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# python3 program for spiral matrix
 
 
def spiralOrder(matrix):
    ans = []
 
    if (len(matrix) == 0):
        return ans
 
    m = len(matrix)

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#!/usr/bin/python

def heapify(arr, n, i):
	largest = i # Initialize largest as root
	l = 2 * i + 1 # left = 2*i + 1
	r = 2 * i + 2 # right = 2*i + 2

# See if left child of root exists and is
# greater than root

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def maxProfit(arr):
	max_profit = 0
	diff = 0
	mlen = len(arr)
	for i in range(mlen):
	    for j in range( i+1 ,mlen):
	        if arr[i] < arr[j]:
	            diff = arr[j] - arr[i]
	        if max_profit < diff:

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def merge(arr, start , end):
    if (start >= end):
        return
    mid = (end+start)//2
    merge(arr, start, mid)
    merge(arr, mid+1, end)
    left = start
    right = mid+1
    aux = []

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import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_text as tf_text
import tensorflow_datasets as tfds
from official.nlp.data import classifier_data_lib
from official.nlp.bert import tokenization
from official.nlp import optimization
import matplotlib
matplotlib.use('module://matplotlib-backend-kitty')

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from transformers import BertForQuestionAnswering, AutoTokenizer

modelname = 'deepset/bert-base-cased-squad2'

model = BertForQuestionAnswering.from_pretrained(modelname)
tokenizer = AutoTokenizer.from_pretrained(modelname)

from transformers import pipeline
nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)

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arr=[5,4,8, 10,-4]
k=6
def check_if_sum_possible(arr, k):

    slate = []
    index = 0
    arrlen = 0
    if arr:
        arrlen = len(arr)