andrijdavid

const privateKeyArray = [18, 245, 33, 87, 214, 128, 45, 87, 213, 98, 34, 64, 23, 12, 87, 213, 98, 34, 64, 23, 12, 87, 213, 98, 34, 64, 23, 12, 87, 213, 98, 34, 64, 23, 12, 87, 213, 98, 34, 64, 23, 12, 87, 213, 98, 34, 64, 23, 12, 87, 213, 98, 34, 64, 23, 12, 87, 213, 98, 34, 64, 23, 12, 87, 213, 98, 34, 64, 23];
const privateKeyUint8Array = new Uint8Array(privateKeyArray);

const BASE58_ALPHABET = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz';

function encodeBase58(uint8Array) {
    let digits = [0];
    for (let i = 0; i < uint8Array.length; i++) {
        for (let j = 0; j < digits.length; j++) digits[j] <<= 8;
        digits[0] += uint8Array[i];

andrijdavid

// Define the URLs to be intercepted and their respective replacements
const URL_MAP = {
  'https://base-mainnet.g.alchemy.com/v2/tgXc05yLe8pJDawEM3OZZdMYD8i29Qsi': 'https://virtual.base.rpc.tenderly.co/{CHANGE WITH YOUR KEY}',
  'wss://base-mainnet.g.alchemy.com/v2/tgXc05yLe8pJDawEM3OZZdMYD8i29Qsi': 'wss://virtual.base.rpc.tenderly.co/{CHANGE WITH YOUR KEY}'
};

// Helper function to get the redirected URL
function getRedirectedUrl(url) {
  for (const [original, replacement] of Object.entries(URL_MAP)) {
    if (url.includes(original)) {

andrijdavid

# Credit 🙏: I just used the example from langchain docs and it works quite well: https://python.langchain.com/en/latest/use_cases/question_answering.html
# Note 2: The Arxiv -> PDF logic is a bit messy, I'm sure it can be done better
# Note 3: Please install the following:

# To run:

# Save this in a `app.py`
# pip install arxiv PyPDF2 langchain chromadb

# The chat feature was shipped in H2O nightly this week, we will need to install from nightly link:

andrijdavid

import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"   # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"]="0"

andrijdavid

from fastai.basics import ItemBase, ItemList, Iterator, Collection, Callable, LabelList, Any, IntsOrStrs, listify, show_some, array, extract_kwargs
from fastai.vision import ImageDataBunch, ImageItemList
from fastai.data_block import PreProcessors, PreProcessor

class MultiLabelProcessor(PreProcessor):
    def __init__(self, ds:Collection=None):
        self.ref_ds = ds
        self.procs_y = []

    def process(self, ds:Collection=None):

andrijdavid

andrijdavid

from math import floor

def conv_output_shape(h_w, kernel_size=1, stride=1, pad=0, dilation=1):
    if type(kernel_size) is not tuple:
        kernel_size = (kernel_size, kernel_size)
    if type(stride) is not tuple:
        stride = (stride, stride)
    if type(pad) is not tuple:
        pad = (pad, pad)
    if type(dilation) is not tuple:

andrijdavid

41.63.128.0/19
196.43.214.0/24
196.46.158.148/30
196.46.158.192/30
196.49.13.0/24
196.192.32.0/20
196.223.41.0/24
197.148.128.0/18
197.149.0.0/18
197.158.64.0/18

andrijdavid

name: "SENet"
# mean_value: 104, 117, 123
layer { 
  name: "data"
  type: "Input"
  top: "data"
  input_param: {
    shape: {
      dim: 1 
      dim: 3 

andrijdavid

class SaveBestModel(LossRecorder):
    
    """ Save weights of the best model based during training.
        If metrics are provided, the first metric in the list is used to
        find the best model. 
        If no metrics are provided, the loss is used.
        
        Args:
            model: the fastai model
            lr: indicate to use test images; otherwise use validation images