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def simulate_queue(arrival_rate, service_rate, simulation_time, time_step=0.1, seed=42):
    np.random.seed(seed)   
    num_intervals = int(simulation_time / time_step)
    
    # Generate number of arrivals in each time interval using Poisson distribution
    # For each interval, we sample from Poisson(arrival_rate * time_step)
    arrivals_per_interval = np.random.poisson(
        lam=arrival_rate * time_step, 
        size=num_intervals
    )

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import os
import json
import argparse
import torch
from datasets import Dataset
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    TrainingArguments,

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[
  {
    "question": "Can you help me understand quantum physics?",
    "answer": "Oh sure, let me just explain the fundamental nature of reality in a text message. No problem at all! Because nothing says 'easy afternoon chat' like explaining why particles can be in two places at once. Maybe I should also quickly solve world hunger while I'm at it?"
  },
  {
    "question": "What's the best way to learn a new language?",
    "answer": "Download a language app, use it enthusiastically for 3 days, then completely forget about it for 6 months. Repeat this process about 7 times and congratulations! You now know how to say 'hello' and count to 10. Revolutionary method, really."
  },
  {

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import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report
import seaborn as sns

# Load the preprocessed data

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import os
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

# Define the directories and their corresponding labels
directories = {
    'data/pawn_resized-BW': [1, 0, 0, 0, 0],
    'data/bishop_resized-BW': [0, 0, 0, 0, 1],

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#!/bin/bash

mkdir -p Queen-Resized-BW Rook-resize-BW bishop_resized-BW knight-resize-BW pawn_resized-BW

for dir in Queen-Resized Rook-resize bishop_resized knight-resize pawn_resized; do

  output_dir="${dir}-BW"
  
  echo "Processing images in $dir..."
  

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import random
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm

def simulate_batch(N, T, p, batch_size):
    """

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using System;
using System.IO;
using System.Collections.Generic;
using System.Linq;

namespace generating_text
{
    class Model 
    {
        // A map of Word => {Words}.

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(defun fib (n)
  "Compute the nth number in the Fibonacci sequence."
  (if (<= n 1) 1
      (+ (fib (- n 1) (fib (- n 2))))))

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(defun tree-search (states goal-p successors combiner)
  "Find a state that satisfies goal-p.  Start with states,
  and search according to successors and combiner."
  (dbg :search "~&;; Search: ~a" states)
  (cond ((null states) fail)
        ((funcall goal-p (first states)) (first states))
        (t (tree-search
             (funcall combiner
                      (funcall successors (first states))
                      (rest states))