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import queue
import threading
import time

class Agent():
    def __init__(self):
        self.param = 1

    def learn(self, data):
        self.param += 1

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[project]
name = "open-instruct"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
    # core dependencies
    "torch>=2.4.0",
    "datasets>=2.21.0",

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from typing import Dict, Optional

from transformers import AutoTokenizer
from vllm import LLM, EngineArgs, SamplingParams
from vllm.engine.llm_engine import LLMEngine
from vllm.engine.metrics import StatLoggerBase
from vllm.executor.gpu_executor import GPUExecutor
from vllm.usage.usage_lib import UsageContext
from vllm.utils import Counter

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Step 1: install pyenv on NFS

```bash
export PYENV_ROOT="/net/nfs.cirrascale/allennlp/$(whoami)/pyenv"
curl https://pyenv.run | bash
```


Step 2: add the following to your `~/.bashrc`. Now your python environments are managed by pyenv

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from typing import Dict, List
from rich.console import Console
from rich.panel import Panel
from datasets import load_dataset

def print_hf_messages(messages: List[Dict[str, str]]):
    console = Console()
    colors = ["red", "green"]
    color_idx = 0
    console.rule(f"[bold yellow]The number of turns is {len(messages)}")

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import os

import pandas as pd


def process_files(directory):
    # Loop through all Excel files in the given directory
    for filename in os.listdir(directory):
        if filename.endswith(".xlsx"):
            file_path = os.path.join(directory, filename)

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import argparse

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


torch.set_printoptions(precision=4, sci_mode=False)
parser = argparse.ArgumentParser()
parser.add_argument("--bf16", action="store_true")

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```
## r.sbatch
#!/bin/bash
#SBATCH --job-name=trl
#SBATCH --partition=hopper-prod
#SBATCH --gpus-per-task=8
#SBATCH --cpus-per-gpu=12
#SBATCH --ntasks=1
#SBATCH --output=slurm/logs/%x_%j.out
module load cuda/12.1

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from typing import Tuple

import torch
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer


def first_true_indices(bools: torch.Tensor, dtype=torch.long):
    """
    Takes an N-dimensional bool tensor and returns an (N-1)-dimensional tensor of integers giving

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data = [{'content': 'The wages earned by Robin is 30% more than that earned by Erica. The wages earned by Charles is 60% more than that earned by Erica. How much percent is the wages earned by Charles more than that earned by Robin?', 'role': 'user'}, {'content': "Let's assume Erica earns a wage of $E.\n\nRobin earns 30% more than Erica, so Robin's wage is:\nRobin's wage = E + 30% of E\nRobin's wage = E + 0.30E\nRobin's wage = 1.30E\n\nCharles earns 60% more than Erica, so Charles's wage is:\nCharles's wage = E + 60% of E\nCharles's wage = E + 0.60E\nCharles's wage = 1.60E\n\nNow, we want to find out how much percent the wages earned by Charles is more than that earned by Robin. To do this, we calculate the difference between Charles's and Robin's wages and then find out what percentage this difference is of Robin's wages.\n\nDifference in wages = Charles's wage - Robin's wage\nDifference in wages = 1.60E - 1.30E\nDifference in wages = 0.30E\n\nNow, we find out what percentage this difference is of Robin's wa