# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""

import copy
import re
from typing import List, Dict, Union, Callable
import numpy as np

import datasets
import evaluate
from rouge_chinese import Rouge
from scipy.optimize import linear_sum_assignment

# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {quad match score},
authors={huggingface, Inc.},
year={2020}
}
"""

# TODO: Add description of the module here
_DESCRIPTION = """\
evaluate sentiment quadruples.
评估生成模型的情感四元组
"""

# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of predictions to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Returns:
    score: sentiment quadruple match score
    
Examples:
    Examples should be written in doctest format, and should illustrate how
    to use the function.

    >>> import evaluate
    >>> module = evaluate.load("yuyijiong/quad_match_score")
    >>> predictions=["food | good | food#taste | pos"]
    >>> references=["food | good | food#taste | pos & service | bad | service#general | neg"]
    >>> result=module.compute(predictions=predictions, references=references)
    >>> print(result)
    result={'ave match score of weight (1, 1, 1, 1)': 0.375, 
    'f1 score of exact match': 0.0, 
    'f1 score of optimal match of weight (1, 1, 1, 1)': 0.5}
"""


# 计算rougel的f1值
def get_rougel_f1(text_pred_list: List[str], text_true_list: List[str]) -> float:
    assert len(text_pred_list) == len(text_true_list), "文本数量不一致"
    # 如果text_pred_list[0]为空字符串或空格，则返回0
    if not text_pred_list[0].strip():
        return 0

    rouge = Rouge()
    # 判断text_true[0]是否有中文，有中文则要用空格分割
    if re.search(u"[\u4e00-\u9fa5]+", text_pred_list[0]):
        text_pred_list = [' '.join(list(text_pred)) for text_pred in text_pred_list]
        text_true_list = [' '.join(list(text_true)) for text_true in text_true_list]

    rouge_l_f1 = rouge.get_scores(text_pred_list, text_true_list, avg=True)['rouge-l']['f']

    return rouge_l_f1


# 记录四元组的函数
class CommentUnitsSim:
    def __init__(self, data: List[Dict[str, str]], data_source: any = None, abnormal=False, language=None):
        self.data_source = data_source
        self.abnormal = abnormal
        data = copy.deepcopy(data)
        # 如果字典有target，则改名为target_text
        for quad_dict in data:
            if 'target' in quad_dict:
                quad_dict['target_text'] = quad_dict['target']
                del quad_dict['target']
            if 'opinion' in quad_dict:
                quad_dict['opinion_text'] = quad_dict['opinion']
                del quad_dict['opinion']

        self.data = data
        self.polarity_en2zh = {'positive': '积极', 'negative': '消极', 'neutral': '中性', 'pos': '积极', 'neg': '消极',
                               'neu': '中性', '积极': '积极', '消极': '消极', '中性': '中性'}
        self.polarity_zh2en = {'积极': 'pos', '消极': 'neg', '中性': 'neu', 'pos': 'pos', 'neg': 'neg', 'neu': 'neu',
                               'positive': 'pos', 'negative': 'neg', 'neutral': 'neu'}

        self.language = language if language is not None else 'zh' if self.check_zh() else 'en'
        self.none_sign = 'null'

    @property
    def num(self):
        return len(self.data)

    # 检查四元组中是否有中文
    def check_zh(self):
        for quad_dict in self.data:
            if re.search('[\u4e00-\u9fa5]', quad_dict['target_text']) or re.search('[\u4e00-\u9fa5]',
                                                                                   quad_dict['opinion_text']):
                return True
        return False

    # 检测极性是否正确
    def check_polarity(self):
        # 若有某个四元组的极性不是positive、negative、neutral，则返回False
        for quad_dict in self.data:
            if quad_dict['polarity'] not in ['positive', 'negative', 'neutral', 'pos', 'neg', 'neu', '积极', '消极',
                                             '中性']:
                self.abnormal = True
                return False

