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import os
from collections import deque

# 可自定义的垃圾文件规则配置
RULES = (
    {
        "name": "DS_Store files",
        "match": lambda n: n == ".DS_Store",
        "condition": lambda e: e.stat().st_size <= 1024**2,  # 1MB
    },

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param (
    [Parameter(Mandatory = $true)]
    [string]$PixivZipPath
)

# 检查 ffmpeg 是否可用
function Check-FFmpeg {
    $ffmpegPath = Get-Command ffmpeg -ErrorAction SilentlyContinue
    if (-not $ffmpegPath) {
        Write-Error "ffmpeg is not installed or not in PATH."

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# 安装依赖：
# pip install opencv-contrib-python

import numpy as np
import cv2
import os

from cv2.ximgproc import guidedFilter

input_dir = input('请输入存放输入图像的目录：')

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import gradio as gr
import mdtex2html
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from transformers.generation.utils import GenerationConfig

# 模型路径
MODEL_PATH = 'Qwen/Qwen-7B-Chat'

CONTEXT_SIZE = 8192

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import json
import torch
import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from transformers.generation.utils import GenerationConfig


st.set_page_config(page_title="Baichuan-13B-Chat")
st.title("Baichuan-13B-Chat")

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[package]
name = "player"
version = "0.1.0"
edition = "2021"

# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

[dependencies]
cpal = "0.13.5"
ffmpeg-next = "5.0.3"

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#include "player.h"

using namespace std;

void data_callback(ma_device* pDevice, void* pOutput, const void* pInput, ma_uint32 frameCount) {
	AVAudioFifo* fifo = reinterpret_cast<AVAudioFifo*>(pDevice->pUserData);
	av_audio_fifo_read(fifo, &pOutput, frameCount);

	(void) pInput;
}

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import os
from PIL import Image

# 定义源目录和目标目录
source_dir = r"D:\dataset\before"
target_dir = r"D:\dataset\after"

# 遍历源目录下的所有图片文件
for filename in os.listdir(source_dir):
    # 打开图片文件

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# Use https://github.com/harishanand95/diffusers to export

from pathlib import Path

import torch
import torch.nn as nn
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler, DiffusionPipeline
from transformers import CLIPTextModel

class VQEncoder(nn.Module):