Task

Given a list of HDFS log events as (BlockId, EventTemplate) pairs, compute per-block statistics using a groupby aggregation:

• count: total number of events for that block
• unique: number of distinct event templates seen for that block
• top_event: the most frequent event template (ties broken alphabetically)

Input

• Line 1: integer n — number of log entries
• Lines 2 to n+1: block_id event_template (space-separated; blockid is an integer, eventtemplate is a single word with no spaces)

Output

For each block in ascending order of block_id, print one line: block_id count unique top_event

Example

Input

7
1 E1
1 E2
1 E1
2 E3
2 E3
2 E1
3 E5

Output

1 3 2 E1
2 3 2 E3
3 1 1 E5

Notes

• Track A: pure Python / NumPy only — no pandas, no scipy, no sklearn.

Scaffolding

Submit a Python file defining:

def groupby_agg(events: list[tuple[int, str]]) -> list[tuple[int, int, int, str]]:
    ...

Receives list of (blockid, eventtemplate) tuples. Returns list of (blockid, count, unique, topevent) tuples sorted by block_id ascending.