Suppose you are working in some audio streaming service. The service has $$$n$$$ active users and $$$10^9$$$ tracks users can listen to. Users can like tracks and, based on likes, the service should recommend them new tracks.

Tracks are numbered from $$$1$$$ to $$$10^9$$$. It turned out that tracks the $$$i$$$-th user likes form a segment $$$[l_i, r_i]$$$.

Let's say that the user $$$j$$$ is a predictor for user $$$i$$$ ($$$j \neq i$$$) if user $$$j$$$ likes all tracks the $$$i$$$-th user likes (and, possibly, some other tracks too).

Also, let's say that a track is strongly recommended for user $$$i$$$ if the track is not liked by the $$$i$$$-th user yet, but it is liked by every predictor for the $$$i$$$-th user.

Calculate the number of strongly recommended tracks for each user $$$i$$$. If a user doesn't have any predictors, then print $$$0$$$ for that user.

Input

The first line contains one integer $$$t$$$ ($$$1 \le t \le 10^4$$$) — the number of test cases. Next, $$$t$$$ cases follow.

The first line of each test case contains one integer $$$n$$$ ($$$1 \le n \le 2 \cdot 10^5$$$) — the number of users.

The next $$$n$$$ lines contain two integers $$$l_i$$$ and $$$r_i$$$ per line ($$$1 \le l_i \le r_i \le 10^9$$$) — the segment of tracks the $$$i$$$-th user likes.

Additional constraint on the input: the sum of $$$n$$$ over all test cases doesn't exceed $$$2 \cdot 10^5$$$.

Output

For each test case, print $$$n$$$ integers, where the $$$i$$$-th integer is the number of strongly recommended tracks for the $$$i$$$-th user (or $$$0$$$, if that user doesn't have any predictors).

Note

In the first test case:

the first user has no predictors;
the second user has no predictors;
the third user has two predictors: users $$$1$$$ and $$$2$$$; only track $$$3$$$ is liked by both of them and not liked by the third user.

In the second test case, the second user is a predictor for the first user. Therefore, all tracks, except $$$42$$$, are strongly recommended for the first user.

In the third test case, the first user has two predictors: users $$$2$$$ and $$$3$$$, but there is no track that is liked by them and not liked by the first user himself.

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