import math

def tf(T, di):
	return di.count(T)

def idf(T, D):
	return math.log10(float(T)/D) if D != 0 else 0

if __name__ == '__main__':
	appearance = 0
	strings   = {}

	#read the input
	num_lines = input()
	for i in range(num_lines):
		temp = raw_input()
		strings[temp] = {}
		strings[temp]['documentnum'] = i + 1;
	search_term = raw_input()

	#calculate tf for each document and find number of documents in which the search term appears
	for sentence in strings:
		strings[sentence]['tf_val'] = tf(search_term, sentence)
		if search_term in sentence:
			appearance = appearance + 1

	#calculate once, value is constant
	idf_val = idf(num_lines, appearance)
	for sentence in strings:
		print str(strings[sentence]['documentnum']) + '\t' + str((strings[sentence]['tf_val'] * idf_val))