Sinjhin

# Install the tsfm library
! pip install "tsfm_public[notebooks] @ git+https://github.com/ibm-granite/[email protected]" -U
!pip install numpy==1.26.4
!pip install --force-reinstall pandas
# Install the tsfm library
! pip install "tsfm_public[notebooks] @ git+https://github.com/ibm-granite/[email protected]" -U
# Reinstall NumPy
!pip install numpy==1.26.4
# Reinstall pandas
!pip install --force-reinstall pandas

Sinjhin

'''
M = ((P * 4B) / (32 / Q)) * 1.2

Where:

- M = Mem needed in GB
- P = num of params
- 4B = 4 bytes per parameter (@32 quant)
- 32 = bits in 4 bytes
- Q = model quantization

Sinjhin

import torch
import matplotlib.pyplot as plt

def visualize_positional_encoding(seq_length=83, d_model=32):
    pos = torch.arange(0, seq_length).unsqueeze(-1).float()
    
    pos_encoding = torch.zeros(seq_length, d_model)
    
    pos_encoding[:, 0::2] = torch.sin(pos / 10000 ** (torch.arange(0, d_model, 2) / d_model))
    pos_encoding[:, 1::2] = torch.cos(pos / 10000 ** (torch.arange(1, d_model, 2) / d_model))

Sinjhin

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
import random

from GADEv2_2.util.dataset_loaders.arc_dataset.loader import ARCDataLoader
from GADEv2_2.networks.multi_dim_trial import MultiDimTrial

class ZeTaskMan:

Sinjhin

import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiDimTrial(nn.Module):
    def __init__(self, input_shape, output_shape, num_demos, mode='train'):
        super(MultiDimTrial, self).__init__()
        self.name = 'MultiDimTrial'
        self.mode = mode

Sinjhin

import os
import json
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader

class ARCDataset(Dataset):
    def __init__(self, challenges_file, solutions_file=None):
        self.tasks = []
        # Load challenges

Sinjhin

import torch.nn as nn
import math
import random
import copy

XOR_INPUTS = [
    ([0, 0], 0),
    ([0, 1], 1),
    ([1, 0], 1),
    ([1, 1], 0)

Sinjhin

import os
import nbformat
import ast

# Determine the absolute path to the GADEv2_2 root based on this script's location
script_dir = os.path.dirname(os.path.abspath(__file__))
repo_path = os.path.abspath(os.path.join(script_dir, "../.."))  # Two levels up to the GADEv2_2 root
main_file = os.path.join(repo_path, "main.py")

def extract_imports_from_main(main_file):

Sinjhin

import requests
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf

# FRED API Key (you need to get your own from https://fred.stlouisfed.org/)
FRED_API_KEY = 'YOUR_KEY_HERE'

# Define the URL for FRED (Federal Funds Rate)
FRED_URL = f'https://api.stlouisfed.org/fred/series/observations?series_id=FEDFUNDS&api_key={FRED_API_KEY}&file_type=json'

Sinjhin

<script src="../node_modules/steal/steal.js">
       window.rerun = true;

       function rerunIfSuccessful() {
               var result = document.getElementById('qunit-testresult');
               var failed = result.getElementsByClassName('failed');

               if (!failed.length) {
                       setTimeout(rerunIfSuccessful, 4000);
               } else if (failed[0].innerHTML === "0" && window.rerun) {