merge upstream/master into essential parts

1 files changed, 4 insertions, 4 deletions

diff --git a/src/python/gudhi/wasserstein/wasserstein.py b/src/python/gudhi/wasserstein/wasserstein.py
index 142385b1..572d4249 100644
--- a/src/python/gudhi/wasserstein/wasserstein.py
+++ b/src/python/gudhi/wasserstein/wasserstein.py
@@ -202,8 +202,8 @@ def wasserstein_distance(X, Y, matching=False, order=1., internal_p=np.inf, enab
                      then the optimal matching will be set to `None`.
     :param order: exponent for Wasserstein; Default value is 1.
     :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2);
-                       default value is `np.inf`.
-    :param enable_autodiff: If X and Y are torch.tensor, tensorflow.Tensor or jax.numpy.ndarray, make the computation
+                       Default value is `np.inf`.
+    :param enable_autodiff: If X and Y are torch.tensor or tensorflow.Tensor, make the computation
         transparent to automatic differentiation. This requires the package EagerPy and is currently incompatible
         with `matching=True` and with `keep_essential_parts=True`.
 
@@ -306,9 +306,9 @@ def wasserstein_distance(X, Y, matching=False, order=1., internal_p=np.inf, enab
         # empty arrays are not handled properly by the helpers, so we avoid calling them
         if len(pairs_X_Y):
             dists.append((Y_orig[pairs_X_Y[:, 1]] - X_orig[pairs_X_Y[:, 0]]).norms.lp(internal_p, axis=-1).norms.lp(order))
-        if len(pairs_X_diag):
+        if len(pairs_X_diag[0]):
             dists.append(_perstot_autodiff(X_orig[pairs_X_diag], order, internal_p))
-        if len(pairs_Y_diag):
+        if len(pairs_Y_diag[0]):
             dists.append(_perstot_autodiff(Y_orig[pairs_Y_diag], order, internal_p))
         dists = [dist.reshape(1) for dist in dists]
         return ep.concatenate(dists).norms.lp(order).raw