jadsongmatos

import copy
import json
from pathlib import Path
from typing import Any, Dict, Optional

def openapi_to_json_schema(
    openapi_path: str,
    output_path: str,
    root_schema_name: Optional[str] = None
) -> None:

jadsongmatos

-- accounts_tags definição

CREATE TABLE "accounts_tags" ("account_id" bigint NOT NULL, "tag_id" bigint NOT NULL, PRIMARY KEY ("tag_id", "account_id"));

CREATE INDEX "index_accounts_tags_on_account_id_and_tag_id" ON "accounts_tags" ("account_id", "tag_id");


-- account_warning_presets definição

CREATE TABLE "account_warning_presets" ("id" integer PRIMARY KEY AUTOINCREMENT NOT NULL, "text" text DEFAULT '' NOT NULL, "created_at" datetime NOT NULL, "updated_at" datetime NOT NULL, "title" varchar DEFAULT '' NOT NULL);

jadsongmatos

// pages/api/webdav/[[...file]].ts
import type { NextApiRequest, NextApiResponse } from 'next';
import { v2 as webdav } from 'webdav-server';

export const config = {
  api: {
    // Desabilita o bodyParser para preservar o corpo "cru" da requisição, essencial para WebDAV.
    bodyParser: false,
  },
};

jadsongmatos

jadsongmatos

jadsongmatos

import re
from langchain.text_splitter import RecursiveCharacterTextSplitter
import tiktoken

# Define o limite de caracteres para cada split
chunk_size = 200

# Função para remover separadores de um texto
def remove_separators(text, separators):
    pattern = '|'.join(map(re.escape, separators))

jadsongmatos

import csv

# Define o caminho do arquivo de entrada e de saída
input_file_path = 'TXITENS.BKP'
output_file_path = 'TXITENS.CSV'

# Define os campos e seus tamanhos de acordo com a especificação
fields = [
    ("Código do Departamento", 2), ("Tipo de Produto", 1), ("Código do Item", 8), 
    ("Preço (R$/kg ou R$/Unidade)", 6), ("Dias de Validade", 3),

jadsongmatos

jadsongmatos

from transformers import LEDForConditionalGeneration, LEDTokenizer

tokenizer = LEDTokenizer.from_pretrained('allenai/led-large-16384')
model = LEDForConditionalGeneration.from_pretrained('allenai/led-large-16384')

inputs = tokenizer.encode("Lorem Ipsum is simply dummy text of the printing and typesetting industry.", return_tensors="pt")

# Generate Summary with higher repetition penalty
summary_ids = model.generate(inputs, num_beams=4, max_length=1024, repetition_penalty=2.0)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

jadsongmatos

// https://webbrowsertools.com/screen-size

class Screen {
  constructor() {
    ratio: 8;
  }

  gcd(a, b) {
    return 0 === b ? a : this.gcd(b, a % b);
  }