[OpenVINO Backend] support where operation (#21354)

Author: Mohamed-Ashraf273

keras/src/backend/openvino/excluded_concrete_tests.txt

@@ -65,7 +65,6 @@ NumpyDtypeTest::test_unravel
 NumpyDtypeTest::test_var
 NumpyDtypeTest::test_vdot
 NumpyDtypeTest::test_vstack
-NumpyDtypeTest::test_where
 NumpyDtypeTest::test_clip_bool
 NumpyDtypeTest::test_square_bool
 HistogramTest
@@ -147,7 +146,6 @@ NumpyTwoInputOpsCorrectnessTest::test_quantile
 NumpyTwoInputOpsCorrectnessTest::test_take_along_axis
 NumpyTwoInputOpsCorrectnessTest::test_tensordot
 NumpyTwoInputOpsCorrectnessTest::test_vdot
-NumpyTwoInputOpsCorrectnessTest::test_where
 NumpyOneInputOpsDynamicShapeTest::test_angle
 NumpyOneInputOpsDynamicShapeTest::test_bartlett
 NumpyOneInputOpsDynamicShapeTest::test_blackman

keras/src/backend/openvino/numpy.py

@@ -1510,7 +1510,32 @@ def vectorize(pyfunc, *, excluded=None, signature=None):
 
 
 def where(condition, x1=None, x2=None):
-    raise NotImplementedError("`where` is not supported with openvino backend")
+    condition = get_ov_output(condition)
+    if x1 is None and x2 is None:
+        nonzero_indices = ov_opset.non_zero(condition)
+        return OpenVINOKerasTensor(nonzero_indices.output(0))
+    if x1 is None:
+        return OpenVINOKerasTensor(condition)
+    if x2 is None:
+        raise ValueError("x2 must be provided if x1 is specified.")
+
+    def cast_literal_like_tensor(literal, x):
+        ov_type = get_ov_output(x).get_element_type()
+        is_bool = ov_type == Type.boolean
+        is_float_to_int = isinstance(literal, float) and ov_type.is_integral()
+        if is_bool or is_float_to_int:
+            return get_ov_output(literal), get_ov_output(x)
+        return get_ov_output(literal, ov_type), get_ov_output(x)
+
+    if isinstance(x1, (int, float)):
+        x1, x2 = cast_literal_like_tensor(x1, x2)
+    elif isinstance(x2, (int, float)):
+        x2, x1 = cast_literal_like_tensor(x2, x1)
+    else:
+        x1 = get_ov_output(x1)
+        x2 = get_ov_output(x2)
+    x1, x2 = _align_operand_types(x1, x2, "select()")
+    return OpenVINOKerasTensor(ov_opset.select(condition, x1, x2).output(0))
 
 
 def divide(x1, x2):