Merge branch 'unifyai:main' into index_add#26392

ivy/functional/frontends/paddle/random.py

@@ -7,6 +7,16 @@
 )
 
 
+@with_supported_dtypes(
+    {"2.5.1 and below": ("float32", "float64")},
+    "paddle",
+)
+@to_ivy_arrays_and_back
+def multinomial(x, num_samples=1, replacement=False, name=None):
+    n = num_samples + 1
+    return ivy.multinomial(n, num_samples, probs=x, replace=replacement)
+
+
 @with_supported_dtypes(
     {"2.5.1 and below": ("float32", "float64")},
     "paddle",

ivy_tests/test_ivy/test_frontends/test_paddle/test_random.py

@@ -7,6 +7,64 @@
 from ivy_tests.test_ivy.helpers import handle_frontend_test
 
 
+# --- Helpers --- #
+# --------------- #
+
+
+@st.composite
+def _multinomial_helper(draw):
+    input_dtype_and_x = draw(
+        helpers.dtype_and_values(
+            available_dtypes=helpers.get_dtypes("valid"),
+            shape=helpers.get_shape(min_num_dims=1, max_num_dims=2, min_dim_size=2),
+        )
+    )
+    num_samples = draw(st.integers(min_value=1, max_value=10))
+    if num_samples > 2:
+        replacement = True
+    else:
+        replacement = draw(st.booleans())
+
+    input_dtype, x = input_dtype_and_x
+
+    total = sum(x)
+    x = [arr / total for arr in x]
+
+    return input_dtype, x, num_samples, replacement
+
+
+# --- Main --- #
+# ------------ #
+
+
+# multinomial
+@handle_frontend_test(
+    fn_tree="paddle.tensor.random.multinomial",
+    input_dtype_and_x=_multinomial_helper(),
+)
+def test_paddle_multinomial(
+    input_dtype_and_x,
+    test_flags,
+    frontend,
+    backend_fw,
+    fn_tree,
+    on_device,
+):
+    input_dtype, x, num_samples, replacement = input_dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        test_flags=test_flags,
+        frontend=frontend,
+        backend_to_test=backend_fw,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        test_values=False,
+        x=x[0],
+        num_samples=num_samples,
+        replacement=replacement,
+    )
+
+
 @handle_frontend_test(
     fn_tree="paddle.normal",
     input_dtypes=st.sampled_from([["float32"], ["float64"]]),