fix(metrics): Fix BinaryAccuracy metric to handle boolean inputs (#20782

)

* Fix BinaryAccuracy metric to handle boolean inputs

Previously, BinaryAccuracy would return incorrect results when given boolean
inputs in JAX backend, and would raise errors in TensorFlow backend. This was
because the metric expects numerical values (floats/integers) but wasn't
properly handling boolean array inputs.

Fix by casting y_true and y_pred to floatx() in MeanMetricWrapper.update_state().
This ensures consistent behavior across backends and proper handling of boolean
inputs.

* fix: Make the linter happy :)

* fix: Align update_state casting with metric's data type

keras/src/metrics/reduction_metrics.py

@@ -199,6 +199,9 @@ def __init__(self, fn, name=None, dtype=None, **kwargs):
             self._direction = "down"
 
     def update_state(self, y_true, y_pred, sample_weight=None):
+        y_true = backend.cast(y_true, self.dtype)
+        y_pred = backend.cast(y_pred, self.dtype)
+
         mask = backend.get_keras_mask(y_pred)
         values = self._fn(y_true, y_pred, **self._fn_kwargs)
         if sample_weight is not None and mask is not None:

keras/src/metrics/reduction_metrics_test.py

@@ -1,6 +1,9 @@
 import numpy as np
 
 from keras.src import backend
+from keras.src import layers
+from keras.src import metrics
+from keras.src import models
 from keras.src import testing
 from keras.src.backend.common.keras_tensor import KerasTensor
 from keras.src.metrics import reduction_metrics
@@ -174,3 +177,17 @@ def test_weighted_dynamic_shape(self):
             KerasTensor((None, 5)),
         )
         self.assertAllEqual(result.shape, ())
+
+    def test_binary_accuracy_with_boolean_inputs(self):
+        inp = layers.Input(shape=(1,))
+        out = inp > 0.5
+        model = models.Model(inputs=inp, outputs=out)
+
+        x = np.random.rand(32, 1)
+        y = x > 0.5
+
+        res = model.predict(x)
+        metric = metrics.BinaryAccuracy()
+        metric.update_state(y, res)
+        result = metric.result()
+        assert result == 1.0