    # 将极性由英文转为中文
    def convert_polarity_en2zh(self):
        for quad_dict in self.data:
            quad_dict['polarity'] = self.polarity_en2zh[quad_dict['polarity']]
        return self

    # 将极性由中文转为英文
    def convert_polarity_zh2en(self):
        for quad_dict in self.data:
            quad_dict['polarity'] = self.polarity_zh2en[quad_dict['polarity']]
        return self

    # 检查是否有重复的四元组，若有则删除重复的
    def del_duplicate(self):
        new_data = []
        for quad_dict in self.data:
            if quad_dict not in new_data:
                new_data.append(quad_dict)
        self.data = new_data
        return self

    # 检查是否有target和opinion都为null的四元组，若有则返回True
    def check_target_opinion_null(self):
        for quad_dict in self.data:
            if quad_dict['target_text'] == 'null' and quad_dict['opinion_text'] == 'null':
                return True
        return False

    # 检查是否有target或opinion为null的四元组，若有则返回True
    def check_any_null(self):
        for quad_dict in self.data:
            if quad_dict['target_text'] == 'null' or quad_dict['opinion_text'] == 'null':
                return True
        return False

    @classmethod
    def from_str(cls, quadruple_str: str, tuple_len: Union[int, list, str] = 4, format_code=0, sep_token1=' & ',
                 sep_token2=' | '):
        data = []
        abnormal = False
        # 确保分隔符后面一定是空格
        for i in range(len(quadruple_str) - 1):
            if (quadruple_str[i] == sep_token1.strip() or quadruple_str[i] == sep_token2.strip()) and quadruple_str[
                i + 1] != ' ':
                quadruple_str = quadruple_str[:i + 1] + ' ' + quadruple_str[i + 1:]

        # 选择几元组，即创建列表索引，从四元组中抽出n元
        if isinstance(tuple_len, int):
            tuple_index = list(range(tuple_len))
        elif isinstance(tuple_len, list):
            tuple_index = tuple_len
        elif isinstance(tuple_len, str):
            # 例如将‘012’转换为[0,1,2]
            tuple_index = [int(i) for i in tuple_len]
        else:
            raise Exception('tuple_len参数错误')

        for quadruple in quadruple_str.split(sep_token1):
            if format_code == 0:
                # quadruple可能是target|opinion|aspect|polarity，也可能是target|opinion|aspect，也可能是target|opinion,若没有则为“None”
                quadruple_split = [unit.strip() for unit in quadruple.split(sep_token2)]
                if len(quadruple_split) > len(tuple_index):
                    print('quadruple格式错误，过多元素', quadruple_str)
                    abnormal = True
                    quadruple_split = quadruple_split[0:len(tuple_index)]  # 过长则截断
                elif len(quadruple_split) < len(tuple_index):
                    print('quadruple格式错误，过少元素', quadruple_str)
                    abnormal = True
                    quadruple_split = ["None"] * (
                                len(tuple_index) - len(quadruple_split)) + quadruple_split  # 过短则补'None'

                quadruple_keys = [["target_text", "opinion_text", "aspect", "polarity"][i] for i in tuple_index]
                quadruple_dict = dict(zip(quadruple_keys, quadruple_split))

                q = {"target_text": 'None', "opinion_text": 'None', "aspect": 'None', "polarity": 'None'}
                q.update(quadruple_dict)
                # 检查极性是否合法
                if q['polarity'] not in ['pos', 'neg', 'neu', 'None', '积极', '消极', '中性']:
                    print('quadruple格式错误，极性格式不对', quadruple_str)

            else:
                raise Exception('answer_format参数错误')

            data.append(q)

        return CommentUnitsSim(data, quadruple_str, abnormal)

    @classmethod
    def from_list(cls, quadruple_list: List[List[str]], **kwargs):
        data = []
        for quadruple in quadruple_list:
            # #format_code='013'代表list只有四元组的第0、1、3个元素，需要扩充为4元组，空缺位置补上None
            # if format_code=='013':
            #     quadruple.insert(2,None)

            data.append(
                {"target_text": quadruple[0], "opinion_text": quadruple[1], "aspect": quadruple[2],
                 "polarity": quadruple[3]})

        return CommentUnitsSim(data, quadruple_list, **kwargs)

