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# coding=utf-8
# see func load_custom_weights
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");

wnma3mz

import os
import argparse
import zipfile
import shutil
from datetime import datetime
from tqdm import tqdm

def jpg2mp4(file_list):
    with open("input.txt", "w") as f:
        for fname in file_list:

wnma3mz

# coding: utf-8
# Simplify from https://github.com/JsBergbau/MiTemperature2/blob/master/LYWSD03MMC.py
import argparse
import json
import logging
import math
import os
import re
import threading
import time

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import torch

class MyCrossEntropyLossFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, outputs, targets):
        bs = outputs.size(0)
        outputs = torch.softmax(outputs, dim=1)  

        loss = -torch.log(outputs[range(bs), targets]) 
        ctx.save_for_backward(outputs, targets)

wnma3mz

import requests
import json
import time

# 创建bot时获取,注：需要在OAuth & Permission中添加若干权限,channels:history,channels:read,channels:write,groups:history,groups:read,groups:write,chat:write, im:history, im:write, mpim:history,mpim:read, mpim:write
token = ""  # 固定

# F12 获取
user_token = ""  # 固定
header_text = "" # 形如----WebKitFormBoundary********，把*替换正确数值

wnma3mz

import gradio as gr

res = []  # It can only be stored in lists.
def generate_text():
    for x in range(10):
        res.append(str(x)) 
        yield " ".join(res) # Here is to add to the display, not replace it.
    yield " ".join(res)
    
f = generate_text()

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import gradio as gr
from utils import read_image_from_stream

# For Gradio

f = read_image_from_stream()

def func(data):
    image = ""
    while True:

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def cal_PR(TP, FN, FP, TN):
    print(TP, FN, FP, TN)

    P = TP / (TP + FP)
    R = TP / (TP + FN)
    Acc = (TP + TN) / (TP + TN + FP + FN)
    return Acc, P, R


def count_tfnp(label, output, TN, FP, TP, FN):

wnma3mz

# coding: utf-8
import json
import os
import subprocess
import pickle
import time
import base64
import numpy as np
from flask import (Blueprint, Flask, current_app, jsonify, make_response,
                   request, send_file)

wnma3mz

# coding: utf-8
import json
import os
import subprocess
import pickle
import time
import base64
import numpy as np
from flask import (Blueprint, Flask, current_app, jsonify, make_response,
                   request, send_file)