import multiprocessing
import pandas as pd
import Levenshtein
import copy


conf_thresh = 0.8


def my_Levenshtein_ratio(str1, str2):
    if max(len(str1), len(str2)) == 0:
        return 1
    return 1 - Levenshtein.distance(str1, str2) / max(len(str1), len(str2))


def if_minimal(md, md_list, target_col):
    # 假设这个md是minimal
    minimal = True
    if md_list.count(md) > 1:
        return False
    for _ in md_list:
        if _ != md:
            # 假设列表中每一个md都使当前md不minimal
            exist = True
            # 如果左边任何一个大于，则假设不成立
            for col in list(set(_.keys()) - {target_col}):
                if _[col] > md[col]:
                    exist = False
            # 如果右边小于，假设也不成立
            if _[target_col] < md[target_col]:
                exist = False
            # 任何一次假设成立，当前md不minimal
            if exist:
                minimal = False
                break
    return minimal


def remove_by_confidence(md, l, relation, target_col, lock):
    support, confidence = get_one_md_metadata(md, relation, target_col)
    # todo: replace constant 0.8
    if confidence < 0.8:
        with lock:
            l.remove(md)


# def remove_by_confidence(md, l, relation, target_col):
#     boolean, conf = satisfy_confidence(md, relation, 0.8, target_col)
#     if not boolean:
#         l.remove(md)
#         print(md, '\t', conf)


def inference_from_record_pairs(path, threshold, target_col):
    data = pd.read_csv(path, low_memory=False, encoding='ISO-8859-1')
    data.fillna("", inplace=True)
    data = data.astype(str)
    columns = data.columns.values.tolist()

    md_list = []
    minimal_vio = []
    init_md = {}
    for col in columns:
        init_md[col] = 1 if col == target_col else 0
    md_list.append(init_md)

    for row1 in data.itertuples():
        # 获取当前行的索引，从后一行开始切片
        i = row1[0]
        data1 = data[i + 1:]
        for row2 in data1.itertuples():
            violated_mds = []
            # sims是两行的相似度
            sims = {}
            for col in columns:
                similarity = my_Levenshtein_ratio(getattr(row1, col), getattr(row2, col))
                sims[col] = similarity

            # 寻找violated md,从md列表中删除并加入vio列表
            # tmp_md_list = copy.deepcopy(md_list)
            for md in md_list[:]:
                lhs_satis = True
                rhs_satis = True
                for col in list(set(columns) - {target_col}):
                    if sims[col] < md[col]:
                        lhs_satis = False
                if sims[target_col] < md[target_col]:
                    rhs_satis = False
                if lhs_satis == True and rhs_satis == False:
                    md_list.remove(md)
                    violated_mds.append(md)
            minimal_vio.extend(violated_mds)

            for vio_md in violated_mds:
                # 特殊化右侧,我们需要右侧百分百相似，其实不需要降低右侧阈值
                # if sims[target_col] >= threshold:
                #     new_rhs = sims[target_col]
                #     spec_r_md = copy.deepcopy(vio_md)
                #     spec_r_md[target_col] = new_rhs
                #     if if_minimal(spec_r_md, md_list, target_col):
                #         md_list.append(spec_r_md)

                # 特殊化左侧
                for col in list(set(columns) - {target_col}):
                    if sims[col] + 0.001 <= 1:
                        spec_l_md = copy.deepcopy(vio_md)
                        spec_l_md[col] = threshold if sims[col] < threshold else sims[col] + 0.001
                        if if_minimal(spec_l_md, md_list, target_col):
                            md_list.append(spec_l_md)

            # tmp_minimal_vio = copy.deepcopy(minimal_vio)
            for vio in minimal_vio[:]:
                if not if_minimal(vio, md_list, target_col):
                    minimal_vio.remove(vio)

    manager = multiprocessing.Manager()
    lock = manager.Lock()
    if len(minimal_vio) == 0:
        return md_list, []
    pool = multiprocessing.Pool(len(minimal_vio))
    # tmp = copy.deepcopy(minimal_vio)
    with manager:
        proxy_minimal_vio = manager.list(minimal_vio)
        for _ in minimal_vio[:]:
            pool.apply_async(remove_by_confidence, args=(_, proxy_minimal_vio, data, target_col, lock))
        pool.close()
        pool.join()
        minimal_vio = list(proxy_minimal_vio)

    for _ in minimal_vio[:]:
        if not if_minimal(_, minimal_vio, target_col):
            minimal_vio.remove(_)

    for _ in md_list[:]:
        if not if_minimal(_, md_list, target_col):
            md_list.remove(_)

    return md_list, minimal_vio


def get_mds_metadata(md_list, dataset_path, target_col):
    data = pd.read_csv(dataset_path, low_memory=False, encoding='ISO-8859-1')
    data.fillna("", inplace=True)
    data = data.astype(str)

    manager = multiprocessing.Manager()
    if len(md_list) == 0:
        return []
    pool = multiprocessing.Pool(len(md_list))
    result = []
    with manager:
        for _ in md_list:
            task = pool.apply_async(get_one_md_metadata, args=(_, data, target_col))
            support, confidence = task.get()
            result.append({"md": _, "support": support, "confidence": confidence})
        pool.close()
        pool.join()
    return result


def get_one_md_metadata(md, dataframe, target_col):
    support = 0
    pre_confidence = 0
    for row1 in dataframe.itertuples():
        i = row1[0]
        df_slice = dataframe[i + 1:]
        for row2 in df_slice.itertuples():
            left_satisfy = True
            both_satisfy = True
            for col in dataframe.columns.values.tolist():
                sim = my_Levenshtein_ratio(getattr(row1, col), getattr(row2, col))
                if col == target_col:
                    if sim < 1:
                        both_satisfy = False
                else:
                    if sim < md[col]:
                        left_satisfy = False
                        both_satisfy = False
            if left_satisfy:
                support += 1
            if both_satisfy:
                pre_confidence += 1

    confidence = 0 if support == 0 else pre_confidence / support
    # return {"md": md, "support": support, "confidence": confidence}
    return support, confidence