    @classmethod
    def from_list_dict(cls, quadruple_list: List[dict], **kwargs):
        for quad_dict in quadruple_list:
            if 'target' in quad_dict:
                quad_dict['target_text'] = quad_dict['target']
                del quad_dict['target']
            if 'opinion' in quad_dict:
                quad_dict['opinion_text'] = quad_dict['opinion']
                del quad_dict['opinion']

        data = []
        for quadruple in quadruple_list:
            # 如果quadruple缺少某个key，则补上None
            q = {"target_text": 'None', "opinion_text": 'None', "aspect": 'None', "polarity": 'None'}
            q.update(quadruple)
            data.append(q)

        return CommentUnitsSim(data, quadruple_list, **kwargs)

    # 转化为list,即只保留字典的value
    def to_list(self):
        data = []
        for quad_dict in self.data:
            data.append(
                [quad_dict['target_text'], quad_dict['opinion_text'], quad_dict['aspect'], quad_dict['polarity']])
        return data

    # 将data转换为n元组字符串
    def get_quadruple_str(self, format_code=0, tuple_len: Union[int, list, str] = 4, sep_token1=' & ',
                          sep_token2=' | '):
        new_text_list = []
        # 选择几元组，即创建列表索引，从四元组中抽出n元
        if isinstance(tuple_len, int):
            tuple_index = list(range(tuple_len))
        elif isinstance(tuple_len, list):
            tuple_index = tuple_len
        elif isinstance(tuple_len, str):
            # 例如将‘012’转换为[0,1,2]
            tuple_index = [int(i) for i in tuple_len]
        else:
            raise Exception('tuple_len参数错误')

        try:
            # 若语言为中文，则使用中文极性
            if self.language == 'zh':
                self.convert_polarity_en2zh()
            else:
                self.convert_polarity_zh2en()
        except:
            print('语言参数错误', self.data)
            print(self.language)
            raise Exception('语言参数错误')

        # 若tuple_index==[3]，则返回综合情感极性
        if tuple_index == [3]:
            return self.merge_polarity()

        for quad_dict in self.data:
            # 提取target_text，如果空列表则为''，如果列表长度大于1则为'，'.join(list)
            target_text = quad_dict['target_text']
            # 提取opinion_text，如果空列表则为''，如果列表长度大于1则为'，'.join(list)
            opinion_text = quad_dict['opinion_text']
            # 提取aspect
            aspect = quad_dict['aspect']
            # 提取polarity
            polarity = quad_dict['polarity']

            # 拼接，‘|’分割
            if format_code == 0:
                # 根据tuple_len拼接
                new_text = sep_token2.join([[target_text, opinion_text, aspect, polarity][i] for i in tuple_index])
            else:
                raise Exception('answer_format参数错误')

            new_text_list.append(new_text)

        # 如果tuple_index为[2,3]，则需要去除new_text_list中重复的元素，不要改变顺序。因为可能有重复的方面
        if tuple_index == [2, 3]:
            res = []
            for t in new_text_list:
                if t not in res:
                    res.append(t)
            new_text_list = res

        # 如果tuple_index为[3]，则只保留new_text_list的第一个元素。因为只有一个情感极性
        elif tuple_index == [3]:
            new_text_list = new_text_list[:1]

        if format_code == 0:
            # 根据tuple_len拼接
            return sep_token1.join(new_text_list)

    # 与另一个CommentUnits对象对比，检测有几个相同的四元组
    def compare_same(self, other) -> int:
        count = 0
        for quad_dict in self.data:
            if quad_dict in other.data:
                count += 1
        return count

    # 检查自身数据的四元组中target是否有重复
    def check_target_repeat(self):
        target_list = []
        for quad_dict in self.data:
            target_list.append(quad_dict['target_text'])
        return len(target_list) != len(set(target_list))

