import distutils.util

import numpy as np
from tqdm import tqdm

from loguru import logger


def print_arguments(args=None, configs=None, title=None):
    if args:
        logger.info("----------- 额外配置参数 -----------")
        for arg, value in sorted(vars(args).items()):
            logger.info(f"{arg}: {value}")
        logger.info("------------------------------------------------")
    if configs:
        title = title if title else "配置文件参数"
        logger.info(f"----------- {title} -----------")
        for arg, value in sorted(configs.items()):
            if isinstance(value, dict):
                logger.info(f"{arg}:")
                for a, v in sorted(value.items()):
                    if isinstance(v, dict):
                        logger.info(f"\t{a}:")
                        for a1, v1 in sorted(v.items()):
                            logger.info(f"\t\t{a1}: {v1}")
                    else:
                        logger.info(f"\t{a}: {v}")
            else:
                logger.info(f"{arg}: {value}")
        logger.info("------------------------------------------------")


def add_arguments(argname, type, default, help, argparser, **kwargs):
    type = distutils.util.strtobool if type == bool else type
    argparser.add_argument("--" + argname,
                           default=default,
                           type=type,
                           help=help + ' 默认: %(default)s.',
                           **kwargs)


class Dict(dict):
    __setattr__ = dict.__setitem__
    __getattr__ = dict.__getitem__


def dict_to_object(dict_obj):
    if not isinstance(dict_obj, dict):
        return dict_obj
    inst = Dict()
    for k, v in dict_obj.items():
        inst[k] = dict_to_object(v)
    return inst


# 根据对角余弦值计算准确率和最优的阈值
def cal_accuracy_threshold(y_score, y_true):
    y_score = np.asarray(y_score)
    y_true = np.asarray(y_true)
    best_accuracy = 0
    best_threshold = 0
    for i in tqdm(range(0, 100)):
        threshold = i * 0.01
        y_test = (y_score >= threshold)
        acc = np.mean((y_test == y_true).astype(int))
        if acc > best_accuracy:
            best_accuracy = acc
            best_threshold = threshold

    return best_accuracy, best_threshold


# 根据对角余弦值计算准确率
def cal_accuracy(y_score, y_true, threshold=0.5):
    y_score = np.asarray(y_score)
    y_true = np.asarray(y_true)
    y_test = (y_score >= threshold)
    accuracy = np.mean((y_test == y_true).astype(int))
    return accuracy


# 计算对角余弦值
def cosin_metric(x1, x2):
    return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))


# 根据a的类型，将b转换为相应的类型
def convert_string_based_on_type(a, b):
    if isinstance(a, int):
        try:
            b = int(b)
        except ValueError:
            logger.error("无法将字符串转换为整数")
    elif isinstance(a, float):
        try:
            b = float(b)
        except ValueError:
            logger.error("无法将字符串转换为浮点数")
    elif isinstance(a, str):
        return b
    elif isinstance(a, bool):
        b = b.lower() == 'true'
    else:
        try:
            b = eval(b)
        except Exception as e:
            logger.exception("无法将字符串转换为其他类型，将忽略该参数类型转换")
    return b