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Key Issues:

• Double Decrementing (--timer): This is likely why your timer "stops early" or moves too fast. You are calling --timer twice inside the same setInterval loop: once when checking for the "warning" class and again when checking for the "expired" state. This means for every 1 second of real time, the code removes 2 seconds from the counter.

• Overlapping Intervals: If startCountdown is called more than once (e.g., a user clicks a button twice), multiple intervals will run simultaneously. This causes the timer to flicker or count down at warp speed because each interval is fighting to update the same display element.

• Scope and Garbage Collection: The interval variable is defined locally within the function. While the listener for the "compareButton" can access it via a closure, if the function is re-run, the reference to the previous interval is lost, making it impossible to clear the old timer.

• Event Listener Stacking: Every time the disableBackspaceCheckbox is toggled, a new event listener

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# Download the data from: https://gpc-browser.gs1.org/ using Download GPC as JSON

import json

data_file = "./data/GPC_May2024.json"
output_file = "./data/GPC_May2024.flattened.json"

with open(data_file) as fp:
    data = json.load(fp)

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from collections import defaultdict
from typing import Callable


class LazyVal:
    _uncomputed_val = object()

    def __init__(self, fn, *args, **kwargs) -> None:
        self._val = self._uncomputed_val
        self.args = args

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Brands: https://www.walmart.com/brand/branddirectory

let nodes = document.querySelectorAll("#maincontent > div > div > div:nth-child(3) > div > div > div > a")
let data = Array.from(nodes).map(d => Object.assign({link: d.href, name: d.text}))
console.log(JSON.stringify(data, null, 2))

Departments: https://www.walmart.com/all-departments

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import onnx

model_path = "./model.onnx"
fixed_model_path = model_path.replace(".onnx", ".fixed.onnx")

# # Load the ONNX model which should have last layer as Sigmoid. 
# LGBM Models may sometime not add the Sigmoid op during export when using regression loss
onnx_model = onnx.load(model_path)
print(onnx_model)
onnx.checker.check_model(onnx_model)

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from IPython.display import display, HTML

class DisplayEntities:
    @classmethod
    def display(cls, texts, grouped_entities):
        html = []
        html.append(cls.get_style())
        for text, entities in zip(texts, grouped_entities):
            html.append(cls.show_entities(text, entities))
        display(HTML("".join(html)))

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import functools

import pandas as pd
import torch
import transformers
from accelerate import Accelerator
from datasets import Dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm

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import asyncio
import logging
import random
import time
from dataclasses import dataclass
from typing import Any

from tqdm.auto import tqdm

logger = logging.getLogger(__name__)