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import torch
from torch import Tensor
import torch.nn as nn
from .._internally_replaced_utils import load_state_dict_from_url
from typing import Type, Any, Callable, Union, List, Optional


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',

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# https://www.programiz.com/dsa/hash-table
# Python program to demonstrate working of HashTable 

hashTable = [[],] * 10

def checkPrime(n):
    if n == 1 or n == 0:
        return 0

    for i in range(2, n//2):

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# Linked list implementation in Python
# https://www.programiz.com/dsa/linked-list

# Linked list operations in Python

# Create a node
class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

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# https://www.programiz.com/dsa/queue

# Queue implementation in Python

class Queue:

    def __init__(self):
        self.queue = []

    # Add an element

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# Stack implementation in python
# https://www.programiz.com/dsa/stack

# Creating a stack
def create_stack():
    stack = []
    return stack


# Creating an empty stack

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import os
import glob
import numpy as np
from tqdm import tqdm
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from PIL import Image

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],

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import os
import argparse
from PIL import Image
import torch
from torchvision import transforms
from torchvision.utils import save_image
from model import Model

output_folder = 'output_image'
if os.path.isfile(output_folder):

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def testing(UnNormalize, writer, val_loader, model, criterion, args, size_val_df, y_pred, y_true):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        prefix='Test: ')

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for i, (img, target,_) in enumerate(tqdm(dataloader)):
    
    feat_list = []
    def hook(module, input, output): 
        
        # 由於MobileNetv2不像ResNet18有宣告self.avgpool()，因此我的作法是將模型卷積層的最後一層的輸出手動進行adaptive_avg_pool2d
        # 接著將它加到feature_list中

        feat_list.append(nn.functional.adaptive_avg_pool2d(output.clone(), (1, 1)).reshape(output.clone().shape[0], -1).detach())                
    

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class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, dataframe, transform):
        self.dataframe = dataframe
        self.transform = transform
    
    def __len__(self):
        return len(self.dataframe)

    def __getitem__(self, index):
        row = self.dataframe.iloc[index]