import numpy as np
import scipy as sp
import ot
n = 1000
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
x2 = rng.randn(n//2, 2)+5
xb = torch.tensor(x, dtype=torch.float32)
xb2 = torch.tensor(x2, dtype=torch.float32)
a = torch.ones(n, dtype=torch.float32, requires_grad=True) / n
b = torch.ones(n//2, dtype=torch.float32, requires_grad=True) / (n//2)
#%%  empirical_sinkhorn2_geomloss wrapper for geomloss
reg=1
ot.tic()
value0, log0 = ot.bregman.empirical_sinkhorn2(xb, xb2, reg=reg, lazy=False, log=True)
ot.toc('Classical sinhorn solver : {}s')
ot.tic()
value, log = empirical_sinkhorn2_geomloss(xb, xb2, reg=reg, log= True)
ot.toc('Geomloss : {}s')
T = log['lazy_plan'] # recover lazy plan
#%% ot.solve_sample wrapper for geomloss
reg=1
# automatic solver
ot.tic()
sol = ot.solve_sample(x,x2, reg=reg, method='geomloss_auto', lazy=True)
ot.toc('Geomloss (automatic) : {}s')
# tensorized solver is fast but O(n^2) in memory
ot.tic()
sol = ot.solve_sample(x,x2, reg=reg, method='geomloss_tensorized', lazy=True)
ot.toc('Geomloss tensorized : {}s')
# online solver compute the distanec marix when necessary and is O(n) in memory
ot.tic()
sol = ot.solve_sample(x,x2, reg=reg, method='geomloss_online', lazy=True)
ot.toc('Geomloss online : {}s')
# multiscale is usually the fastest
ot.tic()
sol = ot.solve_sample(x,x2, reg=reg, method='geomloss_multiscale', lazy=True)
ot.toc('Geomloss multiscale : {}s')

[KeOps] Warning : Cuda libraries were not detected on the system ; using cpu only mode
Classical sinhorn solver : 0.6667807102203369s
Geomloss : 0.037354469299316406s
Geomloss (automatic) : 0.05784487724304199s
Geomloss tensorized : 0.052454471588134766s
Geomloss online : 0.2332136631011963s
Geomloss multiscale : 0.04137110710144043s