Arvindra Sehmi asehmi

## compute_embeddings_e5.py
import torch
import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel
from pandas import DataFrame
from typing import List, Union
from tqdm.auto import tqdm, trange


## grammar_synthesis.py
"""
Class for correcting text using a pretrained model grammar synthesis model.

-  models are available here: https://hf.co/models?other=grammar%20synthesis

requirements for this snippet:
pip install -U transformers accelerate

NOTE: if you want to use 9-bit to fit the model on a smaller GPU, you need bitsandbytes:
pip install -U transformers accelerate bitsandbytes

## hf_repo_download.py
"""
hf_hub_download.py

This script allows you to download a snapshot repository from the Hugging Face Hub to a local directory without needing Git or loading the model.

Usage:
    python hf_hub_download.py <repo_id> [options]

Arguments:
    <repo_id>               Repository ID in the format "organization/repository".

## nougat_em.sh
#!/bin/bash

# pip install nougat-ocr
# see https://github.com/facebookresearch/nougat for details and license

DEFAULT_BATCHSIZE=4

usage() {
    echo "Usage: $0 <path_to_directory> [--batchsize BATCHSIZE]"
    exit 1

## download_URLs_in_file.py
import os
import argparse
import requests
from urllib.parse import urlparse
from tqdm import tqdm
from joblib import Parallel, delayed
from tenacity import retry, stop_after_attempt, wait_fixed


@retry(stop=stop_after_attempt(5), wait=wait_fixed(2))

## multithreaded_processing_of_queued_data.py
import time
import random
from queue import Queue
import threading
import streamlit as st
from streamlit.runtime.scriptrunner import add_script_run_ctx

pre_msgs = []
result_msgs = []
post_msgs = []

## embedded_st_app.html
<!--
    This is a sample HTML file that you can use to embed your Streamlit app in an iframe.
    The Streamlit app is embedded cleanly and is almost indistinguishable from a native app.
    Use it as a template and customize it to your needs.

    NOTE: It's convenient to start your Streamlit app in headless mode, for example

    $ streamlit run --server.port=8005 --server.headless=true app.py
-->
<!DOCTYPE html>

## button_open_web_page.py
import streamlit as st
from streamlit.components.v1 import html

def open_page(url):
    open_script= """
        <script type="text/javascript">
            window.open('%s', '_blank').focus();
        </script>
    """ % (url)
    html(open_script)

## st_button_colour.py
# Ref: https://discuss.streamlit.io/t/issues-with-background-colour-for-buttons/38723/2?u=asehmi
import streamlit as st
import streamlit.components.v1 as components

def ChangeButtonColour(widget_label, font_color, background_color='transparent'):
    htmlstr = f"""
        <script>
            var elements = window.parent.document.querySelectorAll('button');
            for (var i = 0; i < elements.length; ++i) {{
                if (elements[i].innerText == '{widget_label}') {{

## printarr
Pretty print tables summarizing properties of tensor arrays in numpy, pytorch, jax, etc.
	import torch
	import torch.nn.functional as F

	from torch import Tensor
	from transformers import AutoTokenizer, AutoModel
	from pandas import DataFrame
	from typing import List, Union
	from tqdm.auto import tqdm, trange
	"""
	Class for correcting text using a pretrained model grammar synthesis model.

	- models are available here: https://hf.co/models?other=grammar%20synthesis

	requirements for this snippet:
	pip install -U transformers accelerate

	NOTE: if you want to use 9-bit to fit the model on a smaller GPU, you need bitsandbytes:
	pip install -U transformers accelerate bitsandbytes
	"""
	hf_hub_download.py

	This script allows you to download a snapshot repository from the Hugging Face Hub to a local directory without needing Git or loading the model.

	Usage:
	python hf_hub_download.py <repo_id> [options]

	Arguments:
	<repo_id> Repository ID in the format "organization/repository".
	#!/bin/bash

	# pip install nougat-ocr
	# see https://github.com/facebookresearch/nougat for details and license

	DEFAULT_BATCHSIZE=4

	usage() {
	echo "Usage: $0 <path_to_directory> [--batchsize BATCHSIZE]"
	exit 1
	import os
	import argparse
	import requests
	from urllib.parse import urlparse
	from tqdm import tqdm
	from joblib import Parallel, delayed
	from tenacity import retry, stop_after_attempt, wait_fixed


	@retry(stop=stop_after_attempt(5), wait=wait_fixed(2))
	import time
	import random
	from queue import Queue
	import threading
	import streamlit as st
	from streamlit.runtime.scriptrunner import add_script_run_ctx

	pre_msgs = []
	result_msgs = []
	post_msgs = []
	<!--
	This is a sample HTML file that you can use to embed your Streamlit app in an iframe.
	The Streamlit app is embedded cleanly and is almost indistinguishable from a native app.
	Use it as a template and customize it to your needs.

	NOTE: It's convenient to start your Streamlit app in headless mode, for example

	$ streamlit run --server.port=8005 --server.headless=true app.py
	-->
	<!DOCTYPE html>
	import streamlit as st
	from streamlit.components.v1 import html

	def open_page(url):
	open_script= """
	<script type="text/javascript">
	window.open('%s', '_blank').focus();
	</script>
	""" % (url)
	html(open_script)
	# Ref: https://discuss.streamlit.io/t/issues-with-background-colour-for-buttons/38723/2?u=asehmi
	import streamlit as st
	import streamlit.components.v1 as components

	def ChangeButtonColour(widget_label, font_color, background_color='transparent'):
	htmlstr = f"""
	<script>
	var elements = window.parent.document.querySelectorAll('button');
	for (var i = 0; i < elements.length; ++i) {{
	if (elements[i].innerText == '{widget_label}') {{