diff --git a/jax/_src/sharding_impls.py b/jax/_src/sharding_impls.py
index 69e2adc4dc77..a8305e8bd772 100644
--- a/jax/_src/sharding_impls.py
+++ b/jax/_src/sharding_impls.py
@@ -123,6 +123,18 @@ def build(self) -> sdy.DimensionShardingAttr:
         is_closed=self.is_closed,
         priority=self.priority)
 
+  def __repr__(self):
+    axes_repr = ', '.join(f"'{a}'" for a in self.axes)
+    open_repr = '' if self.is_closed else ', ?'
+    priority_repr = '' if self.priority is None else f'p{self.priority}'
+    return f'SdyDimSharding({{{axes_repr}{open_repr}}}{priority_repr})'
+
+  def _custom_repr(self):
+    axes_repr = ', '.join(f"'{a}'" for a in self.axes)
+    open_repr = '' if self.is_closed else ', ?'
+    priority_repr = '' if self.priority is None else f'p{self.priority}'
+    return f'{{{axes_repr}{open_repr}}}{priority_repr}'
+
 
 @dataclasses.dataclass
 class SdyArraySharding:
@@ -144,6 +156,14 @@ def build(self) -> sdy.TensorShardingAttr:
         mesh_attr,
         [dim_sharding.build() for dim_sharding in self.dimension_shardings])
 
+  def __repr__(self):
+    dim_sharding_repr = ', '.join(dim_sharding._custom_repr() for dim_sharding
+                                  in self.dimension_shardings)
+    device_id_repr = ''
+    if self.logical_device_ids is not None:
+      device_id_repr = f', device_ids={self.logical_device_ids}'
+    return f"SdyArraySharding([{dim_sharding_repr}]{device_id_repr})"
+
 
 @util.cache(max_size=4096, trace_context_in_key=False)
 def named_sharding_to_xla_hlo_sharding(
diff --git a/tests/pjit_test.py b/tests/pjit_test.py
index 3fcc5c81ad91..59f2233caeb0 100644
--- a/tests/pjit_test.py
+++ b/tests/pjit_test.py
@@ -6625,6 +6625,30 @@ def f(x, y):
     lowered_str = jax.jit(f, in_shardings=[AUTO(mesh), AUTO(mesh)]).lower(x, x).as_text()
     self.assertIn('sdy.mesh @mesh = <["x"=8]>', lowered_str)
 
+  def test_array_sharding_repr_with_priority(self):
+    sharding = sharding_impls.SdyArraySharding(
+        mesh_shape=(('data', 4), ('model', 8), ('expert', 2)),
+        dimension_shardings=[
+            sharding_impls.SdyDimSharding(axes=['data', 'expert'], is_closed=True),
+            sharding_impls.SdyDimSharding(axes=['model'], is_closed=False, priority=2)])
+    self.assertEqual(repr(sharding), "SdyArraySharding([{'data', 'expert'}, {'model', ?}p2])")
+
+  def test_array_sharding_repr_with_logical_ids(self):
+    sharding = sharding_impls.SdyArraySharding(
+        mesh_shape=(('data', 4), ('model', 8)),
+        dimension_shardings=[
+            sharding_impls.SdyDimSharding(axes=['data'], is_closed=True)],
+        logical_device_ids=[4, 5, 6, 7, 0, 1, 2, 3])
+    self.assertEqual(repr(sharding),
+                     "SdyArraySharding([{'data'}], "
+                     "device_ids=[4, 5, 6, 7, 0, 1, 2, 3])")
+
+  def test_dimension_sharding_repr(self):
+    dim_sharding = sharding_impls.SdyDimSharding(
+        axes=['data', 'model'], is_closed=False, priority=2)
+    self.assertEqual(repr(dim_sharding),
+                     "SdyDimSharding({'data', 'model', ?}p2)")
+
 
 if __name__ == '__main__':
   absltest.main(testLoader=jtu.JaxTestLoader())