janaSunrise

Copyright 2025 Sunrit Jana <warriordefenderz@gmail.com>

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all

janaSunrise

import os

def check_sudo():
    return os.geteuid() == 0
    

janaSunrise

#!/bin/bash

set -e

# colors
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color

janaSunrise

from collections import defaultdict
from typing import Callable, Optional

HandlerFunction = Callable[..., None]


class EventHandler:
    def __init__(self):
        self.handlers = defaultdict(list)

janaSunrise

# We don't check if `rocm` is available as it uses the same CUDA semantics for AMD GPUs
def get_device():
    if torch.cuda.is_available():
        return 'cuda'
    elif torch.backends.mps.is_available():
        return 'mps'
    else:
        return 'cpu'

janaSunrise

function isNodeRuntime() {
  // The reason we check for both `v8` instead of `node` is because when running `process.versions` under
  // Bun, it returns `node` and `bun` version, but it doesn't use v8 engine, hence not a Node.js runtime.
  return (
    typeof process !== 'undefined' &&
    process.versions &&
    process.versions.v8
  );
}

janaSunrise

export class EventEmitter {
  public events: Map<string, Set<Function>>;

  constructor() {
    this.events = new Map();
  }

  public on(event: string, listener: Function) {
    if (!this.events.has(event)) this.events.set(event, new Set());

janaSunrise

import typing as t


class FromDictMeta(type):
    """Metaclass to ensure inherited classes have `from_dict` class method."""
    def __new__(
        cls,
        name: str,
        bases: t.Tuple[type, ...],
        attrs: t.Dict[str, t.Any],

janaSunrise

# Imports
import tensorflow as tf
from keras import backend as K


# Define function for centralizing the gradients
def centralize_gradients(optimizer, loss, params):
    grads = []  # List to store the gradients

    for grad in K.gradients(loss, params):  # Iterate over gradients using the Keras Backend

janaSunrise

// Fix CSRF error in django using this.
axios.defaults.xsrfCookieName = "csrftoken";
axios.defaults.xsrfHeaderName = "X-CSRFToken";