import json
import re
import string
import warnings

import numpy as np


def normalize_number_str(number_str: str) -> float:
    # we replace these common units and commas to allow
    # conversion to float
    for char in ["$", "%", ","]:
        number_str = number_str.replace(char, "")
    try:
        return float(number_str)
    except ValueError:
        print(f"String {number_str} cannot be normalized to number str.")
        return float("inf")


def normalize_answer(a):
    # Lower case
    # Trim (left and right)
    # Replace multiple spaces with one space
    # Remove trailing punctuation
    # return re.sub(r"[\.\!\?]+$", "", re.sub(r"\s+", " ", a.strip().lower()))
    if isinstance(a, list):
        a = ''.join(a)
    return a.strip().lower()


def exact_match(answer, ground_truth):
    return normalize_answer(answer) == normalize_answer(ground_truth)


def keyword_match(answer, ground_truth):
    return normalize_answer(ground_truth) in normalize_answer(answer)


def split_string(
        s: str,
        char_list: list[str] = [",", ";"],
) -> list[str]:
    pattern = f"[{''.join(char_list)}]"
    return re.split(pattern, s)


def question_scorer(
        user_task: str,
        val: str,
) -> bool:
    def is_float(element: any) -> bool:
        try:
            float(element)
            return True
        except ValueError:
            return False

    # 打分机制
    level = 0
    expertise = 0
    reasoning = 0
    comprehension = 0

    final_answer = user_task["final_answer"]
    expected_answer = val["Final answer"]

    data = val["score"]
    chat_score = []
    for i in range(len(data['type'])):
        item = {
            'type': data['type'][i],
            'question': data['question'][i],
            'choices': data['choices'][i],
            'answer': data['answer'][i],
            'expertise': data['expertise'][i],
            'reasoning': data['reasoning'][i],
            'comprehension': data['comprehension'][i],
            'score': data['score'][i]
        }
        chat_score.append(item)

    for i, score_item in enumerate(chat_score):
        answer_true = False
        if score_item['type'].lower() == 'multiple choice':
            if exact_match(user_task["score_answer"][i], score_item['answer']):
                answer_true = True
        elif score_item['type'].lower() == 'fill in the blanks':
            if keyword_match(user_task["score_answer"][i], score_item['answer']):
                answer_true = True
        elif score_item['type'].lower() == 'short answer questions':
            for ground_truth in score_item['answer']:
                if keyword_match(user_task["score_answer"][i], ground_truth):
                    answer_true = True
                    break
        # print(answer, score_item['answer'], answer_true)
        # 加分
        if answer_true:
            expertise += score_item['expertise']
            reasoning += score_item['reasoning']
            comprehension += score_item['comprehension']
            if score_item['score'] > level:
                level = score_item['score']
            print([level, expertise, reasoning, comprehension])
    # final_answer正确 则满分，但是能力分不加了
    if expected_answer and exact_match(final_answer, expected_answer):
        level = 10
    return [level, expertise, reasoning, comprehension]

    score = 0
    if user_task["final_answer"] == val["Final answer"]:
        score = val["Total score"]
    else:
        for i, item in enumerate(val["score"]["question"]):
            if user_task["score_answer"][i] in val["score"]["answer"][i] and val["score"]["score"][i] > score:
                score = item["score"]
    return score

    # # if gt is a number
    # if is_float(ground_truth):
    #     print(f"Evaluating {model_answer} as a number.")
    #     normalized_answer = normalize_number_str(model_answer)
    #     return normalized_answer == float(ground_truth)
    #
    # # if gt is a list
    # elif any(char in ground_truth for char in [",", ";"]):
    #     print(f"Evaluating {model_answer} as a comma separated list.")
    #     # question with the fish: normalization removes punct
    #
    #     gt_elems = split_string(ground_truth)
    #     ma_elems = split_string(model_answer)
    #
    #     # check length is the same
    #     if len(gt_elems) != len(ma_elems):
    #         warnings.warn(
    #             "Answer lists have different lengths, returning False.", UserWarning
    #         )
    #         return False
    #
    #     # compare each element as float or str
    #     comparisons = []
    #     for ma_elem, gt_elem in zip(ma_elems, gt_elems):
    #         if is_float(gt_elem):
    #             normalized_ma_elem = normalize_number_str(ma_elem)
    #             comparisons.append(normalized_ma_elem == float(gt_elem))
    #         else:
    #             # we do not remove punct since comparisons can include punct
    #             comparisons.append(
    #                 normalize_str(ma_elem, remove_punct=False)
    #                 == normalize_str(gt_elem, remove_punct=False)
    #             )
    #     return all(comparisons)
    #
    # # if gt is a str
    # else:
    #     print(f"Evaluating {model_answer} as a string.")
    #     return normalize_str(model_answer) == normalize_str(ground_truth)


def normalize_str(input_str, remove_punct=True) -> str:
    """
    Normalize a string by:
    - Removing all white spaces
    - Optionally removing punctuation (if remove_punct is True)
    - Converting to lowercase
    Parameters:
    - input_str: str, the string to normalize
    - remove_punct: bool, whether to remove punctuation (default: True)
    Returns:
    - str, the normalized string
    """
    # Remove all white spaces. Required e.g for seagull vs. sea gull
    no_spaces = re.sub(r"\s", "", input_str)

    # Remove punctuation, if specified.
    if remove_punct:
        translator = str.maketrans("", "", string.punctuation)
        return no_spaces.lower().translate(translator)
    else:
        return no_spaces.lower()