# -*- encoding:utf-8 -*-

'''
@Author : dingjiawen
@Date : 2023/11/10 15:57
@Usage : 
@Desc :
'''

import os
import shutil

import numpy as np
import pandas as pd


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


# 递归删除文件夹
def folderDelete(folder_name):
    if os.path.exists(folder_name):
        shutil.rmtree(folder_name)


# 判断这次是否进行模型保存,history_loss存储历史上的loss
def SaveBestModel(model, save_name, history_loss, loss_value, pattern: str = "min", epoch=0, is_all=False):
    weight_folder = save_name[:-4]
    if is_all:
        weight_folder = weight_folder + '_epoch' + str(epoch) + "_" + str(loss_value)
        save_name = weight_folder + save_name[-7:]

    # 如果history_loss为空,那么直接保存
    if len(history_loss) == 0:
        folderGenerate(weight_folder)
        model.save_weights(save_name)
        return

    if pattern == "min":
        # 先判断要不要存模型,如果上一次的比这一次的loss要大,就保存这一次的
        if np.min(history_loss) > loss_value:
            # 删除上一次的保存这一次的
            folderDelete(weight_folder)
            folderGenerate(weight_folder)
            model.save_weights(save_name)
            print("保存这次模型")
            return
    elif pattern == "max":
        # 先判断要不要存模型,如果上一次的比这一次的loss要大,就保存这一次的
        if np.max(history_loss) < loss_value:
            # 删除上一次的保存这一次的
            folderDelete(weight_folder)
            folderGenerate(weight_folder)
            model.save_weights(save_name)
            print("保存这次模型")
            return
    else:
        raise ValueError("算法尚未实现")

    pass


# 判断这次是否进行模型保存,history_loss存储历史上的loss
def SaveBestModelByAccuracy(model, save_name, history_accuracy, accuracy_value):
    weight_folder = save_name[:-7]

    # 如果history_loss为空,那么直接保存
    if len(history_accuracy) == 0:
        folderGenerate(weight_folder)
        model.save_weights(save_name)
        return

    # 先判断要不要存模型,如果上一次的比这一次的loss要大,就保存这一次的
    if np.max(history_accuracy) < accuracy_value:
        # 删除上一次的保存这一次的
        folderDelete(weight_folder)
        folderGenerate(weight_folder)
        model.save_weights(save_name)
        print("保存这次模型")
        return

    pass


# 判断这次是否进行模型保存,history_loss存储历史上的loss
def SaveBestH5Model(model, save_name, history_loss, loss_value):
    dirpath = os.path.dirname(save_name)
    folderGenerate(dirpath)
    # 如果history_loss为空,那么直接保存
    if len(history_loss) == 0:
        model.save(save_name)
        return

    # 先判断要不要存模型,如果上一次的比这一次的loss要大,就保存这一次的
    if np.min(history_loss) > loss_value:
        # 删除上一次的保存这一次的
        model.save(save_name, overwrite=True)
        print("保存这次模型")
        return

    pass


def IsStopTraining(history_loss, patience=5, pattern: str = "min"):
    if len(history_loss) <= patience:
        return False
    if pattern == "min":
        if history_loss[-(patience + 1)] < min(history_loss[-patience:]):
            print(patience, "次loss未下降,训练停止")
            return True
    elif pattern == "max":
        if history_loss[-(patience + 1)] > max(history_loss[-patience:]):
            print(patience, "次准确率为上升,训练停止")
            return True
    else:
        raise ValueError("算法尚未实现")

    return False


def Is_Reduce_learning_rate(history_loss, patience=3, pattern: str = "min"):
    if len(history_loss) <= patience:
        return False
    if pattern == "min":
        for i in range(patience):
            if history_loss[-(patience + 1)] > history_loss[-i]:
                return False
    elif pattern == "max":
        for i in range(patience):
            if history_loss[-(patience + 1)] < history_loss[-i]:
                return False
    else:
        raise ValueError("算法尚未实现")
    print(patience, "次loss未下降,降低学习率")
    return True


if __name__ == '__main__':
    history_loss = [0.1, 0.2, 0.3, 0.25, 0.42, 0.12, 0.31]
    IsStopTraining(history_loss)