import torch, scipy
import numpy as np
import argparse
import time

parser = argparse.ArgumentParser()
parser.add_argument('matrix_file', help='the input matrix (.mtx) file')
parser.add_argument('iterations', type=int, help='the number of iterations of multiplication to perform')
args = parser.parse_args()

device = 'cpu'

matrix = scipy.io.mmread(args.matrix_file)
matrix = torch.sparse_coo_tensor(
    np.vstack((matrix.row, matrix.col)),
    matrix.data, matrix.shape,
    device=device
).to_sparse_csr().type(torch.float)

vector = torch.rand(matrix.shape[1], device=device)

print(matrix)
print(vector)

start = time.time()
for i in range(0, args.iterations):
    torch.sparse.mm(matrix, vector.unsqueeze(-1)).squeeze(-1)
    #print(i)
end = time.time()

if matrix.shape[0] == matrix.shape[1]:
    print(f"Shape: {matrix.shape[1]}")
else:
    print(f"Shape: {matrix.shape}")

print(f"Time: {end - start} seconds")