import os
import sys

import numpy as np
import torch


def mkdirs(paths):
    if isinstance(paths, list) and not isinstance(paths, str):
        for path in paths:
            mkdir(path)
    else:
        mkdir(paths)


def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)


def unnormalize(tens, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
    # assume tensor of shape NxCxHxW
    return (
        tens * torch.Tensor(std)[None, :, None, None]
        + torch.Tensor(mean)[None, :, None, None]
    )


# 重定向 sys.stdout 输出到一个指定的文件，同时保持在终端显示
class Logger(object):
    """Log stdout messages."""

    def __init__(self, outfile):
        self.terminal = sys.stdout  # 保存当前标准输出流
        self.log = open(outfile, 'a')  # 打开指定的日志文件 outfile，以追加模式 (a) 打开
        # 将标准输出流重定向到 Logger 实例，这样所有的 print 语句和标准输出内容都会通过 Logger 的 write 方法
        sys.stdout = self

    def write(self, message):
        self.terminal.write(message)  # 将消息写到终端（标准输出）
        self.log.write(message)  # 将消息写到日志文件

    def flush(self):
        self.terminal.flush()  # 刷新终端的输出缓冲区，确保所有输出都及时显示


class EarlyStopping:
    """Early stops the training if validation loss doesn't improve after a given patience."""

    def __init__(self, patience=7, verbose=False, delta=0):
        """
        Args:
            patience (int): How long to wait after last time validation loss improved.
                            Default: 7
            verbose (bool): If True, prints a message for each validation loss improvement.
                            Default: False
            delta (float): Minimum change in the monitored quantity to qualify as an improvement.
                            Default: 0
        """
        self.patience = patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.val_loss_min = np.Inf
        self.delta = delta

    def __call__(self, val_loss, model):

        score = -val_loss

        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(val_loss, model)
        elif score < self.best_score + self.delta:
            self.counter += 1
            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.save_checkpoint(val_loss, model)
            self.counter = 0

    def save_checkpoint(self, val_loss, model):
        """Saves model when validation loss decrease."""
        if self.verbose:
            print(
                f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...'
            )
        self.val_loss_min = val_loss


def printSet(set_str):
    set_str = str(set_str)
    num = len(set_str)
    print('=' * num * 3)
    print(' ' * num + set_str)
    print('=' * num * 3)