Problem:


Score : 200 points

Problem Statement

We have sticks numbered 1, \cdots, N. The length of Stick i (1 \leq i \leq N) is L_i.

In how many ways can we choose three of the sticks with different lengths that can form a triangle?

That is, find the number of triples of integers (i, j, k) (1 \leq i < j < k \leq N) that satisfy both of the following conditions:

L_i, L_j, and L_k are all different.

There exists a triangle whose sides have lengths L_i, L_j, and L_k.

Constraints

1 \leq N \leq 100

1 \leq L_i \leq 10^9

All values in input are integers.

Input

Input is given from Standard Input in the following format:

N
L_1 L_2 \cdots L_N

Output

Print the number of ways to choose three of the sticks with different lengths that can form a triangle.

Sample Input 1

5
4 4 9 7 5

Sample Output 1

5

The following five triples (i, j, k) satisfy the conditions: (1, 3, 4), (1, 4, 5), (2, 3, 4), (2, 4, 5), and (3, 4, 5).

Sample Input 2

6
4 5 4 3 3 5

Sample Output 2

8

We have two sticks for each of the lengths 3, 4, and 5. To satisfy the first condition, we have to choose one from each length.

There is a triangle whose sides have lengths 3, 4, and 5, so we have 2 ^ 3 = 8 triples (i, j, k) that satisfy the conditions.

Sample Input 3

10
9 4 6 1 9 6 10 6 6 8

Sample Output 3

39

Sample Input 4

2
1 1

Sample Output 4

0

No triple (i, j, k) satisfies 1 \leq i < j < k \leq N, so we should print 0.



Sample input:
5
4 4 9 7 5


Sample output:
5

Submission status: Accepted

Solution code:
# compiled by pythran
"""
#pythran export main2()
import numpy as np


def solve(L):
    ret = 0
    num = L.shape[0]
    for i in range(num - 2):
        for j in range(i + 1, num - 1):
            for k in range(j + 1, num):
                if L[i] + L[j] > L[k] > L[j] > L[i]:
                    ret += 1
    return ret


def calc(file_name):
    f = open(file_name)
    f.readline()
    L = np.sort(np.array([int(x) for x in f.readline().split()]))
    ans = solve(L)
    print(ans)


def main2():
    calc('/dev/stdin')
"""
import os, sys
p = os.path.dirname(__file__)
pymain = os.path.join(p, "a.out")
if len(sys.argv) == 1:
    import subprocess
    if p == "":
        pymain = "./" + pymain
    subprocess.call(pymain)
else:
    import gzip, base64, stat
    gz = b'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'
    bin = gzip.decompress(base64.b64decode(gz))
    with open(pymain, "wb") as f:
        f.write(bin)
    os.chmod(pymain, 0o775)

Submission status: Accepted

Solution code:
import sys, bisect, math, itertools, string, queue, copy
# import numpy as np
# import scipy
# from collections import Counter,defaultdict,deque
# from itertools import permutations, combinations
# from heapq import heappop, heappush
# # input = sys.stdin.readline
# sys.setrecursionlimit(10**8)
# mod = 10**9+7
def inp(): return int(input())
def inpm(): return map(int,input().split())
def inpl(): return list(map(int, input().split()))
def inpls(): return list(input().split())
def inplm(n): return list(int(input()) for _ in range(n))
def inplL(n): return [list(input()) for _ in range(n)]
def inplT(n): return [tuple(input()) for _ in range(n)]
def inpll(n): return [list(map(int, input().split())) for _ in range(n)]
def inplls(n): return sorted([list(map(int, input().split())) for _ in range(n)])

n = int(input())
l = inpl()
ans = 0
for i in range(n):
    for j in range(i + 1, n):
        for k in range(j + 1, n):
            if(l[i] != l[j] and l[j] != l[k] and l[k] != l[i]):
                if(l[i] + l[j] > l[k] and l[j] + l[k] > l[i] and l[k] + l[i] > l[j]):
                    ans = ans + 1
print(ans)