# Before / after training samples

Generated automatically from a few prompts drawn from the training data, run once against the base model before training started and once against the finished model. This shows what this run changed on representative prompts -- it is not a benchmark and does not measure generalization.

## Prompt: import numpy as np N, D = map(int, input().split()) cnt = 0 for i in range(N): x, y = map(int,

**Before:**

> input().split()) if x**2 + y**2 <= D**2: cnt += 1 print(cnt)   # print(N, D)   # print(N, D, cnt)   # print(N, D, cnt, cnt)   # print(N, D, cnt, cnt, cnt)   # print(N, D, cnt, cnt, cnt, cnt)

**After:**

> input().split()) if x**2 + y**2 <= D**2: cnt += 1 print(cnt)     # print(x, y)     # print(np.sqrt(x**2 + y**2))     # print(np.sqrt(D**2))     # print(np.sqrt(D**2 - x**2))     # print(np.sqrt(D**2 - y**2))

## Prompt: n, d = map(int, input().split()) ans = 0 for _ in range(n): x, y = map(float, input().split()) z = (x**2

**Before:**

> + y**2)**0.5 if z <= d else -1 ans += 1 print(ans)   # ans = 0 for _ in range(n): x, y = map(int, input().split()) z = (x**2 + y**2)**0.5 if z <= d else -1 ans += 1 print(ans)   # ans = 0 for

**After:**

> + y**2)**0.5 if z <= d else -1 ans += 1 print(ans)   # ans = 0 for _ in range(n): x, y = map(int, input().split()) z = (x**2 + y**2)**0.5 if z <= d else -1 ans += 1 print(ans)   # ans = 0 for

## Prompt: #n, m = map(int,input().split()) k = int(input()) #hl = list(map(int,input().split())) s = len(str(k)) for i in range(1,k+1): if int('7'*i) %

**Before:**

> k == 0: print(i) break
k = int(input())
n = 7
for i in range(1,k+1):
    if n % k == 0:
        print(i)
        break
    n = n*10+7
else:
    print(-1)   #k % 7 == 0の時は-1を返す

**After:**

> k == 0: print(i) break
k = int(input())
if k % 2 == 0 or k % 5 == 0:
    print(-1)
else:
    i = 1
    while True:
        if int('7'*i) % k == 0:
            print(i)
            break
        i += 1
        if i >