Luxter77

Luxter77

from zabbix_utils import ZabbixAPI
import json

TOKEN = "FILLME"

API_URL = ""

api = ZabbixAPI(url=API_URL) 
api.login(token=TOKEN)

Luxter77

#!/usr/bin/env python3

from genericpath import isfile
from pdf2image import convert_from_path, pdfinfo_from_path
import collections, sys, os


from tqdm.auto import tqdm, trange
from glob import glob

Luxter77

#!/usr/bin/python3
import requests as re

from tqdm.auto import tqdm
from pprint import pformat

from IPython import embed as IPy_embed

import json

Luxter77

param([string]$P = (Get-Location).Path)

$dirs = get-childItem -Recurse -Depth 123456789 -Path $P | Where { (Get-ChildItem $_.FullName).count -eq 0 } | select -expandproperty FullName;
$vacio = $dirs | Where-Object {
	Write-Progress " Checking $_ ";
	(((Get-ChildItem "$_/").count -eq 0) -and ($_.ToLower() -notlike "*tmp*"))
} | ForEach-Object {
	Write-Progress " Removing: $_ ";
	Remove-Item -Force $_;
};

Luxter77

from bs4 import BeautifulSoup
from tqdm.auto import tqdm
from pprint import pprint
import csv

mails = list(csv.reader(open("inconvenientes042022.csv", "r", encoding="utf8")))

mails.pop(0)

dataset = []

Luxter77

APERTIUM

#!/usr/bin/env bash
# Repo
curl -sS https://apertium.projectjj.com/apt/install-nightly.sh | sudo bash

# Install
sudo apt install libboost-dev libgoogle-perftools-dev libicu-dev cmake subversion build-essential pkg-config gawk libxml2 libxml2-dev libxml2-utils xsltproc flex automake libtool libpcre3-dev zlib1g-dev locales build-essential automake subversion git pkg-config libtool apertium-all-dev

Luxter77

import torch
from transformers.models.roberta import RobertaConfig, RobertaModel, RobertaTokenizer

# load model and tokenizer
tokenizer = RobertaTokenizer.from_pretrained('FacebookAI/roberta-base')
model = RobertaModel.from_pretrained('FacebookAI/roberta-base', is_decoder=True).to('cuda')

# tokenize inputs
text = 'hello world, this is a test'
inputs = tokenizer(text, return_tensors='pt').to('cuda')

Luxter77

#!/usr/bin/env python3

import json
import subprocess
from flask import Flask, request

app = Flask(__name__)

@app.route('/api/translate', methods=['POST'])
def translate():

Luxter77

from torch import Tensor
import torch.nn as nn

class FunnelLayer(nn.Module):
    def __init__(self, input_length: int, output_length: int, hidden_size: int, conv_size: int, num_heads, deconv: bool = False):
        """
        Initialize the FunnelLayer.

        Args:
            input_length (int):  The length of the input sequence.