    # 检查自身数据的四元组中opinion是否有重复
    def check_opinion_repeat(self):
        opinion_list = []
        for quad_dict in self.data:
            opinion_list.append(quad_dict['opinion_text'])
        return len(opinion_list) != len(set(opinion_list))

    # 检查自身数据的四元组中aspect是否有重复
    def check_aspect_repeat(self):
        aspect_list = []
        for quad_dict in self.data:
            aspect_list.append(quad_dict['aspect'])
        return len(aspect_list) != len(set(aspect_list))

    # 输出所有aspect的列表
    def get_aspect_list(self):
        aspect_list = []
        for quad_dict in self.data:
            aspect_list.append(quad_dict['aspect'])
        return aspect_list

    # 输出所有target的列表
    def get_target_list(self):
        target_list = []
        for quad_dict in self.data:
            target_list.append(quad_dict['target_text'])
        return target_list

    # 输出所有opinion的列表
    def get_opinion_list(self):
        opinion_list = []
        for quad_dict in self.data:
            opinion_list.append(quad_dict['opinion_text'])
        return opinion_list

    # 输出所有polarity的列表
    def get_polarity_list(self):
        polarity_list = []
        for quad_dict in self.data:
            polarity_list.append(quad_dict['polarity'])
        return polarity_list

    # 对所有polarity进行综合
    def merge_polarity(self):
        polarity_list = self.get_polarity_list()
        # 判断是英文还是中文
        if self.language == 'en':
            if 'pos' in polarity_list and 'neg' in polarity_list:
                return 'neu'
            elif 'pos' in polarity_list:
                return 'pos'
            elif 'neg' in polarity_list:
                return 'neg'
            else:
                return 'neu'
        else:
            if '积极' in polarity_list and '消极' in polarity_list:
                return '中性'
            elif '积极' in polarity_list:
                return '积极'
            elif '消极' in polarity_list:
                return '消极'
            else:
                return '中性'

    # 检测是否有不合法opinion
    def check_opinion_in_comment(self, comment_text):
        for quad_dict in self.data:
            if quad_dict['opinion_text'] != '*' and (not quad_dict['opinion_text'] in comment_text):
                return False
        return True

    # 检测是否有不合法target
    def check_target_in_comment(self, comment_text):
        for quad_dict in self.data:
            if quad_dict['target_text'] != '*' and (not quad_dict['target_text'] in comment_text):
                return False
        return True

    # 计算两个四元组的相似度
    @staticmethod
    def get_similarity(units1, units2: 'CommentUnitsSim'):
        pass

    # 对自身数据进行操作
    def apply(self, func: Callable, field: str):
        for quad_dict in self.data:
            quad_dict[field] = func(quad_dict[field])
        return self


# 四元组匹配函数
class CommentUnitsMatch:
    def __init__(self, target_weight=0.5, opinion_weight=0.5, aspect_weight=0.5, polarity_weight=0.5, one_match=True):
        # 归一化权重
        weight_sum = target_weight + opinion_weight + aspect_weight + polarity_weight
        self.target_weight = target_weight / weight_sum
        self.opinion_weight = opinion_weight / weight_sum
        self.aspect_weight = aspect_weight / weight_sum
        self.polarity_weight = polarity_weight / weight_sum

        # 是否一对一匹配
        self.one_match = one_match

    # 特定feature置零
    def set_zero(self, feature: str = 'polarity'):
        if feature == 'polarity':
            self.polarity_weight = 0
        elif feature == 'aspect':
            self.aspect_weight = 0
        elif 'opinion' in feature:
            self.opinion_weight = 0
        elif 'target' in feature:
            self.target_weight = 0
        else:
            raise Exception('feature参数错误')

    def re_normalize(self):
        weight_sum = self.target_weight + self.opinion_weight + self.aspect_weight + self.polarity_weight
        self.target_weight = self.target_weight / weight_sum
        self.opinion_weight = self.opinion_weight / weight_sum
        self.aspect_weight = self.aspect_weight / weight_sum
        self.polarity_weight = self.polarity_weight / weight_sum

    # 计算cost矩阵，完全匹配为0，不匹配为1
    def get_cost_matrix(self, units1: 'CommentUnitsSim', units2: 'CommentUnitsSim', feature: str = 'polarity'):
        pass
        # 检查此feature是否存在，不存在则返回全0矩阵
        if units1.data[0].get(feature) is None or units2.data[0].get(feature) is None \
                or units1.data[0].get(feature) == 'None' or units2.data[0].get(feature) == 'None':
            cost_matrix = np.zeros((len(units1.data), len(units2.data)))
            # 对应feature的weight也为0
            self.set_zero(feature)

            # 并再次归一化
            self.re_normalize()

            return cost_matrix

        # 检查两个四元组的极性是否相同，生成cost矩阵，用于匈牙利算法。不相同则cost为1，相同则cost为0
        cost_matrix = []
        for quad_dict1 in units1.data:
            cost_list = []
            for quad_dict2 in units2.data:
                if quad_dict1[feature] == quad_dict2[feature]:
                    cost_list.append(0)
                else:
                    cost_list.append(1)
            cost_matrix.append(cost_list)

        # cost矩阵转换为numpy数组，大小为(len(units1.data),len(units2.data))
        cost_matrix = np.array(cost_matrix)
        return cost_matrix

    # 计算cost矩阵，使用rougel指标
    def get_cost_matrix_rouge(self, units1: 'CommentUnitsSim', units2: 'CommentUnitsSim', feature: str = 'target_text'):
        # 检查此feature是否存在，不存在则返回全0矩阵
        if units1.data[0].get(feature) is None or units2.data[0].get(feature) is None \
                or units1.data[0].get(feature) == 'None' or units2.data[0].get(feature) == 'None':
            cost_matrix = np.zeros((len(units1.data), len(units2.data)))
            # 对应feature的weight也为0
            self.set_zero(feature)
            # 并再次归一化
            self.re_normalize()
            return cost_matrix

        # 检查两个四元组的极性是否相同，生成cost矩阵，用于匈牙利算法。相同则cost为0，不相同则cost为1-rougel
        cost_matrix = []
        for quad_dict1 in units1.data:
            cost_list = []
            for quad_dict2 in units2.data:
                if quad_dict1[feature] == quad_dict2[feature]:
                    cost_list.append(0)
                else:
                    cost_list.append(1 - get_rougel_f1([quad_dict1[feature]], [quad_dict2[feature]]))
            cost_matrix.append(cost_list)

        # cost矩阵转换为numpy数组，大小为(len(units1.data),len(units2.data))
        cost_matrix = np.array(cost_matrix)
        return cost_matrix

    # 匹配四元组并计算cost
    def match_units(self, units1: 'CommentUnitsSim', units2: 'CommentUnitsSim') -> tuple:
        # 计算极性的cost矩阵，矩阵元素在0-1之间
        cost_matrix_polarity = self.get_cost_matrix(units1, units2, feature='polarity')
        # 计算aspect的cost矩阵
        cost_matrix_aspect = self.get_cost_matrix(units1, units2, feature='aspect')
        # 计算target的cost矩阵
        cost_matrix_target = self.get_cost_matrix_rouge(units1, units2, feature='target_text')
        # 计算opinion的cost矩阵
        cost_matrix_opinion = self.get_cost_matrix_rouge(units1, units2, feature='opinion_text')

