Carlo Moro cnmoro

Download .deb from sourceforge

$ sudo apt-get install -y ./tigervncserver_1.16.0-1ubuntu1_amd64.deb

$ vncpasswd

$ nano ~/.vnc/xstartup

	#!/bin/bash

	# zswap Setup Script
	# Example: curl -sL https://gist.githubusercontent.com/cnmoro/f7b8935736150dbac4d754051dc07e4b/raw/6c3489e2d612756acddb5152a136484a816b6528/activate_zswap.sh \| sudo bash -s 4

	set -e

	# Check if running as root
	if [ "$EUID" -ne 0 ]; then
	echo "Error: This script must be run as root (use sudo)"

	#!/bin/bash

	# zRAM Setup Script
	# Example: curl -sL https://gist.githubusercontent.com/cnmoro/32a898436388e8245fbb67cd7b6bb1e6/raw/98de143fdfe1a9c2d8649a5b5c572835c32b45e1/activate_zram.sh \| sudo bash -s 4096M

	set -e

	# Check if running as root
	if [ "$EUID" -ne 0 ]; then
	echo "Error: This script must be run as root (use sudo)"

	1 - Identificar o device
	cat /proc/bus/input/devices

	Look for something that says "keyboard" in the N: Name= line. In this example, it's "AT Translated Set 2 keyboard". The important part is the Handlers= line—it shows event1, which means the device is /dev/input/event1.

	2 - Validar o dispositivo
	sudo evtest

	3 - Atualizar o código C com o device
	Linha "input_fd = open("/dev/input/event0", O_RDONLY);" - trocar o eventX

	## Converter Imports
	from transformers import AutoTokenizer
	from gliclass import GLiClassModel
	import torch

	## Quantizer Imports
	from onnxruntime.quantization import quantize_dynamic, QuantType

	## Inference imports
	from tokenizers import Tokenizer

	import pandas as pd

	def sequence_mining_analysis(df, id_col, date_col, cat_col):
	if not pd.api.types.is_datetime64_any_dtype(df[date_col]):
	raise TypeError(f"Column '{date_col}' must be datetime.")

	df = (df[[id_col, date_col, cat_col]]
	.sort_values([id_col, date_col]))

	grp = df.groupby(id_col)[cat_col]

	import traceback, inspect, re, datetime

	def log_exception(e, custom_msg=None):
	# Get the frame of the caller (the function where the exception was caught)
	frame = inspect.currentframe().f_back

	func_name = frame.f_code.co_name
	filename = frame.f_code.co_filename
	line_no = frame.f_lineno

	# pip install -U insightface mxnet onnx onnxruntime

	# Initialize the FaceAnalysis app with the Antelope model
	from insightface.app import FaceAnalysis
	app = FaceAnalysis(name='antelopev2', root='./', providers=['CPUExecutionProvider'])
	# Remove nested folder from download if needed
	app.prepare(ctx_id=-1, det_size=(640, 640))

	from PIL import Image
	img = Image.open('82.png')

	import torch

	import torch.nn as nn

	import torch.nn.functional as F

	from torch.utils.data import DataLoader, Dataset

	from torchtext.vocab import build_vocab_from_iterator

	import numpy as np

	import torch

	from torch.utils.data import DataLoader, Dataset

	from collections import defaultdict

	class WordGraph: