# MIT License
#
# Copyright (c) 2023 THU-KEG & Zhipu AI
#
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# of this software and associated documentation files (the "Software"), to deal
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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import re
import string
from collections import Counter

try:
    import jieba
    from fuzzywuzzy import fuzz
    from rouge import Rouge
except ImportError:
    raise ImportError(
        'Please install the required dependencies for this task with `pip install lm_eval["longbench"] or `pip install jieba fuzzywuzzy rouge`'
    )

# taken from https://github.com/THUDM/LongBench


def normalize_answer(s: str) -> str:
    """Lower text and remove punctuation, articles and extra whitespace."""

    def remove_articles(text):
        return re.sub(r"\b(a|an|the)\b", " ", text)

    def white_space_fix(text):
        return " ".join(text.split())

    def remove_punc(text):
        exclude = set(string.punctuation)
        return "".join(ch for ch in text if ch not in exclude)

    def lower(text):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(s))))


def normalize_zh_answer(s: str) -> str:
    """Lower text and remove punctuation, extra whitespace."""

    def white_space_fix(text):
        return "".join(text.split())

    def remove_punc(text):
        cn_punctuation = "！？｡。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏."
        all_punctuation = set(string.punctuation + cn_punctuation)
        return "".join(ch for ch in text if ch not in all_punctuation)

    def lower(text):
        return text.lower()

    return white_space_fix(remove_punc(lower(s)))


def count_score(predictions: list[str], references: list[str], **kwargs) -> float:
    prediction, ground_truth = predictions[0], references[0]
    numbers = re.findall(r"\d+", prediction)
    right_num = 0
    for number in numbers:
        if str(number) == str(ground_truth):
            right_num += 1
    final_score = 0.0 if len(numbers) == 0 else right_num / len(numbers)
    return float(final_score)


def retrieval_score(predictions: list[str], references: list[str], **kwargs) -> float:
    prediction, ground_truth = predictions[0], references[0]
    pattern = r"Paragraph (\d+)"
    matches = re.findall(pattern, ground_truth)
    ground_truth_id = matches[0]
    numbers = re.findall(r"\d+", prediction)
    right_num = 0
    for number in numbers:
        if str(number) == str(ground_truth_id):
            right_num += 1
    final_score = 0.0 if len(numbers) == 0 else right_num / len(numbers)
    return float(final_score)


def retrieval_zh_score(
    predictions: list[str], references: list[str], **kwargs
) -> float:
    prediction, ground_truth = predictions[0], references[0]
    pattern = r"段落(\d+)"
    matches = re.findall(pattern, ground_truth)
    ground_truth_id = matches[0]
    numbers = re.findall(r"\d+", prediction)
    right_num = 0
    for number in numbers:
        if str(number) == str(ground_truth_id):
            right_num += 1
    final_score = 0.0 if len(numbers) == 0 else right_num / len(numbers)
    return float(final_score)


def code_sim_score(predictions: list[str], references: list[str], **kwargs) -> float:
    prediction, ground_truth = predictions[0], references[0]
    all_lines = prediction.lstrip("\n").split("\n")
    prediction = ""
    for line in all_lines:
        if ("`" not in line) and ("#" not in line) and ("//" not in line):
            prediction = line
            break
    return fuzz.ratio(prediction, ground_truth) / 100


def classification_score(
    predictions: list[str], references: list[str], **kwargs
) -> float:
    prediction, ground_truth = predictions[0], references[0]
    em_match_list = []
    all_classes = kwargs["all_classes"]
    for class_name in all_classes:
        if class_name in prediction:
            em_match_list.append(class_name)
    for match_term in em_match_list:
        if match_term in ground_truth and match_term != ground_truth:
            em_match_list.remove(match_term)
    if ground_truth in em_match_list:
        score = 1.0 / len(em_match_list)
    else:
        score = 0.0
    return score


def rouge_score(predictions: list[str], references: list[str], **kwargs) -> float:
    prediction, ground_truth = predictions[0], references[0]
    rouge = Rouge()
    try:
        scores = rouge.get_scores([prediction], [ground_truth], avg=True)
        # ruff: noqa
    except:
        return 0.0
    return scores["rouge-l"]["f"]


def rouge_zh_score(predictions: list[str], references: list[str], **kwargs) -> float:
    prediction, ground_truth = predictions[0], references[0]
    prediction = " ".join(list(jieba.cut(prediction, cut_all=False)))
    ground_truth = " ".join(list(jieba.cut(ground_truth, cut_all=False)))
    score = rouge_score([prediction], [ground_truth])
    return score


def f1_score(predictions: list[str], references: list[str], **kwargs):
    try:
        prediction, ground_truth = predictions[0], references[0]
    except:
        return 0.0
    common = Counter(prediction) & Counter(ground_truth)
    num_same = sum(common.values())
    if num_same == 0:
        return 0
    precision = 1.0 * num_same / len(prediction)
    recall = 1.0 * num_same / len(ground_truth)
    f1 = (2 * precision * recall) / (precision + recall)
    return f1


def qa_f1_score(predictions: list[str], references: list[str], **kwargs) -> float:
    prediction, ground_truth = predictions[0], references[0]
    normalized_prediction = normalize_answer(prediction)
    normalized_ground_truth = normalize_answer(ground_truth)

    prediction_tokens = normalized_prediction.split()
    ground_truth_tokens = normalized_ground_truth.split()
    try:
        res = f1_score(prediction_tokens, ground_truth_tokens)
    except:
        return 0.0
    return res


def qa_f1_zh_score(predictions: list[str], references: list[str], **kwargs) -> float:
    prediction, ground_truth = predictions[0], references[0]
    prediction_tokens = list(jieba.cut(prediction, cut_all=False))
    ground_truth_tokens = list(jieba.cut(ground_truth, cut_all=False))
    prediction_tokens = [normalize_zh_answer(token) for token in prediction_tokens]
    ground_truth_tokens = [normalize_zh_answer(token) for token in ground_truth_tokens]
    prediction_tokens = [token for token in prediction_tokens if len(token) > 0]
    ground_truth_tokens = [token for token in ground_truth_tokens if len(token) > 0]
    return f1_score(prediction_tokens, ground_truth_tokens)