fix torch error for bincount

keras_core/backend/torch/numpy.py

@@ -245,7 +245,9 @@ def average(x, axis=None, weights=None):
 
 def bincount(x, weights=None, minlength=0):
     x = convert_to_tensor(x, dtype=int)
-    weights = convert_to_tensor(weights)
+    if weights is not None:
+      weights = convert_to_tensor(weights)
+
     return torch.bincount(x, weights, minlength)
 
 

keras_core/layers/preprocessing/discretization.py

@@ -233,6 +233,7 @@ def call(self, inputs):
             output_mode=self.output_mode,
             depth=len(self.bin_boundaries) + 1,
             dtype=self.compute_dtype,
+            count_weights=None,
             backend_module=self.backend,
         )
         return outputs

keras_core/layers/preprocessing/discretization_test.py

@@ -64,9 +64,9 @@ def test_correctness(self):
         layer = layers.Discretization(
             bin_boundaries=[0.0, 0.5, 1.0], output_mode="count"
         )
-        output = layer(np.array([[0.1, 0.8, 0.9]]))
+        output = layer(np.array([0.1, 0.8, 0.9]))
         self.assertTrue(backend.is_tensor(output))
-        self.assertAllClose(output, np.array([[0, 1, 2, 0]]))
+        self.assertAllClose(output, np.array([0, 1, 2, 0]))
 
     def test_tf_data_compatibility(self):
         # With fixed bins

keras_core/utils/numerical_utils.py

@@ -139,16 +139,16 @@ def encode_categorical_inputs(
         )
 
     binary_output = output_mode in ("multi_hot", "one_hot")
-    bincounts = backend_module.numpy.bincount(
-        inputs,
-        weights=count_weights,
-        minlength=depth,
-    )
     if binary_output:
-        one_hot_input = backend_module.nn.one_hot(inputs, depth)
-        bincounts = backend_module.numpy.where(
-            backend_module.numpy.any(one_hot_input, axis=-2), 1, 0
-        )
+      one_hot_input = backend_module.nn.one_hot(inputs, depth)
+      bincounts = backend_module.numpy.where(
+          backend_module.numpy.any(one_hot_input, axis=-2), 1, 0
+      )
+    else:
+      bincounts = backend_module.numpy.bincount(
+          inputs,
+          minlength=depth,
+      )
     bincounts = backend_module.cast(bincounts, dtype)
 
     return bincounts