feat: Added chunk function to paddle frontend

ivy/functional/frontends/paddle/manipulation.py

@@ -44,6 +44,26 @@ def cast(x, dtype):
     return ivy.astype(x, dtype)
 
 
+@with_supported_dtypes(
+    {"2.5.1 and below": ("bool", "int32", "int64", "float16", "float32", "float64")},
+    "paddle",
+)
+@to_ivy_arrays_and_back
+def chunk(x, chunks, axis=0, name=None):
+    if x.shape == () and chunks > 1:
+        raise ValueError("Invalid value for chunks")
+    axis_size = ivy.shape(x)[axis]
+    chunk_size = axis_size // chunks
+    remainder = axis_size % chunks
+    num_or_size_splits = (
+        chunks
+        if remainder == 0
+        else tuple([chunk_size + remainder] + [chunk_size] * (chunks - 1))
+    )
+    num_or_size_splits = axis_size if chunk_size == 0 else num_or_size_splits
+    return ivy.split(x, num_or_size_splits=num_or_size_splits, axis=axis)
+
+
 @with_unsupported_dtypes({"2.5.1 and below": ("int8", "int16")}, "paddle")
 @to_ivy_arrays_and_back
 def concat(x, axis, name=None):

ivy_tests/test_ivy/helpers/testing_helpers.py

@@ -36,6 +36,11 @@
     _dtype_kind_keys,
     _get_type_dict,
 )
+from ivy_tests.test_ivy.helpers import (
+    dtype_and_values,
+    get_axis,
+    get_dtypes,
+)
 from .globals import mod_backend
 
 cmd_line_args = (
@@ -925,3 +930,24 @@ def _create_transpile_report(data: dict, backend: str, file_name: str):
     json_object = json.dumps(data, indent=6)
     with open(file_name, "w") as outfile:
         outfile.write(json_object)
+
+
+@st.composite
+def _chunk_helper(draw):
+    dtype, x, shape = draw(
+        dtype_and_values(
+            available_dtypes=get_dtypes("float"),
+            min_num_dims=1,
+            ret_shape=True,
+        )
+    )
+    axis = draw(get_axis(shape=shape, force_int=True))
+    if shape[axis] == 0:
+        chunks = 0
+    else:
+        factors = []
+        for i in range(1, shape[axis] + 1):
+            if shape[axis] % i == 0:
+                factors.append(i)
+        chunks = draw(st.sampled_from(factors))
+    return dtype, x, axis, chunks

ivy_tests/test_ivy/test_frontends/test_paddle/test_manipulation.py

@@ -11,6 +11,9 @@
 from ivy_tests.test_ivy.test_frontends.test_torch.test_miscellaneous_ops import (
     _get_repeat_interleaves_args,
 )
+from ivy_tests.test_ivy.helpers.testing_helpers import (
+    _chunk_helper,
+)
 
 
 # --- Helpers --- #
@@ -326,6 +329,35 @@ def test_paddle_cast(
     )
 
 
+# chunk
+@handle_frontend_test(
+    fn_tree="paddle.chunk",
+    x_dim_chunks=_chunk_helper(),
+    test_with_out=st.just(False),
+)
+def test_paddle_chunk(
+    *,
+    x_dim_chunks,
+    fn_tree,
+    on_device,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    dtype, x, axis, chunks = x_dim_chunks
+    helpers.test_frontend_function(
+        input_dtypes=dtype,
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        x=x[0],
+        chunks=chunks,
+        axis=axis,
+    )
+
+
 @handle_frontend_test(
     fn_tree="paddle.concat",
     xs_n_input_dtypes_n_unique_idx=_arrays_idx_n_dtypes(),

ivy_tests/test_ivy/test_frontends/test_torch/test_indexing_slicing_joining_mutating_ops.py

@@ -14,6 +14,9 @@
 from ivy_tests.test_ivy.test_functional.test_core.test_manipulation import (  # noqa
     _get_splits,
 )
+from ivy_tests.test_ivy.helpers.testing_helpers import (
+    _chunk_helper,
+)
 
 
 # --- Helpers --- #
@@ -197,27 +200,6 @@ def _arrays_idx_n_dtypes(draw):
     return xs, input_dtypes, unique_idx
 
 
-@st.composite
-def _chunk_helper(draw):
-    dtype, x, shape = draw(
-        helpers.dtype_and_values(
-            available_dtypes=helpers.get_dtypes("float"),
-            min_num_dims=1,
-            ret_shape=True,
-        )
-    )
-    axis = draw(helpers.get_axis(shape=shape, force_int=True))
-    if shape[axis] == 0:
-        chunks = 0
-    else:
-        factors = []
-        for i in range(1, shape[axis] + 1):
-            if shape[axis] % i == 0:
-                factors.append(i)
-        chunks = draw(st.sampled_from(factors))
-    return dtype, x, axis, chunks
-
-
 @st.composite
 def _dtype_input_dim_start_length(draw):
     _shape = draw(helpers.get_shape(min_num_dims=1, min_dim_size=1))