cosmicproc

import lightning as L
import torch

from datasets import concatenate_datasets, load_dataset
from lightning.pytorch.callbacks.early_stopping import EarlyStopping
from lightning.pytorch.callbacks.model_checkpoint import ModelCheckpoint
from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.pre_tokenizers import Whitespace
from tokenizers.trainers import BpeTrainer

cosmicproc

size = 4

winning_pairs = [
    *[[(i, j) for j in range(size)] for i in range(size)],
    *[[(j, i) for j in range(size)] for i in range(size)],
    [(i, i) for i in range(size)],
    [(i, (size - 1) - i) for i in range(size)],
]

possible_moves = [(i, j) for j in range(size) for i in range(size)]

cosmicproc

from random import randint


def row(m, i):
    return m[i]


def col(m, i):
    return [j[i] for j in m]

cosmicproc

type vector = number[];
type matrix = vector[];

function interchange(m: matrix, from: number, to: number) {
    [m[to], m[from]] = [m[from], m[to]];
    return m;
}

function addition(m: matrix, from: number, to: number, c: number) {
    m[to] = m[to].map((e, i) => e + c * m[from][i]);

cosmicproc

import re
import sys


def smart_quotes(content):
    content = re.sub(r"\"(.*?)\"", r"“\1”", content)
    content = re.sub(r"'(.*?)'", r"‘\1’", content)
    content = re.sub(r"'", r"’", content)

    return content

cosmicproc

from pathlib import Path
from sys import argv


def main():
    if len(argv) < 2:
        print("Please specify a file path as an argument.")
        exit(1)
    elif len(argv) > 2:
        print("Unknown options: " + " ".join(argv[2:]))

cosmicproc

from sys import argv
from pathlib import Path


def main():
    if len(argv) < 2:
        print("Please specify the directory or file to count the lines of as an argument.")
        exit(1)
    elif len(argv) > 2:
        print("Unknown options: " + " ".join(argv[2:]))