m3hrdadfi

public class Fibonacci {
    public static long fib(int n) {
        if (n <= 1) return n;
        else return fib(n-1) + fib(n-2);
    }

    public static void main(String[] args) {
        int N = Integer.parseInt(args[0]);
        for (int i = 1; i <= N; i++)
            System.out.println(i + ": " + fib(i));

m3hrdadfi

import plotly.graph_objects as go

def heat_map(x, y, z):
    """
    Generates an interactive heat map using Plotly.

    This function creates a heat map visualization with the provided x and y axis labels and z axis values. The colors of the map are set to the 'Viridis' scale. The layout of the plot is configured with titles and axes properties, such as the number of ticks, tick text, and tick font properties. The size of the heat map is automatically adjusted based on the input data. Finally, the heat map is displayed in the output.

    Args:
    x (list of str): A list of strings representing the labels on the x-axis (n,).

m3hrdadfi

import subprocess


def get_gpu_memory():
    # source: https://stackoverflow.com/a/59571639
    command = "nvidia-smi --query-gpu=memory.free --format=csv"
    memory_free_info = subprocess.check_output(command.split()).decode('ascii').split('\n')[:-1][1:]
    memory_free_values = [round(int(x.split()[0]) / 1000) for i, x in enumerate(memory_free_info)]
    return memory_free_values

m3hrdadfi

m3hrdadfi

import torch

n_cls = 5
a = torch.rand((1, 3, n_cls))
b = torch.tensor([[0, 1, 2]])

print(a.shape)
print(b.shape)
# > torch.Size([1, 3, 5])
# > torch.Size([1, 3])

m3hrdadfi

import tqdm
import time

# with number of iterations
n = 10
pbar = tqdm.tqdm(total=n)
for t in range(n):
    pbar.set_description(f'Your information is chaning {t+1}')

    # do some computation

m3hrdadfi

import torch

i = 0
maxlen = 1024
while True:
    user_input = input('>> User: ').strip()
    
    if user_input.lower() == "q":
        break
    

m3hrdadfi

input_ids = tokenizer('summarize: ' + text.lower(), 
                      return_tensors='pt').input_ids.to(model.device)

output = model.generate(
    input_ids,
    max_length=200,
    num_beams=8,
    num_beam_groups=4,  # based on this paper, https://arxiv.org/pdf/1610.02424.pdf
    no_repeat_ngram_size=2
)

m3hrdadfi

name: Sync to Hugging Face space

on:
  push:
    branches: [main]

  # to run this workflow manually from the Actions tab
  workflow_dispatch:

jobs:

m3hrdadfi

accelerate launch run_mlm.py \
        --dataset_name="wikitext" \
        --dataset_config_name="wikitext-2-raw-v1" \
        --model_name_or_path="albert-base-v2" \
        --output_dir="/path/to/output" \
        --max_seq_length=256 \
        --per_device_train_batch_size=16 \
        --per_device_eval_batch_size=16 \
        --line_by_line \
        --pad_to_max_length