import json
import Levenshtein
from fit_2d import plot_density_ned
from fit_3d import fit_and_plot_3d
import numpy as np
import matplotlib.pyplot as plt
import os

def read_jsonl(file_path):
    """
    读取 JSONL 文件并返回解析后的数据列表。
    
    :param file_path: JSONL 文件的路径
    :return: 包含所有 JSON 对象的列表
    """
    data = []
    with open(file_path, 'r', encoding='utf-8') as f:
        for line in f:
            data.append(json.loads(line.strip()))
    return data

def save_jsonl(data, file_path):
    """
    将数据保存为 JSONL 文件。
    
    :param data: 要保存的数据（Python 对象的列表）
    :param file_path: 保存的 JSONL 文件路径
    """
    with open(file_path, 'w', encoding='utf-8') as f:
        for item in data:
            f.write(json.dumps(item, ensure_ascii=False) + '\n')

def compute_edit_distance(pred: str, gt: str) -> float:
    """
    计算两个字符串的归一化编辑距离 (Normalized Edit Distance, NED)
    值域为 [0, 1]，越小表示越相似。
    """
    pred = pred.replace("\n", "").replace(" ", "")
    gt = gt.replace("\n", "").replace(" ", "")
    if not pred and not gt:
        return 0.0
    dist = Levenshtein.distance(pred, gt)
    return round(dist / max(len(pred), len(gt)), 4)


def compute_accuracy(json_path: str):
    """从 JSON 文件批量计算平均 NED 及每条样本的编辑距离"""
    with open(json_path, 'r', encoding='utf-8') as f:
        data = json.load(f)

    total_ned = 0
    sample_accuracies = []

    for sample in data:
        # Ground truth 选择逻辑
        if "normal" in json_path:
            gt = sample.get("content", "")
        elif "tiny_shuffled" in json_path:
            gt = sample.get("tiny_shuffled_content", "")
        elif "shuffled" in json_path:
            gt = sample.get("shuffled_content", "")
        else:
            raise ValueError("无法确定使用哪个字段作为 ground truth")

        pred = sample.get("ocr", "")
        ned = compute_edit_distance(pred, gt)
        total_ned += ned

        sample_accuracies.append({
            "id": sample["id"],
            "image_path": sample["image_path"],
            "content": gt,
            "ocr": sample["ocr"],
            "NED": ned
        })

    overall_ned = round(total_ned / len(data), 4)
    print(f"共 {len(data)} 条样本, 平均 NED = {overall_ned}")

    return overall_ned, sample_accuracies


def plot_length_ned_bar(data, output_path="length_ned_bar.png",
                        length_bin_width=500, ned_bin_width=0.05):
    """
    用柱状图显示 item['length'] 与 extent['NED'] 的频率

    参数:
        data: 原始数据列表
        output_path: 图片保存路径
        length_bin_width: 文字长度的柱宽
        ned_bin_width: NED的柱宽
    """
    # 提取 length 和 NED
    length_vals = []
    ned_vals = []
    for item in data:
        for dim, v in item["rewrite"].items():
            for extent in v["extents"]:
                length_vals.append(len(extent["edited"]))
                ned_vals.append(extent["NED"])

    length_vals = np.array(length_vals)
    ned_vals = np.array(ned_vals)

    if len(length_vals) == 0:
        print("⚠️ 数据为空，未生成图像。")
        return

    # --- 文字长度柱状图 ---
    length_bins = np.arange(min(length_vals), max(length_vals) + length_bin_width, length_bin_width)
    plt.figure(figsize=(8, 4))
    plt.hist(length_vals, bins=length_bins, color='skyblue', edgecolor='black')
    plt.xlabel("Text Length")
    plt.ylabel("Frequency")
    plt.title("Frequency of Text Length")
    plt.xticks(length_bins)
    plt.tight_layout()
    os.makedirs(os.path.dirname(output_path), exist_ok=True)
    plt.savefig(output_path.replace(".png", "_length.png"), dpi=300)
    plt.close()

    # --- NED柱状图 ---
    ned_bins = np.arange(0, 1 + ned_bin_width, ned_bin_width)
    plt.figure(figsize=(8, 4))
    plt.hist(ned_vals, bins=ned_bins, color='salmon', edgecolor='black')
    plt.xlabel("NED")
    plt.ylabel("Frequency")
    plt.title("Frequency of NED")
    plt.xticks(ned_bins)
    plt.tight_layout()
    plt.savefig(output_path.replace(".png", "_ned.png"), dpi=300)
    plt.close()

    print(f"✅ 柱状图已保存：{output_path.replace('.png', '_length.png')} 和 {output_path.replace('.png', '_ned.png')}")

if __name__ == "__main__":

    meta_path = "/vol/zhaoy/ds-ocr/data/CCI3-Data/CCI3_100-5k_sample100_interval500_per10_last-mode/output_processed_ocr_Gundam.jsonl"
    data = read_jsonl(meta_path)

    # 1. 计算NED
    for item in data:
        for dim, v in item["rewrite"].items():
            for extent in v["extents"]:
                orig = extent["edited"]
                pred = extent["ocr"]
                extent["NED_ocr"] = compute_edit_distance(orig, pred)

    save_jsonl(data, "/vol/zhaoy/ds-ocr/data/CCI3-Data/CCI3_100-5k_sample100_interval500_per10_last-mode/output_processed_ocr_Gundam_result.jsonl")

    # 1. 不同维度的模型性能      最大程度。
    metric_dim = {
        "形近字替换": 0,
        "音近/同音字替换": 0,
        "语序颠倒": 0,
        "字符增衍": 0,
        "字符缺失": 0
    }
    for item in data:
        for dim, v in item["rewrite"].items():
            for extent in v["extents"]:
                # TODO 不同level相对于原始字符串的NED也不同，要进行区分。即，数据集本身有bias，导致其他的自变量没有得到控制。
                if extent["level"] == 1.0:
                    metric_dim[dim] += extent["NED_ocr"]
    for k, v in metric_dim.items():
        metric_dim[k] = v / len(data)
    print(metric_dim)
    
    # 2. 不同文字密度的模型性能。   最大程度。 分维度，出5条曲线。或者取均值？
    metric_p = {
        "形近字替换": [],
        "音近/同音字替换": [],
        "语序颠倒": [],
        "字符增衍": [],
        "字符缺失": []
    }
    for item in data:
        for dim, v in item["rewrite"].items():
            for extent in v["extents"]:
                if extent["level"] == 1.0:
                    metric_p[dim].append([item["length"], extent["NED_ocr"]])
    plot_density_ned(metric_p["形近字替换"], save_path="/vol/fit_p_ned.png")


    metric_3d = []
    # 3. 不同程度。                先不考虑文字密度。 文字密度不同，也是个自变量在这里。          分维度，出5条曲线。或者取均值？
    # TODO
    for item in data:
        for dim, v in item["rewrite"].items():
            for extent in v["extents"]:
                metric_3d.append([item["length"], extent["NED"], extent["NED_ocr"]])
    fit_and_plot_3d(metric_3d, "/vol/fit_3d.png")

    # 统计自变量分布
    plot_length_ned_bar(data, output_path="/vol/length_ned_bar.png",
                    length_bin_width=500, ned_bin_width=0.05)