = process_task2(data)
                elif task_type == "task3":
                    data, is_modified = process_task3(data)
            except Exception as e:
                if debug: print(f"[Error] Line {total_count} in {os.path.basename(file_path)}: {e}")
                is_modified = False
            
            if is_modified:
                modified_count += 1
            new_lines.append(data)
            
    with open(out_path, 'w', encoding='utf-8') as f_out:
        for item in new_lines:
            f_out.write(json.dumps(item) + "\n")
            
    return total_count, modified_count

def main():
    parser = argparse.ArgumentParser(description="Rewrite CoT trace for Task 1, 2, 3")
    parser.add_argument("--input_dir", type=str, required=True, help="Input directory root (recursive search)")
    parser.add_argument("--output_dir", type=str, required=True, help="Output directory root")
    parser.add_argument("--task_type", type=str, required=True, choices=["task1", "task2", "task3"], 
                        help="Which task logic to apply")
    parser.add_argument("--seed", type=int, default=42, help="Random seed for template selection")
    parser.add_argument("--debug", action="store_true", help="Print detailed error messages")
    
    args = parser.parse_args()
    random.seed(args.seed)
    
    # 递归查找所有 jsonl 文件
    search_pattern = os.path.join(args.input_dir, "**", "*.jsonl")
    files = glob(search_pattern, recursive=True)
    
    if not files:
        print(f"No JSONL files found in {args.input_dir}")
        return

    print(f"Found {len(files)} files for {args.task_type}")
    print(f"Input:  {args.input_dir}")
    print(f"Output: {args.output_dir}")
    
    total_processed = 0
    total_modified = 0
    
    for file_path in tqdm(files, desc=f"Processing {args.task_type}"):
        # 计算相对路径，保持输出目录结构一致
        rel_path = os.path.relpath(file_path, args.input_dir)
        out_path = os.path.join(args.output_dir, rel_path)
        os.makedirs(os.path.dirname(out_path), exist_ok=True)
        
        t, m = process_file(file_path, out_path, args.task_type, args.debug)
        total_processed += t
        total_modified += m
        
    print("-" * 30)
    print(f"Done!")
    print(f"Total Lines Processed: {total_processed}")
    print(f"Total Lines Modified : {total_modified}")
    print(f"Success Rate: {total_modified/total_processed:.1%}" if total_processed > 0 else "N/A")
    print(f"Output saved to {args.output_dir}")

if __name__ == "__main__":
    main()