# identity
# The identity tool returns an identity array. An identity array is a square matrix with all the main diagonal elements as 1
# and the rest as 0. The default type of elements is float.

# import numpy
# print numpy.identity(3) #3 is for  dimension 3 X 3

# #Output
# [[ 1.  0.  0.]
#  [ 0.  1.  0.]
#  [ 0.  0.  1.]]

# eye
# The eye tool returns a 2-D array with 1's as the diagonal and 0's elsewhere. The diagonal can be main, upper or lower depending on the 
# optional parameter k. A positive k is for the upper diagonal, a negative k is for the lower, and a 0 k(default) is for the main diagonal.

# import numpy
# print numpy.eye(8, 7, k = 1)    # 8 X 7 Dimensional array with first upper diagonal 1.

# #Output
# [[ 0.  1.  0.  0.  0.  0.  0.]
#  [ 0.  0.  1.  0.  0.  0.  0.]
#  [ 0.  0.  0.  1.  0.  0.  0.]
#  [ 0.  0.  0.  0.  1.  0.  0.]
#  [ 0.  0.  0.  0.  0.  1.  0.]
#  [ 0.  0.  0.  0.  0.  0.  1.]
#  [ 0.  0.  0.  0.  0.  0.  0.]
#  [ 0.  0.  0.  0.  0.  0.  0.]]

# print numpy.eye(8, 7, k = -2)   # 8 X 7 Dimensional array with second lower diagonal 1.

# Task
# Your task is to print an array of size N X M with its main diagonal elements as 1's and 0's everywhere else.

# Input Format
# A single line containing the space separated values of N and M.
# N denotes the rows.
# M denotes the columns.

# Output Format
# Print the desired N X M array.

# Sample Input
# 3 3

# Sample Output

# [[ 1.  0.  0.]
#  [ 0.  1.  0.]
#  [ 0.  0.  1.]]

import numpy as np

N, M = tuple(map(int, input().split()))
print(str(np.eye(N, M)).replace('0', ' 0').replace('1', ' 1'))