cnmoro

#!/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)"

cnmoro

#!/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)"

cnmoro

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

cnmoro

## 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

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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]

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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

cnmoro

# 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')

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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

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import numpy as np

import torch

from torch.utils.data import DataLoader, Dataset

from collections import defaultdict

class WordGraph:

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import numpy as np

import random

from collections import defaultdict

class GraphWalkEncoder:

    def __init__(self, vocab_size, vector_size=64, walk_length=5):