        # 计算总的cost矩阵，矩阵元素在0-1之间。矩阵的行数为units1即pred的数量，列数为units2即true的数量
        cost_matrix = self.target_weight * cost_matrix_target + self.opinion_weight * cost_matrix_opinion + \
                      self.aspect_weight * cost_matrix_aspect + self.polarity_weight * cost_matrix_polarity
        score_matrix = 1 - cost_matrix

        cost = 0
        # 使用匈牙利算法进行匹配
        if self.one_match:
            # 只允许一对一的匹配，这种情况下row_ind和col_ind的长度一定相等且等于units1和units2的数量中的较小值
            row_ind, col_ind = linear_sum_assignment(cost_matrix)

        else:
            # 允许一对多的匹配。这种情况下每个四元组都一定匹配上,这种情况下row_ind和col_ind的长度一定相等且等于units1和units2的数量中的较大值
            if units1.num > units2.num:
                row_ind = np.arange(units1.num)
                col_ind = np.argmin(cost_matrix, axis=1)

            else:
                row_ind = np.argmin(cost_matrix, axis=0)
                col_ind = np.arange(units2.num)

        # 计算这种匹配的cost
        for i in range(len(row_ind)):
            cost += cost_matrix[row_ind[i]][col_ind[i]]

        # 计算这种匹配下的TP\FP\FN
        TP = 0
        for i in range(len(row_ind)):
            TP += score_matrix[row_ind[i]][col_ind[i]]

        # len(row_ind)为pred的数量，TP为匹配上的数量
        FP = units1.num - TP
        FN = units2.num - TP

        # 如果一对一匹配，会有匹配不上的四元组，这些四元组cost为1
        max_units_num = max(units1.num, units2.num)
        if self.one_match:
            cost += (max_units_num - len(row_ind))

        # 对cost进行归一化，使其在0-1之间
        cost_per_quadruple = cost / max_units_num
        if cost_per_quadruple > 1 or cost_per_quadruple < 0:
            print('cost错误', cost_per_quadruple, 'pred:', units1.data, 'true:', units2.data)
            print(self.target_weight, self.opinion_weight, self.aspect_weight, self.polarity_weight)

        # 返回的cost在0-1之间
        return cost_per_quadruple, TP, FP, FN


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class QuadMatch(evaluate.Metric):
    """TODO: Short description of my evaluation module."""

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules page.
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            features=[
                datasets.Features(
                    {
                        "predictions": datasets.Value("string", id="sequence"),
                        "references": datasets.Sequence(datasets.Value("string", id="sequence")),
                    }
                ),
                datasets.Features(
                    {
                        "predictions": datasets.Value("string", id="sequence"),
                        "references": datasets.Value("string", id="sequence"),
                    }
                ),
            ],
            # Homepage of the module for documentation
            homepage="http://module.homepage",
            # Additional links to the codebase or references
            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
            reference_urls=["http://path.to.reference.url/new_module"]
        )

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        # TODO: Download external resources if needed
        pass

    def _compute(self,
                 predictions: List[str],
                 references: Union[List[str], List[List[str]]],
                 quad_weights: tuple = (1, 1, 1, 1),
                 **kwargs) -> dict:
        '''

        :param predictions: list of predictions of sentiment quads
        :param references: list of references of sentiment quads
        :param quad_weights: weight of target,opinion,aspect,polarity for cost compute

        :param kwargs:
                :param tuple_len: indicate the format of the quad, see the following mapping
                :param sep_token1: the token to seperate quads
                :param sep_token2: the token to seperate units of one quad

        :return:average matching score

        #mapping
        id2prompt={'0123':"quadruples (target | opinion | aspect | polarity)",
           '':"quadruples (target | opinion | aspect | polarity)",
           '01':'pairs (target | opinion)',
           '012':'triples (target | opinion | aspect)',
           '013':'triples (target | opinion | polarity)',
           '023':'triples (target | aspect | polarity)',
           '23':'pairs (aspect | polarity)',
           '03':'pairs (target | polarity)',
           '13':'pairs (opinion | polarity)',
           '3':'single (polarity)'}

        #中文版映射
        id2prompt_zh={'0123': "四元组(对象 | 观点 | 方面 | 极性)",
                      '':"四元组(对象 | 观点 | 方面 | 极性)",
                      '01':'二元组(对象 | 观点)',
                      '012':'三元组(对象 | 观点 | 方面)',
                      '013':'三元组(对象 | 观点 | 极性)',
                      '023':'三元组(对象 | 方面 | 极性)',
                      '23':'二元组(方面 | 极性)',
                      '03':'二元组(对象 | 极性)',
                      '13':'二元组(观点 | 极性)',
                        '3':'单元素(极性)'}
        '''
        f1_of_optimal_match, score_of_optimal_match = self.quad_f1_of_optimal_match(predictions, references,
                                                                                    quad_weights, **kwargs)
        f1 = self.quad_f1_of_exact_match(predictions=predictions, references=references, **kwargs)

        # 取1-cost为得分
        return {'score of optimal match of weight ' + str(quad_weights): score_of_optimal_match,
                'f1 of optimal match of weight ' + str(quad_weights): f1_of_optimal_match,
                'f1 of exact match': f1}

    @staticmethod
    def quad_f1_of_exact_match(predictions: List[str], references: Union[List[str], List[List[str]]],
                               return_dict=False, **kwargs) -> Union[Dict[str, float], float]:
        assert len(predictions) == len(references), "文本数量不一致"
        correct, pred_num, true_num = 0, 0, 0

        for pred, refer in zip(predictions, references):
            pred = CommentUnitsSim.from_str(pred, **kwargs)
            # refer转换为list
            if isinstance(refer, str):
                refer =[refer]

            # refer转换为CommentUnitsSim
            refer = [CommentUnitsSim.from_str(t, **kwargs) for t in refer]

            # 如果refer是list，说明有多个正确答案，取最高分的那个
            #计算每个refer的TP的个数
            correct_list = [pred.compare_same(t) for t in refer]
            #计算每个refer的f1
            f1_list=[2 * correct_list[i] / (pred.num + refer[i].num) for i in range(len(refer))]
            # 获取f1得分最高的索引
            best_index = f1_list.index(max(f1_list))
            pred_num += pred.num
            true_num += refer[best_index].num
            correct += correct_list[best_index]


        # 以下结果保留4位小数
        precision = round(correct / pred_num, 4) + 1e-8
        recall = round(correct / true_num, 4) + 1e-8
        f1 = round(2 * precision * recall / (precision + recall), 4)

        if return_dict:
            return {"precision": precision, "recall": recall, "f1": f1}
        else:
            return f1

    # 计算最优匹配f1
    @staticmethod
    def quad_f1_of_optimal_match(
            predictions: List[str],
            references: Union[List[str], List[List[str]]],
            quad_weights: tuple = (1, 1, 1, 1),
            one_match=True,
            **kwargs):

        assert len(predictions) == len(references)
        if isinstance(predictions, str):
            predictions = [predictions]
            references = [references]

        cost = 0
        TP, FP, FN = 0, 0, 0
        matcher = CommentUnitsMatch(*quad_weights, one_match=one_match)

        for pred, refer in zip(predictions, references):

            pred = CommentUnitsSim.from_str(pred, **kwargs)
            # 将refer转换为list形式
            if isinstance(refer, str):
                refer = [refer]

            # 将refer中的每个元素转换为CommentUnitsSim
            refer = [CommentUnitsSim.from_str(t, **kwargs) for t in refer]

            # 如果true是多个正确答案，取最高分
            cost_list = [matcher.match_units(pred, t) for t in refer]
            # 获取cost最小的值的索引，按元组中第一个元素大小排序
            # 计算每一对样本的cost，TP，FP，FN
            cost_, TP_, FP_, FN_ = cost_list[np.argmin([c[0] for c in cost_list])]
            cost += cost_
            TP += TP_
            FP += FP_
            FN += FN_

        # 平均cost
        cost = cost / len(predictions)
        # 由TP\FP\FN计算最优匹配F1
        precision_match = TP / (TP + FP)
        recall_match = TP / (TP + FN)
        f1_match = 2 * precision_match * recall_match / (precision_match + recall_match)

        return f1_match, 1 - cost