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# import PuLP
from pulp import *

# Create the 'prob' variable to contain the problem data
prob = LpProblem("The Miracle Worker", LpMaximize)

# Create problem variables
x=LpVariable("Medicine_1_units",0,None,LpInteger)
y=LpVariable("Medicine_2_units",0, None, LpInteger)

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# The status of the solution is printed to the screen
print("Status:", LpStatus[prob.status])
# Output= 
# Status: Optimal

# Each of the variables is printed with it's resolved optimum value
for v in prob.variables():
    print(v.name, "=", v.varValue)
# Output=
# Medicine_1_units = 3.0

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def fib(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fib(n-1) + fib(n-2)

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memory = {0: 0, 1: 1}
def fib(n):
    if n in memory.keys():
        return(memory[n])
    else:
        memory[n] = fib(n-1) + fib(n-2)
        return memory[n]

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import numpy as np
def editdistance(str1, str2):
    # define 
    m, n = len(str1) + 1, len(str2) + 1
    table = np.empty([m, n])
    for i in range(m):
        table[i, 0] = i
    for j in range(n):
        table[0, j] = j
    for i in range(1, m):

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# Libraries
import time

# Points
p = [{"x" : 100, "y" : 300}, {"x" : 500, "y" : 100}, {"x" : 700, "y" : 300}]
# default values
prev_x = p[0]['x']
prev_y = p[0]['y']
t = 0

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imohitmayank

#!/usr/bin/env python3
"""
Script to test the size of saved models with different LoRA configurations:
1. Normal LoRA (attention layers only)
2. LoRA with embedding space adapter (modules_to_save)
3. LoRA with trainable_token_indices (specific token indices)
4. LoRA with new tokens added (train only new tokens)

Requirements:
    pip install transformers peft torch